Acknowledgments
FACULTY OF SCIENCES
Predominant fecal microbiota of captive adult
cheetahs housed in European
zoos: a cross-sectional study
Niloufar MAJDZADEH
Master Dissertation
Master of Biochemistry and Biotechnology
Major Microbial Biotechnology
Academic year 2013-2014
Promoter: Prof. Dr. Geert Huys
Mentor: drs. Anne Becker
Department of Biochemistry and Microbiology (WE10)
Laboratory of Microbiology
1
Acknowledgments
ACKNOWLEDGMENTS
This Master project was only possible because of the involvement of few people to whom I
wish to dedicate the following lines. Hereby I want to thank them for their inspiration,
motivation and help.
My special thanks go to Professor Dr. Huys (Laboratory of Microbiology, University of Gent)
for making this research possible. I would like to appreciate and show my gratefulness to his
guidance, support and advice throughout this research project.
Hereby I would like to express my deepest gratitude to my scientific supervisor, Drs. Anne
Becker (Laboratory of Microbiology, University of Gent) for her thorough support,
encouragement and guidance throughout this project. She helped me warmheartedly and
genuinely despite her busy agenda. Her enthusiasm, open mind, perseverance and brilliance
enlightened my scientific world. Dear Anne, I sincerely wish you the best personal and
professional life ever!
I am also grateful to all the people in BCCM/LMG bacteria collection and Laboratory of
Microbiology for providing a friendly working atmosphere with their kindness and
helpfulness during my master thesis.
I would like to bring this acknowledgment to an end by showing my appreciation to my
beloved family for believing in me all the way through my education. My deepest gratitude
goes to my husband for providing the best possible atmosphere to study and work in. Your
patience, love and support were the key to my success.
2
Table of Contents
TABLE OF CONTENTS
Acknowledgments...................................................................................................................... 2
Abbreviation list ......................................................................................................................... 6
Chapter I. Summary ................................................................................................................... 7
Chapter II. Introduction ............................................................................................................. 8
II.1. Gut microbiota ................................................................................................................ 8
II.1.1. The intestinal tract as microbial ecosystem ............................................................ 8
II.1.2. Function of gut microbiota ...................................................................................... 9
II.1.3. Role of diet on gut microbiota ............................................................................... 12
II.2. Techniques for studying diversity of gut microbiota .................................................... 14
II.3. Cheetah (Acinonyx jubatus) .......................................................................................... 17
II.3.1. Classes based on dietary habits ............................................................................. 17
II.3.2. Vulnerable status of cheetahs ............................................................................... 18
II.3.3. Cheetah’s diet and its role in cheetah’s metabolism ............................................ 19
II.3.4. Domestic cat as a model for studying cheetah gut microbiota ............................. 20
Chapter III. Goals ...................................................................................................................... 23
Chapter IV. Results ................................................................................................................... 24
IV.1. DGGE data .................................................................................................................... 24
IV.1.1. Analysis of all DGGE fingerprints .......................................................................... 26
IV.1.2. Band richness of the DGGE fingerprints per zoo .................................................. 32
IV.2. Linking the DGGE data with the clone library ............................................................. 35
IV.2.1. Common band-classes in the fingerprints ............................................................ 35
IV.2.2. Linking band-classes with fingerprint clusters ..................................................... 36
IV.3. quantitative-PCR results .............................................................................................. 40
VI.3.1. The quantity of Clostridium cluster XIVa per zoo ................................................. 40
3
Table of Contents
VI.3.2. The quantity of Clostridium cluster XIVa per animal ............................................ 41
VI.3.3. Clostridium cluster XIVa versus the DGGE band intensity.................................... 41
Chapter V. Discussion .............................................................................................................. 43
Chapter VI. Conclusion ............................................................................................................. 46
Chapter VII. Materials and methods ........................................................................................ 47
VII.1. Fecal sample collection ............................................................................................... 47
VII.2. Homogenization of fecal samples .............................................................................. 47
VII.3. DNA extraction ........................................................................................................... 47
VII.4. DNA quantity and quality check ................................................................................. 47
VII.4.1. Agarose gel electrophoresis ................................................................................ 47
VII.4.2. Optical density measurement by spectrophotometer ........................................ 48
VII.5. V3-16s rRNA PCR ........................................................................................................ 48
VII.6. DGGE analysis and gel processing .............................................................................. 49
VII.7. Data analysis ............................................................................................................... 49
VII.8. quantitative PCR ......................................................................................................... 50
VII.8.1. Calibration of a standard curve ........................................................................... 50
VII.8.2. Quantifying Clostridium cluster XIVa in the fecal samples .................................. 51
Chapter VIII. References .......................................................................................................... 52
Chapter IX. Addendum ............................................................................................................. 63
IX.1. Fecal samples collection .............................................................................................. 64
IX.2. Homogenization of the fecal samples ......................................................................... 64
IX.3. DNA extraction ............................................................................................................. 64
IX.4. DNA quantity and quality check .................................................................................. 65
IX.4.1. Agarose gel electrophoresis ................................................................................. 65
IX.4.2. Optical density measurement by spectrophotometer ......................................... 65
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Table of Contents
IX.5. V3-16s rRNA PCR .......................................................................................................... 65
IX.6. DGGE ............................................................................................................................ 66
IX.7. Band extraction and sequencing ................................................................................. 67
IX.8. q-PCR ............................................................................................................................ 67
IX.8.1. Building a standard curve ..................................................................................... 67
IX.8.2. Quantification of Clostridium cluster XIVa ........................................................... 68
5
Abbreviations
ABBREVIATION LIST
CFU----------------------------------------------Colony-Forming Unit
MAMP-------------------------------------------Microbe-Associated Molecular Pattern
TLR-----------------------------------------------Toll-Like Receptor
SCFA --------------------------------------------Short-Chain Fatty Acid
EAZA-------------------------------------------- European Association of Zoos and Aquaria
DGGE------------------------------------------- Denaturing Gradient Gel Electrophoresis
GALT --------------------------------------------Gut-Associated Lymphoid Tissue
TH17 --------------------------------------------T Helper 17 cell
TTGE --------------------------------------------Temporal Temperature Gradient Gel Electrophoresis
TGGE --------------------------------------------Temperature Gradient Gel Electrophoresis
RFLP ---------------------------------------------Restriction Fragment Length Polymorphism
ARISA -------------------------------------------Automated Ribosomal Intergenic Spacer Analysis
SSCP ---------------------------------------------Single-Strand Conformation Polymorphism
qPCR---------------------------------------------quantitative PCR
OTU----------------------------------------------Operational Taxonomic Units
PL------------------------------------------------- Planckendael
GA------------------------------------------------Gaia Zoo
OV------------------------------------------------Zoo Parc Overloon
BB------------------------------------------------Safaripark Beekse Bergen
EB------------------------------------------------Ree Park Ebeltoft Safari
SI-------------------------------------------------Réserve Africaine de Sigean
6
Summary
CHAPTER I. SUMMARY
The gastrointestinal tract of mammals is associated with a diverse, dynamic and complex
microbial community, in which the biggest group is represented by bacteria. The gut
microbiota is responsible for production of essential vitamins, differentiation of the immune
system, tissue homeostasis, energy harvest and short-chain fatty acid production. The
cheetah (Acinonyx jubatus) is an endangered animal with limited metabolic ability to digest
anything except proteins. This carnivore hunts gazelle, rabbit or antelope in the wild.
However, they are mostly fed either meat or prey in captivity. One of the biggest problems
that endanger cheetahs is the feeding mismanagement in the zoos that increases
suboptimal health, gastrointestinal and metabolic diseases. Diet, microbiota and health are
strongly correlated and several studies have been conducted on the diet and health of these
carnivores. Yet, there is not sufficient information about their gut microbiota, which form
the main go-between of diet and health. Since the current information about the gut
microbiota of captive cheetahs has been obtained from 2 animals in Belgium, we aim to
collect more fecal samples from animals in different zoos with various conditions. The aim is
to investigate the diversity of their gut microbiota and whether this analysis reveals the
presence of a core microbiota.
Using population fingerprinting by denaturing gradient gel electrophoresis (DGGE) of V3-16S
rRNA amplicons in a cross-sectional study, we studied the fecal microbial diversity of 55
fecal samples from 36 captive cheetahs in 6 members of EAZA zoos. After homogenization
of the fecal samples, community DNA extraction was carried out followed by V3-16S rRNA
PCR. Afterwards, DGGE profiling was performed followed by data analysis. The fingerprints
were compared with the available clone library and phylogenetic tree, previously built
based on two captive adult cheetahs in Belgium. Afterwards, Quantitative PCR (qPCR) was
performed in order to quantify Clostridium cluster XIVa.
As a result of DGGE analysis, there was no zoo-specific or diet type specific clustering in the
DGGE fingerprints, which suggested that the fingerprints are affected by a combination of
factors such as zoo, diet, genetic background and housing. Clostridium cluster XIVa, XI and I
were present in almost all the DGGE profiles, which indicate that these clusters might be
members of the core microbiota of these 55 fecal samples. Band-classes 28.3% and 41.3%
did not correlate with any clone in the clone library, which belonged to Lactobacillus spp.
after sequencing. Furthermore, the boxplot analysis of the fingerprints revealed the
variation of the predominant bacterial diversity between the fingerprints per zoo, per
animal and per diet type. The quantification of Clostridium cluster XIVa depicted that all the
DNA samples consisted of this cluster except for one animal that suffered from a renal
disease, which may probably be one of the reasons that this cluster was not detectable.
In the future studies, more fecal samples could be collected and analyzed by DGGE to
confirm the diversity and the gut microbiota in captive adult cheetahs. Moreover, more
bands, which belong to potentially discriminating band-classes, can be sequenced to identify
the unknown bacterial genera and species in the clone library. Since multiple taxa can
occupy the same position in the DGGE gel, as it is one of the shortcomings of DGGE, more
zoomed DGGE can be performed.
7
Introduction
CHAPTER II. INTRODUCTION
II.1. GUT MICROBIOTA
II.1.1. THE INTESTINAL TRACT AS MICROBIAL ECOSYSTEM
The gastrointestinal tract of mammals, including humans, is an organ system that starts
from the mouth and ends with the anal canal (Martin, 2010) (Figure 1). The intestine, one
essential organ in this system in which the mucosal immune system operates, has several
fundamental tasks in humans. One is nutrient absorption made possible by its 200m2 total
surface area in humans (Hooper et al, 2012). The other is to provide an environment in
which bacteria can grow and survive, which is done through the few micrometers of mucus
layer between the bacterial community and the layer underneath (O’Hara & Shanahan,
2006).
The gastrointestinal tract is not only associated with eukaryotic tissues and cells as in other
organs, but also with a diverse, dynamic and complex microbial community, in which the
biggest group is represented by bacteria. The gastrointestinal tract of human and mammals
also includes archaea, viruses, fungi and protozoa (Hooper et al, 2002). These bacteria are
spread in almost all parts of the gastrointestinal tract of most mammals, with a relatively
low intensity in the proximal and middle small intestine and high intensity in the large
intestine. The number of the bacteria reaches to 1011–1012 CFU/g in the human colon
(Mackie et al, 1999) (Figure 1).
The human gastrointestinal tract (Matamoros et al, 2013) is sterile at birth (Matamoros et
al, 2013). The bacteria start colonization of the host gastrointestinal tract during birth from
the mother, the surrounding environment, infant feeding, gestational age, hospitalization,
and antibiotic use by the infant (Greiner & Bäckhed, 2011; Penders et al, 2006). Therefore,
the microbial composition of gut microbiota changes after birth both in diversity and
richness throughout development. The diversity and richness of the gut microbiota in each
individual reaches its highest complexity and homeostatic climax composition in adulthood
(Sommer & Bäckhed, 2013; Scholtens et al, 2012). The gut microbiota remain relatively
stable during most of adult life and is expected to become less sensitive to modification
(Jalanka-Tuovinen et al, 2011).
Both anaerobic and aerobic bacteria exist in the gastrointestinal tract of humans in which
those that are anaerobic, such as Bacteroides and Clostridium (Vedantam & Hecht, 2003),
are dominant by 100- to 1,000-fold over the aerobic and facultative anaerobic bacteria in
the human gastrointestinal tract (Clemente et al, 2012). In total, the human intestinal
microbiota consists of approximately 500–1,000 species that belong to only a few of the
known bacterial phyla (Qin et al, 2010) (Figure 1). Amongst them, Bacteroidetes and
Firmicutes account for more than 90% of all the phyla of human intestinal bacteria (Tehrani
et al, 2012).
8
Introduction
The bacterial
composition,
concentration
and
spatial distribution in
the human intestinal
tract. As depicted, the
highest concentration
of bacteria among the
human gut microbiota
belongs to the colon
with
1012
CFU/g.
Figure obtained from:
Figure 1.
http://mpicel.myweb.
uga.edu/
Human gastrointestinal tract harbors eukaryotes, viruses, archaea and bacteria. Culturebased techniques have illustrated that all healthy human adults share most of the same gut
bacterial species referred to as a core microbiota. For instance, Escherichia coli can be
isolated from most people. However, culture-independent techniques have demonstrated a
vast microbial diversity with high variability over time and across populations (Knight et al,
2012). Studies have shown that Bacteroidetes and Firmicutes are the phyla in human adults
that dominate their microbiota, whereas Actinobacteria, Proteobacteria and
Verrucomicrobia are generally minor phyla (Eckburg et al, 2005). Additionally, the diversity
of gut microbiota is hugely influenced by age, genetics, environment, diet, human health
and medicine (Knight et al, 2012).
II.1.2. FUNCTION OF GUT MICROBIOTA
Microbiota have coevolved over millions of years with mammalian hosts (Ley et al, 2008). A
balanced gut microbiota are important for host health due to its important role in numerous
developmental, nutritional, physiological and immunological processes (Mackie et al, 1999;
Hooper et al, 2001). The most important functions are:
Differentiation of the immune system: The gut microbiota is essential for differentiation of
the immune system in the host through providing signals. These signals promote the
maturation of immune cells and the normal development of immune functions in order to
eventually protect the host against invasion by opportunistic pathogens and to suppress
inflammatory responses (Chow et al, 2010; Smith et al, 2007; Round et al, 2011). It has been
shown that both the innate and adaptive immune systems in mammals have evolved to
require microbial interactions through their development (Chow et al, 2010; O’Hara &
Shanahan, 2006). The human innate immune system recognizes general microbe-associated
molecular patterns (MAMPs) that are parts of the bacteria such as their flagella or their cell
walls. These antigens are recognized by the host Toll-like receptors (TLRs). In case of
9
Introduction
absence or mutation of TLRs, the gut and mucosal immune systems do not develop normally
in human (O’Hara & Shanahan, 2006). The human gut microbiota also suppress
inflammatory responses and promote immunological tolerance through TLRs (O’Hara &
Shanahan, 2006). The human gut microbiota also help educating the T cells in the adaptive
immune system to discriminate the self from non-self-cells to protect the body from killing
its own cells, which leads to autoimmunity diseases (Kuhn & Stappenbeck, 2013) (Figure 2).
Production of essential vitamins: Production of vitamins is one of the many functions of gut
microbiota in mammals (Smith et al, 2007; LeBlanc et al, 2013). Yet, some vitamins are not
synthesized by the host. Therefore, these vitamins should be obtained from external
sources with the help of the gut microbiota (Hill, 1997). Biotin (B7) is one of the many watersoluble vitamins, in which our body absorbs through the help of gut microbiota. Vitamin B12
is another important water-soluble vitamin that is crucial for DNA synthesis and is also
produced by the gut microbiota by consumption of proteins since B12 is bound to proteins.
Vitamin K, a fat-soluble vitamin, is of high importance for blood clotting and if absent the
host may die due to bleeding. The daily requirement of this vitamin comes from the gut
microbiota in mammals (Hill, 1997). These vitamins are synthesized by several intestinal
bacterial genera, including Bacteroides, Eubacterium, Propionibacterium, and Fusobacterium
(Hooper et al, 2002; Wilson & Nicholson, 2009).
Tissue homeostasis: This function involves a tightly regulated cell cycle and requires a
balance between cell renewal and cell death, which is mediated by the gut microbiota in
mammals (Sommer & Bäckhed, 2013). Studies conducted on mice have shown that TLR
signals derived from the gut microbiota are required for regaining tissue homeostasis
following injury in the intestine (Rakoff-Nahoum et al, 2004). Moreover, gut microbiota can
influence tissue homeostasis in bones by decreasing the bone mass when more Ca2+ is
needed in the body through recruiting specific cells called “osteoclasts”. These cells that are
present in bones, are responsible for resorption of boney tissues when the body needs
calcium ion (Sommer & Bäckhed, 2013) (Figure 2). The gut microbiota modulate bone
homeostasis through modulation of T cell function, serotonin levels and cytokine (Sjogren et
al, 2012).
Energy harvest and short-chain fatty acid production: Carbohydrates are important energy
sources for human and microbial cells, in which the human enzymes cannot degrade most
complex carbohydrates and plant polysaccharides (Tremaroli & Bäckhed, 2012). Therefore,
the undigested and unabsorbed dietary carbohydrates, such as fibers, reach the colon and
are fermented by the microbiota, resulting in the production of CO2, H2 and most
importantly “short-chain fatty acids” (SCFA), which are organic fatty acids with 1 to 6 carbon
atoms. Acetate, propionate and butyrate are the main short-chain fatty acids with certain
advantages for the human and mammal’s body (Willing & Van Kessel, 2007; Hijova &
Chmelarova, 2007). The production rate of short-chain fatty acids in several mammal’s
body, is the highest for butyrate, while is the lowest for acetate, with the ratio of 3 to 1
(Topping & Clifton, 2001). Short-chain fatty acids are rapidly absorbed in the cecum and
colon and only 5-10% is eventually excreted in the feces (Macfarlane & Macfarlane, 2003). It
has been shown that SCFAs are associated with reduced risk of inflammatory bowel disease,
10
Introduction
cardiovascular diseases and different sorts of cancers such as colon cancer (Floch & HongCurtiss, 2001; Radulian et al, 2009).
Human enterocytes and colonocytes use butyrate as the major substrate to maintain energy
producing pathways and 70-90% of this metabolite is metabolized by the colonocytes
(Hijova & Chmelarova, 2007). Butyrate is regarded as a metabolite that is beneficial for the
host health due to its positive influences on cell growth, differentiation and antiinflammatory effects (Hamer et al, 2008) . Inhibition of the transcription factor NF-kB, which
is implicated in innate immunity, cell cycle control and apoptosis, is amongst the antiinflammatory effects of butyrate (Luhrs et al, 2001; Segain et al, 2000). Butyrate also
regulates cell proliferation in mammals through the release of growth factors or
gastrointestinal peptides or by modulation of mucosal blood flow. Moreover, butyrate is the
main short-chain fatty acid that can act directly on genes that regulate cell proliferation
(Blottiere et al, 2003).
Acetate and propionate are taken up by the portal circulation and have an impact on lipid
metabolism in an opposite manner. While acetate contributes to lipid and cholesterol
synthesis in liver, propionate inhibits the effects of acetate (Laparra & Sanz, 2010). In
addition, liver cells metabolize residual butyrate and propionate in order to use them in
glycogenesis. While 50-70% of acetate is taken up by the liver, residual acetate is used by
the muscles in order to generate energy (Hijova & Chmelarova, 2007). Moreover, several
studies have shown that acetate is considered to be the predominant short-chain fatty acid,
in concentration in the human colon. Yet, butyrate has the most beneficial effects on human
health (Tedelind et al, 2007).
Many factors determine the production of short-chain fatty acids in the gut such as the
transit time (Hijova & Chmelarova, 2007), substrate source (Cook & Sellin, 1998) and
bacterial composition in the colon (Roberfroid, 2005). The fermentation takes place in the
proximal colon since the substrate availability is the greatest (Hijova & Chmelarova, 2007).
By the increase in the amount of short-chain fatty acids in the human gut, several beneficial
bacterial species, such as Lactobacillus and Bifidobacterium, which are associated with
improved health, increase as well (Roy et al, 2006). In contrast to most clostridial species in
the mammal’s gastrointestinal tract, Lactobacillus and Bifidobacterium are generally
regarded as beneficial microbes due to their ability to eliminate harmful bacteria and
pathogens by producing various antimicrobial agents such as antibiotics (Rastall, 2004). In
addition to antimicrobial production, these microbes are highly capable of fermentation of
dietary fibers. Therefore, Lactobacillus and Bifidobacterium are considered to ferment
dietary fibers much more effectively than the pathogenic species in the gut (Hijova &
Chmelarova, 2007). Increase in the amount of all short-chain fatty acids results in a decrease
in the pH that indirectly affects the composition of the colonic microbiota, increases mineral
absorption, decreases solubility of bile acids and reduces ammonia absorption (Radulian et
al, 2009; Cook & Sellin, 1998) (Figure 2).
Inhibition of pathogen from causing infection: Another important function of the gut
microbiota in the mammals is protection of gut from infection by pathogenic bacteria.
During pathogen infection, three main functions may be seen:
11
Introduction
i) The gut microbiota might block the pathogen growth and thus interfere with the infection
(Stecher & Hardt, 2011). ii) The microbiota might prime the host innate and adaptive
immune system to prevent the development of infection and diseases, which is caused by
the virulence factors of pathogen bacteria (Sekirov et al, 2010). iii) The gut microbiota
eliminate the pathogens from the host gut at the end of the infection (Endt et al, 2010).
The effects of human gut microbiota on host physiological processes. Arrows represent either
stimulatory or inhibitory effects of gut microbiota on host physiological processes. Figure obtained from:
http://www.nature.com/nrmicro/journal/v11/n4/full/nrmicro2974.html (Sommer & Bäckhed, 2013)
Figure 2.
II.1.3. ROLE OF DIET ON GUT MICROBIOTA
Diet and different food habits are important factors in microbiota composition and
development of mammals including the human being, either in the early life or in the late
life. For instance, the diversity and composition of microbiota vary significantly between
breast-fed and formula-fed infants in the early life. The intestinal microbiota of the breastfed infants is more heterogeneous and more taxonomically diverse than the formula-fed
infants (Schwiertz et al, 2010) with bifidobacteria as the key player in the gut microbial
community of neonates (Turroni et al, 2012). The gut microbiota composition in the human
and mammals is also under the influence of food habits. For instance malnutrition results in
the lower abundance of Bacteroidetes in human gut microbiota, which are specialized for
breaking down the carbohydrates in energy rich diet types (Ottman et al, 2012).
Carbohydrates form the bulk of most human and animal diets and are important nutrients
for both host and microbiota. Mammals easily absorb simple sugars such as glucose and
12
Introduction
galactose, as well as disaccharides and starch, in the proximal part of their small intestine
(Ferraris, 2001). Yet, they are limited to hydrolyze and utilize more complex polysaccharides
(Hooper et al, 2002). Therefore, a large quantity of undigested dietary carbohydrates
including polysaccharides from plant cell wall components such as cellulose, xylan, pectin
and fibrous substrates as well as undigested starch reaches the distal part of the
gastrointestinal tract. This is where the gut microbiota play an important role by degrading
these biomolecules (Hooper et al, 2002). Gut microbiota of most mammals and humans
ferment these carbohydrate polymers that reach the distal gut and help extract nutrient
value from otherwise poorly utilized dietary substrates. Therefore, the distal regions of the
gut of several mammals are highly colonized with bacteria (Zoetendal et al, 2002). Gut
microbiota hydrolyze these carbohydrates and consequently the hosts avoid the need to
evolve the complex repertoire of glycosylhydrolases that would be required to break down
the wide variety of linkages in dietary polysaccharides. The gut microbiota, in turn, gain
access to a nutrient-rich environment, including complex dietary plant polysaccharides and
host-derived glycans secreted on the surface of the epithelium and on the mucus layer,
which is rich in carbohydrates (Corfield et al, 2001). Among these bacteria, bifidobacteria
play the biggest role for carbohydrate fermentation in several mammals and humans
(Ottman et al, 2012).
Proteins form a portion of diet of humans, carnivores and omnivores. Several bacteria use
proteins as a carbon and nitrogen source and it has been demonstrated that protein-rich
diets increase Clostridium populations (Zentek et al, 2004). The amount of protein that
reaches the lower bowel may be consumed by gut microbiota, in which this amount
depends greatly on the protein quality. Lower quality proteins, which are poorly digested,
will provide more protein to microbes in the lower bowel. Therefore, either lower quality
proteins or higher amounts of proteins can increase the occurrence of proteolytic bacteria
such as Bacteroides (Wu et al, 2011). These bacteria can be pathogenic and/or produce
putrefactive compounds such as ammonia, indoles, phenols, and sulphur-containing
compounds. These compounds, which are regarded as detrimental for gut health in either
humans or several animals, are toxic at high levels and are associated with several disease
states (Backus et al, 2002) such as Inflammatory bowel disease (Pedersen et al, 2002; Tuohy
et al, 2006) and chronic renal failure (Niwa, 2010).
According to the study conducted by Wu et al., short-term diet does not change the
microbial composition in the human gut, while long-term diet has a tremendous role on
modulating gut microbiome composition and enterotype partitioning (Wu et al, 2011). For
instance, long term diet rich in animal protein, sugar, starch and fat and low in fiber, will
result in Firmicutes and Proteobacteria dominance in the human gut microbiota. On the
other hand, a diet type rich in vegetables and thus plant fibers, will result in the dominance
of Actinobacteria and Bacteroidetes phyla (Wu et al, 2011; De Filippo et al, 2010).
Recent studies have shown that obese and lean people have different composition in their
gut microbiota, specifically in the proportion of Bacteriodetes and Firmicutes. In obese
people the Firmicutes are dominant while the Bacteriodetes are dominant in lean people
(Ley et al, 2006). The Bacteroidetes degrades complex and otherwise indigestible dietary
polysaccharides in the large intestine, resulting in the production of short-chain fatty acids,
13
Introduction
which are the energy source for the host (Tremaroli & Bäckhed, 2012; Thomas et al, 2011).
In conclusion, the studies conducted to date about the role of diet on gut microbiota,
endorse the fact that the intestinal microbiota in several mammals thrives on using
polysaccharides and peptides, which are indigestible to mammals (Guarner & Malagelada,
2003).
II.2. TECHNIQUES FOR STUDYING DIVERSITY OF GUT MICROBIOTA
Several techniques are available for studying the diversity of gut microbiota in either
humans or animals. Early experiments in this field used culture-based techniques, with a
certain level of discriminatory power for identification of microorganisms (Temmerman et
al, 2004). Most bacteria in the gastrointestinal tract are fastidious and hard to culture since
they are anaerobic. Besides there is no uniform substrate preference for all of them (Inglis
et al, 2012). Consequently only a fraction of the microorganisms in the gastrointestinal tract
can be cultured and studied by culture-dependent techniques. Therefore, these techniques
are limited in scope and lack precision for studying the diversity or functionality of
microbiota, and the progress in this field was greatly hindered due to this fact (Kerr et al,
2013a).
The fact that 99% of the bacteria cannot be cultured or has not yet been cultured
(Handelsman, 2004), leads the scientists to use culture-independent or molecular
techniques, which is one of the most important developments of environmental
microbiology since the 1980s (Keller & Zengler, 2004). These culture-independent
techniques have several advantages over the culture-dependent techniques. Recent
developments and rapid advances in molecular biology and sequencing have resulted in a
more accurate understanding of the gut microbiota composition, the genetic elements of all
the microorganisms in the gastrointestinal tract (microbiome), their dynamics, their
interaction with the other gastrointestinal organs and the functionality of the intestinal
ecosystem in human and many mammalian species (Ritchie et al, 2010). There are many
molecular techniques of which phylogenetic clone libraries, phylogenetic microarray,
community DNA profiling and metagenomics are most commonly used to study gut
microbiota diversity (Lau & Liu, 2007).
Phylogenetic clone library is a sequencing-based technique that targets the 16S-rRNA gene,
which is present and conserved among all members of a bacterial community, to study
phylogenetic and bacterial community structures. In this technique, primers are used to
amplify a portion of the 16S-rRNA gene, followed by ligation of the amplified region of the
universal gene in bacterial plasmids. Afterwards, the plasmids are transformed into
competent cells, such as E. coli, to generate a clone library (Inglis et al, 2012). The
advantages are relative simplicity, representation of a single individual from the bacterial
community by possessing a single insert in each clone; and the ability to near complete
sequencing of 16S-rRNA gene (Inglis et al, 2012). On the other hand, the disadvantages are
the underrepresentation of low abundance bacteria in the community, efficiency
dependency on the quality of the inserts (Lovell et al, 2008) and the high expenses of
sequencing as well. Despite all these aforementioned limitations, this method has still merit
14
Introduction
and has provided invaluable information about the bacterial community and composition of
gut microbiota either in humans or mammals (Inglis et al, 2012) (Figure 3).
Phylogenetic microarray is a glass surface covered by thousands of covalently linked DNA
probes, in which they will be hybridized by DNA or RNA. Subsets of probe sequences, which
are specific to the ecological environment of interest, are required. The probes for studying
the gastrointestinal microbial community are targeting only one or several parts of 16SrRNA gene such as the V3 variable region (Claesson et al, 2009). This technique is widely
used for monitoring gene expression, mutation in genomic DNA and DNA sequence
polymorphism. Yet, it is not capable of detecting the unknowns, hybridization conditions are
not optimal for all the hybrids and many probes are required for exploring the diversity
(Gentry et al, 2006) (Figure 3).
Community DNA profiling is either sequence-dependent, such as Denaturing Gradient Gel
Electrophoresis (DGGE) and Temperature Gradient Gel Electrophoresis (TGGE), or sizedependent electrophoresis such as Restriction Fragment Length Polymorphism (RFLP),
Automated Ribosomal Intergenic Spacer Analysis (ARISA) and Single-Strand Conformation
Polymorphism (SSCP).
In DGGE, a set of amplicons equal in size but different in sequence obtained from the 16S
rRNA gene (Inglis et al, 2012), is applied to an polyacrylamide electrophoresis gel. The
polyacrylamide gel has a denaturing agent including urea and formamide, so that the
amplicons melts at various stages through the gel based on their GC content (Muyzer et al,
1993). The ability to excise and clone individual bands, is a significant advantage of DGGE
analysis. Yet, visual identification of the bands in samples with complex banding patterns
and high diversity can be difficult (Inglis et al, 2012) (Figure 3). TTGE is a form of
electrophoresis that uses temperature to denature the sample as it moves across an
acrylamide gel. Therefore, the amplicons are separated based on differences in melting
temperature, sequence length, and GC content (Inglis et al, 2012). RFLP is a technique that
uses fluorescently labeled PCR primers to amplify the gene of interest, followed by
electrophoresis (Inglis et al, 2012). In order to study different environmental samples, ARISA
can be used that uses PCR on the specific region of rRNA gene operon between 16S and 23S
subunits (Ranjard et al, 2001). In SSCP electrophoresis is carried out to distinguish
sequences fragments according to their different conformations (Hori et al, 2006).
Metagenomics is the study of genetic material recovered directly from environmental
samples such as soil or water samples obtained from the environment. This technique
enables studies of organisms that are not easily cultured in a laboratory, as well as studies of
organisms in their natural environment (Sleator et al, 2008; Handelsman, 2004). This
technique has been made possible by advances in next generation DNA sequencing
technologies and bioinformatics, including sequencing the 16S-rRNA gene (Inglis et al, 2012)
(Figure 3). Therefore, this technique has provided us with invaluable insight about the
lifestyle and metabolic capabilities of the bacteria from environmental samples (Tringe et al,
2005). For instance, metagenomics allows to study the physiology and ecology of
environmental microorganisms, novel genes and gene products, novel proteins and novel
molecules with an antimicrobial activity (Handelsman, 2004). It also allows simultaneous
15
Introduction
monitoring of a diversity of microorganisms and does not have the biases of DNA
amplification. Yet, it is not possible to obtain finished genomes from all the microorganisms,
present in the sample, after performing assembly and annotation due to several reasons
such as incompleteness of reference databases. This results in several gaps and
misannotations in the chromosomes (Handelsman, 2004; Sleator et al, 2008). Particularly
from less abundant members of the microbiota or in situations where a community contains
many closely related species. Therefore, it is difficult to assign a function to specific species
within one community (Sleator et al, 2008).
Techniques used for studying the diversity of gut microbiota. A) Phylogenetic clone library is a library
in which the nucleotide sequences of interest are preserved as inserts to a plasmid that has been used to infect
bacterial cells. B) Phylogenetic microarray is a high-throughput analytical method for microbial community
analysis and consists of a glass covered with thousands of covalently linked DNA probes to which the DNA from
the samples is added. C) Community DNA profiling is either “sequence dependent electrophoresis”, such as
DGGE, TGGE and TGGE or “size dependent electrophoresis” such as RFLP, ARISA and SSCP. D) Metagenomics is
the study of genetic material recovered directly from environmental samples, in which all community DNA is
sequenced. This technique provides us with not only diversity but also the functionality of gut microbiota.
Figures derived from: http://metagenomicsrevealed.yolasite.com/process-1.php
http://www.envirologek.com/products_detail.php?indid=3&compid=15&cid=6&appid=&prodid=344&langid=1
http://biointelligence.wordpress.com/microarray_research/
Figure 3.
16
Introduction
II.3. CHEETAH (ACINONYX JUBATUS)
II.3.1. CLASSES BASED ON DIETARY HABITS
Mammals can be divided into three general classes based on dietary habits, namely
carnivores, herbivores and omnivores. Carnivores are animals that derive their energy and
nutrients from mainly or exclusively animal tissues. They can either hunt other animals or
scavenge animal bodies. Therefore, they have organs for capturing prey such as sharp teeth,
strong jaws and claws (Gittleman et al, 2001). The cheetah (Acinonyx jubatus) is one of the
274 species in the mammalian order of Carnivora (Britannica, 2011), which is the only
member of the genus Acinonyx (Treves & Karanth, 2003). This fast and relatively large
animal has a deep chest and a relatively small head. It is also well-known by the tear marks
running down its eyes in order to protect its eyes from sunlight and help this animal hunt
(Hayward et al, 2006) (Figure 4).
Herbivores, such as deer and goat, consume plant material and the structure of their body
and the inner organs is well adapted to eating only plants (Karban & Agrawal, 2002).
They typically have wide flat teeth adapted to grinding the plant material like grass, tree
bark and other tough plant material (Hummel et al, 2011) (Figure 4).
Omnivores, such as most bears as well as the human being, derive their energy and
nutrients from sources that may include animals, plants, algae and fungi that possess teeth
proper for consumption of various sorts of tissues such as plant and animal tissues
(Deweerdt, 2011) (Figure 4).
The scull and teeth structure in a) carnivores,
b) herbivores and c) omnivores. a) The carnivores
have sharp teeth appropriate for eating and tearing
meat, tendons, etc. apart as well as strong jaws. b)
The herbivores, on the other hand, have flat teeth
suitable for eating plant materials, in which their
teeth are stronger in the back side of their jaws. c)
Omnivores have the kind of teeth that potentially can
consume both animal and plant materials.
Figure 4.
Figure obtained from: http://avonapbiology20112012.wikispaces.com/Maya+Regalado
17
Introduction
II.3.2. VULNERABLE STATUS OF CHEETAHS
Cheetah, the world’s fastest mammal, is a predator carnivore. As a result these animals have
limited metabolic ability to digest anything except proteins (Bradshaw et al, 1996). Cheetahs
inhabit various places such as grassland savannahs and woodland habitats. They are listed
as an endangered animal in the last decades and their distribution has declined dramatically
in the wild (Nowell K., 1996). This is mostly due to the human actions such as destroying
their natural habitat like much of Africa, Middle East and South Asia. Other human actions
that further endanger this animal is hunting cheetahs and their prey such as gazelle and
especially antelope, which is the most important prey for the cheetahs in the wild (O’Brien
et al, 1985). Occasionally road accidents can cause danger to this animal as well (Williams,
2007). Based on an investigation in Zimbabwe, it was also found that several cheetahs have
been killed in the last twenty years due to ingestion of anthrax infected meat, pneumonia,
nephritis, asphyxiation, flea infection, accidental poisoning and fractures in their bones
(Williams, 2007).
Cheetahs are mid-sized predators. Therefore, if there is not enough prey in the
environment, they cannot compete for their food requirements with the larger predators
such as lions, leopards, tigers and the African wild dogs. Obviously this unfortunate event
can cause their starvation (Marker et al, 2003; Durant, 2004; Radloff & Du Toit, 2004;
Hayward et al, 2006). It has also been seen and reported in the wild and also in the
preserved areas that the cheetah cubs are preyed by other large predators such as lions and
jaguars (Gros, 1998).
Lack of genetic variation, polymorphism and heterozygosity are other problems that may
cause susceptibility and vulnerability of these endangered animals to specific diseases such
as renal failure. Additionally, the level of variation in the aforementioned factors is higher in
other mammals. Therefore, the fact that they compete with the other animals and survive in
the wild, as well as adaptation to the ecological niche, is of great significance (O’brien et al,
1983).
The increase in the habitat destruction of wild animals has led to the prospect of protecting
these animals in captive breeding programs (O’Brien et al, 1985). In wildlife preservation,
with the aim of protecting animals from extinction, conservation of threatened and
endangered species is quite challenging. Captive breeding of cheetahs is very difficult to
perform since a large number of the cubs die at birth due to disease susceptibility and
maternal neglect (O’Brien et al, 1985; van Gelder, 1973).
Another problem that endangers cheetahs is the feeding mismanagement in the zoos that
increases suboptimal health, gastrointestinal and metabolic diseases in these vulnerable
animals. Additionally, improper feeding habits in captivity can lower the breeding
performance in the cheetahs (Kotsch et al, 2002; Munson et al, 2005). Therefore, wellbalanced diets can be a therapeutic intervention with representing an important route to
prevent these feeding and breeding problems (Garcia-Mazcorro et al, 2011; Gaggìa et al,
2010).
18
Introduction
II.3.3. CHEETAH’S DIET AND ITS ROLE IN CHEETAH’S METABOLISM
Cheetahs are specialized in predation and have a protein-rich diet due to consuming mostly
meat (Treves & Karanth, 2003). In the wild, these carnivores, generally prefer to capture
and kill the most available medium-sized prey within a body mass range of 23-56 kg, such as
antelope and gazelle (Nowak, 1999; Hayward et al, 2006). Though they are also able to
utilize both larger and smaller prey (Mills et al, 2004). They mostly feed from the flesh of
their prey, due to their relatively fragile teeth, skull and jaw (Van Valkenburgh et al, 1990).
They also consume indigestible animal tissues such as small bones, tendons, skin, hair, and
feathers derived from the prey (Depauw et al, 2011). These indigestible materials, which
cannot be enzymatically digested in the body, have the potential to act as a substrate for
microbial fermentation in the intestine of carnivores with the ability to produce high
amounts of short-chain fatty acids. These short-chain fatty acids have numerous benefits on
colonic function and metabolism such as energy source for colonocytes, stimulation of
colonic blood flow and motility as well as decrease of the growth of pathogenic microbiota
(Rondeau, 2003; Wong et al, 2006).
Wild cheetahs consume tendons, small bones and cartilage on a regularly basis. On the
other hand, cheetahs in captivity are fed either raw meat diet supplemented with a vitamin
and mineral premix or by a prey-based diet containing the whole carcasses, which has high
amounts of indigestible animal tissues (Depauw et al, 2011). Therefore, a big difference
between the diet of captive cheetahs and wild cheetahs is the nutrition composition of their
diet. According to recent studies about the role of animal fibers on the cheetah gut
microbiota and their health, the undigested part of an animal-based diet can be a source of
short-chain fatty acids in the captive cheetahs and potentially other carnivores (Depauw et
al, 2011). Moreover, the presence of animal fibers in the diet of captive cheetahs has a
modifying effect on the bacterial fermentation in the intestine (Depauw et al, 2011).
Therefore, it is of high importance to investigate whether the presence or absence of these
indigestible animal tissues causes changes in intestinal fermentation. Moreover, the effect
of prey-based diet type on the gut microbiota of cheetahs is promising due to the beneficial
actions of the gut microbiota.
In general, the fermentation of protein sources is regarded as detrimental for gut health in
either human or mammals. This process results in many by-products, such as ammonia,
indolic and phenolic compounds. These compounds are toxic and have been linked to
intestinal diseases e.g. inflammatory bowel disease (Pedersen et al, 2002; Tuohy et al, 2006)
and chronic renal failure (Niwa, 2010), which is one of the causes of death in the captive
cheetahs (Depauw et al, 2011). These putrefactive compounds have the potential to
damage the gut ecosystem and influence the general metabolism of carnivores as they can
be converted into toxic metabolites. To this end, several studies have been carried out, in
which it has been observed that putrefactive compounds, namely indole, phenol and pcresol, are higher when cheetahs are fed supplemented beef in comparison with cheetahs
fed whole prey such as rabbit (Vester et al, 2008, 2009).
19
Introduction
II.3.4. DOMESTIC CAT AS A MODEL FOR STUDYING CHEETAH GUT MICROBIOTA
Domestication is a continuous transition that differs by species, genes and environment, in
which some characters may vary with circumstances (Price, 1984). In addition, it is reported
that all domesticates manifest a considerable tolerance of proximity to people and also
adaptations to a new diet. Domestication is not a single trait but a group of traits that
consists of elements affecting emotion, mood and social communication, in which all have
been modified in one way or another (Driscoll et al, 2009). The metabolic and morphological
changes that result in behavioral adaptation to the human environment can lead to a
remarkable dependence on humans for food and shelter, such as for domestic cats (Driscoll
et al, 2009).
Patterns of sequence variation have been seen in the genome of domestic cats, which
reflect a history of domestication and breed development (Driscoll et al, 2007). It is
probable that the cat began its association with humans as a commensal, feeding on the
rodent pests that infested the grain stores of the first farmers (Flood, 2001). It is generally
considered that domestic cats have descended from the old world wildcats. Yet, they differ
from wildcats in behavior, tameness, and coat color diversity (Peters et al, 2009).
The domestic cat has been proposed as a model for basic nutrients and energy
requirements and feeding methods for captive felid populations such as the cheetah (Vester
et al, 2008; Kerr et al, 2013b). It has been proposed by Dierenfeld et al. that nutritional
requirements of domestic cats should form the basis of comparison in managed feeding
programs for captive cheetahs (Dierenfeld, 1993). They both share several nutrition-related
aberrations including food allergies, obesity and hyperlipidemia. Additionally, nutritionists
and veterinarians can take blood samples from domestic cats, which is easier in comparison
with the cheetahs. The blood samples are used to explore the blood metabolite
concentrations, digestibility of food materials and intrinsic feline metabolism with specific
nutritional requirements. Therefore, an extrapolation of dietary recommendations for
domestic cats as well as their dietary profile, has been applied to other exotic felines like
cheetahs (Lubbs et al, 2009; Vester et al, 2009).
It is not thoroughly scientifically applicable to extrapolate the data obtained from the
domestic cats, including the data from dietary requirements and dietary profile, to the
cheetahs. The behavioral, anatomical and nutritional characteristics of these two animals
vary tremendously. For instance, domestic cats are frequently fed carbohydrate-rich
extruded kibble diets (MacDonald et al, 1984; Zoran, 2002) while captive cheetahs are fed a
meat-based or prey-based diet (Vester et al, 2009). Different sources of dietary fiber, such
as pectin and cellulose, are present in domestic cats’ diet. Taxonomic and functional studies
of the intestinal microbial communities have shown that domestic cats possess a welldeveloped complex microbial community which can also change depending upon their diet
type (Lubbs et al, 2009; Vester et al, 2009). For instance, a higher protein level present in
their diet has been associated with an increase in Escherichia coli, Lactobacillus spp.,
Clostridium perfringens and Fusobacterium spp. and a decrease in Bifidobacterium
population in the fecal samples of cats (Lubbs et al, 2009; Vester et al, 2009). Moreover, C.
perfringens, which has been identified as an intestinal pathogen in cheetahs (Citino, 1995),
20
Introduction
E. coli and Lactobacillus spp., were shown to increase when the cat’s diet was supplemented
with pectin, whereas Bifidobacterium increased upon fructooligosaccharide
supplementation (Barry et al, 2012). Bermingham et al. showed that the diversity of the
bacterial population in cats fed dry diets were lower compared with cats fed wet diets
(Bermingham et al, 2013). Moreover, it has been reported that cats fed a diet containing 4%
pectin have a higher percentage of Firmicutes and Spirochaetes in comparison with cats that
were fed a diet comprising 4% cellulose (Barry et al, 2010).
Recently, it was reported by Tun et al. that Bacteroidetes is the most predominant (68%)
bacterial phylum in the feline intestinal microbiome followed by Firmicutes and
Actinobacteria, as it was found in the fecal microbiota of domestic cats (Khin et al, 2012).
The current information about the gut microbiota of adult captive cheetahs has been
obtained from only two captive cheetahs housed in Belgium. This information is collected in
a clone library based on these two captive animals (Becker et al, 2014). In order to build a
clone library, fecal samples are collected and homogenization and community DNA
extraction is carried out. Afterwards, 16S rRNA PCR is performed to prepare the clone
library followed by two distinct steps. The first one is to perform V3-16S rRNA PCR, DGGE
and data analysis. The second step is partial sequencing PCR followed by sequencing,
assembly and annotation in order to build a phylogenetic tree by comparison against The
Ribosomal Database Project II (RDP) (Figure 5). The group of sequences with ≤3% sequence
divergence was regarded as an Operational Taxonomic Unit (OTU) or phylotypes. This
phylogenetic tree works as a reference to identify the bacterial groups on the DGGE
fingerprints of fecal samples under study.
Unexpectedly, Bacteroidetes and Bifidobacteriaceae, which can contribute in intestinal
homeostasis, were not present in the 16S rRNA clone libraries of two Belgian captive
cheetahs studied recently. Yet, a hardly detectable concentration of Bacteroidetes phylum
was seen by qPCR. On the other hand, Firmicutes were in high number as the dominant
group while the minority were Actinobacteria, Proteobacteria and Fusobacteria. Amongst
the Firmicutes, Clostridium cluster XIVa, XI and I were in majority with 43%, 38% and 13% of
all the bacteria respectively (Becker et al, 2014).
21
Introduction
Constructing a clone library based on 16S rRNA gene. In order to build a clone library, fecal samples
are collected followed by their processing to prepare the clone library. On one hand, sequencing, assembly and
annotation are performed to eventually build a phylogenetic tree. On the other hand, V3-16S rRNA PCR is
performed followed by DGGE to have the clones on the gel. The results obtained from both steps are used as a
reference when analyzing the fecal samples under study.
Figure 5.
To sum up, the current information about the gut microbiota of captive adult cheetahs is
scarce. Consequently, more fecal samples should be collected from cheetahs housed in
various zoos with different backgrounds and diet in order to eventually bring the correlation
between diet, microbiota and health to light. By analyzing multiple fecal samples from these
animals, more knowledge will be obtained about the diversity, composition and core
microbiota of captive cheetahs.
22
Goals
CHAPTER III. GOALS
The cheetah is a carnivore that suffers from gastrointestinal disorders and suboptimal
health in captivity, in which one reason can be due to an unbalanced diet. Diet, microbiota
and health are strongly correlated. Diet has an effect on the gut microbiota since several
nutritional components are enzymatically digested and absorbed by the host while the
indigestible animal and plant tissues, such as complex carbohydrates, are fermented by the
gut microbiota. This fermentation results in short-chain fatty acid production that benefits
the host by decreasing inflammatory bowel disease, cardiovascular diseases, colon cancer
and inflammation. In addition, gut microbiota benefit the host’s health through
differentiation of the immune system, production of vitamins and tissue homeostasis.
Therefore, gut microbiota should be taken into account when formulating the diet of
captive cheetahs in order to improve their overall health status.
The relationship between diet and health of cheetahs in captivity has been explored in
several studies. In contrast, the information about their gut microbiota remains scarce since
the current knowledge about the fecal microbiota of adult captive cheetahs has only been
obtained from a few animals from a single zoo. Therefore, in this study, we aim to collect
more fecal samples from various cheetahs housed in different European zoos, with different
genetic backgrounds, housing conditions and diets, in order to bring the fecal microbial
composition of captive adult cheetahs to light. To this end, Denaturing Gradient Gel
Electrophoresis (DGGE) and quantitative PCR (qPCR) will be used to further explore the fecal
bacterial community composition.
The main goals of this Master thesis are:


To describe the fecal microbial community composition of captive adult cheetahs
housed in different European zoos. The goal is to investigate whether a core microbiota
exists in the gut microbiota of captive cheetahs as well as to explore the potential
impact of their diet, zoo and genetic backgrounds on overall gut microbiota composition.
 In order to perform DGGE, fecal samples should be homogenized followed by
community DNA extraction. Afterwards, V3-16S rRNA PCR is performed to
produce V3-16S rRNA amplicons for DGGE. Eventually, the data from the DGGE
fingerprints will be analyzed with BioNumerics software followed by comparison
of the fingerprints with the available clone library, previously built for the fecal
samples of two captive adult cheetahs in Planckendael. By linking the clone
library with the DGGE fingerprints, it will be possible to characterize the bacterial
groups present in DGGE fingerprints of fecal samples.
To quantify specific groups of bacteria in the fecal samples of captive adult cheetahs.
This is carried out through qPCR for a prevailing group of bacteria in the clone library,
namely Clostridium cluster XIVa. This technique quantifies Clostridium cluster XIVa and
confirms the presence of this cluster in the DGGE fingerprints.
23
Results
CHAPTER IV. RESULTS
IV.1. DGGE DATA
In this study, 55 fecal samples were collected from 36 captive cheetahs housed in 6 zoos,
which were members of European Association of Zoos and Aquaria (EAZA). For each animal
one sample was available and statistical randomization was used for animals with more than
one sample to determine which samples to be included in this study. The zoos included
Planckendael (PL), Ree Park Ebeltoft Safari (EB) and Réserve Africaine de Sigean (SI) located
in Belgium, Denmark and France respectively and Gaia Zoo (GA), Safaripark Beekse Bergen
(BB) and Zoo Parc Overloon (OV) in The Netherlands (Table 1). The goal was to characterize
and compare the microbial community composition of the fecal microbiota of different
animals from different zoos. To this end, DGGE fingerprinting was performed based on the
V3 region of the 16S rRNA gene obtained from V3-16S rRNA PCR.
Animal Information
Zoo
Name
Sex
Planckendael
In Belgium
Poko
M
Year
of
Birth
2001
Papole
M
2001
Gaia Zoo in The
Netherlands
Safaripark
Beekse
Bergen in The
Netherlands
Zoo Parc
Overloon in
The
Netherlands
Kivuli
Sample Information
Housing
Parents
Sample code
DNA
code
Diet
Diet
type
Dietary
Regime
I
Sire: 22313
Dam: ALL285
B1-PL-P-T1
CH126
Rabbit
A
1
B1-PL-M-T4
CH141
Beef
B
Sire: 22313
Dam: ALL285
B2-PL-P-T1
CH128
Rabbit
A
B2-PL-M-T10
CH366
Horse
B
NL13-GA-M-T1
CH437
Beef
B
Jinsi
II
NL14-GA-M-T1
CH438
Beef
B
Sawah
NL15-GA-M-T1
CH439
Beef
B
NL1-BB-P-T1
CH146
Rabbit
A
NL2-BB-P-T1
CH147
Rabbit
A
NL3-BB-P-T1
CH148
Rabbit
A
NL4-BB-P-T1
CH149
Rabbit
A
NL5-BB-P-T1
CH150
Rabbit
A
NL6-BB-P-T1
CH151
Rabbit
A
NL7-BB-P-T1
NL7-BB-P-T4
NL8-BB-P-T1
CH152
CH468
Ch153
Rabbit
Rabbit
Rabbit
A
A
A
Impie
NL17-BB-P-T1
CH469
Rabbit
A
Raha
NL16-BB-P-T1
CH472
Rabbit
A
Sire: 041065
Dam: 041066
NL9-OV-M-T3
CH202
Horse
B
NL9-OV-M-T5
CH234
Chicken
B
Sire: 22313
Dam: 21867
NL10-OV-M-T3
CH203
Horse
B
NL10-OV-P-T1
CH144
Rabbit
A
Sire: 22359
Dam: ACI004
NL11-OV-P-T4
CH207
Rabbit
A
NL11-OV-M-T6
CH398
Horse
B
Barafu
F
2007
Omari
M
2005
Luna
F
2000
Farida
F
2008
Marina
F
2001
Kijini
F
2001
Kayenda
F
2005
Noemi
F
2008
Hakkari
Lomai
Xavannah
M
M
F
2000
2002
2005
III
Sire: 2221
Dam: 6135
Sire: 041104
Dam: 041077
Sire: M95213
Dam: M95138
Sire: M98166
Dam: M00088
Sire: 2552
Dam: 2963
Sire: M95212
Dam: M95137
Sire: 20044H
Dam: M00088
Sire: 23257
Dam: 22688
IV
2
3
4
24
Results
Duma
Réserve
Africaine de
Sigean in
France
2011
Icare
M
2009
Imaya
F
2009
Malik
Abari
Asali
Bahati
Ree Park
Ebeltoft Safari
in Denmark
M
Nuru
M
M
F
F
M
V
2012
2010
2010
1999
2005
VI
Sire: MIG1230075237
Dam: MIG1229026496
Sire: 6219
Dam: 6214
NL12-OV-M-T13
CH434
Beef
B
NL12-OV-P-T14
CH436
Antelope
A
FR4-SI-PC-T1
CH455
Chicken
A
FR4-SI-PC-T2
CH456
Chicken
A
Sire : 6219
Dam: 6214
FR5-SI-PC-T1
CH457
Chicken
A
FR5-SI-PC-T2
CH458
Chicken
A
FR5-SI-PC-T3
CH459
Chicken
A
Sire: 4583
Dam: 4825
FR6-SI-PC-T1
CH460
Chicken
A
FR6-SI-PC-T2
CH461
Chicken
A
Sire: 4583
Dam: 4825
FR1-SI-PC-T1
CH462
Chicken
A
FR1-SI-PC-T2
CH463
Chicken
A
Sire: 205011
Dam: 2010046
FR2-SI-PC-T1
CH464
Chicken
A
FR2-SI-PC-T2
CH465
Chicken
A
Sire: 576
Dam: 1041
FR3-SI-PC-T1
CH466
Chicken
A
FR3-SI-PC-T2
CH467
Chicken
A
FR3-SI-PC-T1
CH473
Chicken
A
Sire: 041104
Dam: 041077
DK1-EB-P-T1
CH440
Rabbit
A
DK1-EB-PC-T2
CH441
Chicken
A
Split
F
2009
Sire: ACI006
Dam: ACI014
DK2-EB-PC-T1
CH442
Chicken
A
DK2-EB-P-T2
CH443
Rabbit
A
Izzy
F
2009
Sire: ACI006
Dam: ACI014
DK3-EB-PC-T1
CH444
Chicken
A
DK3-EB-P-T2
CH445
Rabbit
A
DK3-EB-PC-T1
CH446
Chicken
A
Sire: 3865
Dam: 3204
Sire: 041104
Dam: 041077
Sire: ACI006
Dam: ACI014
DK5-EB-PC-T1
CH447
Chicken
A
DK8-EB-PC-T1
CH448
Chicken
A
DK7-EB-PC-T1
CH449
Chicken
A
DK7-EB-PC-T2
CH450
Chicken
A
Sire: 5187
Dam: 610583
Sire: ACI018
Dam: ACI030
Hollander
Sire: ACI018
Richard
Dam: ACI030
Hollander
Sire: 2697
Dam: 2553
DK9-EB-P-T1
CH451
Rabbit
A
DK10-EB-P-T1
CH452
Rabbit
A
DK11-EB-P-T1
CH453
Rabbit
A
DK12-EB-M-T1
CH454
Beef
B
Abayomy
M
2004
Aduke
F
2005
Suna
F
2007
Hollander
M
2005
Hurley
M
2010
Jack
M
2010
Richard
M
2002
5
6
Metadata of all the animals in this study. Dietary regimen is defined as the diet an animal has in a
specific period of time while diet is defined by the animal that the cheetahs are fed, such as antelope, chicken
or horse. Diet type has two categories in this study which is prey and meat. A: Prey, which is the whole carcass
with variable amounts of indigestible animal tissues, such as raw bones, tendons, cartilage, skin, hair or feather.
B: Chunked raw meat supplemented with vitamin and mineral premix.
1: Horse meat supplemented with Carnicon vitamins and minerals. These animals are sometimes fed rabbit and
they have fasting days with less amounts of meat. 2: Minced meat and chopped meat with water, Royal Canin
Renal chunk, Pancrex and Prineral 20mg for the “Kivuli”. “Jinsi” receives the same diet with addition of Ipakitine,
Gistocal and blood. “Sawa” receives meat, Gistocal, water and blood. 3: Rabbit and doves as prey. These
animals are sometimes fed meat supplemented with minerals and vitamins. 4: Rabbit as prey and they are
Table 1.
25
Results
sometimes fed horse and beef meat. They have a fasting day on Thursdays. 5: Whole chicken as prey, which are
freshly killed. These cheetahs have a fasting day on Mondays. 6: Chicken without a head or rabbit as a whole
prey.
I: Outside is grass and indoor is concrete. These two brothers always live together and their sleeping beds are
made of straws. II: Concrete inside with a bed of straw, they each have their separate inside house, grassland
outside which they share. III: Inside is concrete and they sleep separately. Outside is covered with grass and is
for running and (females and males are separate). IV: Grass outside. The males stay together while females
are separate. Inside is covered with sand and concrete sometimes. V: Different places for females and males in
the zoo. Females live in the enclosure. VI: Nuru, Abayomy, Aduke and Richard live alone while Split and Izzy live
together in the concrete inside. Hollander, Hurley and Jack share the outside grass enclosure together.
IV.1.1. ANALYSIS OF ALL DGGE FINGERPRINTS
Clustering the fingerprints: The DGGE fingerprint profiles obtained from 55 fecal samples
were compared by hierarchical clustering using the Pearson correlation coefficient (Figure
6), as well as the Dice correlation coefficient (Figure 7). Pearson correlation coefficient is
based on the intensity of the bands whereas Dice correlation coefficient is based on the
presence or absence of bands in the fingerprints. The analysis of the DGGE fingerprints
showed variation in intensity of bands between the animals, which were labeled by zoo, diet
and diet type. No zoo-specific or diet type-specific clustering was observed in the DGGE
profiles. Yet, several smaller clusters were obtained that were not entirely correlated with
zoo or diet type (see pages 37 and 38).
Pairwise comparison of all the fingerprints: The fingerprints were compared in a pairwise
manner with the Pearson correlation coefficient, which resulted in a data set of 1540
pairwise combinations. The Pearson correlation coefficient was chosen over Dice since
intensity of the bands was more important than only their presence, in order to study the
predominant bacteria. Moreover, the boxplots made for pairwise similarities between
fingerprints per zoo or per diet type did not differ drastically from Pearson to Dice.
26
Results
Clusters obtained from all fingerprints based on Pearson correlation coefficient. The fingerprints were
clustered according to Pearson correlation coefficient. The numbers on the branches indicate the similarity
percentages. Dark green: SI - Blue: EB – Red: OV – light green: GA – Orange: BB – Light blue: PL
Figure 6.
27
Results
Clusters obtained from all fingerprints based on Dice correlation coefficient. The fingerprints were
clustered according to Dice correlation coefficient. The numbers on the branches indicate the similarity
percentages. Light green: GA – Red: OV – Blue: EB – Orange: BB – Light blue: PL – Green: SI
Figure 7.
28
Results
Pairwise comparison of the fingerprints per zoo: The variation of the predominant bacterial
diversity within the 6 zoos was assessed by determining pairwise similarity values, according
to the Pearson correlation coefficient, between all fecal fingerprints in each group. Further
comparison was done by using boxplot analysis per zoo. As depicted in (Figure 8), the
highest variability between the fingerprints per zoo was observed in the DGGE profiles in
Réserve Africaine de Sigean and Zoo Parc Overloon, while the lowest variability was
observed between the samples from Planckendael.
8. Boxplot for pairwise
comparisons
of
the
DGGE
fingerprints per zoo based on
Pearson correlation coefficient. The
DGGE fingerprints of fecal samples
grouped in a zoo were compared in
a pairwise manner followed by
boxplot analysis. PL: Planckendael
GA: Gaia Zoo OV: Zoo Parc Overloon
BB: Safaripark Beekse Bergen EB:
Ree Park Ebeltoft Safari SI: Réserve
Africaine de Sigean
Figure
Pairwise comparison of the fingerprints per diet type: The variation of the predominant
bacterial diversity in the animals fed meat versus prey was assessed by determining pairwise
similarity values, according to the Pearson correlation coefficient, between all fecal
fingerprints in each group. Samples were further compared by boxplot analysis (Figure 9).
The boxplot analysis revealed that without taking the number of samples into account, the
fingerprints from cheetahs fed meat had less variability compared to the animals fed prey.
Yet, the median was the same.
29
Results
Boxplot for
pairwise
comparisons of the
DGGE fingerprints
based on the diet
type, with Pearson
correlation
coefficient.
The
fingerprints of the
fecal samples of
animals fed meat
were compared in a
pairwise
manner.
The
same
was
performed
for
animals fed prey.
Figure 9.
Pairwise comparison of the fingerprints per animal: The variation of the predominant
bacterial diversity within each animal, with more than one sample, was assessed by
determining pairwise similarity values, according to Pearson correlation coefficient. The
samples were further compared by boxplot analysis per animal (Figure 10). A trend was
visualized in the boxplots that 75% of these animals had more than 80% similarity between
their fingerprints form the fecal samples. The remaining 25% had more variability in their
fingerprints.
30
Results
Boxplot for pairwise comparisons of the DGGE fingerprints per animal, with Pearson correlation
coefficient. The fingerprints from each animal, with more than 1 fecal sample, were compared in a pairwise
manner. The animals with one fecal sample were excluded from this study. Animals FR5, FR3 and DK3 had 3
fecal samples, while the remaining had 2 samples.
Figure 10.
31
Results
IV.1.2. BAND RICHNESS OF THE DGGE FINGERPRINTS PER ZOO
The bands from the fingerprint profiles were assigned to band-classes depending upon their
position in the fingerprint. Following, these band-classes were correlated to the clones in
the clone library. As a result, a total of 42 band-classes were assigned. The band richness,
which is the number of bands per fingerprint, was determined for each DGGE profile in
order to assess the variability and richness of the intestinal ecosystem of these animals. The
lowest variability in band richness belonged to the fingerprints of cheetahs from Gaia Zoo,
while the highest variability of band richness belonged to the fingerprints from Safaripark
Beekse Bergen (Table 2).
Zoo
Planckendael
Réserve Africaine de Sigean
Safaripark Beekse Bergen
Zoo Parc Overloon
Gaia Zoo
Ree Park Ebeltoft Safari
Average of band richness
20
17
19
17
11
16
Range of band richness
14-23
9-23
13-25
13-22
9-12
12-24
Table 2. The average and range of band richness per zoo.
Also, the band richness of DGGE fingerprints per zoo was analyzed in boxplots (Figure 11). A
large variability was seen in fingerprints from OV and SI, whereas fingerprints from GA had
the least variability among the zoos. Moreover, the mean of band richness was lower in
fingerprints from GA compared with the other zoos.
Boxplot analysis of
band richness of the DGGE
fingerprints per zoo. The
number of bands of DGGE
fingerprints, which were
grouped per zoo, was
calculated
followed
by
boxplot analysis. Outlier
values are indicated by open
circles and stars.
Figure 11.
PL: Planckendael
GA: Gaia Zoo
OV: Zoo Parc Overloon
BB: Safaripark Beekse Bergen
EB: Ree Park Ebeltoft Safari
SI: Réserve Africaine de
Sigean
32
Results
In order to visualize the band-classes present in the fingerprints, a histogram was generated
(Figure 12). It was depicted in the histogram that several band-classes such as 50.1%, 54.3%
and 67.5% were present in more than 70% of the fingerprints, whereas, band-classes 39.7%,
47.4% and 80.8% were present in more than 60% of the DGGE profiles. Several band-classes
were not abundant in these fingerprints, which had less than 10% occurrence, such as bandclasses 55.5% and 66.2% (Figure 12). Therefore, it can be deduced that the more prevailing
fingerprints such as band-class 54.3%, correlating to Clostridium cluster XIVa, may be a
member of the core microbiota in these samples.
Figure 12.
Band-classes present in the fingerprints. The percentage of fingerprints that possesses each band-
class.
In order to document the percentage of fingerprints per zoo, that contained a particular
band-class, a table was generated (Table 3). It is illustrated in this table that band-classes
54.3% and 67.5% were present in all the 6 zoos.
33
Results
Band-class %
28.3
29.2
29.9
31.4
32.7
34.4
35.7
37.1
38.6
39.7
41.3
42.4
43.4
44.3
45.9
47.4
49.1
50.1
50.9
52
53.3
54.3
55.5
57.3
58.7
61.4
64
65.4
66.2
67.5
68.2
68.8
71.2
72.4
74.4
75.4
76.1
77.5
78.3
79.7
80.8
81.7
PL
100%
100%
25%
0
50%
0
50%
25%
100%
50%
50%
0
100%
0
0
100%
100%
100%
25%
0
100%
100%
0
100%
75%
0
0
0
0
100%
100%
0
50%
0
0
0
0
0
50%
0
0
100%
GA
0
0
0
33.3%
0
0
0
33.3%
0
100%
100%
100%
0
0
0
33.3%
0
0
0
33.3%
0
100%
0
0
100%
0
0
0
0
100%
0
0
0
0
0
0
0
33.3%
0
33.3%
100%
0
BB
9%
27%
0
0
0
9%
54%
0
0
45%
18%
0
27%
0
0
81%
63%
100%
0
0
100%
100%
0
27%
27%
0
9%
0
0
81%
0
27%
27%
0
0
54%
0
9%
0
0
36%
63%
OV
50%
37%
25%
0
0
0
50%
50%
0
87%
25%
0
50%
0
12%
50%
100%
0
50%
0
37%
100%
0
12%
50%
0
12%
25%
0
75%
25%
25%
37%
0
25%
25%
25%
25%
0
0
50%
50%
EB
33.3%
33.3%
0
0
0
33.3%
13%
33.3%
26%
60%
0
13%
6%
0
0
73%
66.6%
100%
0
33.3%
6%
100%
0
33.3%
46.6%
0
0
33.3%
0
100%
100%
0
20%
6%
0
20%
33.3%
20%
33.3%
0
100%
100%
SI
42%
0
28%
28%
0
0
0
0
57%
0
78%
35%
50%
14%
35%
0
0
100%
0
21%
0
42%
14%
0
0
14%
21%
25%
42%
42%
0
0
0
0
0
14%
28%
14%
14%
0
50%
50%
The percentages of fingerprints per zoo, containing each band-class. For each band-class, the
percentage of fingerprints per zoo was calculated.
Table 3.
34
Results
IV.2. LINKING THE DGGE DATA WITH THE CLONE LIBRARY
IV.2.1. COMMON BAND-CLASSES IN THE FINGERPRINTS
To bring to light the common bacterial diversity in the fecal samples of these cheetahs, we
focused on 3 clusters to which most phylotypes of the clone libraries were assigned to,
which might be members of the core microbiota in these 55 fecal samples.
Clostridium cluster XIVa: The DGGE fingerprints were compared with the clone library
previously constructed from the fecal samples of 2 cheetahs in Belgium. According to this
comparison, all the fingerprints consisted of band-classes 50.1% and 54.3%, with different
intensities. The Operational Taxonomic Units (OTUs) correlating to these band-classes were
OTU 21, 10, 7 and 9 from the clone library, representing Clostridium cluster XIVa. Moreover,
other band-classes, namely 47.4%, 49.1% and 53.3%, correlated as well to OTUs
representing this cluster. However, latter band-classes were only present in some of the
fingerprints (Figure 13).
Clostridium cluster XI: All the fingerprints had a band assigned to either band-class 80.8% or
81.6%, with different intensities. OTUs 3 and 4 correlated to these band-classes that
represented Clostridium cluster XI in the clone library (Figure 13).
Clostridium cluster I: Band-classes 67.5% and 68.2%, with low intensity, were present in 70%
of the fingerprints, representing Clostridium cluster I. Due to the limitations of DGGE such as
difficulty in visualization, this band-class might be present in almost all the fingerprints. The
OTU, correlating to these band-classes, was closely related to Clostridium perfringens
(Figure 13).
35
Results
Clostridium clusters XIVa, XI and I in the fingerprints. Clostridium cluster XIVa and XI were present in
all the fingerprints whereas Clostridium cluster I was present in 70% of the DGGE profiles. There were 5 bandclasses representing Clostridium cluster XIVa in the fingerprints. The clustering was based on Pearson
correlation coefficient. The numbers on the branches indicate the similarity percentages.
Figure 13.
IV.2.2. LINKING BAND-CLASSES WITH FINGERPRINT CLUSTERS
Clusters based on Pearson correlation coefficient: Pearson correlation coefficient clustering
of all fingerprint profiles resulted in two complete separate clusters with 34.5% similarity
(Figure 6). The first cluster consisted of 5 fingerprints from cheetahs in Réserve Africaine de
Sigean and was separated from the rest mostly due to band-classes 28.3% and 41.3% (Figure
14). These band-classes did not correlate to any clones in the clone library. After excising
and sequencing these bands, it was revealed that they both belonged to Lactobacillus spp.
The OTUs 15, 26 and 42 correlated to this genus in the clone library as well.
36
Results
Compared to the other animals, these 5 animals were the only ones on a chicken only
dietary regimen.
Figure 14. Band-classes 28.3% and 41.3% were distinct in 5 fingerprints belonging to Réserve Africaine de
Sigean. The clustering was based on Pearson correlation coefficient. The numbers on the branches indicate the
similarity percentages.
Two clusters were obtained within the remaining 49 fingerprints with 55.4% similarity due
to the band intensity in band-classes 50.1% and 54.3%, correlating to Clostridium cluster
XIVa. One of these clusters consisted of 14 fingerprints with low intensity in these 2 bandclasses, while the other cluster, containing 35 fingerprints, had relatively more intense
bands in these two band-classes (Figure 6).
Fingerprint obtained from the fecal sample of NL12-OV-M-T13 clustered separately from 49
fingerprints with 45.5% similarity. This animal had a distinct band in band-class 31.4% that
does not correlate with any of the clones in the clone library. Yet, it displayed bands
correlating to Clostridium cluster XIVa, XI and I as all the other fingerprints do (Figure 6).
According to Pearson correlation coefficient, the profiles of two animals from the same zoo
and on the same diet, namely DK8-EB-PC-T1 and DK7-EB-PC-T1, clustered with 99.3%
similarity together. These profiles had very intense bands for Clostridium clusters XIVa, XI
and I and the same band-classes (Figure 15).
Figure 15. The 2 fingerprints clustered together with 99.3% similarity based on Pearson correlation coefficient.
These two fingerprints were obtained from 2 animals in Ree Park Ebeltoft Safari. The clustering was based on
Pearson correlation coefficient. The numbers on the branches indicate the similarity percentages.
Clusters based on Dice correlation coefficient: Based on Dice correlation coefficient, 2
separate clusters were obtained from all fingerprints with 50.3% similarity. One of these
clusters consisted of only two fingerprints, namely NL13-GA-M-T1 and NL15-GA-M-T1 from
Gaia Zoo, with 55.6% similarity (Figure 7). A cluster with 6 fingerprints was made with 68.7%
similarity, in which all profiles belonged to animals fed chicken in Réserve Africaine de
Sigean. They all had band-classes 28.3% and 41.3% in common, which did not correlate to
any clones in the clone library ( Figure 16), which belonged to Lactobacillus spp. after
sequencing. This data confirmed the clustering based on the Pearson correlation coefficient.
37
Results
Figure 16. The 6 fingerprints from fecal samples of animals in Réserve Africaine de Sigean. These profiles have
2 band-classes, not correlating to the clones in the clone library. The clustering was based on Pearson
correlation coefficient. The numbers on the branches indicate the similarity percentages.
According to Dice correlation coefficient, two animals from different zoos and on different
dietary regimes, namely NL9-OV-M-T3 and B2-PL-M-T10, clustered with 95% similarity
together. Their fingerprints shared 90% of their band-classes ( Figure 17).
Figure 17. Fingerprints from two animals from different zoos and on different diets with 95% similarity.
Animals NL9-OV-M-T3 and B2-PL-M-T10 belonged to different zoos and they had different diets. Yet, the
fingerprints from their fecal samples were 95% similar. The clustering was based on Pearson correlation
coefficient. The numbers on the branches indicate the similarity percentages.
There were no band-classes specifically associated with the profiles in BB, SI or GA zoos,
whereas it was the case for fingerprints from PL, OV and EB.
Band-classes in Planckendael: The fingerprints from Planckendael displayed more than 50%
similarity to one another. They also displayed more intense bands for band-classes 67.5%
and 68.2%, correlating to Clostridium cluster I, in comparison with the other fingerprints.
Band-classes 28.3% and 29.2%, which had very low intensity, were present in all 4 DGGE
fingerprints of the animals in Planckendael. They did not correlate to any of the clones
present in the clone library (Figure 18).
Band-classes 28.3% and 29.2% were present in the fingerprints from animals housed in
Planckendael. These band-classes did not correlate to any of the clones in the clone library. The clustering was
based on Pearson correlation coefficient. The numbers on the branches indicate the similarity percentages.
Figure 18.
38
Results
Band-classes from Ree Park Ebeltoft Safari and Zooparc Overloon: The fingerprints from Ree
Park Ebeltoft Safari and Zooparc Overloon had all the 5 band-classes correlating to
Clostridium cluster XIVa (47.4%, 49.1%, 53.3%, 50.1% and 54.3%) while the other zoos
displayed only 1-4 of these band-classes, which varied among zoos (Figure 19).
The fingerprints of the fecal samples from Ree Park Ebeltoft Safari and Zoo Parc Overloon had more
band-classes correlating to Clostridium cluster XIVa. There were 5 band-classes, namely 47.4%, 49.1%, 50.1%,
53.3% and 54.3%, correlating to Clostridium cluster XIVa in these fingerprints. The clustering was based on
Pearson correlation coefficient. The numbers on the branches indicate the similarity percentages.
Figure 19.
39
Results
IV.3. QUANTITATIVE-PCR RESULTS
Clostridium cluster XIVa, present in all the DGGE fingerprints, was quantified by qPCR in
order to confirm the DGGE fingerprints and to link their quantity to the intensity of the
bands, correlating to this cluster.
Clostridium cluster XIVa, was present in all the DNA samples except for NL14-GA-M-T1. This
cluster was not detectable in this sample. Yet, its DGGE fingerprint had very low intense
bands in band-classes 47.4% and 50.3%, correlating to this cluster. The variability of the
quantity of this cluster was between 3.04E+06 CFU/g and 1.13E+11 CFU/g in the feces,
which belonged to FR6-SI-PC-T2 and B2-PL-M-T10 respectively.
VI.3.1. THE QUANTITY OF CLOSTRIDIUM CLUSTER XIVA PER ZOO
The quantity of Clostridium cluster XIVa was calculated for each sample followed by boxplot
analysis per zoo (Figure 20) in order to see the quantitative variability of Clostridium cluster
XIVa per zoo. It was illustrated by the boxplot that the highest variability of Clostridium
cluster XIVa belonged to samples from SI, whereas the lowest variability belonged to OV.
Boxplot analysis for
quantification of Clostridium
cluster XIVa per zoo. The
calculated amount of this
cluster is depicted by boxplot
per zoo. The biggest variability
is seen in Gaia Zoo, while the
smallest variability is seen in
Zooparc Overloon. The outliers
are indicated by open circles.
Figure 20.
PL: Planckendael
GA: Gaia Zoo
OV: Zoo Parc Overloon
BB: Safaripark Beekse Bergen
EB: Ree Park Ebeltoft Safari
SI: Réserve Africaine de Sigean
40
Results
VI.3.2. THE QUANTITY OF CLOSTRIDIUM CLUSTER XIVA PER ANIMAL
A histogram was assessed for the quantity of Clostridium cluster XIVa in the fecal sample of
all animals (Figure 21). Among 36 animals in this study, 16 animals had more than 1 fecal
sample; therefore, the mean quantity of Clostridium cluster XIVa was calculated for these 16
animals. The highest quantity belonged to animal B2 with 2 fecal samples, while Clostridium
cluster XIVa was not detectible in animal NL14.
Figure 21. Histogram of
quantity of Clostridium cluster XIVa per animal. As depicted, the highest value belonged
to animal B2, whereas Clostridium cluster XIVa was not detectable in animal NL14. Among the animals, 16
animals had more than 1 sample. Animals FR2, DK1, FR4, FR1, NL11, B1, B2, DK2, DK7, NL9, NL7 and NL10 had
2 fecal samples while animals DK3, FR3 and FR5 had 3 samples. The rest had one fecal sample in this study.
VI.3.3. CLOSTRIDIUM CLUSTER XIVA VERSUS THE DGGE BAND INTENSITY
In order to verify whether the DGGE band intensity of the band-classes, correlating to
Clostridium cluster XIVa, correlate with their quantity in each sample, a scatter plot was
generated (Figure 22). It was depicted in the scatter plot that with an increase in the
intensity of DGGE bands, the CFU/g did not change in an ascending pattern. This result
illustrates that the band intensity in the DGGE profiles is not proportional to the
concentration of Clostridium cluster XIVa determined by qPCR.
41
Results
Figure 22. The
scatter plot of the intensity of band-classes, correlating to Clostridium cluster XIVa, against its
quantity in each sample. The mean intensity of band-classes 47.4%, 49.1%, 50.1%, 53.5% and 54.3%, which
correlate to Clostridium cluster XIVa, was plotted against the absolute quantity of this cluster obtained from
qPCR.
42
Discussion
CHAPTER V. DISCUSSION
This study set out to describe the fecal microbial community composition in 55 fecal
samples collected from captive adult cheetahs housed in 6 different European zoos. To this
end, denaturing gradient gel electrophoresis (DGGE) was performed followed by
comparison of the DGGE fingerprints with the available clone library, which was previously
made from fecal samples of two cheetahs in Belgium (Becker et al, 2014). In addition to
DGGE profiling, this study included quantitative PCR (qPCR) with the aim to quantify
Clostridium cluster XIVa, which is one of the most prevailing bacterial groups in the clone
library.
In the current study, DGGE fingerprinting on the cross-sectional data set gave rise to
different community profiles. The DGGE fingerprinting revealed a largely animal-specific
clustering and indicated a clear difference in community structure in the fecal samples. This
inter-animal variation has also been shown in mice (Hildebrand et al, 2013) and cats (Desai
et al, 2009) and many other mammals including humans (Knight et al, 2012). This study also
revealed that the microbiota is affected by a combination of factors including age, sex, diet,
diet type, genetic background and housing of the captive adult cheetahs. Other studies have
also revealed that factors such as age, genetics, environment, diet and drugs, might play a
role in the DGGE fingerprints of humans as well (Wu et al, 2011; Knight et al, 2012). For
instance, it was shown that by intake of antibiotic, the band richness in the DGGE profiles
might decrease in the patients with cystic fibrosis as well as result in an increase or decrease
in the intensity of some bands (Duytschaever et al, 2011). It was also shown that the mice,
which were born and raised in the same housing, showed little variation in their microbiota
profiles compared to the mice in different housings. It was also revealed that their gut
microbiota composition changed by changing their housings (Spor et al, 2011). Yet, the
degree of the divergence in their gut microbiota composition depended on the genotype of
the mice (Deloris Alexander et al, 2006).
By clustering the fingerprints according to the Pearson and Dice correlation coefficients, no
diet type or zoo specific clustering was visualized since these factors do not solely contribute
to the band patterns. These factors, as well as many other environmental factors, may have
contributed to the observation that the fingerprints were unique, which indicates large
inter- and intra-individual variations (Duytschaever et al, 2013). Yet, a separate cluster was
visualized by Pearson correlation coefficient with 5 fingerprints from SI as well as a separate
cluster with 6 fingerprints from SI with Dice correlation coefficient. Both clusters were
obtained mainly due to band-classes 28.3% and 41.3%, which belonged to Lactobacillus spp.
after band excision and sequencing.
The number of bands per fingerprint and per zoo was studied and analyzed, which revealed
a lower mean and range of band richness in the 3 fingerprints from Gaia Zoo. These 3
animals had consumed beef meat with renal disorder sustaining supplements such as
Ipakitine and Gistocal. Moreover, it has been shown in other studies that beef meat is
digested mostly by the host (Vester et al, 2010) and leaves less substrates for the gut
microbiota to ferment (Niba et al, 2009). Therefore, more fecal samples should be collected
and analyzed from these animals in Gaia Zoo to substantiate the diversity in their gut
microbiota. Band richness is considered to be a valuable parameter for measuring the
intestinal ecosystem’s fitness and flexibility (Wittebolle et al, 2009). The fewer bands
43
Discussion
present in a fingerprint, the more negative impact it has on the ecosystem’s functional
stability because it leads to lower productivity as well as less flexibility to compensate for
natural fluctuations (Yachi & Loreau, 1999).
All DGGE profiles had band-classes correlating to Clostridium cluster XIVa and XI, which
indicate that they are part of the core microbiota in these animals as it was also shown for 2
captive cheetahs in Planckendael (Becker et al, 2014). Among the fingerprints, 70% of the
samples displayed band-classes correlating to cluster I. These samples did not have any
factors, such as diet type or zoo, in common. This visualization can either be precise or can
be due to the limitations of DGGE in band visualization (Inglis et al, 2012). The band-classes
present in most of the zoos, might also be members of core microbiota in these fecal
samples but might not be detected by DGGE due to technical limitations. DGGE
fingerprinting is an efficient technique to study the fecal microbial community composition
of captive adult cheetahs as well as other animals and humans (Inglis et al, 2012). The ability
to excise and clone individual bands, is a significant advantage of DGGE analysis. However,
multiple taxa can occupy the same position in the gel (Muyzer et al, 1993; Muyzer & Smalla,
1998), which results in an under-estimation of diversity. Moreover, multiple DGGE bands
can also correlate to a single taxon since a species can contain multiple 16S rRNA genes
(Acinas et al, 2004), which results in an over-estimation of abundance and/or diversity
(Inglis et al, 2012). Therefore, visual identification of the bands in samples with complex
banding patterns and high diversity can be difficult (Inglis et al, 2012). Moreover, the steps
before performing DGGE and the quality of the gel may lead to lack of visualization of the
bands (Inglis et al, 2012). In order to address some of these disadvantages, other techniques
can be used in the future in order to substantiate the results obtained from DGGE (Inglis et
al, 2012).
Since the fecal samples were homogenized and community DNA was extracted from the
homogenate (Pitcher et al, 1989), it is always possible that the DNA has impurity, low quality
or degradation as well as loss of DNA. Therefore, the DNA quality and quantity should
always be checked prior to performing V3-16S rRNA PCR and DGGE. Homogenization, DNA
extraction and PCR may bring biases, which are inevitable. Occasionally, they lead to an
increase or decrease in the intensity of some bands on the DGGE fingerprint profiles
(Ariefdjohan et al, 2010; Claassen et al, 2013).
The presence of Clostridium cluster XIVa, which made up to 43% of the 702 clones in the
clone library (Becker et al, 2014), was confirmed in all the fecal samples by qPCR, except for
one animal. On one hand, qPCR is not capable of detecting all the bacterial cells present in
the fecal sample, as for the majority of techniques (Inglis et al, 2012). On the other hand,
there is a possibility that inaccurate measurement take place due to nonspecific
amplification if the amplicons and the primers specificity are not checked properly. This
results in overestimation of cell densities in the samples (Inglis et al, 2012). One of the major
advantages of qPCR is the fact that it has relatively high throughput and it is simple and fast
after developing the primers and the PCR program (Inglis et al, 2012). Clostridium cluster
XIVa, which is a phylogenetically heterogeneous cluster, consists of gram-positive bacterial
groups in the Firmicutes phylum. The cluster consists of several families such as
Ruminococcaceae, which are the representative of Clostridium cluster XIVa in several
phylogenetic clone libraries (Hoskins, 1993). Blautia is one of the members of this cluster
44
Discussion
(Park et al, 2012), which produces short-chain fatty acids such as butyrate as a source of
energy for colonic epithelial cells. The members of this genus constitute about
1010−1011 bacteria cells per gram in the human colon (Hayashi et al, 2006). This genus can
also produce lactate and acetate as the major end product of carbohydrate fermentation in
the human gut (Park et al, 2013). In addition, most members of this genus do not produce
indole as a putrefactive compound (Park et al, 2012; Willems & Collins, 1995), which is
regarded as detrimental for gut health in either humans or several animals. This compound
can be associated with several disease states (Backus et al, 2002) such as chronic renal
failure (Niwa, 2010).
Clostridium cluster XIVa was not detectable in the fecal sample of animal NL14-GA-M-T1,
though it had vague bands on its DGGE fingerprint correlating to this cluster. This animal
suffered from renal disorder and it was on a special diet including supplements Ipakitine and
Gistocal (Table 1). It has been reported in cats and dogs that inflammatory bowel diseases,
which are associated with damaged mucus layer, can be associated with reduced proportion
of Clostridium cluster XIVa as well (Inness et al, 2007). Moreover, the decrease in this cluster
results in a decline in butyrate production under anaerobic condition (Eeckhaut et al, 2010).
This metabolite has important biological functions such as providing energy for colonocytes,
maintenance of the gastrointestinal barrier, eliciting an anti-inflammatory response, as well
as being involved in prevention of colorectal cancer (Hamer et al, 2008; Canani et al, 2011).
Therefore, the renal disease might be among the reasons for the decreased proportion of
Clostridium cluster XIVa in this animal in Gaia Zoo (Niwa, 2010). Yet, the gut microbial
ecosystem has a broad phylogenetic spectrum of bacteria that are capable of producing
butyrate. Consequently, it is not clear to which extent the reduction of these bacterial
groups may have health consequences (Collins et al, 1994).
Clostridium cluster XI, present in all the DGGE fingerprints in the current study, accounts for
22% of the fecal microbiota in healthy cats (Ritchie et al, 2010) and many members of this
genus produce short-chain fatty acids from mono- and disaccharides (Kitahara et al, 2001).
Clostridium hiranonis is one of the representatives of this cluster and was first described in
human feces (Kitahara et al, 2001) and was also detected in the 2 captive cheetahs in
Planckendael (Becker et al, 2014).
In the future, more fecal samples from captive adult cheetahs can be collected in order to
understand the fecal microbial community composition of captive adult cheetahs in detail as
well as to study its diversity. Moreover, in a longer period of time, temporal stability can be
studied in the gut microbiota of captive cheetahs, as it has been performed for several
animals such as mice (Dimitriu et al, 2013). In order to better understand the DGGE profiles
and the present bacterial groups, more zoomed DGGE can be performed to better visualize
the profiles with complex banding patterns and high diversity. Furthermore, the bands
belonging to band-classes, which do not correlate to the clones in the clone library, can be
excised and sequenced to bring the bacterial composition to light. In addition to the
description of the diversity of gut microbiota of captive cheetahs, the functionality of the
bacterial groups should be studied as well to understand the role of the members of the gut
microbiota in captive cheetahs. For the band-classes present in most of the zoos groupspecific qPCR can be performed in order to quantitatively determine all the members of the
core microbiota in the captive adult cheetahs.
45
Conclusion
CHAPTER VI. CONCLUSION
This study aimed to characterize the fecal microbial community composition of captive adult
cheetahs housed in several European zoos. To this end, DGGE was performed based on the
V3 region of the 16S rRNA gene, which revealed complex banding patterns and distinct
bacterial community. Next, the fingerprints were compared with the available clone library
from 2 captive cheetahs in Belgium. This study revealed a complex microbial diversity in
which Firmicutes were dominant. Among the Firmicutes, Clostridium cluster XIVa and XI
were present in all the fingerprints whereas cluster I was present in 70% of them.
According to data analysis, neither diet type nor zoo specific clustering was visualized in the
fingerprints. This indicates that the gut microbiota composition is affected by a combination
of factors as it is for humans (Knight et al, 2012). Clostridium cluster XIVa and XI were
present in all the DGGE profiles, while cluster I was visualized in 70% of the fingerprints,
which might have been due to the limitations of DGGE in band visualization (Inglis et al,
2012). Therefore, these 3 clusters of Clostridium might be part of the core microbiota of
these 55 fecal samples as it has been shown in 2 captive cheetahs in Planckendael (Becker et
al, 2014).
The boxplot analysis of the fingerprints per animal, per zoo and per diet type revealed the
variation of the predominant bacterial diversity between the fingerprints. It was illustrated
by boxplot analysis of the band richness per zoo that the mean and range of band richness
was less in Gaia zoo compared to the other zoos. The biggest mean of band number
belonged to Planckendael since they had more bacterial variety in their fingerprints. In
addition, the highest variability of band richness belonged to Safaripark Beekse Bergen,
which can be a reflection of the number of fingerprints in this zoo, since it had the highest
number of samples, namely 15, among these 6 zoos.
The quantification of Clostridium cluster XIVa revealed that this cluster was present in all the
fecal samples with the range of 3.04E+06_1.13E+11 CFU/g, except for animal NL14. This
animal suffered from renal disease, which might have led to a decline in the quantity of
Clostridium cluster XIVa (Van den Abbeele et al, 2010; Niwa, 2010).
In the future, more fecal samples should be collected and analyzed by DGGE to confirm the
members of core microbiota and to assess the other members of the core microbiota.
Different sorts of sequencing can be performed to determine the members of gut
microbiota of captive cheetahs if necessary. Moreover, bands belonging to potentially
discriminating band-classes can be sequenced to identify the unknown bacterial genera and
species in the fingerprint profiles. Since multiple taxa can occupy the same position in the
DGGE gel, as it is one of the shortcomings of DGGE, more zoomed DGGE can be performed
to bring the members of gut microbiota of cheetahs to light.
As a general conclusion, this study has shown that Clostridium cluster XIVa, XI and I might be
members of the core microbiota in these 36 captive cheetahs. Moreover, no zoo specific or
diet type specific clustering was visualized in the DGGE profiles, which may indicate that the
microbiota is affected by a combination of factors. Clostridium cluster XIVa was quantified in
all the fecal samples except for an animal suffering from renal disease.
46
Materials and Methods
CHAPTER VII. MATERIALS AND METHODS
VII.1. FECAL SAMPLE COLLECTION
In this project, 55 fecal samples were collected from 36 adult cheetahs housed at 6 EAZA
zoos in 4 European countries. The information about the housing, diet and background of
the animals are summarized in (Table 1). Fecal samples were immediately collected upon
defecation into sterile plastic bags or tubes, transported on dry ice and stored at -80°C until
further analysis.
VII.2. HOMOGENIZATION OF FECAL SAMPLES
Since fecal samples from cheetahs are heterogeneous, homogenization is required prior to
further analysis. This step is carried out in a biohazard.
In this procedure, 25 grams of each fecal sample was placed in a stomacher bag followed by
homogenization in 225 ml bacteriological pepton (L37; Oxoid, Basingstoke, United
Kingdom). The homogenate was then filtered on a Buchner funnel to remove the large
undigested particles from the diet such as tendons and bones. Afterwards, the homogenate
was centrifuged followed by removing the supernatant and adding 1x TE buffer to the
pellet. In the last step the final homogenate was divided into 7 aliquots, which were stored
at -800C (See IX.2. page 64).
VII.3. DNA EXTRACTION
Total bacterial DNA was extracted from the fecal samples by using a slightly modified
version (Vanhoutte et al, 2004) of the protocol of Pitcher and coworkers (Pitcher et al,
1989). In this protocol, enzymatic and chemical lysis are used to recover DNA from the
bacterial cells (See IX.3. page 64).
VII.4. DNA QUANTITY AND QUALITY CHECK
The DNA size and integrity were determined using 1% agarose electrophoresis gels stained
with SYBR Safe. Additionally, DNA concentration and purity were assessed by
spectrophotometric measurements at 234, 260, and 280 nm (See IX.4. page 65).
VII.4.1. AGAROSE GEL ELECTROPHORESIS
Agarose is a porous linear polysaccharide of beta-galactose with hydrogen bonds that break
while heating.
In order to perform agarose gel electrophoresis, DNA samples with loading dye were loaded
on 1% agarose gel located in a Tris-Borate-EDTA (TBE) buffer tank. After electrophoresis, the
gel was stained in a SYBR Safe bath followed by visualization under blue light.
47
Materials and Methods
VII.4.2. OPTICAL DENSITY MEASUREMENT BY SPECTROPHOTOMETER
In order to determine the quantity and quality of the extracted DNA, spectrophotometric
measurement at 260nm, the maximum absorption of light by DNA, 234nm, the minimum
absorption of light by DNA and 280nm, the maximum absorption of light by proteins was
performed. The ratio of OD260/OD280, a measure for DNA purity, should be between 1.8
and 2.2, while OD234/OD260, which can indicate protein contamination, should be between
0.5 and 0.8.
VII.5. V3-16S RRNA PCR
Prior to DGGE, amplification of small fragments of DNA is required. The V3 region of the 16S
rRNA was targeted with universal bacterial primers F+GC/F357 and R518. The forward
primer contained a GC clamp to prevent the amplicons from complete denaturation by the
gradient (Temmerman et al, 2003; Yu & Morrison, 2004) (Table 4). The final volume of each
PCR mixture was 50ul. V3-16S rRNA PCR results in the production of amplicons 194 bp in
length (Vanhoutte et al, 2004) (See IX.5. page 65).
Primer Target
Primer Sequence
Product size
Reference
F+GCF357
5’-CGCCCGCCGCGCGCGGCGGGC
194 bp
(Temmerman et
al, 2003; Yu &
Morrison, 2004)
V3-16S
rRNA
GGGGCGGGGGCACGGGGGCCTAC
GGGAGGCAGCAG-3’
R518
V3-16S
rRNA
5’-ATTACCGCGGCTGCTGG-3’
(Yu & Morrison,
2004)
The primers used for V3-16S rRNA PCR prior to DGGE. The forward primer, containing a GC clamp, and
the reverse primer target the V3 region of 16S rRNA gene in the bacteria. The generated amplicons are 194 bp
in size.
Table 4.
PCR consists of 3 major steps namely denaturation, annealing and elongation. In the first
step double stranded DNA denatures by the high temperature (950C). During annealing, the
forward and reverse primers bind to the conserved regions alongside the V3 region of the
16S rRNA gene. Afterwards, the extension or elongation is carried out by Taq polymerase
that uses the dNTPs to build double stranded DNA, namely the V3 amplicons. The PCR
program used for V3-16S rRNA PCR is depicted in (Table 5). The size of the amplicons was
checked on 1% agarose gel electrophoresis.
N0
Primer set
Target
Temperature program
Reference
15
F+GC-F357/ R518
V3-16S rRNA
 1x 95oC (1min)
(Huys et al,
 30x 95oC (30s)-55oC (45s)-72oC 2008)
(1min)
 1x 72oC (7min)
 4oC
Table 5. The PCR program used for V3-16S rRNA PCR.
48
Materials and Methods
VII.6. DGGE ANALYSIS AND GEL PROCESSING
The V3-16S rRNA amplicons were analyzed by DGGE fingerprinting using 35 to 70%
denaturing polyacrylamide gels. Urea and formamide are the denaturing agents added to
polyacrylamide to obtain a denaturing gradient on the gel. These components cause the
amplicons to denature, based on their GC contents, while moving through the
polyacrylamide gel during electrophoresis (Muyzer & Smalla, 1998).
Afterwards, the DGGE gels were stained in 1x SYBR Gold nucleic acid gel stain TAE buffer
(catalog no. S-11494, Molecular Probes), and UV light was used for visualization. Digital
images were generated from the DGGE profiles in order to perform data analysis (See IX.6.
page 66).
DGGE band extraction: Bands belonging to potentially discriminating band-classes were
excised from the DGGE gel, eluted in 40ul of 1x TE buffer and heated for 10 min at 650C.
Subsequently these DNA solutions were re-amplified using the same V3-16S rRNA primers
as those of the community PCR.
Sequencing of V3-16S rRNA amplicons: Bands were sequenced using an ABI PRISM 3130x1
Genetic Analyzer (Applied Biosystems, Foster city, California USA). Sequences were obtained
using both forward (F357 without GC clamp) and reverse (R518) primers. Sequence
assemblies were achieved with Bionumerics v7.1 (Applied maths). Taxonomic classification
was obtained by comparison against the NCBI database.
VII.7. DATA ANALYSIS
Similarities between DGGE fingerprint profiles of fecal samples were determined using the
Pearson and Dice correlation coefficients and the unweighted pair group method with
arithmetic mean (UPGMA) in BioNumerics version 7 (Applied Maths, St.-Martens-Latem,
Belgium). Boxplots were generated in Statistical Package for the Social Sciences (SPSS)
version 22, to compare the variability of pairwise similarities between all 55 fecal samples
and between fecal samples per zoo, per diet type and per animal.
The band richness of each DGGE profile, which is the total number of bands per fingerprint,
was determined using BioNumerics, followed by generating boxplots for band richness per
zoo in SPSS version 22.
The fingerprints were then compared with the clone library, previously obtained from the
fecal samples of 2 captive cheetahs in Belgium. The clone library consisted of 16S rRNA
sequences of clones. Groups of sequences with ≤ 3% sequence divergence (≥ 97% similarity)
were defined as an operational taxonomic unit (OTU) or phylotype. Representative clones
per OTU were loaded on DGGE fingerprints and these bands were correlated with unknown
bands in the fingerprints of the 55 samples.
49
Materials and Methods
VII.8. QUANTITATIVE PCR
VII.8.1. CALIBRATION OF A STANDARD CURVE
Clostridium symbiosum (R25411), which is a member of Clostridium cluster XIVa, freezedried in an ampulla, was used from the R collection of the laboratory of microbiology to
construct a qPCR standard curve for Clostridium cluster XIVa. The ampulla was broken in a
nutrient broth to awake the cells. From the broth, droplets were poured on a plate with
reinforced clostridial agar medium and incubated anaerobically overnight at 37 oC. The cells
were cultivated for several generations in the anaerobic chamber. Afterwards, the cells
were harvested from massive end plates to make a bacterial suspension. A serial dilution
was made from the main suspension until 10-8, followed by pouring 50ul of each dilution on
M37 plates in triplicates (Figure 23). Consequently, plates were incubated overnight at 37
o
C. The whole procedure was performed in the anaerobic chamber (See IX.8. page 67).
Preparation of serial dilution to construct the standard curve for quantitative PCR followed by
plating the dilutions to calculate CFU/ml. A serial dilution was made from Clostridium symbiosum (R25411), a
representative of Clostridium cluster XIVa.
Figure 23.
From the countable triplicate plates (between 25-250 colonies), the mean of colonies were
calculated as well as the CFU/ml by multiplying the dilution in the colonies mean in 20. From
the initial bacterial suspension, DNA was extracted, according to the DNA extraction
protocol for Gram positive bacteria (Pitcher et al, 1989). Quantitative PCR was then
performed on the resulting DNA in order to build the standard curve for Clostridium cluster
XIVa to enable the quantification of Clostridium cluster XIVa in the samples.
50
Materials and Methods
VII.8.2. QUANTIFYING CLOSTRIDIUM CLUSTER XIVA IN THE FECAL SAMPLES
Quantitative PCR amplification and detection were performed for quantification of
Clostridium cluster XIVa, using the Roche Light Cycler 480 machine. The total volume of each
PCR reaction was 20ul. Parts of V3 and V5 regions of the 16S rRNA gene of Clostridium
cluster XIVa, as well as the whole V4 region were targeted by group specific primers (Table
6). The generated amplicons were 429bp in size.
Primer
Target
Primer Sequence
Product
size
Reference
CcocRT-F
V3,4,516S
rRNA
5’-CGGTACCTGACTAAGAAGC-3’
429 bp
(Rinttilӓ
2004)
CcocRT-R
V3,4,516S
rRNA
5’-AGTTT(C/T)ATTCTTGCGAACG-3’
et
al,
(Yu & Morrison,
2004)
The primers used for qPCR of Clostridium cluster XIVa. The forward and reverse primers target the V3,
V4 and V5 regions of 16S rRNA gene in the bacteria. The generated amplicons are 429 bp in size.
Table 6.
The program used for amplification of Clostridium cluster XIVa consists of 4 major steps
(Table 7).
Program
Cycles
Temperature (oC)
Pre-incubation
Amplification
1
40
Melting curve
1
95
95
55
72
80
95
65
97
10 m
15 s
20 s
30 s
30 s
5s
1m
Continuous
Cooling
1
40
10 s
Duration
Table 7. The quantitative PCR program for targeting Clostridium cluster XIVa.
51
References
CHAPTER VIII. REFERENCES
Van den Abbeele P, Grootaert C, Marzorati M, Possemiers S, Verstraete W, Gérard P, Rabot S, Bruneau A, El
Aidy S, Derrien M, Zoetendal E, Kleerebezem M, Smidt H & Van de Wiele T (2010) Microbial community
development in a dynamic gut model is reproducible, colon region specific, and selective for
Bacteroidetes and Clostridium cluster IX. Applied and environmental microbiology 76: 5237–5246
Acinas SG, Marcelino LA, Klepac-Ceraj V & Polz MF (2004) Divergence and redundancy of 16S rRNA sequences
in genomes with multiple rrn operons. Journal of bacteriology 186: 2629–2635
Ariefdjohan MW, Savaiano DA & Nakatsu CH (2010) Comparison of DNA extraction kits for PCR-DGGE analysis
of human intestinal microbial communities from fecal specimens. Nutrition journal 9: 23
Backus RC, Puryear LM, Crouse BA, Biourge VC & Rogers QR (2002) Breath hydrogen concentrations of cats
given commercial canned and extruded diets indicate gastrointestinal microbial activity vary with diet
type. The Journal of nutrition 132: 1763S–6S
Barry K a, Middelbos IS, Vester Boler BM, Dowd SE, Suchodolski JS, Henrissat B, Coutinho PM, White B a, Fahey
GC & Swanson KS (2012) Effects of dietary fiber on the feline gastrointestinal metagenome. Journal of
proteome research 11: 5924–33 Available at: http://www.ncbi.nlm.nih.gov/pubmed/23075436
Barry KA, Wojcicki BJ, Middelbos IS, Vester BM, Swanson KS & Fahey GC (2010) Dietary cellulose,
fructooligosaccharides, and pectin modify fecal protein catabolites and microbial populations in adult
cats.
Journal
of
Animal
Science
88:
2978–2987
Available
at:
http://www.ncbi.nlm.nih.gov/pubmed/20495116
Becker AA, Hesta M, Hollants J, Janssens GP & Huys G (2014) Phylogenetic analysis of faecal microbiota from
captive cheetahs reveals underrepresentation of Bacteroidetes and Bifidobacteriaceae. BMC
microbiology 14: 43 Available at: http://www.ncbi.nlm.nih.gov/pubmed/24548488
Bermingham EN, Young W, Kittelmann S, Kerr KR, Swanson KS, Roy NC & Thomas DG (2013) Dietary format
alters fecal bacterial populations in the domestic cat (Felis catus). MicrobiologyOpen 2: 173–81 Available
at:
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3584222&tool=pmcentrez&rendertype=abs
tract
Blottiere HM, Buecher B, Galmiche J-P & Cherbut C (2003) Molecular analysis of the effect of short-chain fatty
acids on intestinal cell proliferation. The Proceedings of the Nutrition Society 62: 101–6 Available at:
http://www.ncbi.nlm.nih.gov/pubmed/12740064
Bradshaw JWS, Goodwin D, Legrand-Defrétin V & Nott HMR (1996) Food selection by the domestic cat, an
obligate carnivore. Comparative Biochemistry and Physiology - A Physiology 114: 205–209
Britannica E (2011) Encyclopedia - Britannica Online Encyclopedia. EBU: 1 Available
http://www.school.eb.com.au/all/comptons/article-9275557?query=martin luther&ct=null
at:
Canani RB, Costanzo M Di, Leone L, Pedata M, Meli R & Calignano A (2011) Potential beneficial effects of
butyrate in intestinal and extraintestinal diseases. World journal of gastroenterology : WJG 17: 1519–
1528
Chow J, Lee SM, Shen Y, Khosravi A & Mazmanian SK (2010) Host-bacterial symbiosis in health and disease.
Advances in Immunology 107: 243–274 Available at: http://www.ncbi.nlm.nih.gov/pubmed/21034976
52
References
Citino SB (1995) Chronic, intermittent Clostridium perfringens enterotoxicosis in a group of cheetahs (Acinonyx
jubatus jubatus). Journal of Zoo and Wildlife Medicine 26: 279–285 Available at:
http://grlinker.coalliance.org/grlinker/grl000106/grl-UNM.cgi?genre=article&issn=10427260&volume=26&issue=2&spage=279&date=1995&title=Journal+of+Zoo+and+Wildlife+Medicine&aula
st=Citino&auinit=S.B.&atitle=Chronic,+intermittent+Clostridium+perfringens+enterotoxicosis+in+a+grou
p+of+cheetahs+(Acinonyx+jubatus+jubatus)&id=info:lanl-repo/biosis/PREV199698693516&id=info:lanlrepo/isi/A1995TV00800014
Claassen S, Du Toit E, Kaba M, Moodley C, Zar HJ & Nicol MP (2013) A comparison of the efficiency of five
different commercial DNA extraction kits for extraction of DNA from faecal samples. Journal of
Microbiological Methods 94: 103–110
Claesson MJ, O’Sullivan O, Wang Q, Nikkilä J, Marchesi JR, Smidt H, De Vos WM, Ross RP & O’Toole PW (2009)
Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial
community structures in the human distal intestine. PLoS ONE 4:
Clemente JC, Ursell LK, Parfrey LW & Knight R (2012) The Impact of the Gut Microbiota on Human Health: An
Integrative
View.
Cell
148:
1258–1270
Available
at:
http://linkinghub.elsevier.com/retrieve/pii/S0092867412001043
Collins MD, Lawson PA, Willems A, Cordoba JJ, Fernandez-Garayzabal J, Garcia P, Cai J, Hippe H & Farrow JA
(1994) The phylogeny of the genus Clostridium: proposal of five new genera and eleven new species
combinations. International journal of systematic bacteriology 44: 812–826
Cook SI & Sellin JH (1998) Review article: short chain fatty acids in health and disease. Alimentary
pharmacology & therapeutics 12: 499–507
Corfield AP, Carroll D, Myerscough N & Probert CS (2001) Mucins in the gastrointestinal tract in health and
disease. Frontiers in bioscience : a journal and virtual library 6: D1321–D1357
Deloris Alexander A, Orcutt RP, Henry JC, Baker J, Bissahoyo AC & Threadgill DW (2006) Quantitative PCR
assays for mouse enteric flora reveal strain-dependent differences in composition that are influenced by
the microenvironment. Mammalian Genome 17: 1093–1104
Depauw S, Hesta M, Whitehouse-Tedd K, Vanhaecke L, Verbrugghe A & Janssens GPJ (2011) Animal fibre: The
forgotten nutrient in strict carnivores? First insights in the cheetah. Journal of Animal Physiology and
Animal Nutrition Available at: http://www.ncbi.nlm.nih.gov/pubmed/22074361
Desai AR, Musil KM, Carr AP & Hill JE (2009) Characterization and quantification of feline fecal microbiota using
cpn60 sequence-based methods and investigation of animal-to-animal variation in microbial population
structure. Veterinary microbiology 137: 120–128
Deweerdt S (2011) The omnivore ’ s labyrinth. Nature 471: 22–24
Dierenfeld E (1993) Nutrition of captive cheetahs: food composition and blood parameters. Zoo Biology 12:
143–152
Dimitriu PA, Boyce G, Samarakoon A, Hartmann M, Johnson P & Mohn WW (2013) Temporal stability of the
mouse gut microbiota in relation to innate and adaptive immunity. Environmental Microbiology Reports
5: 200–210
Driscoll CA, Macdonald DW & O’Brien SJ (2009) From wild animals to domestic pets, an evolutionary view of
domestication. Proceedings of the National Academy of Sciences of the United States of America 106
Suppl : 9971–9978
53
References
Driscoll CA, Menotti-Raymond M, Roca AL, Hupe K, Johnson WE, Geffen E, Harley EH, Delibes M, Pontier D,
Kitchener AC, Yamaguchi N, O’brien SJ & Macdonald DW (2007) The Near Eastern origin of cat
domestication. Science (New York, N.Y.) 317: 519–523
Durant S (2004) Survival of the Fastest - The Cheetahs of Serengeti. Africa Geographic June 2004: 30–33
Available at: Durant_2004_Survival_of_Serengeti_cheetahs.pdf
Duytschaever G, Huys G, Bekaert M, Boulanger L, De Boeck K & Vandamme P (2011) Cross-Sectional and
Longitudinal Comparisons of the Predominant Fecal Microbiota Compositions of a Group of Pediatric
Patients with Cystic Fibrosis and Their Healthy Siblings. Applied and Environmental Microbiology 77:
8015–8024
Duytschaever G, Huys G, Bekaert M, Boulanger L, De Boeck K & Vandamme P (2013) Dysbiosis of bifidobacteria
and Clostridium cluster XIVa in the cystic fibrosis fecal microbiota. Journal of Cystic Fibrosis 12: 206–215
Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, Gill SR, Nelson KE & Relman DA (2005)
Diversity of the human intestinal microbial flora. Science (New York, N.Y.) 308: 1635–1638
Eeckhaut V, Van Immerseel F, Pasmans F, De Brandt E, Haesebrouck F, Ducatelle R & Vandamme P (2010)
Anaerostipes butyraticus sp. nov., an anaerobic, butyrate-producing bacterium from Clostridium cluster
XIVa isolated from broiler chicken caecal content, and emended description of the genus Anaerostipes.
International journal of systematic and evolutionary microbiology 60: 1108–1112
Endt K, Stecher B, Chaffron S, Slack E, Tchitchek N, Benecke A, Van Maele L, Sirard JC, Mueller AJ,
Heikenwalder M, Macpherson AJ, Strugnell R, Von Mering C & Hardt WD (2010) The microbiota mediates
pathogen clearance from the gut lumen after non-typhoidal salmonella diarrhea. PLoS Pathogens 6:
Ferraris RP (2001) Dietary and developmental regulation of intestinal sugar transport. The Biochemical journal
360: 265–276
De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, Collini S, Pieraccini G & Lionetti P
(2010) Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe
and rural Africa. Proceedings of the National Academy of Sciences of the United States of America 107:
14691–14696
Floch MH & Hong-Curtiss J (2001) Probiotics and functional foods in gastrointestinal disorders. Current
gastroenterology reports 3: 343–350
Flood PF (2001) A Natural History of Domesticated Mammals, 2nd ed. The Canadian Veterinary Journal 42: 876
Gaggìa F, Mattarelli P & Biavati B (2010) Probiotics and prebiotics in animal feeding for safe food production.
International Journal of Food Microbiology 141:
Garcia-Mazcorro JF, Lanerie DJ, Dowd SE, Paddock CG, Grützner N, Steiner JM, Ivanek R & Suchodolski JS
(2011) Effect of a multi-species synbiotic formulation on fecal bacterial microbiota of healthy cats and
dogs as evaluated by pyrosequencing. FEMS microbiology ecology 78: 542–54 Available at:
http://www.ncbi.nlm.nih.gov/pubmed/22067056
Van Gelder RG (1973) The Serengeti Lion The Serengeti Lion. A Study of Predator-Prey Relations George B.
Schaller. BioScience 23: 499–499
Gentry TJ, Wickham GS, Schadt CW, He Z & Zhou J (2006) Microarray applications in microbial ecology
research. Microbial ecology 52: 159–175
54
References
Gittleman JL, MacDonald DW, Wayne RK & Gosling M (2001) Why “carnivore conservation”? In Carnivore
Conservation, Gittleman JL Funk SM MacDonald DW & Wayne RK (eds) pp 1–7. Cambridge University
Press
Greiner T & Bäckhed F (2011) Effects of the gut microbiota on obesity and glucose homeostasis. Trends in
Endocrinology and Metabolism 22: 117–123
Gros PM (1998) Status of the cheetah Acinonyx jubatus in Kenya: A field-interview assessment. Biological
Conservation 85: 137–149
Guarner F & Malagelada JR (2003) Gut flora in health and disease. Lancet 361: 512–519
Hamer HM, Jonkers D, Venema K, Vanhoutvin S, Troost FJ & Brummer R-J (2008) Review article: the role of
butyrate on colonic function. Alimentary pharmacology & therapeutics 27: 104–119
Handelsman J (2004) Metagenomics: application of genomics to uncultured microorganisms. Microbiology and
molecular biology reviews : MMBR 68: 669–685
Hayashi H, Sakamoto M, Kitahara M & Benno Y (2006) Diversity of the Clostridium coccoides group in human
fecal microbiota as determined by 16S rRNA gene library. FEMS microbiology letters 257: 202–207
Hayward MW, Hofmeyr M, O’Brien J & Kerley GIH (2006) Prey preferences of the cheetah (Acinonyx jubatus)
(Felidae : Carnivora): morphological limitations or the need to capture rapidly consumable prey before
kleptoparasites
arrive?
Journal
of
Zoology
270:
615–627
Available
at:
http://onlinelibrary.wiley.com/doi/10.1111/j.1469-7998.2006.00184.x/full
Hijova E & Chmelarova a (2007) Short chain fatty acids and colonic health. Bratislavské lekárske listy 108: 354–
8 Available at: http://www.ncbi.nlm.nih.gov/pubmed/18203540
Hildebrand F, Nguyen TLA, Brinkman B, Yunta RG, Cauwe B, Vandenabeele P, Liston A & Raes J (2013)
Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut
microbiota variation in common laboratory mice. Genome biology 14: R4 Available at:
http://www.ncbi.nlm.nih.gov/pubmed/23347395
Hill MJ (1997) Intestinal flora and endogenous vitamin synthesis. European journal of cancer prevention : the
official journal of the European Cancer Prevention Organisation (ECP) 6 Suppl 1: S43–S45
Hooper L V, Midtvedt T & Gordon JI (2002) How host-microbial interactions shape the nutrient environment of
the mammalian intestine. Annual review of nutrition 22: 283–307
Hooper L V, Wong MH, Thelin A, Hansson L, Falk PG & Gordon JI (2001) Molecular analysis of commensal hostmicrobial relationships in the intestine. Science (New York, N.Y.) 291: 881–884
Hooper L V., Littman DR & Macpherson AJ (2012) Interactions Between the Microbiota and the Immune
System. Science 336: 1268–1273
Hori T, Haruta S, Ueno Y, Ishii M & Igarashi Y (2006) Direct comparison of single-strand conformation
polymorphism (SSCP) and denaturing gradient gel electrophoresis (DGGE) to characterize a microbial
community on the basis of 16S rRNA gene fragments. Journal of Microbiological Methods 66: 165–169
Hoskins LC (1993) Mucin degradation in the human gastrointestinal tract and its significance to enteric
microbial ecology. European Journal of Gastroenterology & Hepatology 5: 205–213
55
References
Hummel J, Findeisen E, Südekum K-H, Ruf I, Kaiser TM, Bucher M, Clauss M & Codron D (2011) Another one
bites the dust: faecal silica levels in large herbivores correlate with high-crowned teeth. Proceedings.
Biological sciences / The Royal Society 278: 1742–1747
Huys G, Vanhoutte T, Joossens M, Mahious AS, De Brandt E, Vermeire S & Swings J (2008) Coamplification of
eukaryotic DNA with 16S rRNA gene-based PCR primers: possible consequences for population
fingerprinting of complex microbial communities. Current microbiology 56: 553–557
Inglis GD, Thomas MC, Thomas DK, Kalmokoff ML, Brooks SPJ & Selinger LB (2012) Molecular Methods to
Measure Intestinal Bacteria: A Review. Journal of AOAC INTERNATIONAL 95: 5–23 Available at:
http://www.ingentaconnect.com/content/aoac/jaoac/2012/00000095/00000001/art00003
Inness VL, McCartney AL, Khoo C, Gross KL & Gibson GR (2007) Molecular characterisation of the gut
microflora of healthy and inflammatory bowel disease cats using fluorescence in situ hybridisation with
special reference to Desulfovibrio spp. Journal of animal physiology and animal nutrition 91: 48–53
Jalanka-Tuovinen J, Salonen A, Nikkilä J, Immonen O, Kekkonen R, Lahti L, Palva A & De Vos WM (2011)
Intestinal microbiota in healthy adults: Temporal analysis reveals individual and common core and
relation to intestinal symptoms. PLoS ONE 6:
Karban R & Agrawal AA (2002) HERBIVORE OFFENSE. Annual Review of Ecology and Systematics 33: 641–664
Keller M & Zengler K (2004) Tapping into microbial diversity. Nature reviews. Microbiology 2: 141–150
Kerr KR, Beloshapka AN & Swanson KS (2013a) 2011 and 2012 Early Careers Achievement Awards : Use of
genomic biology to study companion animal intestinal microbiota 1. Journal of animal science 91: 2504–
2511
Kerr KR, Morris CL, Burke SL & Swanson KS (2013b) Influence of dietary fiber type and amount on energy and
nutrient digestibility, fecal characteristics, and fecal fermentative end-product concentrations in captive
exotic felids fed a raw beef-based diet. Journal of animal science 91: 2199–210 Available at:
http://www.ncbi.nlm.nih.gov/pubmed/23463568
Khin N, Leung FC-C, Jun L, Hui RK-H, Brar MS, Dowd SE & Tun HM (2012) Gene-centric metagenomics analysis
of feline intestinal microbiome using 454 junior pyrosequencing. Journal of Microbiological Methods 88:
369–376
Kitahara M, Takamine F, Imamura T & Benno Y (2001) Clostridium hiranonis sp. nov., a human intestinal
bacterium with bile acid 7alpha-dehydroxylating activity. International journal of systematic and
evolutionary microbiology 51: 39–44
Knight R, Jansson JK, Stombaugh JI, Gordon JI & Lozupone CA (2012) Diversity, stability and resilience of the
human gut microbiota. Nature 489: 220–230
Kotsch V, Kubber Heiss A, Url A, Schmidt P & Walzer C (2002) Diseases of captive cheetahs (Acinonyx jubatus)
within the European endangered species program (EEP) - A 22-year retrospective histopathological
study. Wiener Tierarztliche Monatsschrift 89: 341–350
Kuhn KA & Stappenbeck TS (2013) Peripheral education of the immune system by the colonic microbiota.
Seminars in Immunology
Laparra JM & Sanz Y (2010) Interactions of gut microbiota with functional food components and
nutraceuticals. Pharmacological Research 61: 219–225
56
References
Lau SCK & Liu W-T (2007) Recent advances in molecular techniques for the detection of phylogenetic markers
and functional genes in microbial communities. FEMS microbiology letters 275: 183–190
LeBlanc JG, Milani C, De Giori GS, Sesma F, Van Sinderen D & Ventura M (2013) Bacteria as vitamin suppliers to
their host: A gut microbiota perspective. Current Opinion in Biotechnology 24: 160–168
Ley RE, Lozupone CA, Hamady M, Knight R & Gordon JI (2008) Worlds within worlds: evolution of the
vertebrate gut microbiota. Nature reviews. Microbiology 6: 776–788
Ley RE, Turnbaugh PJ, Klein S & Gordon JI (2006) Microbial ecology: human gut microbes associated with
obesity.
Lovell CR, Decker P V., Bagwell CE, Thompson S & Matsui GY (2008) Analysis of a diverse assemblage of
diazotrophic bacteria from Spartina alterniflora using DGGE and clone library screening. Journal of
Microbiological Methods 73: 160–171
Lubbs DC, Vester BM, Fastinger ND & Swanson KS (2009) Dietary protein concentration affects intestinal
microbiota of adult cats: a study using DGGE and qPCR to evaluate differences in microbial populations
in the feline gastrointestinal tract. Journal of animal physiology and animal nutrition 93: 113–121
Luhrs H, Gerke T, Boxberger F, Backhaus K, Melcher R, Scheppach W & Menzel T (2001) Butyrate inhibits
interleukin-1-mediated nuclear factor-kappa B activation in human epithelial cells. Dig Dis Sci 46: 1968–
1973
Available
at:
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1
1575451
MacDonald ML, Rogers QR & Morris JG (1984) Nutrition of the domestic cat, a mammalian carnivore. Annual
review of nutrition 4: 521–562
Macfarlane S & Macfarlane GT (2003) Regulation of short-chain fatty acid production. The Proceedings of the
Nutrition Society 62: 67–72
Mackie RI, Sghir A & Gaskins HR (1999) Developmental microbial ecology of the neonatal gastrointestinal tract.
The American journal of clinical nutrition 69: 1035S–1045S
Marker LL, Mills MGL & Macdonald DW (2003) Factors Influencing Perceptions of Conflict and Tolerance
toward Cheetahs on Namibian Farmlands. Conservation Biology 17: 1290–1298 Available at:
http://doi.wiley.com/10.1046/j.1523-1739.2003.02077.x
Martin E (2010) Concise medical dictionary
Matamoros S, Gras-Leguen C, Le Vacon F, Potel G & De La Cochetiere MF (2013) Development of intestinal
microbiota in infants and its impact on health. Trends in Microbiology 21: 167–173
Mills MGL, Broomhall LS, Du Toit JT & Toit JT (2004) Cheetah Acinonyx jubatus feeding ecology in the Kruger
National Park and a comparison across African savanna habitats: is the cheetah only a successful hunter
on open grassland plains? Wildlife Biology 10: 177–186
Munson L, Terio KA, Worley M, Jago M, Bagot-Smith A & Marker L (2005) Extrinsic factors significantly affect
patterns of disease in free-ranging and captive cheetah (Acinonyx jubatus) populations. Journal of
wildlife diseases 41: 542–548
57
References
Muyzer G, Dewaal EC & Uitterlinden AG (1993) Profiling of Complex Microbial-Populations by Denaturing
Gradient Gel-Electrophoresis Analysis of Polymerase Chain Reaction-Amplified Genes-Coding for 16s
Ribosomal-Rna. Applied and Environmental Microbiology 59: 695–700 Available at:
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=202176&tool=pmcentrez&rendertype=abst
ract
Muyzer G & Smalla K (1998) Application of denaturing gradient gel electrophoresis (DGGE) and temperature
gradient gel electrophoresis (TGGE) in microbial ecology. AntonieKluwer Academic Publishers van
Leeuwenhoek 73: 127–141 Available at: http://www.ncbi.nlm.nih.gov/pubmed/9602286
Niba AT, Beal JD, Kudi AC & Brooks PH (2009) Bacterial fermentation in the gastrointestinal tract of nonruminants: Influence of fermented feeds and fermentable carbohydrates. Tropical Animal Health and
Production 41: 1393–1407
Niwa T (2010) Uremic toxicity of indoxyl sulfate. Nagoya journal of medical science 72: 1–11
Nowak RM (1999) Walker’s Mammals of the World Nowak RM (ed) Johns Hopkins University Press Available
at: http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21&path=ASIN/080183970X
Nowell K. JP (1996) Wild cats. Status Survey and Conservation Action Plan. IUCN, Gland, Switzerland.: 106–110
O’Brien SJ, Roelke ME, Marker L, Newman A, Winkler CA, Meltzer D, Colly L, Evermann JF, Bush M & Wildt DE
(1985) Genetic basis for species vulnerability in the cheetah. Science (New York, N.Y.) 227: 1428–1434
O’brien SJ, Wildt DE, Goldman D, Merril CR & Bush M (1983) The cheetah is depauperate in genetic variation.
Science (New York, N.Y.) 221: 459–462
O’Hara AM & Shanahan F (2006) The gut flora as a forgotten organ. EMBO reports 7: 688–93 Available at:
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1500832&tool=pmcentrez&rendertype=abs
tract
Ottman N, Smidt H, De Vos WM & Belzer C (2012) The function of our microbiota: who is out there and what
do they do? Frontiers in Cellular and Infection Microbiology 2:
Park S-K, Kim M-S & Bae J-W (2013) Blautia faecis sp. nov., isolated from human faeces. International journal of
systematic
and
evolutionary
microbiology
63:
599–603
Available
at:
http://www.ncbi.nlm.nih.gov/pubmed/22544782
Park S-K, Kim M-S, Roh SW & Bae J-W (2012) Blautia stercoris sp. nov., isolated from human faeces.
INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY 62: 776–779
Pedersen G, Brynskov J & Saermark T (2002) Phenol toxicity and conjugation in human colonic epithelial cells.
Scandinavian Journal of Gastroenterology 37: 74–79
Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, Van den Brandt PA & Stobberingh EE (2006)
Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics 118: 511–521
Peters G, Baum L, Peters MK & Tonkin-Leyhausen B (2009) Spectral characteristics of intense mew calls in cat
species of the genus Felis (Mammalia: Carnivora: Felidae). Journal of Ethology 27: 221–237
Pitcher DG, Saunders N a. & Owen RJ (1989) Rapid extraction of bacterial genomic DNA with guanidium
thiocyanate.
Letters
in
Applied
Microbiology
8:
151–156
Available
at:
http://doi.wiley.com/10.1111/j.1472-765X.1989.tb00262.x
58
References
Price EO (1984) Behavioral Aspects of Animal Domestication. The Quarterly Review of Biology 59: 1
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende
DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, et al (2010) A human gut microbial gene
catalogue established by metagenomic sequencing. Nature 464: 59–65
Radloff FGT & Du Toit JT (2004) Large predators and their prey in a southern African Large predators its prey
size range a predator ’ s size determines savanna : Journal of Animal Ecology 73: 410–423
Radulian G, Rusu E, Dragomir A & Posea M (2009) Metabolic effects of low glycaemic index diets. Nutrition
journal 8: 5
Rakoff-Nahoum S, Paglino J, Eslami-Varzaneh F, Edberg S & Medzhitov R (2004) Recognition of commensal
microflora by toll-like receptors is required for intestinal homeostasis. Cell 118: 229–241
Ranjard L, Poly F, Lata JC, Mougel C, Thioulouse J & Nazaret S (2001) Characterization of bacterial and fungal
soil communities by automated ribosomal intergenic spacer analysis fingerprints: biological and
methodological variability. Applied and environmental microbiology 67: 4479–4487
Rastall RA (2004) Bacteria in the gut: friends and foes and how to alter the balance. The Journal of nutrition
134: 2022S–2026S
Rinttilӓ T, Kassinen A, Malinen E, Krogius L & Palva A (2004) Development of an extensive set of 16S rDNAtargeted primers for quantification of pathogenic and indigenous bacteria in faecal samples by real-time
PCR. Journal of Applied Microbiology 97: 1166–1177
Ritchie LE, Burke KF, Garcia-Mazcorro JF, Steiner JM & Suchodolski JS (2010) Characterization of fecal
microbiota in cats using universal 16S rRNA gene and group-specific primers for Lactobacillus and
Bifidobacterium spp. Veterinary microbiology 144: 140–146
Roberfroid MB (2005) Introducing inulin-type fructans. The British journal of nutrition 93 Suppl 1: S13–S25
Rondeau M (2003) Short chain fatty acids stimulate feline colonic smooth muscle contraction. Journal of Feline
Medicine
Surgery
5:
167–173
Available
at:
http://linkinghub.elsevier.com/retrieve/pii/S1098612X03000020
Round JL, Lee SM, Li J, Tran G, Jabri B, Chatila TA & Mazmanian SK (2011) The Toll-like receptor 2 pathway
establishes colonization by a commensal of the human microbiota. Science 332: 974–7 Available at:
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3164325&tool=pmcentrez&rendertype=abs
tract
Roy CC, Kien CL, Bouthillier L & Levy E (2006) Short-chain fatty acids: ready for prime time? Nutr Clin Pract 21:
351–366 Available at: http://www.ncbi.nlm.nih.gov/pubmed/16870803
Scholtens PAMJ, Oozeer R, Martin R, Amor K Ben & Knol J (2012) The Early Settlers: Intestinal Microbiology in
Early Life. Annual Review of Food Science and Technology 3: 425–447
Schwiertz A, Taras D, Schäfer K, Beijer S, Bos NA, Donus C & Hardt PD (2010) Microbiota and SCFA in lean and
overweight healthy subjects. Obesity (Silver Spring, Md.) 18: 190–195
Segain JP, Raingeard de la Blétière D, Bourreille A, Leray V, Gervois N, Rosales C, Ferrier L, Bonnet C, Blottière
HM & Galmiche JP (2000) Butyrate inhibits inflammatory responses through NFkappaB inhibition:
implications for Crohn’s disease. Gut 47: 397–403
59
References
Sekirov I, Russell SL, Antunes LCM & Finlay BB (2010) Gut microbiota in health and disease. Physiological
reviews 90: 859–904
Sjogren⁎ K, Engdahl C, Henning P, Lerner UH, Tremaroli V, Lagerquist MK, Bäckhed F & Ohlsson C (2012) The
gut microbiota regulates bone mass in mice. Bone 50: S91–S92
Sleator RD, Shortall C & Hill C (2008) Metagenomics. Letters in applied microbiology 47: 361–366
Smith K, McCoy KD & Macpherson AJ (2007) Use of axenic animals in studying the adaptation of mammals to
their commensal intestinal microbiota. Seminars in Immunology 19: 59–69
Sommer F & Bäckhed F (2013) The gut microbiota--masters of host development and physiology. Nature
reviews. Microbiology 11: 227–38 Available at: http://www.ncbi.nlm.nih.gov/pubmed/23435359
Spor A, Koren O & Ley R (2011) Unravelling the effects of the environment and host genotype on the gut
microbiome. Nature reviews. Microbiology 9: 279–290
Stecher B & Hardt WD (2011) Mechanisms controlling pathogen colonization of the gut. Current Opinion in
Microbiology 14: 82–91
Tedelind S, Westberg F, Kjerrulf M & Vidal A (2007) Anti-inflammatory properties of the short-chain fatty acids
acetate and propionate: a study with relevance to inflammatory bowel disease. World journal of
gastroenterology : WJG 13: 2826–2832
Tehrani AB, Nezami BG, Gewirtz A & Srinivasan S (2012) Obesity and its associated disease: a role for
microbiota?
Neurogastroenterology
and
Motility
24:
305–311
Available
at:
http://dx.doi.org/10.1111/j.1365-2982.2012.01895.x
Temmerman R, Huys G & Swings J (2004) Identification of lactic acid bacteria: Culture-dependent and cultureindependent methods. In Trends in Food Science and Technology pp 348–359.
Temmerman R, Masco L, Vanhoutte T, Huys G & Swings J (2003) Development and validation of a nested-PCRdenaturing gradient gel electrophoresis method for taxonomic characterization of bifidobacterial
communities. Applied and environmental microbiology 69: 6380–6385
Thomas F, Hehemann J-H, Rebuffet E, Czjzek M & Michel G (2011) Environmental and gut bacteroidetes: the
food connection. Frontiers in microbiology 2: 93
Topping DL & Clifton PM (2001) Short-chain fatty acids and human colonic function: roles of resistant starch
and nonstarch polysaccharides. Physiological reviews 81: 1031–1064
Tremaroli V & Bäckhed F (2012) Functional interactions between the gut microbiota and host metabolism.
Nature 489: 242–249
Treves a & Karanth KU (2003) Human-carnivore conflict and perspectives on carnivore management
worldwide. Conservation Biology 17: 1491–1499
Tringe SG, Von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, Podar M, Short JM, Mathur EJ, Detter
JC, Bork P, Hugenholtz P & Rubin EM (2005) Comparative metagenomics of microbial communities.
Science (New York, N.Y.) 308: 554–557
60
References
Tuohy KM, Hinton DJS, Davies SJ, Crabbe MJC, Gibson GR & Ames JM (2006) Metabolism of Maillard reaction
products by the human gut microbiota: implications for health. Molecular nutrition food research 50:
847–857 Available at: http://centaur.reading.ac.uk/12928/
Turroni F, Peano C, Pass DA, Foroni E, Severgnini M, Claesson MJ, Kerr C, Hourihane J, Murray D, Fuligni F,
Gueimonde M, Margolles A, De Bellis G, O’Toole PW, Van Sinderen D, Marchesi JR & Ventura M (2012)
Diversity of bifidobacteria within the infant gut microbiota. PLoS ONE 7:
Van Valkenburgh B, Teaford MF & Walker A (1990) Molar microwear and diet in large carnivores: inferences
concerning diet in the sabretooth cat, Smilodon fatalis. Journal of Zoology 222: 319–340
Vanhoutte T, Huys G, Brandt E & Swings J (2004) Temporal stability analysis of the microbiota in human feces
by denaturing gradient gel electrophoresis using universal and group-specific 16S rRNA gene primers.
FEMS microbiology ecology 48: 437–446
Vedantam G & Hecht DW (2003) Antibiotics and anaerobes of gut origin. Current Opinion in Microbiology 6:
457–461
Vester BM, Beloshapka AN, Middelbos IS, Burke SL, Dikeman CL, Simmons LG & Swanson KS (2010) Evaluation
of nutrient digestibility and fecal characteristics of exotic felids fed horse-or beef-based diets: Use of the
domestic cat as a model for exotic felids. Zoo Biology 29: 432–448
Vester BM, Burke SL, Dikeman CL, Simmons LG & Swanson KS (2008) Nutrient digestibility and fecal
characteristics are different among captive exotic felids fed a beef-based raw diet. Zoo biology 27: 126–
136
Vester BM, Dalsing BL, Middelbos IS, Apanavicius CJ, Lubbs DC & Swanson KS (2009) Faecal microbial
populations of growing kittens fed high- or moderate-protein diets. Archives of Animal Nutrition 63: 254–
265 Available at: http://www.tandfonline.com/doi/abs/10.1080/17450390902860000
Willems A & Collins MD (1995) Phylogenetic analysis of Ruminococcus flavefaciens, the type species of the
genus Ruminococcus, does not support the reclassification of Streptococcus hansenii and
Peptostreptococcus productus as ruminococci. International journal of systematic bacteriology 45: 572–
575
Williams S (2007) Status of the Cheetah in Zimbabwe. Africa: 32–36
Willing BP & Van Kessel AG (2007) Enterocyte proliferation and apoptosis in the caudal small intestine is
influenced by the composition of colonizing commensal bacteria in the neonatal gnotobiotic pig. Journal
of animal science 85: 3256–3266
Wilson ID & Nicholson JK (2009) The role of gut microbiota in drug response. Current pharmaceutical design
15: 1519–1523
Wittebolle L, Marzorati M, Clement L, Balloi A, Daffonchio D, Heylen K, De Vos P, Verstraete W & Boon N
(2009) Initial community evenness favours functionality under selective stress. Nature 458: 623–626
Wong JMW, De Souza R, Kendall CWC, Emam A & Jenkins DJA (2006) Colonic health: fermentation and short
chain fatty acids. Journal of Clinical Gastroenterology 40: 235–243 Available at:
http://www.ncbi.nlm.nih.gov/pubmed/16633129
Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y-Y, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R,
Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li H, Bushman FD & Lewis JD (2011) Linking Long-Term
61
References
Dietary Patterns with Gut Microbial Enterotypes. Science
http://www.sciencemag.org/cgi/doi/10.1126/science.1208344
105:
105–8
Available
at:
Yachi S & Loreau M (1999) Biodiversity and ecosystem productivity in a fluctuating environment: the insurance
hypothesis. Proceedings of the National Academy of Sciences of the United States of America 96: 1463–
1468
Yu Z & Morrison M (2004) Comparisons of different hypervariable regions of rrs genes for use in fingerprinting
of microbial communities by PCR-denaturing gradient gel electrophoresis. Applied and environmental
microbiology 70: 4800–4806
Zentek J, Fricke S, Hewicker-Trautwein M, Ehinger B, Amtsberg G & Baums C (2004) Dietary protein source and
manufacturing processes affect macronutrient digestibility, fecal consistency, and presence of fecal
Clostridium perfringens in adult dogs. The Journal of nutrition 134: 2158S–2161S
Zoetendal EG, Von Wright A, Vilpponen-Salmela T, Ben-Amor K, Akkermans ADL & De Vos WM (2002) Mucosaassociated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and
differ from the community recovered from feces. Applied and environmental microbiology 68: 3401–
3407
Zoran DL (2002) The carnivore connection to nutrition in cats. Journal of the American Veterinary Medical
Association 221: 1559–1567
62
Addendum
CHAPTER IX. ADDENDUM
The original DGGE gels. Pictures were taken from the DGGE gels to perform normalization in
BioNumerics.
Figure 24.
63
Addendum
IX.1. FECAL SAMPLES COLLECTION
1. The fresh fecal sample was collected with a sterile spoon and it was then placed in a
sterile container.
2. The fecal sample was then placed on the ice and transferred to the laboratory of
Microbiology.
IX.2. HOMOGENIZATION OF THE FECAL SAMPLES
1. The fecal samples were taken out of the baggage in the biohazards. The entire
sample was placed in a sterile stomacher bag and mixed manually for further
homogenization.
2. 25 grams of the fecal sample was measured and put in a sterile stomacher bag.
3. 225 ml of 1g/l bacteriological pepton (LP0037; Oxoid, Basingstoke, United Kingdom)
was added to 25 grams of the fecal sample.
4. The bag was placed in the stomacher machine at 230spm for 30 seconds to blend
thoroughly.
5. The homogenate was filtered on a Buchner funnel and the large undigested particles
on the filter were thrown away.
6. Tubes were filled with 45 ml of the homogenate.
7. The homogenate was centrifuged for 10 minutes at 1000rpm at 15oC.
8. The supernatant was removed.
9. The pellet was resuspended in 5 ml of 1× TE (1mM EDTA, 10mM Tris-HCl) buffer.
10. 7 aliquots, each containing 1 ml, from each sample were prepared.
IX.3. DNA EXTRACTION
1. 1 ml of the homogenate in an Eppendorf tube was centrifuged for 10 minutes at
13000rmp.
2. The supernatant was removed and 1 ml 1x TE buffer was added to the pellet and
centrifuged for 5 minutes.
3. After removing the supernatant, the pellet was resuspeneded in 150μl of the
enzyme mix. The enzyme mix consists of 5 mg lysozyme powder, 40μl of
mutanolysine and 110μl 1x TE buffer.
4. The tubes were incubated at 37oC water bath for 40 minutes.
5. 500μl GES (600g/L guanidiumthiocyanate, 200 ml/l 0.5 EDTA, 10g/l sarkosyl) reagent
was added to the mixture and the tubes were mixed gently. Then they were put on
ice for 10 minutes.
6. 250μl of NH4Ac (578.1 g/l NH4OAc) was added to the mixture. The tubes were set on
ice for 10 minutes.
7. Afterwards, 500μl chlorpform/iso-amylalcohol (192 ml chlorpform, 8 ml isoamylalcohol) was added to the mixture followed by thorough homogenization. The
tubes were centrifuged for 20 minutes at 13000rpm.
8. The maximum volume from the top layer was transferred to a new Eppendorf tube
to which 300μl chlorpform/iso-amylalcohol was added. Again it was shaken for
homogenization followed by centrifugation for 20 minutes at 13000 rpm.
64
Addendum
9. From the upper phase, 700μl was transferred to a new Eppendorf tube and ice cold
isopropanol (0.54 of their volumes), was added to them followed by careful
homogenization and centrifugation for 20 minutes at 13000 rpm.
10. The supernatant was removed and the pellet was washed 2 times with 150μl 70%
ethanol. The pellet was air dried and dissolved in 150μl TE 1× buffer overnight at 40C.
11. After two days, 7.5μl RNAse (2mg/ml) was added to the dilution and incubated at
370C for 90 minutes.
IX.4. DNA QUANTITY AND QUALITY CHECK
The DNA quantity and quantity check was performed through optimal density by
spectrophotometer and also through agarose gel electrophoresis.
IX.4.1. AGAROSE GEL ELECTROPHORESIS
1. From the DNA sample, 5μl was mixed with 2μl of the loading dye (4g sucrose, 25mg
bromophenol blue, 6 ml TE).
2. The solidified 1% agarose gel (BIOzym) was placed in TBE buffer tank.
3. The DNA samples were loaded on the gel, located in the tank, as well as the smart
ladder (Eurogentec).
4. Electrophoresis was run for 45 minutes at 75v.
5. The gel was stained in midori green bath for 30-45 minutes.
IX.4.2. OPTICAL DENSITY MEASUREMENT BY SPECTROPHOTOMETER
1. From the DNA sample, 5μl was mixed and vortexed slowly with 95ul of 1x TE buffer
to make a 1/20 dilution.
2. Then they were transferred to Greiner 96 micro titer well plate. The first well was
filled with 100μl of TE buffer.
3. The cuvet was filled with 1 ml of 1x TE buffer and placed in the spectrophotometer.
4. The quantity of the DNA samples in the plate was measured by the
spectrophotometer at 260nm, 234nm and 280nm.
IX.5. V3-16S RRNA PCR
Materials:
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
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33.75μl MilliQ water
6μl AmpliTaq PCR buffer (Applied Biosystems)
2.5μl dNTPs (Applied Biosystems)
2.5μl bovine serum albumin
2μl forward primer with GC clamp (Sigma)
2μl reverse primer (Sigma)
0.25μl heat stable taq polymerase (Applied Biosystems)
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Method:
1.
2.
3.
4.
5.
6.
7.
A master mix was made by combining the PCR components.
From the master mix, 49μl was poured in each PCR tube on ice.
Then 1μl of each DNA sample, in a 1/10 dilution, was added to the PCR mix.
The PCR tubes were put in the thermo cycler.
After the run was over, the tubes were shortly centrifuged.
To each PCR tube, 15μl loading dye was added.
Agarose gel electrophoresis was performed for 45 minutes at 75v.
IX.6. DGGE
1. The buffer tank was filled with 7 liters of 2% TAE buffer (242g Tris-base, 57.1 ml
Glacial acetic acid, 18.61g Na2 EDTA).
2. The 100% acrylamide container was put on the stirrer for 2h to decrystallize.
3. The chambers were cleaned by using milliQ water.
4. Preparation of plates: The spacers and the plates were cleaned with ethanol and
rinsed. Afterwards, the silicon grease was smeared on the back and front of the long
edge of the spacers and they were put on the longer plate facing outside. The
smaller plate was flipped on the bigger one. The clamps were fixed on the right and
left sides of both plates. Consequently they were placed in the tray to further align it
by a standard cupboard. This step was also visually checked and grease was smeared
under the spacers. Then they were placed in the tray with a piece of parafilm
underneath the plates.
5. Casting: Low gradient polyacrylamide gel was made by adding 7.8 ml 0% denaturing
agent, 4.2 ml 100% denaturing agent, 100μl APS 10% and 8μl TEMED. The high
gradient polyacrylamide gel was made by adding 3.6 ml 0% denaturing agent, 8.4 ml
100% denaturing agent, 100μl APS 10% and 8μl TEMED. Afterwards, the low
percentage solution was poured in the left chamber and the faucet was opened and
closed immediately to make sure the bridge is filled with the low concentration
denaturing agent solution. If some low concentration leaks to the right chamber, it
should be taken out and poured in the left chamber with a micropipette. Then the
high concentration gradient agent solution was poured in the right chamber while
checking the level equality in both chambers, followed by putting a magnet in it. The
stirrer was turned on between 2 and 3. The pump and the chamber’s faucet were
turned on simultaneously. The needle of the pump tube was put in between the two
plates to pour the gradient gel in between the two plates. After the gel reached a
certain level, buthanol was added to the top of the gel and the gel was left to solidify
for 2 hours. The tank was set up on 60 degrees and the pump and heater were
turned on.
6. After 2 hours, the buthanol on top of the gel was poured out followed by cleaning it
twice with milliQ water. Absorbing paper was used to rinse between the plates and
an appropriate comb was put between the plates with a 5mm distance from top of
the gel. The stacking gel, which contains 5 ml 0% denaturing agent solution, 50μl APS
10% and 5μl TEMED, was poured on the comb an it was left to solidify for 30
minutes.
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Addendum
7. After 30 minutes, the comb was removed and the wells were washed twice with hot
buffer with avoiding making bubbles. Then they were put on the holder with the
small plate facing inside. To make sure that the plates are tightly fixed on the holder,
some hot buffer was poured between the plates and on the chamber of the holder.
8. Then 25μl of each sample was loaded with a sharp tip, from right to left of the gel.
The holder with the plates was put in the buffer tank. The level of the buffer in the
tank should not be higher than maximum when the holder is placed inside. After that
the lid of the tank was closed and the machine was set on 990 minutes on 70 volt.
9. The day after the tank was turned off and the clamps were unfixed. The plates were
transferred to the ethidium bromide room. The plates were separated and the gel
was marked to make sure of the order of the samples. In order to stain the gel, it was
put in the SYBR gold bath for 30-45 minutes.
10. The gel was slowly taken out and placed on the UV machine to take digital photos.
IX.7. BAND EXTRACTION AND SEQUENCING
1. While taking an image from the DGGE under the UV light, a piece of the desired band
was taken out by using a filter tip.
2. The band was put in 40μl of 1x TE buffer.
3. V3-16S rRNA PCR was performed and checked by 1% agarose gel electrophoresis.
4. The amplicons were poured on Macherey-Nagel 96 plates and it was filtered.
5. From the MilliQ water, 100μl was added to the amplicons in the plates and
vacuumed.
6. From the MilliQ water, 100μl was added to the top of the filters.
7. The water and the amplicons were transferred to new Eppendorfs.
8. A master mix was prepared by combining 1.857μl MilliQ water, 1.857μl sequencing
buffer, 0.286μl BigDye v3.1, 3μl forward primer (F357) and 3μl of the DNA sample.
9. A master mix was prepared by combining 1.857μl MilliQ water, 1.857μl sequencing
buffer, 0.286μl BigDye v3.1, 3μl reverse primer (R518) and 3μl of the DNA sample.
10. The PCR tubes were set in the thermo cycler machine.
11. After the run was over, the amplicons were sent for sequencing.
IX.8. Q-PCR
IX.8.1. BUILDING A STANDARD CURVE
1. The content of a Clostridium symbiosum ampulla (R-25411) was poured in a nutrient
broth in the anaerobic chamber.
2. From the broth, some droplets were poured on M37 plate and incubated in the
anaerobic chamber at 37 oC overnight.
3. Several generations were prepared for the bacteria by the streaking technique as
well as the massive end.
4. The bacterial cells were harvested from massive end plates by ӧse and diluted in 4
ml of bacteriological pepton (L37; Oxoid, Basingstoke, United Kingdom).
5. A serial dilution was made from the main dilution till 10-8.
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Addendum
6. The tubes were thoroughly vortexed. From each tube, 50μl was poured on a M37
plate in triplicates.
7. The plates were incubated at 37 oC overnight in the anaerobic chamber.
8. The countable plates were counted and CFU/ml was calculated by multiplying the
mean number of colonies in 20 in the dilution.
9. The DNA was extracted from the 10o dilution according to Pitcher method (Pitcher et
al, 1989) for gram positive bacteria.
10. V3-16S rRNA PCR was performed.
11. Quantitative PCR was performed as described in IX.8.2.
IX.8.2. QUANTIFICATION OF CLOSTRIDIUM CLUSTER XIVA
1. The PCR mix was prepared by mixing 10μl 2xSensi mix SYBR NO-ROX, 6.4μl MilliQ
water, 0.3μl Ccoc-RT-F, 0.3ul Ccoc-RT-R and 3μl of the DNA sample (1/10 dilution).
The total volume was 20μl per well.
2. The 96 plate was tightly sealed and it was centrifuged for 1 min at 0.7 rpm.
3. The 96 plate was placed in the Roche Light Cycler 480 machine and the protocol for
quantifying Clostridium cluster XIVa was chosen.
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Addendum
Neighbor-joining phylogenetic tree showing the nearest phylogenetic related type strains for
recovered OTUs from two 16S rRNA clone libraries from captive cheetah feces. Bootstrap values, expressed as
percentages of 1000 replications, above 50% are given at branching points. The scale bar shows 5 nucleotide
substitutions per 100 nucleotides. Number of clones in parentheses follows label of either common OTUs
(framed), OTUs solely from CL-B1 (green) or CL-B2 (purple). Figure obtained from:
http://www.biomedcentral.com/1471-2180/14/43/abstract (Becker et al, 2014)
Figure 25.
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