DISS. ETH No. 18938
Differentially Load-Regulated Gene Expression in Mouse
Trabecular Osteocytes
A dissertation submitted to
ETH Zurich
for the degree of
Doctor of Sciences
presented by
ELAD WASSERMAN
M.Sc. Cell Biology and Histology, Tel-Aviv University
born 27th September, 1972
citizen of Israel
accepted on the recommendation of
Examiner: Prof. Dr. Ralph Müller
Co-Examiner: Prof. Dr. Itai Bab
2010
Table of contents:
Acknowledgements………………………………………………………………………………….v
Summary……………………………………………………………………………………………vi
Zusammenfassung………………………………………………………………………………….ix
1. Introduction……………………………………………………………………………………...13
1.1. Hypotheses and specific aims……………………………………………………………….15
1.1.1. Developing method of isolating mRNA from trabecular osteocytes…………………..16
1.1.2. Identify load-induced differentially regulated genes…………………………………..16
1.2. Outline of the thesis…………………………………………………………………………17
2. Background……………………………………………………………………………………...21
2.1. Bone anatomy……………………………………………………………………………….22
2.2. Osteocytes as mechanosensors/endocrine and paracrine organ …………………………….26
2.3. Bone remodeling…………………………………………………………………………….33
2.4. Mechanical load-induced bone adaptation .…………………………………..…………….40
2.5. Mouse genetics……………………………………………………………………………...45
3. Developing a method for isolation of osteocyte RNA………………………………………....67
3.1. Separation of trabecular bone from caudal vertebra………………………………………...68
3.2. Enzymatic digestion of non-osteocytic cells………………………………………………..70
3.3. RNA extraction from denuded trabeculae…………………………………………………..71
3.4. Comparative marker gene mRNA expression in enzymatically isolated cell fractions
and extracted RNA from denuded trabecular bone…………………………………………75
4. Load-induced differential regulation mRNA of trabecular osteocytes……………………...89
4.1. Single loading………………………………………………………………………………89
4.2. Repetitive loading…………………………………………………………………………..99
4.3. Functional genomics for identification of load-regulated pathways……………………...107
4.4. Confirmation of individual load-regulated genes in single loading………………………116
5. Synthesis………………………………………………………………………………………..127
Appendix…………………………………………………………………………………………..133
A1-A4. List of up- and down-regulated genes………………………………………………...134
A5-A6. List of load-regulated signalling pathways………………………………………........184
Curriculum Vitae ………………………………………………………………………………...190
Acknowledgements
The work presented in this thesis is the direct result of a great team effort and I am gratefully
indebted to every member of that team.
First of all I would like to express my deep gratitude to both my supervisors, Professor Dr. Ralph
Müller and Professor Dr. Itai Bab. Without their passion, guidance and leadership the successful
realization of this project would not have been possible. Furthermore, I would like to thank them for
their time, and the open door policy for which I am eternally grateful. I would also like to thank PD
Dr. Franz Weber (University of Zürich) and Dr. Haike Hall-Bozic for their support and for
providing me facilities in their laboratories.
Special thanks go to Dr. Duncan Webster for all of his help in doing the many experiments. His
company and expertise were invaluable and significantly contributed to the outcome of this thesis. I
also would like to thank Dr. Gisela Kuhn and Floor Lambers for their help during loading studies.
During my thesis, I enjoyed a fruitful close collaboration with The Functional Genomics Center
Zurich (FGCZ) located at the University of Zurich. Many members of this institute made it possible
to perform differential expression microarrays and bioinformatics statistical analyses.
Particular thanks go to the past and present members of the Institute of Biomechanics. Their
expertise and companionship created a pleasant environment in which to work. I sincerely hope to
stay in contact and share some good times with you in the future.
I am deeply grateful to my parents. Their unconditional love and support have enabled me to
achieve all my goals. These few words cannot even begin to describe my gratitude and appreciation
for all they have done for me.
Finally, the financial support of the Swiss National Science Foundation (SNF) and the Swiss
Federal Institute of Technology (ETHZ) is gratefully acknowledged.
- v-
Summary
In light of the many bone diseases and injuries for which proper treatment is yet to be developed,
there is a significant need to further address new approaches to stimulate bone healing. Osteoporosis
is a disease characterized by an excessive decrease in bone mass which can lead to an increased
susceptibility to fractures, skeletal deformation and, in more severe cases, death owing to morbidity.
The disease has been attributed to both genetic and age-related factors. There are various
medications available, which have been shown to delay bone loss, however no cure is yet
achievable. To treat the disease, medical research is attempting to target genes which define
osteoporosis, using the mouse as a model. Owing to the recent deciphering of the mouse genome
and the high homology that exists between human and mouse genomes, inbred strains of mice
represent ideal models for genetic studies. Using the mouse to identify genes implicated in the bone
remodeling process could lead to advances in understanding that enable the precise regulation of
genes and proteins responsible for particular bone phenotypes, i.e. bone mineral density or bone
strength. One interesting phenotype under investigation is the response of bone to mechanical
loading or its “mechano-sensitivity”.
Mechanical loading is perhaps the most important single physiological/environmental factor
regulating bone mass and shape. Age-related bone loss and consequent osteoporosis have been
attributed, at least in part, to a reduction in muscle mass/function and the resultant decrease in
mechanical usage of the skeleton. On the other hand, mechanical overloading has been shown to
enhance bone formation and cause a net gain in cancellous bone mass, the major structural
component of skeletal load-bearing sites. However, very little is known about the mechanisms
involved in the load-induced anabolic effects in trabecular bone, mainly due to the lack of in vivo
models to study load-induced molecular events. Building on the studies investigating the effect of
mechanical loading on trabecular bone adaptation in the C56BL/6 mouse tail model that was
recently developed in our group, the next long-term objective of this thesis was to elucidate the
molecular mechanisms involved in the osteogenic anabolic effect of mechanical loading and to find
genes and gene pathways that are regulated by mechanical loading. Mice have a well-characterized
genome accessible to manipulations by transgenic and knockout technologies. An understanding of
the molecular pathways governing load-stimulated bone formation could provide opportunities to
mimic or augment bone mechano-sensitivity using pharmacological and molecular agents thereby
leading to the development of novel strategies in the management of osteoporosis and other skeletal
deficits.
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To investigate the genetic regulation of mechanical loading, an ex-vivo method was established for
the isolation of representative samples of mouse vertebral intact ribonucleic acid (RNA) derived
selectively from trabecular osteoblast/lining cells and osteocytes, by using sequential collagenase
digestions and pulverization. High quality total RNA preparations were isolated immediately
following cell separation using conventional reagents and protocols. The quantity of total RNA
isolated from trabecular osteocytes of a single caudal vertebra was sufficient for further differential
gene expression analysis.
To investigate a single and repetitive mechanical load-induced differential gene expression, the fifth
caudal vertebra (C5) of C57BL/6 (B6) female mice was mechanically stimulated by respective
single or repetitive load doses, each dose consisting of 3’000 cycles at a frequency of 10 Hz with an
amplitude of 0N and 8N via two pins inserted into the adjacent vertebrae (C4 and C6). Mice were
sacrificed six hours after the last mechanical loading and high quality total RNA preparations were
analyzed for gene expression arrays, using Affymethrix Mouse Genome chips. Differential gene
expression analysis of a single mechanical load revealed a total of 331 significantly regulated genes
(P < 0.05), including 281 up-regulated probes and 50 down-regulated probes. Also, functional
genomics analysis of acute loading, using GeneGo MetaCore software, indicated 65 load-regulated
molecular pathways in which significantly regulated probes were present. In particular, upregulation of insulin growth factor 1 (IGF-1, 2.2 fold) and wingless-type MMTV integration site
family, member 5a (Wnt5a, 3.4 fold) genes have been shown which are thought to be activators of
osteoblasts differentiation. In contrast, down-regulation of WNT inhibitor factor 1 gene (WIF-1, 1.8
fold) was shown, an inhibitor of WNT/beta-cathenin pathway for activation of osteoblast
differentiation.
Differential gene expression analysis of repetitive mechanical loading (three times per week of
single load over four weeks) has shown a total of 1342 significantly regulated probes, involving 781
up-regulated and 561 down-regulated genes. In addition, MetaCore software pathway analysis
showed 153 load-regulated molecular pathways in which significantly regulated genes were present.
Particulary observed was an up-regulation of dentin matrix protein 1 (DMP-1, 2.18 fold) which
plays a critical role for bone mineralization and strength; Wnt5a (2.19 fold) an osteoblast
differentiation activator of Wnt signaling; and alpha-actinin (6.3 fold) which is involved in the
cellular mechanoprotective response and raising the amount of alpha-actinin in the cytoskeleton
drives to increase the whole cell resistance to deformation. Quantitative real-time polymerase chain
reaction (PCR) results confirmed that the mRNA levels of Wnt5a and Asporin mRNA were indeed
up-regulated.
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In conclusion, this thesis has provided a method for isolation of RNA from trabecular osteocytes
and using it for cDNA microarrays to reveal the mechanobiological effect of acute and chronic
loading regimes in-vivo on global murine differential gene expression, including also analysis of
signaling pathways. This analysis led to the identification of genes whose expression is regulated by
mechanical loading thus pointing out potential molecular mechanisms involved in the osteogenic
anabolic response to mechanical loading. This in turn paves the way to studying the role of genes in
load-stimulated bone formation in corresponding genetically modified systems.
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Zusammenfassung
Angesichts der vielen Knochenkrankheiten und –verletzungen, für welche angemessene
Behandlungsmethoden erst noch entwickelt werden müssen, besteht die Notwendigkeit, sich mit
neuen Ansätzen zu befassen, die die Knochenheilung stimulieren. Osteoporose ist eine Krankheit,
welche durch eine übermässige Abnahme der Knochenmasse charakterisiert ist, was zu einer
erhöhten Anfälligkeit für Brüche, Skelettdeformationen und - in schwerwiegenderen Fällen - zu Tod
infolge Morbidität führen kann. Die Krankheit wurde sowohl auf genetische als auch auf
altersbedingte Einflussfaktoren zurückgeführt. Verschiedenartige medikamentöse Behandlungen
stehen zur Verfügung, welche Knochenverlust erwiesenermassen verzögern; allerdings ist noch
keine Heilmethode greifbar. Um die Krankheit zu behandeln, versucht die medizinische Forschung,
auf Gene abzuzielen, welche Osteoporose verursachen, indem sie die Maus als Tiermodell
verwendet. Dank der kürzlichen Entschlüsselung des Erbgutes der Maus und infolge der grossen
Homologie zwischen dem Chromosomensatz des Menschen und dem der Maus verkörpern InzuchtMäusestämme das ideale Modell für genetische Studien. Die Verwendung der Maus, um Gene zu
identifizieren, welche in Prozessen des Knochenumbaus eine Rolle spielen, könnte zu Fortschritten
im Verständnis der konkreten Regulierung bestimmter Knochenphänotpyen durch Gene und
Proteine führen, wie beispielsweise im Falle der Knochenmineraldichte oder der Knochenstärke.
Ein interessanter Phänotyp, der untersucht wird, ist das Knochenverhalten unter mechanischer
Belastung beziehungsweise die mechanische Sensitivität.
Mechanische Belastung ist wohl der wichtigste physiologische/umfeldbedingte Einzelfaktor,
welcher die Knochenmasse und die Knochenform reguliert. Altersbedingter Knochenverlust und
folgerichtig, Osteoporose, wurden zumindest teilweise einem Abbau der Muskelmasse/-funktion
und der resultierenden Abnahme des mechanischen Gebrauchs des Skeletts zugeschrieben.
Andererseits wurde gezeigt, dass mechanische Überbeanspruchung Knochenbildung fördert und
einen Nettozuwachs an trabekulärer Knochenmasse verursacht, welche die wichtigste Baugruppe
von lasttragenden Elementen im Skelett ausmacht. Allerdings weiss man nur wenig über die
Mechanismen, welche innerhalb trabekulären Knochens an den lastbedingten anabolischen
Wirkungen beteiligt sind. Dies ist vor allem darauf zurückzuführen, dass Studien lastbedingter
molekularer Vorgänge im lebenden Organismus fehlen. Aufbauend auf Studien in unserer Gruppe,
welche kürzlich die Auswirkungen mechanischer Belastung auf die trabukläre Knochenadaption im
C56BL/6 Mausschwanz-Model untersucht hat, besteht die längerfristige Zielvorgabe dieser
Doktorarbeit darin, die molekularen Mechanismen aufzuklären, welche mittels mechanischer
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Belastung osteogenetische anabolische Auswirkungen hervorrufen und zudem, Gene und genetische
Signalwege
zu
finden,
welche
durch
mechanische
Belastung
reguliert
werden.
Der
Chromosomensatz der Maus ist gut beschrieben und zugänglich für Kunstgriffe transgener und
knockout Technologien. Das Verständnis molekularer Signalwege, welche den Knochenaufbau,
angeregt durch mechanische Belastung, regelt, könnte die Möglichkeit bieten, die mechanische
Sensitivität nachzuahmen oder zu erhöhen, indem pharmakologische und molekulare Mittel
eingesetzt werden. Dies würde zur Entwicklung neuer Strategien für die Handhabung von
Osteoporose und anderer Skelettmängel führen.
Um die genetische Regulierung mechanischer Belastung zu untersuchen, wurde ein Methode
ausserhalb des lebenden Organismus entwickelt, um repräsentative Proben intakter ribonukleinsäure
(RNS)
von
Maus-Rückenwirbeln
zu
isolieren,
welche
selektiv
von
trabekulären
Osteoblastzellen/Wandzellen und Osteozyten abgeleitet wurden, indem sequentielle BindegewebeVerdauung und -Zerstäbung angewandt wurden. Hochwertige Gesamt-RNS-Vorbereitungen
wurden sofort nach der Zellseparierung isoliert, indem konventionelle Reagenzien und Protokolle
verwendet bzw. befolgt wurden. Die Gesamtmenge an RNS, die von trabekulären Osteozyten eines
einzigen Schwanzwirbelknochens isoliert wurden, war ausreichend für eine darauffolgende
differenzielle Genexprimierungs-Analyse.
Um eine einzelne und sich wiederholende, mittels mechanischer Belastung induzierte,
differenzielle Genexpression zu untersuchen, wurde der fünfte Schwanzwirbelknochen von
weiblichen C57BL/6 (B6)-Mäusen mechanisch stimuliert. Die Stimulierung wurde unter
Verwendung von einzelnen respektive sich periodisch wiederholenden Lastdosierungen erreicht,
wobei letztere aus 3000 Zyklen bestanden, die bei einer Frequenz von 10 Hz und einer Amplitude
von 0N und 8N via zwei Stiften in den benachbarten Rückwirbeln (C4 und C6) übertragen wurden.
Die Mäuse wurden sechs Stunden nach der letzten mechanischen Belastung getötet, um danach
hochwertige Gesamt-RNS-Vorbereitungen für Genexprimierungs-Arrays zu untersuchen, indem
Affymethrix Mouse Genome chips verwendet wurden. Differenzielle Genexprimierungs-Analyse
von einzeln applizierten Lastereignissen legte insgesamt 359 sifnifikant regulierte Gene (P<0.05)
offen, 301 davon hochreguliert und 58 runterreguliert. Zudem deutete eine mittels dem
Computerprogramm GeneGo MetaCore durchgeführter funktioneller Genomik-Analyse von akuten
Lastereignissen auf 65 lastregulierte molekulare Signalwege hin, wobei signifikant regulierte
Testergebnisse verzeichnet werden konnten. Namentlich wurden eine Hochregulation des InsulinWachstumsfaktors 1 (IGF-1, 2.2-fach) und flügellosartige MMTV Integrationsseite-Familie,
Mitglied 5a (Wnt5a, 3.4-fach) nachgewiesen, von welchen vermutet wird, dass sie Auslöser der
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Osteoblast-Differenzierung sind. Demgegenüber wurde eine Herunterregulation des WNTHemmfaktors 1-Gen (WIF-1, 1.8-fach) ermittelt, welcher ein Hemmer des WNT/beta-CateninSignalwegs ist und die Aktivierung der Osteoblast-Differenzierung unterdrückt.
Differenzielle Genexprimierungs-Analyse von sich wiederholenden Lastereignissen (drei Mal pro
Woche ein einzelnes Lastereignis, insgesamt über vier Wochen) hat 1585 signifikant hochregulierte
Testergenisse geliefert, wobei 860 hochregulierte und 725 herunterregulierte Gene. Ferner wurden
mittels einer mit dem Computerprogramm MetaCore durchgeführten Signalweg-Analyse 153
lastregulierte molekulare Signalwege ermittelt, in welchen signifikant regulierte Gene gegenwärtig
sind. Im Einzelnen wurde eine Hochregulation des Dentin-Gewebeproteins 1 (DMP-1, 2.18-fach)
beobachtet, welches eine entscheidende Rolle spielt für die Knochenmineralisierung und -stärke;
Wnt5a (2.19-fach), ein Osteoblast-Differenzierungs-Auslöser der Wnt-Signalübertragung; und
alpha-Actinin (6.3-fach), welches in der zellulären Antwort zum mechanischen Schutz und
Aufhebung des Betrags des alphas-Actinin in den Cytoskeleton-Laufwerken beteiligt wird, um den
ganzen Zellwiderstand gegen die Deformierung zu vergrössern. Resultate quantitativer EchtzeitPolymerase-Kettenreaktion (PCR) bekräftigte, dass die mRNS-Niveaux von Wnt5a und Asporin in
der Tat hochreguliert waren.
Zusammenfassend hat diese Doktorarbeit eine Methode für die RNS-Isolierung aus trabekulären
Osteozyten vorgelegt, welche für cDNA-Microarrays angewendet wurde, um die mechanobiologischen Auswirkungen akuter und chronischer Last-Regimes auf globale differentielle
Genexprimierung in der Maus in vivo offenzulegen, wobei auch die Analysie von Signalwegen
berücksichtigt wurde. Diese Untersuchungen hat zur Ermittlung von Genen geführt, deren
Exprimierung durch mechanische Belastung reguliert ist und somit potentielle molekulare
Mechnismen aufzeigt, welche an osteogenetischen anabolischen Reaktionen auf mechnische
Belastung beteilligt sind. Dies wiederum ebnet den Weg, um die Funktionen von Genen zu
studieren, welche sie innerhalb genetisch modifizierter System für lastinduzierte Knochenbildung
ausüben.
- xi-
Chapter 1: Introduction
Chapter 1
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Chapter 1: Introduction
Introduction
The Institute for Biomechanics aims to provide a bridge between biologists, who bring molecular
and cellular components to engineering, and engineers, who bring the methods of measurement,
analysis, synthesis, and control to molecular and cell biology. Biomechanics develops, refines, and
uses bioengineering tools and concepts to explore and understand living systems on the molecular,
cellular and organic levels.
Bone provides life-essential functions as the framework of the body, protecting inner organs from
injury, and representing a storehouse for vital minerals. Nevertheless, because bone is subject to
pathologies or injuries, its ability to exert its essential functions may be lost. For example,
osteoporosis is a disease characterized by an excessive decrease in bone mass which can lead to an
increased susceptibility to fractures, skeletal deformation and, in more severe cases, death owing to
morbidity. The disease has been attributed to both genetic and age-related factors. Appart from the
obvious costs on health, osteoporosis is a global problem and carries with it significant social and
economic costs. This is illustrated by the IOF audit report “Call to Action” published in 2001,
which claims that osteoporosis costs national treasuries in the EU over 4.8 billion Euro annually in
hospital healthcare alone. Various medications are available which have been shown to delay bone
loss, however no cure is yet achievable (1). The concept of bone mass homeostasis maintained by
mechanical loads is widely accepted and supported by a substantial body of experimental evidence
(2). Medical research is now attempting to target genes which define osteoporosis using the mouse
as a model system for human diseases. Owing to the recent deciphering of the mouse genome and
the high homology that exists between the human and mouse genomes (3), inbred strains of mice
represent ideal models for genetic studies. Using the mouse to identify genes implicated in the bone
remodeling process could lead to advances in understanding that enable the precise regulation of the
genes and proteins responsible for particular bone phenotypes, i.e. bone mineral density or bone
strength. One interesting phenotype under investigation is the response of bone to mechanical
loading or its ‘mechano-sensitivity’.
Bone is a specialized connective tissue with basically two functions, both of which are related to its
unique characteristic as a calcified extracellular matrix. The first function is to carry heavy
mechanical loads, derived from weight bearing or from muscle contractions. The second function is
to serve as a reservoir of ions such as calcium, phosphate and magnesium, whereby bone tissue
helps to maintain the homeostasis of these ions in the blood. Bone is a living, continuously selfrenewing tissue. At first sight, the most important cell types involved in the formation, modeling
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Chapter 1: Introduction
and remodeling of bone are the osteoblasts or bone-forming cells and the osteoclasts, the boneresorbing cells. The most abundant cell type in mature bone is, however, the osteocyte. There are
approximately 10 times as many osteocytes as osteoblasts in normal human bone (4). Osteocytes
have a particular location in bone. During bone formation some osteoblasts are left behind while the
bone formation front moves on together with the other retracting osteoblasts. The encapsulated
osteoblasts differentiate into osteocytes. They lose a large part of their cell organelles but gain long
slender cell processes by which the cells remain in contact with earlier incorporated osteocytes and
with osteoblasts lining the bone surface (22). Despite the relative abundance of osteocytes in bone
tissue, they have not yet been shown to have an unequivocal function. Their location in bone and
their organization in a syncytium with two extensive communication systems, one intracellular
(osteocyte-gap junction-osteocyte) and another extracellular (lacuna-canaliculus-lacuna) suggest at
least two possible ways in which osteocytes may function: 1) to ensure communication between
sites deep in the bone and the extra-osseous world and 2) to create an enormous increase in mineral
surface exposed to extracellular fluid and cellular activity. These considerations have led to the
formulation of the following hypotheses about the function of the osteocyte.
1.1. Hypotheses
The primary function of the skeleton is to bear mechanical loads, a principal reason for the
existence of a hard, mineralized extracellular matrix. It has long been recognized that the amount of
mechanical loading to which a piece of bone is exposed and the geometry and mass of that bone are
related. Living bone is continually undergoing processes of remodeling; this allows a continuous
fine tuning of the amount and spatial organization of the tissue, to provide maximal strength with a
minimum of bone mass. This process is called functional adaptation and was originally described as
Wolff's law about 100 years ago (5). Although it is generally considered that functional adaptation
is achieved by the concerted action of osteoblasts and osteoclasts, the mechanism by which these
cells are instructed for such a task remains obscure. To bring about meaningful change in existing
bone tissue, osteoblasts and osteoclasts must be informed about local needs for tissue increase or
reduction; these in turn depend on mechanical overuse or underuse. Both osteoblasts and osteoclasts
act at the surface of bone tissue, while mechanical loads produce displacements, or strains,
throughout the bone. Thus, aberrant strain would best be detected by living elements dispersed
throughout the matrix. Osteocytes are the only cells that can fulfill this demand. Sensor cells that
detect loading deviations need not also be actor cells that carry out adaptation, as long as sensors
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Chapter 1: Introduction
and actors can communicate with one another. In this respect the organization in bone of a cellular
network, where osteocytes, embedded in the matrix, are connected via cell processes and gap
junctions to osteoblasts on the surface of that matrix, assumes significance.
Mechano-transduction in bone involves a number of steps. First, the mechanical load or stress must
be transduced into a physical signal that is sensed by the bone cells. This process is called
mechanical coupling. It is not known which physical signal resulting from stress performs this
function in bone. Mechanical loads produce deformation or strain gradients within the bone tissue;
these in turn cause fluid to flow through the canalicular network (6, 7). Strain resulting from stress,
or flow resulting from strain or both might activate bone cells. In the second step of mechanotransduction, the physical signal is translated by the cell into a biochemical signal. This may be
called biochemical coupling. Many studies have shown that osteocytes in culture react to physical
stress with an enhanced production of prostaglandins, primarily prostaglandin E2 (PGE2). Second
messengers such as cyclic adenosine monophosphates (cAMPs) are also produced (8). In the third
step, the biochemical signal must be communicated to effector cells, i.e., the osteoblasts and
osteoclasts, which react by augmenting or reducing the amount of bone matrix at a specific site.
Is there a role for osteocytes in mechano-transduction? Many authors in recent papers speculate that
this is so, and experimental studies by Lanyon (9) have provided some evidence for this hypothesis,
having shown that osteocytes change metabolic activity when subjected to strain. Recently, a
number of experiments in which isolated osteocytes were subjected to two types of mechanical
stress in vitro have been performed (10). The results suggest that osteocytes are indeed very
sensitive to stress, responding by enhanced production of prostaglandins and other factors. Thus,
our hypotheses are: 1) the anabolic effect of mechanical load in trabecular bone is mediated by
osteocytes and involvs a change in gene expression; 2) some of the differentially regulated genes
affect bone remodeling.
In an effort to continue the search for a cure, this project aims to elucidate the genetic and
biochemical factors of bone formation in response to mechanical loading. It has been demonstrated
in humans and experimental animals that cyclic overloading results in increased bone formation and
a net gain in lamellar bone mass, both cortical and trabecular (11-14). However, very little is known
about the molecular mechanisms involved in the load stimulated bone formation, especially in
trabecular bone which occupies the critical load-bearing sites of the skeleton (vertebrae, hip, distal
radius).
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Chapter 1: Introduction
Recently, the establishment in our group by Webster et al. 2008 of a mouse model for load-induced
anabolic activity in trabecular bone has facilitated molecular approaches in this field because mice
have a well-characterized genome accessible to manipulations by transgenic and knockout
technologies. Building on this model, this study allows the isolation of a well-defined osteocyte
population from cancellous bone for detailed molecular characterization.
To this end, we devised a strategy designed to elucidate load-induced molecular changes in
trabecular bone. This strategy is based on the cyclic compression of individual mouse caudal
vertebrae followed by expression analysis in total RNA isolates from selective osteocytic enriched
cell population. A successful implementation of this strategy is expected to provide a method and
baseline for further studies aimed at elucidating the molecular mechanisms involved in the
trabecular adaptation to mechanical loads. We expect our strategy to yield meaningful advances in
the understanding of bone adaptation to mechanical stimuli, thus uncovering skeletal mechanisms
involved in the skeletal load-bearing function and their role in osteoporosis.
1.2. Specific Aims
1.2.1. Developing a method of isolating intact mRNA from a well-defined trabecular osteocytic
enriched cell population of murine caudal vertebra in the C57BL/6 mouse strain.
Hypothesis to be tested: Assess the feasibility of obtaining a sufficient amount of high quality total
RNA from trabecular osteocytes derived from single caudal vertebra for microarray analysis.
Well-defined trabecular osteocytic population shall be targeted by separation of trabecular bone
from caudal vertebral body, collagenase digestions of medullary soft tissue and pulverization of
remaining trabeculae. Enzymatic “stripping” of cells will be confirmed histologically following
each digestion step. Total RNA from collected bone cell fractions will be subjected to gene
expression profiling by reverse transcripted-polymerase chain reaction (RT-PCR).
1.2.2. Identify load-induced differentially regulated gene expression using trabecular
osteocytic RNA isolated from caudal vertebra of C57BL/6 mice following the administration
of a single and repetitive dose of cyclic mechanical load.
Hypothesis to be tested: A single and repetitive dose of cyclic mechanical loading induces changes
in the expression of osteocytic genes.
A target caudal vertebra will be cyclically compressed via pins inserted into adjacent vertebrae.
Total RNA from trabecular osteocytes will be isolated by the protocol established in Specific Aim
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Chapter 1: Introduction
1.2.1 and their mRNA amplified and analyzed for differential gene expression using cDNA
microarrays. The differential gene expression will be further analyzed to compare transient and
sustained gene expression following a single loading regimen and a repetitive loading regimen
respectively. Genes and molecular pathways will be identified using functional genomics
approaches including advanced bioinformatics tools in close collaboration with the Functional
Genomics Center Zurich. Differentially regulated gene expression of interest will be confirmed by
real-time RT-PCR.
1.3. Outline of the thesis
This thesis is composed of 5 chapters. In order to put the novelty of the proposed research project
into context, chapter 2 will introduce the bone anatomy and physiology, in particular bone
remodeling and the skeletal role of osteocytes as mechanosensors. Relevant aspects of bone cell and
molecular biology relevant loading will be also covered in this chapter. Further, strategies for
finding the genetic determinants related to load-induced bone adaptation in treatment of
osteoporosis will be discussed. This latter point will emphasize the significance of the mouse as a
model for human diseases, demonstrating the potential of this project to yield meaningful advances
in understanding bone adaptation to mechanical stimuli and thus uncovering the skeletal
mechanisms involved in the load-bearing function and their role in osteoporosis. Chapter 3 presents
the development of a robust method for isolating osteocytic RNA from a well-defined trabecular
osteocytic population of a single caudal vertebra to study load-induced molecular events in
osteocytes. This chapter is organized into sections which describe a specific strategy and steps of
the established protocol. Firstly, the developed method isolates intact mRNA from well-defined
trabecular osteocytes using minimal numbers of the animals in the study. The first step in this
process was to physically separate the trabecular bone from the medullar cavity of the target caudal
vertebra without contamination from cortical bone. This initial step is the most important in
determining the yield of the final amount of mRNA. Additional sections present sequential
enzymatic digestion for obtaining a well-defined selective osteoblast/lining cell population,
followed by pulverization of denuded trabeculae and RNA extraction of intact osteocytic RNA. The
accuracy of enzymatic removal of the appropriate cells was confirmed histologically following each
digestion step. Extracted total RNA was then analyzed on quality and quantity with subsequent gene
expression profiling of different bone cell isolates. A minimum of 0.5 ng of total RNA was required
for a single cDNA microarray run. Before attempting to identify load induced gene expression, this
- 17-
Chapter 1: Introduction
protocol was evaluated and optimized by applying it to groups of non-loaded mice. Chapter 4
presents the results of load-induced changes in gene expression in trabecular osteocytes, by the
method developed in chapter 3 and the comparison of differentially regulated genes between single
and repetitive mechanical loads. Functional genomics for the identification of load-regulated
molecular pathways was then outlined and the load-regulated expression of individual genes after
single loading was confirmed by quantitative polymerase chain reaction. Finally, the integration in
Chapter 5 brings together the results and discusses the benefits and limitations of the presented
work, outlining future steps to further advance this field of research.
- 18-
Chapter 1: Introduction
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Guigo R, Guyer M, Hardison RC, Haussler D, Hayashizaki Y, Hillier LW, Hinrichs A, Hlavina
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7. Weinbaum SA, Cowin SC, Zeng YA. 1994 A model for the excitation of osteocytes by
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8. Burger E. and Veldhuijzen JP. 1993 Influence of mechanical factors on bone formation,
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Florida pp. 37–56.
9. Lanyon LE. 1993 Osteocytes, strain detection, bone modeling and remodeling. Calcif Tissue Int
53:S102-106, discussion S106-107.
10. Klein-Nulend J, van der Plas A, Semeins CM, Ajubi NE, Frangos JA, Nijweide PJ, Burger EH
1995 Sensitivity of osteocytes to biomechanical stress in vitro. Faseb J 9(5):441-5.
11. Evans, WJ 1998 Exercise and nutritional needs of elderly people: effects on muscle and bone.
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12. Duncan RL, Turner CH 1995 Mechanotransduction and the functional response of bone to
mechanical strain. Calcif Tissue Int 57(5):344-58.
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13. Biewener AA, Fazzalari NL, Konieczynski DD, Baudinette RV 1996 Adaptive changes in
trabecular architecture in relation to functional strain patterns and disuse. Bone 19(1):1-8.
14. Layne JE, Nelson ME 1999 The effects of progressive resistance training on bone density: a
review. Med Sci Sports Exerc 31(1):25-30.
15. Lean JM, Mackay AG, Chow JW, Chambers TJ 1996 Osteocytic expression of mRNA for c-fos
and IGF-I: an immediate early gene response to an osteogenic stimulus. Am J Physiol 270(6 Pt
1):E937-45.
16. Hillam RA, Skerry TM 1995 Inhibition of bone resorption and stimulation of formation by
mechanical loading of the modeling rat ulna in vivo. J Bone Miner Res 10(5):683-9.
17. Forwood MR, Turner CH 1995 Skeletal adaptations to mechanical usage: results from tibial
loading studies in rats. Bone 17(4 Suppl):197S-205S.
18. Webster D, Morley Pl, van lemthe GH, and Müller R. 2008 A novel in vivo mouse model for
mechanically stimulated bone adaptation – a combined experimental and computational
validation study. Comput Methods Biomech Biomed Engin 11(5):435-41.
19. Webster D, Wasserman E, Weber F, Bab I and Müller R. 2008 Load induced changes in
trabecular and cortical bone are dose dependent in both C57Bl/6 and C3H/Hej mice. Abstracts
30th Annual Meeting ASBMR, Montreal, Canada, J. Bone Miner. Res. 23:S131.
20. Xing W, Baylink D, Kesavan C, Hu Y, Kapoor S, Chadwick RB, Mohan S 2005 Global gene
expression analysis in the bones reveals involvement of several novel genes and pathways in
mediating an anabolic response of mechanical loading in mice. J Cell Biochem 96(5):1049-60.
21. Lau KH, Kapur S, Kesavan C, Baylink DJ 2006 Up-regulation of the Wnt, estrogen receptor,
insulin-like growth factor-I, and bone morphogenetic protein pathways in C57BL/6J osteoblasts
as opposed to C3H/HeJ osteoblasts in part contributes to the differential anabolic response to
fluid shear. J Biol Chem 281(14):9576-88.
22. Palumbo C, Palazzini S, Marotti G. 1990 Morphological study of intercellular junctions during
osteocyte differentiation. Bone 11(6):401–406.
23. Turner CH, Hsieh YF, Müller R, Bouxsein ML, Baylink DJ, Rosen CJ, Grynpas MD, Donahue
LR, Beamer WG 2000 Genetic regulation of cortical and trabecular bone strength and
microstructure in inbred strains of mice. J Bone Miner Res 15(6):1126-31.
24. Robling AG, Turner CH 2002 Mechanotransduction in bone: genetic effects on
mechanosensitivity in mice. Bone 31(5):562-9.
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amplification in osteocyte cell processes. Proc Natl Acad Sci USA 101(47):16689-94.
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Chapter 2: Background
Chapter 2
- 21-
Chapter 2: Background
Background
2.1.
Bone Anatomy
Bone is a specialized connective tissue that together with cartilage constitutes the skeletal
system. These tissues provide the organism with a rigid structure which, with muscle, facilitates
stable locomotion. Like reinforced concrete, the bone matrix is predominantly a mixture of tough
fibers (made of type I collagen) which resist pulling forces, and solid particles (calcium phosphate
as hydroxyapatite crystals) which resist compression. For all its rigidity, bone is by no means an
inert substance but a living organ. Running throughout the hard extra cellular matrix are channels
and cavities occupied by living cells which account for about 15 % of the weight of compact bone.
These cells are engaged in the life long process of remodeling the bone: one class of cells
(osteoclasts) demolishes old bone matrix while another (osteoblasts) deposits new bone matrix in
the interior of the bone (1).
All bony skeletal structures have an external shell made of a dense layer of calcified matrix, referred
to as the cortex or compact bone (2). Their internal space is partially filled with a boney mesh,
referred to as cancellous bone, spongy bone or trabecular bone (Fig. 2.1). The spaces enclosed by
these trabeculae are filled with hematopoietic bone marrow which also fills the trabecular-free
medullary cavity. The periosteal, endosteal and trabecular surfaces are lined with fibroblastic and
osteogenic cells organized in layers, which comprise the periosteum and endosteum. Cortical bone,
which comprises 80% of the skeleton, is dense and compact, has a low turnover rate and a high
resistance to bending and torsion. The function of the cortical bone is to provide mechanical
strength and protection, but it can also participate in metabolic responses, particularly when there is
severe or prolonged mineral deficit. Trabecular bone represents 20% of the skeletal mass but 80%
of the bone surface is found inside the long bones throughout the bodies of the vertebrae, in the
femoral neck and in the inner portions of the pelvis and other flat bones. Trabecular bone is less
dense, more elastic, and has a high turnover rate. Trabecular bone contributes to mechanical
support, particularly in bones such as the vertebrae and femoral neck, and provides the initial
supplies of mineral in acute deficiency states.
- 22-
Chapter 2: Background
1 mm
Endosteum
Periosteum
Marrow cavity
Trabecular Bone
Cortical Bone
Fig. 2.1: The anatomical structure of caudal vertebra (Bab et al. Micro-tomographic Atlas of the Mouse
Skeleton. 2007).
2.1.1. Bone Matrix
Bone matrix mainly consists of type I collagen fibers (consisting of two α1 chains and one α2 chain)
and non-collagenous proteins. Within lamellar bone, the fibers form arches that allow the highest
density of collagen per unit volume of tissue. The lamellae can run parallel to each other (trabecular
bone and periosteum), or be concentric surrounding a channel centered on a blood vessel (cortical
bone Haversian system). Crystals of hydroxyapatite [Ca3(PO4)2(OH)2] are found in, around and
between the collagen fibers and tend to be oriented in the same direction as the collagen fibers. The
role of numerous noncollagenous proteins present in the bone matrix has not been fully explained.
The major noncollagenous protein produced is osteocalcin (bone Gla protein), whose function in
bone is still unknown, osteonectin (phosphoprotein), bone sialoprotein (glycoprotein), osteopontin
and bone morphogenetic proteins which also may play an important role in bone mineralization (4-
- 23-
Chapter 2: Background
6). Biglycan, a proteoglycan, is expressed in the bone matrix and probably positively regulates bone
formation (7).
There are four types of bone cells:
1. Osteoblast - mononucleated cell responsible for bone formation (Fig. 2.2). Osteoblasts do not
function individually but are found in sheaths along the bone surface on the layer of bone matrix
that they are producing. They originate from multipotent stromal stem cells, which have the
capacity to differentiate into osteoblasts, adipocytes, chondrocytes, myoblasts, or fibroblasts (8).
Gene deletion studies have shown that absence of runtrelated transcription factor 2 (Runx2) or of a
downstream factor, osterix, is critical for osteoblast differentiation (9). Toward the end of the
matrix-secreting period, 15% of mature osteoblasts are entrapped in the new bone matrix and
differentiate into osteocytes, whereas some cells remain on the bone surface, becoming flat lining
cells.
Bone formation occurs in three successive phases: the production and the maturation of osteoid
matrix, followed by mineralization of the matrix. In normal adult bone, these processes occur at the
same rate so that the balance between matrix production and mineralization is equal. Initially,
osteoblasts produce osteoid by rapidly depositing collagen. This is followed by an increase in the
mineralization rate to equal that of collagen synthesis. In the final stage the rate of collagen
synthesis decreases and mineralization continues until the osteoid becomes fully mineralized.
Osteoblasts produce a range of growth factors under a variety of stimuli including the insulin-like
growth factors (IGF), platelet-derived growth factor (PDGF), basic fibroblast growth factor (bFGF),
transforming growth factor-beta (TGF- β) and the bone morphogenetic proteins (BMP), (10-14).
These factors regulate bone remodeling in an autocrine/paracrine manner by activating receptors
found on osteoblasts. In turn, the activity of these factors is controlled by systemic endocrine factors
such as the gonadal, parathyroid and pituitary hormones and by the autonomic nervous system (1523).
2. Osteoclast – a giant multinucleated cell up to 100 mm in diameter derives from hematopoietic
cells of the mononuclear lineage (26) and is the bone lining cell responsible for bone resorption. It
is usually found in contact with a calcified bone surface and within a lacuna (Howship’s lacunae) as
a result of its own resorptive activity (Fig. 2.3).
Osteoclasts have abundant Golgi complexes, mitochondria, and transport vesicles loaded with
lysosomal enzymes. They present deep foldings of the plasma membrane in the area facing the bone
matrix (called ruffled border) and the surrounding zone of attachment (called sealing zone).
- 24-
Chapter 2: Background
Lysosomal enzymes such as tartrate-resistant acid phosphatase and cathepsin K are actively
synthesized by the osteoclast and are secreted via the ruffled border into the bone-resorbing
compartment (27).
The process of the osteoclast attachment to the bone surface involves the binding of integrins
expressed in osteoclasts with specific amino acid sequences within proteins at the surface of the
bone matrix (28). After osteoclast adhesion to the bone matrix, αvβ3 integrin binding activates
cytoskeletal reorganization within the osteoclast (29). Attachment usually occurs via dynamic
structures called podosomes. Through their continual assembly and disassembly they allow
osteoclast movement across the bone surface during which bone resorption proceeds. Integrin
signaling and subsequent podosome formation is dependent on a number of adhesion kinases
including the proto-oncogene src (30). Osteoclasts resorb bone by acidification, disintegration of the
hydroxyapatite crystals and proteolysis of the bone matrix and of the hydroxyapatite crystals
encapsulated within the sealing zone.
Osteoclast function is regulated both by locally acting cytokines, mainly macrophage-colony
stimulating factor (M-CSF), receptor activator of NF-kappa B ligand (RANKL) and osteoprotegerin
(OPG) and by systemic hormones (31-39).
3. Bone-lining cells – essentially inactive osteoblasts on the surface of most bones in an adult,
which are responsible for calcium and phosphate exchange in the bone (Fig. 2.3).
Fig. 2.2: Deposition of bone
matrix by osteoblasts:
osteoblasts lining the surface
of bone secrete the organic
matrix of bone (osteoid) and
are converted into osteocytes
as they become embedded in
the matrix. The osteoblasts
themselves are thought to
derive from osteogenic stem
cells that are closely related to
fibroblasts (Alberts et al.
Molecular biology of the cell.
4 ed. 2002).
Osteogenic cell
(osteoblast precursor)
Osteoblast
Osteoid (uncalcified bone
matrix)
Calcified bone matrix
Cell process in canaliculus
Osteocyte
Bone is a dynamic tissue constantly being reshaped by osteoblasts, which build bone and
osteoclasts, which resorb bone (Fig. 2.3).
- 25-
Chapter 2: Background
Fig. 2.3: The remodeling of
compact bone: osteoclasts tunnel
through old bone, while
osteoblasts form new bone
(Alberts et al. Molecular biology
of the cell. 4 ed. 2002).
Quiescent osteoblast
(bone lining cell)
Small blood vessel
Endothelial cell
New bone
Fibroblast
New bone matrix not yet
calcified
Osteocyte
Osteoclast tunneling
through old bone
Osteoblast about to lay down
new bone
4. Osteocyte - a mature osteoblast surrounded by bone matrix, the most abundant cells found in
bone (Fig. 2.2). Even though the metabolic activity of the osteoblast decrease once it is fully
encased in lacunae surrounded by bone matrix, these cells still produce matrix and regulatory
proteins (177). Osteocytes have numerous long cell processes rich in microfilaments that are
organized during the formation of the matrix and before its calcification. They form a network of
narrow canaliculi permeating the entire bone matrix. Osteocyte morphology varies according to cell
age. A young osteocyte has most of the structural characteristics of the osteoblast but a decreased
cell volume. An older osteocyte, located deeper within the calcified bone, exhibits a further
decrease in cell volume and an accumulation of glycogen in the cytoplasm. The osteocytes are
finally phagocytosed and digested during osteoclastic bone resorption (24).
Despite the complex organization of the osteocytic network, the exact function of these cells
remains purely understood. Recent evidence suggests that osteocytes produce sclerostin, a protein
that inhibits bone formation and whose expression is decreased by mechanical stimuli (25).
2.2. Osteocytes as mechanosensors/endocrine and paracrine function
Osteocytes, composing over 90–95% of all bone cells in the adult animal (121), are defined as
cells embedded in the mineralized bone matrix, but clear functions have not yet been ascribed to
these cells, unlike to osteoblasts and osteoclasts. Osteocytes are regularly dispersed throughout the
mineralized matrix within ‘caves’ called lacunae, connected to each other and cells on the bone
surface through slender, cytoplasmic processes or dendrites passing through the bone in thin
- 26-
Chapter 2: Background
‘tunnels’ (100–300 nm) called canaliculi (Fig. 2.4). Not only do these cells communicate with each
other and with cells on the bone surface, but their dendritic processes are also in contact with the
bone marrow (122), implying that osteocytes can communicate with marrow resident cells. One
means for communication with other cell types is through gap junctions, and another is through
release of signaling molecules into the bone fluid that flows through the lacuno-canalicular system.
The most popular theory regarding the major function of osteocytes is that they translate mechanical
strain into biochemical signals between osteocytes and to cells on the bone surface to affect
(re)modeling (123), but this yet remains to be definitively proven. Recent data suggest additional
important functions for osteocytes, such as the regulation of mineral metabolism (124) and the
alteration of the properties of their surrounding matrix (125).
Fig. 2.4. Scanning electron micrograph of isolated osteocytes. (C) Two osteocytes have made contact with
each other via their cell processes after 24 h of culture. (D) Extensive network of flattened osteocytes with
many branched cell processes after 48 h of culture. Scale bar = 10 μm. (Reproduced from J. Bone Miner.
Res. 1992; 7, 389–396 with permission of the American Society for Bone and Mineral Research.)
2.2.1. Osteocytes as mechanosensors directing bone formation and/or resorption
A known key regulator of osteoblast and osteoclast activity in bone is mechanical strain. The
skeleton is able to continually adapt to mechanical loading by adding new bone to withstand
increased amounts of loading, and by removing bone in response to unloading or disuse (reviewed
in (126,127)). Galileo, in 1638, is documented as first suggesting that the shape of bones is related
to loading. Julius Wolff, in 1892, more eloquently proposed that bone accommodates or responds to
strain. The cells of bone with the potential for sensing mechanical strain and translating these forces
into biochemical signals include bone lining cells, osteoblasts, and osteocytes. Of these, the
osteocytes, with their sheer numbers and distribution throughout the bone matrix and their high
- 27-
Chapter 2: Background
degree of interconnectivity, are thought to be the major cell type responsible for sensing mechanical
strain and translating that strain according to the intensity of the strain signals (123).
Various studies have demonstrated load-related responses in osteocytes in vivo, supporting their
proposed role as mechanotransducers in bone. Within a few minutes of loading, glucose-6phosphate dehydrogenase, a marker of cell metabolism, is increased in osteocytes and lining cells
(128). By 2 hours, c-fos mRNA is evident in osteocytes and by four hours, transforming growth
factor-β and insulin-like growth factor-1 mRNAs are increased (129). Additional osteocyte selective
markers, such as E11/gp38, dentin matrix protein 1 (DMP1), MEPE, and sclerostin, are also
regulated by mechanical loading. The DMP-1, is activated in a few hours in response to mechanical
loading in osteocytes in the tooth movement model (130) and in the mouse ulna loading model of
bone formation (131). E11/gp38, a membrane protein that is osteocyte-selective and thought to play
a role in dendrite elongation, is also activated within 4 hours after mechanical load, not only in cells
near the bone surface, but also in deeply embedded osteocytes (132). As detailed below, the
osteocyte specific marker sclerostin, the protein product of the SOST gene, is decreased in response
to anabolic loading
(133).
Anabolic signals that are released within seconds after loading in osteocytes include nitric oxide
(NO), prostaglandins, and other small molecules such as ATP. NO, a short-lived free radical that
inhibits resorption and promotes bone formation is generated within seconds in both osteoblasts and
osteocytes in response to mechanical strain (134). Primary osteocytes and primary calvarial bone
cells have also been shown to release prostaglandins in response to fluid flow treatment, and a
number of studies have suggested that osteocytes are the primary source of these load-induced
prostaglandins (135). In vivo studies have shown that new bone formation induced by loading can
be blocked by the prostaglandin inhibitor, indomethacin (136), and agonists of the prostaglandin
receptors have been shown to increase new bone formation (137).
Another anabolic pathway that appears to be activated rapidly in osteocytes within one hour in
response to load is the canonical Wnt/β-catenin pathway. Johnson and colleagues, discoverers of the
high bone mass (HBM) gene, a mutated low-density lipoprotein receptor-related protein 5 gene
(LRP5) encoding the LRP5 receptor, hypothesized as early as 2002 that LRP5 is a major player in
the way that bone cells respond to mechanical load (138). They reasoned that the HBM mutation
results in a skeleton that is over-adapted in relation to the actual loads being applied, but yet is in
homeostatic equilibrium. They found that wild-type bone experienced 40% greater strain than HBM
bone with the same load (139). Based on these observations in humans and mice, they hypothesized
- 28-
Chapter 2: Background
that the set-point for load responsiveness was lower in the HBM skeleton. Loss of function
mutations in LRP5 result in low bone mass, and mice with mutations in LRP5 do not respond to
mechanical load (140), again supporting the notion that LRP5 is involved in mechanotransduction.
At the most recent annual meeting of the ASBMR, Robling et al. showed that sclerostin, an
inhibitor of the Wnt pathway that binds to LRP5 and that is produced exclusively by mature
osteocytes, decreases 24 hours after loading (133). These investigators proposed that Wnt/β-catenin
is the initiator and SOST/sclerostin is the inhibitor of load-induced new bone formation. Also at this
meeting, Kamel et al. showed that prostaglandin released by bone cells in response to fluid flow can
activate the Wnt/β-catenin pathway independent of LRP5 (141). These investigators suggested that
prostaglandin can bypass the inhibitory effects of sclerostin present in the bone matrix.
Osteocytes may also send signals for bone resorption. Isolated avian osteocytes have been shown to
support osteoclast formation and activation (142), as has the osteocyte-like cell line, MLO-Y4.
However, unlike any previously reported stromal cell lines, MLO-Y4 cells did so in the absence of
any osteotropic factors (143). These cells express RANKL along their dendritic processes and
secrete large amounts of macrophage colony-stimulating factor, both essential for osteoclast
formation. Expression of RANKL along osteocyte dendritic processes, and the capacity of osteocyte
dendritic processes to extend into the marrow space (122) provide a potential means for osteocytes
within bone to interact and stimulate osteoclast precursors at the bone surface. Another means by
which osteocytes can support osteoclast activation and formation is through apoptosis. Osteocyte
apoptosis occurs at sites of microdamage, where the dying osteocyte may send signals to osteoclasts
for targeted removal of bone (144). Investigators found that Bax (apoptotic biomarker) was elevated
in osteocytes immediately at the microcrack locus, whereas Bcl-2 (anti-apoptotic biomarker) was
expressed 1–2 mm from the microcrack, suggesting that damaged osteocytes send signals of
resorption, whereas those osteocytes that do not undergo apoptosis are prevented from doing so by
active protective mechanisms. It is still unclear if signals of resorption sent by dying osteocytes are
the same or different from those sent by viable osteocytes.
The parameters for inducing bone formation or bone resorption in vivo are fairly well-known and
well-characterized. Bone mass is influenced by peak applied strain (145), and bone formation rate is
related to loading rate (146). At bending frequencies of 0.5 to 2.0 Hz, bone formation rates increase
as much as four-fold, while no increase is observed at frequencies lower than 0.5 Hz. When rest
periods are inserted, the loaded bone shows increased bone formation rates when compared to bone
subjected to a single bout of mechanical loading (147). Improved bone structure and strength is
greatest if loading is applied in shorter versus longer increments (148). Therefore, for optimal
- 29-
Chapter 2: Background
anabolic loading, frequency, intensity, and timing of loading are all important parameters. The
major challenge has been to translate these known in vivo parameters of mechanical loading to in
vitro cell culture models.
2.2.2. Mechanisms whereby osteocytes sense mechanical loading
Even though osteocytes are thought to be mechanosensors, there is little conclusive data to show
how mechanical loading is sensed by these cells. One of the more accepted forms of strain is the
flow of bone interstitial fluid driven by extravascular pressure in combination with applied
mechanical loading (149,150). Recently, the first real-time attempts to measure solute transport in
bone through dye diffusion within the lacunar-canalicular system were conducted ex vivo (151).
Fluid flow imposes a shear stress on osteocytes that appears to deform the cells within their lacunae
and the dendrites within their canaliculi (150). Theoretical modeling predicts osteocyte wall shear
stresses resulting from peak physiologic loads in-vivo in the range of 8 to 30 dynes/cm2. However, it
is not clear if the dendritic processes, the osteocyte cell body, and/or cilia are the mechanosensors
(Fig. 2.5).
Fig. 2.5: Cartoon showing
potential ways that an osteocyte
may sense fluid flow shear
stress. (A). Fluid flow shear
stress could perturb tethering
elements between the canalicular
wall and the cell membrane. (B).
Fluid flow shear stress may also
affect the cell body, causing cell
deformation. (C). Fluid flow may
perturb primary cilia leading to
mechanosensation. Both matrix
and cell deformation are also
proposed to play a role in
osteocyte
mechanosensation.
Bonewald LF 2006
A model of strain amplification in osteocyte cell processes has been proposed by Weinbaum and
coworkers (152). One of the requirements of the model is that osteocyte dendritic processes be
tethered to the canalicular wall and anchored to hexagonal actin bundles within the cell processes.
The model predicts that fluid flow through this canalicular space will deform the shape of these
- 30-
Chapter 2: Background
tethering elements, creating a drag force that then imposes a hoop strain on the central actin bundles
inside the osteocyte cell process. This model, however, does not take into account that the dendritic
processes of osteocytes may not always be firmly anchored to their canaliculi. The osteocyte has
been viewed as a quiescent cell until recently, when Dallas and co-workers showed cell body
movement and the extension and retraction of dendritic processes (153). Calvarial explants from
transgenic mice with green fluorescent protein (GFP) expression targeted to osteocytes were used to
dynamically image living osteocytes within their lacunae.
Surprisingly, these studies revealed that, far from being a static cell, the osteocyte may be highly
dynamic. These data suggest that dendrites, rather than being permanent connections between
osteocytes and with bone surface cells, may have the capacity to connect and disconnect. These
studies also partially explain why a protein thought to play a role in dendrite elongation, E11/gp38,
would be regulated by mechanical load in cells embedded in mineralized matrix (132).
Fluid flow shear stress may induce mechanosensation in osteocytes through perturbation of
integrins (154). Integrins, comprised of heterodimers of α and β subunits, are major receptors/
transducers that connect the cytoskeleton to the extracellular matrix (155) and interact with plasma
membrane proteins such as metalloproteases, receptors, transporters, and channels mainly through
the extracellular domain of their α subunits (156). The integrin α5 subunit may act as a tethering
protein that, when perturbed by shear stress, opens hemichannels in osteocytes, allowing the release
of prostaglandin (157).
It has also been proposed that mechanical information is relayed in part by matrix and cell
deformation (158–160). Typical in vivo strains in humans are on the order of 1,200 to 1,900 μE and
were determined using strain gauges that covered an area approximately 1.8 mm by 3.6 mm; this
area would contain thousands of cells and the strains measured are therefore averages.
Microstructural strains measured at or near osteocyte lacunae were up to 3 times greater than the
average strains measured with an external strain gauge (159,160). This suggests that the osteocyte is
subjected to larger strains than those measured on the external bone surface.
Recently, it has been shown that polycystin-1 and 2 (PKD1 and PKD2), known mechanosensory
proteins in the kidney, do play a role in normal bone structure and that cilia do exist on both
osteoblasts and osteocytes (161). Primary cilia clearly function as sensors of odors, light, and
movement, depending on cell type (162). It remains to be determined whether the bone defect in
animals with reduced or defective PKD1 function is due to defective mechanosensory function in
bone cell cilia, as has been shown in kidney epithelial cells. Recently, Jacobs and coworkers
provided preliminary data that loss of cilia resulted in decreased sensitivity to flow (163). It will be
- 31-
Chapter 2: Background
important to determine how a single cilium on an osteocyte cell body can mediate the
mechanosensory functions ascribed to the osteocyte.
In vivo, it has been shown that physiological loading prevents osteocyte apoptosis (164) and,
conversely, that reduced mechanical loading in the tail suspension model increases osteocyte
apoptosis (165). In vitro experiments have shown that fluid flow shear stress inhibits osteocyte
apoptosis induced by serum starvation (166) and that substrate stretching prevents dexamethasoneinduced apoptosis (167). Fluid flow shear stress has recently been shown to prevent both
dexamethasone- and tumor necrosis factor-α-induced apoptosis, and this effect was shown to be
mediated by prostaglandin production (168). Mechanical loading is therefore protective against
apoptosis and this effect is mediated through prostaglandin production. From this reason,
prostaglandin can now be added to the list of potential anti-apoptotic factors for osteocytes.
2.2.3. Osteocytes as regulators of mineralization and mineral metabolism
The osteoid-osteocyte may control deposition of mineral that begins to surround and encase this
cell while it is embedding (169,170). It is also likely that this cell is subjected and responsive to
loading. Mechanosensation may play a role in the process of selection of targeted osteoblasts on the
bone surface to become osteocytes. Osteocytes in cortical bone are orderly and linearly arrayed.
Signals passing from embedded cells to selected cells on the bone surface may be delivered through
gap junctions to select a cell that will maintain this ordered network. Mature osteocytes also have
the capacity to modify their local microenvironment. Glucocorticoid treatment causes mature
osteocytes to enlarge their lacunae and remove mineral from their microenvironment (125).
Osteocytes may be able to modify their microenvironment in response to other factors.
Osteocytes may also play a major role in mineral homeostasis. Genes that are highly expressed in
osteocytes are known regulators of mineralization and mineral homeostasis. The most convincing
evidence that osteocytes are regulators of mineralization comes from studies of SOST/sclerostin.
The SOST gene encodes a protein, sclerostin that is highly expressed in mature (not early)
osteocytes and functions as an inhibitor of bone formation (171). The human conditions of
sclerostosis and van Buchem disease are due to mutations in the SOST gene, and transgenic mice
lacking sclerostin have increased bone mass. It appears that sclerostin is an indirect inhibitor of
BMP, but specifically antagonizes the Wnt pathway (172) as an antagonist of LRP5, a gene shown
to be important as a positive regulator of bone mass (173). Both Wnt/β-catenin and SOST are
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Chapter 2: Background
regulated by mechanical strain in osteocytes, positively and negatively, respectively. Is this one
means by which loading regulates the bone formation and resorption responses?
Deletion or mutation of genes that are highly expressed in embedding osteocytes and mature
osteocytes, such as dentin matrix protein 1 (DMP1) and phosphate-regulating gene with homologies
to endopeptidases on the X chromosome (PHEX), results in hypophosphatemic rickets (144,174).
PHEX is a cell surface membrane metalloendoproteinase and DMP1 is expressed along the
canaliculi of osteocytes. Other players in mineral metabolism include MEPE and FGF23, also
highly expressed in osteocytes (175,176). Therefore, it has been proposed that the osteocyte
network be viewed as an endocrine gland that can regulate mineral metabolism.
DMP1, a promoter of mineralization and mineral homeostasis, and MEPE, an inhibitor of
mineralization, both increase sequentially in response to mechanical load (130). This raises the
question whether mineral metabolism could be regulated by mechanical loading. Another level of
complexity, but a challenge for further investigation.
2.3.
Bone Remodeling
Bone remodeling, a complex process by which old bone is continuously replaced by new tissue,
requires interaction between different cell phenotypes and is regulated by a variety of biochemical
and mechanical factors allowing the maintenance of the shape, quality, and size of the skeleton (3).
This is accomplished through the repairing of microfractures and the modification of structure in
response to stress and other biomechanical forces. This process is characterized by the coordinated
actions of osteoclasts and osteoblasts, organized in bone multicellular units (BMU) that follow an
activation-resorption-formation sequence of events.
In a homeostatic equilibrium, resorption and formation are balanced so that old bone is continuously
replaced by new tissue and adapts to mechanical load and strain (40). In cortical bone the BMU
forms a cylindrical canal about 2,000 µm long and 150–200 µm wide and gradually burrows
through the bone with a speed of 20–40 µm/day. During a remodeling cycle, osteoclasts dig a
circular tunnel in the dominant loading direction (41) and then are followed by several thousands of
osteoblasts that fill the tunnel (42). In this manner, between 2% and 5% of cortical bone is being
remodeled each year. The trabecular bone is more actively remodeled than cortical bone due to the
much larger surface to volume ratio. Osteoclasts travel across the trabecular surface with a speed of
approximately 25 µm/day, digging a trench with a depth of 40–60 µm.
In a remodeling cycle resorption begins with the migration of partially differentiated mononuclear
preosteoclasts to the bone surface where they form multinucleated osteoclasts. After the completion
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Chapter 2: Background
of osteoclastic resorption, there is a reversal phase when mononuclear cells appear on the bone
surface. These cells presumably prepare the surface for new osteoblasts to begin bone formation and
provide signals for osteoblast differentiation and migration. The formation phase follows with
osteoblasts laying down bone until the resorbed bone is completely replaced by new mineralized
matrix. When this phase is complete, the surface is covered with flattened lining cells and a
prolonged resting period begins until a new remodeling cycle is initiated. The stages of the
remodeling cycle have different lengths. In humans, resorption continues for about 2 weeks, the
reversal phase may last up to 4 or 5 weeks, while formation can continue for 4 months until the new
bone structural unit is completely created (46).
2.3.1.
Regulation of Bone Remodeling
The overall integrity of bone appears to be controlled by hormones and many other proteins
secreted by both hemopoietic bone marrow cells and bone cells. There is both systemic and local
regulation of bone cell function:
1) Systemic regulation – gonadal hormones (estrogens and androgens) are the most important
regulators of bone remodeling. The majority of postmenopausal women show a marked decrease in
bone mineral density and high-turnover bone metabolism. This phenomenon leads to
postmenopausal
osteoporosis
(184,185).
Experimentally-induced
estrogen
deficiency
by
ovariectomy in female animals causes similar bone alterations. When estrogen-deficient animals
and postmenopausal women are treated with exogenous estrogen, the decrease in bone mass and
increase in bone turnover are reversed. Estrogens decrease the responsiveness of the osteoclast
progenitor cells to RANKL, thereby preventing osteoclast formation (53). Furthermore, besides
reducing osteoclast life span (54) estrogens stimulate osteoblast proliferation and decrease their
apoptosis. They affect gene coding for enzymes, bone matrix proteins, hormone receptors,
transcription factors, and they also up-regulate the local production of OPG, IGF-I, IGF-II, and
TGF-β (55). This suggests that estrogens have a bone-protective effect. Estrogen effects are
mediated via estrogen receptor α and/or β (ERα, ERβ) and receptor function is species and gender
specific (186). ERα mainly functions in various estrogen target organs (187), however, ERα
knockout (ERαKO) mice exhibit increased bone volume: tissue volume ratios (BV/TV) regardless
of gender (188). ERαKO mice have low-turnover bone metabolism. The numbers of osteoclasts and
osteoblasts are reduced, and both the bone resorption and bone formation rates are slower, as
determined by a bone morphometric analysis. Trabecular bone mineral density (BMD) is increased
in male but not in female ERαKO mice. Testosterone levels are markedly increased regardless of
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Chapter 2: Background
sex. In contrast, ERβKO mice have a higher BV/TV, though BMD is similar to that in wild-type
mice. In brief, female ERKO mice including ERα and ERβ double knockout mice do not exhibit
bone loss characteristic of postmenopausal osteoporosis in humans (189). That estrogen has bone
protective effects in the human is virtually indisputable. Despite studies of bone metabolism in
ERKO mice, however, the mechanism behind these effects has remained elusive up until the
generation of osteoclast-specific ERαKO mice as described below. Estrogen deficiency is also
important in men. The increase in BMD in young men and its decrease with aging is related to
circulating free estrogen, rather than testosterone. There is evidence to suggest that estrogens
regulate bone resorption and that both estrogen and testosterone regulate bone formation in men
(185).
On the other hand, male bone has a higher mineral density and lower risk for fracture or
osteoporosis compared to that of the female (190). The greater strength of male bone has been
attributed to the anabolic effect of androgenic hormones. Androgens are essential for skeletal
growth and maintenance via their effect on androgen receptor, which is present in all types of bone
cells (56). Androgens are synthesized from cholesterol through several enzymatic pathways in
which the side chain of cholesterol is shortened through oxidation from 27 carbons to 19 carbons
(191). In men, androgens are secreted almost exclusively from the testes as testosterone. The
adrenal glands also secrete dehydroepiandrosterone (DHEA), which is a minor androgen that also
serves as a substrate for peripheral aromatization to estradiol (E2). Testosterone is either converted
by 5a-reductase to dihydrotestosterone (DHT), or metabolized to E2 by aromatase, a widely
distributed microsomal cytochrome P450 enzyme. The former pathway amplifies androgen action
locally while the latter pathway diversifies androgen action (191). Hence, enzymatic androgen
activation leads to testosterone acting directly or via its more potent metabolite DHT through the
androgen receptor (AR), or indirectly via aromatization to E2 through the estrogen receptors (ERs).
Thus, testosterone functions as a precursor for peripheral conversion into biologically highly active
hormones. Estradiol, which is thought to play a major role in bone metabolism in men, is largely
synthesized by extratesticular aromatization of circulating testosterone with only a small proportion
of E2 (approximately 15–20%) being directly secreted by the testes (192). Depending on the
relative activity of aromatase, 5a-reductase, and dehydrogenases, and the relative distribution of
ARs and ERs in peripheral target tissues, testosterone and its metabolites may predominantly
activate either the AR or the ER. In bone tissue, the expression of aromatase (193), 5α-reductase
(194), 17beta-hydroxysteroid dehydrogenase (17β-HSD (195)), and 3β-HSD (196) has been
documented, supporting the concept of tissue-specific peripheral activation of gonadal hormones.
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Chapter 2: Background
The AR has been identified inmost bone cells, including osteoblasts (197), osteocytes (198), and
osteoclasts (199). Estrogen action on bone, in men and women, is mediated via ERs. These nuclear
hormone receptors are also expressed in osteoblasts, osteoclasts, and osteocytes (200). Two ERs
have been identified: ERa is predominantly expressed in cells resident in cortical bone, whereas
ERb shows higher levels of expression in cells found in cancellous bone (201). Alternate, nongenomic pathways have also been described in which ARs and ERs modulate transcription
indirectly, via protein–protein interactions. Indeed, AR knockout (ARKO) mice exhibit high bone
turnover with increased bone resorption, which results in reduced trabecular and cortical bone mass
without affecting bone morphology. Bone loss in gonadectomized male ARKO mice is only
partially prevented by treatment with aromatizable testosterone. Examination of primary cultured
osteoblasts and osteoclasts of ARKO mice reveals that AR function is necessary for the suppressive
effects of androgens on osteoclastogenesis (185). Whether the bone-forming osteoblast or boneresorptive osteoclast is the direct target of androgen-AR signaling remains to be clarified.
Parathyroid hormone (PTH) is also thought to play an important role in calcium homeostasis. It
maintains serum calcium concentrations by stimulating bone resorption, increasing renal tubula
calcium reabsorption and renal calcitriol production. PTH stimulates bone formation when given
intermittently and bone resorption when secreted continuously (48). Calcitriol is essential in
enhancing intestinal calcium and phosphorus absorption, and in this way it promotes bone
mineralization. In addition, vitamin D3 possesses important anabolic effects on bone, thus exerting
a dual effect on bone turnover (49). Calcitonin, in pharmacologic doses, mediates loss of the ruffled
border, cessation of osteoclast motility, and inhibition of the secretion of proteolytic enzymes
through its receptor on osteoclasts. This effect, however, is dose limited and its physiologic role is
minimal in the adult skeleton. The growth hormone (GH)/IGF-1 system and IGF-2 are important for
skeletal growth, especially at the cartilaginous end plates and during endochondreal bone formation.
They are among the major determinants of adult bone mass through their effect on regulation of
both bone formation and resorption (50). Glucocorticoids exert both stimulatory and inhibitory
effects on bone cells. They are essential for osteoblast maturation by promoting their differentiation
from mesenchymal progenitors but they decrease osteoblast activity. Furthermore, glucocorticoids
sensitize bone cells to regulators of bone remodeling and they augment osteoclast recruitment (51).
Thyroid hormones stimulate both bone resorption and formation. Thus, bone turnover is increased
in hyperthyroidism and therefore bone loss can occur (52).
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Chapter 2: Background
The discovery that the brain controls bone remodelling has provided a new paradigm for
understanding of neuroskeletal biology. Recently, the discovery of central control of bone mass by
leptin shed light on a novel pathway controlling bone mass: osteoporosis is considered to be not just
a bone disease, but also a ‘neuronal’ disease (202). Successively, other neuropeptides such as
neuropeptideY (NPY), cocaine- and amphetamine-regulated transcript (CART) and, more recently,
neuromedin U (NMU) have been demonstrated to be bone-regulating neuropeptides (203–205).
Leptin is a 16-kDa peptide hormone synthesised by adipocytes that affects appetite and energy
metabolism through its binding to the leptin receptor located in the hypothalamus (206). ob⁄ob mice
that lack functional leptin are obese and sterile (6). In spite of hypogonadism, the most common
cause of osteoporosis, ob ⁄ ob mice and db ⁄db mice that lack a functional leptin receptor display
high bone mass (207). Importantly, ob⁄ob mice fed a low fat diet have a normal weight and high
bone mass (207). Moreover, mouse models of lypodystrophy, such as A-ZIP transgenic mice
expressing a dominantnegative protein inhibiting B-ZIP adipocyte transcription factors or Pparchyp
⁄ hyp mice carrying a hypomorphic mutation at the PPARc2 locus (208), have decreased leptin
serum levels, due to low fat, and display high bone mass. Thus, regardless of their body weight, low
serum leptin level induces an increase of bone mass, demonstrating that a leptin-signalling defect is
the bona fide cause of high bone mass. ob/ob mice have a higher bone formation rate with a
concomitant increase in bone resorption (207). There are two regions in the hypothalamus, namely
arcuate (Arc) nuclei and ventromedial hypothalamic (VMH) nuclei, which are rich in leptin
receptors (209). Destruction of Arc by monosodium glutamate in wild-type mice induces obesity,
but not high bone mass. By contrast, destruction of VMH by gold thioglucose in wild-type mice
recapitulates the bone phenotype of ob⁄ob mice; high bone mass due to an increase in bone
formation. More importantly, i.c.v. leptin infusion to VMH-lesioned ob ⁄ ob mice decreases body
weight, but does not affect bone mass. Conversely, i.c.v. leptin infusion to Arc-lesioned ob⁄ob mice
decrease bone mass, but does not affect body weight. These results suggest that VMH neurones are
necessary for the leptin-dependent central regulation of bone mass (210). Along with its
anorexigenic effect, leptin exerts various physiological roles including sympathetic nervous system
(SNS) regulation (211). For example, the sympathetic tone of ob ⁄ ob mice is low and i.c.v. leptin
infusion increases catecholamine secretion (212). In addition, the stereotactic infusion of leptin to
VMH nuclei, and not other nuclei, induces SNS activation (213). Along with these observations,
many osteoblasts reside next to sympathetic neurones in bone marrow and also express the beta2adrenergic receptors (adrb2) specifically, indicating the interaction of SNS and bone remodelling
(210). Indeed, mice treated with isoproterenol, a betaagonist, display a massive decrease in bone
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Chapter 2: Background
mass and mice that are blocked SNS signalling, either genetically (adrb2-deficient mice or
dopamine b-hydroxylase-deficient mice) or pharmacologically (wildtype mice treated with a betablocker), all exhibit a high bone mass phenotype due to an increase in bone formation (204,210).
These mice are also protected from the inhibition of bone formation by leptin. Thus, SNS is a
major, if not the only, pathway that is responsible for the inhibitory role on bone formation by
leptin. Leptin and SNS also regulate osteoclastic resorption (204). In addition to increased bone
formation, adrb2-deficient mice also display decreased bone resorption and possess fewer numbers
of osteoclasts.
Serotonin is an indoleamine produced in enterochromaffin cells of the duodenum and in
serotonergic neurons of brainstem that does not cross the blood brain barrier (214). Thus, it is de
facto a molecule with two distinct functional identities depending on its site of synthesis: a hormone
when made in the gut and a neurotransmitter when made in the brain (215,216). Study by Yadav et
al. (216) showed that brain-derived serotonin (BDS) promotes bone mass accrual when acting as a
neurotransmitter. The central function of serotonin is mediated through the Htr2c receptor expressed
in ventromedial hypothalamic neurons (VMH). Htr2c_/_ mice are markedly osteopenic before any
metabolic modification is detectable, indicating that serotonin regulation of bone mass occurs
independently of its effects, through Htr2c, on energy metabolism.
Neuropeptide Y receptor (NPY) is expressed in the central and peripheral nervous system and has
been shown to exhibit various physiological actions including food intake regulation. To date, five
receptors (Y1, 2, 4, 5 and 6) have been identified as NPY receptors (217). Of these, Y1 and Y5 are
considered important for appetite regulation through the analysis of knockout mice (217). Y2deficient or hypothalamic specific Y2-deficient mice develop a high bone mass phenotype
accompanied by an increase in bone formation, demonstrating that Y2 signalling affects bone
formation through the CNS (218). Recently, Y1-deficient mice have also been shown to display
high bone mass due to an increase in bone formation (203). However, hypothalamus-specific Y1
deletion does not affect bone mass, suggesting that the nature of Y1 receptor signalling affecting
bone remodelling is peripheral. Interestingly, germline deletion of Y2 significantly reduced the
expression of Y1 in osteoblasts, indicating that high bone mass in Y2-deficient mice may be
attributable to that, at least in part (219). Although Y4-deficient mice have normal bones, Y2⁄Y4
double mutant mice display a higher bone mass and lower serum leptin level than Y2 single mutant
mice, suggesting an indirect effect of bone remodelling through leptin-signalling by Y4 (220). To
date, there is no evidence of the interaction between Y receptor signalling and SNS for bone
remodelling.
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Chapter 2: Background
The cannabinoid system, known to regulate analgesia, appetite and energy expenditure, has also
been shown to regulate bone mass in vivo. There are two cannabinoid receptors: CB1, encoded by
the CNR1 gene, is predominantly expressed in the CNS and SNS, as well as peripheral tissues, and
CB1 is responsible for most of the actions of the CNS with respect to cannabinoid drugs and
endocannabinoids. By contrast, CB2 is more specific for peripheral tissues, including osteoblasts
and osteoclasts. CNR1-deficient mice on an outbred CD1 background exhibit high bone mass with
normal bone formation and resorption (221), whereas CNR1-deficient mice on an inbred C57Bl ⁄6J
background display a low bone mass associated with a decrease in bone formation and an increase
in osteoclast number (222). The molecular basis for this discrepant phenotype is unknown, but it is
interesting that CNR1-deficient mice on a C57Bl ⁄6J background are hypersensitive to i.c.v. leptin,
which explains the low bone mass phenotype, at least in part. By contrast, CNR2-deficient mice
display a low bone mass phenotype with an increase in bone formation and in osteoclast number
(223). The fact that CB2 agonists stimulate osteoclast formation in vitro indicates that these
compounds act directly on osteoclasts (223).
A growing number of studies describing ‘unexpected’ bone phenotypes in mutant mice deficient for
neuropeptides or neurotransmitters have now established a new research area linking skeletal and
neuronal biology.
2) Local regulation - as far as the local regulation of bone cell function is concerned, the recent
discovery of the OPG/RANKL/RANK system, has given a clearer picture regarding the control of
osteoclastogenesis and bone remodeling in general. RANKL, expressed on the surface of
preosteoblastic/stromal cells and a subsets of T-cells (47) binds to RANK on the osteoclast
precursor cells and is critical for the differentiation, fusion into multinucleated cells, activation, and
survival of osteoclastic cells (43). OPG inhibits the entire system by competitively binding to
RANKL (44,45). Macrophage colony-stimulating factor (M-CSF), which binds to its receptor, cFms, on preosteoclastic cells is also necessary for osteoclast development (57).
The opposite phenotypes of OPG overexpression or with RANKL deletion mice (osteopetrosis) and
OPG-deficient or with RANKL overexpression (osteoporosis), have led to the hypothesis that OPG
and RANKL can be the mediators for the stimulatory or inhibitory effects of a variety of systemic
hormones, growth factors, and cytokines on osteoclastogenesis. This has been referred to as “the
convergence hypothesis” in that the activity of the resorptive and antiresorptive agents “converges”
at the level of these two mediators, whose final ratio controls the degree of osteoclast
differentiation, activation, and apoptosis (58).
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Chapter 2: Background
A number of cytokines such as TNF-α and IL-1 modulate this system primarily by stimulating MCSF production and by directly increasing RANKL expression (59). In addition, a number of other
cytokines and hormones exert their effects on osteoclastogenesis by regulating cell production of
OPG and RANKL (60-66). Furthermore, IL-6, a pleiotropic cytokine secreted by osteoblasts,
osteoclasts, and stromal cells, appears to be an important regulator of bone remodeling by
stimulating osteoclastic bone resorption (67) but also by promoting osteoblast generation in
conditions of high bone turnover (68). Recent studies have also suggested that osteoblast-derived
PTHrP promotes the recruitment of osteogenic cells and prevents the apoptotic death of osteoblasts,
thus being an important regulator of bone cell function (69).
Abnormalities of bone remodeling can produce a variety of skeletal disorders, mainly osteoporosis
the most abundant degenerative disease in western societies. The recent advances concerning
systemic and local regulation of bone remodeling have led to new approaches in the diagnosis and
treatment of these disorders. In particular, the newer methods in molecular and cellular biology aid
the definition of the abnormalities in cells of the osteoblastic and osteoclastic lineages that lead to
bone disease and the development of new therapeutic approaches based on a better understanding of
the pathogenetic mechanisms. These involve production of recombinant molecules of cytokines and
their soluble receptors, development of inhibitory peptides, and specific inhibition of key signaling
pathways.
2.4. Mechanical load induced bone adaptation
The skeleton is able to continually adapt to mechanical loading by adding new bone to withstand
increased amounts of loading, and by removing bone in response to unloading or disuse (reviwed in
(126,127)). Galileo, in 1638, is documented as first suggesting that the shape of bones is related to
loading. Julius Wolff, in 1892, more eloquently proposed that bone accommodates or responds to
strain.
Mechanical loading is perhaps the most important single physiological/environmental factor
regulating bone mass and shape. Although the basic form and development of bone are genetically
encoded, their final mass and architecture are governed by adaptive mechanisms sensitive to the
mechanical environment. Mechanical signals are transmitted to bone mainly by muscle contractions
generating strains in the bone matrix (70). Loss of bone (osteoporosis) and muscle strength
(sarcopenia) develop together with increasing age (71); characteristic to osteoporosis is the failure
of structural adaptation by bones to the mechanical environment, which results in increased
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Chapter 2: Background
incidence of fractures in response to physiological loads or minimal trauma. Although age-related
bone loss cannot be ascribed entirely to sarcopenia (71), a growing number of studies in humans
report that resistance training is an effective means of preserving and increasing the mass of both
muscle and bone at all ages (72-74). Likewise, a handful of studies in experimental animals have
demonstrated a mechanical load-induced stimulation of bone formation.
Anatomically, the crucial structural component of all major skeletal load-bearing sites, namely
proximal femur, vertebrae and distal radius, is trabecular bone. During growth increasingly vigorous
mechanical usage increases global bone deposits by enhancing longitudinal growth through the
addition of new spongiosa and new cortex, in addition to stimulating cortical modeling drifts of
increased cortical cross-sectional area. However, in the adult organism, in which modeling drifts are
usually ineffective and cortical bone turnover is relatively low, the effects of vigorous mechanical
usage are targeted mainly to the spongiosa and endosteal cortical surfaces where losses and marrow
cavity expansion are retarded (75). Furthermore, significant gains in trabecular bone mass have
been reported in exercising healthy humans (76). By contrast, decreased mechanical usage results in
increasing numbers of BMUs and high bone turnover, with a clear shift from a balance between
bone resorption and formation towards increased resorption and decreased formation (77).
These observations in humans have been repeatedly supported by experimental work in laboratory
animals, thus confirming Frost’s mechanostat theory (75). This theory defines four mechanical
usage windows, with thresholds defined by minimum effective strains (MES): (i) trivial
(subphysiological) loads which result in a negative, high trabecular bone turnover; (ii) physiological
loads responsible for normal, balanced turnover; (iii) overload, which induces positively balanced
turnover; and (iv) pathological excessive loading, or failure loads, which result in microfractures
and in addition to enhanced lamellar bone formation produce woven bone, apparently as part of the
fracture healing process (75,78). The effect of trivial loads has been confirmed in models employing
immobilization, by methods such as schiatic denervation (79), limb fixation (80), hypogravity (8183) and tail suspension (84,85). Decreased loading also occurs in joint injuries (86), and the effect
on bone in the joint region results in decreased bone volume (87,88), primarily through architectural
adaptation (89). The effect of overloading has been studied using a wide variety of approaches. For
example, increased bone formation indices have been reported in animals forced into excessive
exercising regimens (90). A 6-fold increase has been reported in woven spongiosa formed in vitro
in intermittently pressurized hydraulic bone chambers (91). Woven bone is also produced on
trabeculae in response to extraordinary loading conditions (92-94). Lower load magnitudes produce
increases in trabecular lamellar bone (94-97). Mechanical loading also reduced ovariectomy
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Chapter 2: Background
induced loss of metaphyseal spongiosa (80); at least in rats, the respective signaling and anabolic
effects generated by overloading are parathyroid hormone and estrogen dependent (98-101).
Furthermore, trabecular anabolic bone adaptation can be affected by specific load profiles such as
high frequency, low magnitude vibration (97), and is reflected both in bone architecture (102) and
mechanical strength (103).
Judex et al. (224) have demonstrated that extremely small magnitude forces, induced non-invasively
to the skeleton as whole body vibrations, can be perceived as osteogenic (233). In the proximal tibia
of adult BALB/cByJ mice, for example, 10 min per day of a 45 Hz, 0.3g acceleration (1g =
acceleration on Earth, or 9.8 m/s2) was anabolic to trabecular bone, while disuse was catabolic and
may also suppress bone formation (238). This study reported relation of these mechanically
mediated changes in bone formation rates (BFR) to the expression of a broad set of genes
anticipated to play a role in regulating bone adaptation. The thirteen genes considered all have
critical, but not necessarily unique, tasks in (mechanically induced) bone formation (Cbfa1 (225),
osterix (226), BMP-2 (227), IGF-1 (228), MMP-2 (229), collagen type I (230), integrin b3 (231),
osteonectin 232)) and bone resorption (RANKL(234), iNOS (235), osteopontin (236), MMP-9
(237), cathepsin K (237)). They hypothesized that alterations in load bearing (increase or decrease)
will stimulate differential responses in the activity of these formation and resorption gene
‘‘families,’’ including their temporal expression patterns. These data emphasized that the molecular
events involved in mechanically mediated bone adaptation was both subtle and complex. Further,
the similarity in expression patterns between many distinct genes responding to the catabolic and/or
anabolic signals accentuated an intricate co-dependence of molecular events involved in bone’s
adaptation to mechanical signals.
Another studies by Roling et al. (239) showed how long bones was adapted in response to loading.
Cyclic mechanical loads were applied axially along the ulna of adult rats three times per week for
16 weeks. The rat ulna has a natural curvature in the medial-lateral direction, so axial loads induced
bending of the bone. Under load, the medial surface of the bone was subjected to compressive
stresses and the lateral surface was in tension. The ratio of compressive to tensile stress magnitude
in the loaded adult rat ulna was about 1.5, indicating that the highest stress in the ulna occured at the
medial surface in compression. The pattern of bone formation induced by loading resembles the
stress distribution, with more bone formation where the stresses were highest. The improvement in
bone structure was evidenced by a 69% increase in second moment of area. The ulnar bone strength
of loaded limbs was 64% greater than controls and energy absorbed before fracture increased by
94%, yet the improvement in bone mineral content (BMC) was only a modest 7%. In this animal
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Chapter 2: Background
model, loading induced dramatic improvements in bone biomechanical properties, even with small
changes in BMC. The structural efficiency of the ulna was improved by bone formation,
specifically in highly stressed areas where it was most needed.
A variety of in vitro models have been proposed to study the cellular and molecular mechanisms
involved in the anabolic effect of loading. However, the relevance of these models to the in vivo
situation is equivocal mainly due to the inability for definitive identification of mechano-sensitive
cells (e.g., osteocytes, osteoblasts, lining cells) and the absence of pathways by which loads applied
to the cortical envelope are transferred to trabecular bone cells (e.g., cell and cell attachment
molecule deformation, fluid flow (106)). Still, a few studies have assigned a mechanosensitive role
to osteocytic, periosteal and endosteal cells by demonstrating changes in signalling molecules such
as integrins and the glutamate receptor, tanacin-C, and RoBo-1 (107-110). However, these studies
were carried out in cortical bone only and their relevance to trabecular bone cells remains to be
investigated. It is known that the effect of stresses applied at different rates at an object is largely
determined by the material properties of that object. Low magnitude (<10με) and high frequency
(10–100 Hz) loading can stimulate bone growth and inhibit disuse osteoporosis, while high loading
rates have been shown to increase bone mass and strength after jumping exercises in middle-age
osteopenic ovariectomized rats (249).
For bone cells, Bacabac and colleagues [29–31] have shown that the production of signaling
molecules in response to an in vitro fluid shear stress (at 5 and 9 Hz) and vibration stress (5–100
Hz) correlated with the applied stress rate (241–243). The faster the stress was applied, the stronger
the observed response of the cells (244), suggesting that the bone cellular response to loading and
mechanical properties of the cell are related, which implies that the response of bone cells to
loading is related to cytoskeletal properties. The same group developed a novel application of twoparticle microrheology, for which a 3D in vitro system was devised to quantify the forces induced
by cells on attached fibronectin-coated probes (4μm). The frequency at which the cells generate
forces on the beads is related to the metabolic activity of the cell (245). With this device and using
NO production as a read-out, the material properties of round suspended MLO-Y4 osteocytes and
flat adherent MLO-Y4 osteocytes were characterized. Osteocytes with round suspended
morphology required lower force stimulation in order to show an increase in NO production, even
though they were an order-of-magnitude more elastic compared to flat adherent cells (246).
Apparently, elastic osteocytes seem to require less mechanical forces in order to respond than stiffer
cells (246). In contrast, flat adherent MLO-Y4 osteocytes, primary chicken osteocytes, MC3T3-E1
osteoblasts, and primary chicken osteoblasts all showed a similar elastic modulus of less than 1 kPa
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Chapter 2: Background
(245). This indicates that differences in mechanosensitivity between osteocytes and osteoblasts
might not only be directly related to the elasticity of the cell, but also to other cell-specific
properties, i.e., the presence of receptors or ion channels in the membrane, or how cells change their
material properties in relation to deformation.
Studies addressing load-induced cellular and molecular mechanisms in trabecular bone are rather
few. Compared to cortical bone, the spongiosa is enclosed in the cortical envelope and is
substantially less accessible to cell isolation techniques, particularly in small laboratory animals
such as rats and mice. Similar considerations also apply to the assessment of mechanical load
induced trabecular deformation. Of particular relevance to the present thesis is the structural
modeling of trabecular deformation generated by force applied to the vertebral cortex using microtomographic imaging (μCT) (111) and work emanating from the Chambers group. The latter
investigators have devised a rat model in which the body of the eighth caudal vertebra is subjected
to controlled, atraumatic cyclic compression administered via pins introduced into the bodies of the
seventh and ninth vertebrae (99,112). Using electron microscopy and in situ hybridization they have
recently demonstrated activation of trabecular lining cells and an early (1 h) increase in c-fos and
IGF-I expression in trabecular osteocytes. Apparently, the c-fos response is associated with Ca2+
signaling pathways and integrin binding (113). A late (72 h) increase in transcripts for collagen type
I and osteocalcin was observed in trabecular osteoblasts/lining cells (114-116). While contributing
important information, by and large these data remain incomplete inasmuch as it is very unlikely
that increases in c-fos and IGF-I transcripts comprise the entire inter- and intracellular signaling
cascade evoked by overloading. Nevertheless, these and a few other studies (108,116-118) assigned
for the first time an experimentally supported physiologic role for osteocytes and lining cells, which
the present project proposes to further substantiate and define in mice (119).
Recently, Webster et al. (120,183) devised a mouse tail loading device (CVAD) and investigated
the trabecular bone adaptation by cyclic mechanical stimulation of the fifth caudal vertebrae (C5) of
C57BL/6 (B6) female mice. They reported a genotype dependent dose response in trabecular bone
in several microarchitectural parameters following regular bouts of mechanical stimulation. Mice
were randomly divided into 4 loading groups: 0N (sham-loaded), 2N, 4N and 8N. Using the CVAD
the C5 of all mice were subject to an acute loading regime (3000 cycles, 10 Hz, 3 times a week for 4
weeks). Analysis of microCT image and histomorphometry data revealed that trabecular bone
formation was successfully induced. At trabecular sites global bone volume density (BV/TV)
increased by 25.9% in B68N loading group comparing to sham-loaded (P < 0.05). This was
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Chapter 2: Background
accompanied by a significant global increase of 21.9% (P < 0.001) in trabecular thickness (Tb.Th)
and local significant increases of up to 14.6% (P < 0.001) in trabecular number (Tb.N).
The findings show that trabecular bone in both biological strains is responsive to mechanical
loading and that in terms of absolute and percentage increases, B6 mice are more mechano-sensitive
for a given load. This study has established a mouse model which, for the first time, will allow the
study of load regulated gene expression in both trabecular and cortical bone; furthermore it has also
demonstrated the potential candidacy of B6 and C3H mice for a functional genomics approach at
isolating the mechano-sensitive gene(s) specific to both cortical and trabecular bone.
2.5.
Mouse Genetics
With so many aspects contributing to the strength of bone discovering the genes responsible
presents a sizeable task and cannot be done by studying human biology alone. The study of genetics
in humans is limited to some degree by the tremendous heterogeneity among population, as well as
multiple genetic, heritable and environmental determinants of the target phenotype. If the challenge
is to be realized a more controllable genetic model is required, which is why the mouse has become
such an important tool. The biological similarities between man and mouse make this small animal
the ideal experimental surrogate and via the field of comparative genomics this small animal greatly
increases the likelihood that candidate genes and effective therapies will be found (178).
This section provides an overview about current efforts in mouse genetics. It is adapted from Silver
et al. (179).
Many features of human biology at the cell and molecular levels are shared across the spectrum of
life on earth; our more advanced organism-based characteristics are shared in a more limited
fashion with other species. At one extreme are a small number of human characteristics (brain
functions and behavior) that are shared by no other species or only by primates. But at a step below
there is a whole set of characteristics, which are shared only with mammals. In this context, the
importance of mice in genetic studies was first recognized in the biomedical fields of immunology
and cancer research, for which a mammalian model was essential. Although it has been obvious that
many other aspects of human biology and development should be amenable to mouse models, until
recently, the tools just did not exist to allow for a genetic dissection of these systems.
The movement of mouse genetics to the forefront of modern biomedical research was catalyzed by
the recombinant DNA revolution, which began 30 years ago. With the ability to isolate cloned
copies of genes and to compare DNA sequences from different organisms came the realization that
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Chapter 2: Background
mice and humans as well as all other placental mammals are even more similar genetically than
they were thought to be previously. An astounding finding has been that all human genes have
counterparts in the mouse genome which can almost always be recognized by cross-species
hybridization. Thus, the cloning a of human gene leads directly to the cloning of a mouse homolog
which can be used for genetic, molecular, and biochemical studies that can then be extrapolated
back to an understanding of the function of the human gene. Although the haploid chromosome
number associated with different mammalian species varies tremendously, the haploid content of
mammalian DNA remains constant at approximately three billion base pairs. It is not only the size
of the genome that has remained constant among mammals; the underlying genomic organization
has also remained the same as well. Large genomic segments (on average, 10-20 million base pairs)
have been conserved virtually intact between mice, humans, and other mammals. In fact, the
available data suggest that a rough replica of the human genome could be built by simply breaking
the mouse genome into 130-170 pieces and pasting them back together again in a new order
(180,181). Although all mammals are remarkably similar in their overall body plan, there are some
differences in the details of both development and metabolism, and occasionally these differences
can prevent the extrapolation of mouse data to humans and vice versa (182). Nevertheless, the
mouse has proven itself over and over again as being the model experimental animal par excellence
for studies of nearly all aspects of human genetics.
Besides the strong homology in the genome, among mammals the mouse is ideally suited for
genetic analysis for several other reasons too. First it is one of the smallest mammals known, second
it has a short generation time, in the order of 10 weeks from being born to giving birth. Third,
females breed prolifically in the laboratory with an average of 5-10 pups per litter. Fourth, an often
forgotten advantage is the fact that fathers do not harm their young and that laboratory-bred strains
are relatively docile and easy to handle. Finally, investigators are even able to control the time of
pregnancies.
Manipulation of the mouse genome and micro-analysis
The close correspondence discovered between the genomes of mice and humans would not have
been sufficient to drive researchers into mouse genetics without the simultaneous development,
during the last decade, of increasingly more sophisticated tools to study and manipulate the
embryonic genome. Today, genetic material from any source (natural, synthetic or a combination of
the two) can be injected directly into the nuclei of fertilized eggs; two or more cleavage-stage
embryos can be teased apart into component cells and put back together again in new "chimeric"
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Chapter 2: Background
combinations; nuclei can be switched back and forth among different embryonic cytoplasma;
embryonic cells can be placed into tissue culture, where targeted manipulation of individual genes
can be accomplished before these cells are returned to the embryo proper. Genetically altered live
animals can be obtained subsequent to all of these procedures, and these animals can transmit their
altered genetic material to their offspring.
Progress has also been made at the level of molecular analysis within the developing embryo. With
the polymerase chain reaction (PCR) protocol, DNA and RNA sequences from single cells can be
characterized and enhanced versions of the somewhat older techniques of in situ hybridization and
immunostaining allow investigators to follow the patterns of individual gene expression through the
four dimensions of space and time.
Finally, with the automation and simplification of molecular assays that has occurred over the last
several years, it has become possible to determine chromosomal map positions to a very high
degree of resolution. Genetic studies of this type are relying increasingly on extremely polymorphic
microsatellite loci to produce anchored linkage maps, and large insert cloning vectors, to move from
the observation of a phenotype to a map of the loci that cause the phenotype, to clones of the loci
themselves. All of these techniques provide the scientific community with the ability to search for
answers to the many questions posed. This will invariably lead to more questions, but the potential
is there to elucidate the mechanisms of many diseases and realize effective treatments.
The mouse and osteoporosis
Rodent models for testing hypotheses to skeletal disorders are not new. In fact this is how many
of the established treatments came to market. The overiectomised rat is a well established tool and
was used to test how estrogen deprivation affects the bone remodeling unit. At the forefront of
technology today is the mouse model. Numerous mouse models exist, each of which attempt either
to identify or evaluate candidate genes associated with osteoporosis.
Differential gene expression arrays
The use of cDNA microarrays offers substantial advantage for the study of load-induced
molecular changes and signaling pathways in bone. A DNA microarray is a multiplex technology
used in molecular biology and in medicine.The cDNA microarrays have revolutionized the way in
which gene expression is now analyzed, by allowing the RNA product of thousands of genes to be
monitored at once. By examining the expression of so many genes simultaneously it is now possible
to identify and study gene expression patterns that underlie cellular physiology. For example
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Chapter 2: Background
scientists can now see which genes are switched on (or off) as cells grow, divide or respond to
hormones or toxins. DNA microarrays are little more than glass microscope slides studded with a
large number of DNA fragments, each containing a nucleotide sequence that serves as a probe for a
specific gene. The mostly dense arrays may contain tens of thousands of these fragments in an area
smaller than a postage stamp. These arrays are generated from DNA probes which have been
produced by RT-PCR and then spotted onto the slide by a robot thus the exact sequence and
position of every probe on the array is known. Any nucleotide fragment that hybridizes to a probe
on the array can now be identified as the product of a specific gene simply by detecting the position
to which it is bound.
DNA probes describing
known genes are amplified
using PCR and printed into a
matrix on a glass slide
RNA from sample 2, labeled
with green fluorochrome
Hybridization
RNA from sample 1, labeled
with red fluorochrome
Fig. 2.8: Use of DNA microarrays to monitor the expression of thousands of genes simultaneously (Alberts
et al. Molecular biology of the cell. 4 ed. 2002).
To use DNA microarrays to monitor gene expression RNA from the cells being studied is extracted
and converted to cDNA. The cDNA is then labeled with a fluorescent probe. The microarray is
incubated with this labeled cDNA sample and hybridization is allowed to occur (Fig. 2.8). The array
is then washed to remove cDNA that is not tightly bound, and the positions in the microarray to
which labeled cDNA fragments have bound are identified by an automated scanning-laser
microscope. The array positions are then matched to the particular gene whose sample DNA was
spotted in this location. In figure 2.9 RNA has been collected from two different cell samples for a
direct comparison of their relative levels of gene expression. Theses samples are labeled, one with a
red fluorochrome, and the other with a green fluorochrome. Hence red spots in the hybridized array
indicate that the gene in sample 1 is expressed at a higher level than the corresponding gene in
sample 2. Green spots indicate that expression of the gene is higher in sample 2 than in 1. Yellow
spots indicate that the genes are expressed in equal amounts in both samples while the dark spots
indicate little or no expression in either sample.
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Chapter 2: Background
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223. Ofek O, Karsak M, Leclerc N, Fogel M, Frenkel B, Wright K, Tam J, Attar-Namdar M, Kram
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225. Ontiveros CA, McCabe LR. 2003 Simulated microgravity suppresses osteoblast phenotype,
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226. Nakashima K, Zhou X, Kunkel G, Zhang Z, Deng JM, Behringer RR, de Crombrugghe B.
2002 The novel zinc finger-containing transcription factor osterix is required for osteoblast
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227. Sato M, Ochi T, Nakase T, Hirota S, Kitamura Y, Nomura S, Yasui N. 1999 Mechanical
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228. Kawata A, Mikuni-Takagaki Y. 1998 Mechanotransduction in stretched osteocytes–temporal
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229. Blumenfeld I, Srouji S, Peled M, Livne E. 2002. Metalloproteinases (MMPs-2, -3) are
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230. Moalli MR, Caldwell NJ, Patil P, Goldstein SA. 2000 An in vivo model for investigations of
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231. Weyts FA, Li YS, van Leeuwen J, Weinans H, Chien S.2002. ERK activation and alpha v beta
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232. Pioletti DP, Muller JM, Rakotomanana LR, Corbeil JA, Wild E. 2003. Effect of
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233. Rubin C, Turner AS, Bain SM, Mallinckrodt CA, McLeod K. 2001a. Anabolism: Low
mechanical signals strengthen long bones. Nature 412:603–604.
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235. Watanuki M, Sakai A, Sakata T, Tsurukami H, Miwa M, Uchida Y, Watanabe K, Ikeda K,
Nakamura T. 2002. Role of inducible nitric oxide synthase in skeletal adaptation to acute
increases in mechanical loading. J Bone Miner Res 17:1015–1025.
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237. Rantakokko J, Uusitalo H, Jamsa T, Tuukkanen J, Aro HT, Vuorio E. 1999. Expression
profiles of mRNAs for osteoblast and osteoclast proteins as indicators of bone loss in mouse
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238. Judex S, Donahue LR, Rubin C. 2002. Genetic predisposition to low bone mass is paralleled
by an enhanced sensitivity to signals anabolic to the skeleton. FASEB J 16:1280–1282.
239. Robling AG, Hinant FM, Burr DB, Turner CH. 2002 Shorter, more requent mechanical
loading sessions enhance bone mass. Med Sci Sports Exe 34(2):196-202.
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241. Bacabac RG, Smit TH, Mullender MG et al. 2004 Nitric oxide production by bone cells is
fluid shear stress rate dependent. Biochem Biophys Res Commun 315:823–829.
242. Bacabac RG, Smit TH, Van Loon JJWA et al. 2006 Bone cell responses to high-frequency
vibration stress: does the nucleus oscillate within the cytoplasm? FASEB J 20:858–864.
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prostaglandin E2, by bone cells depends on fluid flow frequency. J Orthop Res 24:1170–1177.
244. Bacabac RG, Smit TH, Mullender MG et al. 2005 Initial stresskick is required for fluid shear
stress-induced rate dependent activation of bone cells. Ann Biomed Eng 33:104–110.
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Chapter 3: Developing a method for isolation of osteocyte RNA
Chapter 3
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Chapter 3: Developing a method for isolation of osteocyte RNA
Developing a method for isolation of osteocyte RNA
Introduction
This was perhaps the most ambitious part of the present thesis, aimed at developing a robust method
for the isolation of representative samples of total RNA derived from well-defined trabecular
osteocytes of single mouse caudal vertebra.
Osteocytes are usually regularly dispersed throughout the mineralized matrix and are connected to
each other and cells on the bone surface through their processes, giving them the potential to recruit
osteoclast precursors to stimulate bone resorption (1-5) and to regulate mesenchymal stem cell
differentiation (6). Osteocytes are thus ideal cellular candidates for initiating biochemical responses
culminating in tissue adaptation during bone growth and remodeling. Osteocytes are generally
agreed to play a role in mechano-adaptation (7-9). Only a few osteocyte-specific proteins have been
described. Recently, dentin matrix protein 1 (DMP1) has been reported to be specifically expressed
only in osteocytes (10). DMP-1 is an acidic non-collagenous protein and is known to be present in
the mineralized matrix of both dentin and bone (10-12). The monoclonal antibody (Mab) OB 7.3
against avian osteocytes has recently been shown to recognize PHEX (phosphate-regulating gene
with homology to endopeptidases on the X chromosome) protein (13). The most compelling
paradigm by which osteocytes influence the function and number of the executive cells of
remodeling is symbolized by SOST/sclerostin. Osteocytes, but no other cells in bone, express
sclerostin – the product of the SOST gene (14,15). As expected for an osteocyte-derived secreted
protein, high levels of sclerostin are detected in the lacunar-canaliculi system (16).
However, osteocytes are still poorly characterized because of their location and the lack of primary
osteocyte isolation methods. Investigations of osteocyte functions are impeded by their difficult
accessibility, especially from trabecular bone, which occupies the critical load-bearing sites of the
skeleton. Compared to cortical bone, the spongiosa is enclosed in the cortical envelope and is
substantially less accessible to cell isolation techniques, particularly in small laboratory animals
such as mice. Apparently, similar considerations also apply to the assessment of mechanical load
induced trabecular deformation. Hence, studies addressing load-induced cellular and molecular
mechanisms in trabecular bone are rather few. Most of the published studies are based on
histological and cytochemical observations of cortical bone sections (17,18). Wong and Cohn
(1974) have developed a sequential enzyme digestion method to isolate various bone cell fractions
from calvarial bones (19). Their main interest was to develop an isolation method for osteoblasts. A
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Chapter 3: Developing a method for isolation of osteocyte RNA
number of studies have subsequently been performed that suggest that osteocytes from calvaria can
be isolated by a series of enzymatic digestions (19-22). Nijweide and Mulder (1986) and van der
Plas and Nijweide (1992) have applied this method to isolate osteocytes from chicken calvaria.
The work presented here is aimed at developing a protocol to isolate trabecular osteocytic intact
RNA from a single mouse caudal vertebra (Fig. 3.1) for detailed characterization of load-induced
changes in osteocyte gene expression.
B
A
0.45 mm
1 mm
Fig. 3.1: Micro-computered tomography (µCT) images of mouse caudal vertebra: (A) Two-dimensional bone structure;
(B) Three-dimensional bone structure. (Bab et al. Micro-tomographic Atlas of the Mouse Skeleton. 2007).
3.1.
Separation of trabecular bone from caudal vertebra
The first step in this process was to physically extract the trabecular bone from the medullar cavity
of the target caudal vertebra without contamination from cortical bone. This step is the most
important in determining the yield of the final amount of total RNA. The method and tools
employed here must therefore maximize the extraction of trabecular bone.
To isolate total RNA from trabecular osteocytes, 12-week old C57BL/6 female mice (Füllinsdorf,
Switzerland) were sacrificed by CO2 inhalation. Immediately, the skin was peeled from the tail and
caudal vertebrae C5-C7 separated individually. The cartilaginous ends of the vertebrae were cut off
and the medullary trabecular bone (total of approximately 8 mm3), which contained bone marrow,
was separated mechanically using a 21 Gauge sterile syringe needle (BD Microlance, Ireland),
syringe of 1 ml volume and flushed into cold RNAlater (Ambion Inc., Austin, Texas) for RNA
preservation. Then, a dental Micro-Drill with a steel burr (Hage&Meisinger GmbH, Germany) was
used in order to collect the remaining trabeculae (Fig. 3.2)
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Chapter 3: Developing a method for isolation of osteocyte RNA
A
B
Fig. 3.2: Perforation by needle (A) a central core of medullary cavity of caudal vertebra. (B) Removal remaining
trabecula by MicroDrill with a burr.
All manipulations were done in a ribonuclease (RNase) free environment to prevent RNase
contamination. The accuracy of trabecular bone mechanical extraction was confirmed
histologically, indicating well-defined separation of the central core of medullary cavity without
contamination by cortical bone (Fig. 3.3).
Histology
In order to confirm histologically the accuracy of trabecular bone extraction, caudal vertebrae were
fixed in neutral buffered formalin, decalcified with 5% EDTA (pH 7.0) for 2–3 days, and embedded
in paraffin. Paraffine sections 5-µm of thickness were performed using microtome for paraffineembedded specimens. For Haematoxylin and Eosin (Sigma-Aldrich, Switzerland) staining: sections
were deparaffinized and rehydrated: 3 x 3 min Xylene, 3 x 3 min 100% ethanol, 1 x 3 min 95%
ethanol, 1 x 3 min 80% ethanol and 1 x 5 min deionized H2O.
Haematoxylin staining: 1 x 3 min Haematoxylin, rinsing in deionized water, 1 x 5 min Tap water (to
allow stain to develop), dipping 8-12 times (fast) acid ethanol (to destain), rinsing 2 x 1 min in Tap
water, rinsing 1 x 2 min in deionized water. Blotting excess water from a slide holder. Eosin
staining and dehydration: 1 x 30 sec Eosin, 3 x 5 min 95% ethanol, 3 x 5 min 100% ethanol
(blotting excess ethanol before going into xylene), 3 x 15 min xylene.
Toluidine Blue staining: 1 x 1 min in Toluidine Blue (Sigma-Aldrich, Switzerland), rinsing in
deionized water for 5 min, dehydration in acetone for 3-5 min. Slides were cover-slipped using
xylene-based Permount (Daigger Inc., USA).
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Chapter 3: Developing a method for isolation of osteocyte RNA
A
Cortical bone
Trabecular bone space
1 mm
B
Harvested trabeculae with bone marrow
125 µm
3.2.
Fig. 3.3: Histological appearance of
(A)
caudal
vertebra
following
mechanical removal of central core of
medullary cavity content, H&E stain.
(B) Harvested central core of
medullary
cavity
content
with
trabecular bone without contamination
of cortical bone, Toluidine Blue stain.
Enzymatic digestion of non-osteocytic cells
Once the trabecular bone was harvested, the cell populations enriched with bone marrow and
osteoblast/lining cells were extracted using collagenase A (Roche, Switzerland), enzmyme that
breaks the peptide bonds in collagen, which is a key component of the animal extracellular matrix
(32). The enzymatic extraction sequence of non-osteocytic cells in the trabecular bone consisted of
the following steps carried out by gently shaking at 4°C: (1) Initial digest, 15 minutes, in 2 mg/ml of
collagenase A in RNAlater. The volume of RNAlater was at least 50-fold greater than the tissue
volume. (2) First extract of osteoblasts/lining cells (OBL1), 30 minutes digest in 3 mg/ml of
collagenase A in RNAlater. (3) Second extract of osteoblasts/lining cells (OBL2), same conditions
as in (2), constituted “Step 3”. At the end of each step the supernatant was collected, centrifuged for
10 min at 12000 xg at 4°C and the cell pellet re-suspended in RNAlater and kept at 4°C for further
use. Some samples of trabecular bone tissue remaining after each step were fixed in phosphate
buffered formalin, decalcified, and processed for histological analysis to confirm the degree of
enzymatic “stripping” of the target cells (see above previous section). Following “Step 3”, the
histological appearance was that of denuded trabeculae that contained histologically intact
osteocytes (Fig. 3.4).
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Chapter 3: Developing a method for isolation of osteocyte RNA
A
B
Lacunae with
osteocytes
Remaining bone marrow and
osteoblast/lining cells
125 µm
125 µm
Fig. 3.4: Histological appearance of (A) medullary central core following initial digest of bone marrow and
osteoblast/lining cells. (B) Denuded trabeculae following Step 3 of sequential collagense digestions without removed
osteoblast/lining cells.
3.3.
RNA extraction from denuded trabeculae, Step 4 – osteocytes (OST)
The remaining fragments of trabecular bone containing osteocyte population, were pulverized by
grinding for 2-3 min in liquid nitrogen using a motorized pestle. The pulverized tissue was quickly
collected into 1 ml of TRIzol Reagent (Molecular Research Center, Inc., Cincinnati, OH) and
homogenised for 30 seconds. Total RNA further prepared using the chloroform-phenol procedure,
according to the manufactures instructions (30).
The detailed protocol for RNA extraction includes the following steps:
Homogenization
Adding 1 ml of TRI Reagent to 1-50 mg of pulverized tissue and vigorous vortexing for 30 seconds.
Sample volume did not exceed 10% of the volume of TRI Reagent used for homogenization.
Phase separation
The homogenate was left for 5 minutes at room temperature to permit the complete dissociation of
nucleoprotein complexes. Next, the homogenate was supplemented with 0.1 ml of Phase Separation
Reagent (BCP, Molecular Research Center Inc.,) per 1 ml of TRI Reagent, the samples covered
tightly and shaken vigorously for 60 seconds. After 5 minutes at room temperature and the mixture
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Chapter 3: Developing a method for isolation of osteocyte RNA
was centrifuged at 12,000 xg for 15 minutes at 4°C. Following centrifugation, the mixture separates
into a lower red phenol-chloroform phase, interphase and the colorless upper aqueous phase. RNA
remains exclusively in the aqueous phase whereas DNA and proteins are in the interphase and
organic phase.
RNA precipitation
The aqueous (upper) phase was then separated and RNA precipitated by adding isopropanol (0.5 ml
of isopropanol per 1 ml of TRI Reagent used for the initial homogenization). After 5 minutes at
room temperature and the samples were centrifuged at 12,000 xg for 8 minutes at 4°C.
RNA wash
The supernatant was then removed and the RNA pellet washed (by vortexing) with 75% ethanol and
subsequent centrifugation at 7,500 xg for 10 minutes at 4°C. Adding at least 1 ml of 75% ethanol
per 1 ml TRI Reagent used for the initial homogenization.
RNA solubilization
The ethanol wash was removed and the RNA pellet briefly air-dried for 3 - 5 min. RNA was
dissolved in 6 μl of RNase-free water by diethyl pyrocarbonate (DEPC) treatment. The RNA thus
obtained fraction was then analyzed for quality and quantity using pico-kit of Agilent Bioanalyzer
2100 (Agilent Technologies, Foster City, CA). Specimens free of DNA and protein (260/280 light
absorbance ratio of 1.6 – 1.9) were selected for RT-PCR, microarray and real-time RT-PCR
analyses (Fig. 3.6).
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Chapter 3: Developing a method for isolation of osteocyte RNA
Flow chart
Rapid separation of caudal vertebra and chopping off its cartilaginous ends
Separation of trabecular bone into cold RNAlater
Initial digestion by Collagenase A (2 mg/ml) in RNAlater for 15 min at 4ºC
Supernatant collection, RNA extraction 1st fraction
Second Collagenase A digestion (3 mg/ml) in RNAlater for 30 min at 4ºC
Supernatant collection, RNA extraction 2nd fraction
Third Collagenase A digestion (3 mg/ml) in RNAlater for 30 min at 4ºC
Supernatant collection, RNA extraction 3rd fraction
Pulverization of “stripped” trabeculae in liquid nitrogen
Collection pulverized tissue and immediate RNA extraction, 4th fraction
Fig. 3.5: Flow chart describing total RNA isolation and purification from bone cell fractions.
The RNA was prepared from the following cell fractions:
1. Initial digest, consisting mainly of bone marrow cells
2. OBL1 fraction, consisting mainly of osteoblasts and bone lining cells
3. OBL2 fraction, comprised of the remaining osteoblast/lining cells
4. OST fraction, consisting of RNA extracted from enzymatically denuded trabeculae
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Chapter 3: Developing a method for isolation of osteocyte RNA
A
B
C
D
E
Fig. 3.6: (A) Example of analysis of intact total RNA sample with identified 18S and 28S ribosomal RNA subunits
peaks and dominating bands on the electropherogram (murine spleen). (B) Analyzed an intact total RNA derived from
Initial digest. (C) Analyzed extracted total RNA derived from OBL1 fraction. (D) Analyzed extracted total RNA
derived from OBL2 fraction. (E) Analyzed an intact total RNA sample derived from OST fraction, with no degradation
evidence.
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Chapter 3: Developing a method for isolation of osteocyte RNA
3.4. Comparative marker gene mRNA expression between enzymatically isolated
cell fractions and extracted RNA from denuded trabecular bone
Reverse transcription/polymerase chain reaction (RT-PCR) analysis
High quality total RNA from bone cell fractions was reverse transcribed into cDNA and amplified
using Qiagen OneStep RT-PCR kit (Qiagen Inc., Valencia, CA). Omniscript and and Sensicript
Reverse Transcriptases which are included in this “one-step” kit provide highly efficient and
sensitive reverse transcription of RNA template quantity from 1 pg to 2 μg. HotStarTaq DNA
Polymerase included in the kit provided hot-start PCR for highly specific amplification.
For RT-PCR reaction 0.45 ng of RNA template was taken from each fraction. A mixture of 2.5 μl
reverse transcriptase, 0.5 μl of dNTP’s mixture (400 μM of each dNTP), 0.5 μl of DNA polymerase,
10 Units RNase inhibitor (Promega, USA), 0.5 μl of each primer (0.6 μM of each primer) and
RNase free water was added for a final volume of 12.5 μl. Thermal cycler conditions: reverse
transcription 50°C for 30 min, initial PCR activation step 95°C for 15 min, 3-step cycling with
denaturation, annealing and extension (time and temperature varied according to the primer) and
final extension 72°C for 10 min. Cycling was carried out 35 times for all primers.
Primer sets used were as follows:
1. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), product size 190 base pairs, sense
primer: 5'-CCT TCA TTG ACC TCA ACT AC-3', antisense primer: 5'-GGA AGG CCA
TGC CAG TGA GC-3'; Denaturation: 94°C (30 sec), annealing: 58°C (30 sec), extension:
72°C (1 min).
2. Tissue non-specific alkaline phosphatase (TNSALP), 373 bp, sense primer, 5'-GCC CTC
TCC AAG ACA TAT A-3', antisense: 5'-CCA TGA TCA CGT CGA TAT CC-3'; 94°C (20
sec), 60°C (30 sec); and 72°C (40 sec).
3. Osteocalcin, 371 bp, sense: 5'-CAA GTC CCA CAC AGC AGC TT-3', antisense: 5'-AAA
GCC GAG CTG CCA GAG TT-3'; 94°C (30 sec), 58°C (30 sec); and 72°C (1 min).
4. Phosphate-regulating gene with homology to endopeptidases on the X chromosome
(PHEX), 414 bp, sense: 5'-GCT TGA GCA AAA AGC CTG CC-3', antisense: 5'-ACC
AGG GTG CCA CCA ATA AAC-3'; 94°C (1 min), 55°C (1 min) and 72°C (1 min).
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Chapter 3: Developing a method for isolation of osteocyte RNA
5.
Osteoblast/osteocyte factor gene (OF45), 482 bp, sense: 5'-ACT ATC CAC AAG TGG
CCT CG-3', antisense: 5'-CTG TTG GCT TGC TCA GTT CC-3'; 94°C (1 min), 55°C (1
min) and 72°C (1 min).
6. GLAST-1 (384 bp) sense: 5'-TCA ATG CCC TGG GCC TCG TTG T-3'; antisense: 5'-GGG
TGG CAG AAC TTG AGG AGG-3'; 94°C (30 sec), 58°C (30 sec) and 72°C (1 min).
7. DMP-1, 395 bp, sense: 5'-CGG CTG GTG GAC TCT CTA AG-3', antisense: 5'-CGG GGT
CGT CGC TCT GCA TC-3'; 94°C (30 sec), 55°C (30 sec) and 72°C (1 min).
8. Mechno-growth factor (MGF), 353 bp, sense: 5'-GCT TGC TCA CCT TCA CCA GC-3',
antisense: 5'-AAA TGT ACT TCC TTT CCT TCT C-3'; 94°C (30 sec), 55°C (45 sec) and
72°C (1 min).
9. SOST/Sclerostin (185 bp) sense: 5'-TCC TCC TGA GAA CAA CCA GAC-3', antisense: 5'TGT CAG GAA GCG GGT GTA GTG-3'; 94°C (30 sec), 55°C (45 sec) and 72°C (1 min).
The oligonucleotide primer sets used crossed intron/exon boundaries so that eventual
contaminations with genomic DNA would not be amplified in the amplification process or would
generate amplicons of larger size. To display amplicons, aliquots of 10 μl of RT-PCR products were
blotted and separated by 1% Agarose gel electrophorosis.
Immunohistochemistry
In order to confirm obtained results from gene expression profiling, protein expression analysis was
performed using antibodies against alkaline phosphatase (ALP), specific to osteoblasts, and
antibodies against DMP-1, specific for osteocytes.
Cryosectioning of undecalcified bone: after surgical removal, caudal vertebrae were immediately
snap-frozen in liquid nitrogen. Bone samples were stored in -80°C. Preceding cryosectioning, bone
samples were embedded in Optimal Temperature Cutting (O.C.T) compound (Sakura, Tissue-Tek).
Cryosections of 8-µm thickness were prepared using the cryomicrotome at a temperature set at 24°C, providing an optimal operating temperature for the CTS. Adhesive tape from CryoJane®
Tape-Transfer system (Instrumedics, USA) was fixed on the bone specimen, serving as an antiroll
device and supporting sectioning performed with a tungsten carbide blade. The still frozen bone
sections adhering to the tape were then transferred to 4x adhesive-coated slides, fixed by physical
pressure. Cryosections were stored at -80°C. Before use, slides were UV-flashed (for duration of 15
seconds) to polymerize the adhesive.
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Chapter 3: Developing a method for isolation of osteocyte RNA
Toluidine blue staining: Bone cryosections were immediately fixed in pre-cooled (-20°C) 70%
ethanol for 2 minutes and washed in diethyl pyrocarbonate (DEPC)-treated water. The sections
were stained in Toluidine Blue for 15 seconds followed by washing in DEPC-treated water and
differentiating in 70% ethanol for 1 minute. Cryosections were dehydrated by increasing grades of
ethanol from 70% to 100% for 1 minute each at -20°C. Dehydration was completed by xylene
incubation for 1 minute at 4°C. Finally, sections were allowed to dry in a dessicator at 47°C for 2-3
minutes each.
Slides were stored at -80°C and allowed to equilibrate to room temperature. Following immersion in
PBS (without Ca2+/Mg2+) at room temperature, slides were placed inside a humid chamber. Sections
were then incubated with 0.025% Trypsin solution and incubated for 15 minutes at room
temperature. Following a rinse with PBS, blocking solution with 10% rabbit serum in PBS was
applied to the sections. Sections were incubated for 1 hour at room temperature. Primary antibody
solution was then added to the samples: 2 µg/ml of ALP goat anti-mouse (AbD Serotec, Düsseldorf,
Germany) and 2 µg/ml of DMP-1 rabbit anti-mouse (Takara Bio, Otsu, Japan). Sections were
incubated in a humid chamber at 4°C overnight. Following incubation, antibody slides were rinsed
in PBS. Fluorescent secondary antibody solution was then added: Alexa488 donkey anti-goat
(1:1000 dilution) (Interchim) and Alexa647 donkey anti-rabbit (1:1000 dilution) (Interchim).
Sections were incubated for 45 minutes. Sections were rinsed with PBS and slides were mounted in
anti-bleach mounting medium and stored at 4°C.
Results: RT-PCR and immunohistochemistry
RT-PCR analysis revealed (Fig. 3.7) the strong presence of TNS-ALP mRNA transcripts in Steps 13 and weak presence of TNS-ALP mRNA in Step 4 (OST fraction). OC mRNA was identified in all
steps. Also, the analysis showed the presence of PHEX, GLAST-1, OF45, DMP-1 and SOST
transcripts in Steps 2 and 4; additionally PHEX, OF45 and SOST transcripts were also presented in
Step 3. These results suggest that steps 2 and 3 were highly enriched with osteoblasts. In this system
SOST, DMP-1, MGF and GLAST-1 are specific for osteocytes (10, 26,27,31). Their markedly
increased levels in step 4, together with the very low TNS-ALP expression in this preparation
indicate that the OST fraction consists mainly of RNA from trabecular osteocytes. These data
demonstrate the feasibility of measuring load-induced change in gene expression in trabecular
osteocytes.
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Chapter 3: Developing a method for isolation of osteocyte RNA
Genes
Cell fractions
GAPDH (190 bp)
Osteocalcin (371)
TNS-ALP (373)
PHEX (414)
OF45 (482)
GLAST-1 (384)
DMP-1 (395)
Fig. 3.7: RT-PCR analysis of sequential
digests from caudal vertebral trabecular bone.
GAPDH was used as a loding control.
Cycling was carried out 35 times for all
primers.
MGF (353)
SOST (185)
LAD
Initial
digest
OBL1 OBL2 OST
Immunohistochemistry of bone cryosections ALP and DMP-1 protein expression has shown that
ALP protein, specific for osteoblasts, was primarily expressed on the periphery of trabeculae,
whereas DMP-1, specific for osteocyte population, was expressed ubiquitously throughout the
extracellular matrix of trabeculae (Fig. 3.8). Immunohistochemical observations have confirmed our
results of gene expression profiling of different bone cell fractions, indicating differential isolation
RNA from trabecular osteoblast/lining cells and osteocytes.
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Chapter 3: Developing a method for isolation of osteocyte RNA
A
Trabecular bone
B
Intact trabecular bone
Cortical bone
Soft tissues
25 μm
50 μm
C
Osteocyte, expressing DMP-1
Osteoblasts, expressing TNSALP
25 μm
Fig. 3.8: Immunohistochemical analysis of intact trabecular bone of caudal vertebra. (A) An overview of cryosection of
caudal vertebra with intact trabecular bone, Toluidine Blue staining. (B) Intact trabecular bone of caudal vertebra,
without staining. (C) TNSALP protein expression (green) and DMP-1 protein expression (red) in trabecular bone.
Interpretation of Results
The diversity of cell types present in bone tissue makes it difficult to assess the functional role of
each cell type separately. Osteocytes in particular are barely accessible because they are confined in
the calcified matrix and their isolation is problematic for ex-vivo and in-vitro studies, as they are
terminally differentiated, and only limited information can be obtained by using transformed cell
lines. Some reports are available giving methods for the enrichment of osteocytes from calvarial
bones and their culture (22,24), but here we report, for the first time, that total RNA from well-
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Chapter 3: Developing a method for isolation of osteocyte RNA
defined trabecular osteocytes can also be isolated and potentially used for further functional
genomics studies.
Histology and gene expression profiling in the various cell fractions revealed that three main types
of cells were obtained from trabecular bone after careful removal of the trabeculae and bone
marrow elements. On the basis of histology, our assumption was that they represented fibroblasts
(or pre-osteoblasts), osteoblasts, and osteocytes. This hypothesis was supported using OF45 primers
for gene expression profiling, where OF45 was highly expressed only in osteoblast/osteocytes
fractions, but not in the initial digest (mainly bone marrow cells). Further, we have shown by RTPCR and immunohistochemical analysis for TNS-ALP and DMP-1 mRNA transcripts in
sequentially isolated digests and protein expression, that osteoblasts have high TNS-ALP activity,
but with their osteogenic differentiation TNS-ALP activity is decreasing (25). On the other hand,
osteocytes express low levels of TNS-ALP but have high activity in DMP-1 expression (22). We
concluded that the RNA we obtained in the final fraction was mainly from osteocytes. This
assumption was further supported by the following observations.
DMP-1, a member of the SIBLING family of acid phosphoproteins, is expressed in teeth and bone.
DMP-1 mRNA and protein are highly and selectively expressed in osteocytes in which the protein
is localized along dendritic processes (10). DMP-1 can thus be considered as a specific marker for
osteocytes. Our results with respect to DMP-1 mRNA expression in the last fraction were in
accordance with our histological data and confirmed the enrichment of osteocytes.
SOST is strongly expressed in osteocytes within bone and is structurally most closely related to the
DAN/cerberus family of BMP antagonists (26). In our experiments, SOST mRNA expression had a
similar pattern to DMP-1, supporting the idea that the isolated cells in the last fraction were mainly
osteocytes and not osteoblastic cells.
Additionally, RT-PCR analysis revealed strong MGF expression only in the final fraction, the
muscle insulin-like growth factor-I (IGF-I) mRNA splice variant (IGF-IEc) which has been
identified in rodents. IGF-IEc or mechano growth factor (MGF) has been found to be up-regulated
by exercise or muscle damage and might be a very promising target for investigation of anabolic
response on bone tissue.
As our purpose was to obtain RNA purely from trabecular osteocytes, we developed a method to
delete as many bone marrow cells and osteoblasts as possible during seqential collagenase
digestions. In conclusion, this method will allow the extraction of RNA well-defined
osteoblast/lining cells populations and osteocytes from cancellous bone for subsequent detailed
load-induced molecular characterization.
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Chapter 3: Developing a method for isolation of osteocyte RNA
Preliminary trials for developing a method for isolation of osteocyte RNA
Different methods have been tried in the preliminary studies of this thesis to obtain a sufficient
amount and a high quality of RNA transcripts from trabecular osteocytes for downstream cDNA
microarrays which unfortunately were unsuccessful. These approaches included: (i) using a
decalcification agent such as chemical compound ethylenediaminetetraacetic acid (EDTA)
combinied with an application of proteolytic enzyme cathepsin K (Calbiochem) for resorption of
bone matrix (33,34); (ii) a laser-captured microdissection (LCM) technique to harvest a pure
trabecular osteocyte population from undecalcified bone cryosections.
Both methods did not yield sufficient qualitative and quantitative RNA extracts from trabecular
osteocytes due to the degradation effect of decalcification reagent (treatment by EDTA) and timeconsuming incubations during sequential digestions at 37ºC of trabecular content to an RNA
integrity of 18S and 28S subunits. Additionally, the LCM approach did not reveal the presence of
RNA transcripts derived from captured osteocytes due to technical problems in obtaining
qualitatively intact RNA from a very low amount of starting cellular material in trabecular bone
cryosections. The quality and quantity of total RNA were determined by an Agilent 2100
Bioanalyzer which showed a total degradation of extracted RNA (3.9).
Fig. 3.9: Bioanalyzer information sheet of
RNA sample derived from trabecular
osteocytes, with evidence for mRNA
degradation.
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Chapter 3: Developing a method for isolation of osteocyte RNA
Flow Chart
Rapid separation of caudal vertebra and chopping off its cartilaginous ends
Separation of trabecular bone into α-MEM full medium
Initial two digestion by Collagenase A (2 mg/ml) in RNAlater for 7 min each at 37ºC
Supernatant removal
Second Collagenase A digestion (3 mg/ml) in RNAlater for 30 min at 37ºC
Supernatant removal
Third Collagenase A digestion (3 mg/ml) in RNAlater for 30 min at 37ºC
Supernatant removal
Three cycles, each consisting of:
a) demineralization by 10 mM of EDTA in RNAlater at 37ºC for 30 min
b) matrix digestion with collagenase A (3 mg/ml) and cathepsin (200 nM) K in RNAlater at 37ºC for 1 hour
Collection and pooling into RNAlater, osteocytic population
RNA extraction using RNAeasy Mini kit (Qiagen Inc.)
Fig. 3.10: Flow chart describing trials of osteocytic RNA extraction by sequential collagenase digestions.
Laser Capture Microdissection experiments:
LCM was used to isolate highly pure cell populations from a heterogenous tissue section via direct
visualization of the cells. There are two general classes of laser-capture microdissection: IR capture
systems and ultraviolet cutting (UV) systems. In this study, a UV cutting system was used. The UV
cutting system included a UV laser microdissection and catapulting (P.A.L.M. Microlaser
Technologies GmbH). The principle components of laser microdisection technology are (i)
visualization of the cells of interest via microscopy (ii) photo volatilization of cells surrounding a
selected area and (iii) removal of cells of interest from the heterogeneous tissue section (35).
Cryosectioning:
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Chapter 3: Developing a method for isolation of osteocyte RNA
After surgical removal, bone specimens were immediately snap-frozen in liquid nitrogen. Bone
samples were stored in -80°C. Preceding cryosectioning, bone samples were embedded in Optimal
Temperature Cutting (O.C.T) compound (Sakura, Tissue-Tek). Cryosections of 4 µm and 8 µm of
thickness were prepared using the cryomicrotome at a temperature set at -24°C, providing an
optimal operating temperature for the CTS. Adhesive tape was fixed on the bone specimen, serving
as an antiroll device and supporting sectioning performed with a tungsten carbide blade. The still
frozen bone sections adhering to the tape were then transferred to 4x adhesive-coated slides, fixed
by physical pressure. Cryosections were stored at -80°C. Before use, slides were UV-flashed (for
duration of 15 seconds) to polymerize the adhesive.
Fixation and staining:
Bone cryosections were immediately fixed in precooled (-20°C) 70% ethanol for 2 minutes and
washed in diethyl pyrocarbonate (DEPC)-treated water. The sections were stained in Toluidine Blue
for 15 seconds and then washed in DEPC-treated water and differentiated in 70% ethanol for 1
minute. Cryosections were dehydrated by increasing grades of ethanol from 70% to 100% for 1
minute each at -20°C. Dehydration was completed by xylene incubation for 1 minute at 4°C.
Finally, sections were allowed to dry in a dessicator at 47°C for 2-3 minutes each.
Total RNA isolation from microdissected cryosections:
The sample was collected into a volume of Buffer RLT. The sample and Buffer RLT were
transferred into a larger reaction vessel. The sample volume was adjusted to 75 µl. 20 ng of carrier
RNA was added to the lysate before homogenization. The solution was then vortexed for 30
seconds. 75 µl of 70% ethanol was then added to the homogenized lysate, and mixed by pipetting.
The sample was then added to an RNeasey MinElute Spin Column in a 2 ml collection tube. The
tube was centrifuged and the flow-through was discarded. 10 µl DNase stock solution was added to
70 µl Buffer RDD. 350 µl Buffer RW1 was pipetted into the RNeasy MinElute Spin Column and
centrifuged for 15 seconds at 10’000 xg. The flow-through was discarded. The RNeasy MinElute
Spin Column was then transferred into a new collection tube and 500 µl Buffer RPE was pipetted
onto the RNeasy MinElute Spin Column. The tube was centrifuged for 15 s at 10000 RPM.
Five hundred µl of 80% ethanol was added to the RNeasy MinElute Spin Column. The tube was
closed gently and centrifuged for 2 min at 10000 RPM to dry the silica-gel membrane. The flowthrough was discarded. The RNeasy MinElute Spin Column was transferred to a new collection
tube. The cap of the spin column was open and centrifuged at full speed for 5 minutes. The flowthrough was discarded. Finally, the spin column was transferred to a new 1.5 ml collection tube and
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Chapter 3: Developing a method for isolation of osteocyte RNA
14 µl of RNase-free water was pipetted directly onto the center of the silica-gel membrane. The tube
was then centrifuged for 1 minute at maximum speed to elute. Finally, the quality and concentration
of RNA was determined by an Agilent 2100 Bioanalyzer (Fig. 3.11).
Fig. 3.11: Bioanalyzer information sheet of
RNA sample from laser-microdissected
trabecular osteocytes, with no evidence for
presence of mRNA transcripts.
Possible causes of RNA degradation
It is possible that not enough material was microdissected to perform RNA isolation. One recent
review on the use of LCM in the context of RT-PCR cited the efficiency to be 18% (36). Thus, in
future studies more material should be microdissected to obtain a sufficient quantity of total RNA
for downstream RT-PCR analysis. Trabecular bone has a large surface area (67% of bone surface)
and thus is more prone to contamination by ubiquitous RNAses than cortical bone (37). Moreover,
by sectioning the bone with 4- and 8 μm thick slices, the internal structure of the cells were exposed
(38). These two factors may have further contributed to RNA degradation. The role of cryosection
thickness should be assessed regarding the degree of RNA preservation.
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Chapter 3: Developing a method for isolation of osteocyte RNA
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4. Zhao S, Zhang YK, Harris S, Ahuja SS, Bonewald LF. 2002 MLO-Y4 osteocyte-like cells
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Hul, W. 2001 Hum. Mol. Genet. 10:537–543
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15. Balemans W, Patel N, Ebeling M, Van Hul E, Wuyts W, Lacza C, Dioszegi M, Dikkers FG,
Hildering P, Willems PJ, Verheij JB, Lindpaintner K, Vickery B, Foernzler D, and Van Hul W.
2002 J. Med. Genet. 39:91–97
16. Staehling-Hampton K, Proll S, Paeper BW, Zhao L, Charmley P, Brown A, Gardner JC, Galas
D, Schatzman RC, Beighton P, Papapoulos S, Hamersma H, and Brunkow ME. 2002 Am. J.
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17. Marotti G, Farneti D, Remaggi F, Tartari F 1998 Morphometric investigation on osteocytes in
human auditory ossicles. Anat Anz 180:449–453.
18. Palumbo C, Ferretti M, Marotti G 2004 Osteocyte dendrogenesis in static and dynamic bone
formation: an ultrastructural study. Anat Rec 278A:474–480.
19. Wong G, Cohn DV 1974 Separation of parathyroid hormone and calcitonin-sensitive cells from
non-responsive bone cells. Nature 252:713–715.
20. Hefley T, Cushing J, Brand JS 1981 Enzymatic isolation of cells from bone: cytotoxic enzymes
of bacterial collagenase. Am J Physiol 240:C234–C238
21. Mikuni-Takagaki Y, Kakai Y, Satoyoshi M, Kawano E, Suzuki Y, Kawase T, Saito S 1995
Matrix mineralization and the differentiation of osteocyte-like cells in culture. J Bone Miner Res
10:231–242.
22. Plas A van der, Nijweide PJ 1992 Isolation and purification of osteocytes. J Bone Miner Res
7:389–396.
23. Nijweide PJ, Mulder RJ 1986 Identification of osteocytes in osteoblast-like cell cultures using a
monoclonal antibody specifically directed against osteocytes. Histochemistry 84:342–347.
24. Hefley TJ 1987 Utilization of FPLC-purified bacterial collagenase for the isolation of cells from
bone. J Bone Miner Res 2:505–516.
25. McCarthy TL, Centrella M, Canalis E 1988 Further biochemical and molecular characterization
of primary rat parietal bone cell cultures. J Bone Miner Res 3:401–408.
26. Winkler DG, Sutherland MK, Geoghegan JC, Yu C, Hayes T, Skonier JE, Shpektor D, Jonas M,
Kovacevich BR, Staehling-Hampton K, Appleby M, Brunkow ME, Latham JA 2003 Osteocyte
control of bone formation via sclerostin, a novel BMP antagonist. EMBO J 22:6267–6276.
27. Iida K, Itoh E, Kim DS, del Rincon J, Soschigano K, Kopchick J, Thorner M. 2004 Muscle
mechano growth factor is preferentially induced by growth hormone in growth-hormonedeficient lit/lit mice. J Physiol 560(2):341-349.
28. Gu G, Nars M, Hentunen T, Metsikkö K, Väänänen HK 2006 Isolated osteocytes express
functional gap junctions in vitro. Cell Tissue Res 323:263-267.
29. Cheng B, Kato Y, Zhao S, Luo J, Sprague E, Bonewald LF, Jiang JX 2001 PGE(2) is essential
for gap junction-mediated intercellular communication between osteocyte-like MLO-Y4 cells in
response to mechanical strain. Endocrinology 142:3464–3473.
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30. Chomczynski P 1993 A reagent for the single-step simultaneous isolation of RNA, DNA and
proteins from cell and tissue samples. BioTechniques, 15:532-537.
31. Skerry TM, Suva LJ. 2003 Investigation of the regulation of bone mass biomechanical loading:
from quantitative cytochemistry to gene array. Cell Biochem Funct 21:223-229.
32. Tortora GJ, Funke BR and Case CL. 2007 Microbiology: an introduction brief addition. Pearson
Benjamin Cummings. ISBN 03-213-960-30.
33. Harris DC. 2007 Quantitative Chemical Analysis. 7th ed., F.H. Freeman and Compagny, New
York.
34. Troen BRL. 2006 The regulation of cathepsin K gene expression. Ann. N. Y. Acad. Sci. 1068:
165–72.
35. Caler WE, Carter DR. 1989 Bone creep-fatigue damage accumulation. Journal of Biomechanics
22:625-635.
36. Innis M. 1999 PCR applications protocols for functional genomics. San Diego: Academic press.
566.
37. Corwin SC. 2001 Bone mechanics Handbook. Boca Raton, FL: CRS Press.
38. Sakura. LCM for the histology technician. Histologic, 2006.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Chapter 4
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Load-induced differential regulation mRNA of trabecular osteocytes
Hypothesis to be tested: A previous Doctoral Thesis emanating from our team has reported an
increase in trabecular bone formation following the administration of well-defined multiple doses of
mechanical loading. It has been also shown, in the case of individual genes, that their expression in
osteocytes is modulated by applying mechanical loads to bone. Hence, we hypothesized that single
and multiple doses of cyclic mechanical loading induce changes in the expression of osteocyte gene
clusters involved in inter- and intracellular signaling, as well as structural genes.
The technique for mRNA isolation from trabecular osteocytes developed in the previous chapter
provided the basis for further studies aimed at elucidating global molecular events involved in the
trabecular adaptation to different regimes of mechanical load, using cDNA microarrays. It is
rationalized that a gross gain in bone density consequent to multiple load dosing represents the
cumulative effect of repetitive single doses. Therefore, to substantiate the above hypothesis this
study investigated how a single loading dose, as well as repeated daily doses, differentially affects
global gene expression in trabecular osteocytes.
4.1. Single mechanical loading
The C57BL/6 mouse model and mechanical loading apparatus
In order to facilitate the investigation of the molecular events involved in trabecular bone formation
a Caudal Vertebrae Axial compression Device (CVAD) has been developed by D. Webster et al.
2008 to mechanically stimulate the fifth caudal vertebrae (C5) of C57BL/6 (B6) female adult mice,
via two stainless steal pins inserted into caudal vertebrae C4 and C6. The CVAD is able to apply an
uniaxial, cyclical, compressive force to the fifth caudal vertebrae (C5) of C57BL/6 female mice via
pins (0.5 mm diameter) inserted into the adjacent caudal vertebrae.
A closed-loop feedback device complete with a graphical user-interface has been designed to apply
a precisely controlled, cyclical, compressive load to the C5 vertebra in the mouse tail at a frequency
of 10 Hz via two pins surgically inserted into C4 and C6 vertebrae (Fig. 4.1). The device is
controlled via LabView 7.0 software (National Instruments) installed on a desktop computer which
communicates with a servo control board (NI-7344 National instruments). Following amplification
by a signal amplifier (MID-7654 National instruments), the servo board outputs a signal to a linear
electro-magnetic actuator (LA25-42-000A, Bei–Kimco Magnetics). Compression of C5 is achieved
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
by using the actuator to drive a shaft, mounted on linear bearings, connected to the distal-most pin.
The proximal pin is clamped such that the only positive and negative translations along the axis of
compression are permitted. The control system is closed by a load cell (13/2443 -16
TRANSMETRA haltec GmbH). As a quality control measure the feedback signal from the load cell
is recorded and all force maxima and minima determined. Surgical insertion of the stainless steel
pins was performed using a special pinning device, compatible with x-ray fluoroscopy. The device
makes use of a V-clamp to simultaneously secure and automatically locate the cranial-caudal axis of
the mouse tail. The coated pins are loaded into channels integral to the V-clamp and are manually
pushed through the centers of the vertebrae, perpendicular to the cranial-caudal axis. A digital
mobile C-arm (OEC Mini-View 6800, GE Medical Systems) was used to locate C4 and C6 (1,2).
Loading Device - Axis 1
Morphed stainless steel pins
Linear actuator
IAM
A4
Load cell
A3
Anesthetized mouse
Desktop PC
LabView
A2
Loading Device - Axis 2
Servo control board
A1
Servo Motor Drive
IAM
Applied mechanical
signal
Clamped pin
b)
a)
Fig. 4.1: a) Overview of the dual axis Caudal Vertebra Axial Compression Device (CVAD). b) Fluoroscopic image
of a mouse, graphically edited to show the location and form of the stainless steel pins once they have been
surgically inserted. The mechanical signal is applied to the distal most pin whilst the proximal-most pin is clamped
(Webster et al. 2008)
Introduction
In addition to the genetic background of the mammalian species mechanical loading is one of the
most important factors which regulate bone mass and shape. Although the basic form and
development of bone are genetically encoded, their final mass and architecture are governed by
adaptive mechanisms sensitive to the mechanical environment (3). The premise that bone cells are
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
able to perceive and respond to mechanical forces is well accepted (4-6). This perception/response
mechanism, also known as mechanotransduction, involves the conversion of a biophysical force
into a biochemical response leading to changes in gene expression and cellular adaptation. Because
of the inherent difficulties encountered during in vivo evaluations of the cellular, molecular, and
mechanical behavior of bone, the majority of research has been conducted in vitro experiments.
Although these studies have, advanced our understanding of mechanical signal transduction in
bone, it has been difficult to assess whether they have simulated accurately the in vivo conditions.
There also have been large variations in the response of bone cells in culture to exogenous
administration of biochemical mediators (7). This lack of consistent reproducibility may be related
to the absence of several factors experienced by bone cells in vivo including an appropriate
osteoprogenitor cell population, blood supply, and mechanical strain tone. Where the direct effects
of loading are concerned, it is likely that the cells affected are the osteocytes (8,9). These cells are
distributed throughout the mineralized matrix and communicate with each other (10), and so they
form an ideally located network of strain sensors, capable of providing information to the bone
surface on the mechanical environment of a large region of bone. A significant body of
circumstantial evidence supports this hypothesis. Osteocytes have a number of responses to loading
that are consistent with such a role (11,12).
An understanding of the biological pathways by which mechanical forces regulate the structure of
bone qualitatively and quantitatively would provide opportunities to mimic or augment the response
of bone to mechanical stimulation by pharmacological agents and may lead to novel strategies in the
management of osteoporosis. However little is known about the cellular mechanisms responsible for
trabecular bone adaptation, as only a few models are currently available for the elucidation of
molecular mechanisms involved in load induced trabecular bone formation, using mainly rat caudal
vertebrae (13). Studies using this model have been hampered by technical hurdles such as the
unavailability of bone cell isolates used to investigate global gene expression and identify the set of
mechanical load regulated genes. Furthermore the principal limitation imposed by the rat model is
its current inaccessibility to potential genetic manipulations. To overcome this, several studies have
established mouse models for the study of cortical bone adaptation and associated biochemical
pathways in response to mechanical loading using C57BL/6 and C3H/Hej inbred strains (14-16).
However specific genes and or combinations of genes have yet to be discovered, moreover the
focus in these studies has been on cortical bone and not trabecular bone, a significant structural
component which has been shown to have a more enduring sensitivity to mechanical stimulation in
mature human adults than that of cortical bone (17,18).
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Based on published data in mice and rats, changes in vertebral trabecular osteoblast/lining cells and
osteocytes occur 30-60 min to 72 hours after the administration of a single loading dose (13,19).
Accordingly, the present study analyzed an early response (6 hours) in osteocyte population to loadinduced changes in the mouse global gene expression, using cDNA microarrays.
DNA microarrays provide a way to analyze the expression of thousands of genes at a time and to
explore the activity of new genes that are being discovered. Massive data sets in this system
approach can be viewed as maps that reflect the order and logic of the genetic program, rather than
the physical order of genes on chromosomes (20). Methods and computational models allow the
building of gene networks of cell physiology and are under continual development. Initially, cluster
analysis for genome wide expression data derived from DNA microarray uses standard statistical
algorithms to arrange genes according to similarity in pattern of gene expression. The output is
displayed graphically, conveying the clustering and the underlying expression data simultaneously
in a form intuitive for biologists. It was found that co-expression of genes of known function with
poorly characterized or novel genes may provide a simple means of gaining leads regarding the
function of many genes for which information is not currently available (21).
We were able to successfully implement a robust technique for selective RNA extraction from
trabecular osteocytes in order to investigate differential load-regulated changes in gene expression
thus identifying molecular pathways of interest to futher study their role in load-stimulated bone
formation in genetically modified animals.
Experimental Design
Animals: All animal protocols were approved by the Institutional Animal Care and Use Committees
of ETH Zürich. Twenty eight, 8-week old C57BL/6 female mice (Füllinsdorf, Switzerland) were
housed in a husbandry unit to acclimatize to their new environment. After one week mice were
divided into 2 groups (0N and 8N loading groups, Fig. 4.2). Stainless steel pins (Fine Science Tools,
Germany) with a diameter of 0.5 mm were inserted into the C4 and C6 mouse vertebrae of all mice,
which were then given 3 weeks to recover before loading commenced. Mice of the 0N loading
group formed the sham-loading group. They were only anesthetized and and connected to the
CVAD which was not activated. The single load compressive dose, applied on C5 vertebra of the
8N loading group, was a sinusoidal waveform with a frequency of 10 Hz and 3000 cycles. For the
pin insertion and loading procedures mice were anesthetized using an oxygen-isoflurane mixture
(Provet Medical AG). All mice were sacrificed using CO2 inhalation 6 hours after loading.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Settling period (mice 8 weeks old)
Single load/Sacrifice after 6 hours
Pinning & Recovery
W0
W1
W2
W4
W3
W4_0N
14 mice
W4_8N
14 mice
Total 28 mice
Fig. 4.2: Schematic representation of the experimental design
RNA extraction
Isolation of total RNA from trabecular osteocytes was performed from a single caudal vertebra of
each mouse according to the protocol, described above in Chapter 3. Briefly, the skin was peeled
from the tail and caudal vertebra C5 was separated from each mouse. The cartilaginous ends were
then cut off and the medullary trabecular bone which contained bone marrow was separated
mechanically using a syringe needle, a MicroDrill, and flushing with cold RNAlater. The medullary
tissue was then collected by centrifugation. The digestion sequence consisted of the following steps
at 4°C: Initial digestion, 15 minutes, carried out using 2 mg/ml of collagenase A in RNAlater. Step
2, 30 minutes first digest using 3 mg/ml of collagenase A in RNAlater. Step 3, second digest using
the same conditions as in Step 2. The remaining trabeculae were then rapidly grinded in a mortar by
pestle under liquid nitrogen on dry ice, until complete pulverization. Total RNA was extracted from
the pulverized tissue, using a conventional TRI Reagent protocol and dissolved in 6 μl of RNase
free water. Extracted total RNA was analyzed on quality and quantity using Agilent Bioanalyzer
2100, taking 1 μl from the RNA sample for this measurement (Fig. 4.3). A minimum 0.5 ng of total
RNA was required for a single cDNA microarray Affymetrix Mouse Genome 230 chip, following
the application of NuGEN Inc. (USA) pico-RNA amplification kit for preliminary mRNA
amplification.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Fig. 4.3: Bioanalyzer information sheet
of intact RNA sample from C5 vertebra
subjected to a single loading dose, with
no evidence for degradation. This RNA
was further analyzed using cDNA
microarray.
Microarray Experiment
Complementary RNA preparation:
The quality of the isolated RNA was determined with a NanoDrop ND 1000 (NanoDrop
Technologies, Delaware, USA) and a Bioanalyzer 2100 (Agilent, Waldbronn, Germany). The
cDNA was prepared from total RNA using a primer mix and reverse transcriptase (RT)
(WTOvation Pico System, NuGEN, 3300-12). The primers have a DNA portion that hybridizes
either to the 5’ portion of the poly (A) sequence or randomly across the transcript. SPIA
amplification, a linear isothermal DNA amplification process, was used to prepare single-stranded
cDNA in the antisense direction of the mRNA starting material. Single-stranded cDNA quality and
quantity was determined using NanoDrop ND 1000 and Bioanalyzer 2100. Fragmented and biotinlabeled single-stranded cDNA targets were generated with the FL-Ovation cDNA Biotin Module
V2 (NuGEN, 4200-12).
Array hybridization:
Biotin-labeled single-stranded cDNA targets (5 μg) were mixed in 220 µl of Hybridization Mix
(Affymetrix Inc., P/N 900720) containing a Hybridization Controls and Control Oligonucleotide B2
(Affymetrix Inc., P/N 900454). Samples were hybridized to GeneChip® Mouse Genome 430 2.0
arrays for 18 hours at 45°C. Arrays were then washed using an Affymetrix Fluidics Station 450
FS450 0004 protocol. An Affymetrix GeneChip Scanner 3000 (Affymetrix Inc.) was used to
measure the fluorescent intensity emitted by the labeled target.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Data Processing and Quality Control (QC):
Raw data processing was performed using the Affymetrix AGCC software. After hybridization and
scanning, probe cell intensities were calculated and summarized for the respective probe sets by
means of the MAS5 algorithm (Hubbell et al., 2002). To compare the expression values of the genes
from chip to chip, global scaling was performed, which resulted in the normalization of the trimmed
mean of each chip to target intensity (TGT value) of 500 as detailed in the statistical algorithms
description document of Affymetrix (2002). Quality control measures were considered before
performing the statistical analysis. These included adequate scaling factors (between 1 and 3 for all
samples) and appropriate numbers of present calls calculated by application of a signed-rank call
algorithm (Liu et al., 2002).
Results
This study used Affymetrix Mouse Genome 430 2.0 microarray chip to compare the gene
expression profiles of 0N (control) and 8N loaded trabecular osteocyte population, with the aim
being to identify altered gene expression in acute load-induced osteocytic mRNA. The mRNA
levels in four biological samples from the 0N group and five samples of the 8N group were
evaluated. The statistical significance of the differences between the means of the 8N and 0N gene
expression values was determined using Student's t-test. The critical value for significance was
chosen as p <=0.05.
The microarrays analysis revealed that 28000 and 34038 probes, out of a total 45101 probe sets per
chip, showed a signal in the 0N and 8N group, respectively. A total of 331 genes whose expression
levels showed a 2-fold change between the 0N and 8N groups were identified when the statistical
test was carried out at a p <=0.05 level of significance. Of these genes the expression of 281 was
up-regulated and that of 50 was down-regulated (Tables A1, A2, Appendix).
Among the genes with significantly load-regulated expression we were able to identify a group of
differentially regulated genes which have known or suspected roles in bone including regulators of
osteocyte, osteoblast and osteoclast metabolism and matrix proteins. These genes included IGF-1,
WNT5a, IL1rn, Xiap, Asporin, STC1, Stat5a (up-regulated) and WIF1 (down-regulated). These
genes encode secreted molecules (STC1, IGF-1, WNT5a, IL1rn, WIF1), transcription factors
(Stat5a), intracellular signaling molecules (Xiap) and extracellular matrix molecules (Asporin).
Discussion
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
IGF-1, an important growth factor implicated in bone formation, has been associated with both
heritable peak bone mass and the response of bone to mechanical loading. In the IGF-1 –
overexpressing mice used in this preliminary study, young mice (6 weeks old) show elevated bone
formation and bone mass compared with wild-type litter-mates (22). Additionally, Horowitz et al.
demonstrated that IGF-1 is critical for optimal skeletal growth and maintenance (23). They used a
model with congenic strain 6T, which contains a QTL (quantitative trait loci) for reduced serum
IGF-I donated from C3H/HeJ on a pure C57Bl/6J (B6) background. In this study a 30%-50%
reduction in IGF-I expression in bone, liver, and fat of the congenic 6T mouse, as well as lower
circulating IGF-I compared with control B6 were found. 6T mice also had a greater percentage
body fat, but reduced serum leptin. These changes were associated with reduced cortical and
trabecular bone mineral density, impaired bone formation but no change in bone resorption.
Moreover, the anabolic skeletal response to intermittent parathyroid hormone (PTH) therapy was
blunted in 6T compared with B6, potentially in response to greater programmed cell death in
osteocytes and osteoblasts of 6T, indicating that allelic differences in IGF-I expression impact peak
bone acquisition and body composition, as well as the skeletal response to PTH. IGF-1 transgenic
mice also demonstrated an increase in femoral cancellous bone volume and an increase in the
osteocyte lacunae occupancy, suggesting that IGF-I may extend the osteocyte life span (24). Also,
Lean et al. analyzed the expression of IGF-1, during the osteogenic response of bone to mechanical
stimulation, where by this growth factor was strongly expressed in osteocytes of mechanically
stimulated, but not control bones, within 30 min of the osteogenic stimulus (25). IGF-I mRNA
expression increased up to 6 h, was restricted to osteocytes, and was strongly suppressed by
indomethacin. Although early IGF-I mRNA expression was resistant to cycloheximide, there was a
degree of suppression after 6 h, raising the possibility that IGF-I expression might be prolonged by
autocrine mechanisms. Thus, our study has shown that osteocytes respond to mechanical stimulation
with immediate prolonged expression of IGF-I implicating osteocytes in the osteogenic response to
mechanical stimulation.
Another differentially up-regulated gene, Wnt5a, the member of WNT gene family of molecules,
which recently has been revealed as mediator of the adaptive response of bone to mechanical
loading, where the WNT signaling pathway is responsible for a complex array of functions in
maintaining bone homeostasis. The importance of Wnt5a in multiple developmental pathways is
illustrated by the phenotype of Wnt5a_/_ mice, which die at birth and show many defective features
such as truncated bodies, facial abnormalities and short deformed limbs (26). Wnts belong to a
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
family of secreted glycoproteins and have been associated with the adaptative response of bone to
mechanical strain. Inactivating mutations in the human low-density lipoprotein receptor-related
protein 5 (LRP5) were shown to cause osteoporosis, while gain-of-function mutations in the LRP5
co-receptor increased Wnt signaling resulting in higher bone mass (27). Although evidence is
accumulating that Wnts are involved in the regulation of bone mechanical adaptation, it is unknown
which cells produce Wnts in response to mechanical loading. Santos and colleagues have shown
that 1 h of pulsating fluid flow (0.7±0.3 Pa, 5 Hz) up-regulated mRNA expression of Wnt3a as well
as the Wnt antagonist SFRP4 in MLO-Y4 osteocytes at 1 to 3 h after cessation of the fluid flow
stimulus. These results suggest that osteocytes in vitro are able to respond to fluid shear stress by
modulation of mRNA expression of molecules involved in Wnt signaling. The response to PFF was
different in MC3T3-E1 osteoblasts, i.e., the expression of most Wnt-related genes, including Wnt5a
and c-jun, was downregulated in response to PFF which underscores the specificity of the mechanoresponse of osteocytes in terms of Wnt expression. Mechanical loading might thus lead to Wnt
production by osteocytes thereby driving the mechanical adaptation of bone (28). A function of
WNT5a gene in bone formation will be broadly discussed in Chapter 4.2.
In contrast, we identified that WIF-1 was down-regulated in acute load-induced trabecular
osteocytes, extracellular protein which binds to WNT proteins and inhibits their activities. WIF-1
plays a role in bone biology as a negative regulator of bone mass. In situ hybridization of WIF-1
found strong expression in osteoblasts and endosteal lining cells. Microarray profiling of murine
osteoblastic cells lines stimulated by BMP2 supported that WIF-1 and sFRP2 (secreted frizzled
protein) were two of the most strongly up-regulated genes during terminal osteoblasts
differentiation and interestingly, WIF-1 protein was observed to be expressed in vivo in trabecular,
but not in cortical bone during late phase bone cell differentiation (29). Transgenic over-expression
of WIF-1 decreases BMD and increases susceptibility to bone fractures in mice. WIF-1 expression
was increased in mice after 4 weeks of treatment with glucocorticoids (GCs), suggesting that it may
participate in the pathogenesis of the prolonged inhibition of bone formation by GCs. Thus, it
appears that antagonism of WIF-1 may have therapeutic potential in osteoporosis treatment.
Interleukin 1 receptor antagonist (IL1ra) appears to play an inhibitory role in osteoclastogenesis and
decreases bone resorption. IL-1 is a predominant cytokine in inflammatory conditions such as
osteolytic diseases, rheumatoid arthrities and is also implicated in the activation of osteoclasts. It is
considered to be a candidate, in part, for initiation of increased responsiveness of large osteoclasts,
to explain the pathological bone loss noted in inflammatory diseases. Trebec et al. demonstrated
that IL1ra inhibited resorptive osteoclast activity by decreasing the number of nucleuses in
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suppressed osteoclasts (30). Thus, a role of IL1ra in the anabolic effect of load-induced bone
formation is also possible.
In addition, a subset of the genes with roles in osteoblast differentiation, leading to bone
mineralization was identified. Particularly those that encode secreted and extracellular matrix
molecules, including STC1 and asporin which also have potential osteogenic functions. Kalamajski
et al. recently showed that asporin, a member of class I small leucine-rich repeat proteoglycan
(SLRP), which binds to collagen type I and in the presence of asporin molecule the number of
collagen nodules, and mRNA of osteoblastic markers Osterix and Runx2 were increased (31).
These results suggest that asporin directly regulated hydroxiapatite formation and increases collagen
mineralization. Another differentially up-regulated gene, STC1 is a mammalian homolog of the fish
calcium/phosphate-regulating polypeptide whose functions are only beginning to be elucidated.
Recently, it has been demonstrated that STC1 stimulates, in an autocrine/paracrine fashion, bone
mineralization by increasing phosphate uptake in osteoblasts apparently via the functional activity
of the sodium-dependent phosphate transporter, Pit1. Yoshiko et al. have assessed the role of STC1
on osteoblast development in fetal rat calvaria cell cultures. STC1 mRNA and protein were
differentially expressed over the time course of cultures, and dexamethasone, a potent stimulator of
differentiation in this model, shifted peak STC1 expression levels to earlier time. Overexpression
(using recombinant human STC1) and underexpression (antisense oligonucleotides) of STC1
accelerated and retarded, respectively, osteogenic development as well as osteopontin and
osteocalcin mRNA expression in mature osteoblast cultures (32).
An additional observed up-regulated gene in the study was Xiap, which is a member of the inhibitor
of apoptosis family of proteins (IAP). It has been recently reported (33), that Xiap is able to block
glucocorticoid-induced apoptosis in osteocytes by both inhibiting caspase activity and by activating
c-Jun N-terminal kinase (JNK1). Activation of JNK1 did not necessarily correlate with the ability of
IAPs to inhibit caspases. XIAP and c-IAP-2 are both capable of inhibiting caspases, but transient
transfection of XIAP and not c-IAP-2 in COS or 293 cells was able to activate JNK1, suggesting
that XIAP anti-apoptotic properties are achieved by two separate mechanisms. Furthermore,
suppression of XIAP by either siRNA or adenovirus of antisense of XIAP induced programmed cell
death and inhibited Akt-stimulated cell survival in ovarian cancer cells. These data identify Xiap as
a new possible mediator for osteocyte survival for improving bone strength properties.
Stat5a is a known member of STAT family transcription factors. In response to cytokines and
growth factors Stat5a is phosphorylated by the receptor associated kinases, and then form homo- or
heterodimers that translocate to the cell nucleus where they act as a transcription activator. Previous
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
studies have established that the effect of growth hormone (GH) in bone is consistent with the
growth-promoting roles of GH and IGF-I on this target organ (34–36), and prolactin receptor
(PRLR) mRNA has been identified in osteoblasts (37). Furthermore, PRLR-/- animals show reduced
ossification (196) suggesting an important, uncompensatable role for PRL signaling in bone
homeostasis. LeBaron et al. identified a low but specific responsiveness to PRL of chondrocytes
and osteocytes of the rat femur, indicating that at least some of the effects of PRL on bone are direct
(38). GH also induced low but detectable activation of Stat5a in femoral chondrocytes, whereas
osteocytes showed moderate Stat5a activation in response to GH, indicating that Stat5a in
osteocytes possibly plays an important function in the anabolic effect of mechanical loading.
Additional studies will be needed to determine more precisely a role of Stat5a transcription factor in
osteocytes and determine age-dependent differences of relevance for osteoporosis.
In this study we observed small gene expression ratios in microarray analyses. Microarray analysis
is able to reliably detect small (< 2-fold) changes that prove to be biologically relevant. One of
them, Sclerostin/SOST osteocyte specific marker, was down-regulated close to a 2-fold change in
the acute study, suggesting a hypothesis that mechanical loading reduces the expression of
sclerostin protein after 24 hours (19). The insignificant effect on SOST would be explained by 6
hours being suboptimal in the presented loading regime, as the processes should include both
transcriptional down-regulation as well as degradation of the existing mRNA.
This study indicates the value of the microarrays approach and shows that the power of the
microarray analysis method lies in its ability to detect genome-wide, coordinated, or similarly
regulated differential gene expression, pointing to perturbed signaling pathways and important
downstream molecular processes. In performing this study we demonstrated that acute mechanical
loading induced molecular changes in trabecular osteocytes.
4.2.
Repetitive mechanical loading
Introduction
Following the initial study of differential gene expression the second hypothesis to be tested was
that sustained cyclic mechanical loading induces sustained expression of osteocyte inter- and
intracellular signaling, as well as structural genes. Chapter 4.1 investigated the basic cellular
responses to mechanical loading evoked by a single loading cycle; the question however remained
whether these are acute or chronic responses. The established protocol for total RNA isolation
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
(Chapter 3) and the mechanical loading protocol used in Chapter 4.1 were therefore employed for
an extended period of time. Subsequently, the results of repeated loading could be compared and
contrasted with the results of single load-induced changes to elucidate the different responses
between the two loading regimes.
The overall purpose of this research project was to develop a proof of principle for the study of
differential gene expression in a mouse model of mechanically-induced bone formation. It is
proposed here to extend that proof to investigate transient versus sustained gene expression
following two loading protocols: single and repetitive. A successful realization of this strategy will
enable a basis for formulating further questions and developing more rigorous experimental
protocols for investigating gene expression resulting from mechanical stimulation. Most importantly
it would illustrate the enormous potential of this mouse model for bone adaptation research.
The interpretation of the data will follow that of the two previous specific aims identifying genes of
interest, based on a single loading protocol, and will be compared to genes identified in this 4-week
protocol of repeated loading. Those genes present after a single load dose, but no longer expressed
after the 4-week protocol, will be considered transient. The identification of these genes would raise
interesting questions on the time scale of their activation throughout the duration of the chronic
loading protocol; a consideration for future research. Conversely, genes expressed after the 4-week
protocol, but not previously identified in Chapter 4.1, will be considered chronic, and may also lead
to interesting questions for future research. A third possible result for some genes is that there will
be no differences in gene expression between the two loading protocols. This finding would confirm
the assumption that the anabolic adaptation of the cancellous bone is truly an additive process
resulting from three single doses of 5-minute loading regimes per week. These results would have
implications for the development of loading regimes to induce anabolic bone adaptation, and
provide further insight into the fundamental genetic processes governing bone adaptation.
Experimental Design
The mechanical loading protocol described in Chapter 4.1 was employed for a four-week period,
three times loading per week, on two groups of mice treatment. These groups were a mirror of the
experimental design in Chapter 4.1., so that a direct comparison could be made between the acute
loading regime and the longer-term loading regime employed in this specific aim (Fig. 4.4).
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Settling period (mice 8 weeks old)
Pinning/Recovery
W0
W1
W2
W3
Repetitive Loading
W4
W5
Total 28 mice
Sacrifice after 6 hrs
W6
W7
14 mice
W8_0N
14 mice
W8_8N
W8
Fig. 4.4: Schematic representation of the experimental design in chronic loading.
The analysis protocol of differential gene expression was identical to the technique developed in
Chapter 3 and the total osteocytic RNA was isolated 6 hours after the last mechanical load. The
selection of this time-point was based on the set of results from acute loading, which were the most
intriguing.
RNA extraction
Isolation of total RNA from trabecular osteocytes was performed from a single caudal vertebra of
each mouse according to the protocol, described above in Chapter 3. Briefly, the skin was peeled
from the tail and caudal vertebra C5 was separated from each mouse. The cartilaginous ends were
then cut off and the medullary trabecular bone which contained bone marrow was separated
mechanically using a syringe needle, a MicroDrill, and flushing with cold RNAlater. The medullary
tissue was then collected by centrifugation. The digestion sequence consisted of the following steps
at 4°C: Initial digestion, 15 minutes, carried out using 2 mg/ml of collagenase A in RNAlater. Step
2, 30 minutes first digest using 3 mg/ml of collagenase A in RNAlater. Step 3, second digest using
the same conditions as in Step 2. The remaining trabeculae were then rapidly grinded in a mortar by
pestle under liquid nitrogen on dry ice, until complete pulverization. Total RNA was extracted from
the pulverized tissue, using a conventional TRI Reagent protocol and dissolved in 6 μl of RNase
free water. Extracted total RNA was analyzed on quality and quantity using Agilent Bioanalyzer
2100, taking 1 μl from the RNA sample for this measurement (Fig. 4.5). A minimum 0.5 ng of total
RNA was required for a single cDNA microarray Affymetrix Mouse Genome 230 chip, following
the application of NuGEN Inc. (USA) pico-RNA amplification kit for preliminary mRNA
amplification.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Fig. 4.5: Bioanalyzer information sheet
of intact RNA sample from C5 vertebra
subjected to repeated loading doses,
with no evidence for degradation. This
RNA was further analyzed using
cDNA microarray.
4.2.4. Microarray experiment
Complementary RNA preparation:
The quality of the isolated RNA was determined with a NanoDrop ND 1000 (NanoDrop
Technologies, Delaware, USA) and a Bioanalyzer 2100 (Agilent, Waldbronn, Germany). The
cDNA was prepared from total RNA using a primer mix and reverse transcriptase (RT)
(WTOvation Pico System, NuGEN, 3300-12). The primers have a DNA portion that hybridizes
either to the 5’ portion of the poly (A) sequence or randomly across the transcript. SPIA
amplification, a linear isothermal DNA amplification process, was used to prepare single-stranded
cDNA in the antisense direction of the mRNA starting material. Single-stranded cDNA quality and
quantity was determined using NanoDrop ND 1000 and Bioanalyzer 2100. Fragmented and biotinlabeled single-stranded cDNA targets were generated with the FL-Ovation cDNA Biotin Module
V2 (NuGEN, 4200-12).
Array hybridization:
Biotin-labeled single-stranded cDNA targets (5 μg) were mixed in 220 µl of Hybridization Mix
(Affymetrix Inc., P/N 900720) containing a Hybridization Controls and Control Oligonucleotide B2
(Affymetrix Inc., P/N 900454). Samples were hybridized to GeneChip® Mouse Genome 430 2.0
arrays for 18 hours at 45°C. Arrays were then washed using an Affymetrix Fluidics Station 450
FS450 0004 protocol. An Affymetrix GeneChip Scanner 3000 (Affymetrix Inc.) was used to
measure the fluorescent intensity emitted by the labeled target.
Data Processing and Quality Control (QC):
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Raw data processing was performed using the Affymetrix AGCC software. After hybridization and
scanning, probe cell intensities were calculated and summarized for the respective probe sets by
means of the MAS5 algorithm (Hubbell et al., 2002). To compare the expression values of the genes
from chip to chip, global scaling was performed, which resulted in the normalization of the trimmed
mean of each chip to target intensity (TGT value) of 500 as detailed in the statistical algorithms
description document of Affymetrix (2002). Quality control measures were considered before
performing the statistical analysis. These included adequate scaling factors (between 1 and 3 for all
samples) and appropriate numbers of present calls calculated by application of a signed-rank call
algorithm (Liu et al., 2002).
Results
This study used Affymetrix Mouse Genome 430 2.0 microarray chips to compare the gene
expression profiles of control and 8N loaded trabecular osteocyte population, with the aim of
identifying altered gene expression in repeated load-induced osteocytic mRNA. The mRNA levels
in three biological samples from the 0N group and four samples of the 8N group were evaluated by
microarray analysis. The statistical significance of the differences between the means of the 8N and
0N gene expression values was determined using Student's t-test. The critical value for significance
was chosen as P <=0.05.
The microarray analysis revealed that 27997 and 36414 probes out of a total of 45101 probe sets per
chip, showed a signal in the 0N and 8N group, respectively. A total of 1342 genes whose expression
levels showed a 2-fold change between the 0N and 8N groups were identified when the statistical
test was carried out at a P <=0.05 level of significance. Of these genes the expression of 781 was
up-regulated and that of 561 was down-regulated (Tables A.3, A.4).
Among the genes with significantly load-regulated expression we were able to identify a group of
differentially regulated genes which have known or suspected roles in bone including regulators of
osteocyte, osteoblast and osteoclast metabolism and matrix proteins. These genes included WNT5a,
DMP1, Xiap, Asporin, Stat5a, Cyclin D1, alpha-actinin and RUNX2 (up-regulated). These genes
encode secreted molecules (WNT5a, Cyclin D1), transcription factors (Stat5a, RUNX2),
intracellular signaling molecule (Xiap) and extracellular matrix molecules (asporin, DMP-1 and
alpha-actinin).
Discussion
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Wnt5a, a member of WNT signaling pathway, was identified up-regulated in both single and
repeated load-induced studies, indicating that this gene plays an important role as a transient and
chronic mediator of the adaptive response of bone to mechanical loading. It took almost two
decades to obtain evidence that Wnt signaling plays a crucial role in mammalian bone homeostasis
(39,40). Two reviews have broadly covered the increasing number of factors involved in the Wnt
signal transduction pathways (41,42). Wnt proteins signal through canonical (β-catenin-dependent)
and non-canonical mechanisms. The activity of the canonical pathway is mediated through βcatenin, which is inactivated in the absence of Wnt ligands by an oligomeric complex that consists
of glycogen synthase kinase 3β (GSK-3), axin, casein kinase 1 (CK1), adenomatous polyposis coli
(APC), and Disheveled. The binding of Wnt proteins to Frizzled (Fz) receptors and its coreceptor,
low-density lipoprotein receptor-related protein 5 or 6 (LRP5/6) stabilizes cytoplasmic β-catenin
protein, which in turn translocates to the nucleus and activates the transcription of target genes via
transcription factors including lymphoid enhancer-binding factor (LEF) and T cell factors (TCF).
The non-canonical Wnt pathways also require Fz receptors and, in vertebrates, have been
characterized to include at least three intracellular cascades: the protein kinase C (PKC) pathway
(Wnt/Ca2+), the Rho family guanosine-5’-triphosphate (GTP)-ases pathway, and the Jun Nterminal kinase (JNK) cascade. Canonical and non-canonical pathways are involved in coordinating
proper bone development, formation and growth, both pre- and postnatally (43,44). The welldocumented role of canonical Wnt signaling in human bone is related to loss or gain-of-function
mutations in LRP5, causing osteoporosis-pseudoglioma syndrome (39), or a high bone density
syndrome (40), respectively. The abnormal phenotype of high bone mass results from increased
Wnt/β-catenin signaling. Canonical Wnt signaling supports osteogenic differentiation from
precursor lines and stem cell lines. During in vivo bone development, canonical Wnt signaling
prevents osteoblasts from differentiating into chondrocytes (45) and targets Runx2 for osteoblast
differentiation (46). Postnatally, overexpression of Wnt10b in transgenic mice increases bone mass
(47). Interestingly, β-catenin signaling in differentiated osteoblasts has been shown to negatively
control osteoclast formation and bone resorption through an increase in osteoprotegerin production
by osteoblasts (48).
Along with the canonical Wnt pathway, there is now increasing evidence for noncanonical Wnt
signaling pathways influencing intracellular events responsible for skeletal development and
differentiation. Tu et al. (49) have reported the role of Wnt-PKC signaling in osteoblastogenesis in
vitro corresponding with PKC homozygous mutant mice exhibiting a deficit in embryonic bone
formation. Also, mice deficient in the G proteins αq and α11 - required for Wnt-induced PKC
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
activation in osteoprogenitors - have bone defects in the craniofacial skeleton. The anabolic effect
of parathyroid hormone (PTH) on bone formation has been in part attributed to its stimulation of
non-canonical
Wnt4
signaling
promoting
osteoprogenitor
differentiation
and
osteoblast
development, primarily through the protein kinase A pathway (50). Non-canonical Wnt4 activation
of p38 mitogen-activated protein kinase (MAPK) has also been reported to enhance osteogenic
differentiation of mesenchymal stem cells (isolated from human craniofacial tissues) and to promote
bone formation in rodent models (51). Similarly, non-canonical Wnt5a signaling was reported to
potently transdifferentiate adipoprogenitors into osteoblasts in vitro by suppressing peroxisome
proliferator-activated receptor (PPAR), a key transcription factor for adipogenesis, and inducing
Runx2, a key transcription factor for osteogenesis (52). Earlier in vivo data also supported the role
of Wnt5a in osteoblastogenesis with the observation of decreased trabecular bone mass in the
femurs of Wnt5a+/- mice (53). Loss-of-function mutation of Wnt5a in these animals resulted in
truncation of the proximal skeleton and absence of distal digits. Finally, non-canonical Wnt5a has
been shown to prevent the apoptosis of osteoblast progenitors and differentiated osteoblasts,
comparable to anti-apoptotic effects of canonical Wnt1 and Wnt3a (54). Interestingly, the
convention of two independent Wnt pathways has remained for some time, but emerging evidence
suggests that the pathways are not as autonomous as originally thought. For instance, although
Wnt5a is thought to primarily function though the non-canonical pathway, it can, under certain
circumstances, signal through the canonical pathway. The possibility of interaction between these
two pathways may explain in part the uncertainties of the role of Wnt5a in adaptive response to
mechanical loading.
Asporin, Xiap and Stat5a (previously broadly described in the acute-load study in Chapter 4.1.6.)
were also identified as up-regulated genes in both acute and chronic loading regimes. This finding
confirms the assumption that the anabolic adaptation of the cancellous bone is truly an additive
process resulting from three times per week single doses of 5-minute loading regimes. These genes
may have future implications as potential agents for treatment of osteoporosis and provide further
insight into the fundamental genetic processes governing bone adaptation.
DMP-1, extracellular matrix molecule, has been differentially up-regulated only in chronic loadinduced trabecular osteocytes and may also lead to interesting questions for future research on bone
adaptation to sustained mechanical loading. DMP-1, a promoter of mineralization and mineral
homeostasis, increases sequentially in response to mechanical load (55). Deletion or mutation of the
DMP-1 gene, which is highly expressed in embedding osteocytes and mature osteocytes, results in
hypophosphatemic rickets (8). DMP-1 is highly expressed and therefore a good marker for the
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
osteocyte lineage (56,57) and is specifically expressed along and in the canaliculi of osteocytes
within the bone matrix suggesting a role for DMP1 in osteocyte function. DMP-1 is activated in a
few hours in response to mechanical loading in osteocytes in the tooth movement model (58) and in
the mouse ulna loading model of bone formation (8). Potential roles for DMP-1 in osteocytes have
been suggested and are related to the post-translational processing and modifications of the protein
as a highly phosphorylated protein and regulator of hydroxyapatite formation (59). It has been
suggested that DMP-1, depending on the proteolytic processing and phosphorylation state, regulates
local mineralization processes that are carried out within the lacunae and canaliculi of osteocytes in
mature bone, thus keeping the lacunae and canaliculi open to allow bone fluid flow (58). Complex
networks of canaliculi, containing osteocyte dendritic processes, penetrate bone; therefore, increases
or decreases in canalicular volume or changes in canalicular structural integrity could alter the
dynamics of fluid flow thereby altering responses of osteocytes under various physiological or
pathological load conditions. A related function for DMP-1 in osteocyte biology may be to define
the structural, mechanical, and material properties of the canalicular and lacunae wall. The stiffness
of this wall could play an important role in detecting and transmitting mechanical signals.
In addition, a runt-related transcription factor 2 (RUNX2), expressed in mature osteoblasts and early
osteocytes, was also up-regulated in sustained load-induced osteocytes, confirming the hypothesis
that mechanical loading increases the expression of this crucial transcription modulator for
osteogenesis affecting osteoblasts differentiation and plays a fundamental role in osteoblast
maturation and homeostasis. Mutations of the RUNX2 gene in humans cause cleidocranial
dysplasia. Among the various stimuli that modulate Runx2 activity, mechanical loading
(strain/stretching) has been revealed to be one of the most critical signals that connect Runx2 with
osteoblast function and bone remodelling through mechanotransduction (60,61).
Another interesting finding is that alpha-actinin was highly up-regulated (6.3 fold change) in a
load-induced chronic study, suggesting that this cross-linking protein may play an important role for
bone adaptation and homeostasis. Few studies have considered how the cytoskeletal composition
changes in response to mechanical loads. Network polymer models of the cytoskeleton predict that
recruitment of cross-linking proteins to the filament network could be an important mechanism of
controlling cell stiffness, and this has been supported by several studies (62, 63). Alpha-actinin is
involved in the cellular mechanoprotective response (64), and increasing the amount of alphaactinin in the cytoskeleton is sufficient to increase the whole cell resistance to deformation. Jackson
et al. showed a 29% increasing of alpha-actinin by Western blotting in mature MC-3T3-E1 cell line
in vitro after mechanical loading, demonstrating that mature osteoblasts respond to fluid shear by
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
increasing the amount of alpha-actinin that is present in the cytoskeleton. Based on the mechanical
function of this protein, it is likely that this response would be sufficient to increase the whole cell
stiffness, which has been observed to occur in mature osteoblasts/early osteocytes exposed to
identical mechanical loads (65). Our study adds to the wealth of evidence suggesting that alphaactinin contributes to the whole cell response to mechanical loading and may be significant for
mechanical and signaling models of the cytoskeleton and further characterizes the whole cell
responses to mechanical loading.
This study indicates the value of the microarrays approach and shows that the power of the
microarray analysis method lies in its ability to detect genome-wide, coordinated, or similarly
regulated differential gene expression, pointing to perturbed signaling pathways and important
downstream molecular processes. In performing this study we demonstrated that sustained
mechanical loading induced molecular changes in trabecular osteocytes.
4.3.
Functional genomics for identification of load-regulated pathways
Introduction
Microarrays made it possible to survey changes in the mRNA levels of genes on a genome-wide
scale in a single experiment, promising an unbiased overview of changes in the transcriptome. New
or adapted methods for mastering the statistical part of microarray analysis were introduced rapidly,
including the still very popular significance analysis of microarrays (SAMs), (66). However, after
the initial enthusiasm subsided, it became quite clear that even the statistically best-supported lists
of up-regulated and down-regulated genes were most of the time as cryptic as the primary
nucleotide sequence of the genome. There are two reasons for this: first of all many (if not almost
all) genes serve multiple context-dependent functions. Not all changes in mRNA levels are directly
connected to the experiment conducted. Therefore, it is no surprise that soon after 2001 the
necessity to go beyond simple clustering and statistics was recognized (67).
One method of analyzing microarray data which is becoming popular is pathway analysis (also
known as functional enrichment). This integrates the normalized array data and their annotations,
such as metabolic pathways and gene ontology functional classifications. It can use various forms of
currently available software for this purpose. Pathway analysis can identify more subtle changes in
expression than the gene lists that result from univariate statistical analysis. Often stringent criteria
are used to create these lists, for example the statistic P-value P≤0.05 and fold-change ≥2. Although
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
genes with large changes in expression might be interesting, so might those in which there are more
subtle changes, such as small, but consistent, changes in expression of a group of genes with related
function. Pathway analysis is suited to detecting such trends and, as microarray technology
improves, detection of lower levels of expression and smaller changes in expression is becoming
feasible. These methods can also be used for different biological data, such as gene expression, and
metabolomic and proteomic data, which indicate that they will be used increasingly to integrate data
from these sources into metabolic networks.
It is important to define the terms pathways and networks at the outset. The major tools for data
analysis are considered as pathways and networks. Pathways are consecutive reaction steps, which
are either biochemical transformations or sequences of signaling events, such as signal transduction.
Both are static as predefined by previous studies. Networks, in contrast, are dynamic, as they are
built de novo out of building blocks from binary interactions and are specific for each data set. The
process of data analysis therefore consists of narrowing down the list of potentially many thousands
(if not more) data points to something more interpretable. This can be achieved by using statistical
analysis p-values, different scoring methods for the intersections between categories, and
calculation of the relevance of the result to the data set in question (using the relative saturation of
pathways and networks with data).
MetaCore database (GeneGO Inc.) is a commercial package that contains more than 400
mammalian signaling and metabolic pathway maps available for mapping gene expression,
proteomics, metabolic, and high content screening (HCS) data. The data generated can be exported
from individual maps and clusters of maps and analyzed further with networks. MetaCore is a webbased computational platform for multiple applications in systems biology. It is primarily designed
for the analysis of high-throughput molecular data (microarray-based and serial analysis of gene
expression (SAGE) gene expression, array-comparative genomic-hybridization DNA arrays,
proteomics data, metabolic profiles, and so on) in the context of human and mammalian networks,
canonical pathways, diseases, and cellular processes. MetaCore is an integrated system, which
consists of (1) a curated database of mammalian biology, (2) a suite of tools for querying,
visualization, and statistical analysis including pathways maps, network algorithms, and filters, (3) a
toolkit (pathway editor) for custom assembly of functional networks, and (4) a set of parsers for
uploading and manipulating different types of high-throughput molecular data (68-70).
As a foundation, MetaCore has a database of protein–protein, protein–DNA, and protein–compound
interactions, metabolic reactions, pathway maps, bioactive compounds (metabolites, drugs, and
ligands), and diseases. Human pathways have been manually collected from the experimental
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
literature for more than 5 years. This represents one of the most comprehensive databases in the
field, the core of MetaCore consists of more than 4.5 million individual findings resulting in about
50,000 signaling interactions and 20,000 human metabolic transformations (covering both
endogenous and xenobiotic metabolism). The database has interaction information for more than
90% of known human proteins, including 1720 transcription factors and 650 GPCRs. This content
is linked to 3200 human diseases and conditions. The bioactive chemistry component includes more
than 7000 known drugs with protein targets and 5000 endogenous metabolites. The pathway
information is organized in more than 400 signaling and metabolic maps with more than 3000
canonical pathways represented.
The MetaCore software currently runs on an Intel-based 32-bit server running RedHat Linux
Enterprise 3 AS (RedHat, Raleigh, NC) and the web server runs Apache 1.3.x/mod_perl. Software
on the server side is written in Perl, whereas the client side requires HTML/JavaScript and the
Macromedia Flash Player Plug-in (Macromedia Inc, San Francisco, CA). The MetaCore database is
generated from manual annotation of full text articles as well as disease relevant information from
OMIM and EntrezGene.
Every node on the network is associated with genes and proteins through the tables in the general
database schema. The novel database architecture enables mapping of the high-throughput
experimental data associated with genes and proteins onto the networks. Every experimental data
point (e.g., a set of probes on the microarray or a frequency for a certain SAGE tag) represents an
attribute of the unique gene or protein identifier. Therefore, the high-throughput data can be linked
with the corresponding node in the database and visualized on the networks containing this node.
Visually, the altered expression or protein abundance data is presented as a solid circle above the
node (red and blue represent increased and decreased abundance, respectively with a number 1 for
acute and number 2 for repeated loading). The applications of this quite straightforward procedure
are ubiquitous in basic research and in drug discovery. For instance, one can directly compare the
lists of genes derived from different types of high-throughput or “small-scale experiments” on the
same networks. When the same data type and experimental platform is used, the conditional
networks can be readily compared for common and different sub-networks and patterns. Such finegrained mapping can also be performed to compare the tissue and cell type specific response,
different time-points, drug dosage, and different patients from the same cohort, and etc.
Materials and Methods
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
In both the acute and chronic mechanical loading studies, MetaCore (GeneGo, St Joseph, MI) was
used to map the differentially expressed genes into biological networks and for the functional
interpretation of the experimental microarray data. MetaCore is an integrated software suite based
on a manually curated database of mammalian protein-protein interactions, protein-DNA
interactions, transcriptional factors, metabolic, and signaling pathways. Within MetaCore, the
networks were generated as a combination of binary single-step interactions (edges) which
connected proteins and genes (nodes). The nodes and edges were derived from the corresponding
interaction tables in the MetaCore database and were visualized as clusters of interconnected nodes
with the Macromedia Flash Player Plug-in. The end nodes on the networks had only one edge; the
internal nodes had anywhere between two to several hundred edges depending on connectivity with
other nodes. The networks were built from an input list of genes, corresponding to the components
(network classes) in the database. The nodes in the input list were therefore considered as root
nodes.
The list of genes was imported as a text in an Excel file directly from Affymetrix microarray
analysis software (www.affymetrix.com) and uploaded as their Swiss-Prot IDs to MetaCore, for
identification of load-regulated signaling pathways. Before building networks, the interactions were
preselected based on the level of trust, interaction direction, effects, mechanisms, and tissue
specificity (in which only the edges with both nodes belonging to a chosen tissue remain). The
nodes from the input list with no connections with other nodes on the list were removed. The edges
of networks were assigned with weights depending on the type of interactions.
The biological process enrichment was analyzed based on GO Ontology processes. The direct
interactions algorithm was the most stringent, the only edges allowed being those between two
nodes which are root nodes, e.g., objects from the list directly connected to each other. For network
analysis, the shortest path algorithm was used to map the shortest path for interaction, based on
Dijekstra algorithm (94).
Results
To identify potential signaling pathways associated with the skeletal anabolic response to
mechanical loading, we analyzed our microarray expression data using MetaCore software. This
software comes with a built-in natural language processing module MedScan and a comprehensive
database containing more than 150,000 events of regulation, interaction and modification between
proteins and cell processes obtained from PubMed which allows it to generate a biological
association network (BAN) of known biological interactions. In order to characterize change in
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
signaling pathways involved in the response of trabecular osteocytes to mechanical loading, we
imported global microarray data into MetaCore software. Functional genomics analysis revealed
overall 65 load-regulated signaling pathways in single mechanical loading and 153 load-regulated
signaling pathways in repeated loading study between loaded and unloaded samples (Tables A5 and
A6, Appendix).
The prediction with MetaCore based on a whole-genome gene expression profile, highlighted in a
single loading top ten significant load-regulated pathways in trabecular osteocytes in response to
mechanical loading. This includes a calcium regulated α-1A adrenergic receptor-dependent
inhibition of PI3K-Act of cytoskeleton remodeling with an impact factor of 67% (meaning that 8
objects of the pathway from total 12 were differentially regulated with significance of P < 0.05),
signaling pathway for regulation of translation initiation (60%), signal transduction by protein
kinase A (PKA) signaling (39%), TGF-β receptor signaling development process (29%) and IGF-1
receptor 1 signaling development pathway (27%). It has been found that these genes are involved in
a number of signaling pathways and have been implicated in regulating the formation and/or
activity of bone cells in response to mechanical strain (71-74).
In the same manner, MetaCore highlighted top ten significant load-regulated signaling pathways in
repetitive loading. This includes cell adhesion of endothelial cell contacts by non-junctional
mechanisms (79%), calcium regulated α-1A adrenergic receptor-dependent inhibition of PI3K-Act
of cytoskeleton remodeling (75%), cytoskeleton remodeling of integrin outside-in signaling
pathway (61%) growth factor activated extracellular signal-regulated kinases (ERK), (56%),
cytoskeleton remodeling regulation of actin cytoskeleton by Rho GTPases (52%), cytoskeleton
remodeling activation of protein kinase C (PKC) via G-protein coupled receptor (45.45%) and
development of Wnt5a signaling pathway (40%, Fig. 4.6 – 4.7). It has been demonstrated that
Wnt5a signaling influencing intracellular events responsible for skeletal development and
differentiation (49).
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Fig. 4.6: Development of WNT5a signaling pathway map (MetaCore GeneGO). Visually, the altered gene expression or
protein abundance data is presented as a solid circle above the node (red and blue represent increased and decreased
abundance; with a number 1 for acute and number 2 for repeated loading, respectively).
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Fig. 4.7: Legends of signaling pathway map.
Additionally, we compared significantly load-regulated signaling pathways according to the
biological functions between single and repetitive doses of cyclic mechanical loading (Fig. 4.8).
Interestingly, percentage of load-regulated signaling pathways in osteocytes associated with
immune response in repetitive loading was decreased comparing to a single loading, whereas
percentage of load-regulated pathways associated with cell growth/differentiation was increased in
repetitive loading comparing to a single loading. Also, percentage of signaling pathways associated
with apoptosis was increased in repetitive loading comparing to a single loading. We assume that
bone during repetitive loading accommodates with a time to sustained mechanical force, whereas
bone response mainly by inflammation processing following single mechanical stimulation.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
Biological processes
A
1.5%
4.6%
Immune response
4.6%
G-protein signaling
28%
6.2%
Cell growth/differentiation
Chemotaxis
Cell cycle
6.2%
Translation
Signal transduction
Cell death
6.2%
1.5%
Transcription
Transport
Cytoskeleton remodeling
7.7%
Neurophysiological process
1.5%
Unknown
20.0%
9.2%
B
1.5%
Biological processes
Im m une res pons e
Proteolys is
3.3%
3.9%
9.8%
Cell growth/differentiation
0.7%
7.8%
Blood coagulation
G-protein s ignaling
Oxidative s tres s
3.9%
Cell adhes ion
Cell cycle
4.6%
Trans cription
3.3%
29.4%
Cell death
Trans port
Signal trans duction
5.2%
Neurophys iological proces s
2.6%
Trans lation
5.2%
2.6%
Cytos keleton rem odeling
0.7%
6.5%
1.3%
Lipid m etabolis m
5.9%
Unknown
Fig. 4.8: Distribution of load-regulated signaling pathways according to cell processes in response to (A) single and (B)
repetitive mechanical loading.
Discussion
The current study presents for the first time murine global-genome mRNA expression profiles in
trabecular osteocytes in response to acute and chronic mechanical loading in vivo. Whole-genome
microarrays analyses predicted that signaling pathways such as PI3K, ECM-receptor interactions,
TGF-β signaling, and Wnt signaling are involved in trabecular osteocyte loading-driven responses
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
in acute and chronic regimes at 6 h after the last loading. First, PI3K signaling pathway is known to
be activated in response to various extracellular signals such as peptide growth factors, insulin, and
insulin-like growth factors (75). Insulin-like growth factors 1 and 2, for instance, can stimulate bone
formation due to mechanical load (76-78). Signaling pathways downstream of PI3K affect a wide
range of cellular activities including cell growth, cell survival, and cell movement (79). Second,
mRNA levels of many collagens together with integrin, fibronectin, and vitronectin are altered in
the pathway linked to ECM-receptor interactions (80). Thus, our pathways analysis supports the
notion that mechanical loading stimulates remodeling of ECM. Note that previous mouse studies
using four-point bending have identified signaling pathways linked to EGF receptors, fibronectins,
and proteolysis (73), where fibronectins and proteolysis are involved in remodeling of ECM. Third,
TGF-β signaling is known to influence diverse processes in embryogenesis, angiogenesis,
inflammation, and wound healing. It also plays a major role in the development and maintenance of
bone metabolism (81). Lastly, Wnt signaling is one of the central pathways in regulating bone
formation (82). Mice with a nonfunctional Lrp5 receptor in this pathway respond poorly to
mechanical loading with significant reduction in bone formation compared with wild-type controls
(19,83). In osteocytes it is reported that up-regulation of the Wnt pathway together with estrogen
receptor, insulin-like growth factor-I, and bone morphogenetic protein pathways are involved in
shear-induced mechanotransduction (84). Wnt binds to two distinct receptor complexes: a complex
of Frizzled and LRP5/6 and another complex of Frizzled and RORs. The binding of Wnt to the
receptors activate two classes of signaling pathways: a β-catenin-mediated canonical pathway and a
b-catenin-independent non-canonical pathway (85). In the absence of Wnt signaling, glycogen
synthase kinase-3b (GSK-3b) phosphorylates β-catenin in the target cells. Adenomatous polyposis
coli (APC) and axin act as scaffold proteins allowing the association of GSK-3b with β-catenin.
Phosphorylated β-catenin is degraded through the ubiquitin-proteosome pathway. Wnt1 class
ligands such as Wnt1 and Wnt3a activate the canonical pathway through the formation of a complex
of Wnt, Frizzled, and LRP5 or LRP6. This complex in turn promotes the phosphorylation of GSK3b, which inhibits the kinase activity of GSK-3b. Inactivation of GSK-3b induces the accumulation
of β-catenin in the target cells, followed by translocation of accumulated b-catenin into the nucleus.
The nuclear β-catenin, together with transcription factors, T-cell factor/lymphoid enhancer factor
(TCF/LEF) family members, induces the expression of the Wnt target genes. In the other pathway,
Wnt5a binds to a receptor complex of Frizzled and ROR1/2. The binding of Wnt5a to a receptor
complex activates heterotrimeric G proteins, which increase intracellular calcium via protein kinase
C (PKC) - and calcineurin-dependent mechanisms (86-89). Wnt5a also activates the planar cell
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
polarity pathway through Rho- and Rac/c-Jun amino-terminal kinase (JNK)-dependent signals
(26,90-93).
In conclusion, we have examined the in vivo effect of mechanical loading on differentially
expressed genes in the whole genome derived from trabecular osteocytes, and identified a number
of genes and pathways that may play important roles in mediating the skeletal anabolic response to
mechanical force. The current study demonstrates that mechanical loading potentially induces
multiple signaling pathways involved in mechanotransduction and bone metabolism. Future studies
on these unknown genes and signal molecules will provide a better understanding of the molecular
pathways involved in mediating the skeleton’s anabolic response to mechanical stress.
4.4. Confirmation of individual load-regulated genes in single loading
Materials and Methods
We used quantitative reverse transcriptase polymerase chain reaction (Real-Time RT-PCR) for
selected genes to confirm changes in transcription observed in our differential microarray analysis,
using six RNA samples from both control and 8N-loaded groups which were extracted after the
acute loading study (described in Chapter 4.1). Real-Time PCR was performed to evaluate mRNA
levels for four significantly up-regulated genes observed in microarray data in both single and
repeated mechanical loadings, including Wnt5a, Asporin, Xiap and Stat5a. Total RNA (150 pg)
was reverse-transcribed into cDNA and amplified by using sensitive for low amount of template
RNA SuperScript III Platinum One-Step Quantitative RT-PCR kit with Rox (Invitrogen Inc.,
Carlsbad, CA). Real-Time PCR was carried out in a 96-well plate using ABI PRISM 7900HT FAST
sequence detection system (Applied Biosystems Inc., Foster City, CA). All biological samples were
run in duplicates along with primers for housekeeping gene glyceraldehyde-3-phosphate
dehydrogese (GAPDH), as a reference gene to normalize the expression data for each gene. Total
volume for each reaction was 25 μl. We used TaqMan® primer probes (Assay on Demand, Applied
Biosystems Inc.) for GAPDH (assay ID: Mm99999915_g1), Wnt5a (Mm00437347_m1), Asporin
(Mm00445945_m1) Xiap (Mm00776505_m1) and Stat5a (Mm00839861_m1) genes in
concentration of 10 μM each. Primers were designed for each gene that primed in separate exons
and spanned at least one intron to avoid contaminating amplification from genomic DNA. The
thermal cycling conditions for Real-Time PCR were: 15 min hold at 50°C (cDNA synthesis), 2 min
hold at 95°C, followed by 45 cycles of 95°C for 15 seconds, and 60°C for 30 seconds. Real-Time
PCR validation was carried out using the 2-ΔΔCT method (Livak KJ 2001). Normalized gene
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
expression values for each gene based on cycle threshold (CT) values for each of the genes and
housekeeping gene GAPDH were generated. Mean ± standard error (SE) values were generated
from six samples from each group of either the loaded or control samples tested.
The statistical significance of the differences between the means of loaded and control group gene
expression values was determined using two-tailed Student’s t-test. The critical value for
significance was chosen as P < 0.05.
Results
The mRNA levels of four genes (Wnt5a, Asporin, Xiap and Stat5a), whose up-regulation in both
single and repeated loading studies was identified with microarrays, were evaluated by quantitative
real-time PCR using six pairs of loaded and non-loaded samples of trabecular osteocytes. The
results were consistent with the microarray data. Results of mRNA expression for two genes,
including Wnt5a and Asporin (3.14 and 2.41 fold-change respectively), reached statistical
significance (P < 0.05) for differential expression level between loaded and control samples. No
significant change was found for Xiap and Stat5a mRNA expression levels, however, with a trend
to up-regulation (Fig. 4.7).
Real-time PCR
Relative mRNA expression, ddCT
0,014
0,012
*
0,01
0,008
non-loaded
*
0,006
loaded
0,004
0,002
0
Asporin
Wnt5a
Xiap
Stat5a
Fig. 4.7: Quantitative Real-Time PCR analysis for Asporin, Wnt5a, Xiap and Stat5a mRNA transcripts
following single mechanical loading.
Real-Time PCR results indicated that the mRNA levels of Wnt5a and Asporin were indeed
confirmed to be up-regulated, based on microarray data, between loaded and control specimens in
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
single loading samples. Further confirmation by real-time PCR analysis of individual genes after
repeated loading should provide additional information on events that take place in a sustained
loading regime.
Discussion
In this study we observed small gene expression ratios in real-time PCR analyses of load-regulated
genes in microarrays. These are probably contributed by the complex mix of osteocytic cells being
assayed, along with the subtle changes to bone that are observed in response to mechanical loading.
Microarray analysis is able to reliably detect small (< 2-fold) changes that prove to be biologically
relevant (87), and in our study we were able to confirm two of the differentially expressed genes by
real-time PCR analysis, including Wnt5a and asporin. Furthermore, the power of the microarray
analysis approach lies in its ability to detect genome-wide, coordinated, or similarly regulated
differential gene expression, pointing to perturbed signalling pathways and importantly downstream
molecular processes. Our study has identified such relationships between commonly regulated
target genes (via WNT signalling pathways) that play roles, in particular, in osteocytes, potentially
influencing bone formation, mineralization and remodelling.
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Chapter 4: Load-induced differential regulation mRNA of trabecular osteocytes
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Chapter 5: Synthesis
Chapter 5
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Chapter 5: Synthesis
Synthesis
5.1. Background and innovations
Osteoporosis is a disease characterized by an excessive decrease in bone mass leading to an
increased susceptibility to skeletal fracture and deformation, symptoms which can have a dramatic,
negative impact on the quality of a person’s life and which, in more extreme cases, can lead to
death. A common misconception about this disease is that it is considered to only afflict females but
the prevalence in men also increases exponentially with age. The rise in hip fracture rate occurs
about 10 years earlier in women than men. By the age of 90, about 17% of males have had a hip
fracture, compared to 32% of females. Further to the obvious costs on health, osteoporosis is a
global problem and carries with it significant socio-economic costs. This is illustrated by the IOF
audit report “Call to Action” published in 2001, which claims that osteoporosis costs national
treasuries in the EU over 4.8 billion Euro annually in hospital healthcare alone. Clinically approved
strategies aimed at treating the disease employ hormonal based medications which disrupt the bone
remodeling process via provocation of bone forming cells or the inhibition of bone resorbing cells.
However, these strategies have limited effects and in some cases negative consequences.
Medical research is now attempting to target the genes which define osteoporosis using the
mouse as a model system for human diseases. Owing to the recent deciphering of the mouse
genome and the high homology that exists between the human and mouse genomes, inbred strains
of mice represent ideal models for genetic studies. Using the mouse to identify genes implicated in
the bone remodeling process could therefore lead to advances in understanding that enable the
precise regulation of the genes and proteins responsible for particular bone phenotypes i.e. bone
mineral density or bone strength. One interesting phenotype under investigation is the response of
bone to mechanical loading or its ‘mechano-sensitivity’. Mechanical loading is the most important
physiological/environmental factor regulating bone mass and shape. It has been demonstrated in
humans that cyclic overloading enhances bone mass in both cortical and trabecular components. An
understanding of the biological pathways (from gene expression to protein function) governing load
stimulated bone formation could therefore provide opportunities to mimic or augment bone
mechano-sensitivity using pharmacological agents thereby leading to the development of novel
strategies in the management of osteoporosis.
The goal of this thesis was to propose a two-step strategy which calls for demonstrating (i) a
working technique for the isolation of intact total RNA from a well-defined trabecular osteocyte
enriched cell population derived from a single mouse caudal vertebra by sequential enzymatic
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Chapter 5: Synthesis
digestion with subsequent bone tissue pulverization; (ii) single and repeated load-induced
differentially expressed genes in trabecular osteocytes in order to elucidate the molecular
mechanisms and pathways involved in the trabecular adaptation to mechanical loads. This novel
work proposes a new experimental method of mechanically loaded trabecular bone which
combining the isolation of trabecular osteocytic RNA to study load-regulated differential gene
expression globally using cDNA microarrays, and use of the single mouse for one biological
sample, the only mammalian species with a well-defined genome currently accessible to routine
manipulations. A successful implementation of this strategy provides a method and baseline for
further studies elucidating the molecular mechanisms involved in the trabecular adaptation to
mechanical loads.
This project also identified several genes and signaling pathways of interest as targets for future
exploration by genetic manipulations, such as knockout, mutated dominant negative and overexpression transgenic technologies for uncovering molecular mechanisms that translate mechanical
loads into improved cancellous bone properties. The actual design and implementation of such
specific transgenic approaches were beyond the scope of this thesis due to its definition as a
feasibility project.
5.2. Developing a method for isolation osteocyte RNA
Chapter 3 describes a new method for the extraction of a sufficient amount of intact total RNA
of well-defined trabecular osteocytic population derived from a single murine caudal vertebra (C5).
This was perhaps the most ambitious part of the project, aimed at establishing a protocol for the
isolation of representative samples of mouse vertebral RNA derived selectively from trabecular
osteoblasts/lining cells and osteocytes. The first step in this process was to physically excavate the
trabecular bone from the medullar cavity of the target caudal vertebra without contamination from
cortical bone. This first step was the most important in determining the yield of the final amount of
mRNA. The greater the amount of extracted RNA the better the signal achieved for gene
expression. The method and tools employed therefore enabled the maximum extraction of
trabecular bone. The medullar cancellous bone (total of approximately 8 mm3) was mechanically
separated using a sterile needle with syringe and MicroDrill, and flushed into cold RNAlater for
RNA stability. To capture the in vivo expression profile, further ex vivo gene transcription was
blocked by adding a transcriptional inhibitor (e.g., actinomycin D). Once the trabecular bone was
extracted, the cell populations enriched with bone marrow and osteoblast/lining cells were isolated
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Chapter 5: Synthesis
using sequential collagenase digestions and constituted Initial Digest, OBL1 and OBL2 cell
fractions. The remaining bony fragments were then briefly grinded in a mortar under liquid nitrogen
on dry ice until total pulverization in order to isolate a well-defined population of osteocytes (OST
fraction). High quality total RNA preparations were isolated immediately following cell separation
using conventional reagents and protocols (TRI Reagent). The accuracy of enzymatic ‘stripping’ of
the appropriate cells was confirmed histologically following each digestion step and indicated
selective isolation of RNA from osteoblast/lining cell and osteocyte population. Extracted total
RNA was then analyzed on quality and quantity using Bioanalyzer technology which has shown
intact mRNA suitable for downstream RT-PCR applications.
Gene expression profiling and immunohistochmistry analysis were performed in order to
confirm appropriate isolation of different cell populations using specific primers for osteoblast and
osteocyte lineages. A minimum of o.5 ng of total intact RNA was required for a single cDNA
microarray run, using a pico RNA amplification kit. Before attempting to identify load induced
gene expression, this protocol was evaluated and optimized by applying it to groups of non-loaded
mice.
Additionally, in this chapter preliminary trials are described including sequential collagenase
digestions with decalcification/resorption agents and a laser-captured microdissection technique
which enabled a new approach to be found for developing a robust method for the isolation of
representative samples of total RNA derived from well-defined trabecular osteocytes of single
mouse caudal vertebra.
5.3. Single and repetitive mechanical loading
Chapter 4 presents the effect of a single and repetitive mechanical loading on differential gene
expression and signaling pathway analysis in trabecular osteocytes using cDNA microarrays. Once
the protocol for the isolation of total RNA from well-defined trabecular bone cells was shown to
yield a sufficient quality and quantity, a critical proof of principle for testing this C57BL/6 mouse
model was the identification of load-regulated genes in a trabecular osteocyte-enriched cell
population immediately following load application. It was rationalized that a gross gain in bone
density as a consequence of multiple load dosing represents the cumulative effect of repetitive
single
doses.
Therefore,
to
investigate
the
basic
cellular
responses
to
mechanical
loading/overloading we studied the set of molecular events evoked by a single loading cycle. The
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Chapter 5: Synthesis
intention was to load groups of mice at a magnitude of 0N and 8N for 3’000 cycles, at a frequency
of 10 Hz followed by RNA extraction after 6 hours of loading.
The microarrays analysis of a single loading revealed that 28000 and 34038 probes, out of a total
45101 probe sets per chip, showed a signal in the 0N and 8N group, respectively. A total of 331
genes whose expression levels showed a 2-fold change between the 0N and 8N groups were
identified when the statistical test was carried out at a p <=0.05 level of significance. Of these genes
the expression of 281 was up-regulated and that of 50 was down-regulated. In particular, it showed
up-regulation of IGF-1 (2.2 fold change), Wnt5a (3.4 fold) and Asporin (3.24 fold) genes which are
thought to be activators of osteoblast differentiation and are also responsive to mechanical strain. In
contrast, a down-regulation of WNT inhibitor factor 1 gene (WIF-1, 1.8 fold), inhibitor of
WNT/beta-cathenin pathway, thought to play an important role in stimulation of osteoblast
differentiation and bone formation, was identified. In addition, quantitative real-time PCR results
have shown that the mRNA levels of Wnt5a and Asporin were indeed confirmed to be up-regulated,
based on microarray data, between loaded and control samples in the acute loading study.
The single loading study investigated the basic cellular responses to mechanical loading evoked
by a single loading cycle; however the question remained whether these were acute or chronic
responses. Therefore, the mechanical loading protocol used in single loading was employed for an
extended period of time, 3 times a week for 4 weeks to directly compare results to investigate the
response of trabecular osteocytes in single and repeated loading reqimes. The microarray analysis of
the repeated loading revealed that 27997 and 36414 probes out of a total of 45101 probe sets per
chip, showed a signal in the 0N and 8N group, respectively. A total of 1342 genes whose expression
levels showed a 2-fold change between the 0N and 8N groups were identified when the statistical
test was carried out at a P <=0.05 level of significance. Of these genes the expression of 781 was
up-regulated and that of 561 was down-regulated. In particular, it showed up-regulation of Wnt5a
(2.19 fold), Asporin (4.9 fold) and DMP-1 (2.17 fold) genes which are thought to be activators of
osteoblast differentiation and are also responsive to mechanical loading. The genes presented after a
single load dose, but no longer expressed after the 4-week loading protocol, were considered
transient, for example IGF-1. The identification of these genes raised interesting questions on the
time scale of their activation throughout the duration of the repeated loading protocol, a
consideration for future research. Conversely, genes expressed after the 4-week protocol, but not
previously identified in the acute study, were considered chronic, for instance DMP-1, and might
also pose interesting questions for future research. A third possible result for some genes was that
there were no differences in gene expression between the two loading protocols, Wnt5a and asporin
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Chapter 5: Synthesis
genes. This finding confirmed the assumption that the anabolic adaptation of the cancellous bone is
truly an additive process resulting from three times per week single doses of 5-minute loading
regimes. These results will have implications for the development of loading regimes to induce
anabolic bone adaptation, and provide further insight into the fundamental genetic processes
governing bone adaptation.
Also, the results of Real-Time PCR in Chapter 4 indicate that the mRNA levels of Wnt5a and
Asporin genes were indeed confirmed to be up-regulated, based on microarray data, between loaded
and control specimens in single mechanical loading samples.
5.4. Functional genomics for identification of load-regulated pathways
Additionally, Chapter 4 presents load-regulated signaling pathway analysis following single and
repeated mechanical loads using the MetaCore GeneGO software program. Functional genomics
examined the in vivo effect of single and repeated mechanical loading on differentially expressed
genes in the whole genome derived from trabecular osteocytes, and identified a number of signaling
molecules and pathways that may play important roles in mediating the skeletal anabolic response
to mechanical force. MetaCore comes with a built-in natural language processing module MedScan
and a comprehensive database containing more than 150,000 events of regulation, interaction and
modification between proteins and cell processes obtained from PubMed which allows it to generate
a biological association network (BAN) of known protein–protein interactions. By importing
microarray expression data into the BAN, co-expressed genes associated with specific signaling
pathways were identified. Functional genomics analysis revealed overall 65 load-regulated
signaling pathways in a single mechanical loading and 153 load-regulated signaling pathways in a
repeated loading study between loaded and control samples. The current study demonstrated that
mechanical
loading
potentially
induces
multiple
signaling
pathways
involved
in
mechanotransductuion and bone metabolism. Future studies on these unknown genes and signal
molecules will provide a better understanding of the molecular pathways involved in mediating the
skeleton’s anabolic response to mechanical stress.
5.5. Limitations
In this thesis, a number of limitations can be identified. The whole procedure for isolation of
osteocyte RNA was time-consuming and did not allow RNA extraction from more than 12 mice per
day. Mechanical separation of trabecular bone from caudal vertebra did not always yield a sufficient
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Chapter 5: Synthesis
amount of 8 mm3 tissue volume to obtain the final amount of extracted 0.5 ng of total RNA.
Additionally, isolated osteocytic RNA may have some contamination from osteoblastic lineage
which has been shown by the gene expression profiling, described in Chapter 3. Finally, the actual
design and implementation of specific transgenic technologies were obviously beyond the scope of
this thesis due to its definition as a feasibility project.
5.6. Future work
Future work will be focused on applying knockout, mutated dominant negative and over-expression
transgenic technologies to the study of molecular mechanisms involved in the cancellous bone
response to mechanical loading. It is anticipated that several genes of interest which were identified
in this project will be targets for future exploitation by genetic manipulation strategies to elucidate
molecular mechanisms that translate mechanical loads into improved bone properties. To this end,
future work will characterize the trabecular bone response to loading in Wnt5a+/- mutated mice
using micro-computered tomography and bone histomorph0metry
5.7. Conclusions
In conclusion, this thesis provided a method for the isolation of mRNA from well-defined
trabecular osteocytes, and cDNA microarrays revealed the mechanobiological effect of acute and
chronic loading regimes in-vivo on global murine differential gene expression, including analysis of
signaling pathways. It investigated load-induced basic cellular response in trabecular bone and
identified genes of interest that were regulated by mechanical loading in order to elucidate the
molecular mechanisms involved in the osteogenic anabolic effect of mechanical loading. This in
turn will lead the way to studying the role of genes in load-stimulated bone formation in
corresponding transgenic systems.
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Appendix
Appendix
Contents
A1. List of up-regulated genes in single loading…………………………………………………..134
A2. List of down-regulated genes in single loading……………………………………………….143
A3. List of up-regulated genes in repeated loading………………………………………………..146
A4. List of down-regulated genes in repeated loading…………………………………………….168
A5. List of load-regulated signalling pathways in single loading………………………………....184
A6. List of load-regulated signalling pathways in repeated loading……………………………....186
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Appendix
Table A1. Up-regulated gene expression in trabecular osteocytes induced by single loading dose
Cell growth and differentiation
Gene
Symbol
Mpo
Rb1
Slfn4
Braf
Wipi1
Jmjd6
Wnt5a**
Foxc2
Aspn**
Xiap**
Vps13a
Entrez
Gene ID
17523
19645
20558
109880
52639
107817
22418
14234
66695
11798
271564
Gcnt2
14538
Nat12
Ankrd11**
Ccr1
Aldh1a7
Mdm2
Cxadr
Tbc1d1
Vav1
Tgm2
Itk
Pex5
Plxnc1
Slfn2
Plek
Abhd5
Dhx36
Stc1
70646
77087
12768
26358
17246
13052
57915
22324
21817
16428
19305
54712
20556
56193
67469
72162
20855
Maff
17133
Stk4
Pabpn1
58231
54196
Appl2
216190
Nf1
Prlr
18015
19116
Gene Description
Myeloperoxidase
Retinoblastoma 1
Schlafen 4
Braf transforming gene
WD repeat domain phosphoinositide interacting 1
Jumonji domain containing 6
Wingless-related MMTV integration site 5A
Forkhead box C2
Asporin
X-linked inhibitor of apoptosis
Vacuolar protein sorting 13A
Glucosaminyl (N-acetyl) transferase 2, I-branching
enzyme
N-acetyltransferase 12
Ankyrin repeat domain 11
Chemokine (C-C motif) receptor 1
Aldehyde dehydrogenase family 1, subfamily A7
Transformed mouse 3T3 cell double minute 2
Coxsackievirus and adenovirus receptor
TBC1 domain family, member 1
Vav 1 oncogene
Transglutaminase 2, C polypeptide
IL2-inducible T-cell kinase
Peroxisome biogenesis factor 5
Plexin C1
Schlafen 2
Pleckstrin
Abhydrolase domain containing 5
DEAH (Asp-Glu-Ala-His) box polypeptide 36
Stanniocalcin 1
V-maf musculoaponeurotic fibrosarcoma oncogene,
protein F
Serine/threonine kinase 4
Poly(A) binding protein, nuclear 1
Adaptor protein, phosphotyrosine interaction,
leucine zipper 2
Neurofibromatosis 1
Prolactin receptor
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Fold
Change
5.25*
4.35
4.22
3.97
3.87
3.81
3.37
3.28
3.24
3.23
3.17
3.13
3.09
3.05
2.96
2.96
2.95
2.86
2.80
2.76
2.62
2.57
2.49
2.47
2.47
2.47
2.42
2.41
2.39
2.37
2.34
2.36
2.26
2.26
2.25
Appendix
Golgi associated PDZ and coiled-coil motif
containing
Ptpro
19277 Protein tyrosine phosphatase, receptor type, O
Efnb2
13642 Ephrin B2
Igf1
16000 Insulin-like growth factor 1
Ldb1
16825 LIM domain binding 1
Bin1
30948 Bridging integrator 1
Hrb
15463 HIV-1 Rev binding protein
Ddx27
228889 DEAD (Asp-Glu-Ala-Asp) box polypeptide 27
Kng1
16644 Kininogen 1
Zfx
22764 Zinc finger protein X-linked
Mgll
23945 Monoglyceride lipase
Mef2c**
17260 Myocyte enhancer factor 2C
Itga4
16401 Integrin alpha 4
Xdh**
22436 Xanthine dehydrogenase
Adam10
11487 A disintegrin and metallopeptidase domain 10
Ifi204**
15951 Interferon activated gene 204
Hoxb4
15412 Homeo box B4
Ets2
23872 E26 avian leukemia oncogene 2, 3' domain
Sprouty protein with EVH-1 domain 1, related
Spred1
114715
sequence
Ppp3cb
19056 Protein phosphatase 3 catalytic subunit, β-isoform
Dhx30
72831 DEAH (Asp-Glu-Ala-His) box polypeptide 30
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
Gopc
94221
2.25
2.22
2.22
2.22
2.20
2.18
2.17
2.16
2.15
2.14
2.13
2.09
2.08
2.07
2.04
2.03
2.03
2.03
2.02
2.01
2.01
Apoptosis
Camk1d
Luc7l
Rtn4
227541
66978
68585
Gulp1
70676
Tmem19
Rcsd1
Pmaip1
Tmem167
Zfyve26
Becn1
67226
226594
58801
66074
211978
56208
Tia1
21841
Ncf1
17969
Tnfrsf1b
21938
Tmem77
67171
Calcium/calmodulin-dependent protein kinase ID
Luc7 homolog (S. cerevisiae-like)
Reticulon 4
GULP, engulfment adaptor PTB domain containing
1
Transmembrane protein 19
RCSD domain containing 1
Phorbol-12-myristate-13-acetate-induced protein 1
Transmembrane protein 167
Zinc finger, FYVE domain containing 26
Beclin 1, autophagy related
Cytotoxic granule-associated RNA binding protein
1
Neutrophil cytosolic factor 1
Tumor necrosis factor receptor superfamily,
member 1b
Transmembrane protein 77
- 135-
2.99*
2.77
2.76
2.60
2.58
2.55
2.50
2.34
2.19
2.18
2.13
2.09
2.08
2.05
Appendix
Thyn1
77862 Thymocyte nuclear protein 1
*p value smaller than 0.05
2.01
Cell cycle
Btbd11**
Clasp1
74007
76707
BTB (POZ) domain containing 11
CLIP associating protein 1
Oligonucleotide/oligosaccharide-binding fold
Obfc2a
109019
containing 2A
Ccnd3
12445 Cyclin D3
Gas5
14455 Growth arrest specific 5
Psmd11
69077 Proteasome 26S subunit, non-ATPase, 11
Rmnd5a
68477 Required for meiotic nuclear division 5 homolog A
Calm1
12313 Calmodulin 1
Stag1
20842 Stromal antigen 1
Rfc3
69263 Replication factor C (activator 1) 3
Ppp6c
67857 Protein phosphatase 6, catalytic subunit
Gnl3
30877 Guanine nucleotide binding protein-like 3
Kpna4
16649 Karyopherin (importin) alpha 4
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
3.91*
3.21
2.90
2.45
2.42
2.30
2.29
2.26
2.26
2.18
2.03
2.01
2.01
Signal transduction
Cdgap
Adrbk2
12549
320129
Rassf5
54354
Als2cl
Ly6g6c
235633
68468
Csf2rb
12983
Pram1
378460
Rapgefl1
268480
Akap10
56697
Serpina3c
16625
Stat5a**
Ptgfr
Pdpk1
Iqgap1
Plxna2
Rab23
Traf1
20850
19220
18607
29875
18845
19335
22029
Cdc42 GTPase-activating protein
Adrenergic receptor kinase, beta 2
Ras association (RalGDS/AF-6) domain family
member 5
ALS2 C-terminal like
Lymphocyte antigen 6 complex, locus G6C
colony stimulating factor 2 receptor beta,
granulocyte-macrophage
PML-RAR alpha-regulated adaptor molecule 1
Rap guanine nucleotide exchange factor (GEF)-like
1
A kinase (PRKA) anchor protein 10
Serine (or cysteine) peptidase inhibitor, clade A,
member 3C
Signal transducer and activator of transcription 5A
Prostaglandin F receptor
3-phosphoinositide dependent protein kinase-1
IQ motif containing GTPase activating protein 1
Plexin A2
RAB23, member RAS oncogene family
Tnf receptor-associated factor 1
- 136-
4.00*
3.68
2.88
2.87
2.84
2.72
2.68
2.64
2.60
2.56
2.49
2.48
2.42
2.34
2.30
2.26
2.25
Appendix
Lrba
Cd19
Ccr9
Gpr68
Stat1
Stat4
Tm6sf1
Akap12
Map3k3
Rapgef6
Prdx6
80877
12478
12769
238377
20846
20849
107769
83397
26406
192786
11758
LPS-responsive beige-like anchor
CD19 antigen
Chemokine (C-C motif) receptor 9
G protein-coupled receptor 68
Signal transducer and activator of transcription 1
Signal transducer and activator of transcription 4
transmembrane 6 superfamily member 1
A kinase (PRKA) anchor protein (gravin) 12
mitogen-activated protein kinase kinase kinase 3
Rap guanine nucleotide exchange factor (GEF) 6
Peroxiredoxin 6
MARVEL (membrane-associating) domain
Marveld1
277010
containing 1
Lnx2
140887 Ligand of numb-protein X 2
Muskelin 1, intracellular mediator containing kelch
Mkln1
27418
motifs
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
2.24
2.18
2.16
2.15
2.14
2.14
2.11
2.10
2.09
2.09
2.07
2.05
2.01
2.00
Transcription
Ppih
Rbm39
Trim26
Zfp711
Zfp260
Cdyl2
Mycbp
Suz12
66101
170791
22670
245595
26466
75796
56309
52615
Nfkbiz
80859
Zfp207
Cpsf6
Supt6h
Rpo1-3
Rwdd4a
Papola
Cpsf6
Prpf40a
Zfp248
Trps1
Orc2l
Ebf3
Baz1b
22680
432508
20926
20018
192174
18789
432508
56194
72720
83925
18393
13593
22385
Peptidyl prolyl isomerase H
RNA binding motif protein 39
Tripartite motif-containing 26
Zinc finger protein 711
Zinc finger protein 260
Chromodomain protein, Y chromosome-like 2
C-myc binding protein
Suppressor of zeste 12 homolog
Nuclear factor of kappa light polypeptide gene
enhancer, zeta
Zinc finger protein 207
Cleavage and polyadenylation specific factor 6
Suppressor of Ty 6 homolog
RNA polymerase 1-3
RWD domain containing 4A
Poly (A) polymerase alpha
Cleavage and polyadenylation specific factor 6
PRP40 pre-mRNA processing factor 40 homolog A
Zinc finger protein 248
Trichorhinophalangeal syndrome I
Origin recognition complex, subunit 2-like
Early B-cell factor 3
Bromodomain adjacent to zinc finger domain, 1B
- 137-
4.05*
3.92
3.42
3.38
3.37
3.30
3.01
2.95
2.90
2.90
2.89
2.79
2.78
2.76
2.73
2.68
2.67
2.67
2.60
2.55
2.51
2.51
Appendix
Nlrc3
Polr1e
Jarid1b
Xbp1
Rlf
Phf23
Ccdc128
Limd1
Rp9
Sp4
Noc4l
268857
64424
75605
22433
109263
78246
73825
29806
55934
20688
100608
Ctdp1
67655
Ccnt2
72949
Zfp711
245595
Jmjd1c
108829
Trps1
83925
Rnf168
70238
Tcta
102791
Frg1
14300
Tox4
268741
Egr2
13654
Zfp503
218820
Mobkl2a
208228
Hnrpab
15384
Fusip1
14105
Ptrf
19285
Nsun6
74455
Sf3b2
319322
Creb1
12912
Bbx
70508
Rnf169
108937
*p value smaller than 0.05
NLR family, CARD domain containing 3
Polymerase (RNA) I polypeptide E
Jumonji, AT rich interactive domain 1B (Rbp2 like)
X-box binding protein 1
Rearranged L-myc fusion sequence
PHD finger protein 23
Coiled-coil domain containing 128
LIM domains containing 1
Retinitis pigmentosa 9
Trans-acting transcription factor 4
Nucleolar complex associated 4 homolog
Carboxy-terminal domain, polypeptide A
phosphatase, subunit 1
Cyclin T2
Zinc finger protein 711
Jumonji domain containing 1C
Trichorhinophalangeal syndrome I
Ring fnger protein 168
T-cell leukemia translocation altered gene
FSHD region gene 1
TOX high mobility group box family member 4
Early growth response 2
Zinc finger protein 503
MOB1, Mps one binder kinase activator-like 2A
Heterogeneous nuclear ribonucleoprotein A/B
FUS interacting protein (serine-arginine rich) 1
Polymerase I and transcript release factor
NOL1/NOP2/Sun domain family 6
Splicing factor 3b, subunit 2
cAMP responsive element binding protein 1
Bobby sox homolog
Ring finger protein 169
2.45
2.43
2.43
2.41
2.40
2.35
2.29
2.29
2.27
2.26
2.26
2.24
2.22
2.21
2.20
2.16
2.16
2.15
2.15
2.14
2.14
2.13
2.10
2.08
2.07
2.04
2.03
2.02
2.02
2.01
2.00
Immune response
Vpreb1
Msr1
Il1b
Ccl12
22362
20288
16176
20293
Fgr
14191
Ccr2
12772
Pre-B lymphocyte gene 1
Macrophage scavenger receptor 1
Interleukin 1 beta
Chemokine (C-C motif) ligand 12
Gardner-Rasheed feline sarcoma viral oncogene
homolog
Chemokine (C-C motif) receptor 2
- 138-
5.70*
4.10
3.53
3.51
3.22
3.17
Appendix
Chi3l4
104183
Pla2g7
27226
Il1rn
16181
Clec4d
17474
Ier5
15939
Ifitm6
213002
Fcgr2b
14130
Ccr2
12772
Cd300lb
217304
Rac2
19354
*p value smaller than 0.05
Chitinase 3-like 4
Phospholipase A2, group VII
Interleukin 1 receptor antagonist
C-type lectin domain family 4, member d
Immediate early response 5
Interferon induced transmembrane protein 6
Fc receptor, IgG, low affinity IIb
Chemokine (C-C motif) receptor 2
CD300 antigen like family member B
RAS-related C3 botulinum substrate 2
3.13
3.08
3.08
2.90
2.83
2.46
2.25
2.19
2.19
2.07
Muscle contraction
Dtna
13527 Dystrobrevin alpha
Prrx1
18933 Paired related homeobox 1
Cugbp2
14007 CUG triplet repeat, RNA binding protein 2
*p value smaller than 0.05
4.36*
2.40
2.07
Cytoskeleton remodeling
Adamts4
240913
S100a9
20202
Tubb6
67951
Cnp
12799
Evl
14026
Dnajc7
56354
Rai14
75646
Fmnl1
57778
Tsga14
83922
Elmo1
140580
Ttll3
101100
Wdr1
22388
Arhgap10
78514
*p value smaller than 0.05
Disintegrin-like metallopeptidase, thrombospondin
type 1, 4
S100 calcium binding protein A9 (calgranulin B)
Tubulin, beta 6
2',3'-cyclic nucleotide 3' phosphodiesterase
Ena-vasodilator stimulated phosphoprotein
DnaJ (Hsp40) homolog, subfamily C, member 7
Retinoic acid induced 14
Formin-like 1
Testis specific gene A14
Engulfment and cell motility 1, ced-12 homolog
Tubulin tyrosine ligase-like family, member 3
WD repeat domain 1
Rho GTPase activating protein 10
4.11*
2.91
2.91
2.86
2.55
2.45
2.44
2.16
2.15
2.12
2.10
2.07
2.07
Proteolysis
Adamts1
11504
Ermp1
Asb3
226090
65257
Serpinb1a
66222
Disintegrin-like metallopeptidase, thrombospondin
type 1, 1
Endoplasmic reticulum metallopeptidase 1
Ankyrin repeat and SOCS box-containing protein 3
Serine (or cysteine) peptidase inhibitor, clade B,
member 1a
- 139-
3.63*
2.39
2.23
2.21
Appendix
Rnf13
24017 Ring finger protein 13
Lnpep
240028 Leucyl/cystinyl aminopeptidase
Ubfd1
28018 Ubiquitin family domain containing 1
*p value smaller than 0.05
2.19
2.07
2.03
Translation
Tnrc6a
Etf1
Secisbp2
Srp54b
233833
225363
75420
665155
Ppp1r15b
108954
Syk
20963
Stom
13830
Rpl37a
19981
*p value smaller than 0.05
Trinucleotide repeat containing 6a
Eukaryotic translation termination factor 1
SECIS binding protein 2
Signal recognition particle 54b
Protein phosphatase 1, regulatory (inhibitor)
subunit 15b
Spleen tyrosine kinase
Stomatin
Ribosomal protein L37a
3.96*
2.45
2.44
2.34
Dedicator of cytokinesis 8
Formyl peptide receptor, related sequence 2
Diaphanous homolog 1
G protein-coupled receptor 177
G protein-coupled receptor 137B
GTP binding protein 4
3.97*
2.88
2.62
2.33
2.31
2.14
Selectin, endothelial cell
Fermitin family homolog 3
CD38 antigen
Intersectin 1 (SH3 domain protein 1A)
Transmembrane protein 206
Olfactomedin 4
LIM domain containing preferred translocation
partner in lipoma
Pleckstrin homology, Sec7 and coiled-coil
domains, binding protein
Killer cell lectin-like receptor, subfamily A,
member 2
Protocadherin beta 16
Elastin microfibril interfacer 2
3.74*
3.19
2.93
2.83
2.69
2.45
2.19
2.17
2.13
2.05
G-protein signaling
Dock8
76088
Fpr-rs2
14289
Diap1
13367
Gpr177
68151
Gpr137b
83924
Gtpbp4
69237
*p value smaller than 0.05
Cell adhesion
Sele
Fermt3
Cd38
Itsn1
Tmem206
Olfm4
20339
108101
12494
16443
66950
380924
Lpp
210126
Pscdbp
227929
Klra2
16633
Pcdhb16
93887
Emilin2
246707
*p value smaller than 0.05
- 140-
2.33
2.33
2.27
2.23
2.10
Appendix
Chemotaxis
Chi3l3
12655
Chitinase 3-like 3
C5ar1
12273 Complement component 5a receptor 1
*p value smaller than 0.05
2.93*
2.31
Transport
Appbp2
Clca1
66884
12722
Slc8a1
20541
Srr
Atp8b4
Vps33b
Xpo4
Cep350
Exoc4
Nup188
27364
241633
233405
57258
74081
20336
227699
Srpr
67398
Col4a3bp
68018
Slc25a25
227731
Snx9
March1
Xpo1
66616
72925
103573
Tram2
170829
Slc7a2
11988
Kpna3
16648
Uqcrq
22272
Pacs1
Osbpl7
Osbpl6
Ppm1m
107975
71240
99031
67905
Plekha3
83435
Ipo9
226432
*p value smaller than 0.05
Amyloid beta precursor protei binding protein 2
Chloride channel calcium activated 1
Solute carrier family 8 (sodium/calcium exchange),
member 1
Serine racemase
ATPase, class I, type 8B, member 4
Vacuolar protein sorting 33B
Exportin 4
Centrosomal protein 350
Exocyst complex component 4
Nucleoporin 188
Signal recognition particle receptor ('docking
protein')
Collagen, type IV, alpha 3 (Goodpasture antigen)
binding protein
Solute carrier family 25 (mitochondrial carrier),
member 25
Sorting nexin 9
Membrane-associated ring finger (C3HC4) 1
Exportin 1, CRM1 homolog (yeast)
Translocating chain-associating membrane protein
2
Solute carrier family 7 (cationic amino acid
transporter), member 2
Karyopherin (importin) alpha 3
Ubiquinol-cytochrome c reductase, complex III
subunit VII
Phosphofurin acidic cluster sorting protein 1
Oxysterol binding protein-like 7
Oxysterol binding protein-like 6
Protein phosphatase 1M
Pleckstrin homology domain-containing, family A
member 3
Importin 9
3.63*
3.48
Histidine decarboxylase
3.34*
3.11
2.96
2.73
2.67
2.66
2.57
2.50
2.46
2.44
2.41
2.37
2.29
2.28
2.27
2.25
2.24
2.19
2.13
2.08
2.08
2.04
2.04
2.02
2.01
Energy metabolism
Hdc
15186
- 141-
Appendix
Phca
Pmm2
66190
54128
Pip4k2a
18718
Prkaa1
105787
Lipg
Ggps1
Cept1
Ipp
Glb1l
Adc
Angptl4
Dhodh
16891
14593
99712
16351
74577
242669
57875
56749
Ugt1a6a
94284
Glipr2
384009
Tnks
21951
Agxt2l2
Atp11c
Gda
72947
320940
14544
Cyp2d22
56448
Phytoceramidase, alkaline
Phosphomannomutase 2
Phosphatidylinositol-5-phosphate 4-kinase, type II,
alpha
Protein kinase, AMP-activated, alpha 1 catalytic
subunit
Lipase, endothelial
Geranylgeranyl diphosphate synthase 1
Choline/ethanolaminephosphotransferase 1
IAP promoted placental gene
Galactosidase, beta 1-like
Arginine decarboxylase
Angiopoietin-like 4
Dihydroorotate dehydrogenase
UDP glucuronosyltransferase 1 family, polypeptide
A6A
GLI pathogenesis-related 2
Tankyrase, TRF1-interacting ankyrin-related ADPribose polymerase
Alanine-glyoxylate aminotransferase 2-like 2
ATPase, class VI, type 11C
Guanine deaminase
Cytochrome P450, family 2, subfamily d,
polypeptide 22
2.92
2.90
2.76
2.73
2.71
2.66
2.43
2.38
2.38
2.38
2.29
2.26
2.19
2.19
2.15
2.14
2.06
2.03
2.02
*p value smaller than 0.05
Neurophysiological process
Gphn
268566 Gephyrin
*p value smaller than 0.05
2.52*
- 142-
Appendix
Table A2. Down-regulated gene expression in trabecular osteocytes induced by single loading dose
Cell growth and differentiation
Gene
Symbol
Arhgap24
Gprin3
Foxq1
Olfm3
Foxp2
Triobp
Entrez
Gene ID
231532
243385
15220
229759
114142
110253
Spock2
94214
Prdm1
Zfp39
12142
22698
Gprin2
432839
Fzd5
14367
Six4
20474
Col19a1
12823
*p value smaller than 0.05
Gene Description
Fold Change
Rho GTPase activating protein 24
GPRIN family member 3
Forkhead box Q1
Olfactomedin 3
Forkhead box P2
TRIO and F-actin binding protein
Sparc/osteonectin, cwcv and kazal-like domains
proteoglycan 2
PR domain containing 1, with ZNF domain
Zinc finger protein 39
G protein regulated inducer of neurite outgrowth
2
Frizzled homolog 5
Sine oculis-related homeobox 4 homolog
Collagen, type XIX, alpha 1
3.32*
3.08
2.46
2.35
2.30
2.22
OTU domain containing 1
2.30*
2.20
2.19
2.17
2.09
2.05
2.03
2.01
Apoptosis
Otud1
71198
*p value smaller than 0.05
Cell cycle
Uhrf2
109113
Cct4
12464
Brca2
12190
Phgdh
236539
*p value smaller than 0.05
Ubiquitin-like, containing PHD and RING
finger domains 2
Chaperonin subunit 4 (delta)
Breast cancer 2
3-phosphoglycerate dehydrogenase
2.32*
2.10
2.06
2.01
Signal transduction
Lrrc28
67867
Atp2a2
11938
Ppp2r5a
226849
Akt2
11652
*p value smaller than 0.05
Leucine rich repeat containing 28
ATPase, Ca++ transporting, cardiac muscle,
slow twitch 2
Protein phosphatase 2, regulatory subunit B
(B56), α-isoform
Thymoma viral proto-oncogene 2
Transcription
- 143-
3.16*
2.49
2.12
2.02
Appendix
Rian
Foxf1a
Zfp449
75745
15227
78619
Hcfc1r1
353502
Hdac11
232232
Smyd3
69726
Zdhhc2
70546
*p value smaller than 0.05
RNA imprinted and accumulated in nucleus
Forkhead box F1a
Zinc finger protein 449
Host cell factor C1 regulator 1 (XPO1dependent)
Histone deacetylase 11
SET and MYND domain containing 3
Zinc finger, DHHC domain containing 2
2.90*
2.76
2.65
Zinc finger, CCHC domain containing 11
2.00*
Ryanodine receptor 2, cardiac
2.04*
Midline 1
Actin binding LIM protein family, member 3
Chloride channel, nucleotide-sensitive, 1A
2.91*
2.34
2.01
Ubiquitin specific peptidase 25
2.29*
CD34 antigen
2.03*
2.43
2.32
2.20
2.09
Immune response
Zcchc11
230594
*p value smaller than 0.05
Muscle contraction
Ryr2
20191
*p value smaller than 0.05
Cytoskeleton remodeling
Mid1
17318
Ablim3
319713
Clns1a
12729
*p value smaller than 0.05
Proteolysis
Usp25
30940
*p value smaller than 0.05
Cell adhesion
Cd34
12490
*p value smaller than 0.05
Transport
Ndufc1
Sh3gl2
66377
20404
Trpm1
17364
Exoc8
102058
Filip1
70598
*p value smaller than 0.05
NADH dehydrogenase (ubiquinone) 1, 1
SH3-domain GRB2-like 2
Transient receptor potential cation channel,
subfamily M, 1
Exocyst complex component 8
Filamin A interacting protein 1
2.51*
2.39
Oxysterol binding protein-like 6
2.41*
2.36
2.32
2.23
Energy metabolism
Osbpl6
99031
- 144-
Appendix
Ppp1r3c
53412
Ckm
12715
Tkt
21881
*p value smaller than 0.05
Protein phosphatase 1, regulatory inhibitor, 3C
Creatine kinase, muscle
Transketolase
2.38
2.11
2.07
Taxilin beta
Asparaginase like 1
2.50*
2.10
Neurophysiological process
Txlnb
378431
Asrgl1
66514
*p value smaller than 0.05
Unknown process
D1Ertd399e
52296
C79741
97877
C77717
97361
*p value smaller than 0.05
DNA segment, Chr 1, ERATO Doi 399,
expressed
Expressed sequence C79741
Expressed sequence C77717
- 145-
4.08*
3.70
2.74
Appendix
Table A3. Up-regulated gene expression in trabecular osteocytes induced by multiple loading doses
Cell growth and differentiation
Gene
Symbol
Cryab
H19
Tnmd
Ptn**
Ttc8
Dixdc1
Ldb3
Aspn**
Thbs4
Col3a1
Ttn
Gas1
Eno3
Mpz
Tnnt3
Meox2
Fkbp4
Entrez
Gene ID
12955
14955
64103
19242
76260
330938
24131
66695
21828
12825
22138
14451
13808
17528
21957
17286
14228
Plekhb1
27276
Cilp**
Itm2a**
Sema3c
Pcp4
Col9a3
Rpgr
Atm
Sema3d
Itgb1bp2
Fmod
Tmem46
Ankrd6
Prkcdbp
Mapk12
Cgrrf1
Cdh13
Fzd7
Drg1
214425
16431
20348
18546
12841
19893
11920
108151
26549
14264
219134
140577
109042
29857
68755
12554
14369
13494
Gene Description
Crystallin, alpha B
H19 fetal liver mRNA
Tenomodulin
Pleiotrophin
Tetratricopeptide repeat domain 8
DIX domain containing 1
LIM domain binding 3
Asporin
Thrombospondin 4
Collagen, type III, alpha 1
Titin
Growth arrest specific 1
Enolase 3, beta muscle
Myelin protein zero
Troponin T3, skeletal, fast
Mesenchyme homeobox 2
FK506 binding protein 4
Pleckstrin homology domain, family B (evectins)
member 1
Cartilage intermediate layer protein
Integral membrane protein 2A
Semaphorin 3C
Purkinje cell protein 4
Collagen, type IX, alpha 3
Retinitis pigmentosa GTPase regulator
Ataxia telangiectasia mutated homolog
Semaphorin 3D
Integrin beta 1 binding protein 2
Fibromodulin
Transmembrane protein 46
Ankyrin repeat domain 6
Protein kinase C, delta binding protein
Mitogen-activated protein kinase 12
Cell growth regulator with ring finger domain 1
Cadherin 13
Frizzled homolog 7
Developmentally regulated GTP binding protein 1
- 146-
Fold
Change
8.22*
8.03
7.75
7.03
6.67
6.59
5.78
4.91
4.81
4.80
4.76
4.26
4.24
4.15
4.02
4.01
4.00
3.97
3.80
3.62
3.51
3.31
3.30
3.26
3.18
3.16
3.15
3.15
3.10
3.09
3.09
3.03
3.01
3.00
2.95
2.93
Appendix
Kif2a
Tmem58
Lix1l**
Bbs2
Ctnna1
Meg3**
Frzb
16563
77552
280411
67378
12385
17263
20378
Gnptab
432486
Mapk1
Igf2r
26413
16004
Crispld2
78892
Ttc3
Cgref1
Serf1
Ly6c1
Mkx
Mmp2**
Reps2
Cyr61
Xiap
Rbbp9
Mef2a**
Arl13b
Golga3
Csnk2a1
Dym
Vav3
Pftk1
Lgals2
Egln1
Bicc1
Aldh1a1
Cand1
Ndrg4
22129
68567
20365
17067
210719
17390
194590
16007
11798
26450
17258
68146
269682
12995
69190
57257
18647
107753
112405
83675
11668
71902
234593
Sirt1
93759
Nav1
Ghitm
Lgals7
Dph3
Ube2b
215690
66092
16858
105638
22210
Kinesin family member 2A
Transmembrane protein 58
Lix1-like
Bardet-Biedl syndrome 2 homolog
Catenin (cadherin associated protein), alpha 1
Maternally expressed 3
Frizzled-related protein
N-acetylglucosamine-1-phosphate transferase, alpha
and beta
Mitogen-activated protein kinase 1
Insulin-like growth factor 2 receptor
Cysteine-rich secretory protein LCCL domain
containing 2
Tetratricopeptide repeat domain 3
Cell growth regulator with EF hand domain 1
Small EDRK-rich factor 1
Lymphocyte antigen 6 complex, locus C1
Mohawk
Matrix metallopeptidase 2
RALBP1 associated Eps domain containing protein 2
Cysteine rich protein 61
X-linked inhibitor of apoptosis
Retinoblastoma binding protein 9
Myocyte enhancer factor 2A
ADP-ribosylation factor-like 13B
Golgi autoantigen, golgin subfamily a, 3
Casein kinase 2, alpha 1 polypeptide
Dymeclin
Vav 3 oncogene
PFTAIRE protein kinase 1
Lectin, galactose-binding, soluble 2
EGL nine homolog 1
Bicaudal C homolog 1
Aldehyde dehydrogenase family 1, subfamily A1
Cullin associated and neddylation disassociated 1
N-myc downstream regulated gene 4
Sirtuin 1 (silent mating type information regulation 2,
homolog) 1
Neuron navigator 1
Growth hormone inducible transmembrane protein
Lectin, galactose binding, soluble 7
DPH3 homolog (KTI11)
Ubiquitin-conjugating enzyme E2B, RAD6 homolog
- 147-
2.90
2.87
2.79
2.78
2.75
2.69
2.66
2.65
2.64
2.63
2.63
2.61
2.61
2.60
2.60
2.57
2.57
2.54
2.54
2.53
2.52
2.51
2.51
2.49
2.48
2.46
2.46
2.41
2.38
2.38
2.37
2.37
2.36
2.36
2.34
2.32
2.31
2.31
2.30
2.28
Appendix
Grem1
Wwp1
Foxc1
Strn
Ptprg
Jph1
Cyr61
Fgf18**
Grem2
Wnt5a
Edf1
Plec1
Dmp1
Serf2
Nrp1**
Mtap1b**
Mbnl1
Braf
Stoml2
Adcyap1r1
Cfl2
Csda
Hsd17b10
Herpud2
23892
107568
17300
268980
19270
57339
16007
14172
23893
22418
59022
18810
13406
378702
18186
17755
56758
109880
66592
11517
12632
56449
15108
80517
C1galt1
94192
Gremlin 1
WW domain containing E3 ubiquitin protein ligase 1
Forkhead box C1
Striatin, calmodulin binding protein
Protein tyrosine phosphatase, receptor type, G
Junctophilin 1
Cysteine rich protein 61
Fibroblast growth factor 18
Gremlin 2 homolog, cysteine knot superfamily
Wingless-related MMTV integration site 5A
Endothelial differentiation-related factor 1
Plectin 1
Dentin matrix protein 1
Small EDRK-rich factor 2
Neuropilin 1
Microtubule-associated protein 1B
Muscleblind-like 1
Braf transforming gene
Stomatin (Epb7.2)-like 2
Adenylate cyclase activating polypeptide 1 receptor 1
Cofilin 2, muscle
Cold shock domain protein A
Hydroxysteroid (17-beta) dehydrogenase 10
HERPUD family member 2
Core 1 synthase, glycoprotein-Nacetylgalactosamine, 1
Glycoprotein m6b
Fibrillin 1
N-myc downstream regulated gene 3
P21 (CDKN1A)-activated kinase 3
Zinc finger protein 521
Activin receptor IIA
Chondroadherin
Leukocyte cell derived chemotaxin 1
Myocyte enhancer factor 2C
Gpm6b
14758
Fbn1
14118
Ndrg3
29812
Pak3
18481
Zfp521
225207
Acvr2a**
11480
Chad**
12643
Lect1**
16840
Mef2c**
17260
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
2.27
2.26
2.25
2.24
2.22
2.22
2.21
2.21
2.20
2.19
2.19
2.18
2.18
2.17
2.16
2.14
2.14
2.13
2.11
2.11
2.09
2.09
2.08
2.07
2.06
2.06
2.06
2.06
2.04
2.03
2.03
2.02
2.01
2.00
Apoptosis
Clu
12759
Phlda1
21664
Dnm1l
74006
Clusterin
Pleckstrin homology-like domain, family A, member
1
Dynamin 1-like
- 148-
3.82*
3.74
3.38
Appendix
Anxa8
Sbsn
Cdr2
Ank2
Ypel2
Plxdc2
Lrp1
Ccdc46
Dleu2
11752
282619
12585
109676
77864
67448
16971
76380
328425
Mllt11
56772
Klhl7
Txn2
Tia1
Use1
Il7**
Ntn1
Ptpn13
Glrx2
52323
56551
21841
67023
16196
18208
19249
69367
Nsmaf
18201
Magef1
Armcx3
Spg21
76222
71703
27965
Pik3ca
18706
Bcl2l1
Gpatch4
Rusc1
Rnf13
Rnft1
Ankrd28
Ctage5
Letmd1
12048
66614
72296
24017
76892
105522
217615
68614
Mpp5
56217
Uaca
72565
Rora
Becn1
Casp3
Dynll1
Dnajb9
Mtus1
19883
56208
12367
56455
27362
102103
Annexin A8
Suprabasin
Cerebellar degeneration-related 2
Ankyrin 2, brain
Yippee-like 2
Plexin domain containing 2
Low density lipoprotein receptor-related protein 1
Coiled-coil domain containing 46
Deleted in lymphocytic leukemia, 2
Myeloid/lymphoid or mixed-lineage leukemia,
translocated, 11
Kelch-like 7
Thioredoxin 2
Cytotoxic granule-associated RNA binding protein 1
Unconventional SNARE in the ER 1 homolog
Interleukin 7
Netrin 1
Protein tyrosine phosphatase, non-receptor type 13
Glutaredoxin 2 (thioltransferase)
Neutral sphingomyelinase (N-SMase) activation
associated factor
Melanoma antigen family F, 1
Armadillo repeat containing, X-linked 3
Spastic paraplegia 21 homolog
Phosphatidylinositol 3-kinase, catalytic, alpha
polypeptide
Bcl2-like 1
G patch domain containing 4
RUN and SH3 domain containing 1
Ring finger protein 13
Ring finger protein, transmembrane 1
Ankyrin repeat domain 28
CTAGE family, member 5
LETM1 domain containing 1
Membrane protein, palmitoylated 5, subfamily
member 5)
Uveal autoantigen with coiled-coil domains and
ankyrin repeats
RAR-related orphan receptor alpha
Beclin 1, autophagy related
Caspase 3
Dynein light chain LC8-type 1
DnaJ (Hsp40) homolog, subfamily B, member 9
Mitochondrial tumor suppressor 1
- 149-
3.28
3.27
2.99
2.94
2.91
2.87
2.82
2.69
2.65
2.63
2.57
2.57
2.52
2.44
2.39
2.38
2.35
2.34
2.32
2.28
2.27
2.22
2.21
2.17
2.17
2.16
2.15
2.13
2.12
2.11
2.09
2.08
2.08
2.08
2.06
2.06
2.05
2.05
2.04
Appendix
Col4a2
12827 Collagen, type IV, alpha 2
Tmem85
68032 Transmembrane protein 85
Sh3kbp1
58194 SH3-domain kinase binding protein 1
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
2.02
2.01
2.00
Cell cycle
Mbp
Adk
Pkia
Smc2
Cables1
Ccdc16
17196
11534
18767
14211
63955
66983
Nek1
18004
Cdc23
Id4
Gspt1
Nipbl
Pfdn1
Eea1
52563
15904
14852
71175
67199
216238
Nufip2
68564
Ncapg
Mtmr6
Anapc2
Rrm2
Calml4
Pole
54392
219135
99152
20135
75600
18973
Camk2a
12322
Clec11a
Cep76
20256
225659
Rev3l
19714
Nbn
Gas5
Dtymk
Smc1a
Ccnd2
Bola2
Cep68
Lrrcc1
27354
14455
21915
24061
12444
66162
216543
71710
Myelin basic protein
Adenosine kinase
Protein kinase inhibitor, alpha
Structural maintenance of chromosomes 2
Cdk5 and Abl enzyme substrate 1
Coiled-coil domain containing 16
NIMA (never in mitosis gene a)-related expressed
kinase 1
CDC23 (cell division cycle 23, yeast, homolog)
Inhibitor of DNA binding 4
G1 to S phase transition 1
Nipped-B homolog
Prefoldin 1
Early endosome antigen 1
Nuclear fragile X mental retardation protein
interacting protein 2
On-SMC condensin I complex, subunit G
Myotubularin related protein 6
Anaphase promoting complex subunit 2
Ribonucleotide reductase M2
Calmodulin-like 4
Polymerase (DNA directed), epsilon
Calcium/calmodulin-dependent protein kinase II
alpha
C-type lectin domain family 11, member a
Centrosomal protein 76
REV3-like, catalytic subunit of DNA polymerase
zeta RAD54 like
Nibrin
Growth arrest specific 5
Deoxythymidylate kinase
Structural maintenance of chromosomes 1A
Cyclin D2
BolA-like 2 (E. coli)
Centrosomal protein 68
Leucine rich repeat and coiled-coil domain contain. 1
- 150-
15.07*
5.06
4.16
4.01
3.35
3.28
3.15
2.94
2.92
2.89
2.75
2.72
2.70
2.59
2.55
2.47
2.47
2.38
2.36
2.33
2.29
2.28
2.26
2.25
2.24
2.22
2.21
2.20
2.20
2.19
2.17
2.17
Appendix
Ccni
Cul5
Polk
Sept7
Egfr
12453
75717
27015
235072
13649
Nasp
50927
Nek1
18004
Pdgfd
71785
Cops5
26754
Foxn3
Pmp22
Sept5
Mphosph8
71375
18858
18951
75339
Pold3
67967
Seh1l
72124
*p value smaller than 0.05
Cyclin I
Cullin 5
Polymerase (DNA directed), kappa
Septin 7
Epidermal growth factor receptor
Nuclear autoantigenic sperm protein (histonebinding)
NIMA (never in mitosis gene a)-related expressed
kinase 1
Platelet-derived growth factor, D polypeptide
COP9 (constitutive photomorphogenic) homolog,
subunit 5
Forkhead box N3
Peripheral myelin protein
Septin 5
M-phase phosphoprotein 8
Polymerase (DNA-directed), delta 3, accessory
subunit
SEH1-like
2.14
2.11
2.11
2.10
2.10
Claudin domain containing 1
Signal transducer and activator of transcription 5A
Prostaglandin F receptor
Phosphodiesterase 4D interacting protein
(myomegalin)
Phosphodiesterase 7B
Microfibrillar associated protein 5
Twinfilin, actin-binding protein, homolog 2
WAS protein family, member 2
ATPase, Ca++ transporting, cardiac muscle, slow
twitch 2
Ankyrin repeat and SOCS box-containing protein 13
Transmembrane protein 55A
Serine/threonine kinase 39, STE20/SPS1 homolog
Phospholipase C, beta 1
RIO kinase 2
Regulator of G-protein signaling 5
Suppressor of cytokine signaling 4
Unc-51 like kinase 2
Suppressor of cytokine signaling 6
Protein kinase, cAMP dependent regulatory, type II
beta
6.72*
5.95
4.49
2.09
2.07
2.03
2.03
2.02
2.02
2.02
2.02
2.02
2.01
Signal transduction
Cldnd1
Stat5a
Ptgfr
224250
20850
19220
Pde4dip
83679
Pde7b
Mfap5
Twf2
Wasf2
29863
50530
23999
242687
Atp2a2
11938
Asb13
Tmem55a
Stk39
Plcb1
Riok2
Rgs5
Socs4
Ulk2
Socs6
142688
72519
53416
18795
67045
19737
67296
29869
54607
Prkar2b
19088
- 151-
4.15
4.06
4.01
3.92
3.16
3.15
3.13
3.04
3.02
2.82
2.82
2.76
2.66
2.65
2.61
2.59
Appendix
Edg2
14745
BC060632
Mapkap1
Rangap1
Socs2
Arhgap12
Trap1
Map2k1
Tex2
Epha3
Cdc42
Rab32
Rps6kc1
Rabif
Pde5a
Map3k2
Mpp1
Ptpdc1
Mn1**
Rhoj
Rcan1
Nenf
Rapgef6
Itsn1
Arf6
Pde1a
244654
227743
19387
216233
75415
68015
26395
21763
13837
12540
67844
320119
98710
242202
26405
17524
218232
433938
80837
54720
66208
192786
16443
11845
18573
Edg3
13610
Lrba
Asb4
Asb8
Grb10
Cyhr1
80877
65255
78541
14783
54151
Rala
56044
Endothelial differentiation, lysophosphatidic acid Gprotein rec., 2
cDNA sequence BC060632
Mitogen-activated protein kinase associated protein 1
RAN GTPase activating protein 1
Suppressor of cytokine signaling 2
Rho GTPase activating protein 12
TNF receptor-associated protein 1
Mitogen-activated protein kinase kinase 1
Testis expressed gene 2
Eph receptor A3
Cell division cycle 42 homolog
RAB32, member RAS oncogene family
Ribosomal protein S6 kinase polypeptide 1
RAB interacting factor
Phosphodiesterase 5A, cGMP-specific
Mitogen-activated protein kinase kinase kinase 2
Membrane protein, palmitoylated
Protein tyrosine phosphatase domain containing 1
Meningioma 1
Ras homolog gene family, member J
Regulator of calcineurin 1
Neuron derived neurotrophic factor
Rap guanine nucleotide exchange factor (GEF) 6
Intersectin 1 (SH3 domain protein 1A)
ADP-ribosylation factor 6
Phosphodiesterase 1A, calmodulin-dependent
Endothelial differentiation, sphingolipid G-proteincoupled rec., 3
LPS-responsive beige-like anchor
Ankyrin repeat and SOCS box-containing protein 4
Ankyrin repeat and SOCS box-containing protein 8
Growth factor receptor bound protein 10
Cysteine and histidine rich 1
V-ral simian leukemia viral oncogene homolog A
(ras related)
Cdc42 binding protein kinase alpha
A kinase (PRKA) anchor protein (yotiao) 9
Rho GTPase activating protein 18
GTPase activating protein and VPS9 domains 1
Cdc42bpa
226751
Akap9
100986
Arhgap18
73910
Gapvd1
66691
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
- 152-
2.59
2.55
2.50
2.46
2.43
2.41
2.41
2.40
2.39
2.37
2.35
2.33
2.29
2.27
2.27
2.25
2.24
2.23
2.23
2.23
2.22
2.17
2.16
2.16
2.13
2.11
2.11
2.10
2.08
2.07
2.06
2.06
2.06
2.04
2.04
2.03
2.00
Appendix
Transcription
Apobec2
Polr3b
11811
70428
Snapc2
102209
Smyd1
Tnni2
Zfp295
Endod1
Lmo4
Lrrn1
Eya4
Papd4
Zfhx4
Pa2g4
Zxda
Egr3
Hnrnpc
Trps1
Lsm2
AK129302
Suv39h2
Zfp637
Hist2h3c2
Smyd2
Zfp260
Lin54
Prrx1
Ruvbl2
Hif1a
Rad52
Bivm
Nfib
Tead1
Ets2
Sdpr
Atf5
E2f6
Zfp60
Sfrs1
Slu7
N6amt2
12180
21953
114565
71946
16911
16979
14051
100715
80892
18813
668171
13655
15381
83925
27756
245522
64707
232337
97114
226830
26466
231506
18933
20174
15251
19365
246229
18028
21676
23872
20324
107503
50496
22718
110809
193116
68043
Apolipoprotein B editing complex 2
Polymerase (RNA) III (DNA directed) polypeptide B
Small nuclear RNA activating complex, polypeptide
2
SET and MYND domain containing 1
Troponin I, skeletal, fast 2
Zinc finger protein 295
Endonuclease domain containing 1
LIM domain only 4
Leucine rich repeat protein 1, neuronal
Eyes absent 4 homolog
PAP associated domain containing 4
Zinc finger homeodomain 4
Proliferation-associated 2G4
Zinc finger, X-linked, duplicated A
Early growth response 3
Heterogeneous nuclear ribonucleoprotein C
Trichorhinophalangeal syndrome I
LSM2 homolog, U6 small nuclear RNA associated
cDNA sequence AK129302
Suppressor of variegation 3-9 homolog 2
Zinc finger protein 637
Histone cluster 2, H3c2
SET and MYND domain containing 2
Zinc finger protein 260
Lin-54 homolog
Paired related homeobox 1
RuvB-like protein 2
Hypoxia inducible factor 1, alpha subunit
RAD52 homolog
Basic, immunoglobulin-like variable motif containing
Nuclear factor I/B
TEA domain family member 1
E26 avian leukemia oncogene 2, 3' domain
Serum deprivation response
Activating transcription factor 5
E2F transcription factor 6
Zinc finger protein 60
Splicing factor, arginine/serine-rich 1 (ASF/SF2)
SLU7 splicing factor homolog
N-6 adenine-specific DNA methyltransferase 2
- 153-
6.18*
4.80
4.76
4.55
4.24
4.01
3.99
3.86
3.71
3.68
3.66
3.54
3.47
3.43
3.42
3.41
3.38
3.29
3.28
3.21
3.16
3.14
3.12
3.03
2.96
2.93
2.93
2.87
2.85
2.83
2.74
2.73
2.72
2.69
2.67
2.63
2.60
2.57
2.57
2.52
Appendix
Hnrpab
Tcfl5
Prrx2
Pole3
Nfic
Hoxb6
15384
277353
20204
59001
18029
15414
Rcbtb2
105670
Satb1
Tcf4
Cnot2
Zcchc7
Trove2
Nr2c1
Zfp160
Ncoa6
20230
21413
72068
319885
20822
22025
224585
56406
Nfatc4
73181
Satb2
Sfrs18
Zfhx3
Pcbd2
Zbtb41
Bbx
Tshz1
Sfrs3
Zbtb20
Znhit3
Sertad3
Zfp467
Hist1h3d
Tardbp
Pax9
Rnase4
Usp21
Nfia
Zfp760
Npat
212712
66625
11906
72562
226470
70508
110796
20383
56490
448850
170742
68910
319149
230908
18511
58809
30941
18027
240034
244879
Mllt3
70122
Chd4
Hsf2
Nf2
107932
15500
18016
Heterogeneous nuclear ribonucleoprotein A/B
Transcription factor-like 5 (basic helix-loop-helix)
Paired related homeobox 2
Polymerase (DNA directed), epsilon 3 (p17 subunit)
Nuclear factor I/C
Homeo box B6
Regulator of chromosome condensation and BTB
domain 2
Special AT-rich sequence binding protein 1
Transcription factor 4
CCR4-NOT transcription complex, subunit 2
Zinc finger, CCHC domain containing 7
TROVE domain family, member 2
Nuclear receptor subfamily 2, group C, member 1
Zinc finger protein 160
Nuclear receptor coactivator 6
Nuclear factor of activated T-cells, calcineurindependent 4
Special AT-rich sequence binding protein 2
Splicing factor, arginine/serine-rich 18
Zinc finger homeobox 3
Pterin 4 alpha carbinolamine dehydratase 2
Zinc finger and BTB domain containing 41 homolog
Bobby sox homolog
Teashirt zinc finger family member 1
Splicing factor, arginine/serine-rich 3 (SRp20)
Zinc finger and BTB domain containing 20
Zinc finger, HIT type 3
SERTA domain containing 3
Zinc finger protein 467
Histone cluster 1, H3d
TAR DNA binding protein
Paired box gene 9
Ribonuclease, RNase A family 4
Ubiquitin specific peptidase 21
Nuclear factor I/A
Zinc finger protein 760
Nuclear protein in the AT region
Myeloid/lymphoid or mixed-lineage leukemia,
translocated to, 3
Chromodomain helicase DNA binding protein 4
Heat shock factor 2
Neurofibromatosis 2
- 154-
2.51
2.48
2.48
2.47
2.46
2.44
2.43
2.41
2.41
2.37
2.37
2.35
2.35
2.35
2.33
2.33
2.32
2.31
2.28
2.28
2.28
2.26
2.24
2.22
2.21
2.21
2.20
2.19
2.18
2.18
2.17
2.17
2.16
2.16
2.15
2.15
2.15
2.14
2.13
2.12
Appendix
Nfyb
Zhx1
Rbm6
Sfrs2
Six5
Zfp423
Ets1
Gpatch8
Zkscan3
Mettl9
Jmjd1c
Klf12
Ints6
Hoxb8
Tada3l
18045
22770
19654
20382
20475
94187
23871
237943
72739
59052
108829
16597
18130
15416
101206
Xrcc6
14375
Hipk1
15257
Taf4a
228980
Phtf2
Gtf2i
Mxi1
68770
14886
17859
Ibtk
108837
Dr1
Zfp251
Ndn
Snrpd3
Carm1
Sin3a
Strap
Ring1
Arrdc4
Creb3l2
13486
71591
17984
67332
59035
20466
20901
19763
66412
208647
Sirt7
209011
Trip4
56404
*p value smaller than 0.05
Nuclear transcription factor-Y beta
Rinc fingers and homeoboxes 1
RNA binding motif protein 6
Splicing factor, arginine/serine-rich 2 (SC-35)
Sine oculis-related homeobox 5 homolog
Zinc finger protein 423
E26 avian leukemia oncogene 1, 5' domain
G patch domain containing 8
Zinc finger with KRAB and SCAN domains 3
Methyltransferase like 9
Jumonji domain containing 1C
Kruppel-like factor 12
Integrator complex subunit 6
Homeo box B8
Transcriptional adaptor 3 (NGG1 homolog, yeast)
X-ray complementing defective repair, Chinese
hamster cells 6
Homeodomain interacting protein kinase 1
TAF4A RNA polymerase II, TATA box binding
protein
Putative homeodomain transcription factor 2
General transcription factor II I
Max interacting protein 1
Inhibitor of Bruton agammaglobulinemia tyrosine
kinase
Down-regulator of transcription 1
Zinc finger protein 251
Necdin
Small nuclear ribonucleoprotein D3
Coactivator-associated arginine methyltransferase 1
Transcriptional regulator, SIN3A
Serine/threonine kinase receptor associated protein
Ring finger protein 1
Arrestin domain containing 4
cAMP responsive element binding protein 3-like 2
Sirtuin 7 (silent mating type information regulation 2,
homolog) 7
Thyroid hormone receptor interactor 4
2.11
2.11
2.10
2.10
2.10
2.09
2.09
2.08
2.07
2.06
2.06
2.06
2.05
2.05
2.05
2.05
2.04
2.04
2.03
2.03
2.03
2.03
2.03
2.03
2.03
2.02
2.02
2.02
2.02
2.02
2.01
2.01
2.01
2.01
Immune response
Prg4
Asph
96875
65973
Proteoglycan 4 (megakaryocyte stimulating factor)
Aspartate-beta-hydroxylase
- 155-
11.29*
4.04
Appendix
Mlf2
Prkca
Scara3
Il33
Nope
Cd55
Tlr4
Nlrx1
30853
18750
219151
77125
56741
13136
21898
270151
Cyp4f16
70101
C2
12263
Ythdf2
213541
*p value smaller than 0.05
Myeloid leukemia factor 2
Protein kinase C, alpha
Scavenger receptor class A, member 3
Interleukin 33
Neighbor of Punc E11
CD55 antigen
Toll-like receptor 4
NLR family member X1
Cytochrome P450, family 4, subfamily f, polypeptide
16
Complement component 2 (within H-2S)
YTH domain family 2
3.87
3.21
3.19
3.10
2.86
2.68
2.59
2.30
Small muscle protein, X-linked
ATPase, Ca++ transporting, cardiac muscle, fast
twitch 1
Calsequestrin 1
Triadin
Myosin, heavy polypeptide 7, cardiac muscle, beta
Cardiomyopathy associated 5
Myomesin 2
Myosin, heavy polypeptide 1, skeletal muscle, adult
Heat shock factor binding protein 1
Myosin, heavy polypeptide 6, cardiac muscle, alpha
8.22*
Myocilin
Actinin alpha 3
Leiomodin 2 (cardiac)
Tropomyosin 2, beta
Synaptopodin 2
Actinin, alpha 1
Ankyrin 1, erythroid
Nexilin
CAP, adenylate cyclase-associated protein, 2
Drebrin-like
Nucleoporin 98
Kinesin family member 5B
Gene model 114, (NCBI)
7.42*
6.27
4.97
4.89
4.45
4.22
3.46
2.98
2.78
2.59
2.50
2.49
2.39
2.19
2.17
2.01
Mucle contraction
Smpx
66106
Atp2a1
11937
Casq1
12372
Trdn
76757
Myh7
140781
Cmya5
76469
Myom2
17930
Myh1
17879
Hsbp1
68196
Myh6
17888
*p value smaller than 0.05
5.24
5.23
4.83
4.30
4.15
2.34
2.27
2.14
2.08
Cytoskeleton remodeling
Myoc
Actn3
Lmod2
Tpm2
Synpo2
Actn1
Ank1
Nexn
Cap2
Dbnl
Nup98
Kif5b
Gm114
17926
11474
93677
22004
118449
109711
11733
68810
67252
13169
269966
16573
228730
- 156-
Appendix
Cap1
Ttl
Krt10
Kif16b
12331
69737
16661
16558
Lysmd2
70082
Oxr1
AA536749
Rdx
Wdr1
Wdr46
Txndc14
Dmn
Sync
Tubb6
Actr1b
Epb4.1l1
Synpo2
Dync1h1
170719
26936
19684
22388
57315
66958
233335
68828
67951
226977
13821
118449
13424
Sgcd
24052
Rnf19a
30945
Pfn2
18645
*p value smaller than 0.05
CAP, adenylate cyclase-associated protein 1
Tubulin tyrosine ligase
Keratin 10
Kinesin family member 16B
LysM, putative peptidoglycan-binding, domain
containing 2
Oxidation resistance 1
Expressed sequence AA536749
Radixin
WD repeat domain 1
WD repeat domain 46
Thioredoxin domain containing 14
Desmuslin
Syncoilin
Tubulin, beta 6
ARP1 actin-related protein 1 homolog B
Erythrocyte protein band 4.1-like 1
Synaptopodin 2
Dynein cytoplasmic 1 heavy chain 1
Sarcoglycan, delta (dystrophin-associated
glycoprotein)
Ring finger protein 19A
Profilin 2
2.33
2.31
2.28
2.28
2.26
2.22
2.16
2.13
2.13
2.11
2.10
2.10
2.08
2.08
2.08
2.07
2.07
2.06
2.05
2.03
2.00
Proteolysis
Pcolce2
76477
Arih1
23806
Btbd3
Prss23
Siah1b
Capn1
Rnf11
Qpctl
Ddi1
Trip12
Cyld
Dpp7
Pcolce
Dzip3
228662
76453
20438
12333
29864
67369
71829
14897
74256
83768
18542
224170
Psmd11
69077
Procollagen C-endopeptidase enhancer 2
Ariadne ubiquitin-conjugating enzyme binding
protein homolog 1
BTB (POZ) domain containing 3
Protease, serine, 23
Seven in absentia 1B
Calpain 1
Ring finger protein 11
Glutaminyl-peptide cyclotransferase-like
DDI1, DNA-damage inducible 1, homolog 1
Thyroid hormone receptor interactor 12
Cylindromatosis (turban tumor syndrome)
Dipeptidylpeptidase 7
Procollagen C-endopeptidase enhancer protein
DAZ interacting protein 3, zinc finger
Proteasome (prosome, macropain) 26S subunit, nonATPase, 11
- 157-
3.24*
3.10
2.89
2.89
2.86
2.74
2.65
2.61
2.59
2.56
2.54
2.48
2.46
2.41
2.41
Appendix
Dda1
Trim37
Spop
Ntan1
Fbxo33
Usp47
Prepl
Pi16
66498
68729
20747
18203
70611
74996
213760
74116
Psmc6
67089
Bace1
Ube3a
23821
22215
Ube2g1
67128
Erap1
Capn2
Usp15
Usp10
Stch
Rnf11
80898
12334
14479
22224
110920
29864
Adam19
11492
Npepps
19155
Kdelc1
72050
Lonp2
66887
Ube3a
22215
Fbxl16
214931
Tulp4
68842
*p value smaller than 0.05
DET1 and DDB1 associated 1
Tripartite motif-containing 37
Speckle-type POZ protein
N-terminal Asn amidase
F-box protein 33
Ubiquitin specific peptidase 47
Prolyl endopeptidase-like
Peptidase inhibitor 16
Proteasome (prosome, macropain) 26S subunit,
ATPase, 6
Beta-site APP cleaving enzyme 1
Ubiquitin protein ligase E3A
Ubiquitin-conjugating enzyme E2G 1 (UBC7
homolog)
Endoplasmic reticulum aminopeptidase 1
Calpain 2
Ubiquitin specific peptidase 15
Ubiquitin specific peptidase 10
Stress 70 protein chaperone, microsome-associated
Ring finger protein 11
A disintegrin and metallopeptidase domain 19
(meltrin beta)
Aminopeptidase puromycin sensitive
KDEL (Lys-Asp-Glu-Leu) containing 1
Lon peptidase 2, peroxisomal
Ubiquitin protein ligase E3A
F-box and leucine-rich repeat protein 16
Tubby like protein 4
2.40
2.35
2.30
2.30
2.27
2.23
2.22
2.22
Ribosomal protein S20
DNA segment, Chr 10, ERATO Doi 322, expressed
Pumilio 1
Dihydrouridine synthase 3-like
LYR motif containing 2
Nucleotide binding protein 2
Selenophosphate synthetase 2
Ribosomal protein S4, Y-linked 2
Ubiquitin-conjugating enzyme E2D 1, UBC4/5
homolog
DCN1, defective in cullin neddylation 1, domain
containing 2
4.61*
3.71
3.31
3.21
3.20
3.13
3.08
2.93
2.20
2.20
2.13
2.12
2.11
2.10
2.09
2.08
2.08
2.08
2.07
2.05
2.04
2.04
2.04
2.02
2.00
Translation
Rps20
D10Ertd322e
Pum1
Dus3l
Lyrm2
Nubp2
Sephs2
Rps4y2
67427
67270
80912
224907
108755
26426
20768
66184
Ube2d1
216080
Dcun1d2
102323
- 158-
2.82
2.62
Appendix
Arl6ip1
Wars
54208
22375
Nars2
244141
Mrpl15
Hsph1
Eif3h
Pus3
Ppil3
Pdzd4
Niban
Stk32b
Mrp63
Selm
Hspa4
Rps27l
Ubl4
27395
15505
68135
67049
70225
245469
63913
64293
67840
114679
15525
67941
27643
Cpeb3
208922
Rps17
Dph4
Mrpl40
Mars
Mrpl43
Qars
Dnajc21
Atad3a
20068
99349
18100
216443
94067
97541
78244
108888
Ptar1
72351
Tufm
233870
Eif2a
229317
*p value smaller than 0.05
ADP-ribosylation factor-like 6 interacting protein 1
Tryptophanyl-tRNA synthetase
Asparaginyl-tRNA synthetase 2
(mitochondrial)(putative)
Mitochondrial ribosomal protein L15
Heat shock 105kDa/110kDa protein 1
Eukaryotic translation initiation factor 3, subunit H
Pseudouridine synthase 3
Peptidylprolyl isomerase (cyclophilin)-like 3
PDZ domain containing 4
Niban protein
Serine/threonine kinase 32B
Mitochondrial ribosomal protein 63
Selenoprotein M
Heat shock protein 4
Ribosomal protein S27-like
Ubiquitin-like 4
Cytoplasmic polyadenylation element binding protein
3
Ribosomal protein S17
DPH4 homolog (JJJ3)
Mitochondrial ribosomal protein L40
Methionine-tRNA synthetase
Mitochondrial ribosomal protein L43
Glutaminyl-tRNA synthetase
DnaJ (Hsp40) homolog, subfamily C, member 21
ATPase family, AAA domain containing 3A
Protein prenyltransferase alpha subunit repeat
containing 1
Tu translation elongation factor, mitochondrial
Eukaryotic translation initiation factor 2a
2.58
2.56
2.54
2.53
2.53
2.51
2.46
2.45
2.33
2.32
2.30
2.30
2.29
2.25
2.22
2.19
2.18
2.15
2.15
2.09
2.09
2.08
2.08
2.08
2.04
2.04
2.03
2.01
G-protein signaling
Kcnk2
16526
Atp1b1
11931
Sphk2
56632
Git2
26431
Gtpbp2
56055
*p value smaller than 0.05
Potassium channel, subfamily K, member 2
ATPase, Na+/K+ transporting, beta 1 polypeptide
Sphingosine kinase 2
G protein-coupled receptor kinase-interactor 2
GTP binding protein 2
Cell adhesion
- 159-
3.38*
3.20
2.45
2.26
2.19
Appendix
Tmem107
Vcan
Lyve1
Hapln1
Col14a1
Tmem157
Hfe2
Pcdh7
Boc
Dock4
66910
13003
114332
12950
12818
67698
69585
54216
117606
238130
Lpp
210126
Fermt3
Fndc1
Cd34
Thbs2
Itgbl1
Thbs1**
Sspn
Pcdhb14
Flrt2
Abi3bp
108101
68655
12490
21826
223272
21825
16651
93885
399558
320712
Cdon
57810
Fbln1
Dpt
Col6a3
Tmem87b
Ptprk
Cd151
Fn1**
Col6a2
Mpp7
Lamb2
Ptprf
Efs
Lims1
Tmem119
Col27a1
Jup
Cercam
Itga6
Comp
14114
56429
12835
72477
19272
12476
14268
12834
75739
16779
19268
13644
110829
231633
373864
16480
99151
16403
12845
Transmembrane protein 107
Versican
Lymphatic vessel endothelial hyaluronan receptor 1
Hyaluronan and proteoglycan link protein 1
Collagen, type XIV, alpha 1
Transmembrane protein 157
Hemochromatosis type 2 (juvenile)
Protocadherin 7
Biregional cell adhesion molecule-related
Dedicator of cytokinesis 4
LIM domain containing preferred translocation
partner in lipoma
Fermitin family homolog 3
Fibronectin type III domain containing 1
CD34 antigen
Thrombospondin 2
Integrin, beta-like 1
Thrombospondin 1
Sarcospan
Protocadherin beta 14
Fibronectin leucine rich transmembrane protein 2
ABI gene family, member 3 (NESH) binding protein
Cell adhesion molecule-related/down-regulated by
oncogenes
Fibulin 1
Dermatopontin
Collagen, type VI, alpha 3
Transmembrane protein 87B
Protein tyrosine phosphatase, receptor type, K
CD151 antigen
Fibronectin 1
Collagen, type VI, alpha 2
Membrane protein, palmitoylated 7, member 7
Laminin, beta 2
Protein tyrosine phosphatase, receptor type, F
Embryonal Fyn-associated substrate
LIM and senescent cell antigen-like domains 1
Transmembrane protein 119
Collagen, type XXVII, alpha 1
Junction plakoglobin
Cerebral endothelial cell adhesion molecule
Integrin alpha 6
Cartilage oligomeric matrix protein
- 160-
6.01*
5.86
5.42
4.39
4.20
4.16
3.91
3.59
3.55
3.39
3.37
3.34
3.07
3.03
2.97
2.93
2.93
2.90
2.81
2.76
2.71
2.65
2.60
2.53
2.48
2.46
2.46
2.41
2.37
2.36
2.35
2.35
2.31
2.30
2.26
2.23
2.18
2.17
2.17
2.12
2.07
Appendix
Tpbg
21983 Trophoblast glycoprotein
Panx3**
208098 Pannexin 3
Pcdhb9
93880 Protocadherin beta 9
Alcam
11658 Activated leukocyte cell adhesion molecule
Clstn1
65945 Calsyntenin 1
Thbd
21824 Thrombomodulin
Pcnx
54604 Pecanex homolog
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
2.07
2.06
2.05
2.05
2.03
2.02
2.00
Transport
Ipo4
Golt1b
Cox8b
Ryr1
75751
66964
12869
20190
Trpc6
22068
Rab6b
Mfap3l
Chsy3
B3galnt2
Ssr4
Kbtbd7
Myo5a
Srpk2
Ttc30b
270192
71306
78923
97884
20832
211255
17918
20817
72421
Ndufa11
69875
Tmem86a
Ap3m1
Srpr
Slc38a10
67893
55946
67398
72055
Ift122
81896
Kcnma1
16531
Kif1b
Cyb5b
Hook3
Snx6
Stx6
Sec24a
Actr1b
16561
66427
320191
72183
58244
77371
226977
Importin 4
Golgi transport 1 homolog B
Cytochrome c oxidase, subunit VIIIb
Ryanodine receptor 1, skeletal muscle
Transient receptor potential cation channel,
subfamily C, 6
RAB6B, member RAS oncogene family
Microfibrillar-associated protein 3-like
Chondroitin sulfate synthase 3
UDP-GalNAc:betaGlcNAc polypeptide 2
Signal sequence receptor, delta
Kelch repeat and BTB (POZ) domain containing 7
Myosin Va
Serine/arginine-rich protein specific kinase 2
Tetratricopeptide repeat domain 30B
NADH dehydrogenase (ubiquinone) 1 alpha
subcomplex 11
Transmembrane protein 86A
Adaptor-related protein complex 3, mu 1 subunit
Signal recognition particle receptor
Solute carrier family 38, member 10
Intraflagellar transport 122 homolog
(Chlamydomonas)
Potassium large calcium-activated channel, alpha
member 1
Kinesin family member 1B
Cytochrome b5 type B
Hook homolog 3
Sorting nexin 6
Syntaxin 6
SEC24 related gene family, member A
ARP1 actin-related protein 1 homolog B
- 161-
7.22*
5.36
4.40
4.19
4.17
4.09
3.97
3.73
3.71
3.24
3.21
3.14
3.12
3.09
3.09
3.02
2.99
2.99
2.99
2.93
2.89
2.85
2.77
2.77
2.74
2.73
2.70
2.68
Appendix
Ap2b1
71770
Tomm70a
28185
Mgea5
Slc35a4
76055
67843
Ndufa7
66416
Atp1a2
Scfd1
Stxbp5
Tpcn1
Atp1b1
98660
76983
78808
252972
11931
Ppp1r12a
17931
Ndufs8
Myo1c
Kpnb1
Stau2
Uqcrh
225887
17913
16211
29819
66576
Atp5b
11947
Mobkl3
Necap1
Timm17b
Dnaja2
Arfgap2
Slc25a4
Vps52
Kif1b
Txndc13
Snapin
Slc25a24
Hcfc1r1
19070
67602
21855
56445
77038
11739
224705
16561
52837
20615
229731
353502
Tomm40
53333
Stxbp6
Clpb
Cnnm3
217517
20480
94218
Plekha8
231999
Bet1
Sytl4
Arfgap3
Vps11
12068
27359
66251
71732
Adaptor-related protein complex 2, beta 1 subunit
Translocase of outer mitochondrial membrane 70
homolog A
Meningioma expressed antigen 5 (hyaluronidase)
Solute carrier family 35, member A4
NADH dehydrogenase (ubiquinone) 1 alpha
subcomplex, 7
ATPase, Na+/K+ transporting, alpha 2 polypeptide
Sec1 family domain containing 1
Syntaxin binding protein 5 (tomosyn)
Two pore channel 1
ATPase, Na+/K+ transporting, beta 1 polypeptide
Protein phosphatase 1, regulatory (inhibitor) subunit
12A
NADH dehydrogenase (ubiquinone) Fe-S protein 8
Myosin IC
Karyopherin (importin) beta 1
Staufen (RNA binding protein) homolog 2
Ubiquinol-cytochrome c reductase hinge protein
ATP synthase, H+ transporting mitochondrial F1
complex, beta
MOB1, Mps One Binder kinase activator-like 3
NECAP endocytosis associated 1
Translocase of inner mitochondrial membrane 17b
DnaJ (Hsp40) homolog, subfamily A, member 2
ADP-ribosylation factor GTPase activating protein 2
Solute carrier family 25, member 4
Vacuolar protein sorting 52
Kinesin family member 1B
Thioredoxin domain containing 13
SNAP-associated protein
Solute carrier family 25, member 24
Host cell factor C1 regulator 1 (XPO1-dependent)
Translocase of outer mitochondrial membrane 40
homolog
Syntaxin binding protein 6 (amisyn)
ClpB caseinolytic peptidase B homolog (E. coli)
Cyclin M3
Pleckstrin homology domain containing, family A
member 8
Blocked early in transport 1 homolog
Bynaptotagmin-like 4
ADP-ribosylation factor GTPase activating protein 3
Vacuolar protein sorting 11
- 162-
2.67
2.67
2.63
2.62
2.60
2.59
2.58
2.58
2.53
2.52
2.51
2.50
2.48
2.41
2.40
2.38
2.37
2.36
2.36
2.35
2.34
2.34
2.32
2.30
2.29
2.29
2.28
2.27
2.27
2.25
2.25
2.22
2.21
2.21
2.19
2.18
2.16
2.15
Appendix
Sft2d2
108735
Stard4
170459
Ndufv1
Tnpo3
Slc25a17
Dnajc19
Rufy2
Golga4
Nup62
Exoc4
17995
320938
20524
67713
70432
54214
18226
20336
Slc25a12
78830
Plekhb2
226971
Scyl2
Slc38a9
Stx18
Cd320
213326
268706
71116
54219
Atp5o
28080
Ergic3
Dscr3
66366
13185
Mrs2
380836
Ndufa5
68202
Cope
Ccs
Txnl1
Sec61a1
59042
12460
53382
53421
Cacna2d1
12293
Snx12
55988
Vbp1
22327
Tmed10
68581
*p value smaller than 0.05
SFT2 domain containing 2
StAR-related lipid transfer (START) domain
containing 4
NADH dehydrogenase (ubiquinone) flavoprotein 1
Transportin 3
Solute carrier family 25, member 17
DnaJ (Hsp40) homolog, subfamily C, member 19
RUN and FYVE domain-containing 2
Golgi autoantigen, golgin subfamily a, 4
Nucleoporin 62
Exocyst complex component 4
Solute carrier family 25 (mitochondrial carrier,
Aralar), member 12
Pleckstrin homology domain containing, family B
member 2
SCY1-like 2 (S. cerevisiae)
Solute carrier family 38, member 9
Syntaxin 18
CD320 antigen
ATP synthase, H+ transport, mitochondrial F1
complex, O subunit
ERGIC and Golgi 3
Down syndrome critical region gene 3
MRS2 magnesium homeostasis factor homolog (S.
cerevisiae)
NADH dehydrogenase (ubiquinone) 1 alpha
subcomplex, 5
Coatomer protein complex, subunit epsilon
Copper chaperone for superoxide dismutase
Thioredoxin-like 1
Sec61 alpha 1 subunit
Calcium channel, voltage-dependent, alpha2/delta
subunit 1
Sorting nexin 12
Von Hippel-Lindau binding protein 1
Transmembrane emp24-like trafficking protein 10
2.15
2.15
2.14
2.14
2.14
2.13
2.13
2.12
2.11
2.10
2.10
2.09
2.08
2.08
2.07
2.06
2.05
2.05
2.04
2.03
2.03
2.02
2.02
2.02
2.01
2.01
2.01
2.01
2.00
Energy metabolism
Coch
Pgam2
Phkb
Pvalb
Acpl2
12810
56012
102093
19293
235534
Coagulation factor C homolog (Limulus polyphemus)
Phosphoglycerate mutase 2
Phosphorylase kinase beta
Parvalbumin
Acid phosphatase-like 2
- 163-
7.74*
6.29
4.65
4.54
4.21
Appendix
Srl
Adssl1
AW548124
Akr1c14
Acaa2
Scarb1
Art1
106393
11565
106522
105387
52538
20778
11870
Auh
11992
Lias
Ankrd49
Hagh
Dtymk
79464
56503
14651
21915
B3gnt1
108902
Cmbl
Vldlr
69574
22359
Gpt2
108682
Dguok
Dlat
Loxl4
Aco2
Gatm
Ampd1
Tpi1
Acsl3
Gmds
Uck2
27369
235339
67573
11429
67092
229665
21991
74205
218138
80914
Smek2
104570
Shmt2
Gnpda2
Nmral1
Tmem195
Gbe1
Pex19
Paics
Srd5a3
Cpt1a
Gnpnat1
Sgms2
B3galnt2
108037
67980
67824
319660
74185
19298
67054
57357
12894
54342
74442
97884
Sarcalumenin
Adenylosuccinate synthetase like 1
Expressed sequence AW548124
Aldo-keto reductase family 1, member C14
Acetyl-Coenzyme A acyltransferase 2
Scavenger receptor class B, member 1
ADP-ribosyltransferase 1
AU RNA binding protein/enoyl-coenzyme A
hydratase
Lipoic acid synthetase
Ankyrin repeat domain 49
Hydroxyacyl glutathione hydrolase
Deoxythymidylate kinase
UDP-GlcNAc:betaGal beta-1,3-Nacetylglucosaminyltransferase 1
Carboxymethylenebutenolidase-like (Pseudomonas)
Very low density lipoprotein receptor
Glutamic pyruvate transaminase (alanine
aminotransferase) 2
Deoxyguanosine kinase
Dihydrolipoamide S-acetyltransferase
Lysyl oxidase-like 4
Aconitase 2, mitochondrial
Glycine amidinotransferase (L-arginine:glycine)
Adenosine monophosphate deaminase 1 (isoform M)
Triosephosphate isomerase 1
Acyl-CoA synthetase long-chain family member 3
GDP-mannose 4, 6-dehydratase
Uridine-cytidine kinase 2
SMEK homolog 2, suppressor of mek1
(Dictyostelium)
Serine hydroxymethyltransferase 2 (mitochondrial)
Glucosamine-6-phosphate deaminase 2
NmrA-like family domain containing 1
Transmembrane protein 195
Glucan (1,4-alpha-), branching enzyme 1
Peroxisome biogenesis factor 19
Phosphoribosylaminoimidazole carboxylase
Steroid 5 alpha-reductase 3
Carnitine palmitoyltransferase 1a, liver
Glucosamine-phosphate N-acetyltransferase 1
Sphingomyelin synthase 2
UDP-GalNAc:betaGlcNAc beta, polypeptide 2
- 164-
3.96
3.86
3.83
3.77
3.69
3.62
3.35
3.29
3.22
3.06
3.02
2.97
2.93
2.92
2.88
2.88
2.76
2.74
2.71
2.68
2.67
2.65
2.60
2.56
2.55
2.54
2.54
2.53
2.45
2.45
2.44
2.44
2.42
2.39
2.39
2.39
2.37
2.36
2.36
Appendix
Gpx7
Ndufv1
Hsd17b4
Hadh
Cyb5r3
Ampd3
Cyb5r1
Ggta1
67305
17995
15488
15107
109754
11717
72017
14594
Pigp
56176
Srd5a3
Ptdss2
Oxnad1
57357
27388
218885
Pdxdc1
94184
Acad9
Serinc1
Atp10a
Hyou1
Pofut2
Cpt2
Sc4mol
Uxs1
Enpp1
Pgam5
Inpp5f
229211
56442
11982
12282
80294
12896
66234
67883
18605
72542
101490
Chchd3
66075
Phyh
Sod3
Uap1
Ugp2
Daglb
Gss
Gyg
Gsto1
Aldoa
16922
20657
107652
216558
231871
14854
27357
14873
11674
Pck2
74551
Gstm5
Glud1
Csad
14866
14661
246277
Bckdhb
12040
Glutathione peroxidase 7
NADH dehydrogenase (ubiquinone) flavoprotein 1
Hydroxysteroid (17-beta) dehydrogenase 4
Hydroxyacyl-Coenzyme A dehydrogenase
Cytochrome b5 reductase 3
AMP deaminase 3
Cytochrome b5 reductase 1
Glycoprotein galactosyltransferase alpha 1, 3
Phosphatidylinositol glycan anchor biosynthesis,
class P
Steroid 5 alpha-reductase 3
Phosphatidylserine synthase 2
Oxidoreductase NAD-binding domain containing 1
Pyridoxal-dependent decarboxylase domain
containing 1
Acyl-Coenzyme A dehydrogenase family, member 9
Serine incorporator 1
ATPase, class V, type 10A
Hypoxia up-regulated 1
Protein O-fucosyltransferase 2
Carnitine palmitoyltransferase 2
Sterol-C4-methyl oxidase-like
UDP-glucuronate decarboxylase 1
Ectonucleotide pyrophosphatase/phosphodiesterase 1
Phosphoglycerate mutase family member 5
Inositol polyphosphate-5-phosphatase F
Coiled-coil-helix-coiled-coil-helix domain containing
3
Phytanoyl-CoA hydroxylase
Superoxide dismutase 3, extracellular
UDP-N-acetylglucosamine pyrophosphorylase 1
UDP-glucose pyrophosphorylase 2
Diacylglycerol lipase, beta
Glutathione synthetase
Glycogenin
Glutathione S-transferase omega 1
Aldolase 1, A isoform
Phosphoenolpyruvate carboxykinase 2
(mitochondrial)
Glutathione S-transferase, mu 5
Glutamate dehydrogenase 1
Cysteine sulfinic acid decarboxylase
Branched chain ketoacid dehydrogenase E1, beta
polypeptide
- 165-
2.34
2.32
2.27
2.25
2.25
2.23
2.23
2.21
2.21
2.21
2.20
2.17
2.14
2.14
2.14
2.14
2.13
2.13
2.13
2.12
2.12
2.12
2.11
2.11
2.11
2.10
2.09
2.09
2.08
2.07
2.07
2.07
2.06
2.06
2.06
2.06
2.05
2.03
2.03
Appendix
Ggps1
14593
Fnip1
216742
*p value smaller than 0.05
Geranylgeranyl diphosphate synthase 1
Folliculin interacting protein 1
2.02
2.00
Taxilin beta
Sterile alpha motif domain containing 9-like
Ubiquilin 1
Monoamine oxidase B
Tripartite motif-containing 2
Clathrin, light polypeptide (Lcb)
WD repeat domain 21
Leucine rich repeat transmembrane neuronal 4
Bisphosphate 3'-nucleotidase 1
6.93*
3.12
2.96
2.92
2.48
2.46
2.31
2.29
2.03
Neurophysiological process
Txlnb
378431
Samd9l
209086
Ubqln1
56085
Maob
109731
Trim2
80890
Cltb
74325
Wdr21
73828
Lrrtm4
243499
Bpnt1
23827
*p value smaller than 0.05
Unknown process
Nipsnap1
18082
BB125219
AI843755
D7Ertd183e
Uhrf1bp1l
D7Ertd183e
Auts2
Heatr5b
BC034902
Ttc19
Ssna1
Rspry1
Ankrd42
Ehbp1l1
Ccdc124
D6Ertd474e
Lrrc42
Ccdc3
Bola3
Lrrc45
D10Ertd641e
Prr16
Ankrd50
BC046331
105063
100215
52234
75089
52234
319974
320473
228642
72795
68475
67610
73845
114601
234388
52285
77809
74186
78653
217366
52717
71373
99696
230967
4-nitrophenylphosphatase and SNAP25-like protein
homolog 1
Expressed sequence BB125219
Expressed sequence AI843755
DNA segment, Chr 7, ERATO Doi 183, expressed
UHRF1 (ICBP90) binding protein 1-like
DNA segment, Chr 7, ERATO Doi 183, expressed
Autism susceptibility candidate 2
HEAT repeat containing 5B
cDNA sequence BC034902
Tetratricopeptide repeat domain 19
Sjogren's syndrome nuclear autoantigen 1
Ring finger and SPRY domain containing 1
Ankyrin repeat domain 42
EH domain binding protein 1-like 1
Coiled-coil domain containing 124
DNA segment, Chr 6, ERATO Doi 474, expressed
Leucine rich repeat containing 42
Coiled-coil domain containing 3
BolA-like 3
Leucine rich repeat containing 45
DNA segment, Chr 10, ERATO Doi 641, expressed
Proline rich 16
Ankrin repeat domain 50
cDNA sequence BC046331
- 166-
5.05*
3.04
3.01
2.89
2.89
2.78
2.64
2.57
2.45
2.43
2.42
2.42
2.40
2.39
2.38
2.38
2.35
2.31
2.24
2.22
2.20
2.19
2.19
2.18
Appendix
Krcc1
AI597468
D10Ertd641e
Deb1
AI503316
57896
103266
52717
26901
105860
Snhg7
72091
Msl2l1
77853
*p value smaller than 0.05
Lysine-rich coiled-coil 1
Expressed sequence AI597468
DNA segment, Chr 10, ERATO Doi 641, expressed
Differentially expressed in B16F101
Expressed sequence AI503316
Small nucleolar RNA host gene (non-protein coding)
7
Male-specific lethal 2-like 1
- 167-
2.14
2.13
2.09
2.06
2.04
2.03
2.02
Appendix
Table A4. Down-regulated gene expression in trabecular osteocytes induced by multiple loading doses
Cell growth and differentiation
Apcdd1**
Entrez
Gene
ID
494504
Speer4b
73526
Speer8-ps1
Speer7-ps1
Gmcl1l
Prlr
Ctnna2
Speer1-ps1
Prelid2
Metrnl
Mtm1
Dtx1
Aff2
Aplp2
Onecut1
Rgmb
Nrp
Srpk3
Tdrd7
Art2a
Hdgfl1
Aldh1a1
Aldh8a1
74062
75858
71847
19116
12386
70896
77619
210029
17772
14357
14266
11804
15379
68799
654309
56504
100121
11871
15192
11668
237320
Slc22a16
70840
Tmem132d
Iapp**
Rhox2a
Bzw2
Rora
Bbs7
243274
15874
75199
66912
19883
71492
Utp20
70683
Hemt1
Cxxc4
Meg3**
15202
319478
17263
Gene Symbol
Gene Description
Adenomatosis polyposis coli down-regulated 1
Spermatogenesis assocciate-glutamate (E)-rich
protein 4b
Spermatogenesis glutamate (E)-rich protein 8, ps-1
Spermatogenesis glutamate (E)-rich protein 7, ps-1
Germ cell-less homolog 1 (Drosophila-like)
Prolactin receptor
Catenin (cadherin associated protein), alpha 2
Spermatogenesis glutamate (E)-rich protein 1, ps-1
PRELI domain containing 2
Meteorin, glial cell differentiation regulator-like
X-linked myotubular myopathy gene 1
Deltex 1 homolog (Drosophila)
AF4/FMR2 family, member 2
Amyloid beta (A4) precursor-like protein 2
One cut domain, family member 1
RGM domain family, member B
Neural regeneration protein
Serine/arginine-rich protein specific kinase 3
Tudor domain containing 7
ADP-ribosyltransferase 2a
Hepatoma derived growth factor-like 1
Aldehyde dehydrogenase family 1, subfamily A1
Aldehyde dehydrogenase 8 family, member A1
Solute carrier family 22 (organic cation transporter),
16
Transmembrane protein 132D
Islet amyloid polypeptide
Reproductive homeobox 2A
Basic leucine zipper and W2 domains 2
RAR-related orphan receptor alpha
Bardet-Biedl syndrome 7
UTP20, small subunit processome component,
homolog
Hematopoietic cell transcript 1
CXXC finger 4
Maternally expressed 3
- 168-
Fold
Change
6.06*
5.96
5.55
5.51
5.51
4.76
4.64
4.36
4.17
3.84
3.83
3.71
3.67
3.63
3.54
3.50
3.40
3.38
3.37
3.32
3.31
3.26
3.19
3.15
3.12
3.10
3.03
3.00
2.99
2.98
2.94
2.93
2.93
2.92
Appendix
Amtn**
71421
Cbfa2t2
12396
Nell1**
Magea3
Pdzrn3
Npn2
338352
17139
55983
18153
Gab2**
14389
Ambra1
Schip1
Rag1
Oog2
Usmg2
Tex19
Dym
Spata19
Mastl
Kirrel3
Dppa4
Sox10
Gzmn
Mxra8
Edar
Strbp
Lenep
Cd53
Prp2
Prl7c1
Tmc1
Tex15
Gsdmc1
Gpc2
Utrn
Svs5
Ntrk3
Tgm3
Dppa5a
Lefty2
Fezf2
Hmgb3
Phyhipl
Pou3f2
228361
30953
19373
381570
83677
73679
69190
75469
67121
67703
73693
20665
245839
74761
13608
20744
57275
12508
83380
67505
13409
104271
83492
71951
22288
20944
18213
21818
434423
320202
54713
15354
70911
18992
Amelotin
Core-binding factor, runt domain, alpha subunit 2,
transloc., 2
NEL-like 1
Melanoma antigen, family A, 3
PDZ domain containing RING finger 3
Neoplastic progression 2
Growth factor receptor bound protein 2-associated
protein 2
Autophagy/beclin 1 regulator 1
Schwannomin interacting protein 1
Recombination activating gene 1
Oogenesin 2
Upregulated during skeletal muscle growth 2
Testis expressed gene 19
Dymeclin
Spermatogenesis associated 19
Microtubule associated serine/threonine kinase-like
Kin of IRRE like 3
Developmental pluripotency associated 4
SRY-box containing gene 10
Granzyme N
Matrix-remodelling associated 8
Ectodysplasin-A receptor
Spermatid perinuclear RNA binding protein
Lens epithelial protein
CD53 antigen
Proline rich protein 2
Prolactin family 7, subfamily c, member 1
Transmembrane channel-like gene family 1
Testis expressed gene 15
Gasdermin C1
Glypican 2 (cerebroglycan)
Utrophin
Seminal vesicle secretory protein 5
Neurotrophic tyrosine kinase, receptor, type 3
Transglutaminase 3, E polypeptide
Developmental pluripotency associated 5A
Left-right determination factor 2
Fez family zinc finger 2
High mobility group box 3
Phytanoyl-coA hydroxylase interacting protein-like
POU domain, class 3, transcription factor 2
- 169-
2.87
2.86
2.81
2.72
2.70
2.66
2.63
2.58
2.57
2.56
2.50
2.46
2.40
2.37
2.36
2.35
2.32
2.30
2.29
2.27
2.26
2.24
2.22
2.22
2.21
2.20
2.17
2.15
2.15
2.15
2.15
2.13
2.12
2.09
2.08
2.08
2.07
2.05
2.05
2.03
2.03
Appendix
Sprr3
20766 Small proline-rich protein 3
Hs6st2
50786 Heparan sulfate 6-O-sulfotransferase 2
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
2.01
2.01
Apoptosis
Alpk2
Aplp1
Aim1
Pawr
Sp110
Lrrc4
Lrrc4c
Bmf
Ptk2
225638
11803
11630
114774
109032
192198
241568
171543
14083
Cideb
12684
Lck
Psen2
16818
19165
Tnfrsf9
21942
Sh3rf1
59009
Xrcc6
14375
Ccnb1ip1
239083
Rnf7
19823
Rnf216
108086
Cckbr
12426
Klrg2
74253
Mageh1
75625
Pim2
18715
C6
12274
Fastkd2
75619
*p value smaller than 0.05
Alpha-kinase 2
Amyloid beta (A4) precursor-like protein 1
Absent in melanoma 1
PRKC, apoptosis, WT1, regulator
Sp110 nuclear body protein
Leucine rich repeat containing 4
Leucine rich repeat containing 4C
Bcl2 modifying factor
PTK2 protein tyrosine kinase 2
Cell death-inducing DNA fragm. factor, α-subunit
effector B
Lymphocyte protein tyrosine kinase
Presenilin 2
Tumor necrosis factor receptor superfamily, member
9
SH3 domain containing ring finger 1
X-ray complement. defective repair, Chinese hamster
cells 6
Cyclin B1 interacting protein 1
Ring finger protein 7
Ring finger protein 216
Cholecystokinin B receptor
Killer cell lectin-like receptor subfamily G, member 2
Melanoma antigen, family H, 1
Proviral integration site 2
Complement component 6
FAST kinase domains 2
5.22*
4.52
4.49
3.93
3.89
3.05
2.98
2.94
2.71
RAN, member RAS oncogene family
Neural precursor cell expressed, down-regulated gene
8
Ubiquitin D
Anaphase promoting complex subunit 10
Ubiquitin specific peptidase 9, X chromosome
Pleckstrin homology domain interacting protein
4.85*
2.64
2.57
2.55
2.51
2.49
2.40
2.39
2.27
2.26
2.13
2.04
2.03
2.02
2.01
2.01
Cell cycle
Ran
19384
Nedd8
18002
Ubd
Anapc10
Usp9x
Phip
24108
68999
22284
83946
- 170-
3.47
3.03
2.97
2.91
2.83
Appendix
Nek7
Zzef1
Tbc1d12
Mtus1
Aurkc
Psca
Cdc14a
Dclre1a
Cfdp1
Incenp
Brwd3
Polh
Lin52
Spin2
Trpd52l3
Kifc1
Pif1
Ube2c
Hcfc1
Cdca7
Jmjd5
Sept14
Msh3
Dclre1b
Hus1
59125
195018
209478
102103
20871
72373
229776
55947
23837
16319
382236
80905
217708
278240
66745
16580
208084
68612
15161
66953
77035
74211
17686
140917
15574
Ercc4
50505
Dis3l2
208718
*p value smaller than 0.05
NIMA-related expressed kinase 7
Zinc finger, ZZ-type with EF hand domain 1
TBC1D12: TBC1 domain family, member 12
Mitochondrial tumor suppressor 1
Aurora kinase C
Prostate stem cell antigen
CDC14 cell division cycle 14 homolog A
DNA cross-link repair 1A, PSO2 homolog
Craniofacial development protein 1
Inner centromere protein
Bromodomain and WD repeat domain containing 3
Polymerase (DNA directed), eta (RAD 30 related)
Lin-52 homolog
Spindlin family, member 2
Tumor protein D52-like 3
Kinesin family member C1
PIF1 5'-to-3' DNA helicase homolog
Ubiquitin-conjugating enzyme E2C
Host cell factor C1
Cell division cycle associated 7
Jumonji domain containing 5
Septin 14
Mut-S homolog 3 (E. coli)
DNA cross-link repair 1B, PSO2 homolog
Hus1 homolog (S. pombe)
Excision cross-complementing rodent repair
deficiency, 4
DIS3 mitotic control homolog (S. cerevisiae-like) 2
2.80
2.70
2.65
2.64
2.63
2.53
2.51
2.49
2.46
2.46
2.43
2.40
2.38
2.37
2.31
2.31
2.31
2.13
2.12
2.11
2.10
2.07
2.07
2.06
2.04
Vomeronasal 1 receptor, A3
Vomeronasal 1 receptor, D14
Natriuretic peptide receptor 1
Embryonic lethal, abnormal vision, Dros-like 2, Hu
ag B
Embryonic lethal, abnormal vision, Dros-like 1, Hu
ag R
Tnf receptor-associated factor 6
Vomeronasal 2, receptor 29
Tetraspanin 3
Centaurin, gamma 2
Vomeronasal 2, receptor 42
5.33*
5.21
4.67
2.03
2.00
Signal transduction
V1ra3
V1rd14
Npr1
113845
81011
18160
Elavl2
15569
Elavl1
15568
Traf6
Vmn2r29
Tspan3
Centg2
Vmn2r42
22034
76229
56434
347722
22310
- 171-
4.57
3.86
3.78
3.73
3.66
3.55
3.53
Appendix
Olfr2
Sstr3
Dlg2
Mapk10
Rapgefl1
Adcy1
Gnao1
Cabin1
Grk5
Olfml2a
Rhbdl3
Akap3
Prkcb1
Ppp2r5c
Htr1d
18317
20607
23859
26414
268480
432530
14681
104248
14773
241327
246104
11642
18751
26931
15552
Ltbp1
268977
Odz2
Dgkb
Snx29
Ptprn
Map2k6
Gnal
Cd247
Klk6
Plcb1
Ankrd44
Smyd5
Garnl3
V1rb4
Lag3
Rab39b
23964
217480
74478
19275
26399
14680
12503
19144
18795
329154
232187
99326
113854
16768
67790
Mapk8ip1
19099
Depdc5
277854
Ralgps1
241308
Vmn2r1
56544
Ilvbl
216136
Gngt1
14699
Lax1
240754
*p value smaller than 0.05
Olfactory receptor 2
Somatostatin receptor 3
Discs, large homolog 2
Mitogen-activated protein kinase 10
Rap guanine nucleotide exchange factor GEF-like 1
Adenylate cyclase 1
Guanine nucleotide binding protein, alpha O
Calcineurin binding protein 1
G protein-coupled receptor kinase 5
Olfactomedin-like 2A
Rhomboid, veinlet-like 3
A kinase (PRKA) anchor protein 3
Protein kinase C, beta 1
Protein phosphatase 2, subunit B, gamma isoform
5-hydroxytryptamine (serotonin) receptor 1D
Latent transforming growth factor beta binding
protein 1
Odd Oz/ten-m homolog 2
Diacylglycerol kinase, beta
Sorting nexin 29
Protein tyrosine phosphatase, receptor type, N
Mitogen-activated protein kinase kinase 6
Guanine nucleotide binding protein, α-stimulating
CD247 antigen
Kallikrein related-peptidase 6
Phospholipase C, beta 1
Ankyrin repeat domain 44
SET and MYND domain containing 5
GTPase activating RANGAP domain-like 3
Vomeronasal 1 receptor, B4
Lymphocyte-activation gene 3
RAB39B, member RAS oncogene family
Mitogen-activated protein kinase 8 interacting protein
1
DEP domain containing 5
Ral GEF with PH domain and SH3 binding motif 1
Vomeronasal 2, receptor 1
IlvB (bacterial acetolactate synthase)-like
Guanine nucleotide binding protein, polypeptide 1
Lymphocyte transmembrane adaptor 1
Transcription
- 172-
3.51
3.37
3.37
3.34
3.28
3.11
3.05
3.05
2.99
2.92
2.87
2.83
2.69
2.66
2.57
2.45
2.43
2.42
2.41
2.40
2.37
2.37
2.36
2.36
2.33
2.25
2.24
2.22
2.20
2.20
2.17
2.16
2.14
2.13
2.09
2.08
2.02
2.00
Appendix
Hif3a
Rpp25
Zhx1
Snord116
D3Ertd300e
Lztfl1
Zscan22
Cux2
Tbx20
Kri1
Esrrg
Ott
Myt1
Tcfap2b
Wiz
Rsbn1
Ssbp3
Cnot8
Kcnq1ot1
Elk3
Hist2h2bb
Zswim3
Pole2
Gm397
Trim71
Trim27
Shq1
Gmeb1
53417
102614
22770
64243
56790
93730
232878
13048
57246
215194
26381
18422
17932
21419
22404
229675
72475
69125
63830
13713
319189
67538
18974
245109
636931
19720
72171
56809
Runx1t1
12395
Foxn2
Hmga2
Ints10
Zfp133
Zfp609
14236
15364
70885
171588
214812
Pop1
67724
Helz
Ccrn4l
Safb
Zim3
78455
12457
224903
116811
Mllt10
17354
Hypoxia inducible factor 3, alpha subunit
Ribonuclease P 25 subunit
Zinc fingers and homeoboxes 1
Small nucleolar RNA, C/D box 116
DNA segment, Chr 3, ERATO Doi 300, expressed
Leucine zipper transcription factor-like 1
Zinc finger and SCAN domain containing 22
Cut-like homeobox 2
T-box 20
KRI1 homolog
Estrogen-related receptor gamma
Ovary testis transcribed
Myelin transcription factor 1
Transcription factor AP-2 beta
Widely-interspaced zinc finger motifs
Rosbin, round spermatid basic protein 1
Single-stranded DNA binding protein 3
CCR4-NOT transcription complex, subunit 8
KCNQ1 overlapping transcript 1
ELK3, member of ETS oncogene family
Histone cluster 2, H2bb
Zinc finger, SWIM domain containing 3
Polymerase (DNA directed), epsilon 2, p59 subunit
Gene model 397, (NCBI)
Tripartite motif-containing 71
Tripartite motif-containing 27
SHQ1 homolog
Glucocorticoid modulatory element bind protein 1
Runt-related transcription factor 1, translocated, 1
(cyclin D-rel.)
Forkhead box N2
High mobility group AT-hook 2
Integrator complex subunit 10
Zinc finger protein 133
Zinc finger protein 609
Processing of precursor 1, ribonuclease P/MRP
family
Helicase with zinc finger domain
CCR4 carbon catabolite repression 4-like
Scaffold attachment factor B
Zinc finger, imprinted 3
Myeloid/lymphoid leukemia, trithorax homolog,
transloc., 10
- 173-
5.80*
5.04
4.80
4.71
4.20
4.18
4.04
3.96
3.85
3.82
3.79
3.62
3.58
3.52
3.48
3.42
3.40
3.38
3.35
3.10
2.95
2.90
2.85
2.80
2.79
2.74
2.71
2.71
2.70
2.70
2.68
2.68
2.65
2.63
2.60
2.59
2.58
2.58
2.55
2.54
Appendix
Meis2
Pbx1
Jmjd1c
Lmnb1
Tyms
Entpd7
Nr5a1
Klf3
Exosc2
Atxn1
Adi1
Prr8
Grhl1
Ascc2
Lbxcor1
17536
18514
108829
16906
22171
93685
26423
16599
227715
20238
104923
381626
195733
75452
207667
Sip1
66603
Rg9mtd2
108943
Phb2
Mta3
12034
116871
Mllt11
56772
Utx
22289
Polr2a
Zfp532
20020
328977
Auh
11992
Zfp28
Cdx2
Hnf1b
Rnasen
Zfp191
Foxp4
Nr2f2
Trim46
Ppih
Rpap2
Nfe2l3
Aff3
Dpf1
Hif3a
Gtf3c1
22690
12591
21410
14000
59057
74123
11819
360213
66101
231571
18025
16764
29861
53417
233863
Meis homeobox 2
Pre B-cell leukemia transcription factor 1
Jumonji domain containing 1C
Lamin B1
Thymidylate synthase
Ectonucleoside triphosphate diphosphohydrolase 7
Nuclear receptor subfamily 5, group A, member 1
Kruppel-like factor 3 (basic)
Exosome component 2
Ataxin 1
Acireductone dioxygenase 1
Proline rich 8
Grainyhead-like 1
Activating signal cointegrator 1 complex subunit 2
Ladybird homeobox 1 homolog co-repressor 1
Survivor of motor neuron protein interacting protein
1
RNA guanine-9-methyltransferase domain containing
2
Prohibitin 2
Metastasis associated 3
Myeloid/lymphoid leukemia, trithorax homolog,
transloc. 11
Ubiquitously transc. tetratricopeptide repeat gene, Xchrom.
Polymerase (RNA) II, DNA directed polypeptide A
Zinc finger protein 532
AU RNA binding protein/enoyl-coenzyme A
hydratase
Zinc finger protein 28
Caudal type homeo box 2
HNF1 homeobox B
Ribonuclease III, nuclear
Zinc finger protein 191
Forkhead box P4
Nuclear receptor subfamily 2, group F, member 2
Tripartite motif-containing 46
Peptidyl prolyl isomerase H
RNA polymerase II associated protein 2
Nuclear factor, erythroid derived 2, like 3
AF4/FMR2 family, member 3
D4, zinc and double PHD fingers family 1
Hypoxia inducible factor 3, alpha subunit
General transcription factor III C 1
- 174-
2.53
2.52
2.52
2.51
2.51
2.51
2.46
2.41
2.39
2.37
2.35
2.35
2.30
2.28
2.28
2.27
2.27
2.27
2.25
2.24
2.23
2.19
2.18
2.17
2.16
2.16
2.14
2.12
2.11
2.09
2.07
2.07
2.06
2.06
2.05
2.05
2.05
2.05
2.03
Appendix
Chd5
269610
*p value smaller than 0.05
Chromodomain helicase DNA binding protein 5
2.03
Serum amyloid A 4
Pregnancy specific glycoprotein 19
NLR family, pyrin domain containing 4E
Melanoma antigen, family B, 1
T-cell receptor alpha chain
Complement component 9
Histocompatibility 2, M region locus 10.1
Interleukin 1 family, member 5 (delta)
Secreted and transmembrane 1B
GTPase, IMAP family member 7
C-type lectin domain family 2, member h
Lymphocyte antigen 6 complex, locus G6C
NLR family, pyrin domain containing 9B
Leucine rich repeat containing 40
X-ray radiation resistance associated 1
Interleukin 12 receptor, beta 2
CD8 antigen, alpha chain
Histocompatibility 2, T region locus 23
Inducible T-cell co-stimulator
Coiled-coil domain containing 130
Alpha-2-glycoprotein 1, zinc
5.13*
3.76
3.61
3.16
3.04
3.01
3.01
2.87
2.62
2.51
2.50
2.45
2.45
2.36
2.31
2.24
2.17
2.14
2.11
2.03
2.01
Myosin, heavy polypeptide 11, smooth muscle
Syntrophin, basic 1
ADP-ribosylation factor-like 15
SH3-binding domain glutamic acid-rich protein
3.12*
2.45
2.20
2.11
Tropomodulin 1
Obscurin-like 1
Keratin 6B
Coronin, actin binding protein, 2B
midline 1
Tubulin, gamma complex associated protein 5
Sfi1 homolog, spindle assembly associated
7.34*
6.69
4.75
4.42
3.57
3.30
3.17
Immune response
Saa4
20211
Psg19
26439
Nlrp4e
446099
Mageb1
17145
Tcra
21473
C9
12279
H2-M10.1
14985
Il1f5
54450
Sectm1b
58210
Gimap7
231932
Clec2h
94071
Ly6g6c
68468
Nlrp9b
243874
Lrrc40
67144
Xrra1
446101
Il12rb2
16162
Cd8a
12525
H2-T23
15040
Icos
54167
Ccdc130
67736
Azgp1
12007
*p value smaller than 0.05
Muscle contraction
Myh11
17880
Sntb1
20649
Arl15
218639
Sh3bgr
50795
*p value smaller than 0.05
Cytoskeleton remodeling
Tmod1
Obsl1
Krt6b
Coro2b
Mid1
Tubgcp5
Sfi1
21916
98733
16688
235431
17318
233276
78887
- 175-
Appendix
Hmmr
15366
Limk1
16885
Ccdc46
76380
Ccdc51
66658
Sprr2a
20755
Lrrc50
68270
Ccdc67
234964
Capn10
23830
Wasl
73178
Fuz
70300
Evl
14026
Krt39
237934
Tubb4
22153
Kif6
319991
Ccdc83
75338
Diap1
13367
Fcho2
218503
Scaper
244891
Tmsb10
19240
*p value smaller than 0.05
Hyaluronan mediated motility receptor (RHAMM)
LIM-domain containing, protein kinase
Coiled-coil domain containing 46
Coiled-coil domain containing 51
Small proline-rich protein 2A
Leucine rich repeat containing 50
Coiled-coil domain containing 67
Calpain 10
Wiskott-Aldrich syndrome-like
Fuzzy homolog
Ena-vasodilator stimulated phosphoprotein
Keratin 39
Tubulin, beta 4
Kinesin family member 6
Coiled-coil domain containing 83
Diaphanous homolog 1
FCH domain only 2
S phase cyclin A-associated protein in the ER
Thymosin, beta 10
2.95
2.90
2.85
2.83
2.73
2.57
2.49
2.33
2.30
2.30
2.29
2.29
2.27
2.19
2.19
2.18
2.14
2.11
2.05
Proteolysis
Hecw1
94253
Serpinb9g
Klk4
Tmprss2
Pm20d1
Rbck1
Mcpt9
Wdr66
Stfa3
Fbxo36
Serpinb9b
Ptpn3
Ermp1
Ube2j2
Rfwd2
Adam28
Adam1b
93806
56640
50528
212933
24105
17232
269701
20863
66153
20706
545622
226090
140499
26374
13522
280667
Adamts19
240322
Wdr91
101240
HECT, C2 and WW domain E3 ubiquitin protein
ligase 1
Serine peptidase inhibitor, clade B, member 9g
Kallikrein related-peptidase 4
Transmembrane protease, serine 2
Peptidase M20 domain containing 1
RanBP-type, C3HC4-type zinc finger containing 1
Mast cell protease 9
WD repeat domain 66
Stefin A3
F-box protein 36
Serine peptidase inhibitor, clade B, member 9b
Protein tyrosine phosphatase, non-receptor type 3
Endoplasmic reticulum metallopeptidase 1
Ubiquitin-conjugating enzyme E2, J2 homolog
Ring finger and WD repeat domain 2
A disintegrin and metallopeptidase domain 28
A disintegrin and metallopeptidase domain 1b
Disintegrin-like/metallopeptidase, thrombospondin
type 1, 19
WD repeat domain 91
- 176-
5.40*
4.83
4.52
3.24
3.06
2.89
2.68
2.67
2.58
2.46
2.44
2.43
2.42
2.37
2.35
2.28
2.04
2.04
2.01
Appendix
*p value smaller than 0.05
Translation
Cugbp2
Eif1a
Brunol4
Agxt2l1
Gtpbp3
Eif2ak1
Mrpl15
Trit1
Rpl9
14007
13664
108013
71760
70359
15467
27395
66966
20005
Nars2
244141
Rps17
Trim6
Pcmtd1
Tnrc6b
20068
94088
319263
213988
Hs3st3b1
54710
Eef1g
67160
*p value smaller than 0.05
CUG triplet repeat, RNA binding protein 2
Eukaryotic translation initiation factor 1A
Bruno-like 4, RNA binding protein
Alanine-glyoxylate aminotransferase 2-like 1
GTP binding protein 3
Eukaryotic translation initiation factor 2 α-kinase 1
Mitochondrial ribosomal protein L15
tRNA isopentenyltransferase 1
Ribosomal protein L9
Asparaginyl-tRNA synthetase 2 mitochondrial
(putative)
Ribosomal protein S17
Tripartite motif-containing 6
Protein-L-isoaspartate O-methyltransferase domain 1
Trinucleotide repeat containing 6b
Heparan sulfate (glucosamine) 3-O-sulfotransferase
3B1
Eukaryotic translation elongation factor 1 gamma
4.42*
4.16
3.52
3.14
3.08
3.05
3.00
2.85
2.85
Follicle stimulating hormone receptor
Urotensin 2 receptor
G protein-coupled receptor 125
Component of Sp100-rs
Regulator of G-protein signaling 9
Prostaglandin E receptor 3 (subtype EP3)
G-protein-coupled receptor 50
Guanine nucleotide binding protein, alpha
transducing 1
4.63*
4.47
4.20
4.16
2.61
2.44
2.28
Mucin 10, submandibular gland salivary mucin
Zona pellucida 3 receptor
Cingulin
Sushi, nidogen and EGF-like domains 1
Microfibrillar-associated protein 4
Gap junction protein, delta 2
CD84 antigen
6.40*
4.04
3.81
3.47
3.40
3.37
3.33
2.62
2.57
2.39
2.31
2.13
2.08
2.07
G-protein signaling
Fshr
Uts2r
Gpr125
Csprs
Rgs9
Ptger3
Gpr50
14309
217369
70693
114564
19739
19218
14765
Gnat1
14685
2.10
*p value smaller than 0.05
Cell adhesion
Muc10
Zp3r
Cgn
Sned1
Mfap4
Gjd2**
Cd84
17830
22789
70737
208777
76293
14617
12523
- 177-
Appendix
Alcam
11658 Activated leukocyte cell adhesion molecule
Vcan
13003 Versican
Fermt2
218952 Fermitin family homolog 2
Pvrl4
71740 Poliovirus receptor-related 4
Muc4
140474 Mucin 4
Cldn2
12738 Claudin 2
Ceacam1
26365 CEA-related cell adhesion molecule 1
Nrxn3
18191 Neurexin III
Ncam1
17967 Neural cell adhesion molecule 1
Limk2
16886 LIM motif-containing protein kinase 2
Itgb2l
16415 Integrin beta 2-like
Nfasc
269116 Neurofascin
Ephb2
13844 Eph receptor B2
Ncan
13004 Neurocan
Muc13
17063 Mucin 13, epithelial transmembrane
Gpa33
59290 Glycoprotein A33 (transmembrane)
Mog
17441 Myelin oligodendrocyte glycoprotein
Jam3
83964 Junction adhesion molecule 3
*p value smaller than 0.05
**Genes reportedly associated with bone metabolism
3.20
3.07
2.86
2.77
2.75
2.69
2.55
2.51
2.50
2.49
2.48
2.47
2.41
2.37
2.35
2.27
2.10
2.06
Chemotaxis
Prok1
246691
Hebp1
15199
Edn3
13616
*p value smaller than 0.05
Prokineticin 1
Heme binding protein 1
Endothelin 3
2.62*
2.13
2.01
Gamma-aminobutyric acid (GABA-A) receptor, pi
Sperm motility kinase 3A
Rabphilin 3A-like (without C2 domains)
Potassium voltage gated channel, Shab-rel. subfam.,
2
Cyclin M3
Chloride channel calcium activated 3
Solute carrier family 15 (oligopeptide transporter), 1
Calcium channel, voltage-dependent, α- 2/Δ-subunit
2
Retinol binding protein 3, interstitial
K+ voltage-gated channel, subfamily S, 2
Glutamate receptor, ionotropic, NMDA2D, epsilon 4
Translocase, inner mitochondrial membr. 8 homolog
a2
6.55*
5.30
4.57
Transport
Gabrp
Smok3a
Rph3al
216643
545814
380714
Kcnb2
98741
Cnnm3
Clca3
Slc15a1
94218
23844
56643
Cacna2d2
56808
Rbp3
Kcns2
Grin2d
19661
16539
14814
Timm8a2
223262
- 178-
4.51
4.33
4.00
3.96
3.93
3.83
3.78
3.70
3.58
Appendix
Ryr2
Klc1
20191
16593
Kcnj9
16524
Ap2a2
11772
Sgip1
73094
Aqp4
Rtp3
Slc35f1
11829
235636
215085
Trpm1
17364
Aqp11
Rab3d
Itpr2
66333
19340
16439
Trpc7
26946
Slco6b1
67854
Trpc5
22067
Laptm4b
Hbb-bh1
Ap3b2
Slc26a10
114128
15132
11775
216441
Slc7a13
74087
Scn8a
Atp8b1
Csn1s2a
Spnb3
Ipo7
Slc25a2
Slc10a1
Slc25a40
Stam2
Pts
Ift172
Stxbp5l
Sri
Golt1b
Jakmip1
Ipo8
Ap3m2
20273
54670
12993
20743
233726
83885
20493
319653
56324
19286
67661
207227
109552
66964
76071
320727
64933
Ryanodine receptor 2, cardiac
Kinesin light chain 1
Potassium inwardly-rectifying channel, subfamily J,
9
Adaptor protein complex AP-2, alpha 2 subunit
SH3-domain GRB2-like (endophilin) interacting
protein 1
Aquaporin 4
Receptor transporter protein 3
Solute carrier family 35, member F1
Transient receptor potential cation channel, family
M, 1
Aquaporin 11
RAB3D, member RAS oncogene family
Inositol 1,4,5-triphosphate receptor 2
Transient receptor potential cation channel, family C,
7
Solute carrier organic anion transport family, member
6b1
Transient receptor potential cation channel,
subfamily C, 5
Lysosomal-associated protein transmembrane 4B
Hemoglobin Z, beta-like embryonic chain
Adaptor-related protein complex 3, beta 2 subunit
Solute carrier family 26, member 10
Solute carrier family 7, cationic amino acid
transporter, 13
Sodium channel, voltage-gated, type VIII, alpha
ATPase, class I, type 8B, member 1
Casein alpha s2-like A
Spectrin beta 3
Importin 7
Solute carrier family 25, member 2
Solute carrier family 10, member 1
Solute carrier family 25, member 40
Signal transducing adaptor molecule 2
6-pyruvoyl-tetrahydropterin synthase
Intraflagellar transport 172 homolog
Syntaxin binding protein 5-like
Sorcin
Golgi transport 1 homolog B
Janus kinase and microtubule interacting protein 1
Importin 8
Adaptor-related protein complex 3, mu 2 subunit
- 179-
3.49
3.40
3.37
3.33
3.33
3.29
3.27
3.27
3.26
3.17
3.03
2.93
2.92
2.89
2.87
2.84
2.84
2.83
2.70
2.67
2.61
2.59
2.58
2.56
2.56
2.56
2.54
2.52
2.51
2.45
2.44
2.44
2.43
2.40
2.33
2.30
2.30
Appendix
Pkd1l3
Slc25a25
244646
227731
Slc17a2
218103
Pacsin3
D2hgdh
Ces2
Igh-6
Slc5a9
Actr1a
Mfsd11
Best2
Sgpp2
Slc14a2
Kif13a
Slc24a2
Atp1a1
80708
98314
234671
16019
230612
54130
69900
212989
433323
27411
16553
76376
11928
Trpc4ap
56407
Dnajc6
72685
Vps33a
77573
*p value smaller than 0.05
Polycystic kidney disease 1 like 3
Solute carrier family 25, member 25
Solute carrier family 17 (sodium phosphate), member
2
Protein kinase C, casein kinase substrate in neurons 3
D-2-hydroxyglutarate dehydrogenase
Carboxylesterase 2
Immunoglobulin heavy chain 6 (heavy chain of IgM)
Solute carrier family 5, member 9
ARP1 actin-related protein 1 homolog A (yeast)
Major facilitator superfamily domain containing 11
Bestrophin 2
Sphingosine-1-phosphate phosphotase 2
Solute carrier family 14 (urea transporter), member 2
Kinesin family member 13A
Solute carrier family 24, member 2
ATPase, Na+/K+ transporting, alpha 1 polypeptide
Transient receptor potential cation channel,
subfamily C, 4
DnaJ (Hsp40) homolog, subfamily C, member 6
Vacuolar protein sorting 33A
2.21
2.16
2.15
2.12
2.12
2.12
2.11
2.10
2.10
2.09
2.08
2.08
2.06
2.05
2.02
2.02
2.00
2.00
2.00
Energy metabolism
Cyp4a10
13117
Man1a
Gsta2
Gpd2
Pcyt1b
Large
Akr1d1
A
Tbc1d5
17155
14858
14571
236899
16795
208665
50518
72238
Cyp2a5
13087
Cyp4a31
666168
Snca
Pank1
Alg6
Idi2
20617
75735
320438
320581
Cyp19a1
13075
Cytochrome P450, family 4, subfamily a, polypeptide
10
Mannosidase 1, alpha
Glutathione S-transferase, alpha 2 (Yc2)
Glycerol phosphate dehydrogenase 2, mitochondrial
Phosphate cytidylyltransferase 1 choline, β-isoform
Like-glycosyltransferase
Aldo-keto reductase family 1, member D1
Nonagouti
TBC1 domain family, member 5
Cytochrome P450, family 2, subfamily a, polypeptide
5
Cytochrome P450, family 4, subfamily a, polypeptide
31
Synuclein, alpha
Pantothenate kinase 1
Asparagine-linked glycosylation 6 homolog
Isopentenyl-diphosphate delta isomerase 2
Cytochrome P450, family 19, subfamily a,
polypeptide 1
- 180-
6.22*
4.34
4.07
3.87
3.78
3.77
3.73
3.49
3.40
3.24
3.22
3.09
3.01
2.98
2.83
2.78
Appendix
Mettl7a1
Ckmt1
Pigc
Acad9
Pgk2
Ugt3a1
Fggy
Galntl4
70152
12716
67292
229211
18663
105887
75578
233733
Lrpap1
16976
Cyp26a1
13082
Cyp2j5
13109
B4galt6
56386
Asah3l
B4galnt2
230379
14422
Hsd3b6
15497
Alms1
Bdh2
Indol1
236266
69772
209176
Prkag2
108099
Cryzl1
Apoa5
Man2a2
Nagk
Gstm3
Gldc
Man1a2
66609
66113
140481
56174
14864
104174
17156
Pbld
68371
Ddah1
Neu3
Bhmt
69219
50877
12116
Asahl
67111
B3galt5
93961
Aldh1l1
107747
*p value smaller than 0.05
Methyltransferase like 7A1
Creatine kinase, mitochondrial 1, ubiquitous
Phosphatidylinositol glycan anchor biosynthesis, C
Acyl-coenzyme A dehydrogenase family member 9
Phosphoglycerate kinase 2
UDP glycosyltransferases 3 family, polypeptide A1
FGGY carbohydrate kinase domain containing
UDP-N-acetyl-alpha-D-galactosamine 4
Low density lipoprotein receptor-related associated
protein 1
Cytochrome P450, family 26, subfamily a,
polypeptide 1
Cytochrome P450, family 2, subfamily j, polypeptide
5
Beta-GlcNAc beta 1,4-galactosyltransferase,
polypeptide 6
N-acylsphingosine amidohydrolase 3-like
Beta-1,4-N-acetyl-galactosaminyl transferase 2
Hydroxy-delta-5-steroid dehydrogenase, 3 betaisomerase 6
Alstrom syndrome 1 homolog
3-hydroxybutyrate dehydrogenase, type 2
Indoleamine-pyrrole 2,3 dioxygenase-like 1
Protein kinase, AMP-activated, gamma 2 noncatalytic subunit
Crystallin, zeta (quinone reductase)-like 1
Apolipoprotein A-V
Mannosidase 2, alpha 2
N-acetylglucosamine kinase
Glutathione S-transferase, mu 3
Glycine decarboxylase
Mannosidase, alpha, class 1A, member 2
Phenazine biosynthesis-like protein domain
containing
Dimethylarginine dimethylaminohydrolase 1
Neuraminidase 3
Betaine-homocysteine methyltransferase
N-acylsphingosine amidohydrolase (acid
ceramidase)-like
Beta-GlcNAc beta 1,3-galactosyltransferase,
polypeptide 5
Aldehyde dehydrogenase 1 family, member L1
Neurophysiological process
- 181-
2.62
2.60
2.57
2.57
2.56
2.56
2.54
2.53
2.53
2.49
2.48
2.48
2.44
2.42
2.42
2.38
2.33
2.26
2.23
2.20
2.20
2.17
2.16
2.13
2.11
2.07
2.06
2.04
2.04
2.02
2.00
2.00
2.00
Appendix
Syn2
20965
Gria4
14802
Slitrk5
75409
Grid2
14804
Grik1
14805
Gria2
14800
Chgb
12653
Syn3
27204
*p value smaller than 0.05
Synapsin II
Glutamate receptor, ionotropic, AMPA4 (alpha 4)
SLIT and NTRK-like family, member 5
Glutamate receptor, ionotropic, delta 2
Glutamate receptor, ionotropic, kainate 1
Glutamate receptor, ionotropic, AMPA2 (alpha 2)
Chromogranin B
Synapsin III
4.16*
3.52
3.27
3.26
2.66
2.42
2.35
2.13
Radial spokehead-like 2A
Reduced expression 2
Apolipoprotein L 7b
Expressed sequence C77717
Expressed sequence C79741
Transmembrane protein 207
DNA segment, Chr X, ERATO Doi 11, expressed
DNA segment, Chr 15, ERATO Doi 50, expressed
Late cornified envelope 1G
DNA segment, Chr 9, ERATO Doi 496, expressed
DNA segment, Chr 5, ERATO Doi 163, expressed
Testis expressed gene 13
DNA segment, Chr 3, ERATO Doi 246, expressed
DNA segment, Chr 8, ERATO Doi 620, expressed
DNA segment, Chr 2, ERATO Doi 239, expressed
DNA segment, Chr 4, ERATO Doi 571, expressed
Leucine rich repeat containing 52
DNA segment, Chr 9, ERATO Doi 26, expressed
Proline rich region 18
Preferentially expressed antigen in melanoma like 5
MORN repeat containing 1
DNA segment, Chr 5, ERATO Doi 521, expressed
F-box and WD-40 domain protein 14
2'-5' oligoadenylate synthetase 1D
DNA segment, Chr 3, ERATO Doi 162, expressed
2'-5' oligoadenylate synthetase 1E
DNA segment, Chr 2, ERATO Doi 105, expressed
Ankyrin repeat domain 34A
Per-hexamer repeat gene 5
Transmembrane protein 109
DNA segment, Chr 5, ERATO Doi 615, expressed
5.34*
5.09
4.62
4.58
4.46
4.36
4.21
4.14
4.02
3.85
3.84
3.66
3.55
3.45
3.42
3.22
3.06
3.04
3.00
2.91
2.82
2.79
2.77
2.74
2.72
2.65
2.63
2.51
2.51
2.49
2.44
Unknown process
Rshl2a
Rex2
Apol7b
C77717
C79741
Tmem207
DXErtd11e
D15Ertd50e
Lce1g
D9Ertd496e
D5Ertd163e
Tex13
D3Ertd246e
D8Ertd620e
D2Ertd239e
D4Ertd571e
Lrrc52
D9Ertd26e
Prr18
Pramel5
Morn1
D5Ertd521e
Fbxw14
Oas1d
D3Ertd162e
Oas1e
D2Ertd105e
Ankrd34a
Phxr5
Tmem109
D5Ertd615e
66832
19715
278679
97361
97877
224058
52003
52193
66195
52361
52181
83555
52211
52423
51880
52341
240899
52062
320111
384077
76866
52350
50757
100535
52252
231699
52165
545554
18690
68539
52401
- 182-
Appendix
Lce1i
76585
D7Ertd1e
51959
D17Ertd657e
52029
Ankrd41
234396
Rtbdn
234542
Akr1c20
116852
Wdr20b
70948
D7Ertd59e
52006
D15Ertd180e
52498
D18Ertd653e
52662
Tmem86b
68255
BC048546
232400
D2Ertd127e
51873
Krt2-ps1
64819
Heatr5b
320473
D5Ertd215e
52224
Dcpp3
620253
BC050210
381337
BC016548
211039
BC031353
235493
BC030500
234290
*p value smaller than 0.05
Late cornified envelope 1I
DNA segment, Chr 7, ERATO Doi 1, expressed
DNA segment, Chr 17, ERATO Doi 657, expressed
Ankyrin repeat domain 41
Retbindin
Aldo-keto reductase family 1, member C20
WD repeat domain 20b
DNA segment, Chr 7, ERATO Doi 59, expressed
DNA segment, Chr 15, ERATO Doi 180, expressed
DNA segment, Chr 18, ERATO Doi 653, expressed
Transmembrane protein 86B
cDNA sequence BC048546
DNA segment, Chr 2, ERATO Doi 127, expressed
Keratin complex 2, basic, pseudogene 1
HEAT repeat containing 5B
DNA segment, Chr 5, ERATO Doi 215, expressed
Demilune cell and parotid protein 3
cDNA sequence BC050210
cDNA sequence BC016548
cDNA sequence BC031353
cDNA sequence BC030500
- 183-
2.37
2.35
2.33
2.32
2.31
2.29
2.28
2.27
2.26
2.25
2.19
2.18
2.16
2.14
2.14
2.14
2.08
2.07
2.04
2.02
2.02
Appendix
Table A5. List of load-regulated molecular pathways in single loading
Molecular Pathway
Translation _Regulation of translation initiation
Cytoskeleton remodeling_α-1A adrenergic receptor-dependent
inhibition of PI3K
Immune response _IL22 signaling pathway
Immune response _Antigen presentation by MHC class I
Immune response _PIP3 signaling in B lymphocytes
Immune response _CD28 signaling
Immune response _BCR pathway
Signal transduction_Activation of PKC via G-Protein coupled receptor
Development_Transcription regulation of granulocyte development
Signal transduction_PKA signaling
Immune response _ICOS-ICOSL pathway in T-helper cell
Immune response _NFAT in immune response
Development_VEGF signaling and activation
Signal transduction_Calcium signaling
Cell cycle_Spindle assembly and chromosome separation
Apoptosis and survival_Anti-apoptotic action of membrane-bound
ESR1
Cardiac Hypertrophy_Ca(2+)-dependent NF-AT signaling in Cardiac
Hypertrophy
Immune response_Inhibitory action of Lipoxins on TNF-alpha
signaling
Transcription_Transcription factor Tubby signaling pathways
Apoptosis and survival_Inhibition of ROS-induced apoptosis by
17beta-estradiol
Development_EPO-induced Jak-STAT pathway
Muscle contraction_ACM regulation of smooth muscle contraction
Oxidative stress_Role of ASK1 under oxidative stress
Immune response _CXCR4 signaling via second messenger
Signal transduction_IP3 signaling
Regulation of CFTR activity (norm and CF)
Cell cycle_Nucleocytoplasmic transport of CDK/Cyclins
Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/IAP pathway
Apoptosis and survival_Anti-apoptotic TNFs/NF-kB/Bcl-2 pathway
Transcription_Ligand-Dependent Transcription of Retinoid-Target
genes
Transport_RAN regulation pathway
Immune response _PGE2 in immune and neuroendocrine system
interactions
TCA (tricarboxylic acid cycle)
Cytoskeleton remodeling_Neurofilaments
Transcription_Ligand-dependent activation of the ESR1/SP pathway
Development_TGF-beta receptor signaling
Immune response_Bacterial infections in normal airways
Development_IGF-RI signaling
- 184-
Counts
60.00%*
Map ID
498
66.67%
2395
50.00%
42.31%
38.71%
35.14%
33.33%
33.33%
35.48%
39.13%
33.33%
33.33%
34.38%
34.38%
34.38%
522
2100
702
620
655
453
458
675
619
668
539
550
712
37.50%
2736
32.43%
2234
32.35%
2727
50.00%
461
34.62%
2740
31.43%
31.43%
36.36%
36.36%
30.00%
30.00%
42.86%
33.33%
29.27%
737
2657
521
613
557
2269
473
721
720
31.25%
369
40.00%
404
33.33%
2387
35.00%
31.03%
31.03%
29.41%
28.21%
27.27%
814
1491
2208
475
2694
540
Appendix
Transport_ACM3 in salivary glands
Immune response _PGE2 common pathways
Immune response _CCR3 signaling in eosinophils
Chemotaxis_Lipoxin inhibitory action on neutrophil migration
Development_A3 receptor signaling
Transcription_CREB pathway
Development_PIP3 signaling in cardiac myocytes
Cell cycle_Regulation of G1/S transition (part 2)
Development_Mu-type opioid receptor signaling
Immune response _IL10 signaling pathway
DNA damage_Role of SUMO in p53 regulation
Neurophysiological process_Mu-type opioid receptor-mediated
analgesia
Development_TPO signaling via JAK-STAT pathway
Immune response _MIF - the neuroendocrine-macrophage connector
Cell cycle_Role of APC in cell cycle regulation
Immune response _Fc epsilon RI pathway
Signal transduction_AKT signaling
Development_A2A receptor signaling
Inhibitory action of Lipoxins on neutrophil migration
Development_EGFR signaling via PIP3
G-protein signaling_G-Protein alpha-s signaling cascades
CFTR folding and maturation (norm and CF)
Transport_Alpha-2 adrenergic receptor regulation of ion channels
Mucin expression in CF via IL-6, IL-17 signaling pathways
Cell cycle_Regulation of G1/S transition (part 1)
Immune response _IL1 signaling pathway
Signal transduction_cAMP signaling
*p value smaller than 0.05
- 185-
32.00%
30.00%
25.45%
28.57%
28.57%
28.57%
27.50%
30.77%
30.77%
35.29%
35.29%
2652
2386
736
2731
644
409
701
474
2453
531
648
35.29%
2450
31.82%
29.63%
28.13%
26.19%
27.03%
27.03%
27.03%
33.33%
30.43%
35.71%
28.57%
28.57%
26.32%
27.27%
27.27%
469
518
472
566
554
643
2692
692
640
2688
2432
2655
544
658
660
Appendix
Table A6. List of load-regulated molecular pathways in repeated loading
Molecular Pathway
Counts
Cell adhesion_Endothelial cell contacts by non-junctional mechanisms
Cell adhesion_Integrin-mediated cell adhesion and migration
Cytoskeleton remodeling_Integrin outside-in signaling
Cytoskeleton remodeling_FAK signaling
Signal transduction_IP3 signaling
Translation _Regulation activity of EIF2
Development_Endothelin-1/EDNRA signaling
Cytoskeleton remodeling_Cytoskeleton remodeling
Cell adhesion_Role of tetraspanins in the integrin-mediated cell adhesion
Transcription_Ligand-Dependent Transcription of Retinoid-Target genes
Transcription_CREB pathway
G-protein signaling_Proinsulin C-peptide signaling
Signal transduction_Erk Interactions: Inhibition of Erk
Cell adhesion_Chemokines and adhesion
Apoptosis and survival_BAD phosphorylation
Development_Alpha-2 adrenergic receptor activation of ERK
Development_EDG3 signaling pathway
Development_ACM1, ACM3, ACM5 activation of ERK
Development_A3 receptor signaling
Development_Flt3 signaling
Cardiac Hypertrophy_NF-AT signaling in Cardiac Hypertrophy
Immune response _CCR3 signaling in eosinophils
Neurophysiological process_HTR1A receptor signaling in neuronal cells
Development_PDGF signaling via MAPK cascades
Development_EDG1 signaling pathway
Development_VEGF-family signaling
Signal transduction_Calcium signaling
Immune response_Function of MEF2 in T lymphocytes
Cell adhesion_Histamine H1 receptor signaling
Cytoskeleton remodeling_α-1A adrenergic receptor-dependent inhibition
of PI3K
Regulation of lipid metabolism_Insulin regulation of glycogen
metabolism
Immune response _Role of the C5b-9 complement complex in cell
survival
Immune response_HTR2A-induced activation of cPLA2
Cytoskeleton remodeling_Fibronectin-binding integrins in cell motility
Development_FGFR signaling pathway
Immune response_CD28 signaling
Development_Dopamine D2 receptor transactivation of PDGFR in nonneuronal cells
Development_A2B receptor: action via G-protein alpha s
Translation_Insulin regulation of translation
Development_Ligand-independent activation of ESR1 and ESR2
Development_ACM2 and ACM4 activation of ERK
79.17%*
62.22%
60.87%
59.57%
63.16%
63.89%
60.98%
48.96%
62.16%
62.50%
60.00%
57.89%
61.29%
46.24%
57.14%
55.00%
61.54%
55.56%
55.56%
53.66%
49.15%
49.15%
58.62%
55.88%
59.26%
56.25%
56.25%
52.38%
54.05%
Map
ID
727
450
664
449
557
497
2255
714
2023
369
409
2815
447
716
661
2427
2951
2518
644
2236
2235
736
2946
654
2809
445
550
541
2435
75.00%
2395
52.50%
725
60.87%
459
57.14%
57.14%
51.16%
51.16%
2439
451
444
620
61.90%
2456
52.63%
52.63%
52.63%
54.55%
482
723
2210
2516
- 186-
Appendix
Cytoskeleton remodeling_Role of PKA in cytoskeleton reorganisation
Development_Role of CDK5 in neuronal development
Cell adhesion_Alpha-4 integrins in cell migration and adhesion
Neurophysiological process_Receptor-mediated axon growth repulsion
Cell adhesion_Integrin inside-out signaling
Development_VEGF signaling via VEGFR2 - generic cascades
Neurodisease_Parkin disorder under Parkinson's disease
Neurophysiological process_ACM regulation of nerve impulse
Development_Neurotrophin family signaling
Cardiac Hypertrophy_Ca(2+)-dependent NF-AT signaling in Cardiac
Hypertrophy
Development_MAG-dependent inhibition of neurite outgrowth
Development_Activation of ERK by Alpha-1 adrenergic receptors
Development_Dopamine D2 receptor transactivation of EGFR
Regulation of CFTR activity (norm and CF)
Immune response_NTS activation of IL-8 in colonocytes
Development_Angiotensin activation of Akt
G-protein signaling_G-Protein alpha-q signaling cascades
Development_EGFR signaling via PIP3
Neurophysiological process_Dopamine D2 receptor transactivation of
PDGFR in CNS
Muscle contraction_ GPCRs in the regulation of smooth muscle tone
Transcription_PPAR Pathway
Immune response_Fc epsilon RI pathway
Transport_Clathrin-coated vesicle cycle
Development_Glucocorticoid receptor signaling
Transport_Alpha-2 adrenergic receptor regulation of ion channels
Neurophysiological process_EphB receptors in dendritic spine
morphogenesis
Membrane-bound ESR1: interaction with G-proteins signaling
Development_G-Proteins mediated regulation MARK-ERK signaling
Immune response_MIF - the neuroendocrine-macrophage connector
Cell cycle_Regulation of G1/S transition (part 2)
Cell adhesion_Endothelial cell contacts by junctional mechanisms
Cytoskeleton remodeling_TGF, WNT and cytoskeletal remodeling
G-protein signaling_Regulation of p38 and JNK signaling mediated by
G-proteins
Transcription_Transcription factor Tubby signaling pathways
WtCFTR and delta508 traffic / Clathrin coated vesicles formation (norm
and CF)
Development_EDG1 signaling via beta-arrestin
Development_A2A receptor signaling
Regulation of lipid metabolism_ACM stimulation of Arachidonic acid
production
Blood coagulation_GPCRs in platelet aggregation
Immune response_MIF-JAB1 signaling
Oxidative stress_Role of ASK1 under oxidative stress
Development_Kappa-type opioid receptor activation of ERK
Development_Endothelin-1/EDNRA transactivation of EGFR
- 187-
54.84%
54.84%
54.84%
50.00%
50.00%
55.17%
55.17%
52.94%
51.35%
543
2368
2749
527
710
533
666
2656
636
51.35%
2234
55.56%
53.13%
60.00%
50.00%
51.43%
56.00%
56.00%
61.11%
735
2392
2457
2269
722
436
639
692
61.11%
2455
46.30%
50.00%
47.83%
44.12%
56.52%
53.57%
2393
546
566
2640
410
2432
53.57%
528
50.00%
50.00%
51.62%
53.85%
53.85%
40.19%
2212
463
518
474
745
715
50.00%
455
66.67%
461
57.89%
2672
51.72%
48.65%
2808
643
45.83%
2658
44.44%
54.55%
54.55%
54.55%
48.57%
2442
520
521
2552
2254
Appendix
Neurophysiological process_Glutamate regulation of Dopamine D1A
receptor
Apoptosis and survival_HTR1A signaling
Translation _Regulation of translation initiation
Development_Delta-type opioid receptor mediated cardioprotection
Regulation of lipid metabolism_Insulin signaling:generic cascades
Development_GDNF signaling
Development_Delta-type opioid receptor signaling via G-protein alpha14
G-protein signaling_G-Protein alpha-12 signaling pathway
Signal transduction_Activation of PKC via G-Protein coupled receptor
Development_Angiotensin signaling via PYK2
Development_EGFR signaling pathway
Cytoskeleton remodeling_Regulation of actin cytoskeleton by Rho
GTPases
G-protein signaling_G-Protein beta/gamma signaling cascades
Regulation of lipid metabolism_α-1 adrenergic receptors signaling
Transcription_Assembly of RNA Polymerase II preinitiation complex
Signal transduction_PTEN pathway
Development_Membrane-bound ESR1: interaction with growth factors
signaling
Development_EDNRB signaling
Development_Angiotensin activation of ERK
Transcription_Role of Akt in hypoxia induced HIF1 activation
Cell adhesion_Cadherin-mediated cell adhesion
Cytoskeleton remodeling_ACM3 and ACM4 in keratinocyte migration
G-protein signaling_EDG5 signaling
Development_EGFR signaling via small GTPases
Cell cycle_Role of Nek in cell cycle regulation
Development_GDNF family signaling
Translation_Translation regulation by Alpha-1 adrenergic receptors
Cell cycle_Spindle assembly and chromosome separation
Proteolysis_Role of Parkin in the Ubiquitin-Proteasomal Pathway
Apoptosis and survival_Anti-apoptotic action of membrane-bound ESR1
Regulation of lipid metabolism_Insulin regulation of fatty acid
methabolism
Transcription_Receptor-mediated HIF regulation
Muscle contraction_ACM regulation of smooth muscle contraction
Development_EDG6 signaling pathway
Transport_RAB3 regulation pathway
Cytoskeleton remodeling_Reverse signaling by ephrin B
Cytoskeleton remodeling_Slit-Robo signaling
Immune response_PGE2 common pathways
Cytoskeleton remodeling_Neurofilaments
Immune response_BCR pathway
Immune response_NFAT in immune response
Transcription_Androgen Receptor nuclear signaling
G-protein signaling_G-Protein alpha-s signaling cascades
Immune response _IFN gamma signaling pathway
- 188-
50.00%
2425
47.37%
52.00%
52.00%
46.34%
55.00%
2947
498
2666
724
646
55.00%
2664
48.48%
45.45%
47.22%
44.00%
454
453
438
443
52.17%
551
52.17%
46.15%
55.56%
45.24%
641
2385
673
676
47.06%
2211
47.06%
50.00%
50.00%
50.00%
52.38%
52.38%
48.28%
48.28%
45.00%
45.00%
46.88%
50.00%
50.00%
2273
437
448
2122
2651
2814
704
731
495
2390
712
662
2736
43.48%
726
45.71%
45.71%
57.14%
57.14%
47.67%
47.67%
47.67%
48.00%
42.86%
42.86%
42.86%
46.43%
42.22%
416
2657
2952
406
529
2121
2386
1491
655
668
2202
640
432
Appendix
G-protein signaling_Cross-talk between Ras-family GTPases
Development_WNT signaling pathway. Part 1
Oxidative phosphorylation
Cell adhesion_ECM remodeling
Apoptosis and survival_Role of CDK5 in neuronal death and survival
dATP/dITP metabolism
Anandamide biosynthesis and metabolism
Muscle contraction_Delta-type opioid receptor in smooth muscle
contraction
Development_VEGF signaling and activation
Development_Leptin signaling via PI3K-dependent pathway
Development_Regulation of CDK5 in CNS
Development_Mu-type opioid receptor regulation of proliferation
Immune response _ICOS pathway in T-helper cell
NGF activation of NF-kB
Immune response_Antigen presentation by MHC class I
Immune response_IL-2 activation and signaling pathway
ATP/ITP metabolism
Translation _Regulation activity of EIF4F
Development_WNT signaling pathway. Part 2
Transport_RAN regulation pathway
Neurophysiological process_Delta-type opioid receptor in nervous
system
Cell cycle_Role of SCF complex in cell cycle regulation
Development_HGF signaling pathway
Development_Beta-adrenergic receptors transactivation of EGFR
Transcription_ChREBP regulation pathway
*p value smaller than 0.05
- 189-
50.00%
50.00%
37.37%
41.18%
45.16%
40.74%
53.33%
408
515
920
717
2374
865
2428
53.33%
2663
44.12%
44.12%
47.83%
47.83%
43.24%
46.15%
46.15%
41.30%
37.65%
40.82%
40.82%
50.00%
539
719
2226
2424
619
653
2100
430
873
496
516
404
50.00%
2567
44.83%
43.75%
43.75%
53.85%
706
530
2433
464
Curriculum Vitae
Curriculum Vitae
Elad Wasserman
Born 27th September 1972, Kiev, Ukraine
2004 - 2010
Ph.D. Thesis “Differentially load-regulated genes in mouse trabecular
osteocytes” under the supervision of Prof. Dr. Ralph Müller and
Prof. Dr. Itai Bab
2000 – 2003
QBI Inc., Nes-Ziona, Israel, Laboratory Technician
1998 - 2000
Tel-Aviv University, Sackler School of Medicine, Department of Cell Biology
and Histology, Master of Science
1995 - 1997
Tel-Aviv University, Faculty of Life Sciences, Bachelor of Science
1991 - 1994
Ukrainian State Medical University, Faculty of Dentistry, Kiev, Ukraine
1988 - 1991
Ukrainian Medical College, Department of Nursing, Kiev, Ukraine
1980 - 1988
Primary School in Kiev, Ukraine
- 190-