THE PURINE AND PYRIMIDINE METABOLISM IN
LACTATING DAIRY COWS
CHARLOTTE STENTOFT NIELSEN
Ph.D. THESIS ∙ SCIENCE AND TECHNOLOGY ∙ 2014
Aarhus University
Faculty of Science and Technology
Department of Animal Science
Blichers Allé 20
P.O. Box 50
DK-8830 Tjele
Supervisors and Ph.D. assessment committee
Supervisors
Head of Research Unit, Ph.D., Mogens Vestergaard
Aarhus University, Faculty of Science and Technology, Department of Animal Science, Denmark
Senior Scientist, Ph.D., Søren Krogh Jensen
Aarhus University, Faculty of Science and Technology, Department of Animal Science, Denmark
Assistant professor, Ph.D., Mogens Larsen
Aarhus University, Faculty of Science and Technology, Department of Animal Science, Denmark
Senior Scientist, Ph.D., Torben Larsen
Aarhus University, Faculty of Science and Technology, Department of Animal Science, Denmark
Consultant / Project leader, Ph.D., Niels Bastian Kristensen
Knowledge Centre for Agriculture, Aarhus, Denmark
Ph.D. assessment committee
Senior Scientist, Ph.D., Stig Purup (Chair)
Aarhus University, Faculty of Science and Technology, Department of Animal Science, Denmark
Associate Professor, Ph.D., Kristian Fog Nielsen
Technical University of Denmark, Department of Systems Biology, Denmark
Professor, Ph.D., Richard Dewhurst
Scotland’s Rural College, Beef and Sheep Research Centre, Midlothian, EH25 9RG, Scotland,
United Kingdom
I
Preface
The nitrogen efficiency of dairy cows is generally low due to the inherent characteristics of the ruminant digestive system and to the feedstuffs and rations used. Any attempt to optimize the diet is
fundamental for improving nitrogen efficiency and utilization. The search for quantitative improvements in nitrogen utilization has mainly focused on feed nitrogen and ration formulation.
However, a better understanding of the quantitative absorption and intermediary metabolism of the
nitrogenous purine and pyrimidine metabolites, the main constituents of nucleic acids, could most
likely contribute to uncover new ways to improve dairy cow nitrogen utilization. So far, the possible significance of microbial nucleic acids in the nutritional physiology of ruminants has sparsely
been investigated, regardless of the fact that they correspond to approximately 20% of the total microbial nitrogen supply. One reason for not including the nucleic acid metabolism in the search for
improved nitrogen utilization can partly be ascribed to the lack of reliable methods for quantitative
measurements of purine and pyrimidine metabolites in bovine blood plasma.
The aim of the Ph.D. study was to improve our knowledge about the quantitative absorption and
intermediary metabolism of purine and pyrimidine metabolites in lactating dairy cows. Therefore, a
high performance liquid chromatography-based technique coupled to electrospray ionization tandem mass spectrometry, to quantify key purine and pyrimidine metabolites in plasma, was developed and combined with individual matrix-matched calibration standards and isotopically labelled
reference components. Results from the development and employment of this technique in experiments with lactating dairy cows are presented herein. Valuable insight into the mechanisms of the
purine and pyrimidine metabolism was obtained, which adds significantly to the present knowledge
of the nitrogen metabolism in dairy cows. In addition, these results may in the future be used to improve nitrogen utilization through reformation of feeding plans and strategies.
The PhD program and the experimental work were carried out at the Department of Animal Science, Faculty of Science and Technology, Aarhus University from February 1st 2011 until November 30th 2014. There has been collaboration with Dr. Jon M. Moorby, Institute of Biological, Environmental and Rural Sciences, University of Aberystwyth (UK) and Professor Christopher K.
Reynolds, School of Agriculture, Policy and Development, University of Reading (UK). The Ph.D.
scholarship was financed by the Faculty of Science and Technology and the Danish Milk Levy
Board, c/o Food and Agriculture, Aarhus N, Denmark. Funding for the cow animal experiments
were partly provided by the Commission of the European Communities (Brussels, Belgium; Rednex
project FP7, KBBE-2007-1) and the Department of Animal Science, Aarhus University.
Foulum, November 2014, Charlotte Stentoft Nielsen
II
Acknowledgements
I would like to express my sincere gratitude towards my main supervisors Mogens Vestergaard,
Søren Krogh Jensen and Mogens Larsen for their competent and encouraging supervision, constructive criticism on my work, and continuous collaboration during this project. The hard work and effort would not have been as easy to manage without their invaluable support and not half as exhilarating without our inspiring discussions. I would also like to thank Niels Bastian Kristensen for initiating and getting the project funded and for his constructive support throughout the project.
I would also like to thank Jon Moorby for professional and organizational support during my stay at
IBERS (UK) and his warm and kind manner towards me. Also, thanks goes to Felicity Crotty and
Alejandro Belanche Gracia for making my stays in Wales more than just work. Warm thoughts
also go to Chris Reynolds, Cassie Barratt and Les Compton at the University of Reading. Our collaboration on manuscript III has been invaluable to this project.
My deepest thanks go to Peter Løvendahl for introducing me to the vast world of experimental statistics and SAS. Without his sustained technical assistance, this project could not have been conducted. For advice concerning handling of milk samples and for his time analysing milk samples, a
special thanks go to Torben Larsen and his technical staff.
For skilled assistance and essential advice during the experimental work I wish to give a special
thanks to Lis Sidelmann, Birgit Hørdum Løth and Anne Krustrup. I really can never thank this team
of technicians enough, none of this research would have seen the day without their assistance.
Also, special thanks go to members of the Department of Animal Science – Integrative Physiology
group; Adam Storm and Bettina Røjen and especially Vibeke Bjerre-Harpøth for indispensable
sparring during the entire project. The atmosphere in the office, in the laboratory, in the barn, at the
halls, and at breaks has been pleasant and fun and their everyday good spirits and cheers have made
many a bad day into a good one.
Warm thanks go to my family and friends for their indefinite love, for their support and for their
interest in my work. A special thanks to my parents for their faith in me and continuous support.
Finally, thank you Jakob, Mia and Mads for supporting me and bearing with my lack of presence in
the final hours. You are my love and my life.
III
Contents
Supervisors and Ph.D. assessment committee ________________________________________________________I
Preface _______________________________________________________________________________________ II
Acknowledgements ____________________________________________________________________________ III
Contents _____________________________________________________________________________________ VI
Summary _____________________________________________________________________________________ 1
Sammendrag (summary in Danish) _______________________________________________________________ 3
List of scientific papers and manuscripts included in the Ph.D. thesis ___________________________________ 5
List of other scientific contributions from the Ph.D. program __________________________________________ 6
Abbreviations _________________________________________________________________________________ 7
1. Introduction _______________________________________________________________________________ 11
2. Background ________________________________________________________________________________ 14
2.1 Nitrogen metabolism in dairy cattle ___________________________________________________________ 14
2.2 The nucleic acid metabolism ________________________________________________________________ 16
2.2.1 Bases, nucleosides, nucleotides, nucleic acids, and DNA/RNA __________________________________ 16
2.2.2 Purine and pyrimidine nucleotide biosynthesis, regulation, salvage, and catabolism __________________ 17
2.3 The purine and pyrimidine metabolism in dairy cattle _____________________________________________ 20
2.3.1 Degradation of dietary nucleic acids and re-synthesis of microbial nucleic acids ____________________ 20
2.3.2 Degradation of microbial nucleic acids in the small intestine ____________________________________ 21
2.3.3 Absorption and intermediary metabolism of purine and pyrimidine metabolites _____________________ 22
2.3.4 Endogenous purine and pyrimidine metabolites ______________________________________________ 23
2.3.5 Renal clearance of purine and pyrimidine metabolites _________________________________________ 23
3. Hypotheses and objectives ____________________________________________________________________ 25
4. Methods ___________________________________________________________________________________ 27
4.1 The multicatheterized cow model ____________________________________________________________ 27
4.1.1 Blood plasma flow ____________________________________________________________________ 28
4.1.2 Net flux _____________________________________________________________________________ 29
4.1.3 Animals and experimental designs ________________________________________________________ 30
4.1.4 Hepatic fractional removal and renal variables _______________________________________________ 31
4.1.5 Purine and pyrimidine nitrogen estimation __________________________________________________ 32
4.2 Development and validation of an LC-ESI-MS/MS analysis ________________________________________ 33
4.2.1 Target considerations __________________________________________________________________ 35
4.2.2 Chemical properties of the purine and pyrimidine metabolite targets ______________________________ 36
IV
4.2.3 LC-ESI -MS/MS ______________________________________________________________________ 37
4.2.4 Matrix effects ________________________________________________________________________ 43
4.2.5 Calibration and quantification ____________________________________________________________ 44
4.2.6 Internal standards _____________________________________________________________________ 46
4.2.7 Sample preparation and pre-treatment protocol ______________________________________________ 47
4.2.8 Validation and application ______________________________________________________________ 48
5. Brief summary of papers and manuscripts included in the thesis ____________________________________ 52
6. Paper I ____________________________________________________________________________________ 55
7. Paper II ___________________________________________________________________________________ 70
8. Manuscript III _____________________________________________________________________________ 102
9. General discussion _________________________________________________________________________ 133
9.1 Quantitative determination of purine and pyrimidine metabolites in bovine plasma by LC-ESI-MS/MS _____ 133
9.1.2 Method development __________________________________________________________________ 134
9.1.3 Method validation ____________________________________________________________________ 136
9.1.4 Method application ___________________________________________________________________ 138
9.1.5 Pre-treatment ________________________________________________________________________ 141
9.2 Absorption and intermediary metabolism of purine and pyrimidine metabolites________________________ 142
9.2.1 The purine metabolism ________________________________________________________________ 142
9.2.2 The pyrimidine metabolism ____________________________________________________________ 148
9.2.3 The fate of purine and pyrimidine nitrogen_________________________________________________ 151
10. Conclusions ______________________________________________________________________________ 155
11. Perspectives ______________________________________________________________________________ 156
12. References _______________________________________________________________________________ 157
Appendix I __________________________________________________________________________________ 167
IV
Summary
The low nitrogen efficiency in dairy cattle is causing productive challenges and environmental concerns. It is expected that nitrogen utilization may be improved through a better understanding of
mechanisms involved in the nitrogen metabolism. In recent decades focus has primarily been on
feed nitrogen in the form of dietary protein. However, only minor improvements in the utilisation of
nitrogen in ruminants have been achieved. A better understanding of the absorption and intermediary metabolism of the purines and pyrimidines, the main constituents of nucleic acids, could uncover new ways to improve nitrogen utilisation. Microbial nucleic acid corresponds to about 20% of the
total microbial nitrogen synthesized in ruminants; yet, the importance of the microbial nucleic acids
has been sparsely investigated. The nucleic acid metabolism has probably not been part of this effort since methods for determining purines and pyrimidines in bovine blood have not been available. Thus, the overall objective of the Ph.D. study was to improve our knowledge of the quantitative
absorption and intermediary metabolism of purine and pyrimidine metabolites in lactating dairy
cows in order to possibly discover new ways to improve the overall nitrogen efficiency. This Ph.D.
thesis is based on the development of an HPLC tandem mass spectrometry technique (LC-ESIMS/MS) and on two experiments with multicatheterized lactating dairy cows. The results are presented and discussed in three separate papers.
In paper I, an LC-ESI-MS/MS method for simultaneous quantification of 20 purines and pyrimidines in bovine blood plasma was developed and validated. The method was combined with individual matrix-matched calibration standards and isotopically labelled reference components and it
was preceded by a pre-treatment consisting of protein precipitation, ultrafiltration, evaporation, and
resolution. It was hypothesised that the purines and pyrimidines could accurately be quantified in
bovine blood plasma by applying LC-ESI-MS/MS. The procedure covered relevant quantification
ranges and ensured sufficient accuracies and removal of matrix components. Moreover, it was selective, sensitive, stable, and precise enough to detect small venous-arterial concentration differences used for determining net portal-drained viscera (PDV), hepatic, and splanchnic fluxes of purines and pyrimidines from the multicatheterized cow model.
In paper II, the absorption and intermediary metabolism of purines and pyrimidines were described
by studying postprandial patterns of the net PDV and hepatic metabolism. Also, the purine and pyrimidine nitrogen pools were evaluated in this context. It was hypothesised that the purines and pyrimidines, in the form of nucleosides, bases, and degradation products, would be absorbed from the
small intestine and undergo degradation in the intestinal mucosa and the hepatic tissue and that the
purine and pyrimidine nitrogen would be lost following excretion via the kidneys. All of the 20 purines and pyrimidines were released from the PDV; the purines primarily as degradation products
1
and only to a lesser degree as nucleosides and bases and the pyrimidines mainly as nucleosides/degradation products. The bases were found to be almost completely degraded in the small
intestine and intestinal mucosa. Only minor effects of the postprandial pattern were detected. Following an almost complete removal in the hepatic tissue, the purine and pyrimidine metabolism
resulted in a large net splanchnic release of purine nitrogen in the form of allantoin for excretion
into the kidneys and an almost complete removal and anabolic reuse of the pyrimidine nitrogen in
the hepatic tissues.
In manuscript III, the net PDV, hepatic and total splanchnic metabolism of the purines and pyrimidines were studied and influences of dietary protein level and forage sources were evaluated. Also,
the fate of the purine and pyrimidine nitrogen was evaluated by estimating nucleic acid nitrogen
fluxes. It was hypothesised that the net PDV and net hepatic fluxes of the purine and pyrimidine
metabolites would reflect different degrees of microbial biosynthesis with different dietary protein
levels and forage sources in the ration. Protein effects were easiest to detect for metabolites with
considerable levels of fluxes, good precision in the method, and primarily at PDV release. Net fluxes were found to be positively affected by dietary protein levels and the net PDV release reflected
predicted levels of microbial flow. The level of hepatic removal tended to be lower and more variable. Considerable amounts of purine nitrogen were found to be released from the splanchnic tissues.
The pyrimidines were found to be less effectively absorbed, but alternative use in anabolic processes saved some of the absorbed pyrimidine nitrogen. Purine nitrogen was the main contributor to
splanchnic nucleic acid nitrogen release.
Important knowledge of the quantitative absorption and intermediary metabolism of purines and
pyrimidines in lactating dairy cows was obtained in this Ph.D. study. Focusing on the contribution
to the nitrogen metabolism high levels of purines were released from the PDV and due to an efficient degradation in the small intestine and hepatic tissues, most of the purine nitrogen was released
from the splanchnic tissues as the excretion products uric acid and allantoin. The pyrimidines were
less effectively absorbed, presumably resulting in a considerable loss of pyrimidine nitrogen in faeces, but anabolic processes saved the absorbed pyrimidine nitrogen. Overall, the purine nitrogen
was found to be the main contributor to the nucleic acid nitrogen release from the splanchnic tissues; the nucleic acid splanchnic release corresponded to 11% of the overall nitrogen intake. By
combining the purine and pyrimidine nitrogen fluxes obtained in this study, it was revealed that
only 25% of the splanchnic release of nucleic acid nitrogen was excreted in urine and milk, the remaining nucleic acid nitrogen was unaccounted for. Hence, in order to obtain a full understanding
of the nucleic acid nitrogen flow in dairy cows and possibly improve the overall utilization of nitrogen, further studies on especially the endogenous fate of uric acid and allantoin are needed.
2
Sammendrag (summary in Danish)
Den lave nitrogeneffektivitet i malkekøer giver anledning til produktionsmæssige udfordringer såvel som miljømæssige bekymringer. Man forventer, at en øget forståelse af de mekanismer, der er
involveret i kvælstofmetabolismen, kan hjælpe til at finde måder til at forbedre kvælstofudnyttelsen.
I de sidste par årtier har fokus hovedsageligt været på kvælstof i form af foderprotein, men der er på
trods af dette kun opnået mindre fremskridt. En bedre forståelse af, hvordan optaget og den indre
metabolisme af nukleinsyrernes primære byggesten, purinerne og pyrimidinerne, ville kunne afsløre
nye måder til at raffinere nitrogeneffektiviteten. Betydningen af mikrobielle nukleinsyrer er kun
sparsomt blevet undersøgt på trods af, at de svarer til 20 % af det totale mikrobielle nitrogen, der
syntetiseres i drøvtyggere. Nukleinsyremetabolismen har formentlig ikke været undersøgt tidligere,
fordi metoder til at bestemme puriner og pyrimidiner i blod fra kvæg ikke har været tilgængelige.
Det overordnede formål med Ph.D. studiet var at opnå større viden om det kvantitative optag og den
indre metabolisme af purin- og pyrimidinmetabolitterne i lakterende køer for herved potentielt at
opdage nye måder til at forbedre den overordnede kvælstofudnyttelse. Ph.D. afhandlingen er baseret
på udviklingen af en HPLC tandem-massespektrofotometrisk teknik (LC-ESI-MS/MS) og to eksperimenter med multikateteriserede lakterende malkekøer. Resultaterne er beskrevet og diskuteret i tre
artikler.
I artikel I blev en LC-ESI-MS/MS metode til effektiv kvantificering af 20 puriner og pyrimidiner i
blod fra kvæg udviklet og valideret. Metoden var kombineret med individuelle matrix-parrede kalibreringsstandarder og isotopmærkede referencekomponenter og en forbehandling bestående af proteinfældning, ultrafiltrering, inddampning og genopløsning. Følgende hypotese blev opstillet: Purinerne og pyrimidinerne kan kvantificeres nøjagtigt i blodplasma fra køer ved anvendelse af LCESI-MS/MS. Proceduren dækkede relevante kvantificeringsområder, sikrede tilstrækkelig målenøjagtighed og fjernede matrixkomponenter i en tilfredsstillende grad. Den var desuden selektiv, følsom, stabil og præcis nok til at kunne måle de små koncentrationsforskelle imellem venerne og arterierne brugt til at bestemme netto frigivelse fra de portåredrænede væv (PDV), netto bortskaffelse
over leveren og totale fluxe over splanchnicus fra den multikateteriserede komodel.
I artikel II blev optaget og den indre metabolisme af purinerne og pyrimidinerne beskrevet ved at
studere effekten af fodring på deres netto PDV og hepatiske metabolisme. Herudover blev purin- og
pyrimidinnitrogenet evalueret i denne kontekst. Følgende hypotese blev opstillet: Purinerne og pyrimidinerne, i form a nukleosider, baser og nedbrydningsprodukter, vil blive optaget fra tyndtarmen,
nedbrudt i tyndtarmens slimhinde og leveren og deres nitrogen til sidst blive tabt som følge af udskillelse fra nyrerne. Alle de 20 målte puriner og pyrimidiner blev frigivet fra PDV; purinerne primært som nedbrydningsprodukter og kun i mindre grad som nukleosider og baser, og pyrimidinerne
3
hovedsageligt som nukleosider og nedbrydningsprodukter. Baserne blev næsten fuldt nedbrudt i
tyndtarmen og tyndtarmens slimhinde. Kun mindre effekter of fodring blev detekteret. Efter en næsten fuldstændig nedbrydning over leveren, resulterede purin- og pyrimidinmetabolismen i en stor
frigivelse af purinnitrogen fra splanchnicus i form af allantoin til udskillese fra nyrerne og en næsten fuldstændig bortskaffelse og anabolsk genbrug af pyrimidinnitrogen i leveren.
I artikel III blev den netto PDV, hepatiske og totale metabolisme over splanchnicus af purinerne og
pyrimidinerne studeret, og foderproteinniveauet og grovfoderkildens indflydelse herpå evalueret.
Herudover blev purin- og pyrimidinnitrogenets skæbne evalueret ved at estimere nukleinsyrenitrogenfluxe. Følgende hypotese blev opstillet: Den netto PDV og hepatiske metabolisme af purin- og
pyrimidinmetabolitterne vil reflektere forskellene i den mikrobiellesyntese som en følge af forskelle
i foderproteinniveau og grovfoderkilde i rationen. Effekterne af protein niveau var lettest at detektere for metabolitter med betragtelige niveauer af fluxe, god præcision i metoden og primært ved frigivelse fra PDV. Nettofluxene blev positivt påvirket af foderproteinniveauer og den netto PDV frigivelse reflekterede forudsagte niveauer af mikrobiel tilførsel til tyndtarmen. Niveauet af bortskaffelse over leveren havde tendens til at være mindre og mere variabel. Anseelige mængder af purinnitrogen blev frigivet fra splanchnicus. Pyrimidinerne blev mindre effektivt optaget fra tyndtarmen,
men alternative anvendelser i anabolske processor bevarede noget af det optagede pyrimidinnitrogen. Purinnitrogenet var den primære bidragyder til nukleinsyrenitrogen frigivet fra splanchnicus.
Der blev i dette Ph.D. studie opnået væsentlig indsigt i det kvantitative optag og den indre metabolisme af puriner og pyrimidiner i lakterende malkekøer. Med fokus på bidraget til nitrogenmetabolismen: Høje niveauer af puriner blev frigivet fra PDV, og på baggrund af den effektive nedbrydning i tyndtarmen og leveren blev det meste af purinnitrogenet frigivet fra splanchnicus som udskillelsesprodukterne urinsyre og allantoin. Pyrimidinerne blev mindre effektivt optaget, formodentligt
resulterende i et tab af pyrimidinnitrogen i afføring, men det anabolske genbrug bevarede det optagede pyrimidinnitrogen. Generelt set blev det fundet, at purinnitrogenet var hovedbidragyderen til
nukleinsyrenitrogenfrigivelsen fra splanchnicus; nukleinsyrefrigivelsen svarede til 11 % af det totale nitrogenoptag. Ved at kombinere de opnåede purin- og pyrimidinnitrogenfluxe fra dette studie
blev det vist, at kun 25 % af nukleinsyrenitrogenet frigivet fra splanchnicus blev udskilt i urin og
mælk; det resterende nukleinsyrenitrogen var der ikke gjort rede for. For at opnå en fuld forståelse
af bevægelserne af nukleinsyrenitrogen i malkekøer og potentielt forbedre den totale udnyttelse af
nitrogen, er det derfor nødvendigt at foretage yderligere studier til specielt at undersøge den endogene skæbne for urinsyre og allantoin.
4
List of scientific papers and manuscripts included in the Ph.D. thesis
Paper I:
Stentoft C., M. Vestergaard, P. Løvendahl, N.B. Kristensen, J.M. Moorby and S.K. Jensen. 2014.
Simultaneous quantification of purine and pyrimidine bases, nucleosides and their degradation
products in bovine blood plasma by high performance liquid chromatography tandem mass spectrometry. J. Cromatogr. A. 1356:197-210.
Paper II:
Stentoft C., B.A. Røjen, S.K. Jensen, N.B. Kristensen, M. Vestergaard and M. Larsen. 2014. Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows. Accepted
November 11th 2014 by Br. J. Nutr.
Manuscript III:
Stentoft C., C. Barratt, L.A. Crompton, S.K. Jensen, M. Vestergaard, M. Larsen and C.K. Reynolds.
Protein Level Influences the Splanchnic Metabolism of Purine and Pyrimidine Metabolites in Lactating Dairy Cows. To be submitted to J. Dairy Sci.
5
List of other scientific contributions from the Ph.D. program
Conference contributions and other disseminations:
Stentoft C. 2011. The purine and pyrimidine metabolism in lactating dairy cows. Presentation. University of Aberystwyth, Institute of Biological, Environmental and Rural sciences (IBERS), Environmental Impact Research Group, Wales, United Kingdom.
Stentoft C. and M. Vestergaard. 2012. A technique to quantify metabolites of the purine and also of
the pyrimidine metabolism. Abstract and theatre presentation. Page 53 in the Book of Abstracts of
the 63rd Annual Meeting of the European Federation of Animal Science, Bratislava, Slovakia, 27-31
August 2012. EAAP series No.18. Wageningen Academic Publishers, Wageningen, The Netherlands.
Stentoft C. 2012. A technique able to quantify metabolites of the purine and also of the pyrimidine
metabolism. Presentation. University of Reading, School of Agriculture, Policy and Development,
Food Production and Quality, United Kingdom + Rednex WP6 Meeting November 21st, Manchester, United Kingdom.
Stentoft C. 2014. Møde I fællesarbejdsgruppen for ernæring og produktion. Presentation. Aarhus
University, Foulum. Purin og pyrimidin metabolismen i malkekøer.
Stentoft C., S.K. Jensen, M. Vestergaard, M. Larsen. 2014. Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows. Abstract and theatre presentation. Page 351
in the Book of Abstracts of the 65rd Annual Meeting of the European Federation of Animal Science,
Copenhagen, Denmark, 25-29 August 2014. EAAP series No.20. Wageningen Academic Publishers, Wageningen, The Netherlands.
6
Abbreviations
Ade*
Adenine
Ado
Adenosine
ADP
Adenosine diphosphate
AICAR
5-aminoimidazole-4-carboxamide ribonucleotide
AIR
5-aminoimidazole ribonucleotide
Alo
Allantoin
AMP
5’-adenylic acid (adenosine monophosphate)
APCI
Atmospheric pressure chemical ionisation
API
Atmospheric pressure ionisation
ATP
Adenosine triphosphate
CAIR
Carboxyaminoimidazole ribonucleotide
CEC
Capillary electrophoresis chromatography
CMP
5’-cytidylic acid (cytidine monophosphate)
CP
Crude protein
CTP
Cytidine triphosphate
Cyd
Cytidine
Cyt
Cytosine
CV
Coefficient of variation
dAdo
2’-deoxyadenosine
dAMP
2’-deoxyadenosine 5’-monophosphate (deoxyadenosine monophosphate)
DC
Direct current
dCMP
2’-deoxycytidine 5’-monophosphate (deoxycytidine monophosphate)
dCyd
2’-deoxycytidine
Δ[PA]/[P] (%)
Hepatic portal venous-arterial concentration difference / portal
concentration (%)
dGMP
2’-deoxyguanosine 5’-monophosphate (deoxyguanosine monophosphate)
dGuo
2’-deoxyguanosine
DHF
Dihydrofolate
dhThy
Dihydrothymine
dhUra
Dihydrouracil
dIno
2’-deoxyinosine
DM
Dry matter
DMI
Dry matter intake
DNA
Deoxyribonucleic acid
dNDP
Deoxynucleotide diphosphate
7
dNTP
Deoxynucleotide triphosphate
dThd
Thymidine or 2’-deoxythymidine
dTMP
Thymidine 5’-monophosphate
dUMP
2’-deoxyuridine 5’-monophosphate (deoxyuridine monophosphate)
dUrd
2’-deoxyuridine
EMA
European medicines agency
ESI
Electrospray ionisation
FAICAR
5-formaaminoimidazole-4-carboxamide ribonucleotide
FDA
U.S. Food and drug administration
FGAM
Formylglycinamidine ribonucleotide
FGAR
Formylglycinamide ribonucleotide
GAR
Glycinamide ribonucleotide
GDP
Guanosine diphosphate
GMP
5’-guanidylic acid (guanosine monophosphate)
GTP
Guanosine triphosphate
Gua
Guanine
Guo
Guanosine
H3C–
Methyl group
HO-
Hydroxyl group
HPLC
High performance liquid chromatography
Hyp
Hypoxanthine
ICH
Harmonisation of technical requirements for registration of pharmaceuticals for human use
IMP
5’-inosinic acid (inosine monophosphate)
Ino
Inosine
LC
Liquid chromatography
LC-ESI-MS/MS
High performance liquid chromatography electrospray ionisation
tandem mass spectrometry
LLE
Liquid-liquid extraction
LLOQ
Lower limit of quantification
MALDI
Matrix-assisted laser desorption/ionization
MRM
Multiple reaction monitoring
MS
Mass spectrometry
MS/MS
Tandem mass spectrometry
m/z
Mass-to-charge ratio
N
Nitrogen
N-
Amine group
NADPH
Nicotinamide adenine dinucleotide phosphate
NDP
Nucleotide diphosphate
8
N10-formyl-THF
N10-formyltetrahydrofolate
NMP
Nucleotide monophosphate
NorFor
Nordic feed evaluation system
NP%
Percentage of net PDV release
NTP
Nucleotide triphosphate
O=
Carbonyl group
pAH
Para-aminohippuric acid
PDV
Portal-drained viscera
PPT
Protein precipitation
PRA
5-phosphoribosyl-1-amine
PRPP
Phosphoribosyl pyrophosphate
Q1
First quadropole
Q2
Second quadrupole
Q3
Third quadrupole
RF
Radiofrequency
RNA
Ribonucleic acid
R5P
Ribose-5-phosphate
RSD
Relative standard deviation
Rt
Retention time
SAICAR
5-aminoimidazole-4-(N-succinylcarboxamide) ribonucleotide
SAMP
Adenylosuccinate
SIL
Stable isotopically labelled reference component
SPE
Solid phase extraction
THF
Tetrahydrofolate
Thy
Thymine
TI%
Percentage of total influx
TMR
Total mixed ration
TSP
Total splanchnic tissues
Uac
Uric acid
UDP
Uridine diphosphate
ULOQ
Upper limit of quantification
UMP
5’-uridylic acid (uridine monophosphate)
UPLC
Ultra high performance liquid chromatography
Ura
Uracil
Urd
Uridine
UTP
Uridine triphosphate
Xan
Xanthine
Xao
Xanthosine
XMP
5’-xanthylic acid (xanthosine monophosphate)
9
β-ala
β-alanine (3-aminopropionic acid)
β-ami
β-aminoisobutyric acid (3-aminoisobutyric acid)
β-iso
β-ureidoisobutyric acid (3-ureidoisobutyric acid)
β-ure
β-ureidopropionic acid (3-ureidopropionic acid)
*
Abbreviations of the purine and pyrimidine metabolites are from IUPAC, abbreviations and symbols for nucleic
acids, polynucleotides and their constituents (IUPAC, 2014).
10
1. Introduction
The nitrogen efficiency in dairy cows is generally low (Kohn et al., 2005) and the efficiency by
which the dairy production systems convert dietary nitrogen into milk protein is only about 25%
(Børsting et al., 2003; Huhtanen and Hristoc, 2009; Tamminga, 1992). The remaining nitrogen is
excreted and thus lost in urine and faeces. Also, the global efficiency of nitrogen in animal production is only slightly over 10%, with the result that 102 Tg (1012 g) nitrogen is excreted annually
(1998 figures) by domesticated animals globally (Steinfeld et al., 2006). Hence, not only the production efficiency but also the environment would benefit from an optimization of diet and metabolism to improve nitrogen utilization of dairy cows (Calsamiglia et al., 2010; Reynolds and Kristensen, 2008; Steinfeld et al., 2006). It is possible to optimize nitrogen efficiency through dietary management and it is expected that even at high production levels, improved nitrogen utilization may be
achieved through a better understanding of the different components and mechanisms involved in
the nitrogen metabolism of dairy cows (Calsamiglia et al., 2010; Clark et al., 1992; Reynolds and
Kristensen, 2008). The single most important factor contributing to the inefficient use of nitrogen in
ruminants is the rumen metabolism (Tamminga, 1992). Research has shown that simply decreasing
dietary nitrogen intakes compromise animal performance (Cyriac et al., 2008; Huhtanen and Hristov, 2009; Kebreab et al., 2001). Focus in the last decades has instead been on optimising the diet
with attention to dietary nitrogen in the form of protein, amino acids and urea (Calsamiglia et al.,
2010; Firkins, 1996; Tamminga, 1992). This focus has, however, only led to minor improvements
in the utilisation of nitrogen in ruminants.
The importance of other nitrogen containing components like microbial nucleic acids and their involvement in the nutritional physiology of ruminants has so far been sparsely investigated, regardless of the fact that they correspond to more than 20% of the total microbial nitrogen synthesised in
the rumen (Fujihara and Shem, 2011; McDonald et al., 2011; Smith and McAllan, 1974). In this
thesis, with the use of a quantitative multicatheterized cow model and a newly developed liquid
chromatography electrospray ionisation tandem mass spectrometry (LC-ESI-MS/MS) technique, we
have tried to improve the basic understanding of the quantitative absorption and intermediary metabolism of the nitrogenous purine and pyrimidine metabolites, the building blocks of nucleic acids,
in the portal-drained viscera (PDV), hepatic, and peripheral tissues, so as to discover new ways to
possibly improve nitrogen efficiency in dairy cows. We have also used this setup to examine how
the complex purine and pyrimidine metabolism is influenced by dietary factors such as crude protein (CP) level and forage source.
11
Introduction
At present, very little is known about the quantitative aspects of these mechanisms and the purine
metabolic pathways have been examined much more intensely than the pyrimidine metabolic
pathways (Chen and Gomes, 1992; Chen and Ørskov, 2004; Fujihara and Shem, 2011).
Nitrogen from the feed undergoes different processes in ruminants before absorption. The rumen
microbial population uses dietary nitrogen from proteins, amino acids, urea, nucleic acids, and other
non-protein nitrogen for the synthesis of microbial protein (75-85%) and microbial nucleic acid (1525%) (Fujihara and Shem, 2011; McDonald et al., 2011). The rumen microbes and their intrinsic
protein and nucleic acids flow into the small intestine before the nitrogen containing molecules are
digested and absorbed (McAllan, 1980; McAllan, 1982; McAllan and Smith, 1973a,b). Nucleic
acids are the main constituents of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) and
they are derived from and degraded to; purine and pyrimidine nucleosides, bases, and degradation
products. Five main types of purine and pyrimidine metabolites exist; they are adenine, guanine,
cytosine, thymine, and uracil and each of these has a distinctive metabolic pathway (Berg et al.,
2002; Carver and Walker, 1995; McDonald et al., 2011).
In short, it is known that the microbial nucleic acids are hydrolysed into nucleosides, bases, and
degradation products in the small intestine and in those forms absorbed across the intestinal mucosa
(Chen and Gomes, 1992; McAllan, 1980) The high activity of xanthine oxidase [1.17.3.2] in the
small intestinal mucosa and the blood converts most of the purine metabolites into the terminal
degradation products; uric acid and allantoin (Chen et al., 1990a; Verbic et al., 1990). A further
degradation of both the purine and the pyrimidine metabolites probably takes place in the blood and
across the hepatic tissues. The final products of the purine pathways; uric acid and allantoin, are
both cleared rapidly from the blood by the kidneys to be easily detected in the urine (Chen et al.,
1990a; Chen and Ørskov, 2004; Verbic et al., 1990). Research so far suggests that dietary nitrogen,
when incorporated into microbial purine metabolites, can not be re-used and this contributes
considerable to the nitrogen loss in dairy cows (Chen and Ørskov, 2004; Tas and Susenbeth, 2007).
Presumably, the pyrimidines are metabolised during absorption, in the blood, and in the hepatic
tissue in much the same manner as the purines. However, it is known that the pyrimidine degradation products, β-alanine and β-aminoisobutyric acid can be incorporated into other intermediates of
the nitrogen metabolism resulting in a more nitrogen economical degradation than seen for the purine degradation products (Kenehisa et al., 2014: KEGG beta-alanine metabolism and valine, leucine and isoleucine degradation; Loffler et al., 2005). This could indicate that the degradation pathways of the pyrimidines differ from that of the purines in dairy cows but the salvage or excretion
mechanisms involved during pyrimidine degradation is not well described.
12
Introduction
Quantitative analysis of purine and pyrimidine metabolites in dairy cattle research has mainly been
focused on purine degradation products in urine and milk, where uric acid and allantoin excretion
has been used as an indirect marker of rumen microbial synthesis (Giesecke et al., 1994; Gonda and
Lindberg, 1997; Gonzalez-Ronquillo et al., 2004; Tas and Susenbeth, 2007). The microbial supply
can be estimated from the urinary concentration of the purine degradation products as there is a direct relationship between microbial nucleic acids entering the small intestine and that excreted in
the urine (McAllan, 1980; McAllan and Smith, 1973a). Most published methods have thus been
developed for purine metabolites in urine. Consequently, to be able to use the multicatheterized cow
model for examining the purine and pyrimidine metabolism, a major part of this thesis has been
focused on developing and validating a rapid, sensitive, specific and reliable LC-ESI-MS/MS technique combined with matrix-matched calibration standards and stable isotopically-labelled reference components for the simultaneous quantification of 20 key purine and pyrimidine metabolites
in bovine blood plasma.
Thus, the overall objective of the Ph.D. study was to improve our knowledge about the quantitative
absorption and intermediary metabolism of purine and pyrimidine metabolites in lactating dairy
cows in order to possibly discover new ways to improve the overall nitrogen efficiency by
a) developing and validating a quantitative method for determining purine and pyrimidine metabolites in bovine blood plasma.
b) examining the quantitative absorption and intermediate metabolism of the purine and pyrimidine
metabolites by studying their net PDV and net hepatic metabolism and to evaluate how this was
influenced by postprandial pattern, dietary portein level and forage source.
c) evaluating the fate of the purine and pyrimidine nitrogen by estimating net PDV and net hepatic
nucleic acid nitrogen fluxes in the splanchnic tissues.
The objectives of the Ph.D. study were addressed by a synopsis based on the literature, laboratory
work, and experimental work with multicatheterized dairy cows. A thorough background and methods section along with three papers form the main body of the thesis. Following the three papers,
the main results and their implications are discussed in relation to the literature and the overall objective. In the end, an overall conclusion is drawn and perspectives indicated.
13
2. Background
First of all, the overall nitrogen metabolism in ruminants with emphasis on the nucleic acid metabolism will be presented. Secondly, to be able to keep up with the many metabolites of which the purine and pyrimidine metabolism is comprised, an overview of the nucleic acid metabolism and biosynthesis of the purine and pyrimidine metabolites in mammals will be given. In the end, the existing knowledge about the purine and pyrimidine metabolism in dairy cattle, with focus on especially
absorption from the small intestine and hepatic metabolism of the purine and pyrimidine metabolites, will be thoroughly reviewed.
2.1 Nitrogen metabolism in dairy cattle
The primary goal of dairy cattle nutritionists with respect to nitrogen utilisation is to achieve maximum output of milk protein with a minimum of dietary nitrogen input and renal loss. Optimal utilisation of nitrogen is vital, as dairy product systems have low nitrogen efficiency, usually around
15% to 40% (Børsting et al., 2003; Calsamiglia et al., 2010; Kohn et al., 2005). The fundamental
problem when reducing nitrogen intake in dairy cows, is to maintain a ruminal ammonia availability
that sustains rumen microbial activity and hereby ensure that the absorption of volatile fatty acids
and microbial protein are not negatively affected. The inefficient use of nitrogen is related to alterations in dietary protein degradation and the efficiency of capture of ruminally degradable nitrogen
for protein synthesis (Calsamiglia et al., 2010; Sunny et al., 2007). Consequently, a substantial
amount of nitrogen is eliminated in the urine as urea (Chen and Ørskov, 2004; Lapierre and Lobley,
2001). In addition, inevitable nitrogen loss also occurs through protein secretions and cell desquamation in gut tissues. Finally, substantial losses also arise from microbial sequestration of nitrogen
in nucleic acids, which are primarily excreted in the urine as uric acid and allantoin (Chen and
Ørskov, 2004; Fujihara and Shem, 2011; Tas and Susenbeth, 2007). Nitrogen losses also occur
through the inefficient utilisation of absorbed amino acids for the synthesis of milk or body protein
(Tamminga, 1992). Attention in this study is especially turned towards the nucleic acid metabolism,
so as to discover the potential of improving utilisation of nitrogen in ruminants by manipulating this
system of nitrogen turnover.
In the following, a review of the nitrogen metabolism in ruminants, as described by McDonald et
al., with emphasis on the involvement of nucleic acid, will be presented (McDonald et al., 2011). In
the rumen, proteins entering from the feed are hydrolysed to peptides and amino acids, the latter
may further be degraded to organic acids and deaminated to yield ammonia (Fig. 1). Dietary protein is, however, not the only contributor to the ammonia pool in the rumen. As much as 30% of the
nitrogen in ruminant feed may be in the form of free amino acids, amides, amines, nucleic acids or
nitrates. Most of these nitrogen sources are readily degraded in the rumen, and their nitrogen is en14
tering the ammonia pool. Microorganisms use these nitrogen sources, including non-protein sources
of nitrogen, such as urea, to synthesise microbial protein (75-85%), microbial nucleic acids (1525%), and smaller amounts of peptides and free amino acids (Fujihara and Shem, 2011; McDonald
et al., 2011; Volden, 2011). Ruminants are unique among farm animals by their ability to synthesise
true protein in the rumen from non-protein nitrogen (Virtanen, 1966). Subsequently, the microbial
cells pass from the rumen to the small intestine, where they are digested and their components, including protein and nucleic acids, absorbed or passed along for excretion (McDonald et al., 2011).
Besides nitrogen, microbial protein synthesis in the rumen requires energy-rich carbohydrates and
other essential nutrients (sulphur, minerals, branched-chain fatty acids) as well as enzymes and
growth factors. An important feature of microbial protein formation is, the capability of bacteria to
synthesise indispensable (or essential) amino acids, thus rendering their host independent of dietary
intake.
Figure 1. Digestion and metabolism of nitrogenous components (modified from McDonald et al., 2011). N, nitrogen.
The low nitrogen efficiency in dairy cattle has resulted in a large number of studies trying to optimize rumen microbial fermentation and flow of nitrogen to the small intestine, mainly focusing on
different aspects of protein and amino acid utilization (Calsamiglia et al., 2010; Clark et al., 1992;
Firkins, 1996; Steinfeld et al., 2006; Tamminga, 1992). Unfortunately, extensive research in this
area during the last two decades has not led to the level of improvement in the utilization of nitro-
15
gen hoped for. Therefore, in this study, attention was turned to another part of the nitrogen metabolism, namely; the nucleic acid metabolism, also called; the purine and pyrimidine metabolism.
2.2 The nucleic acid metabolism
The basic nucleic acid metabolism is thought to be highly preserved between mammals. The following description as well as appendix I. is based upon by Berg et al. (2002), Carver and Walker
(1995), and McDonald et al. (2011).
2.2.1 Bases, nucleosides, nucleotides, nucleic acids, and DNA/RNA
Nucleic acids are high molecular weight components which play a fundamental role in living organisms as a store of genetic information in the form of DNA. The DNA is located in the nucleus of the
cell as part of the chromosome structure. They are also the means by which this information is utilised in the synthesis of proteins through RNA i.e. the central dogma; DNA → RNA → protein.
Upon hydrolysis, nucleic acids yield a mixture of nitrogenous bases, such as purine bases; adenine
and guanine, and pyrimidine bases; cytosine, thymine, and uracil, as well as pentose sugars and
phosphoric acids. There are five common bases; three are pyrimidine type structures based on a sixmembered ring, and two are purine based structures with a second five-membered ring (Fig 2).
Purine
Adenine
Guanine
Pyrimidine
Cytosine
Uracil
Thymine
Figure 2. The basic purine and pyrimidine structures and the five common purine and pyrimidine bases.
The component formed by linking one of the nitrogenous bases to a pentose sugar; a deoxyribose or
a ribose, is termed a nucleoside. Each sugar and base combination has a unique name (Fig 3).
Base
Adenine
Guanine
Cytosine
Thymine
Uracil
Ribonucleic acid
Ribonucleoside Ribonucleotide
Adenosine
Adenylate
Guanosine
Guanylate
Cytidine
Cytidylate
Uridine
Uridylate
Deoxyribonucleic acid
Deoxyribonucleoside
Deoxyribonucleotide
2’-deoxyadenosine
Deoxyadenylate
2’-deoxyguanosine
Deoxyguanylate
2’-deoxycytidine
Deoxycytidylate
Thymidine
Thymidylate
-
Uridine
Figure 3. Nomenclature of purine and pyrimidine bases, nucleosides and nucleotides.
If a nucleoside is esterified with a phosphoric acid it forms a nucleotide. The nucleoside moiety
(base + sugar) has the phosphate group attached to the 3’ or the 5’ position of the sugar (Fig. 4).
Nucleotides may occur in the mono-, di-, or triphosphate form. Nucleic acids are nucleotides arranged in certain patterns such as DNA and RNA. DNA is typically arranged as double helical
strands. Each strand consists of alternate units of the deoxyribose and phosphate groups. Attached
16
to each sugar group is one of the four bases; adenine, guanine, cytosine, and thymine. The bases on
the two strands are joined in pairs by hydrogen bonds. Guanine always pairs with cytosine, with
three hydrogen bonds, and adenine always with thymine, but with only two hydrogen bonds. The
sequence of bases along the strand carries the genetic information of the cell.
Base pairing in DNA
Nucleic acid structure
Uridine-5’-monophosphate, a nucleotide
Figure 4. Nucleotide and nucleic acid structure and base pairing in DNA. Ade, adenine; Gua, guanine; Cyt, cytosine; Thy, thymine.
Apart from DNA, the cell contains different types of RNA, defined in terms of molecular size, base
composition, and functional properties. There are three main forms with multiple functions in the
cell, namely; messenger RNA, ribosomal RNA, and transfer RNA. RNA differ from DNA in the
sugar moiety (ribose) and also in the present bases; uracil in place of thymine. Most RNA molecules exist in a single folded chain. Apart from being building blocks in the structure of nucleic acids, nucleotides exist free as monomers and play important roles in many different parts of the cellular metabolisms; they are the activated precursors of nucleic acid, they play a major part in the
energy metabolism as stores of energy, they function as physiological and activated mediators, they
are components of coenzymes and can function as allosteric effectors and, they have cellular agonist capabilities.
2.2.2 Purine and pyrimidine nucleotide biosynthesis, regulation, salvage, and catabolism
The metabolic requirements for the nucleotides and their cognate bases can be met by both de novo
biosyntheses; from low molecular weight precursors such as amino acids, and/or from dietary intake
17
(salvage). The pathways for the biosynthesis of nucleotides fall into two classes: de novo pathways
and salvage pathways.
In de novo pathways, the nucleotide bases are assembled from simpler components. The framework
for the pyrimidine bases are assembled first and then attached to a ribose. In contrast, the framework for purine bases are synthesised piece by piece directly onto a ribose-based structure. These
pathways comprise a small number of elementary reactions that are repeated with variation to generate the different nucleotides. In salvage pathways, preformed bases are recovered and reconnected
to a ribose unit. Both de novo and salvage pathways lead to the synthesis of ribonucleotides. The
deoxyribonucleotides are synthesises from the corresponding ribonucleotide. The deoxyribose sugar
is generated by the reduction of ribose within a fully formed nucleotide. Furthermore, the methyl
group that distinguishes thymine from uracil is added in the last step in the pathway.
The salvage pathways conserve energy and permits cells incapable of de novo synthesis to maintain
nucleotide pools. It is interesting to note that the enzyme activities of the salvage pathways have
often been found to be higher than those of the de novo synthetic pathways in humans. A detailed
description of the mechanisms of purine and pyrimidine catabolism is described in the following
section. Details of the purine and pyrimidine nucleotide biosynthesis, regulation, and salvage, as
well as the processes of nucleotide interconversion and formation and regulation of deoxyribonucleotides are given in appendix I.
Purine catabolism: The nucleotides of a cell undergo continual turnover. The degradation pathway
starts with a hydrolytical degradation of the nucleotides to nucleosides catalysed by nucleotidases
(Fig. 5 and Fig. 1 in paper II). The phosphorolytic cleavage of nucleosides to free bases and ribose1-phosphate or deoxyribose-1-phosphate is catalysed by phosphatases. Ribose-1-phosphate is further isomerized by phosphoribomutase to ribose-5-phosphate (R5P), a substrate in the synthesis of
phosphoribosyl pyrophosphate (PRPP). The free bases can possibly be reused to form nucleotides
by salvage pathways. If not salvaged, they are degraded and excreted. Catabolism of purine nucleotides ultimately leads to the production of urate in humans and uric acid and allantoin in ruminants.
To give an example; AMP is degraded to the free base hypoxanthine through deamination and hydrolytic cleavage of the glycosidic bond. Xanthine oxidase [1.17.3.2], a molybdenum- and iron containing flavoprotein, oxidizes hypoxanthine to xanthine and then to uric acid. Molecular oxygen, the
oxidant in both reactions, is reduced to H2O2, which is decomposed to H2O and O2 by catalase
[1.11.1.6]. In ruminants, uricase [1.7.3.3] perform the final degradation of uric acid to allantoin.
18
Figur 5. Reactions of the purine catabolism. Metabolites: R5P, ribose-5-phosphate; AMP, 5’-adenylic acid (adenosine monophosphate); IMP, 5’-inosinic acid (inosine monophosphate); XMP, 5’-xanthylic acid (xanthosine
monophosphate); GMP, 5’-guanidylic acid (guanosine monophosphate). Enzymes: 1. 5’-nucleotidase [3.1.3.5], 2.
AMP deaminase [3.5.4.6], 3. adenosine deaminase [3.5.4.4], 4. purine nucleoside phosphorylase [2.4.2.1], 5. xanthine oxidase [1.17.3.2], 6. guanine deaminase [3.5.4.3].
Figure 6. Reactions of the pyrimidine catabolism. Metabolites: CMP, 5’-cytidylic acid (cytidine monophosphate);
UMP, 5’-uridylic acid (uridine monophosphate); dTMP, thymidine 5’-monophosphate. Enzymes: 1. 5’-
19
nucleotidase [3.1.3.5], 2. cytidine deaminase [3.5.4.5], 3. uridine nucleotidase [3.2.2.3], 4. dihydropyrimidine
dehydrogenase [1.3.1.2], 5. dihydropyrimidinase [3.5.2.2], 6. ureidopropionase [3.5.1.6], 7. thymidine phosphorylase [2.4.2.4].
Pyrimidine catabolism: In the pyrimidine nucleotide catabolism, the nucleosides are degraded to
nucleotides and then to free bases (Fig. 6 and Fig. 2 in paper II). Cytidine is deaminated to uridine
first and then uridine is dephosphorylated to uracil. Uracil and thymine are further degraded by
analogous reactions to β-alanine (CMP and UMP) or β-aminoisobutyric acid (dTMP), and NH3 and
CO2. β-alanine can become part of the β-alanine metabolism and β-aminoisobutyric acid part of the
valine, leucine, and isoleucine metabolism and the citric acid cycle (Kenehisa et al., 2014: KEGG
beta-alanine metabolism and valine, leucine and isoleucine degradation; Loffler et al., 2005).
2.3 The purine and pyrimidine metabolism in dairy cattle
The significance of microbial nucleic acids in nutritional physiology of ruminants has so far not
been of interest regardless of the fact that they correspond to more than 20% of the total microbial
nitrogen pool in ruminants. Consequently, their intermediary metabolism and contribution to the
overall nitrogen metabolism is sparsely described. Most of what is known concern the digestion of
nucleic acids in the rumen and small intestine and excretion of purine degradation products in the
urine (Chen et al., 1990a; Giesecke et al., 1994; Verbic et al., 1990).
2.3.1 Degradation of dietary nucleic acids and re-synthesis of microbial nucleic acids
Most of the dietary nucleic acids entering the rumen are degraded in the rumen and from their nitrogen contribution and the inherent ammonia pool, microbial protein and nucleic acids are formed
(Fujihara and Shem, 2011; McAllan, 1982; McDonald et al., 2011; Volden, 2011). In concentrates
and roughages, nucleic acids correspond to 1-4% and 5-20% of the total nitrogen, respectively
(McAllan, 1982). A small amount of degradable nucleic acid enters the rumen from mucosal secretions and sloughed mucosal cells as well. It is not known exactly how effective the rumen degradation of feed nucleic acid is but, based on work by McAllan and Smith, demonstrating that both
DNA and RNA is rapidly degraded in the rumen, it is assumed that most of the dietary nucleic acids
are catabolised (McAllan, 1982; McAllan and Smith, 1973a,b). The degradation efficiency depends
on the rumen enzymatic activity, reflecting differences in microbial populations between animals
and diets, and the rumen passage rate. The total amount of DNA and RNA synthesised in the rumen
depends largely upon the amount of bacterial growth. Studies with ruminal addition of N15-labeled
ammonia have shown that all the nucleic acid bases were steadily enriched and peaked at about 10h
after introduction (McAllan, 1982; Van Nevel and Demyer, 1977). And this has led to the belief
that microbes in the rumen synthesise microbial nucleic acids mostly through de novo synthesis and
not from salvaged bases and nucleosides. Bacteria are able to utilise some bases and nucleosides,
but probably fail to do so in vivo, as these are rapidly deaminated in the rumen. Rumen protozoa are
20
unable to synthesise purines and pyrimidines as well as ribose, and their nucleic acids are thus
probably derived from bacterial nucleic acids (Coleman, 1968).
2.3.2 Degradation of microbial nucleic acids in the small intestine
In the small intestine, the synthesised microbial nucleic acids are hydrolysed to nucleosides and free
bases before subsequent absorption can take place. The amount of DNA (30-40%) and RNA (6070%) entering the intestine has been estimated at 15-35 g/kg dry matter (DM) digesta, the majority
of microbial origin, with digestibilities of 75-85% and 80-90%, respectively (Fujihara and Shem,
2011; McAllan, 1980; Smith and McAllan, 1971). Enzymes which degrade nucleic acids are present
in pancreatic secretions into the intestinal lumen and the small intestinal mucosa (Barnard, 1969;
Nakayama et al., 1981). The microbial DNA and RNA are hydrolysis by the pancreatic enzymes
through the actions of polynucleotidases, nucleosidases, and phosphatases (Berg et al., 2002; Carver
and Walker, 1995; McDonald et al., 2011). These enzymes catalyse the cleavage of the ester bond
between the sugar and phosphoric acid in the nucleic acids, liberating the nucleosides. Nucleosidases [3.1.3.5] attack the linkage between the sugar and the nitrogenous bases, releasing the free purine
and pyrimidine bases. Phosphatases complete the hydrolysis by separating the orthophosphoric acid
from ribose or deoxyribose. If the nucleosides and/or bases are not absorbed directly, the purine and
pyrimidine bases are probably further degraded into uric acid and/or allantoin, the main purine degradation products in ruminants, and the pyrimidine degradation products; β-alanine and βaminoisobutyric acid. Most likely, a further degradation to ammonia and other nitrogenous degradation products such as urea is also possible. Pancreatic ribonucleases are the rate limiting enzymes in
this multistep break down, and their activity has been found to be 1,200 times higher in ruminants
than in humans (Barnard, 1969). Appreciable amounts of the enzyme xanthine oxidase [1.17.3.2],
able of catalysing the oxidation of hypoxanthine to xanthine and xanthine to uric acid, have been
reported in bovine small intestine compared to little or none in sheep or goat intestine (Al-Khalidi
and Chaglassian, 1965; Chen and Gomes, 1992; Roussos, 1963). The importance of xanthine oxidase [1.17.3.2] in the purine metabolism of cattle will be reviewed in further detail later in this section. In steers, it has been observed that nucleic acid degradation is accompanied by transient appearance of adenosine, guanosine, and pyrimidine nucleosides (McAllan, 1980). In similar experiments, it was shown that of the purine and pyrimidine metabolites infused into the small intestine of
steers, adenine, guanine, and uracil was completely removed, thymine and xanthine to approximately 90%, and hypoxanthine and cytosine to only about 50%. The nucleosides adenosine and cytidine
were also completely removed but were replaced in part by the products inosine/hypoxanthine and
cytosine, respectively. Other nucleosides were removed to approximately half the extent of the cor-
21
responding base. These findings point towards an at least partial degradation of the purine and pyrimidine metabolites already in the intestinal lumen before absorption.
2.3.3 Absorption and intermediary metabolism of purine and pyrimidine metabolites
The absorption and hepatic metabolism of the purine and pyrimidine metabolites is sparsely described and primarily the purine metabolic pathway has been examined (Balcells et al., 1992a; Chen
and Gomes, 1992; Fujihara and Shem, 2011; McDonald et al., 2011). The purine nucleosides, free
bases, and possible degradation products are known to be absorbed from the intestinal lumen and
subjected to degradation and possible salvage in the intestinal mucosa (McAllan, 1980; Verbic et
al., 1990). In humans, mucosal enterocytes have been found to have very limited capacity for de
novo purine synthesis and thus probably depend upon salvage (Carver and Walker, 1995). Mucosal
purine salvage could save valuable energy for the cow, but precisely to what extend this occurs is
unknown. Cattle have a high activity of xanthine oxidase [1.17.3.2] in most tissues, and especially
in the small intestinal mucosa, blood, and hepatic tissue (Al-Khalidi and Chaglassian, 1965; Balcells et al., 1992a; Chen and Gomes, 1992; McDonald et al., 2011). Consequently, it has been proposed that practically all of the absorbed purines are fully degraded during/pre-absorption and enter
the blood and hepatic tissue mainly as non-salvageable uric acid and/or allantoin (Chen et al.,
1990a; Verbic et al., 1990). The purine nitrogen would as such be lost to the host animal. In ruminants, uric acid and allantoin functions as the principal nitrogenous end products of the purine metabolism. Few details on the location and mechanisms of degradation of allantoin to uric acid exists,
and it has been speculated, that it probably take place during/pre-absorption or in the hepatic tissue,
as uricase [1.7.3.3] is present in only trace amounts in the blood (Chen et al., 1990a). In sheep, the
activity of xanthine oxidase [1.17.3.2] is negligible and the purine metabolites are available for incorporation into tissue nucleic acids (Al-Khalidi and Chaglassian, 1965; Chen and Gomes, 1992;
Chen et al., 1990a; Razzaque et al., 1981). Due to the differences in xanthine oxidase [1.17.3.2]
activities in most tissues, sheep and cattle are thought to hold distinct purine and pyrimidine metabolic pathways (Chen and Gomes, 1992). Presumably, the pyrimidine metabolites are degraded
during pre-absorption, in the blood, and in the hepatic tissue in much the same manner as the purine
metabolites. However, data concerning the pyrimidine metabolic pathway and other purine metabolites but uric acid and allantoin in cattle is at present very limited. It is known that the pyrimidine
end products; β-alanine and β-aminoisobutyric acid, can function as intermediate products in other
parts of the nitrogen metabolism (Kenehisa et al., 2014: KEGG beta-alanine metabolism and valine,
leucine and isoleucine degradation; Loffler et al., 2005). This could indicate that the degradation
pathways of the pyrimidine metabolites differ from that of the purine metabolites. In general, how
much and what types of purine and pyrimidine metabolites are being absorbed from the intestinal
22
lumen and to what extent they are being metabolised across the hepatic tissue is unknown. Part of
the objective in this study is to try to describe and give a quantitative picture of the metabolism and
degradation of the purine and pyrimidine metabolites by studying postprandial patterns of net PDV
and net hepatic tissue so as to evaluate purine and pyrimidine nitrogen in this context.
2.3.4 Endogenous purine and pyrimidine metabolites
Some of the purine and pyrimidine metabolites entering the circulating blood may also originate
from the degradation of tissue nucleic acids (endogenous). Presumable, the high xanthine oxidase
[1.17.3.2] activity in cattle divert the released endogenous purine metabolites away from possible
salvage and into the degradation pathway. Measurements of the magnitude of endogenous excretion
have been made with the aid of intragastric infusion or replacement of digesta entering the small
intestine, as reviewed by Chen and Gomes, and the purine metabolites has been found to be three
times higher in cattle than in sheep per kg of metabolic weight (Chen and Gomes, 1992). Since it is
assumed that the exogenous purine and possible pyrimidine metabolites are unavailable to the animal, the loss must be replaced by de novo synthesis. As a result, there is always a net endogenous
contribution to the total pool of purine and pyrimidine degradation products in the arterial blood and
subsequently in the urine.
2.3.5 Renal clearance of purine and pyrimidine metabolites
Purine metabolites, from both endogenous and exogenous sources, entering the blood are removed
with a clearance rate constant of about 30%/h, with urinary excretion as the primary route of disposal (Chen et al., 1990a; Giesecke et al., 1994; Verbic et al., 1990). Even though Boudra et al. recently developed a method able to quantitate β-alanine and β-aminoisobutyric acid in bovine urine,
the mechanisms of renal excretion of pyrimidine metabolites are basically an unwritten chapter
(Boudra et al., 2012). In experiments using sheep and cattle infused with purine components abomasally, the renal clearance rate was volumetrically the major excretory route (Balcells et al., 1991;
Vagnoni et al., 1997). In cattle, 82-93% of the urinary excreted purine degradation products are
allantoin, the remainder is mainly uric acid. Consequently, in cattle, the excretion of allantoin and
uric acid correlates with the concentrations of nucleic acids in the rumen content and small intestine
(Chen and Gomes, 1992; McAllan, 1980; Verbic et al., 1990). Quantitative analysis of purine and
pyrimidine metabolites in dairy cattle research has consequently, prior to this project, almost solely
focused on purine metabolites in urine and milk, where uric acid and allantoin, has been used as an
indirect marker of rumen microbial biosynthesis (Boudra et al., 2012; Chen and Ørskov, 2004;
Gonda and Lindberg, 1997; Gonzalez-Ronquillo, 2004; Tas and Susenbeth, 2007). The microbial
supply can be estimated from the urinary concentration of the purine degradation products as there
is a direct relationship between microbial nucleic acids entering the small intestine and that excreted
23
in the urine (McAllan, 1980; McAllan and Smith, 1973a). This relationship is based on the fact that
most of the purines entering the rumen are broken down and reused by rumen microbes, as described previously, making the majority of urinary uric acid and allantoin of microbial origin (Chen
and Ørskov, 2004; Gonzalez-Ronquillo, 2004; Johnson et al., 1998). The measurement of urinary
purine degradation products avoids the need for surgically modified animals and has been shown to
correlate well with other measures of microbial synthesis in the rumen (Dewhurst et al., 2000;
Titgemeyer, 1997). Other purine degradation products, like xanthine and hypoxanthine, have also
been identified in bovine urine but only in very small concentrations compared with higher levels in
sheep and goats (Chen et al., 1990a; Yanez-Ruiz et al., 2004). Some of the purine degradation
products in the blood can also be disposed of by none renal routes, such as by secretion into the
milk by diffusion from the blood into the mammary alveolar lumen as shown for allantoin
(Giesecke et al., 1994; Gonda and Lindberg, 1997; Tiemeyer et al., 1984).
24
3. Hypotheses and objectives
The overall objective of the Ph.D. study was to improve our knowledge about the quantitative absorption and intermediary metabolism of purine and pyrimidine metabolites in lactating dairy cows
in order to possible discover new ways to improve the overall nitrogen efficiency.
Thus, the specific objectives were:
a) develop a quantitative method for determining purine and pyrimidine metabolites in bovine blood
plasma.
b) examine the quantitative absorption and intermediate metabolism of the purine and pyrimidine
metabolites by studying their net PDV and net hepatic metabolism and to evaluate how this was
influenced by postprandial pattern, CP level and forage source.
c) evaluate the fate of the purine and pyrimidine nitrogen by estimating net PDV and net hepatic
nucleic acid nitrogen fluxes in the splanchnic tissues.
The hypotheses of the Ph.D. study were:
a)
i) purine and pyrimidine metabolites can accurately be quantified in bovine blood plasma
obtained from multicatheterized cows by applying LC-ESI-MS/MS, and ii) the metabolites
can be isolated and concentrated from plasma prior to analysis by applying a pre-treatment
protocol consisting of protein precipitation (PPT), ultrafiltration, evaporation, and subsequent resolution.
b)
i) purine and pyrimidine metabolites are absorbed from the small intestine of the dairy cow,
in the form of nucleosides, bases, degradation products, or a combination of these, and undergo degradation across the intestinal wall and the hepatic tissue, and ii) the absorption and
intermediary metabolism of the purine and pyrimidine nucleosides, bases, and degradation
products will reflect the level of microbial flow to the small intestine as a consequence of
varying degrees of microbial biosynthesis with postprandial pattern as well as different protein levels and forage sources fed to the dairy cows.
c)
i) considerable amounts of purine nitrogen is, as a consequence of very effective intestinal
and intermediate degradation mechanisms, released in the form of uric acid and allantoin
from the splanchnic tissues and as such lost to anabolic processes and ii) pyrimidine nitrogen is, as a result of use in other parts of the nitrogen metabolism within the splanchnic tissues, released in much smaller amounts than purine nitrogen.
25
To investigate the quantitative absorption and intermediary metabolism of purine and pyrimidine
metabolites in lactating dairy cows, three studies were undertaken:
Paper I: “Simultaneous quantification of purine and pyrimidine bases, nucleosides and their
degradation products in bovine blood plasma by high performance liquid chromatography
tandem mass spectrometry”
The hypotheses were i) that LC-ESI-MS/MS can accurately be used to quantitatively determine a
range of purine and pyrimidine metabolites in cow blood plasma when incorporated with matrixmatched calibration standards as well as SIL, and ii) purine and pyrimidine metabolites can be isolated and concentrated from blood plasma by applying an appropriate pre-treatment protocol. The
objective was to develop and validate an LC-ESI-MS/MS method for quantification of a range of
purine and pyrimidine metabolites in cow blood plasma and also to develop a reliable, stable, simple, component-specific, and repeatable pre-treatment protocol for the bovine plasma samples.
Paper II (Exp. I): “Absorption and intermediary metabolism of purines and pyrimidines in
lactating dairy cows”
The hypotheses were i) that the purine (adenine, guanine) and the pyrimidine (cytosine, thymine,
uracil) metabolites, in the form of either a nucleoside, a base, a degradation product, or a combination of these, are absorbed from the small intestine of the dairy cow and undergo degradation across
the intestinal wall and the hepatic tissue and ii) that the purine and pyrimidine metabolites and the
nitrogen they contain to a large extent ultimately are lost following degradation and excretion via
the kidneys. The objective was to describe and give a quantitative picture of the metabolism and
degradation of purine and pyrimidine metabolites by studying postprandial patterns of net PDV and
net hepatic metabolism so as to evaluate purine and pyrimidine nitrogen pools in this context.
Manuscript III (Exp. II): “Protein level influences the splanchnic metabolism of purine and
pyrimidine metabolites in lactating dairy cows”
The hypothesis was the net PDV and net hepatic fluxes of the purine and pyrimidine nucleosides,
bases, and degradation products would reflect different degrees of microbial biosynthesis with different dietary protein levels (12.5, 15.0, and 17.5% CP) and proportions of forage sources (grass vs.
corn silage) in the ration. The objectives were to study the net PDV, net hepatic and total splanchnic metabolism of the purine and pyrimidine metabolites and evaluating how this was affected by
dietary CP level and forage source and, to evaluate the fate of the purine and pyrimidine nitrogen by
estimating nucleic acid nitrogen fluxes in the splanchnic tissues.
26
4. Methods
In the first part of this section, the ruminally cannulated multicatheterized cow model and the calculations applied in the experimental part of this thesis for evaluation of the purine and pyrimidine
metabolism and overall nitrogen metabolism in the net PDV, net hepatic, and net splanchnic tissues
(TSP) will be presented. This cow model is the fundamental basis for obtaining samples to describe
the intermediary metabolism of purine and pyrimidine metabolites. As no method was available, a
major part of the project was to be able to quantify the purine and pyrimidine metabolites. Thus, in
the second part the development and validation of a high performance LC-ESI-MS/MS based technique for simultaneous quantification of purine and pyrimidine nucleosides, bases, and their degradation products in bovine blood plasma will be described along with a review of the methodologies
applied.
4.1 The multicatheterized cow model
To address and investigate the inter-organ net fluxes of purine and pyrimidine metabolites, the multicatheterized cow model was used. The cows were surgically fitted with ruminal cannulas and
permanently implanted with indwelling catheters in major blood vessels supplying and draining the
visceral tissues (Fig. 7).
Figure 7. The multicatheterized cow model with placements of permanent catheters (modified with permission
from D.L. Harmon, University of Kentucky, USA). The mesenteric vein catheter was used for infusion of blood
flow marker and, the hepatic portal, hepatic, and gastrosplenic vein, and an artery (mesenteric/intercostal) were
used for blood sampling.
Blood samples were obtained using this cow model to calculate net fluxes (net uptake or net release) of purine and pyrimidine metabolites across the PDV and hepatic tissue by multiplying the
venous-arterial concentration difference of metabolites with blood flows (Huntington et al., 1989;
Katz and Bergman, 1969a). Plasma concentrations of purine and pyrimidine metabolites were determined by LC-ESI-MS/MS as described in the second part of this methods section.
27
4.1.1 Blood plasma flow
The blood plasma flows in the portal hepatic and hepatic veins were determined simultaneously
under steady state conditions by continuous downstream dilution of the marker; paraaminohippuric acid (pAH), into a mesenteric vein draining the intestines (Katz and Bergman,
1969b). The hepatic portal and hepatic blood plasma flows could then be determined based on the
Fick Principle (Cant et al., 1993; Zierler, 1961). To be able to use the Fick Principle, steady state
conditions, a non-metabolizable blood flow marker such as pAH, the ability to obtain a representative blood sample and, the ability to produce reliable venous-arterial concentration differences are
needed. Steady state conditions were achieved by continuously infusing pAH flow marker 1h preceding the first experimental blood sampling. To avoid overestimation of blood flows, all plasma
samples were deacetylated prior to determination of plasma concentrations of pAH (Kristensen et
al., 2009). To be able to gain a representative blood sample, catheters were placed meticulously in
the hepatic portal vein so as the mesenteric blood containing the flow marker could be thoroughly
mixed with blood from the gastrosplenic vein (Fig. 8). In cattle, this can be tricky as the angle between the junction of the anterior mesenteric and gastrosplenic veins occur at a short distance and
often the hepatic portal vein is very short (Seal and Reynolds, 1993). A verification of the proper
placement of the catheters was completed during autopsy after each animal experiment. The pAH
infusion level was targeted to be higher than the arterial concentration to ensure adequate and reliable venous-arterial concentration differences (Katz and Bergman, 1969b).
Figure 8. Positions of blood vessels, catheters and the infusion site for para-aminohippuric acid (pAH) in the
splanchnic bed of the multicatheterized cow model (modified from Katz and Bergman, 1969b).
In the following the equations for calculating plasma blood flows across the splanchnic tissues are
presented. The plasma flows in the hepatic portal and hepatic veins were calculated from the marker
concentration (Eq. 1 and 2, respectively). The hepatic artery plasma flow was estimated by difference (Eq. 3). A net gastrosplenic plasma flow was estimated so as to be able to determine a net gastrosplenic flux and distinguish between the flux of metabolites coming from the forestomachs or
from the intestines. The net gastrosplenic plasma flow was estimated from the hepatic portal plasma
28
flow presuming that the net gastrosplenic plasma flow was 20% of the hepatic portal plasma flow
(Eq. 4) (Remond et al., 1993; Storm et al., 2011). Hence, when evaluating gastrosplenic fluxes it is
important to keep in mind that this flux calculation is based on estimates and not experimental data
(Paper II).
Eq. 1:
Portal vein plasma flow (PF), L⁄h =
marker infusion rate, mmol/h
([marker]hepatic portal -[marker]arterial ), mmol/h
Eq. 2:
Hepatic vein plasma flow (HF), L⁄h =
marker infusion rate, mmol/h
([marker]hepatic -[marker]arterial ), mmol/h
Eq. 3:
Hepatic artery plasma flow (HAF), L⁄h = hepatic vein plasma flow - hepatic portal vein plasma flow
Eq. 4:
Gastrosplenic vein plasma flow (GF), L⁄h = hepatic portal vein plasma flow × 0.2
The blood flow in the PDV and hepatic tissues is high in lactating dairy cows, in the range of 1,2001,700 L/h and 1,500-2,000 L/h, respectively (Kristensen et al., 2007; Reynolds et al., 1988). Consequently, the venous-arterial concentration differences across the PDV and hepatic tissues can be
relatively small for some of the purine and pyrimidine metabolites (μmol/L) relatively to the high
blood flow (Seal and Reynolds, 1993).
4.1.2 Net flux
The equations for calculating the net hepatic portal flux, net hepatic flux, net splanchnic flux, and
net gastrosplenic flux are presented in the following. The net hepatic portal, splanchnic, and gastrosplenic fluxes are venous-arterial concentration differences multiplied by the respective blood flow
(Eq. 5, 7 and 8, respectively). The calculation of net hepatic flux is more complex, as blood to the
liver is supplied by both the hepatic portal vein and the hepatic artery (Fig. 8). Consequently, two
venous-arterial concentration differences are needed in the calculation (Eq. 6). A positive net flux
indicates a net release from a given tissue bed to the blood and a negative net flux indicates a net
uptake by the given tissue bed.
Eq. 5:
Net hepatic portal flux, μmol⁄h = ([metabolite]hepatic portal - [metabolite]arterial ), μmol/L, × PF, L/h
Eq. 6:
Net hepatic flux, μmol⁄h = ([metabolite]hepatic - [metabolite]hepatic portal ), μmol/L, × PF, L/h, +
29
([metabolite]hepatic - [metabolite]arterial ), μmol/L, × HAF, L/h
Eq. 7:
Net splanchnic flux, μmol⁄h = ([metabolite]hepatic - [metabolite]arterial ), μmol/L, × HF, L/h
Eq. 8:
Net gastrosplenic flux, μmol⁄h = ([metabolite]gastrosplenic - [metabolite]arterial ), μmol/L, × GF, L/h
4.1.3 Animals and experimental designs
The eight dairy cows included in experiment I (Paper II) were Holstein cows in their second lactation coming from the research farm belonging to Aarhus University (DK). The six dairy cows included in experiment II (Manuscript III) were multiparous Holstein cows in mid to late lactation
from the research facility connected to Reading University (UK). Both sets of cows were ruminally
cannulated and permanently catheterised in the mesenteric artery, and the hepatic portal, hepatic,
and mesenteric vein (multicatheterized cow model). In experiment I, a permanent catheter was also
placed in the gastrosplenic vein and in experiment II, a mammary vein catheter was inserted into the
epigastric vein at the beginning of each sampling week. The operations and experiments were performed at Aarhus University and Reading University, respectively.
In experiment I and II, it was of interest to examine and study the patterns of absorption and intermediary metabolism of purine and pyrimidine metabolites in dairy cows first of all; in relation to
the basic metabolism of these nitrogenous metabolites, and secondly; to determine how this metabolism was affected by CP level (12.5%, 15.0%, 17.5%), forage source (grass, corn; different fermentable carbohydrates) and consequently the various levels of microbial biosynthesis and flow to
the small intestine.
The multicatheterized dairy cow was considered to be a suitable model for this study, as it could be
used to address the inter-organ net fluxes of purine and pyrimidine metabolites. Both experiments
were intensive experiments performed with few cows and in both cases the experiments were set up
as parts of other studies. The protocols of experiment I and II were originally designed to be used in
evaluating urea re-circulation and to examine the effect of protein concentration and forage type on
the nitrogen metabolism and nutrient flux across the PDV and hepatic tissue, respectively (Barratt et
al., 2013; Røjen et al., 2011). The reason for using experiment I in this Ph.D. study was, that individual samples taken each hour after feeding were available, so it was possible to study postprandial
flux patterns. Also, numerous nitrogenous variables of interest to this study had already been determined, plenty of sample material was available, and the exact amount and form of the CP in the diet
was known. Additionally, this model had a gastrosplenic vein catheter incorporated which made it
possible to determine net gastrosplenic fluxes. The reason for using experiment II in this study was
30
again, that many of the nitrogenous variables of interest to this study had already been determined,
sample material was available (pooled), and the treatments (CP levels and forage sources) fitted
well with the overall intentions of this study. Additionally, this model had an epigastric vein catheter inserted at the beginning of each sampling week, making it possible to investigate the concentration differences across the mammary gland. Furthermore, these in vivo treatments have immediate
appeal to many dairy cow nutritionists, as protein level and forage sources are the main ‘handles’
used in optimizing a ration, making this study highly relevant to a broader audience. Finally, using
these two independent experiments, performed in two different countries, but with the same cow
model, also gave an opportunity of assessing datasets produced independently of research facility
norms and practices.
In experiment 1, a single treatment was evaluated from an experiment with a randomized triplicate
incomplete 3 × 3 Latin square design (three treatments) including repeated measurements. The statistical model used to evaluate these sub-samples included the fixed effect of sampling time and
cow within square was considered as a random effect.
Experiment 2 was a completely randomized 2 × 3 factorial design (six treatments) including repeated measurements. The statistical model included the fixed effect of square, period within square,
forage, protein, forage × protein interaction, and forage × period within square interaction and the
random effects of animal.
4.1.4 Hepatic fractional removal and renal variables
To evaluate the effectiveness of the hepatic tissue and its degradation enzymes to turn over metabolites entering not only from the PDV but also from the peripheral tissues (arterial contribution), two
types of hepatic fractional removal of the purines and pyrimidines was estimated; the percentage of
net PDV release (NP%) and the percentage of total influx (TI%) (Eq. 9 and 10, respectively). The
NP% indicated the proportion of metabolite removed by the hepatic tissue from the PDV. However,
besides the purine and pyrimidine metabolites being released from the PDV, the circulating blood
contains levels of these metabolites naturally and the removal or release of these from the hepatic
tissue depends on the effectiveness of the hepatic enzymes and the body’s requirements or tolerance
of the particular metabolite. Therefore, in addition to the NP%, the TI% was calculated, indicating
the proportion of metabolite removed by the hepatic tissue from the peripheral tissues as well as the
PDV. Since most metabolites are removed by the hepatic tissue, the net hepatic flux is often negative. Thus, the calculation of the NP% and TI% was added a negative operational sign to achieve
positive values of hepatic fractional removal.
31
Eq. 9:
NP%, % =
- net hepatic flux, μmol/h
([metabolite]hepatic portal -[metabolite]arterial ), μmol/L, × PF, L/h
Eq. 10:
TI%, % =
- net hepatic flux, μmol/h
([metabolite]hepatic portal , μmol/L × PF, L/h) + ([metabolite]arterial ,μmol/L ×HAF, L/h)
For the evaluation of purine excretion from the kidneys, urine/splanchnic and urine/renal ratios
were estimated alongside the renal metabolite clearance (volume of blood metabolite cleared by the
kidney per unit of time) (Eq. 11, 12 and 13, respectively). See paper II for further details on calculations. Given that the urinary excretion of the purine degradation products; uric acid and allantoin
can be used as an indirect marker of rumen microbial biosynthesis, these two metabolites have been
extensively studied (Chen and Gomes, 1992; Gonda and Lindberg, 1997; Gonzalez-Ronquillo,
2004; Tas and Susenbeth, 2007). Therefore, the simplest and possible only way to relate the purine
and pyrimidine levels obtained in this study with other studies in ruminants was to compare renal
metabolite clearance rates of purine degradation product. The urine/splanchnic ratio indicated the
proportion of metabolite excreted into the urine from the splanchnicus. However, as describe previously, besides the purine and pyrimidine metabolites being released from the splanchnic tissue, the
circulating blood contains endogenous metabolite levels and the purine and pyrimidine excretion
depends on the effectiveness of the kidneys. Hence, besides the urine/splanchnic ratio, the
urine/renal ratio was calculated, indicating the proportion of metabolite excreted into the urine from
the peripheral tissues and the splanchnic tissue.
Eq. 11:
Urine/splanchnic ratio, % =
net urine flux, mmol/h
net splanchnic flux, mmol/h
Eq. 12:
Urine/renal ratio, % =
net urine flux, mmol/h
renal influx, mmol/h
Eq. 13:
Renal clearance, L/h =
[metabolite]urine , mmol/L
[metabolite]arterial , mmol/L × diuresis, L/h
4.1.5 Purine and pyrimidine nitrogen estimation
An evaluation of the purine and pyrimidine nitrogen metabolism in the net PDV and net hepatic
tissues is performed based on the following estimations. The total amount of purine nitrogen and
pyrimidine nitrogen entering the small intestine were estimated to 60 g/d (Experiment I) from the
32
flow of microbial CP to the small intestine using the Nordic feed evaluation system (NorFor), assuming that when degraded dietary nitrogen is reused by the microbial population, 80% of the total
microbial nitrogen is being used for the biosynthesis of microbial protein and 20% is being used for
the biosynthesis of microbial nucleic acids (Fujihara and Shem, 2011; McDonald et al., 2011;
Volden, 2011). Given that purines contain 5 nitrogen atoms per metabolite and pyrimidines 2.5 nitrogen atoms per metabolite on average, microbial purine nitrogen and pyrimidine nitrogen entering
the small intestine were estimated to 40 g/d and 20 g/d, respectively, assuming 2/3 purine nitrogen
and 1/3 pyrimidine nitrogen of nucleic acid N (Fig. 3 in paper II). Less approximate estimations of
the purine nitrogen and pyrimidine nitrogen entering/absorbed from the small intestine could have
been determined if the microbial flow to the small intestine and the digestibility of the microbial
nucleic acids had been determined experimentally. The purine and pyrimidine nitrogen fluxes were
calculated from the metabolite net flux and the metabolite nitrogen content for each specific metabolite (Eq. 14).
Eq. 14:
Metabolite nitrogen flux, g/d = net flux, μmol/h × 𝑛𝑛𝑖𝑡𝑟𝑜𝑔𝑒𝑛 × Mw𝑛𝑖𝑡𝑟𝑜𝑔𝑒𝑛 , g/mol × 24, h × 10−6
4.2 Development and validation of an LC-ESI-MS/MS analysis
To be able to use the multicatheterized cow model for evaluation of the purine and pyrimidine metabolism, splanchnic vessels’ concentrations had to be determined. However, a suitable quantification method fit for use with bovine blood plasma was prior to this study not available. Consequently, a sensitive, specific, and reliable LC-ESI-MS/MS technique was developed and validated for
quantification of 20 selected purine and pyrimidine metabolites. The procedure was incorporated
with SIL and matrix-matched calibration standards. Concurrently, a simple and repeatable pretreatment protocol capable of cleaning up the bovine plasma prior to analysis was established.
Quantitative analysis of purine and pyrimidine metabolites in dairy cattle research has prior to this
project almost solely focused on purine degradation products in urine and milk (Balcells et al.,
1992a; Boudra et al., 2012; Chen et al., 1990a; George et al., 2006; Rosskopf et al., 1990; Tiemeyer
et al., 1984) where the purine degradation products; uric acid and allantoin, can be used as an indirect marker of rumen microbial biosynthesis (Boudra et al., 2012; Chen and Ørskov, 2004; Gonda
and Lindberg, 1997; Gonzalez-Ronquillo, 2004; Tas and Susenbeth, 2007). Consequently, the majority of established methods have been focused on only purines and primarily in urine samples.
Only one other method published by Boudra et al., has sought to and accomplished to quantitate
pyrimidines (β-alanine and β-aminoisobutyric acid) as well as purines, however only in urine sam-
33
ples (Boudra et al., 2012). To our knowledge, none have attempted to determine purines or pyrimidine metabolites in bovine blood plasma.
Several analytical separation methods have been used for determining purine and pyrimidine metabolites in standard mixtures and biological matrices, primarily urine. The vast majority of these
have applied either high performance liquid chromatography (HPLC) (Haunschmidt et al., 2008;
Lin et al., 1997; Liu et al., 2008) or capillary electrophoresis chromatography (CEC) (Gong et al.,
2004; Hua and Naganuma, 2007; Kazoka, 2002; Lin et al., 1997). Owing to its ability to resolve
such a wide variety of components and its premier separation capabilities, the most widely employed chromatographic separation technique is reverse-phase HPLC. When trying to improve efficiency and especially if working with small volumes of samples, CEC is favored. In addition, when
high separation selectivity and sensitivity is essential, micellar electrokinetic chromatography, microemulsion electrokinetic chromatography, capillary zone electrophoresis, (Haunschmidt et al.,
2008) and ultra high performance liquid chromatography (UPLC) (Clariana et al., 2010) have been
applied. As the purine and pyrimidine metabolites are structurally and chemically very similar and
the bovine plasma concentrations are very small, an effective separation technique is important.
Concerning spectrophotometric detection, the most commonly used types are; ultra violet, mass or
electrochemical. Tandem mass spectrometry (MS/MS) preceded by HPLC is currently considered
the method of choice for quantitative analysis of components in biological matrices, not only due to
its wide application range but also its availability and ease to use (Matuszewski et al., 2003; Taylor,
2005; Xu et al., 2007). Moreover, it has prior to this study been demonstrated that purine and pyrimidine metabolites can be accurately quantified in plasma and urine employing LC-MS/MS (Boudra et al., 2012; Hartmann et al., 2006). Thus, a novel quantitative purine and pyrimidine technique
based on the LC-ESI-MS/MS platform, fit for use with bovine blood plasma, was developed. Work
was performed on many different aspects of method development, optimization and validation simultaneously, always taking care to re-evaluate and take into consideration former steps and other
parts of the procedure concurrently. Roughly, the working procedure was as follows:
1. Target considerations and chemical properties of chosen targets.
2. Availability assessment and purchase of matching component standards and SIL.
3. Evaluation of standards and SIL on the triple quadrupole mass spectrometer (HPLC bypass).
-
Selection of representative precursor/product ions and their transitions.
-
Optimization/validation of the transitions with regard to ion scan intensity, mass-tocharge ratios (m/z), and the associated cone voltage and collision energies.
4. Optimization of the HPLC separation employing standards and SIL.
34
5. Development and optimization of the LC-ESI-MS/MS method with standard addition and
bovine plasma, specifically taking into consideration issues with matrix effects.
6. Development and optimization of the calibration and quantification model.
7. Development of a pre-treatment protocol (concurrently with everything above).
8. Validation and application of the method.
In the following sections, the main methologies and principles applied for the development of the
purine and pyrimidine LC-ESI-MS/MS method will be given. Relevant parts and important considerations made during method development and validation will be presented, for full details consult
paper I (Stentoft et al., 2014).
4.2.1 Target considerations
Four classes of purine and pyrimidine metabolites were possible targets during this investigation;
the nucleotides, the nucleosides, the bases, and the degradation products (Fig. 9 and table 1 in paper
I). The LC-ESI-MS/MS analysis was established for quantification of 10 metabolites of the purine
metabolism (Fig. 3 and 9, and table 1 in paper I) and 10 metabolites of the pyrimidine metabolism
(Fig. 3 and 9, table 1 in paper I). These 20 metabolites were brought on to pre-treatment and quanti-
Purines
tative analysis.
Nucleotides
Nucleosides
Bases
DP
3
AMP
AMP3
3
Ado
Ado3
Ade
Uac
dAMP33
dAMP
3
dAdo
dAdo3
Gua
Alo
GMP33
GMP
Guo
Hyp
dGMP33
dGMP
dGuo
Xan
IMP33
IMP
Ino
1
XMP
XMP1
dIno
Pyrimidines
1
Xao
Xao1
3
CMP
CMP3
Cyd
Cyt
β-ala
dCMP33
dCMP
dCyd44
dCyd
Thy
β-ure
dTMP33
dThd
Ura
β-ami
UMP33
UMP
Urd
dhUra22
1
β-Iso
β-Iso1
3
dUMP
dUMP3
dUrd
2
dhThy
dhThy2
dTMP
dhUra
Figure 9. Possible targets of the
purine and pyrimidine metabolism.
Abbreviations of the purine and pyrimidine metabolites are from IUPAC, abbreviations and symbols for
nucleic acids, polynucleotides and
their constituents (IUPAC, 2014).
1
No available standard and/or stable
isotopically labelled reference component. 2Excluded due to limits in
method capacity. 3Un-identified
during method development.
4
Sensitivity too low for quantification.
AMP, 5’-adenylic acid (adenosine monophosphate); dAMP, 2’-deoxyadenosine 5’-monophosphate (deoxyadenosine monophosphate); GMP, 5’-guanidylic acid (guanosine monophosphate); dGMP, 2’-deoxyguanosine 5’monophosphate (deoxyguanosine monophosphate); IMP, 5’-inosinic acid (inosine monophosphate); XMP, 5’xanthylic acid (xanthosine monophosphate); CMP, 5’-cytidylic acid (cytidine monophosphate); dCMP, 2’deoxycytidine 5’-monophosphate (deoxycytidine monophosphate); dTMP, thymidine 5’-monophosphate; UMP,
5’-uridylic acid (uridine monophosphate); dUMP, 2’-deoxyuridine 5’-monophosphate (deoxyuridine monophosphate); Ado, adenosine; dAdo, 2’-deoxyadenosine; Guo, guanosine; dGuo, 2’-deoxyguanosine; Ino, inosine; dIno,
2’-deoxyinosine; Xao, xanthosine; Cyd, cytidine; dCyd, 2’-deoxycytidine; dThd, thymidine or 2’deoxythymidine; Urd, uridine; dUrd, 2’-deoxyuridine; Ade, adenine; Gua, guanine; Hyp, hypoxanthine; Xan,
35
xanthine; Cyt, cytosine; Thy, thymine; Ura, uracil; dhUra, dihydrouracil; dhThy, dihydrothymine; Uac, uric acid;
Alo, allantoin; β-ala, β-alanine (3-aminopropionic acid); β-ure, β-ureidopropionic acid (3-ureidopropionic acid);
β-ami, β-aminoisobutyric acid (3-aminoisobutyric acid); β-iso, β-ureidoisobutyric acid (3-ureidoisobutyric acid).
To give a full picture of the purine and pyrimidine metabolism, all of the purine and pyrimidine
metabolites should have been investigated. However, standards/SIL were not available for 5’xanthylic acid, xanthosine, and β-ureidoisobutyric acid. Dihydrouracil/dihydrothymine were excluded pre-analysis to reduce the number of metabolites and heighten method sensitivity (Campbell,
1957; Campbell, 1958; Campbell, 1959). Of all of the possible targets, due to their intermediate-like
character, these two were regarded as the metabolites most likely not present. The nucleotides and
adenosine/2’-deoxyadenosine were not identified during method development and the sensitivity of
2’-deoxycytidine was too low for quantification. The nucleotides were most likely degraded rapidly
in the small intestine pre-absorption and endogenous nucleotides probably degraded before and/or
in the blood (Berg et al., 2002; Carver and Walker, 1995; McAllan, 1980; McDonald et al., 2011;
Smith and McAllan, 1974).
4.2.2 Chemical properties of the purine and pyrimidine metabolite targets
All 20 metabolites were to different degrees polar owing to high contents of hydroxyl (HO-), carbonyl (O=), and amine groups (N-), (Table 1). Based on their polarity, they were classified into
three groups: The very polar group, containing the pyrimidine degradation products; all small molecules with similar linear polar structures, as well as the highly polar; allantoin, cytosine, and cytidine. This group was characteristic by a large number of polar HO- and N- groups, few non-polar
rings, and nearly no sugar units. The polar group included the majority of the bases as well as the
intermediate degradation products with more base-like structures, such as uric acid, hypoxanthine,
and xanthine. This group had, compared to the very polar group, a smaller number of polar HO- and
N- groups, more non-polar rings, and no sugar units. Finally, the semi-polar group comprised the
majority of the nucleosides with large but semi-polar sugar side groups, such as most of the ribonucleosides (ribose, 2× HO-) and deoxyribonucleosides (deoxyribose, 1× HO-). Owing to their nonpolar methyl side groups (H3C-), thymine and thymidine were also placed in the semi-polar group.
With regard to the separation of these metabolites, this will be reviewed in detail in a later section.
Table 1. Polarity of the 20 purine and pyrimidine metabolites based on their structures
Metabolite
Very polar group
Cytosine
β-alanine
β-ureidoisobutyric acid
β-aminoisobutyric acid
Allantoin
Cytidine
Polar group
HO-
1
1
1
3
Polar groups
O=
NH3C1
1
2
1
3
1
1
1
1
1
1
1
Rings
Sugar side group (type)
1
1
1
36
Ribose
Rt (min)1
2.91
2.91
2.91
2.98
3.05
3.19
Adenine
Guanine
Uracil
Uric acid
Hypoxanthine
Xanthine
Semi-polar group
Uridine
2’-deoxyuridine
Inosine
Guanosine
Thymidine
2’-deoxyinosine
2’-deoxyguanosine
Thymidine
1
2
3
1
2
3
2
2
2
1
1
2
1
1
2
1
1
2
2
1
2
2
2
1
1
2
2
1
2
2
1
1
1
1
1
3.81
3.86
3.97
4.28
4.56
5.18
Ribose
Deoxyribose
Ribose
Ribose
Deoxyribose
Deoxyribose
Deoxyribose
4.50
5.34
5.81
6.18
6.21
6.74
7.31
8.52
HO-, hydroxyl group; O=, carbonyl group; N-, amine group; H3C–, methyl group; Rt, retention time.
1
The given Rt are measured values (Table 3 in paper I).
4.2.3 LC-ESI -MS/MS
High performance liquid chromatography electrospray ionisation tandem mass spectrometry is the
preferred mass spectrometric (MS) technique for the fast and sensitive quantification of small molecules, peptides, and proteins in complex matrices such as plasma, blood, urine, feces, and tissue
(Ardrey, 2003; Kang, 2012; Watson and Sparkman, 2007; Xu et al., 2007). It combines the separation ability and versatility of HPLC with the sensitivity and specificity of detection from MS/MS.
For most components, MS/MS is more sensitive and significantly more specific than most other
traditional detectors for liquid chromatography (LC), including electrochemical, fluorescence, ultraviolet-visible, and refractive index detectors (Kang, 2012; Watson and Sparkman, 2007). The
most apparent advantages of mass spectrometers as compared to conventional LC detectors are that
they do not need the presence of a suitable chromophore or depends on derivatisation. Moreover
and most imperative, they do not depend on full LC separation, as they are capable of identifying
components in unresolved chromatographic peaks. Furthermore, but just as importantly, MS/MS
experiments can provide fast qualitative and quantitative data on numerous components simultaneously in the same sample and run in a process known as multiple reaction monitoring (MRM) (Fu et
al., 2010; Holčapek et al., 2012; Lemoine et al., 2012; Nováková, 2013; Prakash et al., 2007).
High performance liquid chromatography
High performance liquid chromatography is a versatile, reproducible, and accessible technique with
the ability to separate and quantitate (analytical) or separate and identify (preparative) the components present in any sample that can be dissolved in a liquid (Ardrey, 2003; Kang, 2012). It can be,
and has been, applied to just about any type of sample, such as; pharmaceuticals, foods, cosmetics,
environmental matrices, forensic samples, and industrial chemicals. In HPLC, a high pressure
(6,000 psi or 400 bar) flow of a solvent (stationary phase) is used to separate the components of a
sample based on their chemical properties by filtering through a column filled with a chromato37
graphic packing material of small particles (stationary phase). Separation is achieved as the components of the sample have different affinities towards the stationary and mobile phases, travelling at
different individual speeds through the column (Fig. 10A). High pressure is needed to create the
desired separation as such small particles (< 10 microns) have a great resistance to flow. A detector,
in this case a triple quadrupole mass spectrometer, is needed to analyse the separated components as
they elute from the HPLC column. Each component elute at a specific location, measured by the
elapsed time between the moment of injection and the time of elution, also known as the retention
time (Rt). By comparing a given peak’s Rt in the resulting chromatogram with that of added standards, each component can be identified. To create a separation of any two or more specified components with HPLC, one must choose between different phase combinations and modes of retention.
The choice of a combination between the stationary phase and the mobile phase will determine the
degree of selectivity. Selectivity is the most powerful factor for determining the chromatographic
resolution, the other is the mechanical separation power or the efficiency created by the column
length, particle size, and packed bed uniformity. Three different modes of retention are most commonly used; polarity, with the use of normal phase HPLC, reversed phase HPLC, hydrophilic interaction chromatography or hydrophobic interaction chromatography, electrical charge, with the use
of cation or anion ion exchange chromatography, and molecular size, with the use of size exclusion
chromatography or gel permeation chromatography.
A
B
Figure 10. High performance liquid chromatography (HPLC) system (modified from Waters Corporation, 2014).
(A) A schematic of a HPLC system. A reservoir holds the mobile phase (Ardrey, 2003; Kang, 2012). A high pressure pump is used to generate a specified flow rate (mL/min). An injector is able to introduce a specific amount
(μL) of a sample, possible from an autosampler, into the continuously flowing mobile phase that carries the sample into the HPLC column. The column contains the chromatographic solid phase needed for separation. A detector is used to detect the eluting components generating a chromatogram. (B) The gradient elution profile used in
this study. Both solvents A and B were prepared from a 0.05 mol/L acetic acid buffer containing 10% or 50%
methanol, respectively. The following elution gradient was used: Initial percentage of solvent B was 5%, this was
38
raised to 100% in 8 min and kept there for 6 min, then lowered to 5% in 30 sec, after which it was kept constant
for 3.5 min to re-equilibrate the column prior to the next injection. The flow rate was 200 µL/min and the injection volume was 5 µL. The column temperature was maintained at 30°C while the auto sampler temperature was
set to 4°C to stabilize the samples during time-consuming analyses. The total run time was 18 min per sample.
Based on its broad applicability, the most common mode of polar separation today is reverse phase
HPLC featuring an aqueous blend of water with a miscible polar organic solvent, such as acetonitrile or methanol, and a column packed with C18-bonded silica (Ardrey, 2003). In this study, based
on the polar properties of the targeted purine and pyrimidine metabolites, chromatographic separation was performed on an Agilent 1100 series HPLC system with a Synergi™ Hydro-RP LC column (non-polar C18) from Waters protected by a conventional guard column of the same material
with aqueous solvents (polar acetic acid buffer/methanol) in a reverse phase mode. In reverse phase
HPLC, the mobile phase is non-polar and the mobile phase polar. Two basic elution modes are most
commonly used; isocratic elution, where the mobile phase remains the same throughout the run, and
gradient elution; where the mobile phase composition changes during the separation. A gradient
elution was applied in this study, as this is useful for samples that contain components that span a
range of polarities (Fig. 10B). In this way, the elution strength of the mobile phase increases during
separation (polarity decrease), initially; eluting the very polar components, secondly; eluting the
polar components and finally; eluting the more strongly retained semi-polar components (Table 1).
The 20 purine and pyrimidine metabolites were separated in five runs with distinct chromatographic
profiles and eluted with Rt from 2.91 min to 8.52 min (Table 1 and table 2 in paper I). To achieve
optimal chromatographic resolution and elution order, a series of conditions were modified and implemented during method development (Paper I). The composition of the mobile phase was based
on the work of Hartmann et al., and no other types of solvent were tested (Hartmann et al., 2006).
Having tested several acetic acid buffer to methanol ratios (95%, 90%, 85%, and 80% v/v), the optimal separation was accomplished with a 90% v/v solvent A and 50% v/v solvent B. By adding a
small amount of methanol to solvent A (aqueous), and by keeping the baseline at 5% solvent B,
mixing became more smooth and transitions between runs more stable. Optimal injection volume (5
µL) and flow rate (200 µL/min) was achieved by testing injections of 5, 10, and 20 µL and flow
rates of 100, 200, 300, and 400 µL/min. Concerning the elution gradient, different profiles were
tested, with more or less steep gradients, the aim to make it as short as possible, while still achieving an adequate resolution (Fig. 4B). A major improvement in precision between runs was achieved
by maintaining the column temperature at 30°C instead of 25°C. An improvement in sample stability was achieved by cooling the autosampler to 4°C.
As a final comment on HPLC, a new UPLC system able to achieve significant increases in resolution, speed, and sensitivity was developed in 2004 (Holčapek et al., 2012; Nováková and Vlčková,
39
2009, Prakash et al., 2007; Swartz, 2005). In this system, columns with smaller particles then with
conventional HPLC are applied, and the instrumentation is designed to deliver the mobile phase at
15,000 psi (1,000 bar). In future experiments, this system could be useful in further improving the
purine and pyrimidine method.
Mass spectrometry
Mass spectrometry is a microanalytic technique that can be used selectively to detect and determine
the amount of a given component (Ardrey, 2003; Kang, 2012; Watson and Sparkman, 2007). Even
though not relevant for this study, MS can also be used for determining the composition and molecular structure of components. The tools of MS are mass spectrometers and the data containing the
desired information are mass spectra. Mass spectrometry is based on the concept that ions are
charged particles and, as such, their position in space can be manipulated with the use of electric
and magnetic fields. In MS, ions are separated and detected according to their m/z – the mass of the
ion divided by the number of charges the ion possesses. Hence, a mass spectrometer does not directly determine mass but, determines the mass of a molecule by measuring the m/z of its ion. The
knowledge of the m/z enables one to determine what is present, while the measured ion intensities
answer the question of how much is present. In order to have individual ions free from other matter,
it is necessary to perform the analysis in the gas phase and in a vacuum where the ions cannot collide with other matter during the separation process. Ions of individual m/z are separated in a mass
analyzer and detected in order to obtain the mass spectrum. The three key modules in the mass
spectrometer are; the ion source, which generates the ions and put them on gas form; the mass analyzer, which sorts the ions; and the detector, which convert the ions into an electrical signal that can
be interpreted into a mass spectrum.
For ionization, most commonly used are different types of atmospheric pressure ionisation (API)
sources; such as electrospray ionization (ESI) (molecules of all sizes and polarity), atmospheric
pressure chemical ionization (APCI) (small nonpolar molecules), and atmospheric pressure photoionization (highly nonpolar molecules, low flow rates) (Kebarle and Verkerk, 2009; Kebarle and
Tang, 1993; Holčapek et al., 2012; Kang, 2012; Watson and Sparkman, 2007). When performing
API, the component molecules are ionized (added positive or negative charges) first at atmospheric
pressure and then mechanically and electrostatically separated from neutral molecules. Matrixassisted laser desorption/ionization (MALDI) is also, due to its great mass range and sensitivity
with regards to ionization of biomolecules, a very popular ionization technique, but with MALDI,
the ionization is performed in a vacuum system (Dreisewerd, 2003; Holčapek et al., 2012). Each
type of ionization is suitable for different classes of components; the nature of the components and
the separation conditions strongly influencing which technique generates the best result. In this
40
study, an ESI source was used for ionization and the purine and pyrimidine metabolites were separated in five distinct runs so as to maximize the sensitivity of each metabolite. Three runs were in
negative ESI mode and two runs were in positive ESI mode (Table 2 in paper I). With ESI, the LC
eluent is nebulized or sprayed into a chamber at atmospheric pressure in the presence of a heated
drying gas and a strong electrostatic field, causing drying of the solvent droplets, dissociation of the
component molecules and eventually fully desolvated ions (Fig. 11A) (Kebarle and Tang, 1993;
Kebarle and Verkerk, 2009). These ions are then attracted to and pass through a capillary sampling
orifice into the mass analyzer. When applying ESI, charged ions have to be generated through electrochemical oxidation in the high voltage spray needle before they reach the mass spectrometer, as
no charge is added during the ionization process as when applying APCI (Fig. 11B).
A
B
Figure 11. Electrospray ionization source (ESI) and atmospheric pressure chemical ionization source (APCI)
(modified from Agilent Technologies, 2014). (A) A schematic of an ESI source. In the ESI source, the LC eluent
flows into a high voltage needle and exits as a fine spray of highly charged droplets, which are directed towards
the mass spectrometer via an electric field between the needle and mass spectrometer orifice (Kebarle and Tang,
1993; Kebarle and Verkerk, 2009). The component-solvent droplets are desolvated by a heated gas, usually nitrogen, which evaporate solvent until the charge density on the droplet surface rises so high that the electrostatic
repulsion force exceeds the surface tension of the solvent. At that point a coulombic explosion occurs, which generates much smaller droplets and deposits the charge onto the component molecules, forming charged ions which
enter the mass analyser as completely desolvated ions. (B) A schematic of an APCI source. Atmospheric pressure
chemical ionization is similar to ESI but unlike ESI, APCI uses a corona discharge from an adjacent electrode to
generate ions instead of applying a voltage to the eluent needle. The desolvated molecules and solvent gas enter
the corona discharge area, where the abundant individual solvent molecules are ionized; these solvent ions collide
with component molecules to form the charged ions. Atmospheric pressure chemical ionization occurs in the gas
phase, whereas ESI occurs in the liquid phase.
The ESI ionization process is especially useful for analysing large biomolecules as well as thermally unstable analytes as the components remain solvated through the ionization process and consequently are not as prone to being fragmented in the source (Huang et al., 2010; Zaikin and Halket,
2006). The primary disadvantage of ESI is the possibility of ion suppression or enhancement, also
41
known as matrix effects, caused by competition between ions for ejection from the droplet during
desolvation (Taylor, 2005; Van Eeckhaut et al., 2009; Xu et al., 2007). Matrix effects and the different efforts made during method development to diminish these will be reviewed in greater detail
in the following section.
With regard to the choice of a mass analyzer; quadrupole, time-of-flight, quadropole ion trap, and
Fourier transform-ion cyclotron resonance mass analyzers are most often used (Forsici et al., 2013;
Kang, 2012; Prakash et al., 2007; Watson and Sparkman, 2007). Each has different accuracy, resolution, mass range, tandem analysis, and scan speed capabilities, providing each with advantages
and disadvantages depending on the requirements of the analysis. In this study, a micromass triple
quadrupole mass spectrometer from Waters was used for analysis. A triple quadrupole mass spectrometer consists of three quadrupole mass analyzers in series or tandem and it is therefore also often referred to as a “tandem-in-space” system (Forsici et al., 2013, Prakash et al., 2007, Watson and
Sparkman, 2007). In short, a quadrupole mass analyzer consists of four parallel cylindrical rods
arranged in a square (Fig. 12). Ions in a selected mass range are focused down the rods centre via
specific oscillating electrical fields generated by a superposition of direct current (DC) and radiofrequency (RF) voltages applied to the rods. The created electromagnetic field determine which ions
(m/z) can pass through the filter at a given time. In addition to mass ranges, individual ions can be
selected for detection in the selected ion monitoring mode resulting in a significantly increase in
sensitivity. In a triple quadrupole instruments, the first quadropole (Q1) is used to select a precursor
ion (m/z), fragmentation takes place in the second quadrupole (Q2), and the third quadrupole (Q3)
serves to category the product ions. The categorized product ions collide with the detector triggering an electron cascade which is converted into an electric current and detected by a sensitive voltmeter. In combination the three mass analyzers and the detector yield information on the ion mass’
and intensities to yield a mass spectrum.
Figure 12. Triple quadrupole tandem mass spectrometry (modified from Agilent Technologies, 2014). A quadrupole mass analyzer consists of four parallel cylindrical rods cubically arranged. Component ions are selectively
guided down the rods via electromagnetic fields generated by voltages applied to the rods (Forsici et al., 2013;
Prakash et al., 2007; Watson and Sparkman, 2007). In a triple quadrupole system, the first quadrupole (Q1) selects
only the precursor ion by varying the direct current (DC) and radiofrequency (RF) voltages so that only the ion of
interest can avoid expulsion and pass completely through Q1 to the second quadrupole (Q2). Q2 acts as a collision
cell where the precursor ion collides with an inert gas, in a process referred to as collision induced dissociation, to
yield smaller fragments also known as product ions. Q2 is designed so that all product ions formed will be sent to
the third quadrupole (Q3). Q3 serves as a mass analyzer to sort and inventory the product ions and, in combination
with the detector, generating a spectrum of the resulting product ions. Q1 and Q3 are RF and DC mass-resolving
quadrupoles, while Q2 simple acts as an RF only collision cell and ion guide.
42
The main advantage of the triple quadrupole, however, is its ability to perform multiple tandem MS
experiments in the same analytical run to gain quantitative information on multiple components
simultaneously, also known as MRM (Fu et al., 2010; Holčapek et al., 2012; Lemoine et al., 2012;
Nováková, 2013; Prakash et al., 2007). Briefly, in MRM, Q1 and Q3 operate in static mode, filtering a precursor ion in Q1 and one or more defined product ions in Q3. The product ions are produced by fragmentation of the selected ions in the Q2 collision cell. Often, two mass transitions
from a single precursor to both a quantifier ion and a qualifier ion are used to quantitate and confirm
the identity of a specific component. In MRM, the instrument is monitoring the mass transition
from one or more specific precursor ions to one or more specific product ions. Hence, with the use
of MRM, multiple predefined components can be quantified with large specificity and sensitivity in
one MS analysis. In this study, the fragment ion spectra of the 20 purine and pyrimidine metabolites
were recorded in both polarities and promising selective precursor ions were tested and optimized in
MRM mode (Table 2 in paper I). Following optimization and validation of the transitions with regard to ion scan intensity, m/z, cone voltage, and collision energies, the most intense transition reactions was used for MRM detection and quantification. Positive identification was based on the correlation of Rt with standards and the selected precursor/product transition. Less intensive second
transitions were used for confirmation.
4.2.4 Matrix effects
A very common problem when applying LC-ESI-MS/MS analysis on biological samples is matrix
effects, first described by Kebarle and Tang in 1993 (Kebarle and Tang, 1993). The term describes
the suppression or enhancement effects molecules originating from the sample matrix can have on
the ionization process in the mass spectrometer when co-eluting with the component of interest
(Taylor, 2005; Van Eeckhaut et al., 2009; Xu et al., 2007). The exact mechanism of matrix effects is
unknown, however, they are known to be both component and matrix dependent. Matrix effects
might be introduced by endogenous sample components, by chemicals used during sample preparation or chromatography or by components released during sample preparation. In theory, it occurs
in either the solution or the gaseous phase and the main cause is a change in droplet solution properties caused by the presence of nonvolatile and less volatile solutes that change the efficiency of
droplet formation and evaporation, which in turn affects the amount of charged ions in the gas
phase that ultimately reach the detector (King et al., 2000). This causes a component’s response to
differ when analyzed in a biological matrix such as plasma compared to a standard solution such as
water resulting in poor accuracy, linearity and inter/intraday precision of the method (Fig. 13) (Taylor, 2005; Gosetti et al., 2010). When discussing matrix effects, it is useful to distinguish between
two types: absolute matrix effect, which is the difference in response between and undiluted solu43
tion and a post-extraction spiked sample, and relative matrix effect; which is the difference between
various lots of post-extraction spiked samples (Matuszewski et al., 2003, Nováková, 2013). Relative
matrix effects will be discussed in a later section concerning application.
Figure 13. An illustration of matrix
effects (modified from Kruve et al.,
2011). The same amount of a component is added to a water or plasma
sample however, the peak responses
are very different due to different
compositions of the two matrices.
To assess absolute matrix effects, three strategies have been employed: post column infusion, post
extraction addition, and a comparison of the slopes of calibration curves (Taylor, 2005, Gosetti et
al., 2010, Van Eeckhaut et al., 2009). In this study, absolute matrix effects were evaluated by comparing the response of SIL in matrix samples before extraction with the response obtained in water
(Fig. 3 in paper I). The applied SIL based method was a modified version of the conventional method described by Matuszewski et al., this strategy was not possible as completely blank matrices
were not available for the purine and pyrimidine metabolites (Matuszewski et al., 2003). As the
matrix effect occurs in the gas phase, it is hard to compensate for by MS alone (Kruve et al., 2008;
Hewavitharana, 2011; Nováková, 2013). Even so, it is still very important to evaluate and if possible decrease or eliminate matrix effects when developing new assays (Jessome and Volmer, 2006;
Tan et al., 2011). Matrix effects can vary between measurements, hence, it is not possible to test for
matrix effects only once and consider it to be constant (Mutavdzic et al., 2012). Matrix effects were
largely eliminated in this study first of all by making the external calibration matrix-matched,
hence, quantifying calibrators and sample components under the same conditions, secondly, by implementing an effective pre-treatment, and thirdly, by implementing SIL (Jessome and Volmer,
2006; Tan et al., 2011; Xu et al., 2007). These initiatives compensated quite well for the signal suppression or enhancement in the plasma samples, thereby achieving accurate quantification. Other
approaches could be to inject smaller volumes or dilute the samples, none of which was compatible
with this analysis.
4.2.5 Calibration and quantification
Quantification in LC-ESI-MS/MS involves the comparison of the response of a component (peak
height or area) in a sample with the response from known amounts of the component standard
44
measured under identical experimental conditions (Ardrey, 2003; Fu et al., 2010; Honour, 2011;
Nováková, 2013). The simplest and most widely used practice for external calibration is the use of
an external standard calibration curve, also known as the external standard method. In this situation,
a number of samples referred to as calibrators, usually around eight serial dilution points, containing
known amounts of the component of interest is made up and analysed. The peak responses from the
calibrators are then plotted against the known concentration of component standard and the data for
a calibration curve obtained. The calibration curve is produced by fitting lines and polynomial
curves to the data points, in most cases producing a linear relationship between signal response and
concentration. The range of concentrations must include the concentrations in the unknown samples
as interpolation and not extrapolation of the results is required. Seven different concentration levels
with a two-fold serial dilution of each component were used for the calibration curves in this study
(Table 4 in paper I). All samples and calibrators were analyzed in duplicate and a standard curve
and quality control samples were analyzed at the beginning and at the end of each sequence. The
response was calculated as the chromatographic peak area by selecting each peaks start and end
points, reviewing e.g. the repeatability of Rt, peak shape/intensity, clear blanks, carry-over, and
changes in baseline with massLynx 4.0 software.
Although widely employed, the use of external standardization takes no account of matrix effects,
pre-treatment mistakes etc. Hence, internal standards can be used to overcome this major source of
inaccuracy and to improve precision (Ardrey, 2003; Fu et al., 2010; Hewavitharana, 2011;
Holčapek et al., 2012; Honour, 2011; Nilsson and Eklund, 2007; Nováková, 2013; Stokvis et al.,
2005; Tan et al., 2011; Wang et al., 2007; Wooding and Auchus, 2013). More details about the applied internal standards will be presented in the following section. In the context of application, an
internal standard is a suitable component added to the sample as early as possible in the analytical
procedure, in this study, as the first step in the pre-treatment protocol. Responses from both the
component and the internal standard are then measured during determination of calibrators and
samples, and the component response conclusively normalized with a normalization factor generated from the mean measured internal standard response divided by the internal standard response for
each sample. The resulting normalized component responses were then used to generate the calibration curve and to determine the amount of component present in each of the samples. This approach
is very similar to but should not be confused with the standard addition method, where most commonly a single point calibration is used (Ardrey, 2003; Nováková, 2013). During method development, focus was on quantifying as low concentrations of metabolites with as broad a calibration
span as possible while still maintaining linearity of the calibration curves.
45
In LC-ESI-MS/MS analysis, besides the use of internal standards, a process known as matrixmatching is often employed when producing standard curves to overcome the potential challenges
of matrix effects (Guideline EMA, 2011; Hewavitharana, 2011; Nováková, 2013; Taylor, 2005;
Van Eeckhaut et al., 2009; Vogeser and Seger, 2010; Xu et al., 2007). Matrix matching is used to
compensate for matrix effects by producing the calibrators in the same matrix as the sample matrix,
in this case plasma, thus analyzing the components and internal standard under the same matrix
conditions. Unfortunately, an exact matrix is not available for calibration of all quantified samples
but, matrix matching is still considered a beneficial for improving quantification with LC-ESIMS/MS. If internal standards eluting concurrently with the quantified metabolites are available,
matrix-matching is rendered unnecessary, but this is only the case for 15 out of the 20 purine and
pyrimidine metabolites (Hewavitharana, 2011). In this study, standard venous plasma, containing
unknown quantities of the metabolites under investigation, was used for matrix matching as this
was considered the matrix most similar to the sample matrices and a “blank” matrix was not available. Consequently, to compensate for endogenous metabolite, the response from a blank standard
plasma not added standard compound was subtracted all calibrators prior to calibration.
4.2.6 Internal standards
Addition of an internal standard is most widely used in LC-ESI-MS/MS quantification, as this
yields a high level of accuracy and precision (Ardrey, 2003; Fu et al., 2010; Hewavitharana, 2011;
Holčapek et al., 2012; Nilsson and Eklund, 2007; Nováková, 2013; Stokvis et al., 2005; Tan et al.,
2011; Vogeser and Seger, 2010; Wang et al., 2007; Wooding and Auchus, 2013). The internal
standard compensates for any fluctuation in the MS response, for sample losses that might occur
during sample preparation and chromatographic steps, as well as for matrix effects. The internal
standard should have the same physical, chemical and chromatographic properties as the component, ideally eluting at the same Rt, and have the same spectrometry behaviour including ionization
and fragmentation. The molecular weight should be distinct from that of the component and it
should not be a constituent of the sample. Isotopically labelled analogs, in this text referred to as
SIL, are thought to be the prime internal standards in LC-ESI-MS/MS analysis, as they meet all of
the above criteria. One exception is with regard to SIL that has a high numbers of deuterium atoms
(5+), with these SIL, a small shift in Rt can occur (Fukusaki et al., 2005; Wang et al., 2007). This
was not a problem in this study as all applied SIL were labelled with the more reliable 13C and/or
15
N (Table 3 in paper I) (Berg and Strand, 2011). The specificity and robustness of the quantifica-
tion incorporated with SIL is enhanced by the requirement that the endogenous response must coincide with the corresponding SIL response. In this manner, if the Rt is altered, the endogenous component can still be quantified. Also, the ratio of the two mass transition signal responses should al46
ways reflect those of the SIL, as a significant difference could indicate a contamination. A drawback of using SIL in LC-ESI-MS/MS is that their use is rather expensive and for many components
they are not commercially available.
All SIL used in this study were purchased from Cambridge Isotope Laboratories, except one from
Sigma-Aldrich. All had purities between 95% and 99%. Unfortunately, exact SIL were not available
for all metabolites studied and a suitable SIL was consequently selected on its similarity to the corresponding metabolite in terms of structure, Rt, fragmentation pattern and polarity. Since a component and its SIL will theoretically co-elute, in order to be able to separate them in the mass analyser
and to prevent cross-talk, it is important that the mass difference between the component is at least
three mass units (3-8) (Bakhtiar and Majumdar, 2007; Stokvis et al., 2005; Tan et al., 2011; Tong et
al., 1999; Vogeser and Seger, 2010). Cross-talk is a term used to describe cross contributions in
responses in MS between component pairs due to chemical impurities and/or isotopic interferences.
Meaning; the component peak might interfere with the signal of the SIL and vice versa. It was important to assess cross-talk contributions in the development of this method as some of the applied
SIL had less than three mass unit differences to the natural metabolite. The absence of standard
component/SIL cross-talk contributions was verified by comparing chromatographic responses for
standards and SIL alone and in a mixture.
4.2.7 Sample preparation and pre-treatment protocol
Proper sample preparation is paramount in LC-ESI-MS/MS analysis, as dirty samples can easily
collapse the HPLC system and ESI is sensitive to matrix effects, and at the same time, it enhances
both the selectivity and the sensitivity of the analysis (Hopfgartner and Bourgogne, 2003; Nováková, 2013; Praksah et al., 2007; Van Eeckhaut et al., 2009). Sample pre-treatment is focused on isolation, clean-up and pre-concentration of components from complex biological matrices such as;
whole blood, plasma, serum, urine, or saliva, containing interfering components, such as; salts, sugars, phospholipids and proteins. The proteins especially, might irreversibly adsorb onto the stationary phase of the HPLC column, resulting in a loss of efficiency and increased backpressure and in
the worst case scenario, a blockage (Nováková, 2013). Moreover, phospholipids and salts are common causes of matrix effects. These interfering components are removed to a greater or lesser extent by employing an effective sample pre-treatment protocol. The choice of a pre-treatment protocol is crucial for the accuracy and precision of the quantification, as the targeted components are
present at very low concentrations, while interfering components from the sample matrix prevail. In
bioanalysis, a well-designed pre-treatment technique should employ small sample sizes and be “just
adequate”, as more steps could introduce more errors. Furthermore, highly selective sample preparation is to be preferred, so as to minimise matrix effects. The main problem with sample prepara47
tion is, that it is often labour intensive and time consuming. Conventional sample preparation methods include; solid-phase extraction (SPE) (Bakhtiar and Majumdar, 2007; Chambers et al., 2007;
Poole, 2003), where different types of stationary phases/liquid phase systems, like the ones used for
HPLC, are used to clean-up samples selectively; liquid-liquid extraction (LLE) (Nováková and
Vlčková, 2009; Peng et al., 2000; Ramos, 2012), achieved by extracting the component from the
sample matrix into another immiscible solvent; and PPT (Kole et al., 2011; Polson et al., 2003),
performed by adding a large proportion of an organic solvent to the sample resulting in a precipitation of unwanted matrix proteins. Multiple extraction steps are most often needed to increase component recovery and obtain cleaner extracts (Jessome and Volmer, 2006). More modern approaches
include different types of microextraction techniques and on-line sample preparation approaches
(Hopfgartner and Bourgogne, 2003; Kole et al., 2011; Praksah et al., 2007). The conventional approaches are highly favoured in most laboratories as they are well-established, well-optimized, reproducible, and easily automated (Nováková, 2013). Even though it is the least selective and effective, the PPT approach is one of the leading sample preparation methods used today, simple because
it is also the easiest, fastest, and cheapest. Compared to PPT, more efficient clean-up and higher
selectively might be obtained with LLE or SPE, both methods also widely used in modern LC analysis. One of the major drawbacks of LLE however in the context of this study, is its incapability for
the isolation of polar components. As compared to PPT and LLE, there are many advantages of
SPE, the major one being the selectivity but, the development and application of a SPE procedure
might be very time consuming and the costs quite high as the cartridges are for single use only.
In this study, the purine and pyrimidine metabolites were isolated and concentrated from plasma
prior to analysis by applying a pre-treatment protocol consisting of PPT, ultrafiltration, evaporation,
and subsequent resolution.
4.2.8 Validation and application
The basis for high quality data is reliable analytical methods. New LC-ESI-MS/MS procedures and
all bioanalytical methods in general, require careful method development including proper standardization followed by a thorough validation (Hartmann et al., 1998; Nováková, 2013; Peters et al.,
2007; Vogeser and Seger, 2010). Keeping in mind that the quality of an analytical method largely
depends on method development and only secondly on the quality of validation, it is imperative that
the analytical method is fit for purpose and only after thorough validation the inherent potential is
warranted. The most widely accepted guideline for method validation is; The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human
Use (ICH) guideline Q2 (R1), used both in pharmaceutical and medical science, where most of bioanalytical methods are developed (Guideline ICH, 2005). Other guidelines, which are more de48
tailed, extensive and strict, are a “Guideline on Bioanalytical Method Validation” by the European
Medicines Agency (EMA) (Guideline EMA, 2011) and “Guidance for Industry, Bioanalytical
Method Validation” by the U.S. Food and Drug Administration (FDA) (Guideline FDA, 2001).
Typical validation parameters and the requirements of individual guidelines are summarised in Table 2. Established conventional validation parameters routinely determined during method validation include selectivity, linearity, stability, precision, and accuracy (recovery), usually at three concentration levels in several replicates (typically three to five). Additionally, as a matter of discussion
in recent years, new parameters required are matrix effects, carry-over, and dilution integrity as well
as detailed studies of the stability of components under various conditions during the method application (Guideline EMA, 2011, Guideline FDA, 2001).
Table 2. A comparison of validation parameters required by ICH, EMA, and FDA
Parameter
Selectivity
ICH
√
FDA
6
Carry-over
LOQ (limit of quantitation)
X
LOQ
LOD (limit of detection)
Calibration curve linearity
LOD
5
X
LLOQ,
ULOQ
X
6-8
Range
√
X
Accuracy (%)
Precision2 (% RSD)
Recovery (%)
Dilution integrity
3×3
3×3
X
X
3×5
3×5
3
X
Matrix effects (%)
Robustness
Stability
SST
X
√
X
√
X
X
√
X
Number of concentration levels × replicates
FDA limits
EMA
EMA limits
No interference
6
No interference,
if <20% LLOQ
X
√
<20% LLOQ
Accuracy ± 20%
LLOQ, Accuracy ± 20%
precision ≤ 20%
ULOQ
precision ≤20%
X
Accuracy ± 15%,
61
Accuracy ± 15%,
at LLOQ 20%
at LLOQ 20%
√
Defined by LLOQ and
ULOQ
±15%, at LLOQ ± 20%
4×5
±15%, at LLOQ ± 20%
≤ 15%, at LLOQ ≤ 20%
4×5
≤15%, at LLOQ ≤20%
Precise and consistent
X
X
5
Accuracy and precision
±15%
X
6
≤15% RSD
X
3
3
√
X
√; the parameter is required, X; the parameter is not required, LLOQ; lower limit of detection, ULOQ; upper limit
of quantification, RSD; relative standard deviation (modified from Nováková, 2013).
1
To be analysed in replicates. 2Precision is further subdivided into within-day (repeatability) and across-day (intermediate) precision and reproducibility. 3Very detailed stability studies are required, for full details consult the
EMA guideline (Guideline EMA, 2011).
Concerning validation of the developed method in this study, once the pre-treatment, LC-ESIMS/MS, and calibration procedures had been established, the performance characteristics of the
method were established by assessment of selectivity, linearity (calibration curve), stability, precision, accuracy (recovery), and absolute matrix effects, followed by tests of the application range.
No single guideline was used for the validation studies, but efforts were made to cover all relevant
parts of the specific method while still keeping in line with conventional approaches. Method validation is a vast area in bioanalytical science and to present all details will be beyond the scope of
49
this thesis. However, the main principles and relevant applications for the validation of the purine
and pyrimidine LC-ESI-MS/MS method will be given in the following subsections, for full details
consult paper I (Stentoft et al., 2014).
With regard to selectivity, this is defined as; the ability of a bioanalytical method to measure and
differentiate the component(s) of interest and internal standard(s) in the presence of other components which may be expected to be present in the sample (Guideline EMA, 2011). The selectivity
should be proved using at least six individual sources of the appropriate blank matrix, which are
individually analysed and evaluated for interference (Table 2). In this study, a blank sample matrix
was not available and other components at the same Rt could not be excluded. Instead, the absence
of component/SIL cross-talk was confirmed by comparing chromatographic responses for standards
and SIL alone and in a mixture. The calibration curve describes the response of the instrument with
regard to the concentration of component over the calibration range (Guideline EMA, 2011). According to the EMA guideline, the calibration standards should; first of all, be matrix-matched; secondly, there should be one calibration curve for each studied component, and for each analytical
run; thirdly, it should cover the calibration range, defined by the LLOQ and ULOQ (Table 2);
fourthly, a minimum of six calibrators should be used in addition to the blank sample (processed
plasma without component or SIL) and a zero sample (processed plasma with SIL); and finally, a
relationship which can simple and adequately describe the response of the instrument with regard to
the concentration of component should be applied. Calibration curve precision and accuracy is vital
for achieving high quality data. The calibration and quantification methodology applied has already
been described in a previous section. All calibration curves used in this study were matrix matched
and covered relevant concentration ranges. Logarithmic and linear calibration models were tested
and the linearity of the log calibration curves studied with a lack of fit hypothesis test. The quantification ranges was determined by homogeneity of variance and the stability between run days accessed. Limits of detection and quantification were not determined, instead, the homogeneity of
variance of the calibration curves was considered. According to the EMA guideline, stability in
method validation is; the chemical stability of a component in a given matrix under specific conditions for given time intervals (Guideline EMA, 2011). An evaluation of stability should be carried
out to ensure that every step taken during sample preparation and sample analysis as well as the
storage conditions, do not affect the concentration of the component. Stability studies should be
carried out so as to investigate conditions and time periods that equal those applied to actual study
samples. In this study, for continuous evaluation of long-term storage stability, a freshly thawed
quality control was analyzed and evaluated in all analytical runs. The stability within runs (6-24 h)
was evaluated by assessing a quality control at the beginning and at the end of each sequence and
50
by analysing a set of spiked standard samples at five different times (different vials) during a 30 h
sequence. To determine the stability of the calibration curves, the across-day variation was assessed
over five consecutive days. Freeze-thaw cycle stability was not explored. If working with very
small concentration differences, as in this study, precision is one of the most critical validation parameters. It is defined as; the ratio of standard deviation/mean (%) (Guideline EMA, 2011). The
precision of an analytical procedure expresses the closeness of agreement between a series of measurements obtained under the prescribed conditions expressed as the CV%. In this study, precision of
the method was determined by analyzing replicate sets of spiked standard plasma samples on five
separate days. The accuracy, more commonly known as the recovery, of an analytical procedure
expresses the closeness of the determined value to the value which is accepted either as a conventional true or an accepted reference value, defined as; (determined value/true value) × 100%
(Guideline EMA, 2011). Accuracy should be assessed on samples spiked with known amounts of
the component, independently from the calibrators, using separately prepared stock solutions. The
samples are analysed against the calibration curve, and the obtained concentrations compared with
the nominal value. Accuracy should be evaluated within-day and across-day as for precision. The
absolute accuracies of the developed method were calculated using the same set of spiked standard
plasma as for the precision evaluation. The LC-ESI-MS/MS analysis developed in this study was
established for use with blood plasma samples from multicatheterized cows. Since jugular vein
plasma was used for method development, to determine the application range of the method, relative matrix effects were evaluated in alternative types of plasma as well as water, urine, and milk
samples.
Based on the validation and the examination of relative matrix effects, it was determined that the
LC-ESI-MS/MS method was suitable for quantification of the 20 targeted purine and pyrimidine
metabolites in bovine blood plasma from the multicatheterized cow model.
51
5. Brief summary of papers and manuscripts included in the thesis
Paper I
Simultaneous quantification of purine and pyrimidine bases, nucleosides and their degradation products in bovine blood plasma by high performance liquid chromatography tandem
mass spectrometry. Stentoft C., M. Vestergaard, P. Løvendahl, N.B. Kristensen, J.M. Moorby and
S.K. Jensen. 2014. J. Cromatogr. A. 1356:197-210.
Hypothesis and objectives
The hypotheses were i) that LC-ESI-MS/MS can accurately be used to quantitatively determine a
range of purine and pyrimidine metabolites in cow blood plasma when incorporated with matrixmatched calibration standards and SIL, and ii) purine and pyrimidine metabolites can be isolated
and concentrated from blood plasma by applying an appropriate pre-treatment protocol. The objective was to develop and validate a LC-ESI-MS/MS procedure and pre-treatment protocol for quantification of a range of purine and pyrimidine metabolites in cow blood plasma.
Materials and methods
A LC-ESI-MS/MS method for simultaneous quantification of 20 purine pyrimidines metabolites in
blood plasma from dairy cows were developed and validated. The technique was combined with
individual matrix-matched calibration standards and SIL and preceded by a novel pre-treatment
procedure.
Data presented

Method development including pre-treatment and LC-ESI-MS/MS procedure

The log-calibration model and quantification ranges

Method validation

Potential application
Conclusions
The method was developed and validated as intended. It was confirmed that using a log-calibration
model resulted in a satisfying linear regression. The method covered concentration ranges for each
metabolite according to that in actual samples. The CV% of the chosen quantification ranges were
below 25%. The method had good repeatability (CV% ≤ 25%) and intermediate precision (CV% ≤
25%) and excellent recoveries (91-107%). All metabolites demonstrated good long-term stability
and stability within-runs (CV% ≤ 10%). Different degrees of absolute matrix effects were observed.
The potential application of the method was demonstrated by evaluating its range of use in different
types of blood plasma from multicatheterized cows.
52
Paper II
Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows.
Stentoft C., B.A. Røjen, S.K. Jensen, N.B. Kristensen, M. Vestergaard and M. Larsen. Accepted
November 11th 2014 by Br. J. Nutr.
Hypothesis and objectives
The hypotheses were i) that the purine and the pyrimidine metabolites, in the form of nucleosides,
bases, and degradation product, are absorbed from the small intestine of the dairy cow and undergo
degradation across the intestinal wall and the hepatic tissue and ii) that the purine and pyrimidine
nitrogen to a large extent ultimately are lost following degradation and excretion via the kidneys.
The objective was to describe the metabolism of purine and pyrimidines by studying postprandial
patterns of net PDV and hepatic metabolism and to evaluate the fate of nitrogen in this context.
Materials and methods
Eight ruminally cannulated Holstein cows in second lactation were permanently catheterised in the
artery and gastrosplenic, mesenteric, hepatic portal, and hepatic vein and randomly allocated to a
triplicate incomplete 3 x 3 Latin square design with 14 d periods. Cows were fed a basal total mixed
ration (TMR) supplying 80% of requirements for metabolisable protein. Four cows assigned to a
treatment of 8.5 g of feed urea/kg (ventral ruminal infusion, 15% CP) of dry matter intake (DMI)
were evaluated. Concentrations of purine and pyrimidine metabolites were determined in plasma
using LC-ESI-MS/MS, splanchnic fluxes calculated, and postprandial pattern evaluated.
Data presented

Plasma concentrations and concentration differences between veins of metabolites

Net portal, net hepatic and, and net splanchnic fluxes of metabolites

Purine and pyrimidine nitrogen metabolism
Conclusions
All of the 20 purine and pyrimidine metabolites were absorbed from the PDV; the purines mainly as
degradation products and only minimally as nucleosides and bases and, the pyrimidines mainly as
nucleosides and bases and, only minimally as degradation products. Most of the bases were degraded during absorption, in the blood or in the hepatic tissue. Eventually, an effective blood and hepatic metabolism further degraded all of the purine metabolites into degradation products for excretion
into the kidneys. The pyrimidine nucleosides was to a much larger extend absorbed intact and an
outlet into other parts of the nitrogen metabolism was detected. The postprandial pattern was not
found to have an effect on neither the net PDV nor the net hepatic metabolism.
53
Manuscript III
Protein level influences the splanchnic metabolism of purine and pyrimidine metabolites in
lactating dairy cows. Stentoft C., C. Barratt, L.A. Crompton, S.K. Jensen, M. Vestergaard, M.
Larsen and C.K. Reynolds. To be submitted to J. Dairy Sci.
Hypothesis and objectives
The hypothesis was i) that the net PDV and net hepatic fluxes of the purine and pyrimidine nucleosides, bases, and degradation products would reflect different degrees of microbial biosynthesis
with different dietary protein levels (12.5, 15.0, and 17.5% CP) and proportions of forage sources
(grass vs. corn silage) in the ration. The objectives were to study the net PDV, net hepatic and total
splanchnic metabolism of the purine and pyrimidine metabolites and evaluating how this was affected by dietary protein level and forage source and, to evaluate the fate of the purine and pyrimidine nitrogen by estimating nucleic acid nitrogen fluxes in the splanchnic tissues.
Materials and methods
Six ruminally cannulated Holstein Friesian cows in mid-late lactation were permanently catheterised in the artery and mesenteric, hepatic portal, and hepatic vein and randomly allocated to a 2 × 3
factorial study design with 21 d periods. Cows were fed a TMR consisting of 50:50 mixture of forage:concentrate. There were six treatment periods with diets containing one forage type (DM was
either 25:75 or 75:25 grass silage:corn silage) and one protein level (12.5%, 15.0%, 17.5% CP) for
each period. Concentrations of purine and pyrimidine metabolites were determined in plasma using
LC-ESI-MS/MS, splanchnic fluxes calculated, and protein and roughage effects evaluated.
Data presented

Arterial concentrations

Net portal, net hepatic and, net splanchnic fluxes of metabolites

Epigastric concentration differences
Conclusions
Protein effects were detectable for metabolites with considerable levels of net fluxes and good precision in the method. The effect of protein level was most easily detectable at the level of release
from the PDV and became harder to trace when passing the hepatic tissue. None of the splanchnic
fluxes were influenced by forage source. Due to a very effective intermediary degradation dependent on the level of protein, considerable amounts of purine nitrogen was found to be lost to the dairy
cow. The effect of protein level seemed to be less relevant in the case of the pyrimidine nitrogen,
since the pyrimidine metabolites has an anabolic outlet into other parts of the nitrogen metabolism.
54
6. Paper I
Simultaneous quantification of purine and pyrimidine bases, nucleosides and their degradation products in bovine blood plasma by high performance liquid chromatography tandem
mass spectrometry.
Stentoft C., M. Vestergaard, P. Løvendahl, N.B. Kristensen, J.M. Moorby and S.K. Jensen. 2014. J.
Cromatogr. A. 1356, 197-210.
55
Journal of Chromatography A, 1356 (2014) 197–210
Contents lists available at ScienceDirect
Journal of Chromatography A
journal homepage: www.elsevier.com/locate/chroma
Simultaneous quantification of purine and pyrimidine bases,
nucleosides and their degradation products in bovine blood plasma by
high performance liquid chromatography tandem mass spectrometry
Charlotte Stentoft a,∗ , Mogens Vestergaard a , Peter Løvendahl b , Niels Bastian Kristensen c ,
Jon M. Moorby d , Søren Krogh Jensen a
a
Department of Animal Science, Aarhus University, Blichers Allé 20, DK 8830 Tjele, Denmark
Department of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, DK 8830 Tjele, Denmark
c
Knowledge Centre for Agriculture, Cattle, Agro Food Park 15, DK 8200 Aarhus N, Denmark
d
Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Gogerddan, Aberystwyth, Ceredigion, SY23 3EE Wales, UK
b
a r t i c l e
i n f o
Article history:
Received 13 July 2013
Received in revised form 9 May 2014
Accepted 11 June 2014
Available online 27 June 2014
Keywords:
Nitrogen
Ruminant
Purine
Pyrimidine
Plasma
LC–MS/MS
a b s t r a c t
Improved nitrogen utilization in cattle is important in order to secure a sustainable cattle production.
As purines and pyrimidines (PP) constitute an appreciable part of rumen nitrogen, an improved understanding of the absorption and intermediary metabolism of PP is essential. The present work describes
the development and validation of a sensitive and specific method for simultaneous determination of 20
purines (adenine, guanine, guanosine, inosine, 2 -deoxyguanosine, 2 -deoxyinosine, xanthine, hypoxanthine), pyrimidines (cytosine, thymine, uracil, cytidine, uridine, thymidine, 2 -deoxyuridine), and their
degradation products (uric acid, allantoin, ␤-alanine, ␤-ureidopropionic acid, ␤-aminoisobutyric acid) in
blood plasma of dairy cows. The high performance liquid chromatography-based technique coupled to
electrospray ionization tandem mass spectrometry (LC–MS/MS) was combined with individual matrixmatched calibration standards and stable isotopically labelled reference compounds. The quantitative
analysis was preceded by a novel pre-treatment procedure consisting of ethanol precipitation, filtration, evaporation and reconstitution. Parameters for separation and detection during the LC–MS/MS
analysis were investigated. It was confirmed that using a log-calibration model rather than a linear calibration model resulted in lower CV% and a lack of fit test demonstrated a satisfying linear regression.
The method covers concentration ranges for each metabolite according to that in actual samples, e.g.
guanine: 0.10–5.0 ␮mol/L, and allantoin: 120–500 ␮mol/L. The CV% for the chosen quantification ranges
were below 25%. The method has good repeatability (CV% ≤ 25%) and intermediate precision (CV% ≤ 25%)
and excellent recoveries (91–107%). All metabolites demonstrated good long-term stability and good
stability within-runs (CV% ≤ 10%). Different degrees of absolute matrix effects were observed in plasma,
urine and milk. The determination of relative matrix effects revealed that the method was suitable for
almost all examined PP metabolites in plasma drawn from an artery and the portal hepatic, hepatic and
gastrosplenic veins and, with a few exceptions, also for other species such as chicken, pig, mink, human
and rat.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
The global efficiency of nitrogen in animal production is only
slightly over 10%, with the result that 102 Tg (1012 gram) nitrogen is
∗ Corresponding author. Tel.: +45 8715 7835; fax: +45 8715 4249.
E-mail addresses: [email protected] (C. Stentoft),
[email protected] (M. Vestergaard), [email protected]
(P. Løvendahl), nielsbk@vfl.dk (N.B. Kristensen), [email protected]
(J.M. Moorby), [email protected] (S.K. Jensen).
http://dx.doi.org/10.1016/j.chroma.2014.06.065
0021-9673/© 2014 Elsevier B.V. All rights reserved.
excreted annually (1998 figures) by domesticated animals globally
[1]. The nitrogen efficiency in dairy cows is generally low [2], and
not only the environment, but also the productive efficiency, would
benefit from an optimization of diet and metabolism to improve
nitrogen efficiency and utilization [1,3,4]. Most research hitherto
has focused on refining protein and amino acid utilization, but this
has only led to minor improvements in efficiency [4–6]. A better
understanding of the quantitative absorption and intermediary
metabolism of other nitrogenous products such as the purines and
pyrimidines (PP), the building blocks of nucleic acids and main
constituents of DNA/RNA, could uncover new ways of improving
198
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
dairy cow nitrogen use-efficiency and propose new feeding strategies [7,8]. So far, the possible significance of microbial PP in the
nutritional physiology of ruminants has not been investigated,
regardless of the fact that they correspond to more than 20% of the
total microbial nitrogen supply [7–9]. Little is known about the
quantitative aspects of PP metabolism. What is known, however, is
that the purines go through an effective multistep degradation to
uric acid and allantoin, and the pyrimidines are similarly degraded
to ␤-alanine, before excretion [8,10].
Quantitative analysis of PP in dairy cattle research has almost
solely focused on purines in urine, as excretion of purine derivatives can be used as an indirect measure of rumen microbial
synthesis [11–14]. Most published methods have thus been developed for purine metabolites in urine. Only recently, Boudra et al.
published a method able to quantify the pyrimidine degradation products (DP) ␤-alanine and ␤-aminoisobutyric acid as well
[14].
Different analytical separation methods have been used for
determining PP in biological matrices of which the majority has
applied high performance liquid chromatography (HPLC) [15–17]
or capillary electrophoresis chromatography [17–20]. When high
separation selectivity and sensitivity were essential, electrokinetic
techniques [16] or ultra high performance liquid chromatography
[21] have been used. Concerning detection, spectrometric, electrochemical or mass spectrophotometric detection methods have
been used, with ultra violet detection coupled to HPLC being the
most common one [15–17]. HPLC coupled with tandem spectrometric detection (LC–MS/MS) is currently considered the method
of choice for quantitative analysis of compounds in biological matrices [22] and LC–MS/MS has been shown to be capable of quantifying
PP and their derivatives accurately in urine.
For this study, we wanted to develop and validate an LC–MS/MS
method for quantification of a range of PP and their derivatives
in cow blood plasma. Into this procedure, we wanted to incorporate matrix-matched calibration standards as well as stable
isotopically labelled reference compounds (SIL). As no appropriate pre-treatment procedure was identified in the literature,
we also wanted to develop a good, stable, simple, componentspecific, and repeatable pre-treatment protocol for the plasma
samples.
Several sets of plasma samples from experiments that
attempted to manipulate urea-recycling and increase nitrogen
utilization using multicatheterized Danish Holstein cows were
employed in the development of this method [23] because these
were representative of the types of samples that this method is
likely to be used for in the future.
2. Materials and methods
2.1. Chemicals, reagents and materials
Water quality was at all times secured by treatment on a
Millipore Synergy® UV water treatment system from Millipore
A.S. (Molsheim, France). Methanol (MeOH) from Poch S.A. (Gliwice, Poland) and ethanol (EtOH 99.9% vol.) from Kemetyl A/S
(Køge, Denmark) were of HPLC grade. Formic acid (98–100%)
(HCOOH), acetic acid (100%) (CH3 COOH), and ammonium solution
(25%) (NH4 OH) from Merck (Darmstadt, Germany) were of analytical reagent grade. Sodium hydroxide (NaOH), also from Merck,
was prepared in a 0.01 M aqueous solution. Tricholoroacetic acid
(≥99.0%) from Sigma-Aldrich (Brøndby, Denmark) was prepared in
a 12% (v/v) aqueous solution (TCA) daily. Contamination between
samples was minimized by the use of disposable materials (vials,
bottles, etc.) where practicable, or through the use of lab equipment
that was cleaned without the use of detergents.
2.2. Standards
The following compound standards (bases (BS), nucleosides (NS), DP) were obtained from Sigma-Aldrich (Brøndby,
Denmark): adenine, guanine, cytosine, thymine, uracil, adenosine, guanosine, cytidine, uridine, inosine, 2 -deoxyadenosine,
2 -deoxyguanosine, 2 -deoxycytidine, thymidine, 2 -deoxyuridine,
2 -deoxyinosine, xanthine, hypoxanthine, uric acid, allantoin,
␤-alanine, ␤-ureidopropionic acid and ␤-aminoisobutyric acid.
␤-ureidoisobutyric acid, one important intermediate pyrimidine
derivate metabolite, was not commercially available and could not
be included. No traces of either adenosine or 2 -deoxyadenosine
were identified during method development in plasma or urine
samples. 2 -deoxycytidine was present in trace amounts but even
after extensive optimization the sensitivity remained too low for
quantification. These three components were therefore not pursued further. The chemical structures of the targeted metabolites
are shown in Table 1.
Stable isotopically labelled reference compounds used as
internal standards were purchased from Cambridge Isotope Laboratories (Andover, USA). These were: adenine (8-13 C), guanine (813 C;7,9-15 N2), thymine (15 N2), uracil (U-13 C4;U-15 N2), guanosine
(U-13 C10;U-15 N5), inosine (U-15 N4), cytidine (U-13 C9;U-15 N3),
uridine (U-13 C9;U-15 N2), 2 -deoxyguanosine (U-15 N5), thymidine
(U-15 N2), xanthine (1,3-15 N2), hypoxanthine (15 N4), uric acid (1,315 N2), and ␤-alanine (U-13 C3;15 N). Cytosine (2,4-13 C2;15 N3) was
purchased from Sigma-Aldrich (Brøndby, Denmark). All were 13 C
and/or 15 N labelled with purities of at least 95% (95–99%). Unfortunately, exact SIL were not available for all metabolites studied;
a suitable SIL was consequently selected on its similarity to the
corresponding metabolite in terms of structure, retention time,
fragmentation pattern and group. Individual stock solutions of all
compound standards and SIL were prepared and kept at −80 ◦ C.
Bases and purine DP were diluted in water and NS and pyrimidine
DP were diluted in 0.01 M NaOH solution. Two stock concentrations
of 500 and 5000 ␮mol/L were made for each compound standard.
The exception was for uric acid and allantoin, where the stock concentration was 500/2000 and 500/40,000 ␮mol/L, respectively. For
SIL only the low concentration stock was prepared. All stocks were
filtered through 0.45 ␮m PALL GHP Membrane syringe filters purchased from VWR (Herlev, Denmark) and kept at −20 ◦ C in dark
vials. Appropriate dilutions of these solutions were made in water
to produce standard mixtures and SIL mixtures for external calibration and quantification.
2.3. Samples
A number of 5 mL aliquots of heparinized plasma to be used
for external calibration and quality control were prepared from
2 L of venous blood [23] drawn from a Danish Holstein dairy cow
fed a traditional total mixed ration. Experimental plasma samples
were obtained from a feeding experiment [24] with multicatheterized dairy cows [25,26]. This set of samples was drawn from four
blood vessels simultaneously, representing blood from an artery
and the portal hepatic, hepatic and gastrosplenic veins. Additional
test plasma samples were obtained on site for relative matrix
effect evaluations. These samples were from five other species
(chicken, pig, mink, human, and rat) for between species comparisons, four multicatheterized cows (jugular vein) for intraspecies
comparisons, and bovine urine and milk samples for matrix effect
evaluations.
2.4. Pre-treatment
Before pre-treatment, plasma samples for quantification of uric
acid and uracil were diluted twenty-fold (5%, v/v) and four-fold
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
199
Table 1
Names, types, empirical formulae and suggestions for fragmentations of the compounds analyzed by the LC–MS/MS method.
Purines
Name
Pyrimidines
Type
Empirical formula
Name
Type
NH2
NH2
Adenine
Frag. 1
Base
N
NH
Empirical formula
N
Cytosine
N
Base
N
NH
O
O
Guanine
Frag. 2
Base
N
NH
O
NH
N
Thymine
Base
H3C
NH
NH2
NH
O
O
N
Guanosine
Frag. 3
NS
HO
O
N
O
NH
N
NH2
Uracil
NH
Base
NH O
OH OH
NH2
O
N
Inosine
NS
HO
O
N
N
NH
N
Cytidine
NS
HO
OH OH
O
N
NS
HO
O
N
O
NH
NH
N
NH2
Uridine
NS
HO
OH
O
N
NS
HO
O
OH
N
O
OH OH
O
O
2 -deoxyinosine
O
OH OH
O
2 -deoxyguanosine
Frag. 4
N
N
H3C
NH
N
ThymidineFrag. 7
NS
HO
NH
O
OH
N
O
200
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
Table 1 (Continued)
Purines
Name
Pyrimidines
Type
Empirical formula
Name
Type
Empirical formula
O
NH
N
Xanthine
Base/DP
NH
O
NH
N
H
2 -deoxyuridine
NS
HO
O
N
O
O
OH
O
N
Hypoxanthine
Frag. 5
Base/DP
N
H
DP
HN
O
N
H
NH2
DP
O
Frag. 8
DP
NH
HO
NH2
O
H
N
O
O
Allantoin
Frag. 6
␤-alanine
N
O
Uric acid
O
NH
␤-ureidopropionic acid
DP
NH
HN
O
H
N
H
N
O
N
H
NH
O
O
␤-aminoisobutyric acid
NH
DP
HO
NH2
NS, nucleoside; DP, degradation product. Illustrated with lines are the eight types of suggested metabolite fragmentations.
(25%, v/v) in water, respectively. This was, in the case of uric acid,
to avoid a non-linear calibration curve with the very high uric acid
concentrations in all samples, and, in the case of uracil, to be able
to distinguish the small uracil signal from the pronounced background noise. Pre-treatment: plasma samples were defrosted and
immediately put on ice. The sample (300 ␮L) was then added to
a SIL mixture and a water/standard mixture (550 ␮L total vol.)
before being precipitated with 1.8 mL ice-cold ethanol (10 min, on
ice, −20 ◦ C). This was followed by centrifugation (15 min, 5500 × g,
4 ◦ C). The supernatant was ultrafiltered on a Pall Nanosep 10K,
Omega membrane spin filter purchased from VWR. A 500 ␮L
aliquot of filtered supernatant was dried down under a flow of
nitrogen on a SuperthermTM fitted with a Mini Oven for AI blocks
and evaporator with valves from Mikrolab A/S (Aarhus, Denmark)
in conical autosampler vials from VWR until dryness (app. 75 min.,
room temp.). The pellet was re-suspended in 100 ␮L cold solvent
(A) (30 min, 4 ◦ C) and transferred to a clean dark LC-vial. Matrixmatched external calibrators were treated similarly to standard
plasma. Milk samples were cleared with ice-cold TCA 12% (end 50%,
v/v) before pre-treatment. Urine samples were handled as plasma
samples throughout.
2.5. LC–MS/MS analysis
Chromatographic separation was performed on an Agilent 1100
series HPLC system (Agilent Technologies, Hørsholm, Denmark)
with a SynergiTM Hydro-RP LC Column (250 mm × 2 mm, 4 ␮m)
protected by a conventional guard column of the same material
purchased from Phenomenex (Værløse, Denmark). Samples were
analyzed in five separate runs, three in negative electrospray (ESI)
mode and two in positive ESI mode. The five groups of metabolites
and their chromatographic profiles are shown in Table 2. Separation was performed using a gradient solvent system. For each run,
HPLC solvents were freshly prepared and cleared on a 0.45 ␮m Pall
hydrophilic polypropylene membrane filter purchased from VWR.
Both solvents (A) and (B) were prepared from a 0.05 mol/L acetic
acid buffer containing 10% or 50% methanol, respectively. The acetic
acid buffer was prepared by adjusting 0.05 mol/L acetic acid to pH
4.0 with ammonium solution and readjusting to pH 2.8 with formic
acid. The following elution gradient was used: initial percentage of
solvent B was 5%, this was raised to 100% in 8 min and kept there for
6 min, then lowered to 5% in 30 s, after which it was kept constant
for 3.5 min to re-equilibrate the column prior to the next injection.
The flow rate was 200 ␮L/min and the injection volume was 5 ␮L.
The column temperature was maintained at 30 ◦ C while the auto
sampler temperature was set to 4 ◦ C to stabilize the samples during time-consuming analyses. The total run time was 18 min per
sample.
A Waters (Hedehusene, Denmark) micromass triple quadropole
mass spectrometer was used for electrospray mass spectrometric
analyses using massLynx 4.0 (Waters) software for data collection
and processing. Capillary voltage was set to 3.2 kV, source temperature to 120 ◦ C, and desolvation temperature to 400 ◦ C. The cone
and desolvation gas flows (nitrogen and argon) were set at 29
and 628 L/h, respectively. Fragment ion spectra were recorded in
both polarities and promising selective fragment ions were tested
and optimized along with the cone voltage in multiple-reaction
monitoring (MRM) mode. The values of the tune parameters were
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
201
Table 2
The 20 metabolites were divided into five groups and run according to ESI−/+ mode and structure.
Metabolite group and ESI mode −/+
Group 1: Base/DP (ESI −)
Adenine (1)
Guanine (2)
Xanthine (3)
Allantoin (4)
(Uric acid 1,3-15 N2) (5)
Group 2: Base/DP (ESI +)
Cytosine (6)
Thymine (7)
Hypoxanthine (8)
␤-alanine (9)
␤-ureidopropionic acid (10)
␤-aminoisobutyric acid (11)
Group 3: NS (ESI−)
Guanosine (12)
Cytidine (13)
Uridine (14)
Inosine (15)
Thymidine (16)
2 -deoxyguanosine (17)
2 -deoxyinosine (18)
2 -deoxyuridine (19)
Group 4: Uracil (ESI +)
Uracil (20)
Group 5: Uric acid (ESI−)
Uric acid (5)
DP, degradation product; NS, nucleoside. Plasma samples and standard plasma for quantification and external calibration of uracil and uric acid were diluted 25% and 5%
(v/v), respectively, in water. A group 5 chromatographic profile (uric acid) is not illustrated in the table since uric acid (1,3-15 N2) can be observed with group 1 (same peak,
same shape, same RT).
optimized by separately infusing a solution (500 ␮mol/L) of each
metabolite in its mobile phase at a flow rate of 10 ␮L/min. The MRM
transitions and the applied cone voltages and collision energies are
summarized in Table 3. Common transitions were originated from
the loss of HCN, NH3 , ribose, deoxyribose, HNCO, HNCONH2 and
H2 O fragments for the various PP metabolites (Table 1). The most
intense transition reaction was used for quantification (Table 3).
Data were collected in centroid mode with a constant dwell time
of 0.05 s and an interscan delay of 0.02 s.
2.6. Calibration and quantification
Quantification was performed by matrix-matched external calibration applying standard plasma spiked with a two-fold serial
dilution of mixed standard solutions to obtain seven different
concentration levels of each compound. The only exception was
with uracil where a two-third-fold serial dilution was applied.
Standard plasma (not spiked) was used for subtraction and quality
control but was not included in the regression analysis. In general, all samples and calibrators were analyzed in duplicate and a
standard curve and quality control samples were analyzed at the
beginning and at the end of each sequence. The response was calculated as the chromatographic peak area for all compounds. When
applying standard plasma, which contained unknown quantities
of the metabolites under investigation, the measured metabolite response was initially normalized and the response from
the standard plasma was subtracted. The mean of the measured
SIL responses/SIL area for each sample was used as the normalization factor. During method development the focus of work
was on quantifying as low concentrations of metabolite as possible.
Matrix-matched calibration curves, within the relevant concentration ranges given in Table 4, were generated for each metabolite
at four (allantoin) or seven concentration levels on five consecutive
days for determining and evaluating the calibration model. As noted
previously, uric acid and uracil were quantified from diluted samples. The coefficient of variation (CV%) for each concentrate level
was then calculated for a logarithmic and a linear calibration model
to test the use of log–log transformation. The linearity of the log
calibration curves were studied with a lack of fit hypothesis test.
Subsequently, the homogeneity of variance was estimated for each
concentration by plotting the CV% against log(concentration) and
the quantification range set to the lowest and highest quantified
concentration giving a CV% below 25%.
2.7. Validation procedure
The method was validated according to reports from the “Analytical methods validation: bioavailability, bioequivalence and
pharmacokinetic studies” conferences held in Washington in 1990
[27] and 2000 [28], as described by Peters et al. [29]. It was validated with respect to assessment of selectivity, stability, precision,
recovery, and matrix effect.
2.7.1. Selectivity
Metabolite and SIL cross-talk was evaluated by analyzing the
standard compounds alone and together with their corresponding SIL (no blank matrix was available). Three groups were studied
and their signals compared; a compound standard group (10%, v/v,
50 ␮mol/L), a SIL group (10%, v/v, 50 ␮mol/L), and a combined group
(5%, v/v, 25 ␮mol/L). Analyses of BS/DP and NS were carried out
separately.
202
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
Table 3
Transition reactions monitored by LC–MS/MS, cone voltages and collision energy for the metabolite/stable isotopically-labelled reference compound (SIL) analyzed, and
suggested corresponding fragments lost.
Metabolite/SIL
Purines
Adenine/
Adenine (8-13 C)
Guanine/
Guanine
(8-13 C,7,9-15 N2)
Guanosine/
Guanosine
(U-13 C10;U-15 N5)
Inosine/
Inosine (U-15 N4)
2 -deoxyguanosine/
2 -deoxyguanosine
(U-15 N5)
2 -deoxyinosine/
2 -deoxyguanosine
(U-15 N5)a
Xanthine/
Xanthine (1,3-15 N2)
Hypoxanthine/
Hypoxanthine (15 N4)
Uric acid/
Uric acid (1,3-15 N2)
Allantoin/
Uric acid (1,3-15 N2)a
Pyrimidines
Cytosine/
Cytosine
(2,4-13 C2;15 N3)
Thymine/
Thymine (15 N2)
Uracil/
Uracil
(U-13 C4;U-15 N2)
Cytidine/
Cytidine
(U-13 C9;U-15 N3)
Uridine/
Uridine
(U-13 C9;U-15 N2)
Thymidine/
Thymidine (U-15 N2)
2 -deoxyuridine/
2 -deoxyguanosine
(U-15 N5)a
␤-alanine/
␤-alanine
(U-13 C3;15 N)
␤-ureidopropionic
acid/
ˇ-alanine
(U-13 C3;15 N)a
␤-aminoisobutyric
acid/
ˇ-alanine
(U-13 C3;15 N)a
Mw (g/mol)
Retention
time (min)
Precursor ion (m/z)
Cone
voltage (V)
Product ion
(m/z)
Collision
energy (eV)
Neutral
loss (NL)
Fragmentation 1–8
135.13
136.12
151.13
154.11
3.81
134
135
150
153
–
35
36
28
30
107
108
133
136
16
17
13
13
27
–HCN
1
17
–NH3
2
3.86
–
283.24
298.13
6.18
282
297
–
33
33
150
160
19
20
132
137
–Deoxyribose
3
268.23
272.20
267.24
272.17
5.81
267
271
266
271
–
26
26
26
28
135
139
150
155
20
20
19
20
132
–Deoxyribose
3
116
–Ribose
4
7.31
–
252.23
–
6.74
–
251
–
–
27
–
135
–
20
–
116
–Ribose
4
152.11
154.10
136.11
140.09
168.11
170.10
158.12
–
5.18
151
153
135
141
167
169
157
–
–
29
31
34
34
26
29
16
–
108
109
92
113
124
125
97
–
16
16
16
19
16
14
16
–
43
44
43
27
43
44
60
–HNCO
5
–HNCO –HCN
51
–HNCO
5
–HNCONH2
6
4.56
4.28
3.05
–
+
–
–
111.95
116.08
2.91
112
117
+
29
30
95
99
20
19
17
18
–NH3
2
126.11
128.10
112.09
118.04
6.21
127
129
113
119
+
27
27
26
27
110
111
96
101
7
16
7
16
17
18
17
18
–NH3
2
–NH3
2
3.97
+
243.22
255.13
3.19
242
254
–
23
21
109
116
14
15
133
138
–Deoxyribose
3
244.20
255.12
4.50
243
254
–
23
28
110
116
15
16
133
138
–Deoxyribose
3
242.23
244.22
228.20
–
8.52
241
243
227
–
–
25
26
22
–
151
153
184
–
12
11
12
–
90
–Rearrangement
7
43
–HNCO
5
5.34
–
–
89.09
93.07
2.91
90
94
+
13
14
72
76
10
7
18
–H2 O
8
132.12
–
3.77
–
133
–
+
11
–
115
–
10
–
18
–H2 O
8
103.12
–
2.98
–
104
–
+
13
–
86
–
10
–
18
–H2 O
8
SIL, stable isotopically-labelled reference compound. All metabolites had a specific retention time and generated single peak shapes.
a
This SIL was selected as the most suitable according to structure, retention time, fragmentation pattern and metabolite group.
2.7.2. Stability
For continuous evaluation of long-term storage stability, a fresh
quality control sample was analyzed in all analytical runs. The stability within runs (6–24 h) was evaluated in two ways. First, a
quality control sample was analyzed at the beginning and at the
end of each sequence (data not shown). Secondly, a set of spiked
standard plasma samples were analyzed at five different times (different vials) during a 30-h sequence. Analysis of variance (ANOVA)
using linear mixed models procedures was used to test the stability over time, both with a trend element and with random changes
over and above the linear trend (regression line) [30,31]. Applying
ANOVA, the across-day variation of the PP calibration curves (intercepts and slopes as interactions with test day) was assessed over
five consecutive days and expressed by their P-values. The stability during repeated freeze-thaw cycles was not explored since all
plasma samples in the present study were only thawed once.
2.7.3. Precision and recovery
Precision of the method, in terms of within-day variation
(repeatability) and across-day variation (intermediate precision),
was determined by analyzing replicate sets of spiked standard
plasma samples on five separate days expressed as their CV%. The
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
203
Table 4
Concentration level, calibration range, lack-of fit, quantification range and precision of the metabolite calibration curves.
Metabolite
Type
Rangea
Precision (test-day)d
Linearity
b
Concentration
levels
Calibration range
(␮mol/L)
Lack of fit
P-value
Quantification
rangec (␮mol/L)
Intercept
P-value
Slope
P-value
Purines
Adenine
Guanine
Guanosine
Inosine
2 -Deoxyguanosine
2 -Deoxyinosine
Xanthine
Hypoxanthine
Uric acid
Allantoin
Base
Base
NS
NS
NS
NS
Base/DP
Base/DP
DP
DP
7
7
7
7
7
7
7
7
7
4
0–5.0
0–5.0
0–5.0
0–5.0
0–5.0
0–5.0
0–5.0
0–5.0
0–200
15–500
0.84
0.15
0.79
0.23
0.06
0.92
0.67
0.40
0.99
0.64
0.08–5.0
0.08–5.0
0.16–5.0
0.08–5.0
0.08–5.0
0.16–5.0
0.16–5.0
0.08–5.0
3.15–200
124–500
0.096
0.041
0.071
0.013
0.029
<0.001
0.087
0.009
<0.001
0.427
0.059
0.994
0.003
0.004
0.294
0.021
0.006
<0.001
0.003
0.897
Pyrimidines
Cytosine
Thymine
Uracil
Cytidine
Uridine
Thymidine
2 -Deoxyuridine
␤-Alanine
␤-Ureidopropionic acid
␤-Aminoisobutyric acid
Base
Base
Base
NS
NS
NS
NS
DP
DP
DP
7
7
7
7
7
7
7
7
7
7
0–7.5
0–5.0
0–5.0
0–5.0
0–7.5
0–5.0
0–5.0
0.25–13
0–75
0–5.0
0.84
0.68
0.88
0.70
0.02
0.48
0.35
0.59
0.87
0.29
1.92–7.5
1.27–5.0
0.66–5.0
5.15–5.0
1.91–7.5
–e
–e
13–13
4.67–75
0.31–5.0
0.566
0.035
<0.001
0.086
0.286
0.741
0.151
<0.001
0.003
0.026
0.274
0.030
0.042
0.670
0.480
0.599
0.309
0.070
0.283
0.571
NS, nucleoside; DP, degradation product. Only four curves were available for uric acid and ␤-ureidopropionic acid. In the case of allantoin, the three lower concentration
levels were excluded to better fit the concentration range of actual samples. For uridine, one observation in one curve was considered an outlier following visual inspection
and was rejected.
a
External calibration was performed with seven concentrations of metabolite on five separate days (n = 5, days), except for allantoin where only four concentration levels
were available. The ranges where chosen according to concentration ranges in actual samples.
b
Lack of fit hypothesis test to validate the linearity of the calibration curves expressed by their P-values (n = 5, curves). P < 0.05 was considered significant.
c
The quantification range was set to the lowest and highest quantified concentration giving an acceptable CV% < 25% (see Fig. 2).
d
The intermediate precision of the calibration curves (intercepts and slopes as interactions with test day) expressed by their P-values (n = 5, days). P < 0.05 was considered
significant, P < 0.1 a tendency.
e
Value is above the highest calibrator concentration.
absolute recoveries were calculated using the same set of spiked
standard plasma, at one level, by comparing the obtained concentrations with the initial spiked level.
2.7.4. Matrix effect
Early tests with spiked water, urine and plasma samples
revealed large variations in matrix effect-induced signal suppression and enhancement between the metabolites included in the
analysis. Following optimization of the pre-treatment procedure,
these matrix effects were evaluated as the difference between
samples of water and standard plasma, urine or milk samples
spiked with constant amounts of SIL before pre-treatment. Thus,
we took advantage of the fact that the incorporated SIL should
behave as their matching metabolite in the ESI source [27]. The
conventional strategy of spiking a blank matrix sample with a
compound standard was again not possible as completely blank
matrices were not available for these metabolites. The applied SILbased method was a modified version of the conventional method
to evaluate matrix effect described by Matuszewski et al. [32].
The observed matrix effect was rendered insignificant by utilizing
matrix-matched external calibration.
2.8. Application
To determine the application range of the method, the relative
matrix effect was evaluated by comparing the response from PP SIL
spiked in standard jugular vein plasma with the response in test
plasma samples. Four different sets of samples were assessed. First,
plasma from the jugular vein of four multicatheterized cows was
used to investigate within-species variation. Next, plasma drawn
from the portal vein, the hepatic vein, the gastrosplenic vein, and
an artery from a multicatheterized dairy cow to represent different possible sampling sites were examined. Third, plasma samples
from different species (chicken, pig, mink, human, rat) were used
for between-species evaluation. Finally, water, urine and milk samples were used to compare different matrices. The relative recovery
determined which of the tested matrices were suitable for the
method. For the same reasons as described previously, SIL replaced
compound standards. Water, urine and milk samples were evaluated in the same manner as plasma samples.
3. Results and discussion
3.1. Method development
The aim of this study was to develop a quantitative LC–MS/MS
analysis and a sample pre-treatment procedure for the simultaneous analysis of several metabolites of the PP metabolism in blood
plasma of dairy cows. The chemical properties of the metabolites
were polar due to high contents of –OH, =O and –N groups. Based on
their polarity, they were roughly divided into three groups: The very
polar group, containing ␤-alanine, ␤-aminoisobutyric acid and ␤ureidopropionic acid, were all small molecules with similar linear
polar structures, as well as the also highly polar allantoin, cytosine
and cytidine. The polar group included the majority of the BS, such
as adenine, guanine and uracil, as well as the intermediate DP with
more base-like structures, such as uric acid, xanthine and hypoxanthine. Finally, the semi-polar group comprised the majority of the
NS with large but semi-polar sugar side groups, such as most of
the ribonucleosides (2× –OH) and deoxyribonucleosides (1× –OH).
Owing to their very non-polar methyl side groups, thymine and
thymidine were also placed in the semi polar group. The very polar
204
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
metabolites were poorly retained on the C18 column with the aqueous solvents and eluted first as expected, offering a longer retention
time of the less polar components.
3.1.1. Pre-treatment development and evaluation
An effective clean-up procedure is crucial when performing
LC–MS/MS analysis as this diminishes cross-talk [33,34] as well
as matrix effects [35] and at the same time enhances both the
selectivity and the sensitivity of the analysis [29]. A novel multistep approach, consisting of protein precipitation, ultrafiltration,
evaporation under nitrogen flow, and subsequent resolution, able
to purify and to concentrate all of the studied metabolites from
bovine plasma simultaneously, in a simple and efficient manner,
was developed and optimized.
Initially, different solvents (acetone, acetonitrile, ethanol,
methanol, sulfo-salicylic acid) were tested for precipitation (data
not shown). Ethanol precipitation resulted in the highest recoveries
and least noise when comparing chromatographic responses and
this less harmful solvent was therefore chosen for the procedure.
The ultrafiltration step was added as this step caused markedly
lower levels of background noise. As a consequence of the approximately eight-fold dilution during pre-treatment, evaporation and
reconstitution steps were included. Overall this resulted in a 1.4
times concentration effect. To try to reduce degradation and instability of the samples caused by reactive oxygen species or enzyme
activities during pre-treatment, all centrifugations and incubations
were performed at 4 ◦ C and samples, stocks, and solvents, etc., were
kept at −4 ◦ C or on ice. Only during evaporation were the samples maintained at room temperature. Other types of pre-treatment
methods such as simple dilution (impractical), solid-phase extraction (different chemical properties) [36,37] and accelerated solvent
extraction [38] were also investigated (data not shown) but were
not found useful.
The effectiveness of the pre-treatment and the stability of the
metabolites during the multiple steps were evaluated during validation of the method, described in Section 3.3, and demonstrated
the ability of this pre-treatment to purify and concentrate all of the
targeted PP simultaneously in an easy and efficient manner without significant losses. To our knowledge, no other publications have
presented a similar and effective pre-treatment procedure, as most
other approaches include dilution of the samples.
3.1.2. LC–MS/MS procedure
Based on the chemical properties of the targeted metabolites, experiences from similar studies [14,39], and available
equipment, a reversed-phase C18 column known to be able to
quantify the majority of the studied metabolites from urine was
applied with an acetic acid buffer/methanol HPLC solvent system.
To achieve adequate separation and elution order, a series of
conditions were modified and implemented. The composition of
the acetic acid buffer and the methanol extraction solvent was
based on the work of Hartmann et al. [39], and no other types
of solvent were tested. Having tested several acetic acid buffer to
methanol ratios (95%, 90%, 85%, and 80%, v/v), assessing peak separation and shapes, it was concluded that the best separation was
accomplished with a 90% (v/v) solvent (A) and 50% (v/v) solvent
(B). The chosen injection volume, 5 ␮L, and flow rate, 200 ␮L/min,
was found by assessing the same parameters, testing first injections of 5, 10, 20 ␮L and then flow rates of 100, 200, 300 and
400 ␮L/min. Concerning the elution gradient, we strived to make
it as short as possible, while still achieving as good a peak separation as possible. Different elution profiles were tested, with more or
less steep gradients. The final profile, described in Section 2.5, gave
a total run time of 18 min. By adding a small amount of methanol
to the otherwise aqueous solvent (A), and, by keeping the baseline
at 5% solvent (B), the solvent mixing became more smooth and
transitions between runs became more stable. A major improvement in precision between runs was achieved by maintaining
the column temperature at 30 ◦ C instead of 25 ◦ C. An improvement in the sample stability during the time-consuming analyses
was achieved by cooling the auto-sampler to 4 ◦ C. In the end,
useful combinations of retention times and peak shapes of each
metabolite were achieved with the parameters described, and the
method was therefore adapted and brought on to further validation.
3.2. The log-calibration model and quantification range
Calibration curves were prepared by linear regression of
log(area) against log(concentration) (log-calibration) and by linear
regression in linear units on both axes (linear calibration) to verify
the use of the log-calibration model. Initially, the normality of residuals around the calibration lines were inspected visually (Q–Q plot)
and found to be approximately normal. The CV% for each concentration level for both the log-calibration and the linear calibration
is illustrated in Fig. 1. A large group of the PP (panel I) considerably
improved their CV% profiles using the log-calibration, especially in
the low ranges. However, a smaller group of PP (panel II) did not
benefit from the log transformation; and the transformation did not
weaken as their CV% profiles either. Exceptions were with allantoin,
␤-ureidopropionic acid, cytosine and ␤-alanine, their CV% at the
high end of their profiles were better without the log-log transformation. Given that quantification at low concentrations was
considered to be most important, these findings validated the use
of log–log transformation in the analysis of all the applied PPs. Performing a lack of fit test, the linearity of the PP calibration curves
were evaluated and expressed by their P-values (Table 4). None
of the PP curves resulted in a significant lack of fit except uridine,
which had a very low sensitivity in the analysis, demonstrating a
satisfying log–log regression.
The homogeneity of variance for the different concentration levels is illustrated in Fig. 2 and the quantification ranges (CV < 25%)
in Table 4. Focusing on the lower concentration range, most of
the PP demonstrated a typical precision profile where the CV%
decreased with higher concentration levels. All purines had acceptable variation levels around the lowest concentration levels except
allantoin, which should not be quantified at concentrations below
∼100 ␮mol/L. The pyrimidine BS and cytidine and uridine had
larger CV%’s with acceptable lower concentration levels from 0.66
to 5.15 ␮mol/L. Thymidine and 2 -deoxyuridine demonstrated a
very large variation with CV%’s above 25% over the entire concentration range. In the case of the pyrimidine DP, they were
reasonably stable over their concentration ranges, not counting ␤alanine which only had a CV% < 25% at its highest calibrator. The
upper part of the quantification range was in all cases the highest
quantified calibrator.
3.3. Method validation
Once the pre-treatment, LC–MS/MS procedure, and calibration
model had been set, the performance characteristics of the method
were established by validation with spiked standard plasma. In
terms of quantification purposes, selectivity, stability, precision,
recovery, and matrix effects were evaluated.
The most intensive fragment ion from each precursor ion was
selected as the transition ion for detection and quantification. Positive identification was based on the correlation of retention time
with the standards and the selected precursor/product transition.
Less intensive second transitions were used for confirmation. All
metabolites generated single peak shapes.
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
Linear calibration I
205
Log-calibration I
Ade
Gua
Guo
Ino
dGuo
Ade
Gua
Guo
Ino
dGuo
dIno
Xan
Hyp
Uac
All
dIno
Xan
Hyp
Uac
All
Urd
β-ure
β-ami
Urd
β-ure
β-ami
1000
100
100
CV%
CV%
1000
10
1
0.01
10
0.1
1
10
100
1
0.01
1000
0.1
Standard concentration (μmol/L)
Linear calibration II
Cyt
Thy
Ura
Thd
dUrd
β-ala
1
10
100
1000
Standard concentration (μmol/L)
Log-calibration II
Cyd
100
100
Thy
Ura
Thd
dUrd
β-ala
Cyd
CV%
1000
CV%
1000
Cyt
10
1
0.01
10
0.1
1
10
100
1000
Standard concentration (μmol/L)
1
0.01
0.1
1
10
100
1000
Standard concentration (μmol/L)
Fig. 1. The coefficient of variation (CV%) for each concentration level using linear regression of area against concentration (linear calibration) and using linear regression of
log(area) against log(concentration) (log-calibration). Panel I present the 13 purines and pyrimidines that considerably improved their CV% profiles using the log-calibration.
Panel II, present the seven purines and pyrimidines that did not benefit from the log transformation. Abbreviations for the 20 metabolites: Ade, adenine; Gua, guanine;
Guo, guanosine; Ino, inosine; dGuo, 2 -deoxyguanosine; dIno, 2 -deoxyinosine; Xan, xanthine; Hyp, hypoxanthine; Uac, uric acid; All, allantoin; Urd, uridine; ␤-ure, ␤ureidopropionic acid; ␤-ami, ␤-aminoisobutyric acid; Cyt, cytosine; Thy, thymine; Ura, uracil; Cyd, Cytidine; Thd, thymidine; dUrd, 2 -deoxyuridine; ␤-ala, ␤-alanine.
3.3.1. Selectivity
A blank sample for selectivity evaluation was not available
for these naturally occurring plasma metabolites. Hence, the
presence of chromatographic peaks from standard plasma at
the same retention times as the targeted metabolites could not
be excluded; such endogenous peaks would be expected to be
present. Instead, the absence of standard compound/SIL crosstalk contributions was verified by comparing chromatographic
responses for standards and SIL alone and in a mixture (data not
shown). It was important to assess cross-talk contributions, as
some of the applied SIL (Table 3) had less than three mass unit
differences (3–8) to the natural metabolite, which is normally
recommended as the lowest mass unit difference for LC–MS/MS
analysis [33,34].
3.3.2. Stability
Good stability was achieved by optimizing the pre-treatment
and LC–MS/MS parameters as described in Section 3.1. Long-term
storage stability was tested by comparing chromatographic profiles of quality control standard plasma on a daily basis. Within-run
stability was evaluated by analyzing a control sample at the beginning and end of each sequence. Long sequence run times have
been of concern and the within-run stability was consequently
also evaluated by performing ANOVA for measurements made at
times 0, 7, 15, 22 and 29 h, during a 30-h sequence with triplicate determinations at each time-point, using either a slope
model: yij = intercept + b × time hour + εij , or a combined model:
yij = intercept + timei + b × time hour + εij , where yij is the area measured in the sample at time i, replicate j, and b is the slope of the area
change per hour, and εij is the random error term. Significance of the
time effects were tested using an F-test with type 1 sum of squares.
Residual mean square error was calculated as the square of the
residual variance estimate and expressed as CV%. The metabolite
responses were normalized as usual but the SIL responses were not
since they could not be used to normalize themselves. The results
are given in Table 5.
206
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
Purine bases
Ade
Gua
Purine nucleosides
Hyp
Xan
Guo
dGuo
dIno
Uac
35
40
50
30
35
45
35
25
15
30
CV%
CV%
20
20
15
10
5
5
0
0.01
0
0.01
0.1
1
10
10
5
0
0.1
Standard concentration (μmol/L)
Thy
1
10
1
Standard concentration (μmol/L)
Pyrimidine bases
Cyt
25
20
15
10
Ura
Cyd
70
Urd
Thd
10
100
1000
Standard concentration (μmol/L)
Pyrimidine nucleosides
80
All
40
30
25
CV%
Ino
Purine degradation products
Pyrimidine degradation products
dUrd
β-ala
120
60
100
50
80
40
β-ure
β-ami
CV%
CV%
50
40
30
CV%
60
60
30
40
20
20
10
20
10
0
0.01
0.1
1
10
0
0.01
0.1
Standard concentration (μmol/L)
1
10
0
0.01
Standard concentration (μmol/L)
0.1
1
10
100
Standard concentration (μmol/L)
Fig. 2. The homogeneity of variance for the different concentration levels of the purine and pyrimidine calibration curves divided into bases, nucleosides and degradation
products (CV%). Abbreviations for the 20 metabolites: Ade, adenine; Gua, guanine; Guo, guanosine; Ino, inosine; dGuo, 2 -deoxyguanosine; dIno, 2 -deoxyinosine; Xan,
xanthine; Hyp, hypoxanthine; Uac, uric acid; All, allantoin; Urd, uridine; ␤-ure, ␤-ureidopropionic acid; ␤-ami, ␤-aminoisobutyric acid; Cyt, cytosine; Thy, thymine; Ura,
uracil; Cyd, Cytidine; Thd, thymidine; dUrd, 2 -deoxyuridine; ␤-ala, ␤-alanine.
Table 5
Stability of each metabolite/stable isotopically labelled reference compound during a 30-h sequence.
Metabolite
Purines
Adenine
Guanine
Guanosine
Inosine
2 -deoxyguanosine
2 -deoxyinosine
Xanthine
Hypoxanthine
Uric acid
Allantoin
Pyrimidines
Cytosine
Thymine
Uracil
Cytidine
Uridine
Thymidine
2 -deoxyuridine
␤-alanine
␤-ureidopropionic acid
␤-aminoisobutyric acid
Concentration
level (␮mol/L)
Slope model
(CV%)
Combined
model (CV%)
4
4
4
4
4
4
4
4
4
40
9
11
12
11
14
11
6
12
12
10
4
8
7
2
6
5
4
2
6
7
4
7
4
4
4
7
7
7
7
7
26
18
18
11
11
136
46
16
10
7
3
10
6
9
4
136
46
13
2
5
Corresponding SIL
Concentration
level (␮mol/L)
Slope model
(CV%)
Combined
model (CV%)
Purines
Adenine (8-13 C)
Guanine (8-13 C,7,9-15 N2)
Guanosine (U-13 C10;U-15 N5)
Inosine (U-15 N4)
2 -deoxyguanosine (U-15 N5)
2 -deoxyguanosine (U-15 N5)
Xanthine (1,3-15 N2)
Hypoxanthine (15 N4)
Uric acid (1,3-15 N2)
Uric acid (1,3-15 N2)
7
7
7
7
7
–d
7
7
35
35
8
9
8
11
11
–d
9
12
9
10
5
5
3
2
3
–d
6
7
3
7
Pyrimidines
Cytosine (2,4-13 C2;15 N3)
Thymine (15 N2)
Uracil (U-13 C4;U-15 N2)
Cytidine (U-13 C9;U-15 N3)
Uridine (U-13 C9;U-15 N2)
Thymidine (U-15 N2)
2 -deoxyguanosine (U-15 N5)
␤-alanine (U-13 C3;15 N)
ˇ-alanine (U-13 C3;15 N)
ˇ-alanine (U-13 C3;15 N)
14
7
14
7
14
40
–a
28
–a
–a
9
11
16
16
15
18
–a
9
–a
–a
9
8
13
12
9
13
–a
6
–a
–a
SIL, stable isotopically labelled reference compound. An appropriate concentration level was chosen for each metabolite/SIL according to their sensitivity in the analysis.
The stability (significance of time) of each metabolite/SIL was expressed by their CV% using either a slope- or a combined model. The data handling was conducted with
metabolite responses in area units. If the CV% ≤10% the stability was considered acceptable over time.
a
SIL used for more than one metabolite.
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
207
Table 6
The recovery and within- and across-day variation of each metabolite investigated.
Metabolite
Purines
Adenine
Guanine
Guanosine
Inosine
2 -Deoxyguanosine
2 -Deoxyinosine
Xanthine
Hypoxanthine
Uric acid
Allantoin
Pyrimidines
Cytosine
Thymine
Uracil
Cytidine
Uridinec
Thymidine
2 -Deoxyuridine
␤-Alanine
␤-Ureidopropionic acid
␤-Aminoisobutyric acid
Concentration
level (␮mol/L)
Concentrationa
(␮mol/L)
Recoveryb (%)
Within-day
variationc (CV%)
Across-day
variationd (CV%)
4.17
4.13
4.15
4.18
4.15
4.11
4.12
4.11
4.08
41.44
4.33
3.77
4.13
4.10
4.27
4.23
4.39
4.07
4.38
45
104
91
100
98
103
103
106
99
78
107
2
2
4
2
4
2
3
1
16
34
5
4
12
9
7
8
9
6
55
49
4.13
6.86
4.11
4.16
4.12
6.90
6.86
6.86
6.91
6.83
4.24
6.72
4.33
6.75
3.89
8.35
10
7.21
6.30
6.86
103
98
105
162
94
121
149
105
91
100
21
4
5
18
7
23
33
12
14
6
24
15
4
24
12
21
37
5
13
7
Only four curves were available for uric acid and ␤-ureidopropionic acid. In the case of allantoin, the three lower concentration levels were excluded to better fit the
concentration range of actual samples. For uridine, one observation in one curve was considered an outlier following visual inspection and was rejected. An appropriate
concentration level was chosen for each metabolite according to the metabolites sensitivity in the analysis.
a
Recovered quantified concentration.
b
The recovery (%) was calculated as: (mean recovery concentration/mean spiked concentration) × 100 (n = 8, samples). Recovery (%) was an average of recoveries obtained
over 5 days (m = 5, days).
c
The within-day variation (n = 8, samples) expressed as CV%.
d
The across-day variation (m = 5, days) expressed as CV%.
In general, the combined model resulted in lower CV%’s than
the slope model, as the irregular time effect was also taken into
consideration in the combined model. All but a few metabolites
demonstrated very stable profiles over the 30-h time span with
CV% ≤ 10%. Exceptions were thymidine (136%), 2 -deoxyuridine
(46%) and ␤-alanine (13%), where especially the former two were
found to be unstable. This was probably due to low sensitivities in
the analysis. The SILs were found to be equally or more stable than
their corresponding metabolites probably due to their higher spike
concentrations. As expected, thymidine (U-15 N2 ) and ␤-alanine
(U-13 C3;15 N) had the same instability issues as their partners. No
2 -deoxyuridine SIL was applied in this analysis. Surprisingly, the
uracil and cytidine SIL had CV%’s above 10%. In the case of uracil
(13%), excessive degradation was avoided by always placing uracil
samples in the beginning of a sequence.
To assess the stability of the calibration curves between
run-days, ANOVA was conducted determining the across-day
(intermediate precision) precision (Table 4). Most PP demonstrated
a significant (P < 0.05) difference between test days on either curve
intercept or slope, or at least a tendency (P < 0.1). Exceptions were
with allantoin, cytosine, uridine, thymidine and 2 -deoxyuridine,
all of which revealed reasonably stable curves over test days. These
results demonstrated the need for renewing calibration curves on
a daily basis.
3.3.3. Precision and recovery
To ensure correct quantification and to evaluate analytical
precision, within-day and across-day variation was determined by
studying replicate sets of spiked standard plasma samples (n = 8,
samples) on five separate days (m = 5, days). Here, precision was
defined as the degree to which repeated measurements under
unchanged conditions showed the same result, expressed as the
CV%. Absolute recoveries were identified by using the same set
of spiked standard plasma samples, comparing the recovered
quantified concentrations with the initial spiked concentrations.
Since linearity ranges were short and close to zero, a single, instead
of the traditional three, recovery concentration levels was chosen.
Precision and recovery outcomes are given in Table 6. The obtained
results showed very good extraction efficiency and precision. The
recoveries were between 91% and 107%, except for uric acid with a
lower recovery of 78%. Also, the low sensitivity and accompanying
instability of cytidine, thymidine and 2 -deoxyuridine was again
highlighted with recoveries of 162%, 121%, and 149%, respectively.
In general, the within- and across-day variations mirrored the
recovery results. The exceptions were with allantoin and cytosine,
both of which had good recoveries, 107% and 103%, but exhibited
large CV%’s, within-day variation 34% and 21%, and across day
variation 49% and 24%, respectively.
3.3.4. Absolute matrix effect
It is useful to distinguish between two types of matrix effects:
absolute matrix effect, which is the difference in response between
an undiluted solution and a post-extraction spiked sample, and relative matrix effect (Section 3.4), which is the difference between
various lots of post-extraction spiked samples [32]. Matrix effects
are very common problems when applying LC–MS/MS analysis
on biological samples [22,35,40]. The term describes the effect
molecules originating from the sample matrix can have on the ionization process in the mass spectrometer when co-eluting with the
compound of interest. It theoretically occurs in either the solution
or the gaseous phase and the main cause is a change in droplet
solution properties caused by the presence of non-volatile and
less volatile solutes that change the efficiency of droplet formation and evaporation, which in turn affects the amount of charged
ions in the gas phase that ultimately reach the detector [35]. As
the effect occurs in the ESI source before detection, it is hard to
compensate for by mass spectrometry alone [41,42]. In this analysis, the matrix effect was quantified by comparing the response of
208
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
Purine degradation products
Adenine (8-13C)
Guanine (8-13C,7,9-15N2)
Guanosine (U-13C10;U-15N5)
Inosine (U-15N4)
2’-deoxyguanosine (U-15N5)
Xanthine (1,3-15N2)
1000
500
0
-500
-1000
-1500
-2000
-2500
Plasma
Urine
Uric acid (1,3-15N2)
60000
40000
20000
0
-20000
-40000
Plasma
Milk
Pyrimidine bases and nucleosides
Uracil (U-13C4;U-15N2)
β-alanine (U-13C3;15N)
Cytidine (U-13C9;U-15N3) Uridine (U-13C9;U-15N2)
80000
Thymidine (U-15N2)
60000
2000
1000
0
-1000
-2000
-3000
-4000
Plasma
Urine
Milk
Urine
Milk
Cytosine and β-alanine
Response relative to water (area)
Response relative to water (area)
Thymine (15N2)
Hypoxanthine (15N4)
80000
Response relative to water (area)
Response relative to water (area)
Purine bases and nucelosides
Cytosine (2,4-13C2;15N3)
40000
20000
0
-20000
-40000
-60000
-80000
Plasma
Urine
Milk
Fig. 3. Matrix effects in plasma, urine and milk expressed as response relative to water (area).
SIL in spiked matrix samples before extraction with the response
obtained in water. Matrix effects for all SILs are illustrated in
Fig. 3.
Recognizing that the nature of matrix effects is varying and the
sensitivity between metabolites are very different the sizes of the
bars are relative indicators of the degree of suppression or enhancement. Signal enhancement was observed in plasma for almost all
metabolites, and only a few, such as inosine, cytidine, ␤-alanine
and cytosine, had their signals suppressed. These metabolites did
not share any obvious similarities in polarity or structure; however, matrix effects are known to be very compound-dependent
[22]. In contrast to the signal enhancement generally encountered
in plasma, in urine all metabolite signals were suppressed. This
demonstrates the different matrix effects a given component experience when present in different matrices in LC–MS/MS analysis. In
milk, only the purines had a common pattern, i.e., signal suppression, and the remaining metabolites were neither suppressed nor
enhanced.
Matrix effects can vary between measurements, hence, it is
not possible to test for matrix effects only once and consider it
to be constant [43]. Matrix effects were largely eliminated in the
analysis first of all by making the external calibrators matrixmatched, hence, quantifying calibrators and sample metabolites
under the same conditions, secondly, by implementing a very
effective pre-treatment [33,44], and thirdly, by implementing
SIL [22,42]. Matrix-matching is necessary when specific SILs are
not available for all metabolites [42]. These initiatives compensated quite well for the signal suppression or enhancement
in the plasma samples, thereby achieving accurate quantification.
3.4. Analytical application (relative matrix effect)
This LC–MS/MS analysis was established for quantification of 20
target metabolites of the PP metabolism in blood plasma samples
from multicatheterized cows. Since jugular vein plasma (representing systemic circulating blood) was used for method development
and because quantification relied on matrix-matched calibration
(jugular vein plasma), the relative matrix effect was evaluated in
alternative types of plasma. The relative matrix effect was evaluated by comparing the response from SIL spiked in standard jugular
vein plasma with the response in tested plasma samples. A relative recovery between 85% and 115% was considered good and
between 75% and 125% acceptable, hence, tested samples exerted
the same matrix effect on the metabolite as the cow jugular vein
plasma sample. The generosity of 75–125% was due to the small
sample size (n = 2 samples) inevitably resulting in less precision.
The PP responses given as recovery (%) are depicted in Table 7.
First of all, it was confirmed that within-species variation
was not an issue with any of the metabolites examined, except
for uridine. Secondly, the results demonstrated that all the
examined metabolites, evaluated in all four plasma types from
feeding experiments with multicatheterized cows with this particular type of cow model, could appropriately be quantified with
the developed LC–MS/MS method. Only xanthine (67%), uridine
(135%/148%/127%) and thymidine (132%) displayed recoveries outside the acceptable range of 75-125% and especially thymidine will
be hard to quantify with this method due to other issues anyway.
Surprisingly, the between-species range was very broad and most
metabolites could be evaluated in plasma from other species tested
with a few exceptions. Further confirmed was also the results from
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
209
Table 7
Comparison of the response from the metabolites (stable isotopically labelled reference compounds) spiked in standard jugular vein plasma with the response obtained in
tested plasma samples from four other cows, four other blood vessels, five other animal species and three other matrices, to evaluate relative matrix effect and the application
range of the method.
SIL
Four cows
Four vessels
Five species
Three matrices
1
2
3
4
P
H
R
A
C
P
M
H
R
W
U
M
Purines
Adenine (8-13 C)
Guanine (8-13 C,7,9-15 N2)
Guanosine (U-13 C10;U-15 N5)
Inosine (U-15 N4)
2 -deoxyguanosine (U-15 N5)
Xanthine (1,3-15 N2)
Hypoxanthine (15 N4)
Uric acid (1,3-15 N2)
101
94
110
113
110
88
103
97
102
91
98
97
101
86
106
86
106
106
99
102
103
96
101
107
101
96
103
101
105
93
105
97
99
92
94
96
95
85
105
111
93
88
115
116
117
76
103
112
94
87
122
121
121
77
108
107
83
79
119
114
107
67
104
98
84
74
110
110
109
66
103
100
87
86
115
114
116
76
108
99
71
67
111
114
117
66
107
60
116
106
112
115
114
117
114
102
91
36
120
121
123
78
90
95
88
94
105
107
99
97
45
71
47
54
93
83
62
64
13
68
58
54
77
68
2
67
116
110
Pyrimidines
Cytosine (2,4-13 C2;15 N3)
Thymine (15 N2)
Uracil (U-13 C4;U-15 N2)
Cytidine (U-13 C9;U-15 N3)
Uridine (U-13 C9;U-15 N2)
Thymidine (U-15 N2)
␤-alanine (U-13 C3;15 N)
95
97
104
97
117
102
105
115
96
93
83
107
106
105
100
98
106
96
101
98
104
107
100
101
96
141
112
101
106
100
101
95
98
108
110
116
98
102
122
135
125
98
104
101
103
109
148
132
107
110
101
105
104
127
118
107
90
93
94
124
62
126
90
82
106
106
116
176
118
96
32
98
97
99
87
135
77
138
102
104
108
80
123
112
98
97
106
99
105
126
99
579
98
88
170
35
95
357
43
79
81
30
14
72
42
999
95
100
48
32
102
149
1, Cow 1; 2, cow 2; 3, cow 3; 4, cow 4; P, portal hepatic vein; H, hepatic vein; G, gastrosplenic vein; A, artery; C, chicken; P, pig; M, mink; H, human; R, rat; W, water; U, urine;
M, milk. The relative recovery was calculated as: (tested sample(area) − jugular(area)) × 100 (n = 2, samples). A relative recovery between 85% and 115% was considered good
and between 75% and 125% was considered acceptable. Shaded areas show recoveries not fulfilling these criteria.
Section 3.3.4, concluding that matrix effects varied significantly
between different types of matrices such as water, plasma, urine
and milk. Hence, it is necessary to design, optimize and validate a
specific LC–MS/MS method for each applied matrix.
some of the plasma samples were obtained was partly provided by
the Commission of the European Communities (Brussels, Belgium;
Rednex project FP7, KBBE-2007-1).
References
4. Conclusions
This work presents the development and validation of a new
method for simultaneous and accurate quantification of 20 targeted
metabolites of PP metabolism with different structures and physiochemical properties in blood plasma from dairy cows. Exceptions
were with cytidine, thymidine and 2 -deoxyuridine, where the
method’s sensitivity for these three PP metabolites was so low that
they caused imprecise quantification over the examined concentration ranges. The metabolites were purified and concentrated using
a novel multi-step pre-treatment procedure consisting of protein
precipitation, ultrafiltration, evaporation under nitrogen flow, and
subsequent reconstitution. This procedure ensured efficient recoveries for most investigated metabolites and efficient removal of
interfering matrix components. The method is selective, sensitive, stable, and precise. The potential application of the method
was demonstrated by evaluating its range of use in different types
of blood plasma from multicatheterized cows, here, only uridine,
showed undesirable matrix effects. The method is adaptable and
can be further developed for the quantitative detection of the same
metabolites in other matrices such as urine or milk.
Acknowledgements
We gratefully acknowledge Lis Sidelmann, Birgit Hørdum Løth
and the barn staff at Department of Animal Science, Aarhus University, Foulum, Denmark for skillful technical assistance. Steven
Lock, Application manager EMEA at ABSCIEX, is recognized for his
assistance in assessing MS/MS fragmentation patterns. We also
thank senior scientists Torben Larsen and Peter Lund for supplying
plasma samples for analytical application experiments. C. Stentoft
holds a PhD scholarship co-financed by the Faculty of Science and
Technology, Aarhus University and a research project supported
by the Danish Milk Levy Fond, c/o Food and Agriculture, Aarhus
N, Denmark. Funding for the cow animal experiments from which
[1] H. Steinfeld, P. Gerber, T. Wassenaar, V. Castel, M. Rosales, C. de Haan, Livestock’s long shadow: Environmental issues and options, 2006, www.fao.org,
Accessed Oct.1, 2012.
[2] R.A. Kohn, M.M. Dinneen, E. Russek-Cohen, Using blood urea nitrogen to predict
nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats,
horses, pigs, and rats, J. Anim. Sci. 83 (2005) 879–889.
[3] C.K. Reynolds, N.B. Kristensen, Nitrogen recycling through the gut and the nitrogen economy of ruminants: an asynchronous symbiosis, J. Anim. Sci. 86 (2008)
293–305.
[4] S. Calsamiglia, A. Ferret, C.K. Reynolds, N.B. Kristensen, A.M. van Vuuren, Strategies for optimizing nitrogen use by ruminants, Animal 4 (2010) 1184–1196.
[5] S. Tamminga, Nutrition management of dairy cows as a contribution to pollution control, J. Dairy Sci. 75 (1992) 345–357.
[6] J.L. Firkins, Maximizing microbial protein synthesis in the rumen, J. Nutr. 126
(1996) 1347–1354.
[7] T. Fujihara, M.N. Shem, Metabolism of microbial nitrogen in ruminants with
special reference to nucleic acids, Anim. Sci. J. 82 (2011) 198–208.
[8] P. McDonald, R.A. Edwards, J.F.D. Greenhalgh, C.A. Morgan, L.A. Sinclair, R.G.
Wilkinson, Animal Nutrition, 7th ed., Pearson Education Limited, Essex, 2011,
ISBN 978-1-4082-0423-8.
[9] R.H. Smith, A.B. McAllan, Some factors influencing the chemical composition
of mixed rumen bacteria, Br. J. Nutr. 31 (1974) 27–34.
[10] X.B. Chen, M.J. Gomes, Estimation of Microbial Protein Supply to Sheep and
Cattle Based On Urinary Excretion of Purine Derivatives—An Overview Of The
Technical Details, Occasional Publication of International Feed Resources Unit,
Rowett Research Institute, Bucksburn, Aberdeen AB2 9SB, UK, 1992, pp. 1–21.
[11] J.M. Moorby, R.J. Dewhurst, R.T. Evans, J.L. Danelon, Effects of dairy cow diet
forage proportion on duodenal nutrient supply and urinary purine derivative
excretion, J. Dairy Sci. 89 (2006) 3552–3562.
[12] B.M. Tas, A. Susenbeth, Urinary purine derivates excretion as an indicator of
in vivo microbial N flow in cattle: a review, Livest. Sci. 111 (2007) 181–192.
[13] M. Gonzalez-Ronquillo, J. Balcells, A. Belenguer, C. Castrillo, M. Mota, A comparison of purine derivatives excretion with conventional methods as indices
of microbial yield in dairy cows, J. Dairy Sci. 87 (2004) 2211–2221.
[14] H. Boudra, M. Doreau, P. Noziere, E. Pujos-Guillot, D.P. Morgavi, Simultaneous analysis of the main markers of nitrogen status in dairy cow’s urine
using hydrophilic interaction chromatography and tandem mass spectrometry
detection, J. Chromatogr. A 1256 (2012) 169–176.
[15] L. Liu, J. Ouyang, W.R.G. Baeyens, Separation of purine and pyrimidine bases by
ion chromatography with direct conductivity detection, J. Chromatogr. A 1193
(2008) 104–108.
[16] M. Haunschmidt, W. Buchberger, C.W. Klampfl, Investigations on the migration
behaviour of purines and pyrimidines in capillary electromigration techniques
with UV detection and mass spectrometric detection, J. Chromatogr. A 1213
(2008) 88–92.
210
C. Stentoft et al. / J. Chromatogr. A 1356 (2014) 197–210
[17] H. Lin, D.K. Xu, H.Y. Chen, Simultaneous determination of purine bases, ribonucleosides and ribonucleotides by capillary electrophoresis electrochemistry
with a copper electrode, J. Chromatogr. A 760 (1997) 227–233.
[18] Y.X. Gong, S.P. Li, P. Li, J.J. Liu, Y.T. Wang, Simultaneous determination of six
main nucleosides and bases in natural and cultured Cordyceps by capillary
electrophoresis, J. Chromatogr. A 1055 (2004) 215–221.
[19] N.-P. Hua, T. Naganuma, Application of CE for determination of DNA base composition, Electrophoresis 28 (2007) 366–372.
[20] H. Kazoka, Analysis of purines and pyrimidines by mixed partition-adsorption
normal-phase high-performance liquid chromatography, J. Chromatogr. A 942
(2002) 1–10.
[21] M. Clariana, M. Gratacós-Cubarsí, M. Hortós, J.A. García-Regueiro, M. Castellari, Analysis of seven purines and pyrimidines in pork meat products by ultra
high performance liquid chromatography–tandem mass spectrometry, J. Chromatogr. A 1217 (2010) 4294–4299.
[22] R.N. Xu, L. Fan, M.J. Rieser, T.A. El-Shourbagy, Recent advances in highthroughput quantitative bioanalysis by LC-MS/MS, J. Pharm. Biomed. Anal. 44
(2007) 342–355.
[23] B.A. Røjen, P.K. Theil, N.B. Kristensen, Effects of nitrogen supply on inter-organ
fluxes of urea-N and renal urea-N kinetics in lactating Holstein cows, J. Dairy
Sci. 94 (2011) 2532–2544.
[24] B.A. Røjen, N.B. Kristensen, Effect of normal and high NaCl intake on PDV urea-N
flux and renal urea-N kinetics in lactating cows, in: EAAP—European Federation of Animal Science, 63rd Annual Meeting, Bratislava 2012, Wageningen
Academic Publishers, Bratislava, Slovakia, 2012, p. 116.
[25] N.B. Kristensen, A. Storm, B.M.L. Raun, B.A. Røjen, D.L. Harmon, Metabolism of
silage alcohols in lactating dairy cows, J. Dairy Sci. 90 (2007) 1364–1377.
[26] G.B. Huntington, C.K. Reynolds, B.H. Stroud, Techniques for measuring bloodflow in splanchnic tissues of cattle, J. Dairy Sci. 72 (1989) 1583–1595.
[27] V.P. Shah, K.K. Midha, S. Dighe, I.J. McGilveray, J.P. Skelly, A. Yacobi, T. Layloff,
C.T. Viswanathan, C.E. Cook, R.D. McDowall, K.A. Pittman, S. Spector, K.S. Albert,
S. Bolton, M. Dobrinska, W. Doub, M. Eichelbaum, J.W.A. Findlay, K. Gallicano,
W. Garland, D.J. Hardy, J.D. Hulse, H.T. Karnes, R. Lange, W.D. Mason, G. McKay,
E. Ormsby, J. Overpeck, H.D. Plattenberg, G. Shiu, D. Sitar, F. Sorgel, J.T. Stewart, L. Yuh, Analytical methods validation–bioavailability, bioequivalence and
pharmacikinetic studies, Pharm. Res. 9 (1992) 588–592.
[28] V.P. Shah, K.K. Midha, J.W. Findlay, H.M. Hill, J.D. Hulse, I.J. McGilveray, G.
McKay, K.J. Miller, R.N. Patnaik, M.L. Powell, A. Tonelli, C.T. Viswanathan, A.
Yacobi, Bioanalytical method validation – a revisit with a decade of progress,
Pharm. Res. 17 (2000) 1551–1557.
[29] F.T. Peters, O.H. Drummer, F. Musshoff, Validation of new methods, Forensic
Sci. Int. 165 (2007) 216–224.
[30] R.C. Littell, G.A. Milliken, W.W. Stroup, R.D. Russell, SAS® System for Mixed
Models, SAS Institute Inc., Cary, NC, 1996, 633 pp. ISBN: 1-55544-779-1.
[31] C.E. McCulloch, S.R. Searle, Generalized, Linear, and Mixed Models, Wiley, New
York, 2004, 358 pp. ISBN: 978-0-471-65404-9.
[32] B.K. Matuszewski, M.L. Constanzer, C.M. Chavez-Eng, Strategies for the
assessment of matrix effect in quantitative bioanalytical methods based on
HPLC-MS/MS, Anal. Chem. 75 (2003) 3019–3030.
[33] A. Tan, I.A. Levesque, I.M. Levesque, F. Viel, N. Boudreau, A. Levesque, Analyte
and internal standard cross signal contributions and their impact on quantitation in LC-MS based bioanalysis, J. Chromatogr. B 879 (2011) 1954–1960.
[34] E. Stokvis, H. Rosing, J.H. Beijnen, Stable isotopically labeled internal standards
in quantitative bioanalysis using liquid chromatography/mass spectrometry:
necessity or not? Rapid Commun. Mass Spectrom. 19 (2005) 401–407.
[35] A. Van Eeckhaut, K. Lanckmans, S. Sarre, I. Smolders, Y. Michotte, Validation of
bioanalytical LC-MS/MS assays: evaluation of matrix effects, J. Chromatogr. B
877 (2009) 2198–2207.
[36] R. Bakhtiar, T.K. Majumdar, Tracking problems and possible solutions in the
quantitative determination of small molecule drugs and metabolites in biological fluids using liquid chromatography-mass spectrometry, J. Pharmacol.
Toxicol. Methods 55 (2007) 262–278.
[37] E. Chambers, D.M. Wagrowski-Diehl, Z. Lu, J.R. Mazzeo, Systematic and comprehensive strategy for reducing matrix effects in LC/MS/MS analyses, J.
Chromatogr. B 852 (2007) 22–34.
[38] B.E. Richter, B.A. Jones, J.L. Ezzell, N.L. Porter, N. Avdalovic, C. Pohl, Accelerated
solvent extraction: a technique for sample preparation, Anal. Chem. 68 (1996)
1033–1039.
[39] S. Hartmann, J.G. Okun, C. Schmidt, C.D. Langhans, S.F. Garbade, P. Burgard,
D. Haas, J.O. Sass, W.L. Nyhan, G.F. Hoffmann, Comprehensive detection of
disorders of purine and pyrimidine metabolism by HPLC with electrospray
ionization tandem mass spectrometry, Clin. Chem. 52 (2006) 1127–1137.
[40] P.J. Taylor, Matrix effects: the Achilles heel of quantitative high-performance
liquid chromatography-electrospray-tandem mass spectrometry, Clin.
Biochem. 38 (2005) 328–334.
[41] A. Kruve, A. Kunnapas, K. Herodes, I. Leito, Matrix effects in pesticide multiresidue analysis by liquid chromatography-mass spectrometry, J. Chromatogr.
A 1187 (2008) 58–66.
[42] A.K. Hewavitharana, Matrix matching in liquid chromatography-mass spectrometry with stable isotope labelled internal standards – is it necessary? J.
Chromatogr. A 1218 (2011) 359–361.
[43] D. Mutavdzic Pavlovic, T. Pinusic, M. Perisa, S. Babic, Optimization of matrix
solid-phase dispersion for liquid chromatography tandem mass spectrometry analysis of 12 pharmaceuticals in sediments, J. Chromatogr. A 1258 (2012)
1–15.
[44] L.L. Jessome, D.A. Volmer, Ion suppression: a major concern in mass spectrometry, LC GC N. Am. 24 (2006) 498–510.
7. Paper II
Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows.
Stentoft C., B.A. Røjen, S.K. Jensen, N.B. Kristensen, M. Vestergaard and M. Larsen.
Accepted November 11th 2014 by Br. J. Nutr.
70
Absorption and Intermediary Metabolism of Purines and Pyrimidines
in Lactating Dairy Cows
Authors
Charlotte Stentoft1*, Betina Amdisen Røjen2, Søren Krogh Jensen1, Niels B. Kristensen2, Mogens
Vestergaard1, Mogens Larsen1
All work was performed at: Department of Animal Science, Aarhus University, Foulum, Blichers
Allé 20, DK-8830 Tjele, Denmark
Current address: 1Department of Animal Science, Aarhus University, Foulum, Blichers Allé 20,
DK-8830 Tjele, Denmark; 2Knowledge Centre for Agriculture, DK-8200 Aarhus N, Denmark
*Corresponding author. Tel: +45 87154286; Fax: +45 87154249
E-mail: [email protected]
Running title
Purine and Pyrimidine Metabolism in Ruminants
Key words: Ruminant, Uric acid, Splanchnic metabolism, Liver
Abbreviations: N, nitrogen; NA, nucleic acid; NT, nucleotide; NS, nucleoside; BS, base; DP, degradation product; PDV, portal-drained viscera; Ade, adenine; Gua, guanine; Cyt, cytosine; Thy,
thymine; Ura, uracil; Uac, uric acid; Alo, allantoin; XO, xanthine oxidase; β-ala, β-alanine; β-ami,
β-aminoisobutyric acid; DMI, dry matter intake; TMR, total mixed ration; NorFor, Nordic feed
evaluation system; pAH, p-aminohippuric acid; Xan, xanthine; Hyp, hypoxanthine; LC-ESIMS/MS, high performance liquid chromatography electrospray ionisation tandem mass spectrometry; SIL, stable isotopically-labeled reference compound; NP%, percentage of net PDV release;
TI%, percentage of total influx; SD, standard deviation; SEM, standard error of the mean; Guo,
guanosine; Ino, inosine; dGuo, 2’-deoxyguanosine; dIno, 2’-deoxyinosine; Cyd, cytidine; Urd, uridine; Thd, thymidine; dUrd, 2’-deoxyuridine; β-ure, β-ureidopropionic acid; ΔPA, concentration
difference between hepatic portal vein and artery; ΔHA, concentration difference between hepatic
vein and artery; ΔPH, concentration difference between hepatic portal vein and hepatic vein; ΔGA,
concentration difference between gastrosplenic vein and artery; TSP, total splanchnic tissue.
71
Abstract
About 20% of ruminal microbial nitrogen (N) in dairy cows derives from purines and pyrimidines
yet; their intermediary metabolism and contribution to the overall N metabolism is sparsely described. In this study, the postprandial patterns of net portal-drained viscera (PDV) and hepatic metabolism were assessed to evaluate purine and pyrimidine N in dairy cows. Blood was sampled simultaneously from 4 veins with eight hourly samplings from four multicatheterised Holstein cows.
Quantification of 20 purines and pyrimidines was performed with HPLC-ESI-MS/MS and net fluxes were estimated across the PDV, hepatic tissue and total splanchnic tissue (TSP). The concentration differences between veins of 15 purine and pyrimidine nucleosides (NS), bases (BS) and degradation products (DP) were different from zero (P ≤ 0∙05) resulting in net PDV releases (mmol/h)
of purine NS (0∙33-1∙3), purine BS (0∙0023-0∙018), purine DP (7∙0-7∙8), pyrimidine NS (0∙30-2∙8)
and pyrimidine DP (0∙047-0∙77). The hepatic removal of purine and pyrimidine was almost equivalent to the net PDV release, resulting in no net TSP release. One exception was uric acid (7∙9) from
which a large net TSP release arose from the degradation of purine NS and BS. A small net TSP
release of pyrimidine DP β-ala and β-ami (-0∙032-0∙37) demonstrated an outlet of N into the circulating N-pool. No effect of time relative to feeding was observed (P ˃ 0∙05). These data indicate
that considerable amounts of N is lost in the dairy cow due to prominent intermediary degradation
of purines but that pyrimidine N is re-usable to a larger extend.
72
Introduction
The nitrogen (N) efficiency in dairy cows is generally low(1) and optimisation of the diet, with focus
on dietary N in the form of protein, amino acids and urea, has only led to minor improvements in
the utilisation of N in ruminants(2-6). The importance of other N-containing compounds like microbial nucleic acids (NA) in the nutritional physiology of ruminants has so far been sparsely investigated, regardless of the fact that they correspond to more than 20% of the total microbial N synthesised in the rumen(7-9). The amount of microbial DNA/RNA entering the intestines has been estimated to 15-35 g/kg DM digesta. Thus, an improved understanding of the quantitative absorption
and intermediary metabolism of the different NA components; nucleotides (NT), nucleosides (NS),
bases (BS) and degradation products (DP), in the portal-drained viscera (PDV), hepatic and peripheral tissues may be of importance in order to discover new ways to improve N efficiency in dairy
cows.
Nitrogen from the feed undergoes different processes in ruminants before absorption. In the rumen,
dietary N is degraded and reused by the microbial population for synthesis of not only microbial
protein (75-85%), but also microbial NA (15-25%)(7-8,10). Nucleic acids are the main constituents of
DNA and RNA and they are derived from and degraded to first, purine and pyrimidine NS, then
purine and pyrimidine BS and finally purine and pyrimidine DP. Five main types of purines and
pyrimidines exist, they are adenine (Ade), guanine (Gua), cytosine (Cyt), thymine (Thy), and uracil
(Ura). The purines and pyrimidines are further divided into two sub-groups; the purines (Ade, Gua)
and the pyrimidines (Cyt, Thy, Ura), and each sub-group has a distinctive metabolic pathway(8)
(Fig. 1 & 2). The re-synthesised microbial NA flow into the small intestine where they are digested
before subsequent absorption takes place(7,11-12).
Quantitative analysis of purines and pyrimidines in dairy cattle research has almost solely focused
on purine derivatives in urine and milk, where the purine DP uric acid (Uac) and allantoin (Alo)
excretion has been used as an indirect marker of rumen microbial synthesis(13-18). Hence, data
concerning the absorption and hepatic metabolism of other purines and pyrimidines than Uac and
Alo is at present very limited.
Of the purine and pyrimidine metabolic pathways, the purine metabolism has mainly been
examined(7,14). In short, it is known that purine NT are hydrolysed into purine NS and BS in the
small intestine and absorbed from the intestinal lumen across the intestinal mucosa(8-9,11,14). Dairy
cows have a high activity of the enzyme xanthine oxidase (XO) [1.17.3.2] in most tissues, and especially in the small intestinal mucosa and the blood, which converts large amounts of purines into the
purine DP Uac(19-20). It is also well-known, that endogenous purine DP, from the degradation of
tissue NA, and exogenous purine DP originating from the microbes, in the form of Uac and Alo, is
73
rapidly cleared from the blood by the kidneys(19-20). Thus, large amounts of purine DP are lost in the
urine and are unavailable for recycling into tissue NA (salvage) or protein. It has been speculated,
that the conversion of Uac to Alo probably takes place in the hepatic tissue, as uricase [1.7.3.3] is
present in only trace amounts in the blood. The inability to use the microbial purine N for the synthesis of amino acids contributes considerably to N loss in dairy cows(13-20).
Presumably, the pyrimidines are metabolised during absorption, in the blood, and in the hepatic
tissue in much the same manner as the purines. However, it is know that the pyrimidine degradation
products, β-alanine (β-ala) and β-aminoisobutyric acid (β-ami) can be incorporated into other intermediate products as part of the N metabolism(21-23). This could indicate that the degradation
pathways of the pyrimidines differ from that of the purines in dairy cows but the salvage or excretion mechanisms involved during pyrimidine degradation is not well described. The purine and pyrimidine metabolic pathways have at least one thing in common; during degradation, small amounts
of ammonia (NH3) are released, possible available for urea-recycling(24). Thus, some N is re-usable
following incorporation into microbial NA.
The objective of the present study was to describe and give a quantitative picture of the metabolism
and degradation of purines and pyrimidines by studying postprandial patterns of net PDV and net
hepatic metabolism so as to evaluate purine and pyrimidine N in this context. We hypothesise that
the purines (Ade, Gua) and the pyrimidines (Cyt, Thy, Ura) in the form of either a NS, a BS, a DP,
or a combination of these, are absorbed from the small intestine of the dairy cow and undergo degradation across the intestinal wall and the hepatic tissue. Furthermore, we hypothesise that the purines and pyrimidines and the N they contain ultimately largely are lost following DP excretion
across the kidneys.
Materials and methods
The present experiment complied with Danish Ministry of Justice Law No. 382 (June 10, 1987),
Act No. 726 (September 9, 1993), concerning experiments with animals and care of experimental
animals.
Animals, experimental design, and samplings
A detailed description of the experiment is provided in a preceding paper(24). Briefly, eight ruminally cannulated Danish Holstein cows in second lactation were permanently catheterised in the gastrosplenic vein as well as in an mesenteric or intercostal artery, mesenteric vein, hepatic portal vein,
and hepatic vein, as described previously(25). Cows were randomly allocated to a triplicate incomplete 3 x 3 Latin square design with 14 d periods. Treatments were ventral ruminal infusion of tap
water (water infusion, 10 L/d), 4∙1 g of feed urea/kg of dry matter intake (DMI), and 8∙5 g of feed
urea/kg of DMI. For the present investigation, four cows assigned to the 8∙5 g of feed urea/kg
74
treatment of DMI were evaluated. The a priori criteria for selection were a functional gastrosplenic
catheter i.e. all gastrosplenic vein plasma samples were available in the sample set, and at least two
cows from each square. All cows were fed the same basal total mixed ration (TMR), formulated
using the Nordic feed evaluation system (NorFor)(26). The basal TMR supplied 80% of requirements
for metabolisable protein. To obtain 8∙5 g of feed urea/kg of DMI, the average voluntary DMI for
each cow was determined during the first week of each experimental period, and for the remaining
of the period, each cow was fed at 95% of voluntary DMI. Cows were fed 3 equal portions at 8 h
intervals and orts were removed and weighed. Infusion lines were inserted through the ruminal cannula and anchored in the ventral ruminal sack. Cows were sampled on the last day of each experimental period. Eight hourly sample sets of blood were obtained beginning 30 min before feeding at
0800 h resulting in samples obtained 0∙5 h before feeding and, at 0∙5, 1∙5, 2∙5, 3∙5, 4∙5, 5∙5, and 6∙5
h after feeding. Eight samples of urine were collected at the same time points as blood sampling by
stimulating the cow to urinate in a cup by sweeping the supra mammary region by hand. Blood was
stabilised in sodium heparin vacuettes (Greiner Bio-One GmbH, Kremsmünster, Austria) immediately after sampling and placed on crushed ice. Plasma was harvested after centrifugation at 3,000 g
at 4°C for 20 min and stored in polystyrene tubes at -20°C. Urine samples were stored at -20°C and
pooled within cow and period. A number of 5 mL aliquots of heparinised plasma to be used for external calibration and quality control were prepared from two liters of venous blood drawn from a
Danish Holstein dairy cow fed a traditional TMR. Splanchnic blood plasma flows were determined
by downstream dilution of p-aminohippuric acid (pAH) continuously infused (28 ± 2 mmol/h) into
the mesenteric vein(27).
Analytical procedures
Heparinised plasma was deacetylated before pAH determination by combining with an equal
amount of 20% trichloroacetic acid (v/v) (Sigma-Aldrich Denmark A/S, Brøndby, Denmark) and
incubating the supernatant for 1 h at 100°C. The pAH concentration in plasma and urine were determined by the method described by Harvey & Brothers(28) using a continuous flow analyser (Autoanalyzer 3, method US-216-72 Rev. 1; Seal Analytical Ltd, Burgess Hill, UK). Urine concentrations of xanthine (Xan), hypoxanthine (Hyp), Uac and Alo were determined applying an in-house
routine procedure based on high performance liquid chromatography according to Thode(29).
The concentration of key purine and pyrimidine metabolites was determined in heparinised plasma
samples using a validated high performance liquid chromatography-based technique coupled to
electrospray ionisation tandem mass spectrometry (HPLC-ESI-MS/MS) combined with individual
matrix-matched calibration standards and stable isotopically-labeled reference compounds (SIL) as
described by Stentoft et al.(30). Quantification was performed by external calibration applying stand75
ard plasma spiked with a two-fold serial dilution of purine and pyrimidine standard mixtures. To fit
within the actual experimental calibration ranges, three to five concentration levels were used for
calibration. All samples were analysed in duplicate and a standard curve and quality controls were
analysed at the beginning and at the end of each sequence. Exploratory data from the analyses is
summarised in Table 1.
Calculations and statistical procedures
The purine and pyrimidine concentrations were determined from their responses calculated as the
chromatographic peak area. Matrix-matched linear calibration curves (start and end) were obtained
by correcting for inherent purine and pyrimidine and regressing log(area) against
log(concentration). The resulting linear correlations were used to determine the purine and pyrimidine concentrations (mean). Preceding quantification, purine and pyrimidine responses were normalised employing the following factor: mean SIL area/SIL area for each sample. Calculation of net
PDV flux, net hepatic flux, net splanchnic flux and hepatic extraction ratios of metabolites were
performed as described by Kristensen et al.(31). A positive net flux indicated a net release from a
given tissue bed to the blood. A negative net flux indicated a net uptake by the tissue bed. The hepatic fractional removal of purine and pyrimidine metabolites were estimated as the percentage of
net PDV release (NP%) and the percentage of total influx (TI%). The NP% indicated the proportion
of metabolite removed by the hepatic tissue from the PDV. The TI% indicated the proportion of
metabolite removed by the hepatic tissue from the PDV and all the other body tissue. The renal
plasma flow was calculated as the infusion rate of pAH divided by the arterial concentration, assuming complete renal extraction of pAH. Diuresis was calculated as the infusion rate of pAH divided by the urinary concentration of pAH. The renal influx was calculated as the renal plasma flow
times the arterial concentration of purine and pyrimidine metabolite. The net urine flux was calculated as diuresis times the urine concentration of the purine and pyrimidine metabolite. The
urine/renal ratio and the urine/splanchnic ratio was estimated as the net urine flux divided by the
renal influx and as the net urine flux divided by the net splanchnic flux, respectively. Metabolite
clearance (volume of blood metabolite cleared by the kidney per unit of time) was calculated as
urinary concentration divided by arterial concentration times diuresis.
The total amount of purine N and pyrimidine N entering the small intestine were estimated to 60 g/d
from the flow of microbial crude protein to the small intestine assuming 20% of total microbial N to
be bound in NA using NorFor(7-8,10, 26). Microbial purine N and pyrimidine N entering the small intestine was estimated to 40 g/d and 20 g/d, respectively, assuming 2/3 purine N and 1/3 pyrimidine
N (5N/purine, 2.5N/pyrimidine) of NA N. The metabolite N flux absorbed from the PDV, removed/produced across the hepatic tissue, removed/produced across the total splanchnic bed, and
76
excreted in the urine was calculated from the metabolite net flux and the metabolite N content for
each specific metabolite by multiplying net flux with nitrogen molecules in the given metabolite,
the molecular weight of N, 24 h and 10-6.
Data was subjected to ANOVA using the MIXED procedure in SAS (Statistical Analysis System
version 9.1 (TS1M3); SAS Institute Inc., Cary, NC). The model included the fixed effect of sampling time (Time) and cow within square was considered as a random effect. Time was considered
as repeated measure using the autoregressive order 1 covariance structure. Denominator degrees of
freedom were estimated using the Kenward-Roger method. Least squares means ± standard deviation (SD) or standard errors of the mean (SEM) are presented. Significance was declared at *P ≤
0∙05 and tendencies were considered at †P ≤ 0∙1. Effects of linear and quadratic orthogonal polynomial contrasts (Lin and Quad) of time relative to feeding were tested.
Results
The total N supply of cows with feed plus infused urea N was equivalent to a dietary crude protein
concentration of 15∙0% of DM corresponding to a moderate supply. The 4 cows used were 69 ± 8
days in lactation at first sampling day and DMI, energy-corrected milk yield, and body weight averaged 19 ± 0∙58 kg/d, 32 ± 1∙0 kg/d, and 577 ± 14 kg, respectively.
Plasma variables
All 20 purine and pyrimidine metabolites were identified in all four types of experimental plasma
samples (Table 2). The purines occurred in the following concentration ranges (μmol/L): guanosine
(Guo) 0∙021 to 1∙1, inosine (Ino) 0∙040 to 0∙79, 2’-deoxyguanosine (dGuo) 0∙015 to 0∙29 and 2’deoxyinosine (dIno) 0∙0056 to 0∙32, higher than the purine BS concentration ranges (μmol/L): Ade
0∙15 to 0∙16, Gua 0∙0045 to 0∙015, Hyp 0∙041 to 0∙059, and Xan 0∙011 to 0∙015. The purine DP concentration ranges (μmol/L) were even higher: Uac 71 to 78 and Alo 117 to 133. Only in the case of
the purine NS (Guo, Ino, dGuo, dIno), a large difference in concentration between types of plasma
was observed with notably higher concentration levels in the hepatic portal vein compared to the
artery, hepatic vein and gastrosplenic vein.
In case of the pyrimidines, the following concentration ranges (μmol/L) occurred: NS cytidine
(Cyd) 2∙4 to 4∙8, uridine (Urd) 2∙1 to 6∙0, thymidine (Thd) 1∙1 to 1∙8, and 2’-deoxyuridine (dUrd)
0∙65 to 1∙0, and these were, as for the purines, higher than for their corresponding pyrimidine BS
concentration ranges (μmol/L): Cyt 0∙0, Ura 0∙19 to 0∙24 and Thy 0∙022 to 0∙042. The concentrations of pyrimidine NS were generally higher than the purine NS, on the contrary, the concentrations of the pyrimidine BS (Cyt, Ura, Thy) was in the same range as the purine BS (Ade, Gua, Hyp,
Xan). The concentration ranges (μmol/L) of the pyrimidine DP β-ala 13 to 14, β-ureidopropionic
77
acid (β-ure) 3∙7 to 4∙6, and β-ami 0∙28 to 0∙35 were considerably lower than for their purine counterparts.
The concentration differences between the hepatic portal vein and artery (ΔPA), the hepatic vein
and artery (ΔHA), hepatic portal vein and hepatic vein (ΔPH) and gastrosplenic vein and artery
(ΔGA) of the purine and pyrimidine metabolites are presented in Table 3. All purines except Ade
and Xan had one or more ΔPA, ΔHA, ΔPH or ΔGA values that differed from zero (P ≤ 0∙05). Most
of the pyrimidines also had ΔPA, ΔHA, ΔPH or ΔGA that were different from zero (P ≤ 0∙1). Yet,
as for Ade and Xan, Cyt, Thy and Ura did not demonstrate differences from zero for neither ΔPA,
ΔHA, ΔPH or ΔGA (P ˃ 0∙1).
Net portal-drained viscera fluxes
Given that neither the ΔPA, ΔHA, ΔPH nor ΔGA for Ade, Xan, Cyt, Thy and Ura were different
from zero (P ˃ 0∙1), net PDV fluxes for these metabolites could not be assessed. This was also the
case for β-ure, which only demonstrated a concentration difference different from zero for ΔGA (P
≤ 0∙01). The net PDV fluxes of the remaining 15 purine and pyrimidine metabolites (Guo, Ino,
dGuo, dIno, Gua, Hyp, Uac, Alo, Cyd, Urd, Thd, dUrd, β-ala, β-ure, β-ami) were all positive (net
release) (Table 4). The following net PDV releases (mmol/h) of the purine NS: Guo 1∙3, Ino 0∙85,
dGuo 0∙33, dIno 0∙35, and BS (μmol/h) occurred: Gua 2∙3, and Hyp 18, and the net PDV releases
(mmol/h) of the purine DP Uac 7∙0 and Alo 7∙8. The net PDV release of Alo increased over time in
a quadratic manner (P = 0∙03) and the release of Guo and Ino tended towards a cubic effect (P =
0.08 and P = 0.09, respectively).
The following net PDV releases (mmol/h) of the pyrimidine NS occured: Cyd 1∙9, Urd 2∙8, Thd
0∙77, and dUrd 0∙30, notably higher than for the pyrimidine DP β-ala 0∙77 and β-ami 0∙047. In the
case of dUrd and β-ala, linear time effects could be observed (P = 0∙07 and P ≤ 0∙01, respectively).
The remaining metabolites did not demonstrate any time dependence relative to feeding (0∙39 ≤ P ≤
0∙79).
Net hepatic fluxes
For the same reason as for the net PDV fluxes, net hepatic fluxes for Ade, Xan, Cyt, Thy, Ura and
β-ure could not be assessed. Except for Uac, the net hepatic fluxes of the remaining 15 purine and
pyrimidine metabolites were negative (net uptake) (Table 4). The following net hepatic uptakes
(mmol/h) occurred for the purine NS: Guo -1∙3, Ino -0∙86, dGuo -0∙32, dIno -0∙36. The net hepatic
uptake (μmol/h) of the purine BS Gua -20 and Hyp -21 was lower. In the case of Gua, a tendency
towards a quadratic time effect could be observed (P = 0∙09). The only purine with a positive net
78
hepatic flux (net release) (mmol/h) was Uac 0∙63. The purine DP Alo -16 had a net negative hepatic
flux (net uptake).
The net hepatic uptake of the pyrimidines was, as for the net PDV release, different from that of the
purines. The pyrimidine NS and BS had the following net hepatic uptakes (mmol/h): Cyd -1∙4, Urd
-5∙0, Thd -1∙0, and dUrd -0∙52. These were again higher than those of the pyrimidine DP β-ala -0∙22
and β-ami -0.095. Apart from Gua, none of the metabolites demonstrated any time dependence
(0∙13 ≤ P ≤ 0∙94).
Hepatic fractional removal
The hepatic fractional removal was estimated as the NP%; the proportion of metabolite removed by
the hepatic tissue from the PDV, and the TI%; the proportion of metabolite removed by the hepatic
tissue from the PDV and all the other body tissue, of individual purine and pyrimidine metabolites
(Table 5). The very small concentration levels of Gua gave rise to very imprecise estimations and
the hepatic fractional removal was therefore not calculated for this metabolite. The NP% of the purine NS and BS was approximately 100%: Guo 99%, Ino 98%, dGuo 98%, dIno 104% and Hyp
117%. In contrast, the purine DP resulted in a NP% of about 0%: Uac -32% and Alo 0∙4%. The
same results were obtained for the TI% of purine NS and BS 97%, 87%, 85%, and 97%, and DP
0.2% and 9%. The only exception was with Hyp, with a TI% of only 20% compared to a NP% of
117%. Only in the case of Uac, a TI% quadratic time effect was observed (P = 0∙02), the remaining
purines demonstrated no effects of time for neither NP% or TI% (0∙27 ≤ P ≤ 0∙93).
The NP% of the NS and BS pyrimidines was also, as for the purines, roughly 100%: Cyd 74%, Urd
191%, Thd 123%, and dUrd 181%. The following NP% of the pyrimidine DP occurred: β-ala 16%
and β-ami 173%. It should be noted that the SEM of the pyrimidines when calculating the NP% was
large (Table 5). The TI% for the NS and BS pyrimidines was lower than the NP%: Cyd 21%, Urd
62%, Thd 49%, and dUrd 33%. The pyrimidine DP β-ala and β-ami demonstrated the same difference, with TI% of -2% and 16%, compared to their NP% of 16% and 173%. None of the metabolites demonstrated any time dependence (0∙29 ≤ P ≤ 0∙99).
Net splanchnic fluxes
The net splanchnic fluxes of the purines and pyrimidines differed between metabolites (Table 4).
The net splanchnic fluxes (mmol/h) of the purine NS were close to zero: Guo 0∙0072, Ino 0∙0014,
dGuo 0∙0085, dIno -0∙0069, as was the splanchnic fluxes of the purine BS Gua -17, and Hyp -1∙1.
Only Gua demonstrated a quadratic time effect (P < 0∙01). In the case of the purine DP, a net release (mmol/h) was observed across the splanchnic tissues (PDV + hepatic tissue): Uac 7∙9. In contrast, the net splanchnic flux (mmol/h) of the purine DP Alo -6∙1 was negative.
79
The net splanchnic flux (mmol/h) of the pyrimidine NS Cyd 0∙49 was positive (net release). In contrast, the net splanchnic flux (mmol/h) of the pyrimidine NS Urd -2∙2, Thd -0∙30, and dUrd -2∙0
were negative (net uptake). The net splanchnic flux (mmol/h) of the pyrimidine DP β-ala 0∙37 and
β-ami -0∙032 was low compared with the rest of the pyrimidines and the purine DP Uac and Alo.
None of the metabolites demonstrated time dependence (0∙55 ≤ P ≤ 0∙99), except for dUrd (P =
0∙06).
Renal variables
Renal variables were estimated for the purine degradation products Uac and Alo (Table 4). Given
that the arterial concentration (μmol/L) of Xan 0∙011 and Hyp 0∙043 was very low and the urinary
concentration level was below detection limits, renal calculations of these two metabolites were not
performed. The urinary excretion of Uac and Alo was equivalent to 47% and 25%, respectively, of
renal influx. Urinary excretion of Uac was equivalent to 13% of the net splanchnic release. Due to
the net splanchnic removal of Alo, the urine/splanchnic ratio could not be determined. The renal
clearance (volume of blood metabolite cleared by the kidney per unit of time) was 15 L/h for Uac
and 89 L/h for Alo. Unfortunately, there was no analytical method available for determining the
pyrimidine degradation products in urine.
Purine and pyrimidine nitrogen metabolism
Microbial NA N was estimated to 60 g/d N entering the small intestine. Microbial purine N and
pyrimidine N entering the small intestine was estimated to 40 g/d N and 20 g/d N, respectively. The
metabolite N fluxes of the purines and pyrimidines mirrored the net PDV, hepatic and splanchnic
fluxes as the calculations of N fluxes were simply added the N dimension (Fig. 3). The total purine
N PDV flux was 27 g/d equal to 67% of purine N assumed being absorbed from the small intestine.
In case of the pyrimidine N, the total 4∙7 g/d pyrimidine N PDV flux only corresponded to 24% of
the pyrimidine N assumed entering the intestine.
Discussion
By employing a novel LC-ESI-MS/MS technique for quantifying purine and pyrimidine metabolites
in arterial and hepatic portal, hepatic and gastrosplenic plasma from lactating dairy cows, we were
able to quantify net PDV absorption and net hepatic metabolism of the 10 main metabolites of the
purine metabolism (Fig. 1) and the 10 main metabolites of the pyrimidine metabolism (Fig. 2)(30).
The purines and pyrimidines were found to be absorbed and metabolised differently and they will
be discussed as two distinct groups.
Ideally, all of the purine metabolites would have been investigated. However, since the purine NT
as well as adenosine and 2’-adenosine were not identified during the method development and,
80
since no standards/SIL were available for XMP and xanthosine, these were excluded from the analysis. The absence of purine NT agreed with the notion that NT is rapidly degraded in the small intestine before absorption and endogenous NT was probably degraded before and/or in the blood(89,11,14,20,32-33)
. The purine and pyrimidine method has a broad application range at low concentration
levels(30). Unfortunately, the broad range also resulted in a method unable to quantify Alo as precisely as hoped for since the within-day and across-day variations in this concentration range ended
up equal to the splanchnic concentration differences. Consequently, the estimated net fluxes of Alo
should be interpreted with caution throughout. Preferably, all of the pyrimidines would have been
considered but the pyrimidine NT were, as for the purine NT, not identified in plasma(8-9,11,14,20,32-33).
Of the pyrimidines, the intermediates dihydrouracil and dihydrothymine were most likely not present and consequently they were excluded from analysis due to limits in method capacity(34-36). In
the case of β-ure, no standard/SIL was available.
Splanchnic metabolism of purines
Portal-drained viscera metabolism of purines
The low net PDV release of the purine BS compared to NS suggests a more effective degradation of
BS than of NS in the enterocytes. The considerable net PDV release of Uac and Alo, compared to
purine NS and BS, are in line with previous observations of high activity of XO [1.17.3.2] in the
intestinal mucosa and the blood in cattle(14,19-20). The XO enzyme, in cooperation with additional
degradation enzymes, such as adenine deaminase [3.5.4.2], guanine deaminase [3.5.4.3], purinenucleoside phosphorylase [2.4.2.1] and uricase [1.7.3.3], produces Uac and Alo and removes purine
BS and NS (Fig. 1). When such substantial amounts of Uac and Alo were released into the hepatic
portal vein, it must be assumed that equimolar amounts of purine NS and BS have to be degraded
either in the intestinal mucosa or prior to absorption or alternatively the purine DP was absorbed
directly(10). Some of the Uac and Alo may also be of endogenous origin i.e. turnover of the mucosal
enterocytes and other parts of the PDV tissue. Actually, mucosal enterocytes are thought to have
limited capacity for de novo purine synthesis; hence, these cells are the only cells thought to be able
to salvage exogenous purines(14).
With the use of ΔGA, a distinction between the purine flux from the forestomachs and the intestines
could be made. Presuming the gastrosplenic plasma flow was around 20% of the hepatic portal
plasma flow, a net gastrosplenic flux could be estimated as ΔGA × 0∙2 × PDV blood plasma flow(3738)
. Under these presumptions, Alo was the only purine with a net gastrosplenic flux that contributed
to the net PDV flux with more than 1% (approx. 40%). As no evidence of ruminal absorption of Alo
exists in the literature, further investigations are needed to clarify the gastrosplenic contribution of
Alo.
81
When studying how the postprandial patterns affect the net PDV metabolism, only the net PDV flux
of Alo increased over time. A time dependent absorption profile could have been observed if studying N components such as urea/ammonia with a simple digestion and absorption itinerary(24). Purine
digestion is more complex and time demanding; first, the feed DNA and RNA has to be broken
down in the rumen, secondly, the microbes have to re-synthesise new DNA and RNA, thirdly, the
microbes have to pass from the rumen to the small intestine, and finally, a second mode of digestion
has to happen before final absorption(7). Thus, postprandial absorption profiles could be hard to detect. Also, effects of postprandial pattern were most likely easiest to detect for metabolites with
considerable levels of net fluxes, such as Alo. The effects would be harder to trace when passing the
hepatic tissue because of the endogenous contribution.
Hepatic and splanchnic metabolism and urinary excretion of purines
The observed net hepatic uptake of the purine NS Guo, Ino, dGuo, and dIno (-0∙32--1∙3 mmol/h)
and BS Gua and Hyp (-20 and -21 μmol/h, respectively), supports the anticipation of a further purine absorption/degradation in the hepatic tissue. The considerable amounts of Uac and Alo excreted by dairy cows, most likely originate from degradation pre/during absorption, added degradation
in the hepatic tissue and endogenous losses(14,20). The hepatic uptake of purine NS and BS and release of Uac (0∙63 mmol/h) agreed with this. Surprisingly, a final degradation of Uac to Alo does
not seem to take place in the hepatic tissue (-16 mmol/h). This could suggest that Alo was either
degraded in the hepatic tissue or that Alo was excreted via biliary secretion. Both of these proposals
seem unlikely, even though Alo has been reported in the bile of dogs(39) and rats(40), it should be the
terminal DP of the purine metabolism and large amounts of Alo is excreted in the urine(13-17,19).
The hepatic fractional removal of the purines NS and BS was approx. 100% indicating that the purine degrading enzymes in the hepatic tissue were capable of degrading all of the entering purine
NS and BS, not only from the PDV but also from the peripheral tissues (Fig. 1). The only exception
was with Hyp, where the hepatic fractional removal was only 20% of total. The efficiency of the
hepatic enzymes may reflect the fact that the main part of the purine NS and BS was already degraded pre-absorption, during absorption or/and in the blood. The hepatic fractional removal of the
purine DP Uac and Alo was approx. 0%, demonstrating that degradation in the hepatic tissue of
these products does not take place as expected.
In consequence of the 100% fractional hepatic removal of the purine NS and BS, the net splanchnic
release was essentially zero. When it comes to Uac, an overall splanchnic release (7∙9 mmol/h)
again demonstrated the degradation of purine NS and BS to Uac in the PDV. As a result of the limitations of Alo analysis, a splanchnic uptake of Alo instead of a release, as was expected, was observed.
82
Purine DP in urine and milk has been examined extensively as purine DP excretion can be used as
an indirect measure of rumen microbial synthesis(13-18). The present study showed, in full agreement
with previous studies, that large amounts of Uac and Alo (1∙0/11 mmol/L), not Hyp and Xan (0
mmol/L), were present in urine from lactating dairy cows(15,20,41,42). The estimated renal clearance of
Uac (15 L/h) and Alo (89 L/h) also correponded well with previous findings(43-44).
In summary, the purines were absorbed mainly as DP Uac and Alo and only in minor proportions as
NS and BS. The absorbed NS and BS was fully degraded to Uac or Alo in the hepatic tissue where
it, alongside with absorbed and endogenously produced DP, was subsequently released to the circulating pool of DP, ready for excretion from the kidneys.
Splanchnic metabolism of pyrimidines
Portal-drained viscera metabolism of pyrimidines
The net PDV release of the pyrimidine NS and BS (0∙30-2∙8 mmol/h) was higher than that of the
purine NS and BS (0∙0023-1∙3 mmol/h) and, the net PDV release of the pyrimidine DP β-ala and βami (0∙047-0∙77 mmol/h) were lower than that of the purine DP (7∙0-7∙8 mmol/h). From these results, it becomes evident that the mechanisms of the purine and the pyrimidine metabolisms differ
in lactating dairy cows in the same way as they differ in humans(21). When such large amounts of
pyrimidine NS were absorbed and such low levels of DP, it would seem that, in contrast to the purines, a prominent degradation of NS to BS before or during absorption does not occur for the pyrimidines. The low levels of pyrimidine DP could also partly be a result of β-ala and β-ami being
incorporated into other intermediate products. The pyrimidine DP are not end-products in the same
manner as the purines(21); β-ala can become part of the β-alanine metabolism(22) and β-ami part of
the valine, leucine, and isoleucine metabolism and the citric acid cycle(23). On the other hand, in
parallel to the purine metabolism, the pyrimidine BS Cyt, Ura, and Thy was rapidly degraded. This
was also the case for the pyrimidine DP β-ure, suggesting that β-ure functions more as an easily
convertible intermediate than as a terminal DP. It follows, that active dihydrouracil dehydrogenase
[1.3.1.1], and/or dihydropyrimidine dehydrogenase [1.3.1.2], and beta-ureidopropionase enzymes
[3.5.1.6] must be present in the intestinal mucosa and/or blood. Some of the released pyrimidines
may also be of endogenous origin and the salvage mechanisms of the mucosal enterocytes may also
play a role in the absorption pattern observed(7,14). If estimating net gastrosplenic fluxes of the pyrimidines, only β-ure had a gastrosplenic flux that contributed to the net PDV flux with more than
20% (i.e., approx. 60%). Since there is no evidence of ruminal absorption of pyrimidines, the gastrosplenic contributions probably were of endogenous origin.
83
When studying how the postprandial pattern affects the PDV metabolism, only the net PDV flux of
dUrd and β-ala increased over time. A time-dependent absorption profile was not expected since,
as for the purines, pyrimidines undergo a comprehensive digestion route before absorption take
place. From this study, we are not able to clarify why an effect of time was detected for these two
pyrimidine metabolites and not the remaining pyrimidines.
Hepatic and splanchnic metabolism of pyrimidines
Concerning the net hepatic fluxes of the pyrimidine NS and BS (-0∙52--5∙0 mmol/h), extensive hepatic uptake was detected as expected. Consistent with the theory that the pyrimidine DP can function as intermediates and as such are not terminal end-products, the pyrimidine DP (-0∙095--0∙22
mmol/h) was also removed by the hepatic tissue(21-23).
The hepatic fractional removal of the pyrimidine NS and BS was approx. 100%, suggesting that the
pyrimidine degrading enzymes in the hepatic tissue on a net basis were able to degrade all of the
pyrimidines at a rate equivalent to the net PDV release. The TI% of the pyrimidine NS and BS was
approx. 50% and, not as for the purines, the same as the NP%. This suggests that the enzymes in the
hepatic tissue was not capable of removing the entire amount of pyrimidines entering from the PDV
and the peripheral tissues and probably reflects the fact that much larger amounts of pyrimidines
enter the hepatic tissue intact as NS, and not as BS or DP. The same pattern of high NP% and lower
TI% was observed for the pyrimidine DP, further demonstrating the notion that the pyrimidine DP
acts more like intermediates than end-products in the pyrimidine metabolism.
In accordance with the calculated net PDV and hepatic fluxes, the net splanchnic fluxes of the pyrimidine NS Cyd was positive (0∙49 mmol/h) and those of the pyrimidine NS Urd, Thd and, dUrd
were negative (-0∙2--2∙2 mmol/h). The net splanchnic fluxes of the pyrimidine DP β-ala and β-ami
(0∙37 and -0∙032 mmol/h, respectively) were also as expected lower than that of the rest of the pyrimidines and the purine DP. Only the net splanchnic flux of dUrd increased over time (P < 0∙06).
In summary, the pyrimidines were absorbed mainly as NS and BS and only in minor proportions as
DP. This was the opposite of what was recorded for the purines, where mainly DP was absorbed. In
both the purine and the pyrimidine metabolism, a pronounced degradation of BS took place. The
absorbed pyrimidines was partly degraded across the hepatic tissue (some release of Cyd and β-ala),
most ending up as intermediates in other parts of the N metabolism. The pyrimidines were as such,
not as exposed to excretion via the kidneys. It must then be assumed that the pyrimidines to a greater extent than the purines can be used for N salvage. Although we would have liked to determine
the pyrimidine metabolites in urine, unfortunately at current no method was available. Hence, calculations of renal pyrimidine variables could not be performed.
84
Purine and pyrimidine N contribution to the nitrogen metabolism
When reviewing the purine and pyrimidine metabolism with focus on the contributions to the N
metabolism, it became evident that considerable amounts of purine N in the form of Uac and Alo
were lost to the dairy cows (Fig. 1 & 3). Even though 67% of the purine N was absorbed from the
small intestine, the very effective degradation of the purine metabolites pre-absorption, in the intestinal mucosa, the blood and, the hepatic tissue, as a consequence of a high activity of XO in these
tissues, as well as the high renal clearance rate of Uac and Alo and the inability of the animal to
salvage Uac and Alo in other cells than the mucosal enterocytes, made it almost impossible to reclaim purine N for microbial synthesis of endogenous purines and/or amino acids in the dairy cow.
Furthermore, the 67% becomes 84% if taking into account that the digestibility of DNA (75-85%)
and RNA (80-90%) is around 80% in the small intestine(11).
Focusing on the intermediary metabolism of the pyrimidines, very different types of degradation
mechanisms seemed to be in function. First of all, only 24% of the pyrimidine N was absorbed from
the small intestine, 30% if taking the digestibility of DNA and RNA into account. Thus, much less
N was available to the cow from this part of the N metabolism. Nevertheless, the pyrimidine metabolites were also, as the purines, degraded before and in the hepatic tissue but, because the endproducts β-ala and β-ami can function as intermediates in other parts of the N metabolism, the pyrimidine N does not seem to be lost to the same extend as for the purines (Fig. 2 & 3). Some βalanine escapes the hepatic tissue and might be excreted in the kidneys but in comparison to Uac
and Alo, the proportion is expected to be minor. Another advantage of the pyrimidines were that
they due to the comprehensive degradation process and less effective hepatic degradation were
available as NS metabolites for N salvage in peripheral tissues.
It should also be noticed that not all of the purine N and pyrimidine N was lost in Uac and Alo and,
β-ami and β-ala; the released ammonia (NH3) from purine and pyrimidine degradation could become part of the urea-recycling system and thereby possible be recycled by the dairy cow for incorporation into valuable amino acids (Fig. 3), though recent research have questioned the true recycling of urea N in ruminants(24,31).
By now the basic intermediary degradation pathways of the purine and pyrimidine metabolism and
the purine and pyrimidine N has been described, further studies examining the effect of i.e. protein
level on the postprandial pattern of the net PDV and hepatic metabolism could reveal if it is possible to manipulate or use this complex system for optimising and making more efficient the utilisation of purine and pyrimidine N in ruminants.
85
Conclusion
All of the 20 examined NA, the 10 key purines and the 10 key pyrimidines, were released to different extends to the PDV of lactating dairy cows; the purines mainly as the DP Uac and Alo and, only
in minor proportions as purine NS and BS and, the pyrimidines mainly as NS and BS and, only in
minor proportions as the pyrimidine DP β-ala and β-ami. Most of the purine and pyrimidine BS was
degraded during absorption, in the blood or the hepatic tissue, resulting in low, yet detectable, concentrations of these metabolites in the blood. A very effective blood and hepatic metabolism consequently degraded all of the purines to Uac and Alo, releasing these non-salvageable N metabolites
to the circulating PD for excretion into the kidneys. The metabolic processes of the pyrimidine metabolism appeared quite differently from those of the purine metabolism. The pyrimidine NS was to
a much larger extend absorbed intact and an outlet into other parts of the N metabolism through βala and β-ami resulted in a more N economical degradation mechanism of these metabolites. The
postprandial pattern was not found to have an effect on neither the net PDV nor the net hepatic metabolism of any of the purine and pyrimidine metabolites examined in this study. Further investigations with varying rumen microbial synthesis are needed to discover the full potential of improving
the utilisation of N in ruminants by manipulating the purine and pyrimidine metabolism.
Acknowledgements
We thankfully acknowledge Lis Sidelmann and Birgit H. Løth at the Department of Animal Science, Faculty of Science and Technology, Aarhus University (Denmark), for skillful and dedicated
technical assistance. We thank Peter Løvendahl at Department of Molecular Biology and Genetics,
Aarhus University (Denmark) for his competent and constructive assistance during statistical handling.
Financial support
C. S. holds a PhD Scholarship co-financed by the Faculty of Science and Technology, Aarhus University (Denmark) and a research project supported by the Danish Milk Levy Fond, c/o Food and
Agriculture (Aarhus, Denmark). Funding for the study was provided by the Commission of the European Communities (Brussels, Belgium; FP7, KBBE-2007-1), the Directorate for Food, Fisheries,
and Agri Business (Copenhagen, Denmark; #3304-VMP-05-005), and the Danish Ministry of Food,
Agriculture, and Fisheries (Copenhagen, Denmark). None of the funding parties had any role in the
design, analysis or writing of this article.
Conflict of interest
There are no conflicts of interest.
Authorship
86
C.S., S.K.J., N.B.K, and M.V. were responsible for project development and formulating the research questions; B.A.R and N.B.K designed the dairy cow study and carried out the experiment;
C.S. developed and performed the purine and pyrimidine analysis; C.S. and M.L. performed the
statistical analysis and drafted the manuscript, and all authors contributed, commented and approved the final content.
87
1
References
2
1. Kohn RA, Dinneen MM & Russek-Cohen E (2005) Using blood urea nitrogen to predict nitrogen
3
excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats. J Anim
4
Sci 83, 879-889.
5
2. Reynolds CK & Kristensen NB (2008) Nitrogen recycling through the gut and the nitrogen
6
economy of ruminants: an asynchronous symbiosis. J Anim Sci 86, 293-305.
7
3. Calsamiglia S, Ferret A, Reynolds CK et al. (2010) Strategies for optimizing nitrogen use by
8
ruminants. Anim 4, 1184-1196.
9
4. Steinfeld H, Gerber P, Wassenaar T et al. (2006) Livestock’s long shadow: Environmental issues
10
and options; available at http://www.fao.org/docrep/010/a0701e/a0701e00.HTM (accessed May
11
2014)
12
5. Tamminga S (1992) Nutrition management of dairy cows as a contribution to pollution control. J
13
Dairy Sci 75, 345-357.
14
6. Firkins JL (1996) Maximizing microbial protein synthesis in the rumen. J Nutr 126, 1347-1354.
15
7. Fujihara T & Shem MN (2011) Metabolism of microbial nitrogen in ruminants with special
16
reference to nucleic acids. Anim Sci J 82, 198-208.
17
8. McDonald P, Edwards RA, Greenhalgh JFD et al. (2011) Animal Nutrition. 7th ed. Essex:
18
Pearson Education Limited.
19
9. Smith RH & McAllan AB (1974) Some factors influencing the chemical composition of mixed
20
rumen bacteria. Br J Nutr 31, 2734.
21
10. McDonald P, Edwards RA, Greenhalgh JFD et al. (2002) Animal Nutrition. 6th ed,. Essex:
22
Pearson Education Limited.
23
11. McAllan AB (1980) The degradation of nucleic acids in, and the removal of breakdown
24
products from the small intestine of steers. Br J Nutr 44, 99-112.
25
12. McAllan AB & Smith RH (1973) Degradation of nucleic acid derivatives by rumen bacteria in
26
vitro. Br J Nutr 29, 467-474.
27
13. Boudra H, Doreau M, Noziere P et al. (2012) Simultaneous analysis of the main markers of
28
nitrogen status in dairy cow's urine using hydrophilic interaction chromatography and tandem mass
29
spectrometry detection. J Chromatogr A 1256, 169-176.
88
30
14. Chen XB & Gomes MJ (1992) Estimation of microbial protein supply to sheep and cattle based
31
on urinary excretion of purine derivatives - an overview of the technical details. Occasional
32
Publication of International Feed Resources Unit, Rowett Research Institute, Bucksburn, Aberdeen
33
AB2 9SB, UK.
34
15. Gonzalez-Ronquillo M, Balcells J, Belenguer A et al. (2004) A comparison of purine
35
derivatives excretion with conventional methods as indices of microbial yield in dairy cows. J
36
Dairy Sci 87, 2211-2221.
37
16. Moorby JM, Dewhurst RJ, Evans RT et al. (2006) Effects of dairy cow diet forage proportion
38
on duodenal nutrient supply and urinary purine derivative excretion. J Dairy Sci 89, 3552-3562.
39
17. Tas BM & Susenbeth A (2007) Urinary purine derivates excretion as an indicator of in vivo
40
microbial N flow in cattle: A review. Livest Sci 111, 181-192.
41
18. Gonda HL & Lindberg JE (1997) Effect of diet on milk allantoin and its relationship with
42
urinary allantoin in dairy cows. J Dairy Sci 80, 364-373.
43
19. Chen XB, Orskov ER & Hovell FDD (1990) Excretion of purine derivatives by ruminants –
44
endogeneous excretion, differences between cattle and sheep. Br J Nutr 63, 121-129.
45
20. Verbic J, Chen XB, Macleod NA et al. (1990) Excretion of purine derivatives by ruminants -
46
effect of microbial nucleic acid infusion on purine derivative excretion by steers. J Agric Sci 114,
47
243-248.
48
21. Loffler M, Fairbanks LD, Zameitat E et al. (2005) Pyrimidine pathways in health and disease.
49
Trends Mol Med 11, 430-437.
50
22. KEGG: Kyoto Encyclopedia of Genes and Genomes, beta-alanine metabolism; available at
51
http://www.genome.jp/kegg/pathway/map/map00410.html (accessed May 2014).
52
23. KEGG: Kyoto Encyclopedia of Genes and Genomes, valine, leucine and isoleucine degradation;
53
available at http://www.genome.jp/kegg-bin/show_pathway?map00280 (accessed May 2014).
54
24. Røjen BA, Theil PK & Kristensen NB (2011) Effects of nitrogen supply on inter-organ fluxes
55
of urea-N and renal urea-N kinetics in lactating Holstein cows. J Dairy Sci 94, 2532-2544.
56
25. Larsen M & Kristensen NB (2009) Effect of abomasal glucose infusion on splanchnic amino
57
acid metabolism in periparturient dairy cows. J Dairy Sci 92, 3306-3318.
58
26. Volden H (2011) NorFor - The Nordic feed evalution system. EAAP publication No. 130.
59
Wageningen: Wageningen Academin Publishers.
89
60
27. Katz ML & Bergman EN (1969) Simultaneous measurements of hepatic and portal venous
61
blood flow in the sheep and dog. Am J Physiol 216, 946-952.
62
28. Harvey RB & Brothers AJ (1962) Renal extraction of para-aminohippurate and creatinine
63
measured by continuous in vivo sampling of arterial and renal-vein blood. Ann N Y Acad Sci 102,
64
46-54.
65
29. Thode S (1999) Bestemmelse af purinderivater (allantoin, urinsyre, hypoxanthin og xanthin)
66
samt kreatinin i urin hos kvæg ved anvendelse af HPLC. DJF Rapport nr. 127. Foulum: Trykt på
67
Forskningscenter Foulum.
68
30. Stentoft C, Vestergaard M, Løvendahl P et al. (2014) Simultaneous quantification of purine and
69
pyrimidine bases, nucleosides and their degradation products in bovine plasma by high performance
70
liquid chromatography tandem mass spectrometry. J Chromatogr A 1356, 197-210.
71
31. Kristensen NB, Storm AC & Larsen M (2010) Effect of dietary nitrogen content and
72
intravenous urea infusion on ruminal and portal-drained visceral extraction of arterial urea in
73
lactating Holstein cows. J Dairy Sci 93, 2670-2683.
74
32. Carver JD & Allan Walker W (1995) The role of nucleotides in human nutrition. J Nutr
75
Biochem 6, 58-72.
76
33. Berg JM, Tymoczko JL & Stryer L (2001) Biochemistry. 5th ed. New York: W. H. Freeman and
77
company
78
34. Campbell LL, Jr. (1957) Reductive degradation of pyrimidines. III. Purification and properties
79
of dihydrouracil dehydrogenase. J Biol Chem 227, 693-700
80
35. Campbell LL, Jr. (1958) Reductive degradation of pyrimidines. IV. Purification and properties
81
of dihydrouracil hydrase. J Biol Chem 233, 1236-1240.
82
36. Campbell LL, Jr. (1960) Reductive degradation of pyrimidines. V. Enzymatic conversion of N-
83
carbamyl-beta-alanine to beta-alanine, carbon dioxide, and ammonia. J Biol Chem 235, 2375-2378.
84
37. Remond D, Chaise JP, Delval E et al. (1993) Net transfer of urea and ammonia across the
85
ruminal wall of sheep. J Anim Sci 71, 2785-2792.
86
38. Storm AC, Hanigan MD & Kristensen NB (2011) Effects of ruminal ammonia and butyrate
87
concentrations on reticuloruminal epithelial blood flow and volatile fatty acid absorption kinetics
88
under washed reticulorumen conditions in lactating dairy cows. J Dairy Sci 94, 3980-3994.
89
39. Yoshimura S (1929) Über das allantoin in der galle des hundes. J Biochem 10, 435-442.
90
90
40. Tay LK, Papp EA & Timoszyk J (1991) Metabolism of 14C-2',3'-dideoxyinosine by the in situ
91
perfused rat liver preparation. Biopharm Drug Dispos 12, 285-297.
92
41. Bristow AW, Whitehead DC & Cockburn JE (1992) Nitrogenous constituents in the urine of
93
cattle, sheep and goats. J Sci Food Agric 59, 387–394.
94
42. Martín-Orúe SM, Balcells J, Guada JA et al. (2000) Microbial nitrogen production in growing
95
heifers: direct measurement of duodenal flow of purine bases versus urinary excretion of purine
96
derivatives as estimation procedures. Anim Feed Sci Technol 88, 171–188.
97
43. Giesecke D, Ehrentreich L, Stangassinger M et al. (1994) Mammary and renal excretion of
98
purine metabolites in relation to energy intake and milk yield in dairy cows. J Dairy Sci 77, 2376-
99
2381.
100
44. Valadares RF, Broderick GA, Valadares Filho SC et al. (1999) Effect of replacing alfalfa silage
101
with high moisture corn on ruminal protein synthesis estimated from excretion of total purine
102
derivatives. J Dairy Sci 82, 2686-2696.
103
45. IUPAC, Abbreviations and Symbols for Nucleic Acids, Polynucleotides and their Constituents;
104
available at http://www.chem.qmul.ac.uk/iupac/misc/naabb.html (accessed May 2014).
105
46. KEGG: Kyoto Encyclopedia of Genes and Genomes, purine metabolism; available at
106
http://www.genome.jp/kegg/pathway/map/map00240.html (accessed May 2014).
107
47. KEGG: Kyoto Encyclopedia of Genes and Genomes, pyrimidine metabolism; available at
108
http://www.genome.jp/kegg/pathway/map/map00230.html (accessed May 2014)
91
Table 1. Abbreviation, type and calibration range of investigated purine and pyrimidine metabolites
Abbreviations1
µmol/L
Pyrines
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Adenine
Guanine
Hypoxanthine
Xanthine
Uric acid
Allantoin
Pyrimidines
Cytidine
Uridine
Thymidine
2’-deoxyuridine
Cytosine
Uracil
Thymine
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
Range (min)2
Type
Range (max)2
Guo
Ino
dGuo
dIno
Ade
Gua
Hyp
Xan
Uac
Alo
NS
NS
NS
NS
BS
BS
BS/DP
BS/DP
DP
DP
0∙0
0∙0
0∙0
0∙0
0∙0
0∙0
0∙0
0∙0
0∙0
125
5∙0
5∙0
5∙0
5∙0
5∙0
5∙0
5∙0
5∙0
200
500
Cyd
Urd
dThd
dUrd
Cyt
Ura
Thy
β-ala
β-ure
β-ami
NS
NS
NS
NS
BS
BS
BS
DP
DP
DP
2∙5
1∙9
2∙5
0∙16
1∙9
0∙0
0∙0
3∙1
0∙0
0∙0
5∙0
7∙5
5∙0
5∙0
7∙5
5∙0
5∙0
13
75
5∙0
BS, base; NS, nucleoside; DP, degradation product; min, minimum concentration; max, maximum concentration.
1
Abbreviations from IUPAC, abbreviations and symbols for nucleic acids, polynucleotides and their constituents (45).
2
External calibration was performed with five concentrations and bottom points were excluded to fit the concentration
range in actual samples.
92
Table 2. Concentrations (µmol/L) of purine and pyrimidine metabolites in plasma samples from lactating dairy cows
Mean1
µmol/L
Purines
Guo
Ino
dGuo
dIno
Ade
Gua
Hyp
Xan
Uac
Alo
Pyrimidines
Cyd
Urd
Thd
dUrd
Cyt
Ura
Thy
β-ala
β-ure
β-ami
Artery
SD1
min
max
Mean1
Hepatic portal vein
SD1
min
max
Mean1
Hepatic vein
SD1
min
max
Mean1
Gastrosplenic vein
SD1
min
max
0∙021
0∙046
0∙015
0∙013
0∙15
0∙012
0∙043
0∙011
73
122
0∙027
0∙033
0∙024
0∙019
0∙017
0∙019
0∙013
0∙0070
33
30
0∙0
0∙012
0∙0
0∙0
0∙12
0∙0
0∙018
0∙0
16
73
0∙097
0∙13
0∙069
0∙064
0∙20
0∙063
0∙073
0∙028
132
170
1∙1
0∙79
0∙29
0∙32
0∙16
0∙015
0∙059
0∙015
78
129
0∙68
0∙60
0∙16
0∙19
0∙016
0∙019
0∙026
0∙0079
34
34
0∙24
0∙058
0∙065
0∙056
0∙13
0∙0
0∙034
0∙0
20
81
2∙8
2∙3
0∙66
0∙81
0∙18
0∙056
0∙13
0∙031
131
215
0∙024
0∙047
0∙019
0∙0082
0∙15
0∙0045
0∙041
0∙011
78
117
0∙035
0∙028
0∙030
0∙013
0∙015
0∙012
0∙010
0∙0055
34
27
0∙0
0∙0
0∙0
0∙0
0∙12
0∙0
0∙021
0∙0017
17
74
0∙10
0∙12
0∙13
0∙041
0∙18
0∙047
0∙064
0∙025
133
188
0∙035
0∙040
0∙020
0∙0056
0∙15
0∙013
0∙042
0∙013
71
133
0∙037
0∙025
0∙028
0∙012
0∙014
0∙020
0∙012
0∙0085
33
32
0∙0
0∙0
0∙0
0∙0
0∙12
0∙0
0∙020
0∙0
18
87
0∙11
0∙12
0∙11
0∙049
0∙18
0∙065
0∙063
0∙028
134
202
3∙3
3∙7
1∙1
0∙82
0∙0
0∙19
0∙042
13
3∙7
0∙31
1∙2
0∙79
1∙1
0∙36
1∙9
2∙7
0∙0
0∙26
0∙0
0∙0
0∙0
2∙7
2∙0
0∙12
6∙6
6∙0
3∙8
1∙6
0∙0
0∙91
0∙20
23
9∙4
0∙60
4∙8
6∙0
1∙8
1∙0
0∙0
0∙23
0∙022
14
4∙2
0∙35
1∙5
1∙4
1∙1
0∙35
2∙4
3∙8
0∙056
0∙60
0∙0
0∙0
0∙0
4∙9
2∙4
0∙17
8∙9
11
3∙6
2∙1
0∙0
1∙1
0∙13
26
9∙3
0∙68
3∙7
2∙1
1∙1
0∙65
0∙0
0∙24
0∙023
14
4∙1
0∙28
1∙3
0∙61
1∙1
0∙33
1∙8
1∙2
0∙0
0∙0
0∙0
0∙0
0∙0
3∙8
2∙3
0∙12
6∙6
4∙3
3∙5
1∙5
0∙0
0∙83
0∙15
23
8∙6
0∙51
2∙4
4∙2
1∙2
1∙0
0∙0
0∙20
0∙029
13
4∙6
0∙34
0∙89
1∙1
1∙4
0∙32
1∙2
2∙7
0∙0
0∙56
0∙0
0∙0
0∙0
4∙7
2∙9
0∙15
4∙9
7∙4
5∙5
2∙0
0∙0
0∙90
0∙15
23
9∙4
0∙61
0∙30
0∙060
5∙6
1∙7
0∙15
0∙36
0∙042
5∙6
2∙1
0∙16
min, minimum concentration; max, maximum concentration.
1
Mean ± SD.
93
0∙29
0∙042
5∙6
1∙9
0∙12
0∙30
0∙043
4∙8
2∙0
0∙16
Table 3. Concentration differences (µmol/L) between each of four blood veins and an artery of purine and pyrimidine metabolites in lactating dairy cows
µmol/L
Purines
Guo
Ino
dGuo
dIno
Ade
Gua
Hyp
Xan
Uac
Alo
Pyrimidines
Cyd
Urd
Thd
dUrd
Cyt
Ura
Thy
β-ala
β-ure
β-ami
Mean1
ΔPA
SEM1
P-value2
Mean1
ΔHA
SEM1
P-value2
1∙2*
0∙75*
0∙29*
0∙30*
0∙0021
0∙0032
0∙016*
0∙0033
5∙5*
6∙6†
<0∙01
0∙20
0∙067
0∙068
0∙0015
0∙0049
0∙0058
0∙0017
1∙2
3∙6
1∙5*
2∙3*
0∙69*
0∙23*
0∙0
0∙042
-0∙021
0∙66†
0∙20
0∙039*
Mean1
ΔPH
SEM1
P-value2
0∙46
0∙40
0∙42
0∙87
0∙12
0∙29
0∙73
0∙25
0∙79
0∙03
0∙0032
0∙00016
0∙0049
-0∙0047
0∙00088
-0∙0091
-0∙0011
-0∙00074
5∙4*
-5∙1†
0∙0082
0∙0039
0∙010
0∙0085
0∙0012
0∙0039
0∙0028
0∙0027
1∙3
2∙8
0∙099
0∙20
0∙16
0∙081
0∙64
0∙73
0∙67
0∙03
0∙090
0∙018
0∙32
0∙24
0∙013
0∙76
0∙67
0∙17
0∙71
0∙43
0∙34*
-1∙5*
-0∙062
-0∙15*
0∙0
0∙060
-0∙019
0∙47
0∙11
-0∙022†
Mean1
ΔGA
SEM1
P-value2
0∙32
0∙55
0∙68
0∙96
0∙78
0∙01
0∙82
0∙59
0∙52
0∙05
1∙1*
0∙75*
0∙28*
0∙31*
0∙0020
0∙013*
0∙018*
0∙0044†
0∙22
11*
0∙22
0∙19
0∙066
0∙070
0∙0014
0∙0036
0∙0092
0∙0029
1∙2
2∙4
0∙12
0∙42
0∙69
0∙79
0∙38
0∙25
0∙76
0∙37
0∙26
0∙70
0∙014*
-0∙0065
0∙0059
-0∙0070*
-0∙0026
-0∙0017
-0∙00038
0∙0028
0∙51
12*
0∙0058
0∙014
0∙0070
0∙0028
0∙0021
0∙0095
0∙0025
0∙0029
1∙8
2∙6
0∙69
0∙53
0∙71
0∙73
0∙94
0∙61
0∙37
0∙29
0∙36
0∙29
0∙070
0∙14
0∙19
0∙080
0∙53
0∙56
0∙76
0∙07
0∙074
0∙016
0∙41
0∙18
0∙013
0∙24
0∙56
0∙54
0∙22
0∙77
1∙2*
3∙9*
0∙74*
0∙39*
0∙0
-0∙0032
-0∙0019
0∙044
0∙15
0∙066*
0∙082
0∙30
0∙22
0∙12
0∙98
0∙47
0∙24
0∙24
0∙17
0∙12
0∙21
0∙051
0∙58
0∙45
0∙59
0∙02
0∙070
0∙0034
0∙40
0∙15
0∙019
0∙29
0∙89
0∙74
0∙09
0∙08
-0∙94*
0∙52*
0∙0098
0∙22*
0∙0
-0∙047
-0∙016
0∙57
0∙61*
0∙029*
0∙11
0∙013
0∙37
0∙23
0∙0097
<0∙01
0∙75
0∙49
0∙38
0∙11
ΔPA, concentration difference between hepatic portal vein and artery; ΔHA, concentration difference between hepatic vein and artery; ΔPH, concentration difference between
hepatic portal vein and hepatic vein; ΔGA, concentration difference between gastrosplenic vein and artery.
1
Mean ± SEM (n = 4). Difference from zero declared when *P ≤ 0∙05, tendency when †P ≤ 0∙1 (t-test).
2
P-values for main effect of time relative to feeding. Significance declared when *P ≤ 0∙05, tendency when †P ≤ 0∙1 (F-test).
94
Table 4. Blood plasma flows (L/h) and net fluxes (µmol/h or mmol/h) of purine and pyrimidine metabolites in lactating dairy cows
Time1
Site
L/h
Plasma flows
Purines
Guo, mmol/h
Ino, mmol/h
dGuo, mmol/h
dIno, mmol/h
Gua, μmol/h
Hyp, μmol/h
Uac, mmol/h
Alo, mmol/h
Pyrimidines
Cyd, mmol/h
Urd, mmol/h
Overall
mean2
SEM2
-0∙5
0∙5
1∙5
2∙5
P-values for time4
3∙5
4∙5
6∙5
SEM3
Lin
Quad
Cubic
PDV
TSP
HA
1221
1440
214
85
105
32
1104
1332
228
1202
1344
143
1284
1574
255
1267
1446
179
1188
1436
248
1258
1574
316
1245
1377
132
115
184
95
0∙34
0∙66
0∙95
0∙36
0∙33
0∙43
0∙37
0∙94
0∙29
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
1∙3
-1∙3
0∙0072
0∙85
-0∙86
0∙0014
0∙33
-0∙32
0∙0085
0∙35
-0∙36
-0∙0069
2∙3
-20
-17
18
-21
-1∙1
7∙0
0∙63
7∙9
7∙8
-16
-6∙1
0∙18
0∙14
0∙012
0∙21
0∙21
0∙0066
0∙064
0∙67
0∙014
0∙067
0∙073
0∙012
5∙9
3∙9
7∙1
6∙0
11
3∙5
2∙1
1∙7
2∙1
3∙9
3∙2
4∙4
1∙3
-1∙2
0∙021
0∙95
-0∙93
0∙019
0∙32
-0∙30
0∙023
0∙33
-0∙33
-0∙0027
35
-12
23
12
-12
0∙13
4∙2
-6∙2
-2∙0
-12
-23
-33
1∙2
-1∙2
-0∙019
0∙87
-0∙88
-0∙028
0∙26
-0∙27
-0∙016
0∙35
-0∙35
0∙0044
4∙6
-14
-9∙5
11
-16
-4∙5
11
-5∙9
5∙3
26
-25
0∙57
0∙86
-1∙0
0∙0062
0∙47
-0∙57
0∙011
0∙26
-0∙31
-0∙028
0∙30
-0∙33
-0∙0079
-16
-34
-38
18
-20
9∙2
8∙1
0∙12
8∙9
-17
-9∙8
-6∙3
0∙93
-0∙94
-0∙0064
0∙56
-0∙57
-0∙0068
0∙30
-0∙31
-0∙018
0∙28
-0∙29
-0∙0071
-0∙43
-14
-14
8∙5
-9∙8
0∙031
4∙5
3∙9
8∙4
17
-15
-8∙1
1∙2
-1∙2
0∙013
0∙83
-0∙83
-0∙0049
0∙30
-0∙30
0∙0015
0∙37
-0∙39
-0∙018
-16
-53
-76
18
-24
-6∙8
7∙3
4∙4
12
26
-12
14
2∙3
-2∙3
0∙038
1∙4
-1∙4
0∙0056
0∙48
-0∙42
0∙055
0∙42
-0∙43
-0∙0060
0∙22
0∙91
1∙1
30
-35
-5∙9
4∙5
9∙9
15
18
-12
6∙2
1∙3
-1∙3
-0∙0017
0∙86
-0∙84
0∙015
0∙39
-0∙35
0∙042
0∙41
-0∙42
-0∙011
9∙1
-12
-2∙5
30
-30
-0∙12
9∙4
-1∙8
8∙5
-2∙7
-12
-15
0∙37
0∙38
0∙021
0∙33
0∙34
0∙016
0∙10
0∙11
0∙033
0∙11
0∙11
0∙018
15
10
13
11
15
7∙6
3∙9
3∙8
5∙1
10
7∙8
9∙7
0∙38
0∙46
0∙73
0∙56
0∙62
0∙62
0∙35
0∙59
0∙26
0∙39
0∙31
0∙43
0∙39
0∙75
0∙21
0∙24
0∙18
0∙59
0∙87
0∙11
0∙12
0∙33
0∙25
0∙14
0∙95
0∙97
0∙90
0∙67
0∙78
0∙34
0∙74
0∙93
0∙32
0∙69
0∙76
0∙70
0∙04*
0∙09†
<0∙01*
0∙69
0∙99
0∙85
0∙77
0∙04*
0∙13
0∙03*
0∙48
<0∙01*
0∙08†
0∙14
0∙07†
0∙09†
0∙15
0∙51
0∙17
0∙57
0∙18
0∙59
0∙64
0∙85
0∙36
0∙51
0∙26
0∙66
0∙63
0∙30
0∙30
0∙21
0∙93
0∙42
0∙96
0∙96
PDV
HEP
TSP
PDV
HEP
TSP
1∙9
-1∙4
0∙49
2∙8
-5∙0
-2∙2
0∙21
0∙10
0∙13
0∙16
0∙24
0∙16
2∙0
-1∙4
0∙64
2∙5
-4∙7
-2∙2
1∙7
-1∙4
0∙35
2∙7
-4∙7
-2∙0
1∙5
-1∙6
0∙24
2∙2
-4∙4
-2∙1
1∙6
-1∙4
0∙26
3∙0
-5∙3
-2∙3
1∙9
-1∙1
0∙84
2∙7
-4∙9
-2∙2
1∙8
-1∙4
0∙40
3∙1
-5∙3
-2∙2
2∙3
-1∙7
0∙67
3∙2
-5∙5
-2∙3
0∙44
0∙38
0∙27
0∙50
0∙48
0∙32
0∙32
0∙70
0∙50
0∙18
0∙11
0∙62
0∙20
0∙54
0∙44
0∙78
0∙77
0∙96
0∙69
0∙53
0∙37
0∙76
0∙67
0∙77
95
Thd, mmol/h
dUrd, mmol/h
β-ala, mmol/h
β-ami, mmol/h
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
0∙77
-1∙0
-0∙30
0∙30
-0∙52
-0∙20
0∙77
-0∙22
0∙37
0∙047
-0∙095
-0∙032
0∙14
0∙34
0∙35
0∙13
0∙16
0∙12
0∙41
0∙58
0∙58
0∙015
0∙029
0∙022
0∙20
-0∙37
-0∙17
0∙22
-0∙45
-0∙22
-0∙46
0∙72
-0∙13
0∙067
-0∙11
-0∙011
0∙40
-0∙46
-0∙26
-0∙22
-0∙25
-0∙47
-0∙10
2∙2
2∙1
0∙041
-0∙037
0∙029
1∙0
-2∙1
-1∙2
0∙76
-0∙80
0∙046
-0∙51
-1∙2
-2∙5
0∙11
-0∙20
-0∙086
0∙80
-1∙7
-0∙91
-0∙30
-0∙43
-0∙73
-0∙10
-0∙93
-1∙0
-0∙014
-0∙049
-0∙062
0∙83
-1∙2
-0∙36
0∙42
-0∙42
0∙0011
0∙90
-0∙65
0∙25
-0∙030
-0∙0023
-0∙032
0∙66
-0∙048
0∙61
0∙76
-0∙93
-0∙17
1∙8
-0∙29
1∙5
0∙054
-0∙15
-0∙049
1∙5
-1∙3
0∙17
0∙44
-0∙39
0∙17
3∙8
-1∙5
2∙4
0∙097
-0∙11
-0∙016
0∙43
0∙64
0∙68
0∙24
0∙26
0∙20
1∙1
1∙7
2∙0
0∙047
0∙0047
0∙052
0∙08†
0∙76
0∙45
0∙07†
0∙67
0∙12
<0∙01*
0∙22
0∙30
0∙92
0∙78
0∙78
0∙94
0∙42
0∙43
0∙94
0∙42
0∙25
0∙24
0∙72
0∙31
0∙12
0∙50
0∙37
0∙29
0∙07†
0∙25
0∙21
0∙45
0∙92
0∙87
0∙86
0∙71
0∙45
0∙75
0∙95
PDV, portal-drained viscera; TSP, total splanchnic tissue; HA, hepatic artery; HEP, hepatic tissue.
1
Hourly samples (time) were collected during an 8-h period, 0∙5 h before feeding, and at 0∙5, 1∙5, 2∙5, 3∙5, 4∙5, 5∙5 and 6∙5 h after feeding, on d 14 of the experimental period.
2
Overall mean ± SEM (n = 4, across the four cows).
3
SEM (n = 4, across the four cows within each sampling time).
4
P-values for linear (Lin), (Quad) and cubic (Cubic) time effects. Significance declared when *P ≤ 0∙05, tendency when †P ≤ 0∙1 (F-test).
96
Table 5. Hepatic fractional removal as percentage of net PDV release and percentage of total influx of purines and pyrimidine metabolites
Percentage of net PDV release (NP%)
P-values for time2
Overall
SEM1
Lin
Quad Cubic
mean1
%
Purines
Guo
Ino
dGuo
dIno
Hyp
Uac
Alo
Pyrimidines
Cyd
Urd
Thd
dUrd
β-ala
β-ami
Percentage of total influx (TI%)
P-values for time2
Overall
SEM1
Lin
Quad
Cubic
mean1
0∙99
0∙98
0∙98
1∙04
1∙17
-0∙32
0∙0037
0∙010
0∙018
0∙097
0∙045
0∙32
0∙31
0∙58
0∙92
0∙33
0∙45
0∙65
0∙29
0∙28
0∙27
0∙99
0∙35
0∙58
0∙80
0∙38
0∙46
0∙11
0∙92
0∙53
0∙15
0∙64
0∙21
0∙16
0∙46
0∙97
0∙87
0∙85
0∙97
0∙20
0∙0018
0∙088
0∙021
0∙041
0∙081
0∙021
0∙10
0∙015
0∙014
0∙77
0∙43
0∙25
0∙14
0∙24
0∙07†
0∙34
0∙94
0∙82
0∙53
0∙49
0∙86
0∙02*
0∙53
0∙16
0∙67
0∙55
0∙23
0∙72
0∙85
0∙85
0∙74
1∙91
1∙23
1∙81
0∙16
1∙73
0∙056
0∙10
0∙38
0∙91
0∙92
1∙03
0∙66
0∙97
0∙87
0∙95
0∙28
0∙32
0∙85
0∙78
0∙90
0∙56
0∙28
0∙41
0∙24
0∙82
0∙16
0∙92
0∙64
0∙23
0∙21
0∙62
0∙49
0∙33
-0∙015
0∙16
0∙025
0∙015
0∙12
0∙092
0∙027
0∙055
0∙60
0∙74
0∙83
0∙54
0∙18
0∙42
0∙31
0∙06†
0∙82
0∙42
0∙44
0∙70
0∙53
0∙23
0∙09†
0∙67
0∙84
0∙52
NP%, percentage of net PDV release; TI%, percentage of total influx.
1
Overall mean ± SEM (n = 4, across the four cows). Only the overall mean and not individual time estimates are given
since almost no effects of time were detected.
4
P-values for linear (Lin), (Quad) and cubic (Cubic) time effects. Significance declared when *P ≤ 0∙05, tendency when
†P ≤ 0∙1 (F-test).
97
Table 6. Renal purine variables in lactating dairy cows
Item
Renal plasma flow, L/h
Diuresis, L/h
Arterial concentration
Xan, μmol/L
Hyp, μmol/L
Uac, mmol/L
Alo, mmol/L
Urine concentration
Xan, μmol/L
Hyp, μmol/L
Uac, mmol/L
Alo, mmol/L
Renal influx, mmol/h
Uac
Alo
Net urine flux, mmol/h
Uac
Alo
Urine/renal ratio
Uac
Alo
Urine/splanchnic ratio
Uac
Alo2
Renal clearance, L/h
Uac
Alo
Mean1
346
0∙89
SEM1
36
0∙072
0∙011
0∙043
73
122
0∙0070
0∙013
33
30
0∙0
0∙0
1∙0
11
0∙11
1∙5
24
41
4∙8
3∙0
0∙89
10
0∙11
1∙2
0∙47
0∙25
0∙10
0∙039
0.13
0.036
15
89
4∙4
21
1
Mean ± SEM (n = 4).
The net splanchnic flux of Alo was negative hence, a urine/splanchnic ratio could not be determined.
2
98
Figure 1
Nucleotide
Nucleoside
2'-deoxyinosine
C10H12N4O4
NH3
Base
7
1
2'-deoxyadenosine
C10H13N5O3
6
AMP
C10H14N5O7P
1
Adenosine
C10H13N5O4
6
2
IMP
C10H13N4O8P
NH3
1
7
Inosine
C10H12N4O5
Adenine
C5H5N5
NH3
6
8
NH3
9
Hypoxanthine
C5H4N4O
10 11
3
XMP
C10H13N4O9P
Degradation product
6
dAMP
C10H14N5O6P
NH3
Intermediate
1
Xanthosine
C10H12N4O6
6
4
Xanthine
C5H4N4O2
NH3
GMP
C10H14N5O8P
1
dGMP
C10H14N5O7P
1
5
Guanosine
C10H13N5O5
6
2'-deoxyguanosine
C10H13N5O4
6
8
10
Uric acid
C5H4N4O3
11
13
14
Allantoin
C4H6N4O3
12
Guanine
C5H5N5O
Fig. 1. Degradation pathways of the purine metabolism. Illustration modified from KEGG: Kyoto Encyclopedia of Genes and Genomes, Purine metabolism(46). Metabolites:
dAMP; 2’-deoxyadenosine 5’-monophosphate (deoxyadenosine monophosphate), AMP; 5’-adenylic acid (adenosine monophosphate), IMP; 5’-inosinic acid (inosine monophosphate), XMP; 5’-xanthylic acid (xanthosine monophosphate), GMP; 5’-guanidylic acid (guanosine monophosphate), dGMP; 2’-deoxyguanosine 5’-monophosphate (deoxyguanosine monophosphate). Enzymes: 1. 5’-nucleotidase [3.1.3.5], 2. AMP deaminase [3.5.4.6], 3. IMP dehydrogenase [1.1.1.205], 4. GMP synthase [6.3.5.2], 5. deoxyguanosine
kinase [2.7.1.113], 6. purine-nucleoside phosphorylase [2.4.2.1], 7. adenosine deaminase [3.5.4.4], 8. guanosine phosphorylase [2.4.2.15], 9. adenine deaminase [3.5.4.2], 10.
xanthine oxidase [1.17.3.2], 11. xanthine dehydrogenase [1.17.1.4], 12. guanine deaminase [3.5.4.3], 13. urate factor-independent hydroxylase [1.7.3.3] or uricase, 14. hydroxyisourate hydrolase [3.5.2.17] (or spontaneous reaction).
99
Figure 2
Nucleotide
CMP
C9H14N3O8P
Nucleoside
Cytidine
C9H13N3O5
1
NH3
UMP
C9H13N2O9P
Intermediate
Degradation product
Cytosine
C4H5N3O
5
4
Uridine
C9H12N2O6
1
Base
NH3
6
9
Uracil
C4H4N2O2
10
11
Dihydrouracil
C4H6N2O2
12
β -ureidopropionic
acid
C4H8N2O3
13
β-alanine
C3H7NO2
NH3
dUMP
C9H13N2O8P
NH3
2'-deoxyuridine
C9H12N2O5
2
NH3
3
dCMP
C9H14N3O7P
dTMP
C10H15N2O8P
1
7
8
4
2'-deoxycytidine
C9H13N3O4
2
Thymidine
C10H14N2O5
8
Thymine
C5H6N2O2
10
11
Dihydrothymine
C5H8N2O2
12
β -ureidoisobutyric
acid
C5H10N2O3
13
β-aminoisobutyric
acid
C4H9NO2
NH3
Fig. 2. Degradation pathways of the pyrimidine metabolism. Illustration modified from KEGG: Kyoto Encyclopedia of Genes and Genomes, pyrimidine metabolism(47). Metabolites: CMP; 5’-cytidylic acid (cytidine monophosphate), UMP; 5’-uridylic acid (uridine monophosphate), dUMP; 2’-deoxyuridine 5’-monophosphate (deoxyuridine monophosphate), dCMP; 2’-deoxycytidine 5’-monophosphate (deoxycytidine monophosphate), dTMP; thymidine 5’-monophosphate. Enzymes: 1. 5’-nucleotidase [3.1.3.5], 2. thymidine
kinase [2.7.1.21], 3. dCMP deaminase [3.5.4.12], 4. cytidine deaminase [3.5.4.5], 5. ribosylpyrimidine nucleosidase [3.2.2.8], 6. uridine nucleosidase [3.2.2.3], 7. purinenucleoside phosphorylase [2.4.2.1], 8. thymidine phosphorylase [2.4.2.4], 9. cytosine deaminase [3.5.4.1], 10. dihydrouracil dehydrogenase [1.3.1.1], 11. dihydropyrimidine dehydrogenase [1.3.1.2], 12. dihydropyrimidinase [3.5.2.2], 13. beta-ureidopropionase [3.5.1.6].
100
Figure 3
NH3
Intestinal mucosa
Pyrimidine
NH3
Total splanchnic tissue
NS-N: 3∙9 ± 0∙92 g/d
NS-N: -4∙0 ± 1∙0 g/d
NS-N: 0∙018 ± 0∙034 g/d
BS-N: 0∙0029 ± 0∙015 g/d
BS-N: -0∙056 ± 0∙015 g/d
BS-N: -0∙022 ± 0∙011 g/d
Uac-N: 9∙3 ± 3∙0 g/d
Uac-N: 0∙71 ± 2∙0 g/d
Uac-N: 10 ± 2∙1 g/d
Alo-N: 14 ± 5∙8 g/d
Alo-N: - (-22 ± 3∙9 g/d)
Alo-N: - (-8 ± 5∙51 g/d)
NH3
NH3
NH3
Hepatic tissue
Total splanchnic tissue
NS-N: 3∙9 ± 0∙33 g/d
NS-N: -5∙1 ± 0∙18 g/d
NS-N: -1∙1 ± 0∙17 g/d
β-ala-N: 0∙28 ± 0∙13 g/d
β-ala-N: -0∙069 ± 0∙15 g/d
β-ala-N: 0∙19 ± 0∙13 g/d
β-ami-N: 0∙016 ± 0∙0012 g/d
β-ami-N: -0∙031 ± 0∙0097 g/d
β-ami-N: -0∙013 ± 0∙074 g/d
NH3
N-outlet
NH3
Uac-N: 1∙2 ± 0∙15 g/d
Alo-N: 13 ± 1∙6 g/d
Portal-drained viscera
Sum: 4∙7 g/d N
Urine
Kidneys
Hepatic tissue
Sum 27 g/d N
Intestine
Microbial pyrimidine-N in
nucleic acids
20 g/d N
Portal-drained viscera
Kidneys
Microbial purine-N in
nucleic acids
40 g/d N
Intestinal mucosa
Purine
Intestine
NH3
Fig. 3. The purine N and pyrimidine N intestinal absorption and intermediary metabolism in the portal-drained viscera, hepatic and total splanchnic tissue in lactating dairy cows.
Purine-N, purine nitrogen; pyrimidine-N, pyrimidine nitrogen; NS-N, purine or pyrimidine nucleoside nitrogen; BS-N, purine or pyrimidine base nitrogen; Uac-N, uric acid nitrogen; Alo-N, allantoin nitrogen; β-ala-N, β-alanine nitrogen; β-ami-N, β-aminoisobutyric acid nitrogen; N-outlet, nitrogen outlet into the β-alanine metabolism(22) and the valine,
leucine, and isoleucine metabolism and the citric acid cycle(23); NH3, ammonia release during degradation available for urea-recycling(24). The purine N and pyrimidine N were
estimated from the microbial crude protein in the small intestine and the notion that when degraded dietary nitrogen is reused by the microbial population, 75-85% (80%) N goes
to microbial protein and 15-25% (20%) N to microbial nucleic acids(7-8,10). Values are means ± SEM (n = 4).
101
8. Manuscript III
Protein level influences the splanchnic metabolism of purine and pyrimidine metabolites in
lactating dairy cows.
Stentoft C., C. Barratt, L.A. Crompton, S.K. Jensen, M. Vestergaard, M. Larsen and C.K. Reynolds.
To be submitted to J. Dairy Sci.
102
PURINE AND PYRIMIDINE METABOLISM IN DAIRY COWS
Protein Level influences the Splanchnic Metabolism of Purine and Pyrimidine metabolites in
Lactating Dairy Cows.
C. Stentoft,*1 C. Barratt, † L. A. Crompton, † S. K. Jensen,* M. Vestergaard,* M. Larsen,* C.
K. Reynolds, †
* Department of Animal Science, Aarhus University, Foulum, DK-8830 Tjele, Denmark
† School of Agriculture, Policy and Development, University of Reading, Early Gate, Reading RG6
6AR, United Kingdom
1
Corresponding author: [email protected]
103
ABSTRACT
The low nitrogen efficiency in dairy cattle causes productive challenges and environmental concerns. Microbial nucleic acid corresponds to about 20% of the total microbial nitrogen synthesized
in ruminants; yet, the importance of microbial nucleic acid metabolism has been sparsely investigated. Thus, the effect of dietary protein supply and forage source on splanchnic metabolism of 20
purine and pyrimidine metabolites was investigated in dairy cows. Six ruminally cannulated midlactation Holstein cows permanently catheterised in the mesenteric artery, and hepatic portal, hepatic, and mesenteric veins were used in a replicated 3 × 3 Latin square design with 2 × 3 factorial arrangement of dietary treatments. Dietary treatments were formulated to contain 12.5, 15.0, and
17.5% crude protein (dry matter basis) in one of two forage mixtures; 25:75 or 75:25 grass:corn
silage on a 50:50 forage to concentrate ratio and was fed ad libitum. Incremental effects of protein
level were observed mainly for net portal-drained viscera release of uric acid, allantoin, cytidine,
and uridine, reflecting predicted flows to the small intestine. Hepatic removal of nucleic acid metabolites, especially nucleosides, tended to be smaller and more variable, probably due to endogenous contribution. Most of the bases were fully degraded during digestion and absorption. None of
the net fluxes were influenced by forage source, presumably due to effects of adjustments made to
the fed concentrates reducing effects of nucleic acid microbial synthesis. While affected by protein
level, considerable amounts of purine nitrogen, in the form of uric acid and allantoin, was released
from the splanchnic tissues and presumably lost to anabolic processes. There was less effects of
protein level on the total splanchnic release of pyrimidine nitrogen, which we suggest was used in
other parts of the nitrogen metabolism within the splanchnic tissues. At a protein level of 15%, approx. 11%of nitrogen intake was released from the total splanchnic tissues as nucleic acid nitrogen
and approx. 15% of total nitrogen loss was in the form of nucleic acid nitrogen.
Key words: ruminant, uric acid, allantoin, liver
104
INTRODUCTION
There has long been great focus on increasing the utilization of dietary nitrogen (N) by reducing
protein level and optimizing amino acid composition of MP (Kohn et al., 2005; Steinfeld et al.,
2006); however, only minimal improvements in the utilization of N in ruminants have been realized
in practice (Tamminga, 1992; Firkins, 1996; Calsamiglia et al., 2010). The symbiosis between rumen microorganisms and the ruminant mammal reinforce the importance of non-protein N containing components like microbial nucleic acids (NuAc) and their involvement in the nutritional physiology of ruminants. So far this has been sparsely investigated, regardless of the fact that microbial
NuAc correspond to more than 20% of the total microbial N synthesised in the rumen (Smith and
McAllan, 1974; McDonald et al., 2011). An improved understanding of the quantitative absorption
and intermediary metabolism of the NuAc components; the purine and pyrimidine metabolites, in
the portal-drained viscera (PDV) and hepatic tissue, may be of importance in order to discover new
ways to improve N efficiency in dairy cows.
Microbial biosynthesis mainly depends on the energy source i.e. available carbohydrates and
protein level (Nocek and Russell, 1988; Clark et al., 1992; Reynolds et al., 2001) and microbial
protein flow to the small intestine generally increases with increasing dietary protein level (Ipharraguerre and Clark, 2005). The rumen microbial population uses dietary N from proteins, amino acids, urea, NuAc, and other sources of non-protein N for synthesis of microbial protein and microbial
NuAc, which represent 75-85% and 15-25%, respectively, of total microbial N (McDonald et al.,
2002; McDonald et al., 2011; Fujihara and Shem, 2011). Hence, ruminants have considerable
amounts of dietary N converted into microbial NuAc before digestion, absorption, and utilization of
nitrogenous components in the small intestine (McAllan and Smith, 1973; McAllan, 1980; McAllan, 1982).
Quantitative analysis of purine and pyrimidine metabolites in dairy cattle research has mainly been
focused on excretion of purine derivatives in urine and milk, where uric acid and allantoin excretion
has been used as an indirect marker of rumen microbial protein flow to the small intestine (Giesecke
et al., 1994; Gonda and Lindberg, 1997; Gonzalez-Ronquillo et al., 2004; Tas and Susenbeth,
2007). Thus, the objectives were to study the net PDV, net hepatic and total splanchnic metabolism
of the purine and pyrimidine metabolites and evaluating how this was affected by dietary CP level
and forage source and, to evaluate the fate of the purine and pyrimidine nitrogen by estimating
NuAc nitrogen fluxes in the splanchnic tissues. It was hypothesised that the net PDV and net hepatic fluxes of the purine and pyrimidine nucleosides (NS), bases (BS), and degradation products (DP)
would reflect different degrees of microbial biosynthesis with different dietary protein levels (12.5,
15.0, and 17.5% CP) and proportions of forage sources (grass vs. corn silage) in the ration.
105
MATERIALS AND METHODS
The present experiment complied with the requirements of the Animal (Scientific Procedures)
Act 1986, concerning the use of animals in research in the United Kingdom (UK).
Animals, Diets, and Experimental Design
A description of the experiment is provided separately (Barratt et al., 2013). Briefly, six ruminally cannulated multiparous Holstein Friesian cows (average BW was 711 ± 3 kg) were permanently
catheterised in the mesenteric artery, and the hepatic portal, hepatic, and two mesenteric veins and
the right carotid artery elevated to a subcutaneous position in early lactation (Huntington et al.,
1989). Cows were used in a repeated 3 × 3 Latin square experimental design with the effect of CP
level tested within squares and forage source as the square effect. Cows were randomly allocated to
a 2 × 3 factorial arrangement of six treatment periods for each cow with each diet designed to contain one CP level and one predominant forage source for each period. Cows were fed hourly equal
meals and for ad libitum intake a TMR consisting of 50:50 mixture of forage:concentrate. Forage
fed was either 25:75 (CS) or 75:25 (GS) grass silage:corn silage on a DM basis, providing differences in the amounts and rate of fermentation of starch and NDF. The rations were formulated to
contain CP levels of 12.5, 15.0, and 17.5% of DM, providing MP below, near, and above estimated
requirements (Thomas, 2004). This was achieved primarily through differences in amounts of rumen-protected soybean meal (SoyPass®) added to the concentrate portion of the diets. Cows were
milked twice daily and milk yield and feed DMI measured daily. Cows were sampled for measurements of net PDV, net hepatic, and total splanchnic fluxes in the final week of each experimental
period.
Experimental Sampling and Data Collection
At the beginning of each sampling week, catheters were inserted into the epigastric mammary
vein and if needed the carotid artery. Eight hourly sample sets of blood were obtained (0730 to
1430), from the mesenteric artery, and the hepatic portal, hepatic, and epigastric vein, resulting in a
set of 32 samples obtained per cow per sampling day. Blood was stabilised in heparin immediately
after sampling and stored on ice. Collected blood was either added to a pooled sample within cow
and period or saved individually. Plasma was harvested after centrifugation at 1,500 g for 10 min,
frozen using dry ice, and stored at -20°C. The plasma samples went through three freeze/thaw cycles prior to analysis. A number of 5 mL aliquots of heparinised plasma to be used for external calibration and quality control were prepared from two litre of venous blood drawn from a Danish Holstein dairy cow. Splanchnic blood plasma flows were determined by downstream dilution of paminohippuric acid continuously infused at a constant rate of 12 g/h (10% v/w) into a mesenteric
vein (Barratt et al., 2013).
106
Analytical Procedures
The concentrations of key purine and pyrimidine metabolites were determined in heparinised
plasma samples pooled within cow and period using a validated HPLC based technique coupled to
electrospray ionisation tandem mass spectrometry (HPLC-ESI-MS/MS) combined with individual
matrix-matched calibration standards and stable isotopically-labelled reference components (SIL) as
described by Stentoft et al. (2014a). Quantification was performed by external calibration applying
standard plasma spiked with a two-fold serial dilution of purine and pyrimidine standard mixtures.
To fit within the actual experimental calibration ranges, five concentration levels were used for calibration. All samples were analysed in duplicate and a standard curve and quality controls were analysed at the beginning and at the end of each sequence. Exploratory data from the analyses is summarised in Table 1.
Calculations and Statistical Procedures
The purine and pyrimidine concentrations were determined from their responses calculated as
the chromatographic peak area. Matrix-matched linear calibration curves (start and end) were obtained by correcting for inherent purine and pyrimidine content and by regressing log(area) against
log(concentration). The resulting linear correlations were used to determine the purine and pyrimidine concentrations (mean). Preceding quantification, purine and pyrimidine responses were normalised employing the following factor: mean SIL area/SIL area for each sample.
Plasma flows were determined according to Katz and Bergman (1969) and calculation of venous-arterial concentration difference and net PDV flux, net hepatic flux, and total splanchnic flux
of metabolites as described by Kristensen et al. (2010) based on the Fick Principle (Zierler, 1961;
Cant et al., 1993). A positive net flux or venous-arterial concentration difference reflects a net release from a given tissue bed to the blood. A negative net flux or venous-arterial concentration difference reflects a net uptake to the tissue bed from blood.
The amount of NuAc N entering the small intestine were estimated from the flow of microbial
CP to the small intestine assuming 20% of total microbial N to be bound in NuAc using the UK
Feed into Milk (FiM) system (McDonald et al., 2002; Thomas, 2004; McDonald et al., 2011). The
share of microbial purine N and pyrimidine N entering the small intestine was assumed to be 2/3
purine N and 1/3 pyrimidine N (5N/purine, 2.5N/pyrimidine) of NuAc N. Total microbial protein,
NuAc N, purine N, and pyrimidine N did not mirror CP levels because the dietary CP level were
achieved to a large extent through the replacement of fibrous co-products with soybean meal protected from rumen degradation. Purine N, pyrimidine N, and NuAc N net PDV, net hepatic, and
total splanchnic fluxes was calculated from the metabolite net flux and the metabolite N content for
each specific metabolite.
107
Data was subjected to ANOVA using the MIXED procedure in SAS (Statistical Analysis System
version 9.1 (TS1M3); SAS Institute Inc., Cary, NC). The model included the fixed effect of square,
period within square, protein, forage, protein × forage interaction, and forage × period within square
interaction and the random effects of animal. Denominator degrees of freedom were estimated using
the Kenward-Roger method. Means ± SEM are presented. In addition, orthogonal polynomial contrasts were used to test for linear and quadratic (Lin and Quad, respectively) effects of dietary CP
level. Paired students t-tests were used to test whether mean venous-arterial concentration differences were different from zero. Due to the limited number of animals in this trial, it is reasonable to
suggest that inherent variability will have occurred. Thus, significance was declared at P ≤ 0.10.
RESULTS
One cow missed one sampling period with the 12.5% CP level on the GS treatment due to illness
and was sampled at a later date than the others. The six cows used were in mid-lactation and their
DMI was 20.5, 22.3, and 22.4 ± 0.7 kg/d on the CS treatment and 19.7, 20.3, and 21.1 ± 0.7 kg/d on
the GS treatment, milk yield was 24.8, 27.7, and 29.7 ± 2.5 kg/d on the CS treatment and 25.2, 27.2,
and 31.1 ± 2.5 kg/d on the GS treatment, and 4% FCM was 25.7, 29.0, and 31.0 ± 1.2 kg/d on the
CS treatment and 26.0, 28.4, and 33.3 ± 1.2 kg/d on the GS treatment, at the 12.5, 15.0, and 17.5%
CP levels, respectively. Increasing CP level linearly increased DMI, milk yield, and 4% FCM (P ≤
0.01) but there was no effect of forage source or forage source by protein interaction detected (Barratt et al., 2013).
Arterial Concentrations
All 20 purine and pyrimidine metabolites were measureable in all four blood vessels except cytosine; which was only detected in arterial plasma (Table 2). The purine DP concentrations were
higher than both the purine NS and BS concentrations and the purine NS concentrations higher than
the purine BS concentrations. The concentrations of the pyrimidine NS were generally higher than
the purine NS, whereas the pyrimidine BS was in the same range as the purine BS. The pyrimidine
NS concentrations were, as for the purine metabolites, higher than for the pyrimidine BS concentrations. The concentrations of the pyrimidine DP were more variable but generally lower than for the
purine DP. Only in the case of the purine NS, large venous-arterial concentration differences, with
the highest levels in the hepatic portal vein, were observed (Table 3).
The arterial concentrations of the purine and pyrimidine NS, BS, and DP were mostly not affected by CP level or forage source (0.13 ≤ P ≤ 0.99; Table 2). The arterial concentrations of inosine
and thymine were unaffected by CP level on the CS treatment, but increased with increasing CP
level on the GS treatment (PPro × For = 0.06 and PPro × For = 0.07, respectively). In the case of 2’deoxyguanosine, concentrations were higher for the GS treatment compared with the CS treatment
108
(PFor = 0.03). In the case of xanthine and 2’-deoxyuridine, the arterial concentration was higher for
the 17.5% CP level compared with the lower CP levels (PLin = 0.02 and PLin = 0.04, respectively).
Both guanine and β-aminoisobutyric acid experienced quadratic effects of CP level, first the arterial
level was high and then low (PQuad = 0.05 and PQuad = 0.07, respectively).
The a priori criteria for calculating net fluxes were that at least one of the venous-arterial concentration differences between the hepatic portal vein and artery (ΔPA), the hepatic vein and artery
(ΔHA), and hepatic portal vein and hepatic vein (ΔPH) differed from zero (P ≤ 0.10). Most of the
purine and pyrimidine metabolites met this criterion for most of the venous-arterial concentration
differences except the BS; hypoxanthine, cytosine, uracil, and thymine (Table 3).
Net Portal-drained Viscera Fluxes
The net PDV fluxes of 16 purine and pyrimidine metabolites (guanosine, inosine, 2’deoxyguanosine, 2’-deoxyinosine, adenine, guanine, xanthine, uric acid, allantoin, cytidine, uridine,
thymidine, 2’-deoxyuridine, β-alanine, β-ureidopropionic acid, and β-aminoisobutyric acid) were all
positive (net release) or close to zero (Table 4). One exception was with allantoin where a single
treatment (17.5% CP, GS treatment) resulted in a net PDV removal. Given that neither the ΔPA of
guanine, hypoxanthine, 2’-deoxuridine, cytosine, uracil, and thymine were different from zero (P ≤
0.10), net PDV releases of these metabolites were not assessed (Table 3).
None of the purine net PDV releases were influenced by forage source, with the exception of adenine where the CS treatment gave rise to a higher net PDV release than the GS treatment (PFor =
0.06). However, the net PDV release of 2’-deoxyguanosine (PLin = 0.07), adenine (PLin < 0.001),
and xanthine (PLin = 0.03) all positively increased with CP level. In the case of allantoin, a noteworthy quadratic effect of CP level was observed (PQuad = 0.09); the net PDV release (mmol/h) increased from the 12.5 to the 15.0% CP level and decreased from the 15.0 to the 17.5% CP level on
both the CS and GS treatments.
None of the pyrimidine net PDV releases were, as for the purine metabolites, influenced by forage source. Although, in the case of net PDV release of cytidine and uridine, positive linear effects
of CP level were detected (PLin = 0.07 and PLin = 0.06). The net PDV release of thymidine was unaffected by CP level on the CS treatment, but decreased with increasing CP level on the GS treatment (PPro × For < 0.01). The CP level and forage source did not affect any of the remaining net PDV
fluxes (0.11 ≤ P ≤ 0.99).
Net Hepatic Fluxes
With the exceptions of the purine and pyrimidine DP (uric acid, allantoin, β-alanine, and βureidopropionic acid), the net hepatic fluxes of the purine and pyrimidine NS and BS were all negative, indicating net uptake from the portal vein and arterial blood (Table 4). Given that neither the
109
ΔPH of adenine, guanine, hypoxanthine, allantoin, cytosine, uracil, and thymine were different from
zero (P ≤ 0.10), net hepatic removal of these metabolites were, with the exception of allantoin, not
assessed (Table 3).
In the case of guanosine and 2’-deoxyguanosine, a quadratic and a linear effect (PQuad = 0.06 and
PLin = 0.09, respectively) of CP level was observed such that more metabolite was removed by the
liver with increasing CP level. The net hepatic removal of 2’-deoxyinosine was unaffected by CP
level on the GS treatment, but increased with the CS treatment (PPro × For = 0.08). The purine DP;
uric acid and allantoin, had net hepatic fluxes (mmol/h) ranging from -0.79 to 0.55 and -11.3 to
1.35, respectively, with an effect of CP level for uric acid only such that less was removed by the
liver with increasing CP level (PLin = 0.09).
The net hepatic removals of the pyrimidine metabolites were, as for the net PDV release, different from that of the purine metabolites. No significant pyrimidine BS net hepatic removals were
measured. None of the pyrimidine net hepatic removals were influenced by forage source, with the
exception of β-ureidopropionic acid, where the net hepatic removal were greater on the CS treatment as compared to the GS treatment (P = 0.08). The net hepatic removal of cytidine were linearly
effected by CP level such that more was removed by the liver with increasing CP level (PLin =
0.07). In the case of β-aminoisobutyric acid, a similar effect was observed, however this effect was
quadratic; first the liver removed less and then more (PQuad < 0.01). The net hepatic removal of βalanine was unaffected by CP level on both the CS and GS treatment at the 12.5 and the 15.0% CP
level but, at the 17.5% CP level, the net hepatic removal was higher, removing less metabolite, on
the GS treatment than on the CS treatment (PPro × For = 0.04). The remaining net hepatic removals
were unaffected by CP level or forage source (0.11 ≤ P ≤ 0.99).
Total Splanchnic Fluxes
With some of the purine and pyrimidine metabolites, the total splanchnic fluxes indicated a net
removal and with some a net release from the splanchnic tissues (Table 4). The ranges of total
splanchnic fluxes (μmol/h) of the purine NS and BS were all close to zero and the ΔHA only differed from zero for allantoin, uridine, and β-ureidopropionic acid (Table 3). The total splanchnic
release of uric acid increased from essentially zero on the 12.5 and 15% CP level, to a total positive
release on the 17.5% CP level (PLin = 0.09). For allantoin, the total splanchnic release increased as
CP level increased from the 12.5% to the 17.0% CP level, with a profile mirroring the net PDV release (PLin = 0.05). None of the purine total splanchnic fluxes were influenced by forage source. A
protein × forage interaction was detected for cytidine, arising from differences in total splanchnic
release especially at the12.5% CP level (PPro × For = 0.09). As for the purine metabolites, the ranges
of total splanchnic fluxes (μmol/h) of the pyrimidine DP were quite different from those of the NS,
110
with the exception of β-aminoisobutyric with a range close to zero. The total splanchnic fluxes
(μmol/h) of the pyrimidine DP; β-alanine (-3378 to 4874) and β-ureidopropionic acid (-61 to 1782),
were high and varying compared with the rest of the pyrimidine metabolites but similar in numerical range to that of uric acid. In the case of β-alanine, at the 17.5% CP level, the GS treatment resulted in a total splanchnic release (4874) compared to a total splanchnic removal (-3378) on the CS
treatment (PPro × For = 0.04). The remaining total splanchnic fluxes were unaffected by CP level or
forage source (0.14 ≤ P ≤ 0.99).
Epigastric Venous-Arterial Concentration Differences
The net removal or release of purine and pyrimidine metabolites across the mammary gland was
assessed by epigastric venous-arterial concentration differences (ΔEA) as epigastric plasma flow
was not available. The ΔEA only differed from zero (P ≤ 0.1) for guanosine, inosine, 2’deoxyinosine, guanine, 2’-deoxyuridine, and uridine (Table 3). The ΔEA of guanosine, inosine, and
uridine were positive indicating a net release from the mammary gland, whereas the ΔEA of 2’deoxyinosine, guanine, and 2’-deoxyuridine were close to zero or negative indicating a net removal
(Table 5). None of the ΔEA were influenced by CP level but the uridine ΔEA was greater on the CS
treatment (PFor < 0.01 and). The ΔEA of guanosine and 2’-deoxyinosine, was greater at the 12.5%
CP level on the CS treatment than on the GS treatment, whereas the ΔEA was lower at the 17.5%
CP level on the CS treatment than on the GS treatment (PPro × For = 0.06 and PPro × For < 0.01, respectively). The relatively large SEM made the ΔEA of 2’-deoxyuridine inconsistent. The remaining
ΔEA were unaffected by CP level or forage source (0.11 ≤ P ≤ 0.96).
Purine and Pyrimidine Nitrogen
Since the calculations of N fluxes was simply added the N dimension, the differences in purine
N, pyrimidine N, and NuAc N fluxes mirrored the net PDV, net hepatic, and total splanchnic fluxes
(Table 6). The net PDV and total splanchnic releases of purine N were affected by CP level in a
quadratic manner (PQuad = 0.04 and PQuad = 0.02, respectively). No effects of CP level were measured in the pyrimidine N fluxes. One exception was with β-aminoisobutyric acid, where a quadratic
effect of CP level was detected in the net hepatic N removal (PQuad < 0.01, data not shown). The
total NuAc N net PDV and total splanchnic releases were affected by CP level in the same manner
as the purine N releases.
The microbial NuAc N entering the small intestine was estimated to 63, 65, and 64 g/d N on the
CS treatment and 60, 58, and 58 g/d N on the GS treatment at the 12.5, 15.0, and 17.5% CP levels,
respectively. Microbial purine N and pyrimidine N entering the small intestine was estimated to
42.0/21.0, 43.3/21.7, and 42.7/21.3 g/d purine N/pyrimidine N on the CS treatment and 40.0/20.0,
38.7/19.3, and 38.7/19.3 g/d purine N/pyrimidine N on the GS treatment. The purine N net PDV
111
release was equal to 21, 174, and 79% on the CS treatment and 11, 144, and 48% on the GS treatment of purine N assumed being absorbed from the small intestine. For the pyrimidine N, the net
PDV release corresponded to 33, 28, and 27% on the CS treatment and 62, 34, and 29% on the GS
treatment.
DISCUSSION
The effect of CP level and forage source on net PDV and hepatic metabolism as well as ΔEA,
of the 20 main purine (Fig. 1) and pyrimidine (Fig. 2) metabolites, were investigated applying a
novel LC-ESI-MS/MS technique for quantifying purine and pyrimidine metabolites in bovine blood
plasma (Stentoft et al., 2014a). In addition, the absorption effectiveness and fate of the purine N,
pyrimidine N, and total NuAc N was revised. The purine and pyrimidine metabolites were found to
be metabolised and affected differently; thus, they will be discussed as two distinct groups.
Arterial Levels of Purine and Pyrimidine Metabolites
The quantitative method for purine and pyrimidine plasma concentration determination has a
broad application at low concentration with excellent recoveries (Stentoft et al., 2014a). However,
the large range of metabolites covered by the method resulted in less precise quantifications near
the low end of the quantification ranges. This might be the reason for some of the variation in data,
especially with regard to allantoin (Table 1-5). Relatively large allantoin SEM was also reported in
a recent study published by this group (Stentoft et al., 2014b). Limitations of the quantitative LCESI-MS/MS method meant that not all of the possible purine and pyrimidine metabolites were investigated (Fig 1 and Fig. 2) (Stentoft et al., 2014b). In line with observations demonstrating a very
effective degradation of NuAc to BS and NS in the small intestine (pre-absorption), no purine or
pyrimidine nucleotides (NT) were detected during method development. The microbial purine and
pyrimidine NT was most likely degraded rapidly in the small intestine before entering the intestinal
mucosa and endogenous NT was probably degraded before and/or in the blood (McAllan, 1980;
McAllan, 1982; McAllan and Smith, 1973). If comparing the arterial concentration levels of the
purine metabolites detected in this study with ones described previously; slightly higher or unchanged levels of purine NS and BS were detected (Stentoft et al., 2014b). In case of the purine DP,
lower concentrations of uric acid and higher concentration of allantoin were reported previously
(Balcells et al., 1992; Stentoft et al., 2014b). The differences in levels between studies could have
been caused by different sample handling (Stentoft et al., 2014b). In this study, the samples went
through three freeze/thaw cycles prior to analysis, whereas samples were frozen after collection and
then only thawed once for analysis in the former study. Thus, enzymatic catalysed purine degradation of uric acid to allantoin in the bovine plasma samples during sample handling cannot be excluded. Urate factor-independent hydroxylase [1.7.3.3] (uricase) and/or hydroxyisourate hydrolase
112
[3.5.2.17] catalyse this reaction, but degradation can also happen spontaneously (Kanehisa et al.,
2014; Kyoto Encyclopedia of Genes and Genomes. Purine metabolism. Accessed Oct. 1, 2014). In
contradiction to this theory is that uricase [1.7.3.3] has only been detected in trace amounts in bovine blood (Chen et al., 1990). The differences in uric acid and allantoin levels between studies
could also be caused by differences in activity of degradation enzymes in the small intestine and/or
in the intestinal mucosa. These enzymes are thought to be very active and it has been proposed that
most of the purine metabolites are fully degraded to uric acid and/or allantoin before released into
the hepatic portal vein (Chen et al., 1990, Verbic et al., 1990). Whatever the reason for the differences in levels, the summed concentration of uric acid and allantoin was quite stable and only the
ratio between them changed (Balcells et al., 1992; Stentoft et al., 2014b). This suggests that estimation of purine DP concentrations in bovine plasma should be based on the sum of uric acid and allantoin.
The arterial concentrations of the pyrimidine metabolites detected in this study were similar to or
slightly higher than the ones observed previously (Stentoft et al., 2014b). The greater ability of the
pyrimidine metabolites to withstand degradation corresponded with previous findings suggesting
that the pyrimidine metabolites to a large extent are absorbed intact; as NS and BS, and that the
pyrimidine metabolism differs from the purine metabolism (Loffler et al., 2005; Stentoft et al.,
2014b). The arterial concentrations of the purine and pyrimidine metabolites were with a few exceptions not affected by either CP level or forage source. The small effects that were detected were
assumed to be the result of influences of the diet on nutrient flows and other metabolic processes.
Splanchnic Metabolism of Purines
When studying how the CP level and forage source affected the net PDV, net hepatic, and total
splanchnic flux of the purine NS, BS, and DP, the fluxes should in theory reflect the level of microbial flow to the small intestine as a consequence of different degrees of microbial biosynthesis with
different CP levels (Nocek and Russell, 1988; Clark et al., 1992; Reynolds et al., 2001; Ipharraguerre and Clark, 2005). However, in the present study, differences in dietary CP level were
achieved to a large extent through the replacement of fibrous co-products with soybean meal protected from rumen degradation, which was expected to achieve differences in MP flow to the small
intestine through less effect on microbial protein flow from the rumen than observed in other studies.
As previously observed (Stentoft et al., 2014b), the net hepatic removal was essentially equivalent to the net PDV release of purine NS and BS resulting in around zero total splanchnic release in
the current study (Table 4). As regards uric acid and allantoin, an overall total splanchnic release
again demonstrated the degradation of purine NS and BS to purine DP in the PDV and hepatic tis113
sue. Further, the level of net PDV and net hepatic fluxes of purine metabolites were similar, or
slightly higher, to that previously observed in lactating dairy cows (Stentoft et al., 2014b).
The net PDV releases of 2’-deoxyguanosine, adenine, and xanthine were linearly and positively
affected by CP levels as hypothesised. This effect was not observed for the remaining purine NS
and BS, most likely due to the very small net PDV release of these metabolites. The low net PDV
releases of the purine BS compared to NS, suggested a more extensive degradation of BS than of
NS in the small intestine and the intestinal mucosa. An effect of CP level was surprisingly not observed for uric acid either, even though a larger amount of uric acid was released from the PDV.
Allantoin was the main purine DP being absorbed from the small intestine. The large net PDV release of uric acid and allantoin, compared to purine NS and BS, agreed with previous findings
demonstrating high activity of xanthine oxidase [1.17.3.2] in the intestinal mucosa and the blood in
cattle (Chen, et al.,1990; Verbic et al., 1990; Balcells et al., 1992). This enzyme along with other
degradation enzymes, degrade purine BS and NS to their purine DP (Fig. 1). A large net PDV release was observed for allantoin. On the CS treatment, as dietary CP level increased from 12.5% to
15.0%, the net PDV release increased correspondingly. However, this effect was quadratic, and the
net release increased with the 17.5% CP level (Table 4). A similar pattern was observed with the
GS treatment however, the levels were lower than those observed with the CS treatment. The decline between the 15.0% and the 17.5% CP level was most likely caused by a decrease in the microbial flow with the greater amount of rumen protected protein fed with the 17.5% CP treatment,
which is in contrast to studies where unprotected proteins are fed (Ipharraguerre and Clark, 2005).
An impairment of the degradation of the microbial NuAc in the small intestine at high CP levels or
some other effect on the absorption mechanism on the high CP level could also partly be the reason
for the decline.
As regards the influence of CP level on the net hepatic removal of the purine NS, BS, and DP,
the endogenous synthesis of metabolites in the liver may conceal effects otherwise detected at the
level of absorption. Nonetheless, the incremental effect of CP level on net PDV release of 2’deoxyguanosine was also observed for the net hepatic removal of this purine NS. The very low net
hepatic removal of the purine NS and BS was likely a consequence of the in most cases almost
complete degradation across the PDV on a net basis. Consequently, no effects of CP level were detected for any of the remaining purine NS or BS. In contrast to net PDV release of uric acid, which
was not affected by CP level, a linear effect was observed for the net hepatic removal such that less
was removed on a net basis with increasing CP level. As expected, no effect of CP level was observed in the net hepatic removal of allantoin and for most treatments there was little net hepatic
metabolism of allantoin appearing in the portal vein (Table 3).
114
Net total splanchnic releases of the purine DP; uric acid and allantoin, linearly increased with increasing CP levels and for most treatments the total splanchnic release suggests that there was degradation of purine NS and BS to purine DP in the PDV and hepatic tissue. The remaining purine
metabolites were, owing to their small total splanchnic fluxes, as expected not affected by CP level,
which reflects the sum of net PDV and net hepatic flux and the fact that net PDV release was essentially matched by net hepatic removal. The total splanchnic release of uric acid mirrored the net
hepatic release; resulting in a total splanchnic release of uric acid increasing with the CP level, in
particular when dietary CP level increased from the 15.0% to the 17.5% CP level. The total
splanchnic release of allantoin mirrored the net PDV release in terms of the pattern of the numerical
changes observed, although the protein effect in this case was linear instead of quadratic.
None of the net PDV, net hepatic, or total splanchnic fluxes of purine metabolites were influenced by forage source. However, diet composition was adjusted to minimize differences in the
total concentrations of starch, water soluble carbohydrates, or NDF across treatments. This meant
that effects of subtle changes in carbohydrate concentrations, forage source (grass vs corn silage),
and rate of degradation on the rumen outflow of purine metabolites were not detectable at the level
of their total splanchnic metabolism (Nocek and Russell, 1988; Clark et al., 1992; Reynolds et al.,
2001).
Splanchnic Metabolism of Pyrimidines
In contrast to the net PDV release of pyrimidine NS and DP, there was no net PDV release of the
pyrimidine BS detected in this study (Table 4). Net hepatic removal of the pyrimidine NS and DP
was in most cases nearly equivalent to their net PDV release, as reported previously, resulting in
there being little total splanchnic release (Stentoft et al., 2014b). These results suggest that in general terms the mechanisms of purine and pyrimidine metabolism in the splanchnic tissues differ
such that there is a net release of purine metabolites but that pyrimidine metabolites absorbed are
largely metabolized within the splanchnic tissues. The pyrimidine metabolites have an outlet into
other parts of the N metabolism; β-alanine can be recycled into the β-alanine metabolism and βaminoisobutyric acid into the valine, leucine, and isoleucine metabolism and citric acid cycle
(Loffler et al., 2005; Kanehisa et al., 2014; Kyoto Encyclopedia of Genes and Genomes. Pyrimidine
metabolism, Beta-alanine metabolism, and Valine, leucine and isoleucine degradation. Accessed
Oct. 1, 2014). A further degradation to ammonia and other nitrogenous degradation products such
as urea is also possible. The level of net PDV and net hepatic fluxes of pyrimidine metabolites were
similar, or slightly higher, than previously observed in lactating dairy cows (Stentoft et al., 2014b).
In general the net PDV release of pyrimidine NS was greater than observed for the purine NS as
a result of there being less degradation of pyrimidine metabolites in the small intestine (Table 4).
115
The net PDV release of cytidine and uridine were linearly increased by increasing CP level. This
effect was not observed for the pyrimidine NS; thymidine and 2’-deoxyuridine, most likely due to
the lower levels and relatively high variability resulting in part from the relatively low precision of
the LC-ESI-MS/MS method for these two metabolites (Stentoft et al., 2014a). The net PDV release
of the pyrimidine DP; β-alanine and β-ureidopropionic acid, were comparable with the net PDV
release of pyrimidine NS but lower than that of the purine DP. β-aminoisobutyric acid had the lowest net PDV release of all of the pyrimidine metabolites. As was the case for uric acid, even though
considerable amounts of pyrimidine DP and especially β-alanine and β-ureidopropionic acid were
released from the PDV, there was no effect of CP level. β-ureidopropionic acid is the precursor of
β-alanine and from the high rate of net PDV release, it seems that this metabolite functions as a pyrimidine DP alongside with β-alanine and not an easily degradable DP intermediate. Some of the
released pyrimidine metabolites may also be of endogenous origin within the PDV tissues (Chen
and Gomes, 1992; Chen and Ørskov, 2004; Fujihara and Shem, 2011).
The effect of dietary CP level on the net PDV release of cytidine was also observed for the net
hepatic removal of cytidine, but this was not the case for the net hepatic removal of uridine. In addition, a quadratic effect of CP level was observed for the net hepatic removal of β-aminoisobutyric
acid. These results are consistent with the theory that the pyrimidine DP can function as intermediates in other pathways of N metabolism and the large net hepatic removal of the pyrimidine NS and
DP demonstrated an extensive and in most cases almost complete hepatic metabolism of the pyrimidine metabolites released into the portal vein on a net basis (Loffler et al., 2005; Kanehisa et al.,
2014; Kyoto Encyclopedia of Genes and Genomes. Pyrimidine metabolism, Beta-alanine metabolism, and Valine, leucine and isoleucine degradation. Accessed Oct. 1, 2014).
As a consequence of the extensive hepatic removal, which largely mirrored rates of net PDV release, the total splanchnic fluxes of the pyrimidine NS and DP were essentially zero and not affected by CP levels. Only a small net release of cytidine and a small net removal of uridine were detected.
None of the pyrimidine net PDV, net hepatic, or total splanchnic fluxes were, as for the purine
metabolites, found to be influenced by forage source. One exception was with β-ureidopropionic
acid, where the higher CS treatments gave rise to a higher net hepatic removal than on the GS
treatment.
Metabolism of Purine and Pyrimidine Metabolites in the Mammary Gland
Former studies have shown that uric acid and allantoin in milk correlate with their plasma and urine
concentrations as well as feed composition (Giesecke et al., 1994; Gonda and Lindberg, 1997).
However, in the present study ΔEA of both uric acid and allantoin did not differ from zero (Table
116
5). This suggests that the rate of transfer from arterial blood to the mammary tissues and milk may
be too small to be measured based on venous-arterial concentration differences. Small amounts of
guanosine, inosine, and uridine, were all shown to be released and small amounts of 2’deoxyinosine and guanine taken up from/by the mammary gland. Only in the case of uridine, an
effect of forage source was detected and none of the ΔEA was affected by CP levels. By estimating
net mammary releases across treatments for inosine (130-270 μmol/h) and uridine (1350-1900
μmol/h), assuming a mammary plasma flow of approximately 450 L plasma/kg milk (Larsen et al.,
2014), it became evident that, compared to the total splanchnic release, net mammary release of NS
is considerable. This indicates a net inter-organ transfer of inosine and uridine from the mammary
gland to the liver in addition to the PDV and that the mammary gland and the PDV are the two main
origins of the inosine and uridine removed by the liver. This is in agreement with the observation
that in lactating dairy cow, the rate of cell proliferation is exceeded by the rate of cell apoptosis
leading to a gradual decrease in the total number of epithelial cells in the udder with advancing lactation (Capuco et al., 2001; Sørensen et al., 2006).
Absorption and Fate of Purine, Pyrimidine, and Nucleic acid Nitrogen
Focusing on the contribution to the overall N metabolism, treatment effects on the purine N net
fluxes (Table 6) largely reflected the effects observed for uric acid and allantoin (Table 4). This
suggests that as a consequence of metabolic interconversions within the splanchnic tissues, considerable amounts of purine N was lost to the dairy cow and released as uric acid and allantoin to the
circulating blood pool that can be excreted in urine, and the magnitude of the loss varies with diet
composition and microbial protein flow to the small intestine. When performing the calculations of
net N flux within purine BS and NS as groups, the small effects of treatments observed previously
were not significant and there was little total splanchnic release of N (data not shown). Very different types of intermediary mechanisms seem to be in function for the pyrimidine metabolites. However, the pyrimidine degradation pathways and especially the absorption level also appeared to be
influenced by CP level (Table 6). The pyrimidine N fluxes showed that the pyrimidine N to a much
greater extent than the purine N was removed by the hepatic tissue. However, the pyrimidine N
fluxes did not as clearly as the purine N fluxes display effects of CP levels. As for the purine metabolites, the pyrimidine metabolites were degraded before and in the liver but, because β-alanine,
β-ureidopropionic acid, and β-aminoisobutyric acid can function as intermediates in other parts of
the N metabolism, the pyrimidine N is not likely to be released into the circulating blood pool to the
same extent as the purine N. Since uric acid and allantoin was the main contributors to NuAc N, the
total splanchnic release and treatment effects of NuAc N generally mirrored those of purine N.
117
Using ration formulation software to predict flow of microbial purine N and pyrimidine N to the
small intestine based on measured DMI, it was estimated that across treatments, approximately 80%
of the purine N and approximately 35% pyrimidine N entering the small intestine was released from
the PDV on a net basis. Considering that the digestibility of DNA/RNA is only about 80% (McAllan, 1980), the net PDV release rate is greater than would be expected (Stentoft et al., 2014b), but
considering the potential errors of measurement, the comparison suggests that the total release rates
measured across the PDV are biologically plausible (Table 4).
Comparing the total splanchnic release or removal of NuAc N (Table 6) with the overall N intake (Barratt et al., 2013), the N release or removal corresponded to -0.8, 14, and 4% on the CS
treatment and 1, 7, and -0.4% on the GS treatment. This suggests that at 15% CP, approximately
11% of the dairy cow N intake is released from the total splanchnic tissues as NuAc N compared to
approximately 0% at 12.5% CP, regardless of forage source. If taking into consideration the milk
efficiency and regarding the N not used for milk production as a loss to the dairy cow milk production, approximately 15% of N loss was in the form of NuAc N.
CONCLUSIONS
The present study reports net PDV, net hepatic, and total splanchnic fluxes of purine and pyrimidine
NS, BS, and DP. Significant splanchnic venous-arterial differences were measured using the LCESI-MS/MS method in the current study. Net PDV and net hepatic fluxes were found to be affected
by dietary protein levels and in general net PDV release of nucleic acid metabolites to the portal
vein reflected predicted flows to the small intestine. Positive effects of dietary protein level were
observed for net PDV release of uric acid, allantoin, cytidine, and uridine in particular. Net removal
of nucleic acid metabolites tended to be smaller and more variable for the liver, perhaps due to endogenous contribution to hepatic metabolism. This was particularly true with the NS metabolites.
For most of the BS their net PDV release was low, suggesting they were fully degraded during digestion and absorption. None of the net PDV, net hepatic, or total splanchnic fluxes of purine or
pyrimidine metabolites were found to be influenced by forage source, presumably due to the effects
of adjustments made to the amounts and types of concentrates fed that reduced potential affects on
microbial synthesis of nucleic acids. Considerable amounts of purine N, in the form of uric acid and
allantoin, was released by the total splanchnic tissues on a net basis and presumably lost to anabolic
processes, and the amount released was affected by dietary protein level. There was less effect of
dietary protein level on the total splanchnic release of the pyrimidine N, which we suggest was used
in other anabolic pathways of N metabolism within the splanchnic tissues. At a dietary protein level
of 15% of DM, approx. 11% of the dairy cow N intake was shown to be released from the total
splanchnic tissues as NuAc N and approx. 15% of the total N loss was in the form of NuAc N.
118
ACKNOWLEDGEMENTS
We thankfully acknowledge Lis Sidelmann and Birgit H. Løth at the Department of Animal Science, Faculty of Science and Technology, Aarhus University (Denmark), for skillful and dedicated
technical assistance. C. Stentoft holds a PhD Scholarship co-financed by the Faculty of Science and
Technology, Aarhus University (Denmark) and a research project supported by the Danish Milk
Levy Fond, c/o Food and Agriculture (Aarhus, Denmark). The contributions of technicians and
staff at the Centre for Dairy Research of the University of Reading for the care and management of
animals used and for technical assistance during the study is also gratefully acknowledged.
119
REFERENCES
Balcells, J., D. S. Parker, C. J. Seal. 1992. Purine metabolite concentrations in portal and
pheripheral blood of steers, sheep and rats. Comp. Biochem. Physiol. 101:633-363.
Barratt, C., L. Crompton, C. Green, D. Humphries, R. Pilgrim, and C. K. Reynolds. 2013. Effect of
dietary protein concentration and forage type on nitrogen metabolism and nutrient flux across the
portal drained viscera and the liver in lactating dairy cows. Pages 407-408 in Energy and protein
metabolism and nutrition in sustainable animal production. J.W. Oltjen, E. Kebreab and H.
Lapierre. EAAP publication No. 134. Wageningen Academic Publishers, Wageningen, NL.
Calsamiglia, S., A. Ferret, C. K. Reynolds, N. B. Kristensen, and A. M. van Vuuren. 2010.
Strategies for optimizing nitrogen use by ruminants. Animal 4:1184-1196.
Cant, J. P., E. J. DePeters, and R. L. Baldwin. 1993. Mammary amino acid utilization in dairy cows
fed fat and its relationship to milk protein depression. J. Dairy Sci. 76:762-774.
Capuco A. V., D. L. Wood, R. Baldwin, K. Mcleod, and M. J. Paape. 2001. Mammary cell number,
proliferation, and apoptosis during a bovine lactation: relation to milk production and effect of
bST1. J. Dairy Sci. 84:2177–2187.
Chen, X. and E. Ørskov. 2004. Research on urinary excretion of purine derivatives in ruminants:
past, present and future. Pages 180-210 in Estimation of microbial protein supply in ruminants
using urinary purine derivatives. H.P.S. Makkar and X.B. Chen. FAO/IAEA, Kluwer Academic
Publishers, Dordrecht, NL.
Chen, X. B. and M. J. Gomes. 1992. Estimation of microbial protein supply to sheep and cattle
based on urinary excretion of purine derivatives - an overview of the technical details. Occasional
Publication of International Feed Resources Unit, Rowett Research Institute, Bucksburn, Aberdeen
AB2 9SB, UK.
Chen, X. B., E. R. Ørskov, and F. D. D. Hovell. 1990. Excretion of purine derivatives by ruminants
– endogeneous excretion, differences between cattle and sheep. Br. J. Nutr. 63:121-129.
Clark, J. H., T. H. Klusmeyer, and M. R. Cameron. 1992. Microbial protein synthesis and flows of
nitrogen fractions to the duodenum of dairy cows. J. Dairy Sci. 75:2304-2323.
Firkins, J. L. 1996. Maximizing microbial protein synthesis in the rumen. J. Nutr. 126:1347-1354.
Fujihara, T. and M. N. Shem. 2011. Metabolism of microbial nitrogen in ruminants with special
reference to nucleic acids. Anim. Sci. J. 82:198-208.
120
Giesecke, D., L. Ehrentreich, M. Stangassinger, and F. Ahrens. 1994. Mammary and renal excretion
of purine metabolites in relation to energy intake and milk yield in dairy cows. J. Dairy Sci.
77:2376-2381.
Gonda, H. L. and J. E. Lindberg. 1997. Effect of diet on milk allantoin and its relationship with
urinary allantoin in dairy cows. J. Dairy Sci. 80:364-373.
Gonzalez-Ronquillo, M., J. Balcells, A. Belenguer, C. Castrillo, and M. Mota. 2004. A comparison
of purine derivatives excretion with conventional methods as indices of microbial yield in dairy
cows. J. Dairy Sci. 87:2211-2221.
Huntington, G. B., C. K. Reynolds, and B. H. Stroud. 1989. Techniques for measuring blood-flow
in splanchnic tissues of cattle. J. Dairy Sci. 72:1583-1595.
Ipharraguerre, I. R. and J. H. Clark. 2005. Impacts of the source and amount of crude protein on the
intestinal supply of nitrogen fractions and performance of dairy cows. J. Dairy Sci. 88:22-37.
Kanehisa, M., S. Goto, Y. Sato, M. Kawashima, M. Furumichi, and M. Tanabe. 2014. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic acids res. 42(Database
issue):D199-205
Katz, M. L. and E. N. Bergman. 1969. Simultaneous measurements of hepatic and portal venous
blood flow in the sheep and dog. Am. J. Physiol. 216:946-952.
Kyoto Encyclopedia of Genes and Genomes. Beta-alanine metabolism. Accessed Oct. 1, 2014.
http://www.genome.jp/kegg/pathway/map/map00410.html.
Kyoto Encyclopedia of Genes and Genomes. Purine metabolism. Accessed Oct. 1, 2014.
http://www.genome.jp/kegg/pathway/map/map00240.html.
Kyoto Encyclopedia of Genes and Genomes. Pyrimidine metabolism. Accessed Oct. 1, 2014.
http://www.genome.jp/kegg/pathway/map/map00230.html.
Kyoto Encyclopedia of Genes and Genomes. Valine, leucine and isoleucine degradation. Accessed
Oct. 1, 2014. http://www.genome.jp/kegg-bin/show_pathway?map00280.
Kohn, R. A., M. M. Dinneen, and E. Russek-Cohen. 2005. Using blood urea nitrogen to predict
nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats.
J. Anim. Sci. 83:879-889.
Kristensen, N. B., A. C. Storm, and M. Larsen. 2010. Effect of dietary nitrogen content and
intravenous urea infusion on ruminal and portal-drained visceral extraction of arterial urea in
lactating Holstein cows. J. Dairy Sci. 93:2670-2683.
121
Larsen, M., H. Lapierre, N. B. Kristensen. 2014. Abomasal protein infusion in postpartum transition
dairy cows: Effect on performance and mammary metabolism. J. Dairy Sci. 97:5608-5622.
Loffler, M., L. D. Fairbanks, E. Zameitat, A. M. Marinaki, and H. A. Simmonds. 2005. Pyrimidine
pathways in health and disease. Trends Mol. Med. 11:430-437.
McAllan, A. B. 1980. The degradation of nucleic acids in, and the removal of breakdown products
from the small intestine of steers. Br. J. Nutr. 44:99-112.
McAllan, A. B. 1982. The fate of nucleic acids in ruminants. Proc. Nutr. Soc. 41:309-317.
McAllan, A. B., R. H. Smith, and 1973. Degradation of nucleic acid derivatives by rumen bacteria
in vitro. Br. J. Nutr. 29:467-474.
McDonald, P., R. A. Edwards, J. F. D. Greenhalgh, and C. A. Morgan. 2002. Animal Nutrition. 6th
ed. Pearson Education Limited, Essex, UK.
McDonald, P., R. A. Edwards, J. F. D. Greenhalgh, C. A. Morgan, L. A. Sinclair, and R. G.
Wilkinson. 2011. Animal Nutrition. 7th ed. Pearson Education Limited, Essex, UK.
Nocek, J. E. and J. B. Russell. 1988. Protein and energy as an integrated system. Relationship of
ruminal protein and carbohydrate availability to microbial synthesis and milk Production. J. Dairy
Sci. 71:2070-2107.
Reynolds, C. K., S. B. Cammell, D. J. Humphries, D. E. Beever, J. D. Sutton, and J. R. Newbold.
2001. Effects of postrumen starch infusion on milk production and energy metabolism in dairy
cows. J. Dairy Sci. 84:2250-2259.
Smith, R. H. and A. B. McAllan. 1974. Some factors influencing the chemical composition of
mixed rumen bacteria. Br. J. Nutr. 31:2734.
Steinfeld, H., P. Gerber, T. Wassenaar, V. Castel, M. Rosales, and C. de Haan. 2006. Livestock’s
long shadow: Environmental issues and options. Accessed Oct. 1, 2014.
http://www.fao.org/docrep/010/a0701e/a0701e00.HTM.
Stentoft, C., M. Vestergaard, P. Lovendahl, N. B. Kristensen, J. M. Moorby, and S. K. Jensen.
2014a. Simultaneous quantification of purine and pyrimidine bases, nucleosides and their
degradation products in bovine blood plasma by high performance liquid chromatography tandem
mass spectrometry. J. Chromatogr. A. 1356:197-210.
Stentoft, C., B. A. Røjen, S. K. Jensen, N. B. Kristensen, M. Vestergaard, and M. Larsen. 2014b.
Absorption and intermediary metabolism of purines and pyrimidines in lactating dairy cows. Br. J.
Nutr. (In Press)
122
Sørensen, M. T., J. V. Nørgaard, P. K. Theil, M. Vestergaard, and K. Sejrsen. 2006. Cell turnover
and activity in mammary tissue during lactation and the dry period in dairy cows. J. Dairy Sci.
89:4632–4639.
Tamminga, S. 1992. Nutrition management of dairy cows as a contribution to pollution control. J.
Dairy Sci. 75:345-357.
Tas, B. M. and A. Susenbeth. 2007. Urinary purine derivates excretion as an indicator of in vivo
microbial N flow in cattle: A review. Livest. Sci. 111:181-192.
Thomas, C. 2004. Feed into milk: An advisory manual. Nottingham University Press, Nottingham,
UK.
Verbic, J., X. B. Chen, N. A. Macleod, and E. R. Ørskov. 1990. Excretion of purine derivatives by
ruminants – effect of microbial nucleic acid infusion on purine derivative excretion by steers. J.
Agric. Sci. 114:243-248.
Zierler, K. L. 1961. Theory of the use of arteriovenous concentration differences for measuring
metabolism in steady and non-steady states. J. Clin. Invest. 40:2111-2125.
123
Table 1. Metabolite type, concentration range (μmol/L) and across day variation (CV%) of purine and pyrimidine metabolites
Metabolite
Purines
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Adenine
Guanine
Hypoxanthine
Xanthine
Uric acid
Allantoin
Pyrimidines
Cytidine
Uridine
Thymidine
2’-deoxyuridine
Cytosine
Uracil
Thymine
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
Type
1
Range2
Min
Max
Levels3
Low
High
Across-day
variation (CV%)3
NS
NS
NS
NS
BS
BS
BS/DP
BS/DP
DP
DP
0.16
0.08
0.08
0.16
0.08
0.08
0.08
0.16
6.25
37.5
5.0
2.5
2.5
5.0
2.5
2.5
2.5
5.0
200
1200
1.0
0.5
0.5
1.0
0.5
0.5
0.5
1.0
10
60
4
2
2
4
2
2
2
4
180
1000
4
6
6
4
5
6
8
7
5
23
NS
NS
NS
NS
BS
BS
BS
DP
DP
DP
2.50
1.25
2.50
2.50
1.25
0.31
0.63
7.20
2.50
0.16
80
40
80
80
40
10
20
230
80
5.0
5.0
2.0
5.0
5.0
5.0
1.0
1.0
15
5.0
1.0
60
30
60
60
30
6
10
200
60
4
6
11
9
52
9
7
20
22
11
1
BS, base; NS, nucleoside; DP, degradation product.
min, minimum concentration; max, maximum concentration. External calibration was performed with six concentrations and points were excluded to fit the concentration range in actual samples.
3
low, lowest concentration level; high, highest concentration level. Two concentration levels were used for determining
across-day variation expressed as coefficient of variation (CV%).
2
124
Table 2. Arterial variables (μmol/L)1
Metabolite
Purines
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Adenine
Guanine
Hypoxanthine4
Xanthine
Uric acid
Allantoin
Pyrimidines
Cytidine
Uridine
Thymidine
2’-deoxyuridine
Cytosine4
Uracil4
Thymine4
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
12.5
Corn
15.0
17.5
12.5
Grass
15.0
17.5
SEM
2
Pro
For
P-values3
Pro × For
Lin
Quad
0.03
0.13
0.003
0.01
0.15
0.01
0
0.01
5.60
201
0.05
0.25
0.01
0.005
0.16
0.002
0.02
0.01
5.57
163
0.04
0.20
0.02
0.02
0.15
0
0
0.03
5.50
162
0.06
0.12
0.02
0.01
0.17
0.02
0
0.01
6.24
155
0.10
0.51
0.04
0.01
0.16
0.004
0
0.01
6.36
146
0.14
0.72
0.03
0.02
0.15
0.003
0
0.04
6.20
147
0.02
0.12
0.01
0.006
0.009
0.005
0.02
0.007
0.63
27.8
0.24
0.07
0.71
0.20
0.27
0.15
0.42
0.02
0.85
0.29
0.18
0.10
0.03
0.86
0.41
0.52
0.32
0.95
0.43
0.55
0.21
0.06
0.92
0.48
0.38
0.99
0.45
0.82
0.97
0.42
0.36
0.59
0.93
0.11
0.29
0.80
0.27
0.02
0.60
0.99
0.18
<0.01
0.46
0.78
0.18
0.05
0.57
0.11
0.87
0.17
2.56
3.16
0.65
6.85
0
0
0.03
10.9
4.87
0.24
2.83
3.76
0.65
7.74
0
0.19
0
9.62
5.53
0.23
2.79
3.97
0.73
9.61
0
0
0.06
11.5
4.65
0.24
2.25
3.37
0.59
7.46
0.03
0
0.01
10.4
5.24
0.20
2.75
3.24
0.81
6.30
0
0
0.02
11.8
4.11
0.22
2.46
3.14
0.89
10.3
0
0
0
10.4
3.89
0.24
0.45
0.47
0.25
2.45
0.03
0.19
0.01
1.43
1.11
0.03
0.37
0.57
0.62
0.13
0.32
0.42
0.35
0.90
0.51
0.17
0.79
0.30
0.49
0.74
0.25
0.32
0.16
0.87
0.48
0.65
0.53
0.18
0.74
0.66
0.19
0.45
0.07
0.24
0.42
0.13
0.77
0.40
0.50
0.04
0.99
0.27
0.15
0.78
0.33
0.24
0.13
0.96
0.44
0.78
0.07
0.57
0.94
0.62
0.42
0.07
1
Cows were feed a TMR containing grass:corn silage (25:75 or 75:25) with 12.5%, 15.0%, or 17.5% CP of DM.
mean ± SEM (pooled) (n = 6).
3
P-values for protein (Pro) describe the effect of feeding different CP levels. P-values for forage (For) describe the effect of feeding mainly corn or grass silage. P-values for protein×forage (Pro × For) describes any interaction between CP level and either corn or grass silage. P-values for linear (Lin) and quadratic (Quad) effects describe the effect of
dietary CP. Significance declared when P ≤ 0.1 (F-test).
4
All metabolites except hypoxanthine, cytosine, uracil, and thymine had one or more ΔPA, ΔHA, ΔPH or ΔMA values that differed from zero (P ≤ 0.10).
2
125
Table 3. Venous-arterial concentration differences (µmol/L) between each of four blood veins and an artery of purine and pyrimidine metabolites in lactating dairy cows 1
Metabolite
Purines, µmol/L
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Adenine
Guanine
Hypoxanthine5
Xanthine
Uric acid
Allantoin
Pyrimidines, µmol/L
Cytidine
Uridine
Thymidine
2’-deoxyuridine
Cytosine5
Uracil5
Thymine5
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
Mean
ΔPA2
SEM3
0.38
0.78
0.21
0.26
0.006
0.008
-0.003
0.01
0.40
15.5
0.11
0.21
0.05
0.05
0.002
0.006
0.003
0.006
0.15
4.99
0.01
<0.01
<0.01
<0.01
<0.01
0.21
0.40
0.05
0.04
<0.01
-0.008
-0.03
0.01
0.002
0.001
-0.003
0.009
-0.002
0.19
12.0
0.009
0.03
0.008
0.004
0.004
0.003
0.006
0.004
0.21
5.45
0.41
0.30
0.16
0.65
0.80
0.38
0.11
0.60
0.38
0.07
0.44
0.84
0.22
0.26
0.004
0.009
-0.01
0.01
0.30
4.13
0.10
0.18
0.04
0.04
0.002
0.007
0.008
0.004
0.16
4.02
<0.01
<0.01
<0.001
<0.01
0.16
0.28
0.16
<0.01
0.11
0.31
0.09
0.36
-0.002
0.01
-0.0005
-0.004
0.002
0.002
-0.21
6.80
0.02
0.08
0.006
0.003
0.003
0.003
0.006
0.004
0.16
3.87
<0.01
<0.01
0.80
0.01
0.86
0.09
0.71
0.58
0.25
0.11
1.69
2.12
0.93
1.10
-0.004
-0.006
0.01
0.98
1.00
0.06
0.11
0.05
0.23
1.22
0.004
0.04
0.01
0.50
0.31
0.01
<0.0001
<0.0001
<0.01
0.40
0.31
0.89
0.44
0.07
0.01
0.01
0.13
-0.88
0.07
-1.11
-0.004
-0.02
0.02
0.58
0.47
-0.007
0.10
0.07
0.16
0.86
0.003
0.02
0.01
0.71
0.21
0.01
0.23
<0.0001
0.69
0.21
0.22
0.42
0.16
0.45
0.06
0.57
1.56
2.99
0.78
3.28
-0.0004
0.003
0.45
0.45
0.06
0.19
0.09
0.14
1.66
0.06
0.01
0.54
0.23
0.01
<0.0001
<0.0001
<0.01
0.10
0.99
0.83
0.42
0.10
<0.01
-0.14
3.14
0.11
-1.46
-0.005
-0.02
-0.008
1.19
0.08
0.004
0.08
0.10
0.15
0.42
0.003
0.04
0.01
0.85
0.22
0.01
0.14
<0.0001
0.48
<0.01
0.17
0.69
0.44
0.21
0.74
0.74
3
P-value
4
3
Mean
ΔHA2
SEM3
P-value
1
4
3
Mean
ΔPH2
SEM3
P-value
4
3
Mean
ΔEA2
SEM3
P-value4
Cows were feed a TMR containing grass:corn silage (25:75 or 75:25) with 12.5%, 15.0%, or 17.5% CP of DM.
ΔPA, concentration difference between hepatic portal vein and artery; ΔHA, concentration difference between hepatic vein and artery; ΔPH, concentration difference between
hepatic portal vein and hepatic vein; ΔEA, concentration difference between epigastric vein and artery.
3
Mean ± SEM (n = 6).
4
P-values for difference from zero. Significance declared when P ≤ 0.10 (t-test).
5
All metabolites except hypoxanthine, cytosine, uracil, and thymine had one or more ΔPA, ΔHA, ΔPH or ΔEA values that differed from zero (P ≤ 0.10).
2
126
Table 4. Net splanchnic fluxes (µmol/h, unless otherwise noted) of purine and pyrimidine metabolites in lactating dairy cows1
Metabolite
Purines
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Adenine
Guanine
Xanthine
Uric acid, mmol/h
Allantoin, mmol/h
Pyrimidines
Cytidine
Uridine
Thymidine
12.5
Corn
15.0
17.5
12.5
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
577
-443
52
868
-727
25
304
-247
43
357
-314
19
15
-4
24
18
-32
-13
35
-32
4
0.72
-0.58
0.14
4.58
-6.03
-1.45
630
-659
-29
1166
-1213
-47
383
-361
22
458
-441
17
-7
-8
-15
21
-22
0
29
-27
-7
0.59
-0.51
0.07
52.6
1.35
54.0
763
-767
-4
1340
-1377
-36
424
-407
18
423
-438
-15
27
-16
9
11
-1
8
8
-29
-6
1.15
0.05
1.20
24.7
-6.12
20.5
492
-526
-34
995
-1058
-63
267
-238
29
366
-369
-3
-5
-5
-10
-15
-19
-34
15
-14
1
0.61
-0.60
0.01
10.9
-10.0
-11.8
567
-614
-23
753
-991
-62
179
-184
-3
337
-323
3
-6
-2
-1
5
-6
0
36
-31
3
0.29
-0.79
-0.20
30.1
-11.3
34.0
551
-540
11
1475
-1583
-108
267
-282
-15
287
-297
-9
16
-3
-6
10
-7
7
3
-21
-18
0.06
0.55
0.61
-4.77
-2.29
-5.06
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
1899
-1459
440
2910
-3989
-1260
1423
-953
624
2401
-2411
-10
3019
-4636
-1617
1701
-1751
-51
3013
-2729
284
3390
-4953
-1563
1571
-898
673
3234
-3124
110
3135
-4209
-1073
1686
-552
1134
2204
-2121
208
2859
-4425
-1334
1297
-1356
16
2725
-2181
544
3120
-4534
-1414
380
-1218
-838
Site
2
Grass
15.0
127
17.5
SEM
3
P-values4
Pro × For
Pro
For
Lin
Quad
266
270
27
505
518
56
112
105
29
113
116
13
8
10
15
13
13
9
12
14
16
0.48
0.53
0.52
15.4
16.7
15.2
0.32
0.04
0.25
0.41
0.26
0.98
0.07
0.06
0.59
0.29
0.24
0.20
<0.01
0.63
0.21
0.66
0.42
0.09
0.06
0.87
0.46
0.89
0.22
0.19
0.07
0.91
0.03
0.57
0.73
0.67
0.85
0.65
0.66
0.42
0.63
0.33
0.73
0.68
0.62
0.06
0.67
0.45
0.39
0.62
0.64
0.87
0.54
0.90
0.32
0.96
0.60
0.81
0.66
0.41
0.40
0.15
0.11
0.71
0.62
0.73
0.39
0.13
0.92
0.11
0.08
0.63
0.56
0.93
0.56
0.54
0.89
0.75
0.38
0.99
0.50
0.72
0.59
0.90
0.44
0.88
0.78
0.42
0.24
0.21
0.31
0.40
0.84
0.07
0.09
0.73
0.57
0.63
0.13
<0.01
0.84
0.42
0.51
0.80
0.75
0.03
0.61
0.82
0.69
0.09
0.09
0.30
0.61
0.05
0.14
0.06
0.90
0.61
0.35
0.92
0.25
0.23
0.31
0.97
0.67
0.70
0.32
0.37
0.24
0.48
0.51
0.03
0.25
0.89
0.26
0.99
0.99
0.57
0.09
0.74
0.21
278
365
196
235
505
437
639
502
465
0.04
0.03
0.07
0.13
0.58
0.86
0.01
0.31
0.22
0.65
0.89
0.77
0.77
0.99
0.48
0.32
0.58
0.34
0.17
0.15
0.09
0.64
0.72
0.94
<0.01
0.20
0.07
0.07
0.29
0.07
0.06
0.60
0.63
0.02
0.39
0.42
0.18
0.07
0.93
0.81
0.65
0.75
<0.01
0.69
0.07
2’-deoxyuridine
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
4779
-8754
-4033
-588
-128
-716
2406
-1577
828
105
-126
-21
-343
-898
664
362
988
942
1796
-1769
27
51
-62
-10
-2200
-1261
-2434
2951
-6329
-3378
59
-359
-61
22
-108
-86
10716
-7107
3609
3398
-1599
1799
1037
745
1782
111
-91
20
-673
-1616
-1631
1689
-1590
2104
1841
-590
896
78
-49
23
1
-1441
-5828
-7269
1892
2982
4874
1282
-390
892
138
-113
-12
3783
4161
3588
1621
2263
2257
860
764
736
37
47
35
0.40
0.69
0.56
0.67
0.63
0.85
0.53
0.46
0.43
0.37
0.03
0.30
0.42
0.76
0.66
0.46
0.26
0.14
0.84
0.08
0.17
0.22
0.70
0.23
0.99
0.87
0.69
0.28
0.04
0.10
0.29
0.11
0.99
0.58
0.26
0.67
0.57
0.87
0.27
0.44
0.49
0.81
0.29
0.30
0.96
0.84
0.27
0.20
0.33
0.45
0.81
0.82
0.97
0.59
0.99
0.39
0.23
0.27
<0.01
0.74
Cows were feed a TMR containing grass:corn silage (25:75 or 75:25) with 12.5%, 15.0%, or 17.5% CP of DM.
PDV, portal-drained viscera; HEP, hepatic tissue; TSP, total splanchnic tissue.
3
mean ± SEM (pooled) (n = 6).
4
P-values for protein (Pro) describe the effect of feeding different CP levels. P-values for forage (For) describe the effect of feeding mainly corn or grass silage. P-values for protein×forage (Pro × For) describes any interaction between CP level and either corn or grass silage. P-values for linear (Lin) and quadratic (Quad) effects describe the effect of
dietary CP. Significance declared when P ≤ 0.10 (F-test).
2
128
Table 5. Venous-arterial concentration differences (µmol/L) between the epigastric vein and artery (ΔEA) of purine and pyrimidine metabolites in lactating dairy cows 1
Metabolite
Purines
Guanosine
Inosine
2’-deoxyinosine
Guanine
Pyrimidines
Uridine
2’-deoxyuridine
12.5
Corn
15.0
17.5
0.08
0.28
0.02
-0.01
0.08
0.36
0.02
-0.002
3.12
0.86
3.52
-2.67
P-values3
Pro × For
12.5
Grass
15.0
17.5
0.05
0.48
-0.01
0.002
0.04
0.28
0.01
-0.02
0.13
0.30
-0.01
0.0004
0.16
0.26
0.02
-0.0001
0.01
0.12
0.01
0.005
0.44
0.56
0.27
0.11
0.32
0.42
0.70
0.74
0.06
0.12
< 0.01
0.96
0.74
0.24
0.51
0.59
0.15
0.85
0.27
0.04
3.38
1.07
2.82
0.26
2.69
-1.28
2.97
-5.12
0.25
1.55
0.84
0.25
< 0.01
0.04
0.40
< 0.01
0.48
0.75
0.80
0.17
SEM
1
2
Pro
For
Lin
Quad
Cows were feed a TMR containing grass:corn silage (25:75 or 75:25) with 12.5%, 15.0%, or 17.5% CP of DM.
Mean ± SEM (pooled) (n = 6).
3
P-values for protein (Pro) describe the effect of feeding different CP levels. P-values for forage (For) describe the effect of feeding mainly corn or grass silage. P-values for protein × forage (Pro × For) describes any interaction between CP level and either corn or grass silage. P-values for linear (Lin) and quadratic (Quad) effects describe the effect of
dietary CP. Significance declared when P ≤ 0.10 (F-test).
2
129
Table 6. The purine and pyrimidine nitrogen (g/d) intestinal absorption and intermediary metabolism in lactating dairy cows 1
2
Item
Purine N
Pyrimidine N
Nucleic acid N
3
Site
PDV
HEP
TSP
PDV
HEP
TSP
PDV
HEP
TSP
12.5
8.80
-9.33
-1.27
7.03
-7.74
-1.77
15.8
-17.1
-3.04
Corn
15.0
75.4
-2.79
72.6
6.09
-7.57
-0.44
81.5
-10.4
72.2
17.5
33.7
-11.3
29.1
5.73
-9.56
-2.94
39.4
-20.8
26.2
12.5
4.47
1.81
-3.53
12.4
-9.45
3.48
16.4
-2.58
6.14
Grass
15.0
55.6
-19.9
37.8
6.56
-7.36
-0.46
62.7
-29.5
37.3
17.5
18.6
-11.1
-1.69
5.67
-7.90
-2.63
25.3
-24.5
-4.22
1
4
SEM
17.5
13.0
15.8
2.3
2.76
2.17
16.4
13.4
15.7
Pro
0.01
0.94
<0.01
0.39
0.83
0.33
<0.01
0.77
<0.01
For
0.36
0.98
0.20
0.60
0.87
0.60
0.37
0.99
0.23
P-values5
Pro × For
0.91
0.61
0.55
0.52
0.84
0.42
0.84
0.43
0.43
Lin
0.03
0.99
0.01
0.77
0.58
0.22
0.02
0.80
<0.01
Quad
0.04
0.61
0.02
0.12
0.84
0.22
0.04
0.37
0.03
Cows were feed a TMR containing grass:corn silage (25:75 or 75:25) with 12.5%, 15.0%, or 17.5% CP of DM.
Purine N, purine nitrogen; pyrimidine N, pyrimidine nitrogen; Nucleic acid N, nucleic acid nitrogen.
3
PDV, portal-drained viscera; HEP, hepatic tissue; TSP, total splanchnic tissue.
4
Mean ± SEM (pooled) (n = 6).
5
P-values for protein (Pro) describe the effect of feeding different CP levels. P-values for forage (For) describe the effect of feeding mainly corn or grass silage. P-values for protein × forage (Pro × For) describes any interaction between CP level and either corn or grass silage. P-values for linear (Lin) and quadratic (Quad) effects describe the effect of
dietary CP. Significance declared when P ≤ 0.10 (F-test).
2
130
Figure 1
Nucleotide
Nucleoside
2'-deoxyinosine
C10H12N4O4
NH3
Base
7
1
2'-deoxyadenosine
C10H13N5O3
6
AMP
C10H14N5O7P
1
Adenosine
C10H13N5O4
6
2
IMP
C10H13N4O8P
NH3
1
7
Inosine
C10H12N4O5
Adenine
C5H5N5
NH3
6
8
NH3
9
Hypoxanthine
C5H4N4O
10 11
3
XMP
C10H13N4O9P
Degradation product
6
dAMP
C10H14N5O6P
NH3
Intermediate
1
Xanthosine
C10H12N4O6
6
4
Xanthine
C5H4N4O2
NH3
GMP
C10H14N5O8P
1
dGMP
C10H14N5O7P
1
5
Guanosine
C10H13N5O5
6
2'-deoxyguanosine
C10H13N5O4
6
8
10
11
Uric acid
C5H4N4O3
13
14
Allantoin
C4H6N4O3
12
Guanine
C5H5N5O
Fig. 1. Degradation pathways of the purine metabolism. Illustration modified from Kyoto Encyclopedia of Genes and Genomes; Purine metabolism (Kanehisa et al., 2014). Metabolites: dAMP, 2’-deoxyadenosine 5’-monophosphate (deoxyadenosine monophosphate); AMP, 5’-adenylic acid (adenosine monophosphate); IMP, 5’-inosinic acid (inosine
monophosphate); XMP, 5’-xanthylic acid (xanthosine monophosphate); GMP, 5’-guanidylic acid (guanosine monophosphate); dGMP, 2’-deoxyguanosine 5’-monophosphate
(deoxyguanosine monophosphate). Enzymes: 1. 5’-nucleotidase [3.1.3.5]; 2. AMP deaminase [3.5.4.6]; 3. IMP dehydrogenase [1.1.1.205]; 4. GMP synthase [6.3.5.2]; 5. deoxyguanosine kinase [2.7.1.113]; 6. purine-nucleoside phosphorylase [2.4.2.1]; 7. adenosine deaminase [3.5.4.4]; 8. guanosine phosphorylase [2.4.2.15]; 9. adenine deaminase
[3.5.4.2]; 10. xanthine oxidase [1.17.3.2]; 11. xanthine dehydrogenase [1.17.1.4]; 12. guanine deaminase [3.5.4.3]; 13. urate factor-independent hydroxylase [1.7.3.3] or uricase;
14. hydroxyisourate hydrolase [3.5.2.17] (or spontaneous reaction).
131
Figure 2
Nucleotide
CMP
C9H14N3O8P
Nucleoside
Cytidine
C9H13N3O5
1
NH3
UMP
C9H13N2O9P
Intermediate
Degradation product
Cytosine
C4H5N3O
5
4
Uridine
C9H12N2O6
1
Base
NH3
6
9
Uracil
C4H4N2O2
10
11
Dihydrouracil
C4H6N2O2
12
β -ureidopropionic
acid
C4H8N2O3
13
β-alanine
C3H7NO2
NH3
dUMP
C9H13N2O8P
NH3
2'-deoxyuridine
C9H12N2O5
2
NH3
3
dCMP
C9H14N3O7P
dTMP
C10H15N2O8P
1
7
8
4
2'-deoxycytidine
C9H13N3O4
2
Thymidine
C10H14N2O5
8
Thymine
C5H6N2O2
10
11
Dihydrothymine
C5H8N2O2
12
β -ureidoisobutyric
acid
C5H10N2O3
13
β-aminoisobutyric
acid
C4H9NO2
NH3
Fig. 2. Degradation pathways of the pyrimidine metabolism. Illustration modified from Kyoto Encyclopedia of Genes and Genomes; Pyrimidine metabolism (Kanehisa et al.,
2014). Metabolites: CMP, 5’-cytidylic acid (cytidine monophosphate); UMP, 5’-uridylic acid (uridine monophosphate); dUMP, 2’-deoxyuridine 5’-monophosphate (deoxyuridine
monophosphate); dCMP, 2’-deoxycytidine 5’-monophosphate (deoxycytidine monophosphate); dTMP, thymidine 5’-monophosphate. Enzymes: 1. 5’-nucleotidase [3.1.3.5]; 2.
thymidine kinase [2.7.1.21]; 3. dCMP deaminase [3.5.4.12]; 4. cytidine deaminase [3.5.4.5]; 5. ribosylpyrimidine nucleosidase [3.2.2.8]; 6. uridine nucleosidase [3.2.2.3]; 7. purine-nucleoside phosphorylase [2.4.2.1]; 8. thymidine phosphorylase [2.4.2.4]; 9. cytosine deaminase [3.5.4.1]; 10. dihydrouracil dehydrogenase [1.3.1.1]; 11. dihydropyrimidine
dehydrogenase [1.3.1.2]; 12. dihydropyrimidinase [3.5.2.2]; 13. beta-ureidopropionase [3.5.1.6].
132
9. General discussion
One way of adding to the existing knowledge of nitrogen metabolism in dairy cows is to improve
the understanding and importance of the microbial nucleic acids. With regard to the purine and pyrimidine metabolism, focus has until now mainly been on purine derivative excretion in urine and
milk, where uric acid and allantoin excretion has been used as an indirect marker of rumen microbial synthesis (Giesecke et al., 1994; Gonda and Lindberg, 1997; Gonzalez-Ronquillo et al., 2004;
Tas and Susenbeth, 2007). In this Ph.D. study, attention is instead tried drawn to the purine and pyrimidine metabolism as an important component of the total nitrogen metabolism. This led to the
overall objective of the Ph.D. study which was; to improve knowledge about the quantitative absorption and intermediary metabolism of purine and pyrimidine metabolites in lactating dairy cows
in order to possibly discover new ways to improve the overall nitrogen efficiency.
The lack of interest in this part of the purine and pyrimidine metabolism could partly be assigned to
the unavailability of applicable methods for quantification of purine and pyrimidine metabolites in
bovine blood. Hence, this study was initiated with the first specific objective; developing and validating a method for the quantitative determination of purine and pyrimidine metabolite concentrations in bovine blood plasma (Paper I). The method needed to cover a large range of metabolites, to
get an almost complete picture of the nucleic acid metabolic mechanisms (Katz and Bergman,
1969a; Huntington et al., 1989). Very importantly, it had to cover relevant quantification ranges in
bovine blood plasma and be precise enough to detect rather small venous-arterial concentration differences used for determining fluxes of these metabolites when sampled from the multicatheterized
cow model. After having achieved the first objective of the study, the second specific objective; to
examine the quantitative absorption and intermediate metabolism of the purine and pyrimidine metabolites by studying their net PDV and net hepatic metabolism and to evaluate how this was influenced by postprandial pattern, CP level and forage source (Paper II and manuscript III), was investigated. In the end, the third objective was taken on, and an overview of the fate of the purine and
pyrimidine nitrogen was made. At this point, the challenges were to apply a novel LC-ESI-MS/MS
method in order to describe a part of the nitrogen metabolism in ruminants which had not been investigated earlier, and to present the results from a fairly complicated cow model in a way so that it
could be easily understood.
9.1 Quantitative determination of purine and pyrimidine metabolites in bovine plasma by LCESI-MS/MS
When using the multicatheterized cow model for evaluation of the purine and pyrimidine metabolism, concentrations of a large number of targeted purine and pyrimidine metabolites with very different chemical properties had to be determined with appropriate detection levels and sufficient
133
precision. A quantification method fit for use with bovine blood plasma was prior to this study not
available. Consequently, a sensitive, specific, and reliable LC-ESI-MS/MS analysis was developed
and validated for quantification of 10 metabolites of the purine metabolism and 10 metabolites of
the pyrimidine metabolism (Fig. 9). The procedure was incorporated with SIL and matrix-matched
calibration standards. Concurrently, a simple and repeatable pre-treatment protocol capable of
cleaning up the bovine plasma prior to analysis was established.
9.1.2 Method development
LC-MS/MS was chosen as the analytic technique (Paper I) primarily because it had previously been
applied for purine and pyrimidine metabolite determination in ruminant urine/milk, instrumentation
accessibility/availability, and the chemical properties/polarities of the targets (Balcells et al., 1992a;
Chen et al., 1990b; George et al., 2006; Rosskopf et al., 1990; Tiemeyer et al., 1984). It was also
chosen because of its high versatility, sensitivity, specificity, speed, fit for use with complex liquid
matrices, and independence of chromophores, derivatisation, and full LC separation (Ardrey, 2003;
Kang, 2012; Matuszewski et al., 2003; Taylor, 2005; Watson and Sparkman, 2007; Xu et al., 2007).
In addition, its quantitative abilities, ease of use, and most importantly ability to perform MRM was
essential for making this choice (Fu et al., 2010; Holčapek et al., 2012; Lemoine et al., 2012; Nováková, 2013; Prakash et al., 2007).
The majority of established methods on purine and pyrimidine quantification have been focused on
only purines and primarily in urine and milk (Balcells et al., 1992a; Boudra et al., 2012; Chen et al.,
1990b; George et al., 2006; Rosskopf et al., 1990; Tiemeyer et al., 1984). Only one other publication seeking to quantitate pyrimidine as well as purine metabolites (urine) was identified in the literature (Boudra et al., 2012). Several analytical separation methods and types of spectrophotometric
detection have been applied for purine and pyrimidine quantification in biological matrices, including MS/MS (Boudra et al., 2012; Clariana et al., 2010; Gong et al., 2004; Haunschmidt et al., 2008;
Hua and Naganuma, 2007; Kazoka, 2002; Lin et al., 1997; Liu et al., 2008). Furthermore, it has
been confirmed that purine and pyrimidine metabolites can accurately be quantified in urine employing LC-MS/MS (Boudra et al., 2012).
The method was developed for the simultaneous quantification of 20 purine and pyrimidine metabolites in bovine plasma. Initially, aiming at getting as full a picture of the metabolism as possible, all metabolites of the purine and pyrimidine metabolism (Fig. 9); nucleotides, nucleosides, bases, and degradation products, were considered as possible analytical targets. However, limits in the
method meant that some of the metabolites were not included in the final analyses (Paper I).
Chromatographic separation of the very polar to semi-polar purine and pyrimidine metabolites (Table 1) was accomplished applying a reversed phase C18 column combined with an acetic acid buff134
er/methanol HPLC solvent system using a gradient elution profile (Ardrey, 2003; Hartmann et al.,
2006; Kang, 2012). The elution profile was designed to be as short as possible while still achieving
a good peak separation (Fig. 10 and paper I). Optimal chromatographic resolution and elution order
was achieved through optimisation of the mobile phase composition, injection volume, flow rate,
gradient profile, and column/autosampler temperatures. Concerning the mass spectrometric analysis, a triple quadropole mass spectrometer was used for detection/MRM and an electrospray for
ionization. The electrospray source was used since ESI is known to have a broad application range
being suitable for molecules of all sizes and polarities (Kebarle and Verkerk, 2009; Kebarle and
Tang, 1993; Holčapek et al., 2012; Kang, 2012; Watson and Sparkman, 2007). Fragment ion spectra were recorded in both polarities and fragment ions tested and optimized along with the cone
voltages and collision energies in MRM mode (Table 3 in paper I). The most intense transition reactions were used for quantification. In order to maximize the sensitivity of each metabolite, the 20
metabolites were analysed in five separate runs, three in negative ESI and two in positive ESI mode
(Table 2 in paper I). The transition pairs of the purine and pyrimidine metabolites established in this
study corresponded with those reported in other species and matrices (Table 3) (Boudra et al., 2012;
Clariana et al., 2010; Hartmann et al., 2006).
Table 3. Comparison of transition reactions monitored by LC-ESI-MS/MS in bovine plasma, human plasma,
bovine urine, and pork meat
Metabolite
Purines
Adenine
Guanine
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Xanthine
Hypoxanthine
Uric acid
Allantoin
Pyrimidines
Cytosine
Thymine
Uracil
Cytidine
Uridine
Thymidine
2’-deoxyuridine
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
Mw
(g/mol)
Bovine plasma
Pair
ESI
(m/z)
135.13
151.13
283.24
268.23
267.24
252.23
152.11
136.11
168.11
158.12
134-107
150-133
282-150
267-135
266-150
251-135
151-108
135-92
167-124
157-97
+
-
111.95
126.11
112.09
243.22
244.20
242.23
228.20
89.09
132.12
103.12
112-95
127-110
113-96
242-109
243-110
241-151
227-184
90-72
133-115
104-86
+
+
+
+
+
+
Human plasma
Pair
ESI
(m/z)
134-107
-
282-150
267-135
266-150
251-135
151-108
135-92
168-124
-
127-110
113-96
+
+
243-200
241-151
227-184
-
133-115
+
Bovine urine
Pair
ESI
(m/z)
153-110
137-94
169-126
159-99
+
+
+
+
90-72
+
104-86
+
Pork meat
Pair
ESI
(m/z)
282-150
267-135
-
151-108
135-92
167-124
-
243-110
-
Mw, molecular weight; Pair, transition pair; ESI, electrospray ionisation mode (constructed from paper I, Boudra
et al., 2012; Clariana et al., 2010; and Hartmann et al., 2006).
135
Quantification was performed by external calibration (Ardrey, 2003; Fu et al., 2010; Honour, 2011;
Nováková, 2013) and linear regression (methods section and paper I) with focus on quantifying as
low concentrations of metabolites as possible. During method development, much attention was
devoted to trying to counteract/diminish matrix effects (absolute) (Jessome and Volmer, 2006;
Matuszewski et al., 2003; Nováková, 2013; Tan et al., 2011). Matrix effects were eliminated by
using specific 13C and/or 15N SIL, by making the external calibration matrix-matched, and by implementing an effective pre-treatment. Matrix matching is believed to be unnecessary when incorporating specific SIL but, this was only the case for 15 out of the 20 purine and pyrimidine metabolites (Hewavitharana, 2011). Using this many different SIL is uncommon, as they are rather expensive and for many components they are not commercially available. However, they also yield a high
level of accuracy and precision in the quantification (Ardrey, 2003; Fu et al., 2010; Hewavitharana,
2011; Holčapek et al., 2012; Nilsson and Eklund, 2007; Nováková, 2013; Stokvis et al., 2005; Tan
et al., 2011; Vogeser and Seger, 2010; Wang et al., 2007; Wooding and Auchus, 2013).
9.1.3 Method validation
The inherent potential of the LC-ESI-MS/MS method was established and validated by assessing
selectivity, linearity (calibration curve), stability, precision, accuracy (recovery) and relative matrix
effects (application range). No single guideline (ICH, FDA, or EMA) was used for the validation,
but efforts were made to cover all relevant parts of the specific method while still keeping in line
with conventional approaches (Table 2 and 4) (Guideline EMA, 2011; Guideline ICH, 2005; Guideline FDA, 2001). In the following section relevant parts of the validation procedure are summarized
and evaluated, for full details consult paper I.
Table 4. A summary of validation parameters required by ICH, EMA, FDA, and if /how these were determined
during method development.
Parameter
Selectivity
Carry-over
LOQ (limit of quantitation)
LOD (limit of detection)
Calibration curve linearity
Range
Precision1 (% RSD)
Recovery (%)
Dilution integrity
Matrix effects (%)
Robustness
Stability
Number of concentration levels × replicates
ICH
FDA
EMA
Paper I
√
6
6
√
X
X
√
√3
LOQ
LLOQ, LLOQ, X
ULOQ
ULOQ
LOD
X
X
X
5
6-8
62
7×2
√
X
√
√
3×3
3×5
4×5
1×8
X
3
X
1×8
X
X
5
√4
X
X
6
√
√
X
X
X
X
√
√
√
√, the parameter is required/determined; X, the parameter is not required/determined; LLOQ, lower limit of detection; ULOQ, upper limit of quantification (modified from Nováková, 2013).
136
1
Precision is further subdivided into within-day(run) (repeatability) and across-day(run) (intermediate) precision
and reproducibility.2To be analysed in replicates. 3Blank samples were injected after samples with an expected
high concentration. 4When dilution is applied i.e. uracil/uric acid, the calibration matrix was diluted accordingly.
With regard to selectivity, a blank sample matrix was not available and consequently the presence
of other components from standard plasma at the same Rt as the targeted metabolites could not be
excluded; endogenous peaks would be expected to be present. Instead, what seemed more relevant
was confirming the absence of component/SIL cross-talk, as some of the applied SIL had less than
three mass unit differences (3-8) to the natural metabolite (Bakhtiar and Majumdar, 2007; Stokvis et
al., 2005; Tan et al., 2011; Tong et al., 1999; Vogeser and Seger, 2010). This was verified by comparing chromatographic responses for standards and SIL alone and in a mixture. Looking back, further approaches to evaluate selectivity should probably have been made. Calibration curve precision
and accuracy is vital for achieving high quality data (Ardrey, 2003; Fu et al., 2010; Honour, 2011;
Nováková, 2013). Consequently, producing satisfactory calibration curves were a very time consuming task. All calibration curves used were matrix matched and covered relevant concentration
ranges (as low as possible) of each of the purine and pyrimidine metabolites (Guideline EMA,
2011; Hewavitharana, 2011; Nováková, 2013; Taylor, 2005; Van Eeckhaut et al., 2009; Vogeser
and Seger, 2010; Xu et al., 2007). Logarithmic and linear calibration models were tested and it was
concluded that the CV% profiles of most of the metabolites benefitted from a log-log transformation (Fig. 1 in paper I). The linearity of the log calibration curves were studied with a lack of fit
hypothesis test (no significant lack of fit) (Table 4 in paper I), the quantification ranges determined
by homogeneity of variance (CV < 25%) (Table 4 and Fig. 2 in paper I) and the stability between
run days accessed (Table 4 in paper I). Limits of detection and quantification were not determined
as the homogeneity of variance of the calibration curves was considered a more comprehensive
demonstration of the limits of the method. Concerning stability, focus was on testing factors that
were relevant to this specific method (Guideline EMA, 2011). For continuous evaluation of longterm storage stability, a freshly thawed quality control was analysed and evaluated in all analytical
runs. The stability within runs (6-24 h) was evaluated in two ways; first of all, a quality control was
reviewed at the beginning and at the end of each sequence (stability of stock and working solutions); secondly, to test the autosampler stability, a set of spiked standard samples were analysed at
five different times (different vials) during a 30 h sequence (Table 5 in paper I). To determine the
stability of the calibration curves, the across-day variation was assessed over five consecutive days
(curve stability) (Table 4 in paper I). The stability during repeated freeze-thaw cycles was not explored as all samples were only thawed once. Not surprisingly, it was shown that, to sustain quantification accuracy, calibration curves had to be incorporated in all analytical runs. No concerns with
regard to any of the remaining stability parameters emerged during testing. Precision of the method,
137
in terms of within-day (repeatability) and across-day variation (intermediate precision), was determined according to conventional methods by analysing replicate sets of spiked standard plasma
samples on five separate days (Table 6 in paper I) (Guideline EMA, 2011). The absolute accuracies
(within and across-day) was calculated using the same set of spiked standard plasma as for the precision evaluation. Since quantification ranges were short and close to zero, a single instead of the
traditional three recovery concentration levels was employed (Table 6 in paper I). The method was
found to have good precision (CV% ≤ 25%) and excellent accuracies (91-107%).
The LC-ESI-MS/MS analysis was established for quantification of 20 purine and pyrimidine metabolites in blood plasma obtained from veins and arteries from multicatheterized cows. Since jugular vein plasma was used for method development and because quantification relied on matrixmatched calibration, relative matrix effects were evaluated in alternative types of plasma to determine the potential application range of the method (Fig. 4 in paper I). The relative matrix effects
were assessed by comparing the response from SIL spiked in standard jugular vein plasma with the
response in first of all, plasma from the jugular vein of four multicatheterized cows; to investigate
within-species variation, secondly, plasma drawn from the portal, hepatic portal, and gastrosplenic
vein, and an artery; to assess different possible sampling sites, thirdly, plasma samples from different species; for between-species evaluation, and finally, water, urine and milk samples; to compare
different matrices. It was revealed that the method was suitable for almost all examined purine and
pyrimidine metabolites in all tested types of plasma with a few exceptions, and also for other species such as chicken, pig, mink, human, and rat.
Based on the extensive validation process as well as the examination of relative matrix effects, it
was determined that the LC-ESI-MS/MS method was suitable for quantification of the 20 targeted
purine and pyrimidine metabolites in bovine blood plasma obtained from the multicatheterized cow
model.
9.1.4 Method application
The developed method had a broad application at low concentrations with excellent accuracies (Paper I). However, the extensive span and individual properties of the different metabolites resulted in
less precise quantifications near the low end of the quantification ranges (Fig. 2 in paper I). This,
and the level of the within-day variation (%), might be the reason for some of the variation in the
results with regard to allantoin especially in paper II (Paper II and manuscript III).
Applying several sets of bovine plasma, relative matrix effects were evaluated during method development and the potential application range of the method was demonstrated (Table 7 in paper I).
In addition to being used for matrix effect evaluation, the concentration levels of the purine and
pyrimidine metabolites in these bovine plasma sets were used as indicators of the levels of metabo138
lites to be expected in experimental cow plasma samples. The established quantification ranges
were based on these data. Freshly prepared calibration curves were used to determine the final
quantification ranges and the span was set to the lowest and highest quantified concentration giving
an acceptable CV < 25% (Table 5 and table 4 and Fig 2 in paper I). Actual experimental concentration levels of the purine and pyrimidine metabolites in plasma were not established until the samples from the two experiments covered by paper II and manuscript III had been analysed. Hence,
when performing the analyses, to be certain to have calibration curves covering the experimental
concentrations, the applied concentration ranges had a wider range than the established quantification range (Paper II and manuscript III). The applied ranges were adjusted according to the sample
concentrations (Table 5). This resulted in applied ranges that were in some cases outside the validated ranges of quantification. Even though the applied ranges were extended and/or reduced versions of the validated quantification ranges, the curves remained linear, especially in the upper end
of the applied range. Moreover, if quantification was performed near or below the lowest verified
quantification point, the results were interpreted with caution (CV ˃ 25%). Especially in the arterial
samples, some of the concentrations of the bases and nucleosides were very small, resulting in
quantification below the lowest verified quantification point. The concentration levels in the hepatic
portal, hepatic, gastrosplenic, and epigastric veins were generally higher and the fluxes based on
these, despite with some variation, very reliable. In addition, so as to be certain to generate reliable
splanchnic fluxes, venous-arterial concentration differences were always tested for difference from
zero before used for estimation of fluxes (Paper II and manuscript III).
Table 5. Quantification ranges (μmol/L) and within-day variation (CV%) established during method development
(paper I) and applied ranges (μmol/L), mean arterial concentration levels (μmol/L), and hepatic portal venousarterial differences (%) used and/or determined in paper II and manuscript III.
Metabolite
Purines
Adenine
Guanine
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Xanthine
Hypoxanthine
Uric acid
Allantoin
Pyrimidines
Cytosine
Thymine
Uracil
Cytidine
Q-range1
(μmol/L)
Applied range3
(μmol/L)
PII
MIII
Arterial levels4
(μmol/L)
PII
MIII
Within-day5
(CV%)
Δ[PA]/[P]6
(%)
PII
MIII
0.08-5.0
0.08-5.0
0.16-5.0
0.08-5.0
0.08-5.0
0.16-5.0
0.16-5.0
0.08-5.0
3.15-200
124-500
0.08-5.0
0.08-5.0
0.16-5.0
0.08-5.0
0.08-5.0
0.16-5.0
0.16-5.0
0.08-5.0
3.2-200
125-500
0.08-2.5
0.08-2.5
0.16-5.0
0.08-5.0
0.08-2.5
0.16-5.0
0.16-5.0
0.08-2.5
6.26-200
37.5-1200
0.15
0.012
0.021
0.046
0.015
0.013
0.011
0.043
73
122
0.16
0.0051
0.068
0.33
0.020
0.013
0.020
0.0032
5.9
161
2%
2%
4%
2%
4%
2%
3%
1%
16%
34%
1%
21%
100%
95%
100%
94%
22%
27%
7%
5%
4%
80%
73%
73%
84%
93%
33%
-7
6%
9%
1.92-7.5
1.27-5.0
0.66-5.0
5.15-5.0
1.9-7.5
1.27-5.0
0.66-5.0
2.5-5.0
1.25-40
0.63-20
0.31-10
2.5-80
0.0
0.042
0.19
3.3
0.0
0.020
0.033
2.6
21%
4%
5%
18%
-7
-7
18%
31%
-7
-7
33%
37%
139
Uridine
Thymidine
2’-deoxyuridine
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
1.91-7.5
-2
-2
13-13
4.67-75
0.31-5.0
1.9-7.5
2.5-5.0
0.16-5.0
3.1-13
4.7-75
0.31-5.0
1.25-40
2.50-80
2.50-80
7.20-230
2.50-80
0.16-5.0
3.7
1.1
0.82
13
3.7
0.31
3.5
0.73
8.12
11
4.7
0.23
7%
23%
33%
12%
91%
6%
38%
38%
23%
5%
5%
11%
37%
55%
11%
8%
18%
21%
Q-range, quantification range; Δ[PA]/[P] (%), hepatic portal venous-arterial concentration difference / portal concentration (%); PII, paper II; MIII, manuscript III (constructed from paper I, paper II and manuscript III).
1
The quantification range was set to the lowest and highest quantified concentration giving an acceptable CV <
25% (Paper I). 2Value is above the highest calibrator concentration.3Applied range (μmol/L) was determined by
external calibration with five concentrations and points were excluded to fit the concentration range in actual samples (Paper II and manuscript III). 4Mean arterial concentrations (µmol/L) of purine and pyrimidine metabolites in
plasma samples (Paper II and manuscript III). 5Within-day variation expressed as CV% (Paper I). 6ΔPA/A (%);
the hepatic portal venous-arterial difference (%) (Paper II and manuscript III). 7Not determined because the Δ[PA]
was essentially zero.
For the method to be precise enough to be used for determining fluxes when analysing sample sets
from the multicatheterized cow model, the within-day variation (%) preferable should be below that
of the venous-arterial difference (%). The hepatic portal venous-arterial concentration differences
were for most metabolites higher or similar to the within-day variation (%) (Table 5). Only for the
degradation products such as uric acid, allantoin and β-alanine, that had natural high levels of endogenous metabolites, the precision of the method was unfortunately not higher than the within-day
variation (%). However, the within-day variation (%) was determined at relatively low concentration levels, resulting in larger CV% than what would be expected at the considerable higher concentration levels used in the experimental sample sets (Paper I, paper II and manuscript III). Also, owing to the discovery of leaking in the HPLC system followed by a repair performed between the
analyse of the samples from the two experiments, as well as further small refinements of the analysis procedures during the study, the across-day variation (CV%) and probably within-day variation
(CV%) of the method had improved when performing analyses for manuscript III (Table 6). Especially the variation of uric acid and allantoin benefitted from the maintenance repair of the instrument. The within-day and across-day variation (CV%) of this method was in most cases in line with
or better than previously reported values (Hartmann et al., 2006).
Table 6. Within-day and across-day variation (CV%) established during method development, re-evaluated in
manuscript III, and reported by Hartmann et al. in human plasma (Hartmann et al., 2006).
Metabolite
Purines
Adenine
Guanine
Guanosine
Inosine
2’-deoxyguanosine
2’-deoxyinosine
Xanthine
Hypoxanthine
Uric acid
Within-day variation (CV%)1
Paper I
Hartmann et al.
Across-day variation (CV%)2
Paper I
Manuscript III
Hartmann et al.
2
2
4
2
4
2
3
1
16
5
4
12
9
7
8
9
6
55
8
16
8
19
8
9
10
18
140
5
6
4
6
6
4
7
8
5
7
9
11
17
9
11
16
6
Allantoin
Pyrimidines
Cytosine
Thymine
Uracil
Cytidine
Uridine
Thymidine
2’-deoxyuridine
β-alanine
β-ureidopropionic acid
β-aminoisobutyric acid
34
21
4
5
18
7
23
33
12
14
6
11
10
14
8
14
8
8
49
23
24
15
4
24
12
21
37
5
13
7
9
7
6
11
9
52
20
22
11
17
13
10
8
9
7
10
CV%, coefficient of variation (%) (constructed from paper I, manuscript III, and Hartmann et al., 2006).
1
The within-day variation (CV%) determined in paper I (conc. level = 4-7 μmol/L, except allantoin 40 μmol/L, n
= 8, samples) and Hartmann et al. (2006) (conc. level = 35-50 μmol/L, except uric acid 200 μmol/L, n = 10, samples). 2The across-day variation (CV%) determined in paper I (conc. level = 4-7 μmol/L, except allantoin 40
μmol/L, n = 8, samples, m = 5, days), manuscript III (Two conc. levels: Low level = 0.5-5 μmol/L, except uric
acid, allantoin and β-alanine; 10, 60, 15 μmol/L and high level = 2-60 μmol/L, except uric acid, allantoin and βalanine; 180, 1000, 200 μmol/L, n = 4, samples, m = 6 days), and Hartmann et al. (2006) (conc. level = 35-50
μmol/L, except uric acid 200 μmol/L, n = 10, samples, m = 7 days).
The fact that the analysis variation (within and across-day) improved from experiment I (Paper II)
to experiment II (Manuscript III) was especially noteworthy with regard to the allantoin fluxes. In
paper II, allantoin could not be quantified as precisely as hoped for and the negative net hepatic flux
of allantoin did not agree with theory that allantoin passes the hepatic tissue without being metabolised. On the other hand in manuscript III, allantoin was shown to pass the hepatic tissue unharmed
and the theory of allantoin functioning as a terminal product for excretion was thus confirmed.
9.1.5 Pre-treatment
An effective pre-treatment was vital in this study as complex biological matrices such as plasma can
easily clot the HPLC column resulting in a loss of efficiency, and ESI is sensitive to matrix effects
caused by salts, sugars, and proteins (Hopfgartner and Bourgogne, 2003; Nováková, 2013; Peters et
al., 2007; Praksah et al., 2007; Van Eeckhaut et al., 2009). Also, a proper clean-up enhances the
selectivity and the sensitivity of the analysis. A novel multi-step approach, consisting of PPT, ultrafiltration, evaporation under nitrogen flow, and subsequent resolution, focused on isolation, cleanup, and pre-concentration, was developed and optimized. In addition, the HPLC system was
equipped with a guard column to try to avoid blockage from contaminants escaping pre-treatment
and/or originating from the HPLC system itself (Ardrey, 2003). The pre-treatment procedure was
able to purify and to concentrate all of the purine and pyrimidine metabolites from bovine plasma
simultaneously, in a simple and efficient manner. Initially, different solvents (acetone, acetonitrile,
ethanol, methanol, sulfo-salicylic acid) were tested for PPT (Nováková, 2013; Polson et al., 2003).
Ethanol PPT was chosen for the procedure as it resulted in the highest recoveries and least noise,
and because it was the least harmful of the tested solvents. The ultrafiltration step was added to remove additional pollutants. Evaporation and reconstitution steps were included to obtain a concen141
tration effect. To try to reduce degradation and instability of the samples caused by reactive oxygen
species or enzyme activities during pre-treatment, all centrifugations and incubations were performed at 4°C, and samples, stocks, and solvents etc. were kept at -4°C or on ice. Other types of
pre-treatment methods such as simple dilution (impractical) (Antignac et al., 2005), SPE (Bakhtiar
and Majumdar, 2007; Chambers et al., 2007; Poole, 2003), and accelerated solvent extraction (Richter et al., 1996), a form of LLE, were also investigated but were not found useful. Different types of
SPE from Waters were tested; HLB (polar components), C8, C18, WCX (basic conditions), and
MCX (acidic conditions), but none was found capable of a satisfactory purification of all the purine
and pyrimidine metabolites in one step. There probably exists other more sensitive and complicated
ways to quantify smaller groups of or even single purine and pyrimidine metabolites but, when analyzing this many components with such different chemical properties simultaneously, in such a
complex matrix, the procedure chosen herein seems like a better choice. The pre-treatment procedure is able to purify and concentrate all of the targeted purine and pyrimidine metabolites simultaneously, in an easy and efficient manner without significant losses.
9.2 Absorption and intermediary metabolism of purine and pyrimidine metabolites
Taking advantage of the inbuilt ability of the multicatheterized cow model to describe the net PDV
and net hepatic fluxes of selected metabolites, the absorption pattern and intermediary metabolism
of the purine and pyrimidine nucelosides, bases, and degradation products were studied using two
feeding experiments (Experiment I and II). Besides describing the basics of these mechanisms and
how the purine and pyrimidine metabolism was influenced by postprandial pattern (Paper II), the
effects of protein level and forage source in the ration (Manuscript III) was assessed. In addition,
the fate of the purine and pyrimidine nitrogen in the dairy cows were evaluated (Paper II and
manuscript III). The purine and pyrimidine metabolites were found to be absorbed and metabolised,
and because they were affected differently, they will be discussed as two distinct groups.
The quantitative absorption and intermediary metabolism of purine and pyrimidine metabolites is an
almost unwritten chapter of the nitrogen metabolism in dairy cows and ruminants. Hence,
information and relevant litterature on the subject are at present very limited. The digestion of
monogastrics is very different from that of ruminants and to exchange knowledge between the two
animal types have therefore been difficult (McDonald et al., 2011). However, the results of the two
experiments (Experiment I and II) have provided a fairly clear picture of the mechanisms involved
and of the importance of the purine and pyrimidine metabolites.
9.2.1 The purine metabolism
All 10 purine metabolites were identified in all four types of experimental plasma samples from the
multicatheterized cows (Table 2 in paper II, data not shown in manuscript III) and arterial levels,
142
venous-arterial concentration differences, net PDV, net hepatic, and total splanchnic fluxes as well
as excretion parameters were determined.
Arterial levels of purine metabolites
It both experiments, arterial concentrations of purine degradation products were higher than the
concentrations of nucleosides and nucleoside concentrations higher than concentrations of bases
(Table 2 in paper II and manuscript III). Only in the case of the purine nucleosides, absorption from
the small intestine was indicated by higher concentrations in the hepatic portal vein compared to the
artery, and the hepatic, gastrosplenic, and epigastric veins. The arterial concentration levels of the
purine nucleosides and the bases detected in the two experiments were very similar. However, more
in line with other studies, lower concentration of uric acid and higher concentrations of allantoin
were identified in experiment II as compared to experiment I (Balcells et al., 1992b). It is believed
that the differences in concentration levels observed between experiments were caused by degradation during handling and/or storage in experiment II, where the samples went through three
freeze/thaw cycles prior to analysis. Storage degradation could have been avoided but the decision
to use the unique set of blood samples from experiment II for this study was taken after blood was
analysed for other purposes (Barratt et al., 2013). In experiment I, measures were taken to avoid
storage degradation i.e. samples were only thawed upon analysis. A difference in the activity of
degradation enzymes in the small intestine and/or intestinal mucosa between the two experiments
could also be the cause for the difference. In contradiction to this theory was that uricase [1.7.3.3],
the enzyme that catalyses the degradation of uric acid to allantoin, has only been detected in trace
amounts in bovine blood (Chen et al., 1990a). On the other hand, the degradation can also happen
spontaneously or be aided by hydroxyisourate hydrolase [3.5.2.17] (Kenehisa et al., 2014: KEGG
purine metabolism). Not surprisingly, as uric acid is the intermediate precursor of allantoin, only the
ratio between uric acid and allantoin changed between experiments and not the total amount (uric
acid + allantoin) (Berg et al., 2002; Carver and Walker, 1995; McDonald et al., 2011). This led to
the conclusion that estimation of purine degradation product concentrations in bovine blood plasma
should be based on the sum of uric acid and allantoin. As anticipated, no notable effects of postprandial pattern, protein level, or forage source was detected in the arterial levels of the purine metabolites. The small effects detected in experiment II were assumed to be the result of influences of
diets on nutrient flow and other metabolic processes.
When calculating net fluxes, venous-arterial concentration differences are multiplied by the respective blood flows. However, the venous-arterial concentration differences across the PDV and hepatic tissues were in both experiments, especially for the purine bases, very small (Table 3 in paper II
and manuscript III) (Kristensen et al., 2007; Reynolds et al., 1988; Seal and Reynolds, 1993).
143
Therefore, the a priori criteria for calculating net fluxes were that at least one of the five venousarterial concentration differences estimated between the 1) hepatic portal vein and artery, 2) hepatic
vein and artery, 3) hepatic portal vein and hepatic vein, 4) gastrosplenic vein and artery, and 5) epigastric vein and artery, of the purine metabolites were different from zero (P ≤ 0.10). All purine
metabolites except adenine and xanthine in experiment I and hypoxanthine in experiment II met this
criterion. When not different from zero, individual venous-arterial concentration differences were
considered when interpreting net fluxes. In general, all of the concentrations and venous-arterial
concentration differences of the purine bases; adenine, guanine, hypoxanthine, and xanthine were
very small.
Release of purine metabolites from the portal-drained viscera
Large amounts of fully degraded purine metabolites in the form of uric acid and allantoin and very
low levels of purine bases and nucleosides were found to be released from the PDV in both experiments (Table 4 in paper II and manuscript III). These findings suggested a very effective degradation of purine metabolites in the small intestine or in the intestinal mucosa, and most likely a combination of these, in dairy cows. The almost non-existing release of bases in general corresponded
with previous findings by McAllan, demonstrating how purine and pyrimidine bases were removed
by 50-100% when infused into the intestine of steers (McAllan, 1980). The extensive purine degradation was also in line with previous observations demonstrating a very effective degradation of
nucleic acids to nucleosides and bases in the small intestine, facilitated by the excreted pancreatic
polynucleotidases, nucleosidases, and phosphatases (Barnard et al., 1969; Berg et al., 2002; Carver
and Walker, 1995; McAllan, 1980; McDonald et al., 2011; Nakayama et al., 1981). If the purine
nucleosides and/or bases are not absorbed directly, a further degradation to uric acid and/or allantoin probably takes place (Fig. 1 in paper II and manuscript III). Also, an even further degradation to
ammonia and urea is possible. The purine nucleosides, bases, and degradation products are known
to be absorbed from the intestinal lumen and subjected to another level of degradation in the intestinal mucosa (McAllan, 1980, Verbic et al., 1990). One of the degradation enzymes known to be
highly active in most tissues, and especially in the small intestinal mucosa, blood, and hepatic tissue
in cattle, is xanthine oxidase [1.17.3.2] (Al-Khalidi and Chaglassian, 1965; Balcells et al., 1992b;
Chen and Gomes, 1992; McDonald et al., 2011; Roussos, 1963; Verbic et al., 1990). Xanthine oxidase in collaboration with additional degradation enzymes in the intestinal mucosa, produces uric
acid and removes purine nucleosides and bases. Some of the released uric acid and allantoin may
also originate from turnover of mucosal enterocytes and other parts of the PDV such as the rumen,
hind gut, pancreas, spleen, and fat. The exact location of degradation of uric acid to allantoin is undetermined. Since uricase [1.7.3.3] is present in only trace amounts in bovine blood, it most likely
144
takes place in the small intestine, intestinal mucosa, and hepatic tissue (Chen et al., 1990a). The
effect of storage on the levels of uric acid and allantoin, but not of their sum, in the samples from
experiment II, points toward alternative mechanisms of uric acid degradation in the blood (Kenehisa
et al., 2014: KEGG purine metabolism). To try to differentiate between purine metabolites absorbed
from the small intestine and released from the forestomachs and other tissues drained by the gastrosplenic vein, net gastrosplenic releases of metabolites were estimated (Paper II, data not shown).
Presuming the gastrosplenic plasma flow was around 20% of the hepatic portal plasma flow, a distinction between the release of purine metabolites from the forestomachs and the intestines was
made with the use of the gastrosplenic-arterial concentration difference (Remond et al., 1993; Storm
et al., 2011). Under these presumptions, allantoin was the only purine metabolite with a net gastrosplenic release contributing considerably to the net PDV release (40% of PDV release). This could
indicate that allantoin was absorbed from the rumen which has been proposed for urea (Abdoun et
al., 2006; Kristensen et al., 2010; Reynolds and Kristensen, 2008; Røjen et al., 2011). Allantoin
would probably in that case, based on its relatively large chemical structure, be actively transported.
The gastrosplenic allantoin contribution could also, at least partly, originate from purine turnover in
the very large tissues of the forestomachs. Still, further investigations are needed to clarify the gastrosplenic contribution of allantoin. This result might also be a consequence of the problematic accurate determination of allantoin in experiment I.
Only in the case of allantoin, a positive effect of postprandial pattern was detected in the PDV release (Table 4 in paper II). Most likely because of their complex digestion and absorption itinerary,
postprandial absorption profiles were not detected for the remaining purine metabolites (Fujihara
and Shem, 2011; McAllan, 1982; McDonald et al., 2011). In addition, any real effects were most
likely easiest to detect for metabolites with considerable fluxes, such as that of allantoin, and at
PDV release, as endogenous contributions of the hepatic metabolism was added to the hepatic fluxes.
Positive effects of dietary protein level was detected for metabolites with large levels of net fluxes
and good precision in the method and mainly at release from the PDV (Table 4 in paper I and manuscript III). Hence, the absorption profile of 2’-deoxyguanosine, adenine, and xanthine were found
to be linearly and positively influenced by an increase in the dietary protein level (12.5, 15.0, and
17.5% CP) (Clark et al., 1992; Ipharraguerre and Clark, 2005; Nocek and Russell, 1988; Reynolds
et al., 2001). Most likely is due to their small net PDV releases, no influences of protein level in the
diet were detected for the remaining purine metabolites. This was also the case for uric acid, even
though considerable levels of uric acid were absorbed. In case of allantoin, the protein level effect
was quadratic and not linear (Ipharraguerre and Clark, 2005). The decline between the 15.0 and the
145
17.5% protein levels were most likely caused by a decrease in the microbial flow due to the fact that
the 17.5% level was achieved by feeding a greater amount of rumen protected protein. So, in the
case that the high protein level had been achieved by feeding protein sources with a lower degree of
rumen protection, the result could have been different. Reduced degradation of nucleic acids in the
small intestine or other negative effects on the absorption mechanisms on the high protein level
could also partly explain why the effect was quadratic.
Removal of purine metabolites in the hepatic tissue
As anticipated, a further and complete removal of the purine bases and nucleosides in the hepatic
tissue was observed in both experiments (Table 4 in paper II and manuscript III). Uric acid was also
almost completely removed, with only small amounts being released from the splanchnic tissues. In
paper II, it was indicated based on the negative net hepatic flux of allantoin, that allantoin was degraded in the hepatic tissue. This was unfortunately probably a result of the method being unable to
quantify allantoin as precisely as hoped for. Following the repair of the instrument performed between the two experiments, the close to zero net hepatic flux of allantoin in manuscript III very
nicely showed what was expected; i.e., that allantoin simple passes through the hepatic tissue. These
results were also reflected in the NP% of the purine nucleosides and bases (approx. 100%) as well
as of uric acid and allantoin (approx. 100%) (Table 5 in paper II). From these results, it becomes
evident that the considerable amounts of uric acid and allantoin excreted by dairy cows (Chen and
Ørskov, 2004; Tas and Susenbeth, 2007; Verbic et al., 1990) first of all, originates from the very
effective degradation in the small intestine and intestinal mucosa and secondly, from the final and
almost complete degradation across the hepatic tissue, and from endogenous losses (Chen and
Gomes, 1992; McAllan, 1980; Verbic et al., 1990). Due to the relatively small net hepatic fluxes
and endogenous contributions, effects of postprandial pattern were not detected in the hepatic metabolism of the purine metabolites (Table 4 in paper II).
The small levels of hepatic removal and endogenous contributions of metabolites in the liver, was
also the reason why effects of dietary protein levels otherwise measurable at the level of PDV release became harder to detect across the hepatic tissue. This was especially the case for the net hepatic removal of the purine nucleosides and bases. Still, a protein level effect was detected in the net
hepatic removal of 2’-deoxyguanosine. In contrast to the missing effect on the PDV release of uric
acid, a linear effect was observed for the net hepatic removal, demonstrating an almost complete
degradation of uric acid in the hepatic tissue. As anticipated, no effect was observed for allantoin,
since allantoin has been shown to pass through the hepatic tissue. Diet composition in experiment II
was adjusted to minimize differences in the total concentrations of starch, water soluble carbohydrates, or neutral detergent fiber across the 2 × 3 treatments. This meant that effects of subtle
146
changes in carbohydrate concentrations, forage source (grass vs corn silage), and rate of degradation on the rumen outflow of purine metabolites could possibly be hard to detect in this study (Clark
et al., 1992; Nocek and Russell, 1988; Reynolds et al., 2001). Hence, no effects of forage source
were detected in any part of the purine PDV or hepatic metabolisms (Table 4 in manuscript III).
This was in agreement with the findings by Baratt et al., who did not identify effects of forage
source in any measured nitrogen parameters either (Barratt et al., 2013).
Excretion of purine metabolites in urine and milk
Urinary excretion has been found to be the primary route of disposal of purine degradation products
(Balcells et al., 1991; Chen et al., 1990a; Vagnoni et al., 1997; Verbic et al., 1990) and the level of
excretion in especially urine but also in milk can be used as an indirect measure of rumen microbial
biosynthesis (Chen and Ørskov, 2004; Giesecke et al., 1994; Gonda and Lindberg, 1997; GonzalezRonquillo, 2004; Tas and Susenbeth, 2007; Verbic et al., 1990). In cattle, 82-93% of the urinary
excreted purine degradation products are allantoin, the remainder is uric acid but other products,
such as xanthine and hypoxanthine, have also been identified in bovine urine in small concentrations (Chen et al., 1990a; Yanez-Ruiz et al., 2004). Renal variables were determined in experiment I
and they showed in full agreement with previous studies that large amounts of allantoin and uric
acid, with typical clearance rates, and not hypoxanthine and xanthine, were excreted in the urine
(Table 6 in paper II) (Bristow et al., 1992; Giesecke et al., 1994; Gonzalez-Ronquillo et al., 2004;
Martín-Orúe et al., 2000; Valadares et al., 1999; Verbic et al., 1990).
Some of the purine degradation products may also be cleared by secretion into milk (Giesecke et al.,
1994; Gonda and Lindberg, 1997; Tiemeyer et al., 1984). Studies have shown that concentrations of
uric acid and allantoin in milk correlate with their plasma and urine concentrations as well as feed
composition. In this study, this route of disposal was assessed by venous-arterial concentration differences between the epigastric vein and artery. Keeping in mind that plasma flows are needed to
calculate actual fluxes, these results could give an indication about the flux of these metabolites
across the mammary gland. In contrast to previous reports, uptake of uric acid and allantoin into the
mammary gland were not detected (Table 5 in manuscript III). This suggested that the rate of transfer from arterial blood to the mammary tissues and milk may be too small to be measured based on
venous-arterial concentration differences. However, 2’-deoxyinosine and guanine was shown to be
taken up by the mammary gland and guanosine and inosine released into the arterial blood. Inosine
was the purine metabolite with the highest venous-arterial concentration difference in the study and
by estimating a mammary plasma flow and a net mammary flux for this metabolite, it became clear
that a release from the mammary gland to the liver of this nucleoside probably exists in dairy cows
(Larsen et al., 2014). This was in agreement with reports showing that with advancing lactation, the
147
rate of cell proliferation in the mammary gland is exceeded by the rate of cell apoptosis and hence,
in all probability, release of degraded nucleic acids from the mammary gland into milk and arterial
blood (Capuco et al., 2001; Sørensen et al., 2006).
9.2.2 The pyrimidine metabolism
The 10 pyrimidine metabolites were identified in all four types of experimental plasma samples
from the multicatheterized cows (Table 2 in paper II and manuscript III) and arterial levels, venousarterial concentration differences, net PDV, net hepatic, and total splanchnic fluxes as well as some
excretion parameters were examined.
Arterial levels of pyrimidine metabolites
It both cow experiments, it was determined that the arterial concentrations of the pyrimidine nucleosides were generally higher than the concentrations of the purine nucleosides, whereas the concentrations of the pyrimidine bases were in the same range as the purine bases (Table 2 in paper II and
manuscript III). The pyrimidine nucleoside concentrations were higher than for the pyrimidine bases. The concentrations of the pyrimidine degradation products were more variable but generally
lower than that of the purine degradation products. Also in the case of the pyrimidine nucleosides,
PDV release was clearly indicated by relatively high concentrations levels in the hepatic portal vein.
The different handling/storage employed during the two experiments did not, as seen for uric acid
and allantoin, induce different arterial levels of the pyrimidine metabolites. The greater ability of
the pyrimidine metabolites to withstand degradation fits with the observation that the pyrimidine
metabolites to a much larger extend were released from the PDV as nucleosides. The differences in
concentration levels clearly indicated that the mechanisms of the purine and pyrimidine absorption
and intermediary metabolism differed (Loffler et al., 2005). The arterial concentrations were, with a
few exceptions, not affected by postprandial pattern, protein level, or forage source.
The calculations of pyrimidine net fluxes were performed as for the purine metabolites and using
the same criteria and considerations. The concentration levels and venous-arterial concentration
differences of the pyrimidine bases were just as small as those of the purine bases (Table 3 in paper
II manuscript III) (Kristensen et al., 2007; Reynolds et al., 1988; Seal and Reynolds, 1993). Thus,
net fluxes were calculated for all pyrimidine metabolites, except β-ureidopropionic acid (only exp.
I), cytosine, thymine, and uracil.
Release of pyrimidine metabolites from the portal-drained viscera
The pattern of net PDV release of the pyrimidine metabolites was found to be quite different from
that of the purine metabolites. The pyrimidine metabolites were to a much larger extend released
from the PDV intact as nucleosides and much smaller amounts of pyrimidine degradation products
than purine degradation products were observed (Table 4 in paper II and manuscript III). From
148
these results, it became evident that the purine and pyrimidine metabolisms in the splanchnic tissues
differed in the dairy cows. The higher levels of released pyrimidine nucleosides and lower levels of
released degradation products suggested less active pyrimidine degradation in the small intestine
and intestinal mucosa. The pyrimidine metabolites in contrast to the purine degradation products,
have a possible outlet into other parts of the nitrogen metabolism; β-alanine can be recycled into the
β-alanine metabolism (Kenehisa et al., 2014: KEGG beta-alanine metabolism) and βaminoisobutyric acid can enter into the valine, leucine, and isoleucine metabolism and/or citric acid
cycle (Kenehisa et al., 2014: KEGG valine, leucine and isoleucine degradation). It could also partly
be a result of reuse of β-alanine and β-aminoisobutyric acid in the mucosal enterocytes or further
degradation to other intermediate products. It seemed that the pyrimidine nucleosides were not as
readily degraded to bases in the small intestine and intestinal mucosa as the purine nucleosides, but
when first degraded to pyrimidine bases, the further degradation to β-alanine, β-ureidopropionic
acid, and β-aminoisobutyric acid was rapid. Hence, even though xanthine oxidase does not degrade
pyrimidine metabolites, degradation enzymes of the pyrimidine metabolism must be active in the
intestinal mucosa and bovine blood (Fig. 2 in paper II and manuscript III). And again, as for the
purine metabolites, some of the absorbed metabolites may also originate from turnover in the intestinal mucosa and PDV (Chen and Gomes, 1992).
When estimating net gastrosplenic release of the pyrimidine metabolites to differentiate between
pyrimidine metabolites absorbed from the small intestine and released from the forestomachs and
other tissues, only β-ureidopropionic acid seemed to contribute significantly to the net PDV release
(60% of PDV release) (Remond et al., 1993; Storm et al., 2011). Yet, until further studies have determined if purine and pyrimidine metabolites can be absorbed from the rumen, this was believed to
be of endogenous origin. Due to the comprehensive digestion route and small concentration levels
and fluxes of most of the pyrimidine metabolites, it was uncertain if postprandial pattern would be
detectable in the net fluxes of the pyrimidine metabolites (Fujihara and Shem, 2011; McAllan,
1982; McDonald et al., 2011). Microbial nucleic acid biosynthesis and digestion is complex and
time demanding; first, feed nitrogenous components has to be broken down in the rumen, secondly,
the microbes have to synthesise microbial DNA and RNA, thirdly, the microbes have to pass from
the rumen to the small intestine, and finally, a second mode of digestion has to happen before final
absorption into the intestinal mucosa (Fujihara and Shem, 2011; McAllan, 1982; McDonald et al.,
2011; Volden, 2011). Even so, effects of postprandial pattern were, as for allantoin, detected for 2’deoxyuridine and β-alanine (Table 4 in paper II).
Positive linear effects of dietary protein levels were measured for the pyrimidine nucleosides; cytidine and uridine (Table 4 in manuscript III). Probably due to their lower levels of PDV release and
149
relatively high variability as a result of their relatively low precision in the method (Paper I), protein
level effects were not detectable for the remaining pyrimidine nucleosides and degradation products
and this even though relatively high levels of β-alanine and β-ureidopropionic acid were released
from the PDV.
Removal of pyrimidine metabolites in the hepatic tissue
Extensive hepatic removal was observed for all of the pyrimidine metabolites in both experiments
(Table 4 in paper II and manuscript III). Consistent with the theory that the pyrimidine degradation
products can function as intermediates in other parts of the nitrogen metabolism, the pyrimidine
degradation products were also almost completely removed in the hepatic tissue (Kenehisa et al.,
2014: KEGG pyrimidine metabolism, beta-alanine metabolism, and valine, leucine and isoleucine
degradation; Loffler et al., 2005). This again clearly demonstrated that the pyrimidine metabolism
differed from the purine metabolism in the splanchnic tissues such that there, in contrast to the net
splanchnic release of purine metabolites, was an absorption and metabolisation of the pyrimidine
metabolites within the splanchnic tissues. These results were also mirrored in the pyrimidine metabolite NP% of approximately 100% (Table 5 in manuscript II). However, a TI% of approximately
50%, suggested that the pyrimidine degradation enzymes in the hepatic tissue were not capable of
removing all of the pyrimidine metabolites entering from the PDV and the peripheral tissues. This
most likely reflected the fact that much higher levels of pyrimidine nucleotides and degradation
products entered the hepatic tissue and that the effectiveness of the hepatic tissue and the body’s
requirements or tolerance of the pyrimidine metabolites were different from that of the purine metabolites. Exactly what happens with the excess pyrimidine metabolites is unknown; maybe they are
used in peripheral tissues or are excreted. As expected, postprandial pattern was not detectable in
the hepatic metabolism of the pyrimidine metabolites (Table 4 in paper II).
As described previously, due to endogenous contributions of metabolites, the influences of dietary
protein levels were hard to detect in the hepatic fluxes. Hence, only the hepatic removal of cytidine
and not uridine was found to be affected by dietary protein level (Table 4 in manuscript III). Even
though the hepatic flux of β-aminoisobutyric was relatively low, a quadratic effect was also observed for β-aminoisobutyric acid. As was the case for the purine PDV and hepatic metabolism, no
effect of forage source was detected in the pyrimidine PDV and hepatic metabolism, presumably
due to the effects of adjustments made to the amounts and types of fed concentrates used in the 2 ×
3 diets (Table 4 in manuscript III). One exception was with β-ureidopropionic acid, where the corn
silage treatment gave rise to a higher net hepatic removal than on the grass treatment. Why an effect
was observed for β-ureidopropionic acid, and only in the hepatic flux, we are not able to explain
from this study. The concentration levels and fluxes of β-ureidopropionic acid were very different
150
between the two cow experiments and the results in general difficult to interpret (Table 3 and 4 in
paper II and manuscript III).
Excretion of pyrimidine metabolites in urine and milk
Despite methods have been developed for quantification of β-alanine and β-aminoisobutyric acid in
bovine urine and small levels have been detected, renal excretion of pyrimidine metabolites have
been sparsely investigated (Boudra et al., 2012). Renal variables of pyrimidine metabolites would
have been very informative but, the developed method was not applicable for quantitating pyrimidine metabolites in urine samples (Paper I). Initial steps toward extending the quantification method
to urine samples were made but, after having revealed large differences in matrix effects between
the two types of matrices (plasma and urine), it became clear that there was not time for further developing the method for use with urine samples (Table 3 and table 7 in paper I). The reported low
concentrations of pyrimidine metabolites in urine samples corresponded with our findings suggesting that the pyrimidine metabolites were being incorporated into other parts of the nitrogen metabolism in the splanchnic tissues, instead of being released and possibly excreted in the urine (Boudra
et al., 2012). To our knowledge, up until now, secretion of pyrimidine metabolites into milk has not
been studied in dairy cows. Mammary exchange of pyrimidine metabolites was evaluated by assessing venous-arterial concentration differences between the epigastric vein and artery (Table 5 in
manuscript III). Relatively large concentrations of uridine was shown to be released into the arterial
blood from the mammary gland and by estimating a mammary plasma flow and a net mammary
flux (Larsen et al., 2014), it was discovered that this nucleoside was released from the mammary
gland to the liver in considerable amounts. This corresponded with the fact that the total splanchnic
fluxes of uridine in general (Table 4 in paper II and manuscript III) were found to be negative, indicating an endogenous contribution and removal in the hepatic tissue. Also, an accelerated release of
degraded nucleic acids from the mammary gland as a consequence of a gradual increase in the rate
of cell apoptosis with advancing lactation fits with the observation of the relatively high net mammary release of uridine to the arterial blood (Capuco et al., 2001; Sørensen et al., 2006).
9.2.3 The fate of purine and pyrimidine nitrogen
The main issue with the very effective degradation and excretion of especially the purine metabolites in the dairy cow is the loss of valuable nitrogen, which could have been used for synthesis of
protein. To be able to understand the consequences of the purine and pyrimidine metabolism with
regard to their inherent nitrogen, the fate of the purine nitrogen, pyrimidine nitrogen, and total nucleic acid nitrogen was examined in experiment I and II by estimating net PDV and net hepatic nitrogen fluxes (Fig. 3 in paper II and table 5 in manuscript III). The metabolite nitrogen fluxes mirrored the metabolite fluxes but, an important difference was the incorporation of the purine and py151
rimidine metabolite nitrogen content. Purine and pyrimidine metabolites on average contain 5 and
2.5 nitrogen molecules per purine or pyrimidine base, respectively. Seeing that the purine and pyrimidine bases are complementary in the DNA and RNA strands, this meant that presumably 2/3
and only 1/3 of the nucleic acid nitrogen entering the small intestine was fixed in purine and pyrimidine metabolites, respectively (McDonald et al., 2011). Hence, the purine metabolites contained
much larger amounts of microbial nucleic acid nitrogen than the pyrimidine metabolites. It experiment I and II, it was shown that considerable amounts of purine nitrogen was released from the
splanchnic tissues as uric acid and allantoin and as such it was lost to anabolic processes following
possible excretion in urine and milk. The pyrimidine nitrogen fluxes on the other hand, revealed
that the pyrimidine nitrogen to a much greater extend was used in alternative anabolic pathways of
the nitrogen metabolism within the splanchnic tissues. It was specifically interesting to assess if
effects of dietary protein level was, as they were in the metabolite fluxes, detectable in the nitrogen
fluxes (Table 5 in manuscript III). The magnitude of the loss of especially the purine nitrogen but
also the pyrimidine nitrogen was found to vary with diet composition, especially with the dietary
protein level, and with the microbial flow to the small intestine. Since uric acid and allantoin was
the main contributor to the estimation of total nucleic acid nitrogen, fluxes and treatment effects of
nucleic acid nitrogen generally mirrored those of purine nitrogen.
A major difference between the purine and pyrimidine metabolism discovered in this study was the
efficiency of absorption. Taking into account that the digestibility of DNA and RNA is around 80%
in the small intestine, in experiment I, approximately 80% of the purine nitrogen entering the small
intestine was found to be absorbed and only 30% of the pyrimidine nitrogen (Paper II) (McAllan,
1980). Similar but slightly higher estimates were obtained in experiment II. Most likely, the purine
and pyrimidine nitrogen not absorbed would be lost directly in the faeces. Hence, much of the purine and especially pyrimidine nitrogen seemed to be lost for the dairy cow prior to absorption from
the small intestine. As some of the purine and pyrimidine metabolites could have been reused directly in the intestinal mucosa or taken up as other nitrogen components than the ones measured,
possible more of the purine and pyrimidine nitrogen was absorbed from the small intestine than
estimated (Kenehisa et al., 2014: KEGG pyrimidine metabolism, beta-alanine metabolism, and valine, leucine and isoleucine degradation; Loffler et al., 2005). The high level of absorption of the
purine metabolites could have resulted in a beneficial reuse of the purine nitrogen in the dairy cow.
However, the very effective intermediary purine metabolism resulted in splanchnic release of the
absorbed purine nitrogen. Since ammonia is released during degradation, some of the purine nitrogen might be saved through urea recycling (Kristensen et al., 2010; Røjen et al., 2011). Concerning
the purine metabolism, a less efficient degradation of the purine metabolites prior to PDV release
152
and in the splanchnic tissues could possible result in larger amounts of purine nucleosides to be absorbed and reused in the splanchnic tissue resulting in less splanchnic release. However, so far no
indications of a possible anabolic reuse of purine metabolites in the splanchnic tissues have been
reported. Thus, even if it was possible to diminish purine degradation in the small intestine, intestinal mucosa, blood, and hepatic tissue, this would most likely not result in a more nitrogen economical metabolism of purine metabolites. Regarding the pyrimidine metabolism, an optimization of the
absorption efficiency of the pyrimidine metabolites could result in less pyrimidine nitrogen loss in
faeces but even if possible, whether the splanchnic metabolism and especially the liver metabolism
would be able to handle elevated levels of pyrimidine metabolites is uncertain. This would most
likely simple result in a larger amount of pyrimidine metabolites released to the arterial blood pool
leading to accumulation and possible negative consequences hereof.
If comparing the total splanchnic release of nucleic acid nitrogen (Table 6 in manuscript III) with
the overall nitrogen intake (Barratt et al., 2013), the nucleic acid nitrogen release corresponded to
approximately 11% of overall nitrogen intake at a dietary level of 15% of DM and only 0% at a
level of 12.5% of DM, across treatments. This demonstrated how the nucleic acid nitrogen release
from the splanchnic tissues became larger when going from a dietary protein level of 12.5% to
15.0% of DM. The inefficient use of nucleic acid nitrogen within the splanchnic tissues were further
demonstrated by taking into consideration the milk nitrogen efficiency and regarding the nitrogen
not used for milk production as a loss for the dairy cow milk production. In that case, approximately
15% of nitrogen unused for milk production was released from the total splanchnic tissue as nucleic
acid nitrogen (CP 15.0% of DM).
By combining the discussed metabolite nitrogen fluxes in the splanchnic tissues with purine and
pyrimidine renal variables (experiment I) and mammary fluxes (experiment II), an overview of the
movements and fates of purine and pyrimidine nitrogen in the dairy cow was constructed (Fig. 14).
In experiment I, the dietary protein level was approximately 15% of DM providing metabolisable
protein near estimated requirements (Thomas, 2004). Hence, to be able to apply data from both experiments, the overview was constructed from both the estimated purine and pyrimidine nitrogen
fluxes and renal variables from experiment I (Paper II) and, the purine and pyrimidine nitrogen
fluxes from the 15% dietary protein level (CP 15% of DM; corn and grass silage treatments) and
estimated nitrogen mammary fluxes from experiment II (Manuscript III). Values of purine nitrogen
in milk were obtained from Gonda and Lindberg (1997). The odd net hepatic and total splanchnic
fluxes of allantoin in experiment I was left out of the calculations (Paper II). Also, the mammary
flux of 2’-deoxyuridine was left out of the estimation of purine nitrogen mammary flux due to inconsistent results (manuscript III). As described previously, pyrimidine renal variables were not
153
determined in this study. Nevertheless, even though not depicted in the overview, relatively low
concentrations of pyrimidine metabolites have been reported in urine samples (Boudra et al., 2012).
So as to keep perspective and because these parameters have already been discussed, effects of
postprandial pattern, dietary protein level, and forage source were not incorporated.
Figure 14. Purine and pyrimidine nitrogen in the dairy cow. N, nitrogen; Pu-N, purine nitrogen; Py-N, pyrimidine
nitrogen.
From this overview, it becomes evident that the movements and fates of the purine and pyrimidine
metabolite nitrogen play a significant role in the overall nitrogen metabolism in dairy cows. What
becomes obvious from this figure is also, that only about 25-30% of the purine nitrogen released
from the splanchnic tissues seems to be excreted in the urine on a daily basis and that secretion into
milk only account for less than 1% of the remaining 75%. What the fate is of the remaining 75% of
the released uric acid and allantoin nitrogen in the dairy cow is unknown.
154
10. Conclusions
The overall objective of this Ph.D. study was to improve our knowledge about the quantitative absorption and intermediary metabolism of purine and pyrimidine metabolites in lactating dairy cows
in order to possible discover new ways to improve the overall nitrogen efficiency.
In conclusion (hyp. a), it was possible to develop and validate a LC-ESI-MS/MS method for simultaneous and accurate quantification of 20 purine and pyrimidine metabolites in bovine blood plasma. The metabolites were prior to analysis isolated and concentrated to a satisfactory level, using a
pre-treatment protocol consisting of protein precipitation, ultrafiltration, evaporation, and resolution. The procedure covered relevant quantification ranges and ensured efficient accuracies and
removal of matrix components. Moreover, it was selective, sensitive, stable, and precise enough to
detect small venous-arterial concentration differences used for determining splanchnic fluxes.
In conclusion (hyp. b), all of the examined purine and pyrimidine metabolites were released to different extends from the PDV. The level of release of the purine and the pyrimidine bases was low.
The purine metabolites were primarily released as fully degraded uric acid and allantoin and only to
minor degrees as purine bases and nucleosides. Following a full removal in the hepatic tissue, the
purine metabolism resulted in a large net splanchnic release of uric acid and allantoin. In addition,
the pyrimidine metabolites were to a much larger extend released from the PDV as nucleosides, as
well as the degradation products β-alanine and β-aminoisobutyric acid, and an almost complete removal in the hepatic tissue resulted in almost no total splanchnic release. Effects of postprandial
pattern, dietary protein level, and forage source were detected for metabolites with considerable
levels of fluxes, good precision in the method, and mainly at release from the PDV. Postprandial
pattern was only found to have an effect on the net PDV release rates of allantoin, 2’-deoxyuridine,
and β-alanine. Net fluxes were found to be positively affected by dietary protein levels and in general the net PDV release reflected predicted levels of microbial flow to the small intestine. This was
especially the case for uric acid, allantoin, cytidine, and uridine. Hepatic removal of the nucleosides
tended to be smaller and more variable. None of the net PDV, net hepatic, or total splanchnic fluxes
of was found to be influenced by forage source.
In conclusion (hyp. c), the fate of purine and pyrimidine nitrogen was found to be different. Considerable amounts of purine nitrogen were released from the splanchnic tissues on a net basis. The pyrimidine metabolites were found to be less effectively absorbed from the small intestine but alternative use in anabolic processes presumably saved some of the absorbed pyrimidine nitrogen. Purine
nitrogen was the main contributor to nucleic acid nitrogen release from the splanchnic tissues but
only about 25% of this seemed to be excreted in urine and milk on a daily basis, the remaining purine nitrogen is so far unaccounted for.
155
11. Perspectives
The low nitrogen efficiency by dairy cows has implications for production performance as well as
for the environment. It is expected that improved nitrogen utilization may be achieved through better understanding of components and mechanisms involved in the nitrogen metabolism of dairy
cows. A better understanding of other nitrogenous components than proteins, such as microbial nucleic acids, and their quantitative absorption and intermediary metabolism in the PDV, hepatic, and
peripheral tissues may offer knowledge to possibly reduce dietary nitrogen requirements in dairy
cows and reduce urinary nitrogen excretion in particular from the very effective intermediary purine
degradation.
In order to obtain a full understanding of the nucleic acid nitrogen flow and improve the utilization
of nitrogen in dairy cows, especially at high levels of dietary protein, it is important to further examine some of the following parameters.
A large proportion of purine nitrogen is lost due to an efficient degradation of the purine metabolites prior to PDV release and in the splanchnic tissues. Some of this purine nitrogen could possibly
be reused in the splanchnic tissues in the form of nucleosides by protecting the purine metabolites
from such a comprehensive degradation in the small intestine and intestinal mucosa. On the other
hand, no indications of a possible anabolic reuse of purine metabolites in the splanchnic tissues
have been reported. Regarding the pyrimidine nitrogen, an optimization of the absorption efficiency
of the pyrimidine metabolites could result in less pyrimidine nitrogen loss in faeces. However,
whether the splanchnic metabolism and especially the liver metabolism would be able to handle
elevated levels of pyrimidine metabolites is uncertain. Most likely, a larger amount of pyrimidine
metabolites released to the arterial blood pool would lead to a disadvantage accumulation.
The questions as to where the majority of the purine nitrogen released from the splanchnic tissues
end up is still unanswered. Further studies to give a more fulfilling picture of especially the movements of purine nitrogen after release of the splanchnic tissues would provide additional knowledge
to the possibility of optimising utilisation of nucleic acid nitrogen in dairy cows.
The developed LC-ESI-MS/MS technique for quantifying purine and pyrimidine metabolites in
bovine blood was developed could be used for examining other aspects of the nucleic acid metabolism not only in dairy cows but also in other species. With regard to the LC-ESI-MS/MS procedure,
additional experiments on the stability of samples during repeated freeze/thaw cycles could provide
more certainty as to why the levels of allantoin and uric acid was so different between experiment I
and II. Also, a re-validation of the within-day variation (%) between experiment I and II could confirm the notion that the repair had improved the precision of especially allantoin detection.
156
12. References
Abdoun, K., F. Stumpff, and H. Martens. 2006. Ammonia and urea transport across the rumen epithelium: A
review. Anim. Health. Res. Rev. 7:43-59.
Agilent Technologies. Accessed Sep. 1, 2014. http://www.agilent.com/home
Al-Khalidi, U. A. and T. H. Chaglassian. 1965. The species distribution of xanthine oxidase. Biochem. J. 97:318310.
Antignac, J.-P., K. de Wasch, F. Monteau, H. De Brabander, F. Andre, and B. Le Bizec. 2005. The ion
suppression phenomenon in liquid chromatography-mass spectrometry and its consequences in the field of
residue analysis. Analytica. Chimica. Acta. 529:129-136.
Ardrey, B. 2003. Liquid chromatography-mass spectrometry: An introduction. 1st ed. John Wiley & Sons Ltd,
Chichester, UK.
Bakhtiar, R. and T. K. Majumdar. 2007. Tracking problems and possible solutions in the quantitative
determination of small molecule drugs and metabolites in biological fluids using liquid chromatographymass spectrometry. J. Pharmacol. Toxicol. Methods. 55:262-278.
Balcells, J., J. A. Guada, C. Castrillo, and J. Gasa. 1991. Urinary excretion of allantoin and allantoin precursors by
sheep after different rates of purine infusion into the duodenum. J. Agric. Sci. 116:309-317.
Balcells, J., J. A. Guada, J. M. Peiro, and D. S. Parker. 1992b. Simultaneous determination of allantoin and
oxypurines in biological fluids by high-performance liquid chromatography. J. Chromatogr. 575:153-157.
Balcells, J., D. S. Parker, and C. J. Seal. 1992a. Purine metabolite concentrations in portal and peripheral blood of
steers, sheep and rats. Comp. Biochem. Physiol. B. 101:633-636.
Barnard, E. A. 1969. Biological function of pancreatic ribonuclease. Nature. 221:340-344.
Barratt, C., L. Crompton, C. Green, D. Humphries, R. Pilgrim, and C. K. Reynolds. 2013. Effect of dietary protein
concentration and forage type on nitrogen metabolism and nutrient flux across the portal drained viscera
and the liver in lactating dairy cows. Pages 407-408 in Energy and Protein Metabolism and Nutrition in
Sustainable Animal Production. EAAP publication No.134. J. W. Oltjen, E. Kebreab, and H. Lapierre, ed.
Wageningen Academic Publishers, Wageningen, NL. (Abstr.)
Berg, J. M., J. L. Tymoczko, and L. Stryer. 2002. Biochemistry. 5th ed. W. H. Freeman and Co., New York, US.
Berg, T. and D. H. Strand. 2011. 13C labelled internal standards - a solution to minimize ion suppression effects in
liquid chromatography-tandem mass spectrometry analyses of drugs in biological samples? J. Chromatogr.
A. 1218:9366-9374.
Boudra, H., M. Doreau, P. Noziere, E. Pujos-Guillot, and D. P. Morgavi. 2012. Simultaneous analysis of the main
markers of nitrogen status in dairy cow's urine using hydrophilic interaction chromatography and tandem
mass spectrometry detection. J. Chromatogr. A. 1256:169-176.
157
Bristow, A. W., D. C. Whitehead, and J. E. Cockburn. 1992. Nitrogenous constituents in the urine of cattle, sheep
and goats. J. Sci. Food Agric. 59:387-394.
Børsting, C. F., T. Kristensen, L. Misciattelli, T. Hvelplund, and M. R. Weisbjerg. 2003. Reducing nitrogen
surplus from dairy farms. Effects of feeding and management. Livest. Prod. Sci. 83:165-178.
Calsamiglia, S., A. Ferret, C. K. Reynolds, N. B. Kristensen, and A. M. van Vuuren. 2010. Strategies for
optimizing nitrogen use by ruminants. Animal. 4:1184-1196.
Campbell, L. L. 1960. Reductive degradation of pyrimidines. V. Enzymatic conversion of N-carbamyl-betaalanine to beta-alanine, carbon dioxide, and ammonia. J. Biol. Chem. 235:2375-2378.
Campbell, L. L. 1957. Reductive degradation of pyrimidines. III. Purification and properties of dihydrouracil
dehydrogenase. J. Biol. Chem. 227:693-700.
Campbell, L. L. 1958. Reductive degradation of pyrimidines. IV. Purification and properties of dihydrouracil
hydrase. J. Biol. Chem. 233:1236-1240.
Cant, J. P., E. J. DePeters, and R. L. Baldwin. 1993. Mammary amino acid utilization in dairy cows fed fat and its
relationship to milk protein depression. J. Dairy Sci. 76:762-774.
Capuco, A. V., D. L. Wood, R. Baldwin, K. McLeod, and M. J. Paape. 2001. Mammary cell number, proliferation, and apoptosis during a bovine lactation: Relation to milk production and effect of bST. J. Dairy Sci.
84:2177-2187.
Carver, J. D. and W. Allan Walker. 1995. The role of nucleotides in human nutrition. J. Nutr. Biochem. 6:58-72.
Chambers, E., D. M. Wagrowski-Diehl, Z. Lu, and J. R. Mazzeo. 2007. Systematic and comprehensive strategy
for reducing matrix effects in LC/MS/MS analyses. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.
852:22-34.
Chen, X. and E. Ørskov. 2004. Research on urinary excretion of purine derivatives in ruminants: Past, present and
future. Pages 180-210 in Estimation of Microbial Protein Supply in Ruminants using Urinary Purine Derivatives. H.P.S. Makkar and X.B. Chen ed. FAO/IAEA, Kluwer Academic Publishers, Dordrecht, NL.
Chen, X. B. and M. J. Gomes. 1992. Estimation of microbial protein supply to sheep and cattle based on urinary
excretion of purine derivatives - an overview of the technical details. Occasional Publication of
International Feed Resources Unit, Rowett Research Institute, Bucksburn, UK.
Chen, X. B., J. Mathieson, F. Hovell, and P. J. Reeds. 1990b. Measurement of purine derivatives in urine of
ruminants using automated methods. J. Sci. Food Agric. 53:23-33.
Chen, X. B., E. R. Ørskov, and F. D. D. Hovell. 1990a. Excretion of purine derivatives by ruminants - endogenous
excretion, differences between cattle and sheep. Br. J. Nutr. 63:121-129.
Clariana, M., M. Gratacós-Cubarsí, M. Hortós, J. García-Regueiro, and M. Castellari. 2010. Analysis of seven
purines and pyrimidines in pork meat products by ultra high performance liquid chromatography-tandem
mass spectrometry. J. Chromatogr. A. 1217:4294-4299.
158
Clark, J. H., T. H. Klusmeyer, and M. R. Cameron. 1992. Microbial protein synthesis and flows of nitrogen
fractions to the duodenum of dairy cows. J. Dairy Sci. 75:2304-2323.
Coleman, G. S. 1968. The metabolism of bacterial nucleic acid and of free components of nucleic acid by the
rumen ciliate Entodinium caudatum. J. Gen. Microbiol. 54:83-96.
Cyriac, J., A. G. Rius, M. L. McGilliard, R. E. Pearson, B. J. Bequette, and M. D. Hanigan. 2008. Lactation
performance of mid-lactation dairy cows fed ruminally degradable protein at concentrations lower than
national research council recommendations. J. Dairy Sci. 91:4704-4713.
Dewhurst, R. J., D. R. Davies, and R. J. Merry. 2000. Microbial protein supply from the rumen. Anim. Feed Sci.
Technol. 85:1-21.
Dreisewerd, K. 2003. The desorption process in MALDI. Chem. Rev. 103:395-426.
Firkins, J. L. 1996. Maximizing microbial protein synthesis in the rumen. J. Nutr. 126:1347-1354.
Forcisi, S., F. Moritz, B. Kanawati, D. Tziotis, R. Lehmann, and P. Schmitt-Kopplin. 2013. Liquid
chromatography-mass spectrometry in metabolomics research: Mass analyzers in ultra high pressure liquid
chromatography coupling. J. Chromatogr. A. 1292:51-65.
Fu, Q., F. S. Schoenhoff, W. J. Savage, P. Zhang, and J. E. Van Eyk. 2010. Multiplex assays for biomarker
research and clinical application: Translational science coming of age. Proteomics. Clin. Appl. 4:271-284.
Fujihara, T. and M. N. Shem. 2011. Metabolism of microbial nitrogen in ruminants with special reference to
nucleic acids. Anim. Sci. J. 82:198-208.
Fukusaki, E., K. Harada, T. Bamba, and A. Kobayashi. 2005. An isotope effect on the comparative quantification
of flavonoids by means of methylation-based stable isotope dilution coupled with capillary liquid
chromatography/mass spectrometry. J. Biosci. Bioeng. 99:75-77.
George, S., M. Dipu, U. Mehra, P. Singh, A. Verma, and J. Ramgaokar. 2006. Improved HPLC method for the
simultaneous determination of allantoin, uric acid and creatinine in cattle urine. J. Chromatogr. B. 832:134137.
Giesecke, D., L. Ehrentreich, M. Stangassinger, and F. Ahrens. 1994. Mammary and renal excretion of purine
metabolites in relation to energy intake and milk yield in dairy cows. J. Dairy Sci. 77:2376-2381.
Gonda, H. L. and J. E. Lindberg. 1997. Effect of diet on milk allantoin and its relationship with urinary allantoin
in dairy cows. J. Dairy Sci. 80:364-373.
Gong, Y. X., S. P. Li, P. Li, J. J. Liu, and Y. T. Wang. 2004. Simultaneous determination of six main nucleosides
and bases in natural and cultured Cordyceps by capillary electrophoresis. J. Chromatogr. A. 1055:215-221.
Gonzalez-Ronquillo, M., J. Balcells, A. Belenguer, C. Castrillo, and M. Mota. 2004. A comparison of purine
derivatives excretion with conventional methods as indices of microbial yield in dairy cows. J. Dairy Sci.
87:2211-2221.
159
Gosetti, F., E. Mazzucco, D. Zampieri, and M. C. Gennaro. 2010. Signal suppression/enhancement in highperformance liquid chromatography tandem mass spectrometry. J. Chromatogr. A. 1217:3929-3937.
Guideline EMA. European Medicines Agency. Committee for medicinal products for human use. Guideline on
bioanalytical method validation. 2011. London, UK. Accessed Oct. 1, 2014.
http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2011/08/WC500109686.pdf
Guideline FDA. U.S Department of Health and Human Services, Food and Drug Administration. Guidance for
industry, bioanalytical method validation. 2001. Rockville, Maryland, US. Accessed Oct. 1, 2014.
http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm368107.pdf
Guideline ICH. International Conference on Harmonization of Technical Requirements for Registration of
Pharmaceuticals for Human Use. Q2 (R1): Text on validation of analytical procedures. 2005. Geneva, CH.
Accessed Oct. 1, 2014.
http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q2_R1/Step4/Q2_R1__
Guideline.pdf
Hartmann, C., J. Smeyers-Verbeke, D. L. Massart, and R. D. McDowall. 1998. Validation of bioanalytical
chromatographic methods. J. Pharm. Biomed. Anal. 17:193-218.
Hartmann, S., J. G. Okun, C. Schmidt, C. D. Langhans, S. F. Garbade, P. Burgard, D. Haas, J. O. Sass, W. L.
Nyhan, and G. F. Hoffmann. 2006. Comprehensive detection of disorders of purine and pyrimidine
metabolism by HPLC with electrospray ionization tandem mass spectrometry. Clin. Chem. 52:1127-1137.
Haunschmidt, M., W. Buchberger, and C. W. Klampfl. 2008. Investigations on the migration behaviour of purines
and pyrimidines in capillary electromigration techniques with UV detection and mass spectrometric
detection. J. Chromatogr. A. 1213:88-92.
Hewavitharana, A. K. 2011. Matrix matching in liquid chromatography-mass spectrometry with stable isotope
labelled internal standards - is it necessary? J. Chromatogr. A. 1218:359-361.
Holčapek, M., R. Jirásko, and M. Lisa. 2012. Recent developments in liquid chromatography-mass spectrometry
and related techniques. J Chromatogr A 1259:3-15.
Honour, J. W. 2011. Development and validation of a quantitative assay based on tandem mass spectrometry.
Ann. Clin. Biochem. 48:97-111.
Hopfgartner, G. and E. Bourgogne. 2003. Quantitative high-throughput analysis of drugs in biological matrices by
mass spectrometry. Mass. Spectrom. Rev. 22:195-214.
Hua, N.-P. and T. Naganuma. 2007. Application of CE for determination of DNA base composition.
Electrophoresis. 28:366-372.
Huang, M. Z., C. H. Yuan, S. C. Cheng, Y. T. Cho, and J. Shiea. 2010. Ambient ionization mass spectrometry.
Annu. Rev. Anal. Chem (Palo Alto Calif). 3:43-65.
Huhtanen, P. and A. N. Hristov. 2009. A meta-analysis of the effects of dietary protein concentration and
degradability on milk protein yield and milk N efficiency in dairy cows. J. Dairy Sci. 92:3222-3232.
160
Huntington, G. B., C. K. Reynolds, and B. H. Stroud. 1989. Techniques for measuring blood-flow in splanchnic
tissues of cattle. J. Dairy Sci. 72:1583-1595.
Ipharraguerre, I. R. and J. H. Clark. 2005. Impacts of the source and amount of crude protein on the intestinal
supply of nitrogen fractions and performance of dairy cows. J. Dairy Sci. 88(E-Suppl):22-37.
IUPAC. Abbreviations and symbols for nucleic acids, polynucleotides and their constituents. Accessed Nov. 1,
2014. http://www.chem.qmul.ac.uk/iupac/misc/naabb.html
Jessome, L. L. and D. A. Volmer. 2006. Ion suppression: A major concern in mass spectrometry. LC GC N. Am.
24:498-510.
Johnson, L. M., J. H. Harrison, and R. E. Riley. 1998. Estimation of the flow of microbial nitrogen to the
duodenum using urinary uric acid or allantoin. J. Dairy Sci. 81:2408-2420.
Kanehisa, M., S. Goto, Y. Sato, M. Kawashima, M. Furumichi, and M. Tanabe. 2014. Data, information,
knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42(Database issue):199-205.
Kang, J.-S. 2012. Tandem Mass Spectrometry - Applications and Principles. Principles and applications of LCMS/MS for the quantitative bioanalysis of analytes in various biological samples. D. J. Prasain, ed. InTech,
Rijeka, HR. (E-book)
Katz, M. L. and E. N. Bergman. 1969a. A method for simultaneous cannulation of the major splanchnic blood
vessels of the sheep. Am. J. Vet. Res. 30:655-661.
Katz, M. L. and E. N. Bergman. 1969b. Simultaneous measurements of hepatic and portal venous blood flow in
the sheep and dog. Am. J. Physiol. 216:946-952.
Kazoka, H. 2002. Analysis of purines and pyrimidines by mixed partition-adsorption normal-phase highperformance liquid chromatography. J. Chromatogr. A. 942:1-10.
Kebarle, P. and L. Tang. 1993. From ions in sollution to the gas phase: The mechanism of electrospray mass
spectrometry. Anal. Chem. 65:972-986.
Kebarle, P. and U. H. Verkerk. 2009. Electrospray: From ions in solution to ions in the gas phase, what we know
now. Mass Spectrom. Rev. 28:898-917.
Kebreab, E., J. France, D. E. Beever, and A. R. Castillo. 2001. Nitrogen pollution by dairy cows and its mitigation
by dietary manipulation. Nutr. Cycl. Agroecosys. 60:275-285.
KEGG beta-alanine metabolism. Kyoto Encyclopedia of Genes and Genomes. Accessed Oct. 1, 2014.
http://www.genome.jp/kegg/pathway/map/map00410.html.
KEGG purine metabolism. Kyoto Encyclopedia of Genes and Genomes. Accessed Oct. 1, 2014.
http://www.genome.jp/kegg/pathway/map/map00240.html.
KEGG pyrimidine metabolism. Kyoto Encyclopedia of Genes and Genomes. Accessed Oct. 1, 2014.
http://www.genome.jp/kegg/pathway/map/map00230.html.
161
KEGG valine, leucine and isoleucine degradation. Kyoto Encyclopedia of Genes and Genomes. Accessed Oct. 1,
2014. http://www.genome.jp/kegg-bin/show_pathway?map00280.
King, R., R. Bonfiglio, C. Fernandez-Metzler, C. Miller-Stein, and T. Olah. 2000. Mechanistic investigation of
ionization suppression in electrospray ionization. J. Am. Soc. Spectrom. 11:942-950.
Kohn, R. A., M. M. Dinneen, and E. Russek-Cohen. 2005. Using blood urea nitrogen to predict nitrogen excretion
and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats. J. Anim. Sci. 83:879-889.
Kole, P. L., G. Venkatesh, J. Kotecha, and R. Sheshala. 2011. Recent advances in sample preparation techniques
for effective bioanalytical methods. Biomed. Chromatogr. 25:199-217.
Kristensen, N. B., B. A. Røjen, B. M. L. Raun, A. C. Storm, L. Puggaard, and M. Larsen. 2009. Hepatic acetylation of the blood flow marker p-aminohippuric affect measurement of hepatic blood flow in cattle. Pages
558-559 in XIth International Symposium on Ruminant Physiology, Clermont-Ferrand, France. Y. Chilliard, F. Glasser, Y. Faulconnier, F. Bocquier, I. Veissier, and M. Doreau, ed. Wageningen Academic Publishers, Wageningen, NL. (Abstr.)
Kristensen, N. B., A. Storm, B. M. L. Raun, B. A. Rojen, and D. L. Harmon. 2007. Metabolism of silage alcohols
in lactating dairy cows. J. Dairy Sci. 90:1364-1377.
Kristensen, N. B., A. C. Storm, and M. Larsen. 2010. Effect of dietary nitrogen content and intravenous urea
infusion on ruminal and portal-drained visceral extraction of arterial urea in lactating Holstein cows. J.
Dairy Sci. 93:2670-2683.
Kruve, A., K. Herodes, and I. Leito. 2011. Accounting for matrix effects of pesticide residue liquid
chromatography/electrospray ionisation mass spectrometric determination by treatment of background
mass spectra with chemometric tools. Rapid Commun. Mass Spectrom. 25:1159-1168.
Kruve, A., A. Kunnapas, K. Herodes, and I. Leito. 2008. Matrix effects in pesticide multi-residue analysis by
liquid chromatography-mass spectrometry. J. Chromatogr. A. 1187:58-66.
Lapierre, H. and G. E. Lobley. 2001. Nitrogen recycling in the ruminant: A review. J. Dairy Sci. 84(E-Suppl):223236.
Larsen, M., H. Lapierre, and N. B. Kristensen. 2014. Abomasal protein infusion in postpartum transition dairy
cows: Effect on performance and mammary metabolism. J. Dairy Sci. 97:5608-5622.
Lemoine, J., T. Fortin, A. Salvador, A. Jaffuel, J. P. Charrier, and G. Choquet-Kastylevsky. 2012. The current
status of clinical proteomics and the use of MRM and MRM3 for biomarker validation. Expert Rev. Mol.
Diagn. 12:333-342.
Lin, H., D. K. Xu, and H. Y. Chen. 1997. Simultaneous determination of purine bases, ribonucleosides and
ribonucleotides by capillary electrophoresis electrochemistry with a copper electrode. J. Chromatogr. A.
760:227-233.
Liu, L., J. Ouyang, and W. R. G. Baeyens. 2008. Separation of purine and pyrimidine bases by ion
chromatography with direct conductivity detection. J. Chromatogr. A. 1193:104-108.
162
Loffler, M., L. D. Fairbanks, E. Zameitat, A. M. Marinaki, and H. A. Simmonds. 2005. Pyrimidine pathways in
health and disease. Trends Mol. Med. 11:430-437.
Martı́n-Orúe, S. M., J. Balcells, J. A. Guada, and M. Fondevila. 2000. Microbial nitrogen production in growing
heifers: Direct measurement of duodenal flow of purine bases versus urinary excretion of purine derivatives
as estimation procedures. Anim. Feed Sci. Technol. 88:171-188.
Matuszewski, B. K., M. L. Constanzer, and C. M. Chavez-Eng. 2003. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Anal. Chem. 75:3019-3030.
McAllan, A. B. and R. H. Smith. 1973a. Degradation of nucleic acids in the rumen. Br. J. Nutr. 29:331-345.
McAllan, A. B. 1980. The degradation of nucleic acids in, and the removal of breakdown products from the small
intestine of steers. Br. J. Nutr. 44:99-112.
McAllan, A. B. 1982. The fate of nucleic acids in ruminants. Proc. Nutr. Soc. 41:309-317.
McAllan, A. B. and R. H. Smith. 1973b. Degradation of nucleic acid derivatives by rumen bacteria in vitro. Br. J.
Nutr. 29:467-474.
McDonald, P., R. A. Edwards, J. F. D. Greenhalgh, C. A. Morgan, L. A. Sinclair, and R. G. Wilkinson. 2011.
Animal Nutrition. 7th ed. Pearson Education Limited, Essex, UK.
Mutavdzic, D., T. Pinusic, M. Perisa, and S. Babic. 2012. Optimization of matrix solid-phase dispersion for liquid
chromatography tandem mass spectrometry analysis of 12 pharmaceuticals in sediments. J. Chromatogr. A.
1258:1-15.
Nakayama, J., T. Fujiyoshi, M. Nakamura, and M. Anai. 1981. Purification and properties of an
endodeoxyribonuclease from nuclei of bovine small intestinal mucosa. J. Biol. Chem. 256:1636-1642.
Nilsson, L. B. and G. Eklund. 2007. Direct quantification in bioanalytical LC-MS/MS using internal calibration
via analyte/stable isotope ratio. J. Pharm. Biomed. Anal. 43:1094-1099.
Nocek, J. E. and J. B. Russell. 1988. Protein and energy as an integrated system. Relationship of ruminal protein
and carbohydrate availability to microbial synthesis and milk production. J. Dairy Sci. 71:2070-2107.
Nováková, L. 2013. Challenges in the development of bioanalytical liquid chromatography-mass spectrometry
method with emphasis on fast analysis. J. Chromatogr. A. 1292:25-37.
Nováková, L. and H. Vlčková. 2009. A review of current trends and advances in modern bio-analytical methods:
Chromatography and sample preparation. Anal. Chim. Acta. 656:8-35.
Peng, S. X., T. M. Branch, and S. L. King. 2000. Fully automated 96-well liquid-liquid extraction for analysis of
biological samples by liquid chromatography with tandem mass spectrometry. Anal. Chem. 73:708-714.
Peters, F. T., O. H. Drummer, and F. Musshoff. 2007. Validation of new methods. Forensic Sci. Int. 165:216-224.
Polson, C., P. Sarkar, B. Incledon, V. Raguvaran, and R. Grant. 2003. Optimization of protein precipitation based
upon effectiveness of protein removal and ionization effect in liquid chromatography-tandem mass
spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 785:263-275.
163
Poole, C. F. 2003. New trends in solid-phase extraction. Trends Analyt. Chem. 22:362-373.
Prakash, C., C. L. Shaffer, and A. Nedderman. 2007. Analytical strategies for identifying drug metabolites. Mass
Spectrom. Rev. 26:340-369.
Ramos, L. 2012. Critical overview of selected contemporary sample preparation techniques. J. Chromatogr. A.
1221:84-98.
Razzaque, M. A., J. H. Topps, R. N. B. Kay, and J. M. Brockway. 1981. Metabolism of the nucleic acids of rumen
bacteria by preruminant and ruminant lambs. Br. J. Nutr. 45:517-527.
Remond, D., J. P. Chaise, E. Delval, and C. Poncet. 1993. Net transfer of urea and ammonia across the ruminal
wall of sheep. J. Anim. Sci. 71:2785-2792.
Reynolds, C. K., S. B. Cammell, D. J. Humphries, D. E. Beever, J. D. Sutton, and J. R. Newbold. 2001. Effects of
postrumen starch infusion on milk production and energy metabolism in dairy cows. J. Dairy Sci. 84:22502259.
Reynolds, C. K., G. B. Huntington, H. F. Tyrrell, and P. J. Reynolds. 1988. Net metabolism of volatile fatty acids,
D-beta-hydroxybutyrate, nonesterifield fatty acids, and blood gasses by portal-drained viscera and liver of
lactating Holstein cows. J. Dairy Sci. 71:2395-2405.
Reynolds, C. K. and N. B. Kristensen. 2008. Nitrogen recycling through the gut and the nitrogen economy of
ruminants: An asynchronous symbiosis. J. Anim. Sci. 86(E-Suppl):293-305.
Richter, B. E., B. A. Jones, J. L. Ezzell, N. L. Porter, N. Avdalovic, and C. Pohl. 1996. Accelerated solvent
extraction: A technique for sample preparation. Anal. Chem. 68:1033-1039.
Røjen, B. A., P. K. Theil, and N. B. Kristensen. 2011. Effects of nitrogen supply on inter-organ fluxes of urea-N
and renal urea-N kinetics in lactating Holstein cows. J. Dairy Sci. 94:2532-2544.
Rosskopf, R., H. Rainer, and D. Giesecke. 1990. Purin- und pyrimidinmetaboliten zur beurteilung des
pansenstoffwechsels: HPLC-analysen in milch und blutplasma. Arch. Anim. Nutr. 41:411-426.
Roussos, G. G. 1963. Studies on a hypoxanthine oxidase from bovine small intestine. Biochim. Biophys. Acta.
73:338-340.
Seal, C. J. and C. K. Reynolds. 1993. Nutritional implications of gastrointestinal and liver metabolism in
ruminants. Nutr. Res. Rev. 6:185-208.
Smith, R. H. and A. B. McAllan. 1971. Nucleic acid metabolism in the ruminant. 3. Amounts of nucleic acids and
total and ammonia nitrogen in digesta from the rumen, duodenum and ileum of calves. Br. J. Nutr. 25:181190.
Smith, R. H. and A. B. McAllan. 1974. Some factors influencing the chemical composition of mixed rumen
bacteria. Br. J. Nutr. 31:2734.
Sørensen, M. T., J. V. Nørgaard, P. K. Theil, M. Vestergaard, and K. Sejrsen. 2006. Cell turnover and activity in
mammary tissue during lactation and the dry period in dairy cows. J. Dairy Sci. 89:4632-4639.
164
Steinfeld, H., P. Gerber, T. Wassenaar, V. Castel, M. Rosales, and C. de Haan. 2006. Livestock’s long shadow:
Environmental issues and options. Accessed Oct. 1, 2014.
http://www.fao.org/docrep/010/a0701e/a0701e00.htm.
Stentoft, C., M. Vestergaard, P. Løvendahl, N. B. Kristensen, J. M. Moorby, and S. K. Jensen. 2014. Simultaneous
quantification of purine and pyrimidine bases, nucleosides and their degradation products in bovine blood
plasma by high performance liquid chromatography tandem mass spectrometry. J. Chromatogr. A.
1356:197-210.
Stokvis, E., H. Rosing, and J. H. Beijnen. 2005. Stable isotopically labeled internal standards in quantitative
bioanalysis using liquid chromatography/mass spectrometry: Necessity or not? Rapid. Commun. Mass
Spectrom. 19:401-407.
Storm, A. C., M. D. Hanigan, and N. B. Kristensen. 2011. Effects of ruminal ammonia and butyrate
concentrations on reticuloruminal epithelial blood flow and volatile fatty acid absorption kinetics under
washed reticulorumen conditions in lactating dairy cows. J. Dairy Sci. 94:3980-3994.
Sunny, N. E., S. L. Owens, R. L. t. Baldwin, S. W. El-Kadi, R. A. Kohn, and B. J. Bequette. 2007. Salvage of
blood urea nitrogen in sheep is highly dependent on plasma urea concentration and the efficiency of capture
within the digestive tract. J. Anim. Sci. 85:1006-1013.
Swartz, M. E. 2005. UPLC™: An introduction and review. J. Liq. Chromatogr. Relat. Technol. 28:1253-1263.
Tamminga, S. 1992. Nutrition management of dairy cows as a contribution to pollution control. J. Dairy Sci.
75:345-357.
Tan, A., I. A. Levesque, I. M. Levesque, F. Viel, N. Boudreau, and A. Levesque. 2011. Analyte and internal
standard cross signal contributions and their impact on quantitation in LC-MS based bioanalysis. J.
Chromatogr. B Analyt. Technol. Biomed. Life Sci. 879:1954-1960.
Tas, B. M. and A. Susenbeth. 2007. Urinary purine derivates excretion as an indicator of in vivo microbial N flow
in cattle: A review. Livest. Sci. 111:181-192.
Taylor, P. J. 2005. Matrix effects: The achilles heel of quantitative high-performance liquid chromatographyelectrospray-tandem mass spectrometry. Clin. Biochem. 38:328-334.
Thomas, C. 2004. Feed into milk: An advisory manual. Nottingham University Press, Nottingham, UK.
Tiemeyer, W., M. Stohrer, and D. Giesecke. 1984. Metabolites of nucleic acids in bovine milk. J. Dairy Sci.
67:723-728.
Titgemeyer, E. C. 1997. Design and interpretation of nutrient digestion studies. J. Anim. Sci. 75:2235-2247.
Tong, X., I. E. Ita, J. Wang, and J. V. Pivnichny. 1999. Characterization of a technique for rapid pharmacokinetic
studies of multiple co-eluting compounds by LC/MS/MS. J. Pharm. Biomed. Anal. 20:773-784.
Vagnoni, D. B., G. A. Broderick, M. K. Clayton, and R. D. Hatfield. 1997. Excretion of purine derivatives by
Holstein cows abomasally infused with incremental amounts of purines. J. Dairy Sci. 80:1695-1702.
165
Valadares, R. F., G. A. Broderick, S. C. Valadares Filho, and M. K. Clayton. 1999. Effect of replacing alfalfa
silage with high moisture corn on ruminal protein synthesis estimated from excretion of total purine
derivatives. J. Dairy Sci. 82:2686-2696.
Van Eeckhaut, A., K. Lanckmans, S. Sarre, I. Smolders, and Y. Michotte. 2009. Validation of bioanalytical LCMS/MS assays: Evaluation of matrix effects. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.
877:2198-2207.
Van Nevel, C. and D. Demeyer. 1977. Determination of rumen microbial growth in vitro from 32P-labelled
phosphate incorporation. Br. J. Nutr. 38:101-114.
Verbic, J., X. B. Chen, N. A. Macleod, and E. R. Ørskov. 1990. Excretion of purine derivatives by rumminants –
effect of microbial nucleic acid infusion on purine derivative excretion by steers. J. Agric. Sci. 114:243248.
Virtanen, A. I. 1966. Milk production of cows on protein-free feed. Science. 153:1603-1614.
Vogeser, M. and C. Seger. 2010. Pitfalls associated with the use of liquid chromatography-tandem mass
spectrometry in the clinical laboratory. Clin. Chem. 56:1234-1244.
Volden, H. 2011. Norfor - The Nordic feed evaluation system. EAAP publication no. 130. Wageningen Academic
Publishers, Wageningen, NL.
Wang, S., M. Cyronak, and E. Yang. 2007. Does a stable isotopically labeled internal standard always correct
analyte response?: A matrix effect study on a LC/MS/MS method for the determination of carvedilol
enantiomers in human plasma. J. Pharm. Biomed. Anal. 43:701-707.
Waters Corporation. Accessed Sep. 1, 2014. http://www.waters.com/waters/home.htm
Watson, J. T. and O. D. Sparkman. 2007. Introduction to mass spectrometry: Instrumentation, applications, and
strategies for data interpretation. 4. ed. John Wiley, Hoboken, N.J.
Wooding, K. M. and R. J. Auchus. 2013. Mass spectrometry theory and application to adrenal diseases. Mol. Cell.
Endocrinol. 371:201-207.
Xu, R. N., L. Fan, M. J. Rieser, and T. A. El-Shourbagy. 2007. Recent advances in high-throughput quantitative
bioanalysis by LC-MS/MS. J. Pharm. Biomed. Anal. 44:342-355.
Yanez-Ruiz, D. R., A. I. M. Garcia, A. Moumen, and E. M. Alcaide. 2004. Ruminal fermentation and degradation
patterns, protozoa population and urinary purine derivatives excretion in goats and wethers fed diets based
on olive leaves. J. Anim. Sci. 82:3006-3014.
Zaikin, V. G. and J. M. Halket. 2006. Derivatization in mass spectrometry. 8. Soft ionization mass spectrometry of
small molecules. Eur. J. Mass Spectrom (Chichester, Eng). 12:79-115.
Zierler, K. L. 1961. Theory of the use of arteriovenous concentration differences for measuring metabolism in
steady and non-steady states. J. Clin. Invest. 40:2111-2125.
166
Appendix I
Purine and pyrimidine ribonucleotide biosynthesis, regulation and salvage
Purine biosynthesis: Purine ribonucleotides are synthesized de novo, beginning with simple starting
materials such as amino acids and bicarbonate (Fig. 15). They are assembled attached to a ribose
ring, beginning with the formation of an intermediate ribonucleotide; IMP.
The de novo purine ribonucleotide synthesis of IMP, AMP, and GMP, requires PRPP, a form of
ribose activated to accept purine and pyrimidine bases. Phosphoribosyl pyrophosphate is synthesized from R5P, formed by the pentose phosphate pathway, by the addition of pyrophosphate to
adenosine triphosphate (ATP). The PRPP construct provides the foundation on which the purine
bases are assembled. The initial committed step is the displacement of pyrophosphate by ammonia
from glutamine to produce 5-phosphoribosyl-1-amine (PRA). Nine steps are required to assemble
the purine ring. The first six are analogous. Each step consists of the activation of a carbon bound
oxygen atom, typically a carbonyl oxygen atom, by phosphorylation. This is followed by the displacement of a phosphoryl group by ammonia or an amine group acting as a nucleophile. The de
novo purine synthesis proceeds as follows: First; glycine is coupled to the amino group of PRA,
secondly; N10-formyltetrahydrofolate (N10-formyl-THF) transfers a formyl group to the amino group
of the glycine residue, thirdly; the inner amide group is phosphorylated and converted into an amidine by the addition of ammonia derived from glutamine, fourthly; an intramolecular coupling reaction forms the five-membered imidazole ring, fifthly, bicarbonate is added first to the exocyclic
amino group and then to a carbon atom of the imidazole ring, finally; the imidazole carboxylate is
phosphorylated, and then the phosphate is displaced by the amino group of aspartate. Three more
steps complete the ring construction. Fumarate, an intermediate in the citric acid cycle, is eliminated, leaving the nitrogen atom from aspartate joined to the imidazole ring. A formyl group from N10formyl-THF is added to this nitrogen atom to form a final intermediate that cycle with the loss of
water to IMP. A few additional steps convert IMP into either AMP or GMP. 5’-adenylic acid is
synthesized from IMP by the substitution of an amino group for the carbonyl oxygen atom. Again,
the addition of aspartate followed by the elimination of fumarate contributes the amino group. Guanosine triphosphate (GTP), rather than ATP, is the phosphoryl donor in the synthesis of the adenylosuccinate intermediate (SAMP) from IMP and aspartate. In accordance with the use of GTP,
the enzyme that promotes this conversion is adenylosuccinate lyase [4.3.2.2]. This enzyme also
catalyses the removal of fumarate from SAMP in the synthesis of AMP and from 5formaaminoimidazole-4-carboxamide ribonucleotide (FAICAR) in the synthesis of IMP. 5’guanidylic acid is synthesized by the oxidation of IMP to XMP, followed by the incorporation of an
167
amino group. 5’-xanthylic acid is activated by the transfer of an AMP group from ATP to the oxygen atom in the newly formed carbonyl group. Ammonia from glutamine finally displaces the AMP
group to form GMP.
Figure 15. An overview of the de novo biosynthetic pathways of purine ribonucleotides. Metabolites: PRPP;
phosphoribosyl pyrophosphate, PRA; 5-phosphoribosyl-1-amine, GAR; glycinamide ribonucleotide, FGAR;
formylglycinamide ribonucleotide, FGAM; formylglycinamidine ribonucleotide, AIR; 5-aminoimidazole ribonucleotide, CAIR; carboxyaminoimidazole ribonucleotide, SAICAR; 5-aminoimidazole-4-(N-succinylcarboxamide)
ribonucleotide, AICAR; 5-aminoimidazole-4-carboxamide ribonucleotide, FAICAR; 5-formaaminoimidazole-4carboxamide ribonucleotide, IMP; 5’-inosinic acid (inosine monophosphate), SAMP; adenylsuccinate, AMP; 5’adenylic acid (adenosine monophosphate), XMP; 5’-xanthylic acid (xanthosine monophosphate), GMP; 5’guanidylic acid (guanosine monophosphate). Enzymes: 1; glutamine phosphoribosyldiphosphate amidotransferase
[2.4.2.14], 2; glycinamide ribonucleotide synthetase [6.3.4.13], 3; glycinamide ribonucleotide transformylase
[2.1.2.2], 4; formylglycinamide ribonucleotide amidotransferase [6.3.5.3], 5; 5’-aminoimidazole ribonucleotide
synthetase [6.3.3.1], 6; phosphoribosylaminoimidazole carboxylase [4.1.1.21], 7; phosphoribosylaminoimidazolesuccinocarboxamide synthase [6.3.2.6], 8; adenylosuccinate lyase [4.3.2.2], 9; phosphoribosylaminoimidazolecarboxamide formyltransferase [2.1.2.3], 10; IMP cyclohydrolase [3.5.4.10], 11; adenylosuccinate synthase
[6.3.4.4], 12; IMP dehydrogenase [1.1.1.205], 13; GMP synthase [6.3.4.1].
Pyrimidine biosynthesis: In the de novo synthesis of pyrimidine ribonucleotides, the ring is formed
first and then it is attached to a ribose. Pyrimidine rings are assembled from bicarbonate, aspartic
acid, and ammonia. 5’-uridylic acid is formed first and then cytidine triphosphate (CTP) and dTMP
is made from UMP.
5’-uridylic acid: The first step in the de novo pyrimidine ribonucleotide synthesis is the synthesis of
carbamoyl phosphate from bicarbonate and ammonia in a multistep process, requiring the cleavage
of two molecules of ATP (Fig. 16). This reaction is catalysed by the multifunctional carmamoyl
168
phosphate synthetase [6.3.5.5]. In the first step, bicarbonate is phosphorylated by ATP to form carboxyphosphate and adenine diphosphate (ADP). The ammonia, derived from hydrolysis of glutamine, then reacts with carboxyphosphate to form carbamic acid and inorganic phosphate. In the
final step, carbamic acid is phosphorylated by another ATP molecule to form carbamoyl phosphate.
In the next step, carbamoyl phosphate reacts with aspartate to form carbamoylaspartate. Carbamoylaspartate then cyclizes to form dihydroorotate which is then oxidized to form orotate. At this
stage, orotate couples to ribose, in the form of PRPP, to form orotidylate, a pyrimidine ribonucleotide. This reaction is driven by the hydrolysis of phosphate. The enzyme that catalyzes this addition,
orotate phosphoribosyltransferase [2.4.2.10], is homologous to the phosphoribosyltransferases described in the previous section used for salvage of purine bases. Orotidylate is then decarboxylated
to form UMP, a major pyrimidine ribonucleotide that is a precursor to RNA.
Cytidine triphosphate: The other major pyrimidine ribonucleotide; CMP, is synthesized from UMP,
but UMP is converted into uridine triphosphate (UTP) before the synthesis can take place (Fig. 16).
The di- and triphosphates are the active forms of the ribonucleotides. Ribonucleoside monophosphates are converted into triphosphates in stages. First, monophosphates are converted into diphosphates by specific nucleoside monophosphate kinases that utilize ATP as the phosphoryl group donor. As an example, UMP → uridine diphosphate (UDP) by UMP kinase [2.7.4.14]. Nucleoside
diphosphates and triphosphates are interconverted by nucleoside diphosphate kinase [2.7.4.6], an
enzyme that has broad specificity. After UTP has been formed, it can be transformed into CTP by
the replacement of a carbonyl group by an amino group. As for the synthesis of carbamoyl phosphate, this reaction requires ATP and uses glutamine as the source of the amino group. The reaction
proceeds through analogous mechanisms in which the O-4 atom is phosphorylated to form a reactive intermediate, and then the phosphate is replaced by ammonia. The CTP ribonucleotide can then
be used in many biochemical processes, including RNA synthesis.
Thymidine 5’-monophosphate: Uracil, produced by the pyrimidine biosynthesis pathway, is not a
component of DNA. Rather, DNA contains thymine, a methylated analog of uracil. An extra step is
required to generate dTMP from uracil (Fig. 17). Thymidylate synthase [2.1.1.45] catalyses the addition of a methyl group derived from N5,N10-methylenetetrahydrofolate to 2’-deoxyuridine 5’monophosphate (dUMP) to form dTMP. The addition of a thiolate from the enzyme activates
dUMP. Opening the five-membered ring of the tetrahydrofolate (THF) derivative prepares the methyl group for a nucleophilic attack by the activated dUMP. The reaction is completed by the transfer of a hydride ion to form dihydrofolate (DHF). Tetrahydrofolate is regenerated from the dihydrofolate that is produced in the synthesis of dTMP. Methylation of dTMP facilitates the identification
of DNA damage for repair and helps preserve the integrity of the genetic information.
169
Figure 16. An overview of the de novo biosynthetic pathways of pyrimidine ribonucleotides. Metabolites: HCO3-;
bicarbonate, UMP; 5’-uridylic acid (uridine monophosphate), UDP; uridine diphosphate, UTP; uridine triphosphate, CTP; cytidine triphosphate. Enzymes: 1; carbamoyl phosphate synthetase [6.3.5.5], 2; aspartate transcarbamylase [2.1.3.2], 3; dihydroorotase [3.5.2.3], 4; dihydroorotate dehydrogenase [1.3.5.2], 5; orotate phosphoribosyltransferase [2.4.2.10], 6; orotodylate decarboxylase [4.1.1.23], 7; UMP kinase [2.7.4.14], 8; nucleoside diphosphate kinase [2.7.4.6], 9; CTP synthase [6.3.4.2].
Figure 17. Biosynthesis and salvage of thymine nucleotides. Metabolites: dUMP; 2’-deoxyuridine 5’monophosphate, dTMP; thymidine 5’-monophosphate, THF; tetrahydrofolate, DHF; dihydrofolate. Enzymes: 1;
thymidylate synthase [2.1.1.45], 2; thymidine kinase [2.7.1.21].
Inhibited by
AMP
Inhibited by IMP, AMP, and GMP
R5P
PRPP
Histidine
PRA
SAMP
AMP
XMP
GMP
IMP
Pyrimidine
nucleotides
Inhibited by
GMP
Figure 18. Regulation of purine ribonucleotide biosynthesis. The committed steps in the purine ribonucleotide
synthesis are the conversion of ribose-5-phosphate (R5P) into phosphoribosyl pyrophosphate (PRPP) and PRPP
further into 5-phosphoribosyl-1-amine (PRA) by glutamine phosphoribosyldiphosphate amidotransferase
[2.4.2.14]. This important enzyme is feed-back inhibited by 5’-adenylic acid (AMP) and 5’-guanidylic acid
(GMP), the final products of this pathway. Combinatorial effects of these two ribonucleotides are greatest when
170
the correct concentration of both adenine and guanine ribonucleotides is achieved. The amidotransferase reaction
is also feed-back inhibited allosterically by binding adenosine triphosphate (ATP), adenosine diphosphate (ADP),
and AMP at one inhibitory site and guanosine triphosphate (GTP), guanosine diphosphate (GDP), and GMP at
another. Conversely, the activity of the enzyme is stimulated by PRPP. 5’-inosinic acid (IMP) is the branch point
in the synthesis of AMP and GMP. The reactions leading away from IMP are also sites of feedback inhibition. 5’adenylic acid inhibits the conversion of IMP into adenylosuccinate (SAMP), its immediate precursor. Similarly,
GMP inhibits the conversion of IMP into 5’-xanthylic acid (XMP). As already noted, GTP is a substrate in the
synthesis of AMP, whereas ATP is a substrate in the synthesis of GMP. This reciprocal substrate relation tends to
balance the synthesis of adenine and guanine ribonucleotides.
Purine regulation: The synthesis of purine ribonucleotides is controlled by feedback inhibition,
controlling the overall rate and the balance between AMP and GMP production (Fig. 18).
Pyrimidine regulation: The pyrimidine ribonucleotide synthesis is regulated by feedback inhibition
at several sites; carbamoyl phosphate synthetase [6.3.5.5], aspartate transcarbamylase [2.1.3.2], orotodylate decarboxylase [4.1.1.23], and CTP synthase [6.3.4.2] are all sites for feedback inhibition
(Fig. 19). The dTMP salvage pathway is controlled by the deoxyribonucleotide kinase enzyme;
thymidine kinase [2.7.1.21]. Thymidine kinase [2.7.1.21] is able to convert thymidine and 2’deoxyuridine to dTMP and dUMP, respectively (Fig. 18). The activity of this enzyme is unique in
that it fluctuates with the cell cycle, rising to peak activity during DNA synthesis. It is feedback
inhibited by its products.
Inhibited by
UMP
HCO3-
Inhibited by UDP,
UTP, CTP
Carbamoyl
phosphate
Carbamoyl
aspartate
Inhibited by UMP
and CMP
Orotidylate
UMP
Inhibited by
CTP
UTP
CTP
Activated by
GTP
Activated by ATP
Figure 19. Regulation of pyrimidine ribonucleotide biosynthesis. Carbamoyl phosphate synthetase [6.3.5.5], the
enzyme converting bicarbonate (HCO3-) into carbamoyl phosphate, is feedback inhibited by 5’-uridylic acid
(UMP). Aspartate transcarbamylase [2.1.3.2] is a multifunctional protein in mammalian cells capable of catalyzing both the formation of carbamoyl phosphate, carbamoyl aspartate, and dihydroorotate. It is inhibited by uridine
diphosphate (UDP), uridine triphosphate (UTP), and cytidine triphosphate (CTP) and activated by adenine triphosphate (ATP) and. It is also regulated by glycine, which acts as a competitive inhibitor of the glutamine binding site. There is also a regulation of orotodylate decarboxylase [4.1.1.23]. This enzyme is competitively inhibited
by UMP and, to a lesser extent, by 5’-cytidylic acid (CMP). Finally, CTP synthase [6.3.4.2], one of the enzymes
involved in the conversion of UTP to CTP, is feedback-inhibited by CTP and activated by guanine triphosphate
(GTP). Adenine triphosphate (ATP) levels also regulate pyrimidine ribonucleotide synthesis at the level of phosphoribosyl pyrophosphate (PRPP) formation. An increase in the level of PRPP results in an activation of pyrimidine ribonucleotide synthesis.
Purine salvage: Free purine bases, derived from the turnover of endogenous nucleotides or from the
diet, have the possibility of being salvaged and thus recycled. Purine salvage is achieved by attaching the base to PRPP to form purine nucleotide monophosphates (Fig. 20A). Two salvage enzymes
with different specificities recover purine bases. Adenine phosphoribosyltransferase [2.4.2.7] catalyses the formation of AMP. Whereas hypoxanthine-guanine phosphoribosyltransferase [2.4.2.8]
171
catalyses the formation of GMP as well as IMP. Generation of AMP and GMP through these salvage reactions shuts off the de novo synthetic pathway. Another important enzyme of purine salvage in rapidly dividing cells is adenosine deaminase [3.5.4.4], able to deaminate adenosine to inosine. The purine nucleotide phosphorylases can also contribute to the salvage of the bases through a
reversal of the catabolic pathways. However, these pathways are less significant than those catalyzed by the phosphoribosyltransferases.
Pyrimidine salvage: Owing to the solubility of the by-products of the pyrimidine catabolism, pyrimidine salvage is considered less significant than purine salvage. Even so, both uracil and thymine
can be salvaged through the action of concerted enzyme reactions (Fig. 20B).
A
B
Figure 20. Salvage reactions of the purine and the pyrimidine metabolism. (A) Purine salvage reations. Metabolites: AMP; 5’-adenylic acid (adenosine monophosphate), IMP; 5’-inosinic acid (inosine monophosphate), GMP;
5’-guanidylic acid (guanosine monophosphate). Enzymes: 1; adenine phosphoribosyltransferase [2.4.2.7], 2; hypoxanthine-guanine phosphoribosyltransferase [2.4.2.8], 3; AMP deaminase [3.5.4.6]. (B) Pyrimidine salvage
reations. Metabolites: UMP; 5’-uridylic acid (uridine monophosphate), dTMP; thymidine 5’-monophosphate,
dCMP; 2’-deoxycytidine 5’-monophosphate (deoxycytidine monophosphate), dUMP; 2’-deoxyuridine 5’monophosphate (deoxyuridine monophosphate). Enzymes: 1; uridine phosphorylase [2.4.2.3], 2; uridine kinase
[2.7.1.48], 3; nucleoside deoxyribosyltransferase [2.4.2.6], 4; thymidine kinase [2.7.1.21], 5; deoxycytidine kinase
[2.7.1.74].
Interconversion and formation and regulation of deoxyribonucleotides
Interconversion: During the catabolism of nucleic acids, nucleoside mono- and diphosphates are
released. These nucleosides do not accumulate rather they are interconverted owing to the action of
nucleoside mono- and diphosphate kinases. The nucleoside monophosphate kinases catalyze ATP
dependent reactions of the type: (d)NMP + ATP ↔ (d)NDP + ADP. The nucleoside diphosphate
kinases catalyze reaction of the type: N1TP + N2DP ↔ N1DP + N2TP where N1 represent a purine
172
ribo- or deoxyribonucleotide and N2 a pyrimidine ribo- or deoxyribonucleotide. The activity of the
nucleoside diphosphate kinases are 10-100 times higher than that of the nucleoside monophosphate
kinases, maintaining a relatively high intracellular level of (d)NTPs relative to that of (d)NDPs.
Formation and regulation of deoxyribonucleotides: The typical cell contains 5-10 times as much
RNA than DNA. Therefore, the main purpose of nucleotide synthesis is to produce ribonucleotides.
However, since proliferating cells need to replicate their genomes, the production of deoxyribonucleotides is also necessary.
Deoxyribonucleotides, the precursors of DNA, are formed through the reduction of ribonucleotides
(Fig. 21). During the reduction, the 2’-hydroxyl group on the ribose moiety is replaced by a hydrogen atom. The substrates are ribonucleoside diphosphates or triphosphates, and the ultimate reductant is nicotinamide adenine dinucleotide phosphate (NADPH). This reduction is chemically a difficult reaction, requiring a very sophisticated catalyst; ribonucleotide reductase [1.17.4.1]. This multifunctional enzyme contains redox-active thiol groups for the transfer of electrons to NADPH during
the reduction reactions.
Figure 21. Formation of deoxynucleotides from ribonucleotides. Metabolites: NDP; nucleotide diphosphate,
dNDP; deoxynucleotide diphosphate, dNTP; deoxynucleotide triphosphate. Enzymes: 1; ribonucleotide reductase
[1.17.4.1], 2; nucleoside diphosphate kinase [2.7.4.6].
The reduction of ribonucleotides to deoxyribonucleotides is precisely controlled by allosteric interactions. Two separate sites on the ribonucleotide reductase enzyme functions to regulate its activity, one controls the overall activity of the enzyme and one controls the substrate specificity. The
overall catalytic activity is diminished by the binding of deoxyadenosine triphosphate, signalling an
abundance of deoxyribonucleotides. Binding of ATP reverses this feedback inhibition. Binding of
deoxyadenosine triphosphate or ATP to the substrate specificity control sites enhances the reduction
of UDP and CTP, the pyrimidine nucleotides. Binding of thymidine triphosphate promotes the reduction of GDP and inhibits the further reduction of pyrimidine ribonucleotides. The subsequent
increase in level of deoxyguanosine triphosphate stimulates the reduction of ATP to deoxyadenosine triphosphate. This complex pattern of regulation supplies the appropriate balance of the four
deoxyribonucleotides needed for the synthesis of DNA.
173