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HIF1α dependant transcriptional networks in macrophages andhepatocytes

Author(s): Müller, Julius

Publication Date: 2009

Permanent Link: https://doi.org/10.3929/ethz-a-005900145

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DISS. ETH Nr. 18594

HIF1αααα dependant transcriptional networks in macrophages and hepatocytes

ABHANDLUNG zur Erlangung des Titels

DOKTOR DER WISSENSCHAFTEN der

ETH ZÜRICH

vorgelegt von Julius Müller

Dipl. Biol., Ruprecht-Karls-Universität Heidelberg

geboren am 16.09.1977

von

Deutschland

Angenommen auf Antrag von Prof. Romeo Ricci Prof. Wilhelm Krek

Prof. Peter Bühlmann

2009

Index

Page 2

1. Index

1. Index .......................................................................................................................... 2

2. Acknowledgements .................................................................................................. 4

3. Summary ................................................................................................................... 5

4. Zusammenfassung ................................................................................................... 6

5. Abbreviations ............................................................................................................ 7

6. Introduction ............................................................................................................... 8

6.1. Hypoxia and the Hypoxia Inducible Factor 1 alpha ............................................................... 8

6.2. Transcriptional- and epigenetic regulation ......................................................................... 15

6.3. Methodology to address Genome wide binding patterns................................................... 22

7. Aim of the project ................................................................................................... 24

8. Material and Methods ............................................................................................. 25

8.1. Media and Buffers used for ChIP and ChIP-chip .................................................................. 25

8.2. Cell lines ............................................................................................................................... 28

8.3. ChIP-on-chip......................................................................................................................... 29

8.4. ChIP-Seq ............................................................................................................................... 35

8.5. mRNA Expression Profiling .................................................................................................. 35

8.6. Identification of ChIP-chip Peaks ......................................................................................... 36

8.7. Identification of ChIP-Seq Peaks .......................................................................................... 37

8.8. De novo Motif Analysis ........................................................................................................ 37

8.9. Q-PCR Validation of ChIP Hits .............................................................................................. 38

8.10. Annotation of sequences and association of expression- to binding data .......................... 38

9. Results ..................................................................................................................... 39

9.1. Regulation of HIF1α and its target genes ............................................................................ 39

9.2. Genome wide expression and binding studies .................................................................... 42

9.3. Genome wide binding study using the murine leukemic monocyte-macrophage cell line

(Raw.264) and ChIP-Seq ............................................................................................................................ 54

9.4. Promoters that are occupied by HIF1α in Raw.264 cells .................................................... 57

9.5. Characterization of the Hypoxia Response Element ........................................................... 63

9.6. Transcription factors interacting with Hif1α ....................................................................... 68

9.7. Downstream regulatory mechanism regulated by Hif1α .................................................... 72

Index

Page 3

10. Discussion............................................................................................................... 77

10.1. Binding of HIF1α is cell type specific ................................................................................... 77

10.2. One out of five genes that are bound by HIF1α are differentially expressed in PMH and

PMM 78

10.3. One out of twenty-five hypoxia responsive genes are bound by HIF1α in PMH ................ 79

10.4. ChIP-Seq reveals markedly more HIF1α binding events during hypoxia in Raw.264 cells .. 80

10.5. HIF1α directly binds to genes associated to glycolysis, angiogenesis and regulation of

transcription, depending on the cell type. ............................................................................................... 81

10.6. HIF1α preferentially binds close to the TSS ......................................................................... 81

10.7. HIF1α preferentially binds to an HRE consisting of nine base pairs or an tandem core HRE

82

10.8. The TRE consensus motif is overrepresented at enhancer regions targeted by HIF1α ...... 83

10.9. SP1 is a potential HIF1α target and might regulate genes in response to hypoxia

independent of HIF1α ............................................................................................................................... 84

10.10. Transcriptional regulation of chromatin modifiers by HIF1α .............................................. 85

10.11. Comparison to previous genome wide HIF1α binding studies............................................ 86

11. Outlook .................................................................................................................... 88

12. References .............................................................................................................. 90

13. Supplements ........................................................................................................... 97

13.1. Top 300 up regulated genes in PMH and PMM ................................................................... 97

13.2. Group III genes of PMM, PMH and Raw.264 cells (Top 300) ............................................. 104

Acknowledgements

Page 4

2. Acknowledgements

Ich möchte meine Arbeit den folgenden Personen widmen, die alle direkt und indirekt

zum erfolgreichen Abschluss meiner Doktorabeit während der letzten vier Jahre

beigetragen haben:

Meinen Eltern, ohne dessen Unterstützung diese Arbeit niemals möglich gewesen wäre.

Romeo Ricci, der mir die Möglichkeit gegeben hat, dieses Projekt bis zum Ende

durchzuführen.

Meinen Kollegen:

Renata Windak, Grzegorz Sumara, Susann Kumpf, Arne Ittner, Helmuth Gehart und

Ivan Formentini

Meine Semesterstudenten:

Yvonne Fink und Andreas Essig

Meinem Thesis Committee:

Wilhelm Krek und Peter Bühlmann

Meinen Kollaboratoren:

Andrea Patrignagni (ChIP-chip), Bernard Jost (ChIP-Seq)

Meinen Geschwistern:

Caroline, Wiebke und Oskar

Weitere wichtige Elemente:

Susann (Administration und Lehre), Arne und Helmuth (Abend Snacks), Nikolai

(Kaffeepausen), Felix (Trainingspartner), Antra (Rauchen), Gerald (Ernährung), Stefan

und Strahil (Golf Pros), Iza (General Support), Ivan (Fluchen)…

Summary

Page 5

3. Summary

HIF1α is the principal transcription factor that mediates responses to low oxygen levels

in eukaryotic cells. By comparing genome-wide promoter binding studies of HIF1α in

primary mouse hepatocytes and primary mouse macrophages, I was able to

demonstrate that HIF1α binding is cell-type specific. Integration of expression data

revealed that only a small fraction of genes bound by HIF1α are differentially expressed.

To explore the transcriptional mechanisms, which modulate the differentially expressed

genes secondary or independent of HIF1α, motif analysis of the respective promoters

was performed. Among others, transcription factors of the activating protein 2 (AP2)

family and SP1 showed a marked overrepresentation suggesting an important function

in the regulation of hypoxia responsive genes. To complement these results with

genome-wide binding data, an unbiased binding study of HIF1α using ChIP-Seq in

Raw.264 cells, was performed. Although the majority of binding events were localized

close to the transcriptional start side (TSS), about 40% of the peaks occurred more than

10kbp away from the TSS.

Motif analysis of the Raw.264 binding study revealed that HIF1α preferably binds to an

extended hypoxia response element (HRE) and in 14% of the cases, a tandem core

HRE seems to be the consensus site bound by HIF1α. Moreover, I showed that AP1

might be an important factor cooperating with HIF1α at enhancer sites to modulate

expression levels of developmental genes and genes associated to apoptosis. Another

mechanism of transcriptional regulation upon hypoxia may involve the JmjC family of

histone demethylases that were found to be direct targets of HIF1α.

Thus, my data refined the HRE motif that appears to be bound by HIF1α in a cell-

specific manner. Furthermore, this work demonstrates that the hypoxia response in

mammalian cells is to a larger extent regulated by other transcription factors and by

dynamic epigenetic changes both of which may be dependent and independent of

HIF1α.

Zusammenfassung

Page 6

4. Zusammenfassung

Sauerstoffmangel oder Hypoxie wird von allen eukaryotischen Zellen hauptsächlich

durch den Transkriptionsfaktor HIF1α in eine transkriptionelle Antwort übersetzt. In

dieser Arbeit wurde durch Genom-weite Promoter-Bindungsanalysen dieses

Transkriptionsfaktors gezeigt, daß HIF1α zelltypspezifisch an die DNA bindet.

Außerdem konnte gezeigt werden, daß nur ein kleiner Teil der HIF1α gebunden Gene

auch eine Änderung der Transkript Konzentrationen erfährt. Um die großen

Unterschiede zwischen der Anzahl der deregulierten und der gebundenen Gene zu

erklären, wurde eine de novo Promoter Sequenzanalyse der deregulierten, aber nicht

gebundenen Gene durchgeführt. Hier zeigte sich, daß die klassischen

Erkennungssequenzen Transkriptionsfaktoren SP1 und AP2 signifikant

überrepräsentiert waren.

Weiterhin wurden die Daten durch ChIP-Seq Daten einer murinen Makrophagen

Krebszelllinie ergänzt. Hier zeigte sich das trotz einer starken Konzentration der

Bindungsstellen am Promoter, etwa 40% der Bindungsstellen mehr als 10tbp entfernt

lokalisiert waren. Weiterhin konnte durch eine de novo Motivanalyse der gebundenen

Sequenzen ein Tandem Erkennungsmotiv von HIF1α, das HRE, in 14% der

gebundenen Sequenzen, gefunden werden. Interessanterweise konnten zusätzlich

Transkriptionsfaktoren der AP1 Familie mit etwa 16% der gebundenen Sequenzen in

Verbindung gebracht werden. Diese Bindungsstellen waren weiter vom

Transkriptionsstart entfernt und es kann spekuliert werden, daß AP1

Transkriptionsfaktoren wichtig für die Co-Regulation von HIF1α gebundenen Genen der

zellulären Entwicklung und der Apoptose ist.

Ein weiterer Mechanismus der induzierten, transkriptionellen Kontrolle durch Hypoxie,

können Histon-demethylasen der JmjC Familie sein, die direkt von HIF1α gebunden und

dereguliert werden.

Zusammenfassend kann gesagt werden, daß HIF1α zelltypspezifisch an sein

Erkennungsmotiv bindet. Weiterhin zeigt diese Arbeit, daß die transkriptionelle Antwort

auf Hypoxie zu einem Großteil auf andere Transkriptionsfaktoren und epigenetischen

Mechanismen beruht, die direkt oder indirekt durch HIF1α moduliert werden.

Abbreviations

Page 7

5. Abbreviations

CDS Coding Sequence

ChIP Chromatin-Immunoprecipitation

ChIP-chip Analysis of the ChIPed DNA by DNA-microarrays

ChIP-Seq Analysis of the ChIPed DNA by Sequencing

DAVID Database for Annotation, Visualization and Integrated Discovery

FDR False discovery rate

GO Gene Ontology

HRE Hypoxia Response Element

LPS Lipopolysaccharide, an endotoxin that triggers an inflammatory response

LSC Location and Size matched, randomized Control region

PMH Primary, Liver perfusion elicited Mouse Hepatocytes

PMM Primary, Thioglycollate elicited Mouse Macrophages

PP Proximal Promoter -> -5000 to +3000kbp to the TSS

TRE TPA Response Element

TSS Transcriptional Start Site

WCE Whole Cell Extract

Introduction

Page 8

6. Introduction

6.1. Hypoxia and the Hypoxia Inducible Factor 1 alpha

6.1.1. Cellular oxygen demand and hypoxia

Molecular oxygen is indispensable to maintain the normal physiological status of all

mammalian cells. Most importantly, oxidative phosphorylation in mitochondria is

dependent on molecular oxygen in order to provide cells with sufficient energy. Non-

mitochondrial oxygen consumption is accounting for up to 10–30% of total cellular O2

consumption (Herst and Berridge, 2007; Rosenfeld et al., 2002). The physiological

oxygen levels to accomplish these needs can span a wide range. Assuming atmospheric

oxygen partial pressure of 21 kPa (equals 21%), blood oxygen levels are about 13 kPa

in the arterial and 9 kPa in the venous blood, respectively. Due to the different diffusion

and vascularization conditions, oxygen levels described for tissues can only be roughly

estimated. For example, in vivo measurements of partial pressure in mouse spleens

revealed values of about 0.5–4.5 kPa, depending on the distance from the artery

(Caldwell et al., 2001).

However, if oxygen supply becomes insufficient, the physiological status of the cell can

undergo drastic changes. Changes in cell physiology include pH status (Chiche et al.,

2009), the abundance of reactive oxygen species (ROS) (Guzy and Schumacker, 2006),

genome integrity (To et al., 2006), growth and cell survival (Carmeliet et al., 1998; Liu et

al., 2006), protein translation (Young et al., 2008) and iron metabolism (Peyssonnaux et

al., 2008). In fact, prolonged hypoxia inevitably leads to cell death.

Therefore, sophisticated systems evolved to provide metazoan organisms with sufficient

oxygen such as the cardiovascular system and the respiratory system. Moreover, all

eukaryotic cells possess complex mechanisms to sense and adapt to low oxygen levels

(hypoxia) and many of these adaptation processes, if deregulated, can play an important

role in the development and progression of a variety of diseases including

atherosclerosis (Sluimer and Daemen, 2009), cancer (Denko, 2008), diabetes (Crawford

et al., 2009) and inflammatory disorders (Sitkovsky and Lukashev, 2005).

Introduction

Page 9

6.1.2. HIF1α, the principal mediator of the hypoxic response in mammals

A central mediator of the hypoxic response in all mammalian cells is the transcription

factor hypoxia inducible factor 1 alpha (HIF1α) (Iyer et al., 1998). It belongs to the family

of the Hypoxia Inducible Factors, and is the only isoform that is ubiquitously expressed.

HIF1α is tightly regulated by oxygen levels. Under normoxic conditions, HIF1α is

targeted by one or more of the three prolyl hydroxylase domain proteins (PHD1–3)

which can hydroxylate the oxygen dependant degradation domain (ODDD) on two

different Proline residues (Kaelin and Ratcliffe, 2008; Schofield and Ratcliffe, 2004). This

hydroxylation leads to rapid recognition and degradation of HIF1α that is mediated by

VHL, a component of an E3 multiprotein ubiquitin-ligase complex (Figure 1A). Since

PHD enzymes belong to the family of highly oxygen-dependent 2-oxoglutarate-

dependent-oxygenases, HIF1α is stabilized under hypoxia, binds to different importins

and gets translocated to the nucleus where it heterodimerizes with constitutively

expressed HIF1β (Depping et al., 2008). The dimerisation between the HIF1α and HIF1β

subunits occurs through the basic helix-loop-helix (bHLH) and PER-ARNT-SIM (PAS) A

and B domains located in the N-terminal region of each subunit, whereas DNA binding

to the hypoxia response element (HRE), occurs through the bHLH domains (Brahimi-

Horn and Pouyssegur, 2009). However, to give rise to a transcriptional response, the

presence and interaction of other co-activators such as CBP/p300 is required. This

interaction can be prevented by the factor inhibiting HIF (FIH), which can hydroxylate

asparagine residue within the carboxy terminal transcriptional activation domain (CTD)

(Lando et al., 2002). Since FIH also belongs to the family of 2-oxoglutarate-dependent-

oxygenases, asparagine hydroxylation represents a second, oxygen-dependent

mechanism to regulate HIF1α activity.

Introduction

Page 10

6.1.3. Alternative stabilization and regulation of HIF1α

HIF regulation is not limited to low oxygen levels. An overview about the current

knowledge of hypoxia-independent regulatory mechanism of HIF1α is depicted in Figure

1B, which was taken from a recent review (Brahimi-Horn and Pouyssegur, 2009). It

depicts the broad variety of possibilities to regulate HIF1α independent of hypoxia and

therefore underlines its importance in the normal physiology of the cell.

Figure 1: Hypoxia-dependent regulation and hypoxia-independent activation of HIF. (A) The scheme depicts the oxygen-dependent degradation of different HIF family members. In brief, under normoxia HIFs can be hydroxylated by PHDs or FIH, which leads to proteasomal degradation or inability to interact with obligatory co activators (CBP/p300) respectively. Adopted from (Schofield and Ratcliffe, 2004) (B) The scheme depicts the most well established means of hypoxia-independent regulation of HIF1α on the transcriptional level, on the translational level, on the posttranslational level and on the level of transcriptional activity of HIF1α. Adopted from (Brahimi-Horn and Pouyssegur, 2009).

Introduction

Page 11

For example, on the transcriptional level, HIF1α mRNA expression can be enhanced by

LPS exposure in macrophages, presumably through a NF-κB-mediated mechanism

(Belaiba et al., 2007; Frede et al., 2006). Also on the translational level, HIF1α protein

can be regulated by enhanced translation via the mTOR/Akt pathway (Harada et al.,

2009), and, less understood, specifically by different proposed mechanisms involving

e.g. CAP-independent translation via its internal ribosomal entry side (IRS) (Yee Koh et

al., 2008). Additionally, HIF1α transcriptional activity can be regulated by a variety of

modifications (Lisy and Peet, 2008). The best-understood modifications, apart from

hydroxylation of asparagines by FIH described above, include mitogen-mediated

phosphorylation (Richard et al., 1999), acetylation of a lysine residue within the ODD

(Jeong et al., 2002), S-nitrosylation within the ODD (Li et al., 2007b) and SUMOylation in

proximity to and within the ODD (Berta et al., 2007).

Additionally, loss of function of different tumor suppressors and gain of function of

different oncogenes regulate different steps that lead to HIF activation (Semenza, 2003).

6.1.4. Hypoxia Inducible Factors 2 and 3

HIF1α is regarded as the principle mediator of the hypoxic response in all mammalian

cell types. This hypothesis is underlined e.g. by genome wide expression studies using

knock down of HIF1α. In this study, it was shown that depletion of HIF1α in fibroblasts

was sufficient to prevent induction of hypoxia-dependent genes, while inactivation of

HIF-2α was not affecting expression of hypoxic genes (Elvidge et al., 2006). These

experiments have been exerted in fibroblasts and the role of HIF2α is well different in

other cell types that are exposed to hypoxia (see below). In fact, HIF2α shares a high

degree of sequence identity with HIF1α, which is also reflected by their shared ability to

heterodimerize with HIF1β and to bind to HREs to induce transcription of target genes

(Raval et al., 2005; Wiesener et al., 2003).

Several genes were described to be bound by both isoforms, but often only one of the

two is required to activate transcription. This was e.g. confirmed for genes associated

with glycolysis (Hu et al., 2007), which were shown to be transcriptionally regulated only

by HIF1α binding. Erythropoietin (EPO) is an example for a gene transcription of which

is mainly regulated by HIF2α (Rankin et al., 2007).

Introduction

Page 12

Moreover, in HIF2α/VHL loss-of-function studies in mice, using for example a liver-

specific HIF2α knock out model, it was shown, that HIF2α is an important regulator of

hepatic lipid metabolism (Rankin et al., 2009). Additionally, mainly due to its well-

described function in cell cycle progression (Gordan et al., 2007), HIF2α has been

implicated to aggressive tumor phenotypes (Qing and Simon, 2009). Generally, the role

and importance of HIF2α, in particular under hypoxic conditions, are less understood

compared to HIF1α and remains to be elucidated.

The role and function of HIF3α, or inhibitory PAS protein (IPAS), which is regulated by

several types of alternative splicing, is even less well understood. The most established

role for HIF3α is the ability to form transcriptionally inactive heterodimers with HIF1α

(Makino et al., 2001).

6.1.5. Known transcriptional, HIF1α-mediated responses

HIF1α deficiency in mice leads to embryonic lethality at E11. HIF1α-deficient embryos

showed neural tube defects, cardiovascular malformations, and marked cell death within

the cephalic mesenchyme (Iyer et al., 1998). In a whole plethora of studies addressing

different biological problems in vivo and in vitro over the last decade, HIF1α was linked

to a broad range of genes associated to a variety of physiological processes. Among

others, these mainly include basic processes such as angiogenesis, vasodilatation,

glucose metabolism, erythropoiesis, oxygen sensing, pH homeostasis, autophagy,

development and cell differentiation. A comprehensive list of HIF1α target genes can be

found in a recently published review (Wenger et al., 2005). In the following, two key in

vivo functions of Hif1α in hepatocytes and macrophages are delineated. These functions

also build the basis of my study.

6.1.6. HIF1α in hepatocytes

Oxygen is essential as an electron acceptor in various metabolic functions of the liver.

Under normal conditions, oxygen levels of the liver are constantly kept at a high level by

a dual blood supply, consisting of the hepatic portal vein and the hepatic arteries and

both vessels supply equal amounts of oxygen.

Introduction

Page 13

During the passage through the sinusoids, a periportal-to-perivenous concentration

gradient of substrates, products, hormones and oxygen supply is formed due to liver

metabolism (Kietzmann et al., 1999). The functional unit of hepatocytes between the

aerobic periportal hepatocytes and the anaerobic perivenous hepatocytes, surrounding a

hepatic centrilobular vein, spans about 15 to 25 cells (Benhamouche et al., 2006).

Oxygen levels drop from 8-9 kPa in the periportal blood to 4-5 kPa in the perivenous

blood (Jungermann and Kietzmann, 1996). Expression studies of enriched

subpopulations of periportal- and perivenous hepatocytes revealed an increased

expression of glycolytic genes in the perivenous hepatocytes, as expected by the

oxygen gradient (Braeuning et al., 2006).

Additionally, several studies demonstrated the importance of hypoxia in the development

and progression of liver diseases. Perivenous hypoxia in particular is regarded to be a

major cause for several primary and secondary liver diseases and it is widely believed

that perivenous hypoxia can contribute to hepatocellular damage. Perivenous hypoxia

plays a crucial role in the etiology of secondary liver diseases such as heart failure

(ischemic hepatitis), gut ischemia, indirect drug hepatotoxicity and in the etiology of

primary liver diseases such as alcohol-induced liver disease (ALD) or exposure to other

xenobiotics like the industrial chemical carbon tetrachloride or the pharmacological

agent acetaminophen (Kietzmann et al., 1999). Moreover it was shown, that mice fed

with a high fat diet show a deregulation of the hepatic oxygen gradient which gives rise

to the progression of NAFLD (non-alcoholic fatty liver disease) to the more serious

NASH (non-alcoholic steatohepatitis) (Mantena et al., 2009).

Although no study could directly show an increased stabilization of Hif1α along the

metabolic zonation towards the hepatic portal vein, direct functional implications of Hif1α

in liver have recently been revealed by studies in mice with liver specific ectopic

expression of a non-degradable form of Hif1α. The livers of these mice show

microvesicular steatosis and a moderately elevated lipid accumulation compared to

control livers (Kim et al., 2006). Additionally, a recent study showed an interesting link

between Hif1α-dependent glycolysis and aggressivity of hepatocellular carcinoma

(Hamaguchi et al., 2008).

Taken together, these studies indicate a crucial function of hypoxia and Hif1α in normal

liver physiology as well as pathophysiology of the liver.

Introduction

Page 14

6.1.7. Hif1α in macrophages

The energy expenditure of activated macrophages reaches a high level to fulfill its

function in immune responses. However, at sites of inflammation, oxygen levels are

typically low. Moreover, macrophages experience sustained periods of hypoxia in

diseased tissues such as malignant tumors (Vaupel et al., 2001), atherosclerotic plaques

(Bjornheden et al., 1999) and arthritic joints (Taylor and Sivakumar, 2005). Therefore,

oxygen consumption has to be tightly regulated and macrophages have to rely on

anaerobic glycolysis as their major energy source.

As described above, Hif1α is the principal transcription factor to regulate the latter

metabolic function. Most importantly, myeloid-specific deletion of Hif1α revealed that

inflammatory processes are impaired due to defects in the glycolytic capacity of

macrophages (Cramer et al., 2003). Altered glycolysis resulted in profound impairment

of myeloid cell aggregation, motility, invasiveness, and bacterial killing demonstrating the

importance of Hif1α in this cell type.

Additionally, under normoxic conditions, Hif1α can be stabilized by exposure of

macrophages to LPS (Jantsch et al., 2008), suggesting a more general importance of

Hif1α in macrophages in host defense against environmental pathogens.

6.1.8. The classical HRE

The canonical DNA motif bound by Hif1α, consists of a well-conserved 4bp core motif 5’-

CGTG-3’. The core motif is part of virtually all described consensus motifs published so

far. However, Wenger et al showed that the 5’-CG-3’ of the core motif can be

methylated, and therefore can be made inaccessible for Hif1α (Wenger et al., 2005).

Therefore binding studies exploring the affinity of Hif1α to specific promoters that are

based solely on artificially introduced, ‘naked’ DNA such as the luciferase assays, have

to be taken with caution. Depending on the study, the core motif can be extended at the

5’ position by an Adenine or a Thymine and a second base in front of the 5’ position of

the core motif seems to be preferentially Thymine (Wenger et al., 2005). Apart from this,

no extended sequence preference was consistently established so far.

Introduction

Page 15

To circumvent limitations indicated above, I explored Hif1α promoter binding under

native conditions using ChIP-chip or ChIP-Seq (see below). In the following, I aim at

introducing general aspects of transcriptional and epigenetic regulation of promoters that

are specifically important in the context of latter methods.

6.2. Transcriptional- and epigenetic regulation

The physiological status of every eukaryotic cell is dependent on the regulated

production of mRNA by RNA polymerase II (PolII). Transcription is preinitiated by TATA

box binding protein (TBP) binding to the promoter. TBP is part of the general

transcription factor TFIID, a multimeric protein complex together with thirteen TBP-

associated factors (TAFs). The consensus motif bound by TBP is a highly conserved

regulatory element called TATA-box with a consensus sequence of TATAA, which is

located 28-34bp upstream of the TSS. Recruitment of other general transcription factors

and subsequent recruitment of PolII leads to the so called preinitiation complex (PIC).

The TATA-box and the Initiator element (Inr), which is defined by the YYANWYY

consensus, where the A is at position +1 of the TSS, are the only core promoter

elements that, alone, can recruit the PIC and initiate transcription. However, only a low,

or basal, rate of transcription is driven by this preinitiation complex and recent genome

wide promoter studies revealed that only 10-20% of all mammalian promoters possess a

functional TATA-box (Kim et al., 2005). Instead, 72% of all human promoters possess a

CpG island (Saxonov et al., 2006), which are stretches in which CG dinucleotides are

overrepresented, and it has been shown that only a fraction of CpG-associated

promoters have TATA-like elements. Furthermore, CpG-island-associated promoters are

most often associated with so-called housekeeping and transcription of these promoters

can be initiated over a ~100 bp region resulting in a population of mRNAs that have

different lengths but usually the same protein-coding content. Therefore CpG-island-

associated promoters are promoters that fall into the ‘broad’ class whereas the

promoters that often have TATA and Inr boxes, use only one or a few consecutive

nucleotides as TSSs and fall into the `sharp` class (Figure 2) (Carninci et al., 2005).

Introduction

Page 16

In order to further enhance or repress transcription of a specific gene, all required

transcription activators have to be present and chromatin structure has to allow for

elongation of the transcript by PolII. The precise regulation of these processes is crucial

to provide the cell with the required amount of a specific transcript at the right moment.

As opposed to 2% of the human genome being protein-coding sequences, one third is

believed to be involved in transcriptional regulation, underlining the complexity of this

task (for review see (Levine and Tjian, 2003)). Transcriptional regulation can be either

indirect by modulation of the chromatin state (see below), or direct by transcription factor

interaction with the PIC.

Transcription factors and many transcriptional co-activators recognize and bind to a few

base pairs spanning, conserved DNA sequences. These motifs are normally located -

6kbp to +4kbp with regard to the TSS. Much of our knowledge about regulatory

elements in the PP region is derived by reporter gene assays, which are done by fusing

the promoter sequence to a reporter gene and then introducing targeted deletions in that

sequence to detect regulatory elements. In order to initiate or modulate transcription,

binding sites can also be located distant of TSSs. Indeed, several studies have shown

long-range interactions of transcription factors to promoters of regulated genes (Dekker,

2008).

Figure 2: Classification of promoters with respect to the TSS they use. Promoters can be classified in two categories, the sharp type promoter and the broad type promoter. The sharp type promoter often possesses a TATA-box and an Inr element and has a defined TSS. The broad promoter is lacking a classical TATA-Box but often has CpG Islands. The broad type promoter is lacking a defined TSS and transcription is initiated from various starting points in front of the coding region. Adopted from (Carninci et al., 2005)

Introduction

Page 17

Apart from these long-range interactions, transcription factors can be found close to

clusters of distant localized genes, where transcription is organized in transcriptional

factories (Sutherland and Bickmore, 2009). It was also shown that some transcription

factors colocalize with sites of active transcription. For example, the progesterone

receptor becomes concentrated in nuclear foci only when the hormone ligand is bound,

and these foci are associated with active transcription (Arnett-Mansfield et al., 2007).

This suggests a physical interaction between clusters of transcription factors and genes,

which can be even located on different chromosomes. However, the spatial arrangement

of transcription is limited by the fact that large parts of the genomic DNA is bound to the

nuclear lamina, organized in so called lamina associated domains (LADs) within the

nucleus and active transcription occurs exclusively in non-Lamin associated sites

(Guelen et al., 2008). Additionally, as a prerequisite for all binding events, the chromatin

status of the binding site must allow the interaction to the DNA binding domain of the

transcription factor. According to current knowledge, the chromatin status is mainly

regulated by DNA methylation, post-translational histone modifications, chromatin

remodeling, histone variant incorporation, and histone eviction (Henikoff et al., 2008; Li

et al., 2007).

6.2.1. The role of histones in the regulation of transcription

Histone octamers consist of four types of histones: H3, H4, H2A and H2B. The DNA is

wrapped in 1.65 turns of in total 147bp around a histone octamer and linked between

octamers by histone H1. Therefore, the most obvious function of histones is the spatial

organization of the genetic material within the nucleus. The four subunits can be post-

translationally modified in a variety of ways, including phosphorylation, ADP-ribosylation,

ubiquitylation, sumoylation, acetylation and methylation. Since many of these

modifications are correlated with defined transcriptional responses, the second important

function of histones is the regulation of transcription (Kouzarides, 2007; Li et al., 2007;

Margueron et al., 2005).

.

Introduction

Page 18

Figure 3: Genome-Wide Distribution Pattern of Histone Modifications and phylogenetic tree of JmjC domain containing proteins. (A) Histone modifications and their distribution over the range of a whole arbitrary gene relative to the promoter is shown. The correlation to transcriptional activity is indicated as well as patterns of the histone modification which were determined by genome wide approaches. Adopted from (Li et al., 2007) (B) Phylogenetic tree of all known JmjC domain containing proteins. Putative oncogenes are in red and putative tumor suppressors in green. (JmjC) Jumonji C domain; (JmjN) Jumonji N domain; (PHD) plant homeodomain; (Tdr) Tudor domain; (Arid) AT-rich interacting domain; (Fbox) F-box domain; (C5HC2) C5CHC2 zinc-finger domain; (CXXC) CXXC zinc-finger domain; (TPR) tetratricopeptide domain; (LRR) leucine- rich repeat domain; (TCZ) treble-clef zinc-finger domain; (PLAc) cytoplasmic phospholipase A2 catalytic subunit. Adopted from (Cloos et al., 2008). (C) α-ketoglutarate and iron (Fe) is used as cofactors by JmjC proteins to hydroxylate the methylated histone substrate. To form the highly reactive oxoferryl group which is reacting with the methyl group, Fe(II) has to activate one molecule of oxygen. The spontaneous degradation of carbinolamine intermediate leads to the release of one molecule of formaldehyde.. Adopted from (Cloos et al., 2008).

Certain modifications, such as acetylation, are altering the net charge of transcriptional

units within the genome, leading to a change from ‘closed’ heterochromatin to an

‘opened’ chromatin state (euchromatin). Of particular interest for transcriptional studies

however, is the histone lysine and arginine methylation, since it has been associated to

transcriptional activation and repression, heterochromatin-mediated transcriptional

silencing, DNA damage response and X chromosome inactivation (Margueron et al.,

2005; Martin and Zhang, 2005).

Introduction

Page 19

The best described histone lysine modifications are histone H3 lysines 4, 9, 27, 36 and

79, and histone H4 lysine 20 (Margueron et al., 2005). While trimethylation marks of H3

lysine 4, 36 and 79 are associated with transcriptional activation, trimethylation of H3

lysine 9 and 27 as well as trimethylation of histone H4 lysine 20 is associated with

transcriptional inactivation (Berger, 2007). Like all histone modifications, lysine

methylations are not unique to one nucleosome. Instead, lysine modifications are spread

over the promoter region and often cover the nucleosomes of whole genes (Figure 3A).

These patterns are highly dynamic and can be rearranged by histone methylases and

histone demethylases. Unlike acetylation, histone methylation and histone demethylation

is often catalyzed by a specific enzyme at a specific site resulting in unique functions

(Figure 3A).

6.2.2. Histone demethylases

Since N-CH3 is one of the thermodynamically most stable bonds in nature, the common

sense within the epigenetic field was that the only way to revert histone methylation was

by histone exchange or by cleavage of the methylated histone tail.

With the discovery of the amine oxidase LSD1 as a histone demethylases, this

assumption changed (Shi et al., 2004). The LSD1-mediated demethylation process uses

flavin adenine dinucleotide (FAD) as a cofactor and can demethylate mono- and

dimethylation. In 2006, a second family of histone demethylases, the Jumonji or JmjC

domain containing proteins (Tsukada et al., 2006) was discovered.

The Jumonji protein family is particularly important in this context as several of its

members were shown to be HIF targets (Xia et al., 2009). These proteins contain the

conserved JmjC domain, which can demethylate all three methylation states of histones

by catalyzing the generation of highly reactive oxygen species (ROS) in the presence of

iron, 2-oxoglutarate, and oxygen. The generated ROS attacks the methyl groups on

histone lysines and produces unstable intermediate oxidized products that

spontaneously release formaldehyde, resulting in the removal of methyl groups from

histone lysines (Figure 3C). Of the 27 described proteins with a JmjC domain, 15

possess a known demethylase function of specific lysines or arginines in the H3 tail of

histones.

Introduction

Page 20

In a variety of studies, JmjC domain containing proteins were functionally linked to

development, differentiation, senescence and X chromosome inactivation. Most recently,

histone demethylases have been described to play a crucial role in differentiation and a

variety of diseases (Cloos et al., 2008). Therefore it becomes more and more clear that

the interplay between histone methylases and demethylases regulate transcriptional

responses in a dynamic manner. In fact, our previous view that histone methylation and

demethylation constitute stable and irreversible modifications has to be revisited. The

first demethylase, which was described to be involved in a dynamic transcriptional

response, was JMJD3, a known histone H3 lysine 27 trimethylation (H3K27me3)

demethylase. H3K27me3 is associated with transcriptional repression mediated by

proteins of the Polycomb group (PcG). JMJD3 can be induced in macrophages upon

exposure to bacterial products and inflammatory cytokines mediated by NFκB. The

accumulation leads to binding to PcG target genes and regulates their transcriptional

activity by removal of the H3K27me3 repressory mark at specific sites (De Santa et al.,

2007) providing an intriguing link between inflammation and reprogramming of the

epigenome.

6.2.3. Epigenetic modulation under hypoxia

A variety of developmental processes have been linked to hypoxic conditions. For

example, it has been speculated that pO2 in the developing embryo is lower than 2%,

implicating an active role of Hif1α in the embryonic development (Lin et al., 2008).

Also the hematopoietic lineage is exposed to hypoxic conditions. By in situ

measurements of oxygen levels within the bone marrow of mice, the oxygen levels have

been determined to be about 2.4% (Ceradini et al., 2004). Additionally it has been

recently shown, that hematopoietic stem cells (HSCs) preferably localize in regions

within the bone marrow of low perfusion and vascularization (Parmar et al., 2007).

Together, these reports and studies in adipogenic-, myogenic- and chondrogenic

differentiation clearly show that hypoxia prevents cellular differentiation and maintains

pluripotency of stem/progenitor cells (Lin et al., 2008).

Introduction

Page 21

As of now, the molecular mechanism how hypoxia contributes to these functions is

unclear. Since the demethylation reaction mediated by JmjC family members is oxygen

dependent (Figure 3C), the general believe is that global methylation is increasing upon

hypoxia. However, due to the wide range of affinities to molecular oxygen among

different JmjC domain subtypes (Ozer and Bruick, 2007), it is not excluded that certain

histone demethylases are active even under severe hypoxia.

Only one study addressed the involvement of Hif1α in the regulation of methylation

marks by showing binding to and differential expression of four JmjC domain containing

proteins upon exposure to hypoxia (Xia et al., 2009).

However two studies were addressing the effect of hypoxia to global methylation levels.

Chen et al showed in 2006 that global H3K9me2 levels are enhanced in various cell

lines (Chen et al., 2006). More recently Johnson et al analyzed global and gene-specific

histone methylation levels (Johnson et al., 2008a). Overall, Johnson et al addressed

global levels of four activating methylation marks (H4R3me2, H3K4me2, H3k4me3 and

H3K79me2) and 5 repressive marks (H3K27me2, H3K27me3, H3K4me1 and

H3K9me2). All modifications tested were 1.4 - 3.6 fold increased upon exposure to 0.2%

oxygen for 48 hours on the global level, indicating an enhanced methylation activity or a

decreased demethylation activity under hypoxia.

However, by a more targeted approach, the promoters of four genes were analyzed for

H3k4me3 and H3K27me3 levels upon exposure to hypoxia. Two of these genes were

previously shown to be repressed (AFP and Albumin) and two were demonstrated to be

enhanced (EGR1 and VEGFA) upon exposure to hypoxia. As expected H3K4me3 levels

were enhanced at all promoters 1.6 – 9.2 fold. Surprisingly however, H3K27me3 levels

were less than 0.4 times of the levels under normoxia at the promoters of AFP, EGR1

and VEGFA. Although not effecting global methylation levels, this unexpected reduction

in methylation levels suggests a possible activity of an H3K27me3 demethylating

enzyme at specific sites under hypoxic conditions.

Introduction

Page 22

6.3. Methodology to address Genome wide binding patterns

6.3.1. ChIP-chip

The combination of chromatin immunoprecipitation (ChIP) and DNA microarray

hybridization to determine the binding sites of transcription factors in a genome-wide

context was first introduced with the study of HNF transcription factors in 2004 (Odom et

al., 2004). DNA microarrays consist of unique, single stranded DNA oligonucleotides

(features), which are immobilized on a solid surface, in spots with a diameter of a few

nanometers. After the ChIP, enriched DNA fragments are linearly amplified by ligation

mediated PCR (LM-PCR) and labeled with a fluorescent dye. The labeled DNA

fragments are subsequently hybridized to DNA microarrays, which are scanned by a

laser to acquire raw intensities of the DNA fragment distribution.

The resolution of a CHIP-chip study depends on the fragment size of the DNA, the size

of the features and the gap between features on the array. Starting with self-spotted

arrays with at most 40000 single features, tremendous progress has been achieved to

increase coverage and sensitivity of the arrays. Modern, commercially available DNA-

arrays suitable for ChIP-chip studies cover millions of features on one array. The

obvious technical limitations introduced by the microarray are mainly probe-specific

behavior, dye bias, resolution and design of the array (Johnson et al., 2008b).

6.3.2. ChIP-Seq

To avoid the technical limitation introduced by the DNA-array, high throughput

sequencing can be applied to the DNA-fragments derived by a ChIP experiment. The

first combination of chromatin immunoprecipitation with genome wide sequencing was

established in 2006 (Chen et al., 2008). Since then, several studies successfully applied

this method.

As opposed to LM-PCR used as an unavoidable amplification step for a ChIP-chip

experiment, the amplification step of common ChIP-Seq experiments is far superior in

terms of linearity. The specific amplification steps of the ChIPed material is achieved by

a manufacturer specific method such as the sequencing-by-synthesis approach of

Illumina (Mardis, 2008).

Introduction

Page 23

A genome-wide readout of the protein binding sites is produced by end-sequencing of

the amplified and immobilized ChIP fragments. The resulting forward and reverse reads

of 36bp (Illumina) are mapped to an existing genome and computationally fused to

peaks.

6.3.3. Genome wide binding patterns of transcription factors

Remarkable progress has been made during the past few years in the characterization

of transcriptional patterns in a genome-wide scale. The main driving force has been the

develop development and improvement of ChIP-chip, ChIP-Seq and other large scale

experimental techniques. Therefore, the characteristic binding pattern of several

transcription factors, insulators and general transcription factors could be studied on a

genome-wide scale including p53 (Chen et al., 2008), PPARg and RXR (Nielsen et al.,

2008), estrogen receptor (Carroll et al., 2006), FoxP3 (Zheng et al., 2007), the insulator

protein CTCF (Kim et al., 2007), TCF3 (Cole et al., 2008), Polycomb (Pokholok et al.,

2005), HNF (Odom et al., 2004), CREB (Zhang et al., 2005), ERRa and ERRg (Dufour

et al., 2007) and p63 (Yang et al., 2006).

A concise overview over the raw experimental results is depicted in Table 1. Together

the binding patterns of different transcription factors are highly varying. Several studies

suggested total binding events in the range of hundreds (e.g. Hif1α), and some

suggested total binding to be in the transcription factor to thousands. (e.g. PPARg). The

overall overlap of the of nee tod

Table 1: Overview about recent genome wide binding studies

Aim of the project

Page 24

7. Aim of the project

The variety of biological processes, which are affected by a HIF-dependent hypoxic

response, highlights the complexity and importance of these transcription factors.

Generally speaking, HIF-dependent hypoxic responses mainly entail a shift of energy

metabolism towards glycolysis, cell cycle arrest, a decrease in protein translation and

induction of neovascularisation factors such as for example VEGF. However, a more

global picture of HIF targets and downstream signaling effects is lacking. My work aims

at elucidating how eukaryotic cells respond to hypoxia at the molecular level. For this

purpose, I generated a global and dynamic regulatory network of the transcription

factors HIF-1α in primary hepatocytes and macrophages using ChIP-on-chip in

combination with cDNA microarrays and complemented data using a ChIP-Seq

approach in a macrophage cell line.

Material and Methods

Page 25

8. Material and Methods

8.1. Media and Buffers used for ChIP and ChIP-chip

Crosslinking Buffer

Stock For 50 ml Final Concentration

1M Hepes-KOH, pH 7.5 2.5 ml 50 mM

5M NaCl 1.0 ml 100 mM

0.5M EDTA, pH 8.0 100.0 µl 1 mM

0.5M EGTA, pH 8.0 50.0 µl 0.5 mM

37% Formaldehyde 14.9 ml 11%

ddH2O 31.5 ml

Block Solution


10x PBS 10 ml 1X

BSA 500 mg 0.5% BSA (w/v)

ddH2O 90 ml

Total 100 ml

Complete Protease Inhibitor Cocktail (Roche) was added

Lysis Buffer 1 (LB1)




0.5M EDTA 0.2 ml 1 mM

50% glycerol 20.0 ml 10%

10% NP-40 5.0 ml 0.5%

10% Triton X-100 2.5 ml 0.25%

ddH2O 64.5 ml


Page 26



1M Tris-HCl, pH 8.0 1.0 ml 10 mM


0.5M EDTA, pH 8.0 0.2 ml 1 mM

0.5M EGTA, pH 8.0 0.1 ml 0.5 mM

ddH2O 94.7 ml





0.5M EDTA, pH 8.0 0.2 ml 1 mM

0.5M EGTA, pH 8.0 0.1 ml 0.5 mM

10% Na-Deoxycholate 1.0 ml 0.1%

20% N-lauroylsarcosine 2.5 ml 0.5%

ddH2O 93.2 ml

Wash Buffer (RIPA)



5M LiCl 25.0 ml 500 mM

0.5M EDTA, pH 8.0 0.5 ml 1 mM

10% NP-40 25.0 ml 1%

10% Na-Deoxycholate 17.5 ml 0.7%

ddH2O 169.5 ml

Elution Buffer



0.5M EDTA, pH 8.0 2.0 ml 10 mM

10% SDS 10.0 ml 1%


Page 27

ddH2O 83.0 ml

Linker Oligonucleotides

Oligo JW102 (5’-GCGGTGACCCGGGAGATCTGAATTC-3‘) and

Oligo JW103 (5’-GAATTCAGATC-3‘)

Blunting Mix

Stock 1X Mix Final Concentration

10X NE Buffer 2 11.0 µL 1x

10 µg/µL BSA (NEB) 0.5 µL 5 µg

10mM each dNTP 1.1 µL 100 µM

3U/µL T4 DNA polymerase (NEB) 0.5 µL 1.5 U

ddH2O 41.9 µL

Total 55 µL

Ligase Mix


5x ligase buffer (Invitrogen) 10.0 µl 1x

15 µM linkers

6.7 µl 2 µM

400U/µl T4 DNA ligase (NEB) 0.5 µl 200U

ddH2O 7.8 µl

Total 25.0 µl

Mix A


10X Thermopol buffer (NEB) 4.00 µL 1x

dNTP mix (2.5 mM each) 5.00 µL 250 µM

oligo JW102 (40 µM) 1.25 µL 1 µM

ddH2O 4.75 µL

Total 15.00 µL


Page 28

Mix B


10X Thermopol buffer (NEB) 1.0 µL 1x

Taq polymerase (5U/µL) 0.5 µL 0.25 U

ddH2O 8.5 µL

Total 10.0 µL

Precipitation Mix


7.5 M Ammonium acetate 25.0 µl 625 mM

100% Ethanol 225.0 µl 75%

Total 250.0 µl

Labeling Mix

Stock 1x Mix Final Concentration

10X dUTP Nucleotide Mix 8.2 µL 112/56 nM

Cy5- or Cy3-dUTP (1 mM) 1.5 µL 17 µM

Klenow (40 U/µL) 1.5 µL 60 U

ddH2O 1.8 µL

Total 13.0 µL

8.2. Cell lines

8.2.1. Primary Mouse Hepatocytes

Primary mouse hepatocytes were harvested from male 12-14 weeks old C57BL/6 mice

using the protocol of (Seglen, 1976). Collected cells are filtered with a 70µm sieve and

washed twice with intermitted spinning steps for 2 minutes at 50 rpm in cold medium.

PMH where subsequently plated in DMEM supplemented with 10% FCS (Difco) and 1%

penicillin-streptomycin. 2 - 4h after plating the cells are washed once with DMEM.


Page 29

8.2.2. Primary Peritoneal Mouse Macrophages

Primary Peritoneal Mouse Macrophages were harvested from male 12-14 weeks old

C57BL/6 mice. Mice were injected with 2ml of 4% Thioglycollate (Sigma) in the

peritoneum and sacrificed after 72h by peritoneal washes with cold PBS. Collected cells

are filtered with a 70µm sieve, pelleted and resuspended RPMI supplemented with 10%

FCS (Difco) and 1% penicillin-streptomycin. 2 - 4h after plating the cells are washed

once with RPMI.

8.2.3. Raw.264 cell line

Raw.264 cells were grown in RPMI (Sigma) supplemented with 10% FCS and 1%

penicillin-streptomycin.

8.2.4. Hypoxic conditions

All cells subjected to hypoxia where grown in 15 cm cell culture dishes and 25 ml of

growth medium. The Invivo2 400 hypoxia workstation (Ruskinn) was set to 0.5% of

oxygen, 37°C and 5% of CO2. All media used within the hypoxic chamber were

preincubated at least for one hour.

8.3. ChIP-on-chip

8.3.1. Preparation of the cells under hypoxia and cross-link proteins to DNA

5 x 107 to 1 x 108 cells were used for each immunoprecipitation that was used for one

ChIP-chip study. On the day of harvesting, cells were incubated in a humid hypoxic

chamber (see above) under standard cell culture conditions (37°C, 5% CO2) for the

respective time points. Crosslinking Buffer containing 0.9% formaldehyde was freshly

prepared and preincubated under hypoxic conditions for at least 3h before addition to

the monolayer. Crosslinking was performed in total for 9 minutes. 1 minute of the

crosslinking procedure was carried out under hypoxic conditions and 8 minutes were

performed at room temperature and at ambient oxygen levels.


Page 30

Formaldehyde crosslinking was quenched by the addition of 1/20 volume of 2.5 M

glycine to plates. Subsequently cells were rinsed with 5 ml 1X PBS and harvested using

a silicone scraper. The cells were then aliquoted into 2 x 50 ml conical tubes and spun at

1,350 x g for 5 minutes at 4°C in a table-top centrifuge with swinging bucket rotor.

The supernatant was discarded and cell pellets were flash frozen in liquid nitrogen

before storage at -80°C.

8.3.2. Preparation of the magnetic beads

100 µl per experiment of Protein G coated Dynal magnetic beads were vigorously

resuspended and added to a 1.5ml microfuge tube. 1 ml of Block Solution was added to

the tube and beads were gently mixed. Beads were collected using the Dynal small-

volume magnetic particle concentrator (Invitrogen) and the supernatant was discarded.

After two additional washing steps, 10µg of the respective antibody plus 250µl of ice

cold Blocking Solution were added to the beads and incubated overnight at 4°C on a

rotating platform. The next day, beads were washed 3x with 1 ml Block Solution as

described in above and spun for 1 minute at 4°C at 17,000 x g to collect and remove the

supernatant. Finally, beads were resuspended in 100µl Block Solution.

8.3.3. Cell lysis

The pellet of approximately 108 cells was resuspended in 5ml of Lysis Buffer 1 and

rocked at 4°C for 10 min. After spinning at 1,350 x g for 5 minutes at 4°C in a tabletop

centrifuge the supernatant was discarded. The pellet was subsequently resuspended in

5ml of Lysis Buffer 2 and rocked gently at room temperature for 10 min. Nuclei were

pelleted in tabletop centrifuge by spinning at 1,350 x g for 5 minutes at 4°C and

supernatant was discarded.

After resuspension of the pelleted nuclei in 3 ml of Lysis Buffer 3 (LB3) cells were

transferred to 15ml polypropylene tube that has been cut at the 7 ml mark (to make

sonication easier). Sonication of the suspension was performed with a microtip attached

to a sonicator (Fisher) at 4°C and on ice, with power settings set to 75% and 30 seconds

bursts between 60 seconds of cooling steps in between.


Page 31

In total the cell suspension was sonicated during 8 of such ON cycles for the normoxic

samples and 9 ON cycles for hypoxic cells, to account for increased crosslinking during

hypoxia. 300 µl of 10% Triton X-100 was then added to the sonicated lysate and mixed

by pipetting up and down several times. The lysates were split into two 1.5 ml microfuge

tubes and spun at 20,000 x g for 10 minutes at 4°C in a microfuge to pellet debris.

Supernatant was then combined from the two 1.5ml microfuge tubes into a new 15 ml

conical tube for immunoprecipitation. 50µl of cell lysate was saved from each sample as

WCE.

8.3.4. Immunoprecipitation of the chromatin

100 µl antibody/magnetic bead mixture were added to 15 ml conical tube containing the

cell lysate and were gently mixed overnight on a rotator or rocker at 4°C.

8.3.5. Wash, elution, and reverse cross-linking

One 1.5 ml microfuge tube was pre-chilled for each immunoprecipitate, beads were

collected using a magnetic stand and subsequently washed for 7 times with 1ml of

Wash Buffer (RIPA). After the last wash beads were washed once with 1 ml TE that

contains 50mM NaCl. Cells were spun at 960 x g for 3 minutes at 4°C in a centrifuge

and any residual TE buffer was removed a pipette.

Elution of the bound Protein DNA complexes was done in 210µl of elution buffer at 65°C

for 15min with a Thermoshaker (Eppendorf). During elution, beads were resuspended

every 2 minutes by mixing briefly on a vortex mixer. Afterwards beads were spun down

at 16,000 x g for 1 minute at room temperature and the supernatant was transferred to a

new 1.5 ml microfuge tube. Cross-links of ChIPed eluted fraction and of 50µl of WCE

complemented with 3 volumes (150µl) of elution buffer were reversed by incubation in a

water bath at 65°C overnight.


Page 32

8.3.6. Digestion of the cellular protein and RNA

200µl of TE was added to each tube of IP and WCE DNA to dilute SDS in elution buffer.

8µl of 10 mg/ml RNaseA (0.2 mg/ml final concentration / Fermentas) were added and

mixed and incubated in a circulating water bath for 2 hours at 37°C. 7µl of CaCl2 stock

solution (300 mM CaCl2 in 10mM Tris pH 8.0) were added to each sample, followed by

4µl of 20 mg/ml Proteinase K (0.2mg/ml final concentration / Sigma).

The samples were then mixed and incubated in a water bath at 55°C for 30 minutes.

400µl of phenol:chloroform:isoamyl (Fluka) alcohol were added to each tube and

samples were thoroughly mixed on a vortex mixer and subsequently centrifuged at

14,000 x g at room temperature for 5 minutes. The supernatant was transferred to a new

1.5ml tube and an equal volume of Chloroform (Fluka) was added.

After centrifugation at 14,000 x g for 5 minutes at room temperature the aqueous layer

was transferred to a new 1.5ml microfuge tube. A precipitation mix including 16µl of 5M

NaCl (200 mM final concentration), 1.5µl of 20µg/µl glycogen (Invitrogen) (30µg total)

and 880µl of EtOH were added to each sample.

After cooling of the sample at -80°C for at least 30min the mixture was spun at 20,000 x

g for 10 minutes at 4°C to create DNA pellets. The pellets were then washed with 500µl

of 70% ice-cold EtOH and dried for 10 minutes with a vacuum desiccator. The dried

pellets were lysed in 70µl of 10mM Tris-HCl, pH 8.0. While the concentration of the IP

samples remains unknown, the concentration of the WCE samples was measured with a

Nanodrop (NanoDrop Technologies) and adjusted to 100ng/µl.

8.3.7. Preparation of linkers for LM-PCR

Oligos JW102 and JW103 were mixed to a final concentration of 40 µM each in 250mM

Tris-HCl pH 7.9 and 100µl were put into a PCR tube. The linkers were annealed in a

Thermal Cycler using the following program:

Step 1: 95°C 5 minutes


Step 3: Ramp down to 4°C (0.4°C/min)

Step 4: 4°C HOLD


Page 33

8.3.8. Blunting of the DNA ends and ligation of the linkers

2µl (200ng) WCE DNA and 53µl ddH2O were added into a PCR tube. 55µl of each IP

sample were transferred into a second PCR tube and on ice. 55µl of blunting mix were

added to all samples and cooled for 20 minutes at 12°C in a thermal cycler and

subsequently placed on ice. After addition of 11.5µl of cold 3 M sodium acetate and

0.5µl of 20µg/µl glycogen (10µg total) to the sample, Phenol DNA Extraction with

subsequent Ethanol precipitation was performed as described above. Pellets were

dissolved in 25µl of water.

25µl of ligase mix was added to 25µl of sample and cooled for 16 hours in a thermal

cycler set to 16°C. 6µl of 3 M sodium acetate and 130µl of 100% EtOH was added to the

sample which was then chilled at -80°C for at least 30min. Pelleting the DNA was done

by spinning at 20,000 x g for 10 minutes at 4°C. The sample was washed with 500µl of

ice-cold 70% EtOH, dried for 10 minutes in a vacuum desiccator and resuspended in

25µl H2O.

8.3.9. Amplification of the IP and WCE samples

25µl each of IP and WCE DNA were put into separate PCR tubes. 15µl of Mix A was

added to each sample and samples were heated in a thermocycler for 2min at 55°C.

Then 10µl of Mix B were added to each tube to hot start the reactions with the following

PCR program:




Step 4: 95°C 30 seconds

Step 5: 60°C 30 seconds

Step 6: 72°C 1 minute

Step 7: GO TO Step 4 x 25 times


Step 9: 4°C HOLD

After PCR samples were mixed with 250µl of Precipitation Mix each and cooled for 30

minutes at -80°C.


Page 34

Precipitation was done by spinning at 20,000 x g for 10 minutes at 4°C. Pellets were

washed with 500 µl of ice-cold 70% EtOH, dried for 10 minutes with a vacuum

desiccator, resuspended in 50µl H2O and concentrations were adjusted to 100ng/µl.

8.3.10. Sample Labeling

Sample labeling and clean up was achieve using Invitrogen’s CGH Labeling kit with a

modified labeling procedure:

20.0µl of LM-PCR product (100ng/µL) was put into a PCR tube and 35µl of random

primer solution and 20µl of water was added. The sample was mixed on a vortex mixer

for 30 seconds, placed in a thermal cycler preheated to 95°C and incubated for 5

minutes. Tubes were then immediately transferred to an ice-water bath and cooled for 5

minutes. Cy5 mix was used for IP DNA and Cy3 for WCE DNA.

3µl of the label mix was added in each tube and mixed by pipetting up and down multiple

times followed by a 3 hour incubation at 37°C in the dark. The reaction was stopped by

adding of 9µl of stop buffer to each tube and subsequent mixing. Samples were

transferred to a 1.5 mL microfuge tube and clean up the samples was done using

Invitrogen’s CGH column as follows:

0.4ml of Purification Buffer A was added to each tube and mixed with a vortex mixer for

30 seconds. Columns were placed into a 2ml collection tube and spun at 8,000 × g for 1

minute at room temperature. After adding 0.6ml of Purification Buffer B to the column

samples were spun in a centrifuge at 8,000 × g for 1 minute at room temperature. Flow-

through was discarded and the tube was placed back in the tube. Then 0.2ml of

Purification Buffer B was added to the column and the sample was centrifuged at 8,000

× g for 1 minute at room temperature before discarding the flow-through. The purification

column was then placed in a new, sterile 1.5-mL collection tube and 50µl of sterile water

was added. After incubation at room temperature for 1 minute samples were centrifuged

at 8,000 × g for 1 minute at room temperature to elute the labeled DNA.


Page 35

8.3.11. Sample Hybridization and Scanning of microarrays

All samples were hybridized to Agilent 244k Mouse Promoter Arrays. The hybridization

procedure was conducted according to the manufacturer’s recommendations. All slides

were treated with the Acetonitrile containing Stabilization and Drying Solution of Agilent

after hybridization and before scanning to prevent Ozone degradation of Cyanine 5.

8.3.12. Design of the Agilent 244k Mouse Promoter Arrays

The Agilent 244k Mouse Promoter Arrays consist of 2 slides. Each slide contains

244.000 unique Oligonucleotides with an average length of 60bp and an isothermal

design (common melting temperature for all features). The slides represent the

Promoters of about 20.000 mouse genes. A promoter is defined as the region of -

5000bp to +2000bp with regard to the TSS. Each gene is covered in average by 25

features.

8.4. ChIP-Seq

ChIP-Seq experiments using the Illumina platform were performed in collaboration by

the IGBMC (Strasbourg).

To prepare the library, 10 ng of chipped DNA was used (~200 bp DNA fragments linked

with 5' and 3' Illumina adapters) using the Illumina kit (Preparing samples for ChIP

sequencing of DNA).

Library (4 pM) of DNA fragments is then hybridized on the flowcell and clusters are

generated using Illumina Cluster Station. Genome Analyzer II (Illumina) is used to

sequence (36 cycles).

8.5. mRNA Expression Profiling

TRIzol Reagent (Invitrogen) harvested total mRNA samples were purified using a DNA

purification Kit (Machery-Nagel) and subjected to DNAse on column digestion treatment.

Total mRNA was quantified using the NanoDrop ND 1000 (NanoDrop Technologies) and

the mRNA integrity was assessed using the Bioanalyzer 2100 (Agilent).


Page 36

Starting material of total RNA for the amplification, which includes cDNA synthesis using

oligodTT7 primer, followed by in vitro transcription, was 1µg. Quality, quantity and dye

incorporation control of each cRNA sample was performed using the NanoDrop.

Gene expression profiling using the Agilent platform was performed in collaboration by

the Functional Genomic Center Zürich.

8.5.1. 1-color array

cRNA of the PMM samples were labeled using the Quick Amp Labeling Kit, One-Color

(Agilent) and the labeled material was hybridized to Agilent whole mouse genome arrays

(G4122F). Arrays were scanned using the Agilent Microarray Scanner G2565BA.

8.5.2. 2-color array

cRNA of the PMH samples were labeled using the Quick Amp Labeling Kit, Two-Color

(Agilent) and the labeled material was hybridized to Agilent whole mouse genome arrays

(G4122F). Arrays were scanned using the Agilent Microarray Scanner G2565BA.

8.5.3. Expression analysis

Raw images of the hybridized arrays were analyzed using the Feature Extraction

Software Package Ver. 10.5 (Agilent). Fold changes were calculated using GeneSpring

GX Ver. 10 (Agilent). Features with raw intensities lower than 500 were labeled as ńot

expressed´ and removed. p-values were estimated using an unpaired students t-test.

Features having a p-value > 0.02 were labeled as ńot significant´ and removed. Fold

change was calculated using normalized intensities and fold changes lower than 1.5

were labeled as ńot differentially expressed´ and removed.

8.6. Identification of ChIP-chip Peaks

ChIP-chip raw images of the hybridized arrays were analyzed using the Feature

Extraction Software Package Ver. 10.5 (Agilent) with the default protocol for ChIP

experiments (ChIP_105_Dec08).


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Resulting raw intensities were analyzed with ChIP-Analytics Ver.1.3 (Agilent) and inter-

arrays median normalization, Intra-array (dye-bias) median normalization and Intra-array

Lowess (intensity-dependent) normalization were applied. Peak detection and p-value

estimation was performed using the Whitehead Error model v1.0 and the Whitehead

Per-Array Neighborhood Model v1.0. Peaks were labeled as ´bound´ if p-values were

lower than 0.0006 (for choice of cut off see results).

8.7. Identification of ChIP-Seq Peaks

Peak detection and normalization was done by CisGenome Software v1.1 (Ji et al.,

2008). The default parameters for peak detection were used with a window size of 100

bp, cutoff >= 10 reads, step size = 25 bp, maximum gap = 0 and minimum peak length =

0. The detected peaks using these settings were labeled as ´bound´.

8.8. De novo Motif Analysis

8.8.1. Weeder

Weeder (Pavesi et al., 2006) was used to detect overrepresented motifs using the

Weeder Cygwin version v1.3.1 with the following settings: ´mouse model´, ´large

search´, ´search on both strands´ and óccurrence can be more than one´.

8.8.2. Gibbs-Motif-Sampler

For low redundant de novo motif search, Gibbs-Motif-Sampler included in CisGenome

package was used with the following settings: Órder of background markov chain´ = 3

and Ńo. of MCMC (simulation) iterations´ = 3000.


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8.9. Q-PCR Validation of ChIP Hits

RNA was purified from total hearts using TRIzol Reagent (Invitrogen) according to

manufacturer’s instructions. 2µg of RNA was used as a template to synthesize cDNA,

using Ready-To-Go You-Prime First-Strand Beads (Amersham). Qantitative RT-PCR

reactions were set up as recommended by the manufacturer (Roche) and were run an

analyzed on the Roche LightCycler 480.

8.10. Annotation of sequences and association of expression- to binding data

All sequences used were annotated with the ENSEMBL release v54 of the 37 NCBI

assembly of the mouse genome using the ENSEMBL Core API. All features on the

Agilent expression array and all sequences derived by the binding studies were

annotated using ENSEMBL Transcript definitions. For comparison of the expression and

binding data, ENSEMBL Gene definition was used.

Results

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9. Results

9.1. Regulation of HIF1αααα and its target genes

9.1.1. Hif1α rapidly accumulates upon hypoxia in PMM, PMH

I first aimed at determining ideal time points and conditions for subsequent chromatin-

immunoprecipitation of Hif1α in cells that we wanted to use as a tool in the following

ChIP-chip and ChIP-Seq experiments, respectively. Protein abundance of Hif1α in PMM

and PMH was assessed during a time course of hypoxia exposure using Western

blotting (Figure 4). In PMH as well as in PMM, Hif1α accumulated most abundantly

already after 1.5h and rapidly decreased after 8h.

After 16h of hypoxia, Hif1α was almost undetectable in primary cells. However,

exposure of PMH to hypoxia for 8 and 16 hours resulted in reduced abundance of Lamin

A (Figure 4) and other reference proteins that I tested (data not shown) most likely due

to the fact that the protein translational machinery is inhibited under hypoxia treatment

as previously reported (Wouters and Koritzinsky, 2008).

Figure 4: HIF1αααα protein is stabilized upon hypoxia in PMM, PMH. Western Blot analysis with 150µg of nuclear protein extracts from PMM and PMH. Protein levels of HIF1α were assessed under normoxic conditions and after four time points of exposure to 0.5% oxygen. Lamin A was used as a loading control.

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9.1.2. Promoter occupancy of Hif1α target genes correlates well with nuclear protein

accumulation

To determine whether nuclear HIF1α accumulation correlates with its promoter

occupancy, I analyzed three established HIF1α-targeted promoters at different time

points of hypoxia by ChIP-QPCR. PMM and PMH showed a significant increase of

HIF1α promoter binding after 3h of hypoxic exposure (Figure 5A-B).

Figure 5: Expression of HIF1αααα target genes was increased several hours after the initial binding event. A-B: ChIP-QPCR of three established HIF1α binding locations at the promoters of GAPDH, LDHA and JMJD1A were performed. ChIP was performed from PMM (A) and PML (B) after different time points of exposure to hypoxic conditions (0.5% O2). Ct values were normalized to percent of input and afterwards to normoxia. C - D: RT-PCR was performed of total mRNA from PMM (C) and PML (D) after different time points of exposure to hypoxic conditions (0.5% O2).

Results

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In both cell types and for all three tested genes, promoter occupancy was maintained

after 16h of hypoxic exposure at levels seen upon 3h of hypoxic conditioning indicating

that HIF1α stabilization leads to its binding to promoters in PMM and PMH.

9.1.3. Expression of the aforementioned HIF1α target genes was enhanced several

hours after the initial binding event

To assess whether promoter occupancy correlates with changes in expression of

respective genes, transcript levels of genes were measured for PMM (Figure 5C) and

PMH (Figure 5D) at different time points after hypoxic exposure using quantitative RT-

PCR. As expected, induction of transcripts was observed several hours after HIF1α

promoter occupancy for all three tested transcripts and in both tested cells. The

expression dataset with highest resolution revealed that a plateau phase is reached

approximately after 8h-16h of exposure to hypoxia (PMH in Figure 5D).

These experiments demonstrated that expected hypoxic responses occur in PMH and

PMM under conditions used making it to a suitable system for subsequent experiments.

Based on the pattern of promoter occupancy and subsequent transcription, we decided

to take the 3h time point for chromatin-IPs and 16 h for the expression analysis (Figure

6).

Figure 6: Experimental design based on promoter occupancy and expression patterns. PMH and PMM will be exposed to 0.5% and 21% Oxygen levels. ChIP and whole mRNA extraction will be performed for each condition individually. Hypoxic conditions will be applied for 3h and 16h for the binding study and the expression study, respectively.

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9.2. Genome wide expression and binding studies

9.2.1. ChIP-chip

To assess the binding events of Hif1α on a genome-wide level, I performed ChIP-on-

chip assays with extracts of PMH and PMM. For hybridization, we chose commercially

available mouse promoter arrays (Agilent) that allowed for assessment of binding events

of Hif1α across promoters (-5kbp to +2kbp with regard to the TSS) of the entire genome

(Figure 6).

9.2.2. HIF1α promoter occupation

To determine the amount of significant binding events, I applied stringent statistical

criteria with a p-value of <0.0006, as determined by ChIP-QPCR (see chapter 9.2.3).

Comparison of the individual hypoxia- to normoxia control experiments revealed that the

amount of significantly bound genes was less than 25% of the binding events observed

under hypoxia. In general the statistical significant peaks measured in cells under

normoxic showed less bias towards the TSS, less enrichment for HRE and a

approximately ten fold lower raw intensity level compared to their relative hypoxic

datasets (data not shown) suggesting nonspecific enrichment. However, around 16% of

the binding events observed in hypoxia experiments were found in cells under normoxic

conditions only, suggesting nonspecific enrichment for most of the genes.

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The amount of all statistically significant binding events under hypoxic conditions of each

experimental set are indicated in group I (Figure 7). The number of genes in group I that

could be associated to current ENSEMBL genes within the proximal promoter (PP), are

indicated in group II. Group III genes are targets in group II that are also represented on

arrays used for my genome-wide expression arrays. (Figure 7). A complete list of group

III genes is provided in the appendix.

Figure 7: HIF1αααα targets distinct genes in different cell types. (A) Significant binding events of HIF1α can be divided into three subclasses. Group I peaks are peaks that are uniquely found and that can be associated to a unique position within the genome. Group II peaks are peaks that can be associated to a maximum of two genes (one upstream and one downstream within either the whole genome or the PP region) or minimal to one gene. All peaks that cannot be associated to a unique gene within the respective region are neglected from Group II. On the other hand a peak can be found twice if a peak is associated to a gene up- and downstream (e.g. at bidirectional promoters). Since Agilent expression arrays cover only a limited amount of ENSEMBL annotated transcripts, Group II peaks can be subdivided to a group of peaks that are covered by the Agilent expression arrays and have therefore present expression data (Group III). The overlap between each dataset is represented by Venn Diagrams on the right.

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9.2.3. Validation of ChIP-chip experiments

In order to validate ChIP-chip data, ChIP-QPCR of a randomized set of significantly

bound target genes in PMH (Figure 8A) and PMM (Figure 8A and B, respectively) was

performed. In total, one gene was randomly chosen out of one group of 50 genes,

ranked according to decreasing ChIP-chip data scores (p-value) until rank number 500.

Additionally, three well established targets as described by Wenger et al (Wenger et al.,

2005) and four negative controls were validated. ChIP samples of each IgG control,

normoxia and hypoxia were included.

The enrichment levels confirmed the ChIP-chip data approximately until group number 5

(top 250 genes) for both datasets in PMH and PMM. Enrichment levels of groups lower

than group number 5 were in general indistinguishable (pvalue > 0.1) compared to the

mean enrichment scores of the negative control genes as well as the IgG ChIP.

To compare datasets in a statistically defined manner, ChIP-chip p-Values were lowered

until the first 5 groups were covered within both datasets. The adjusted ChIP-chip p-

Value used for all ChIP-chip experiments was < 0.0006.

In total, 3 of 16 and 1 of 14 tested peaks within the first 5 groups of PMH and PMM

dataset, respectively could not be confirmed by ChIP-QPCR. Therefore, a false

discovery rate (FDR) of 20% and 14% for PMH and PMM respectively can be estimated.

6 out of 16 and 2 out of 14 tested genes of the first 5 groups in PMH and PMM

respectively, were also significantly bound under normoxia (data not shown). Two genes

in the PMH under normoxia but none in the PMM dataset could be confirmed by ChIP-

QPCR.

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Figure 8: ChIP-chip data validation by ChIP-QPCR. ChIP has been performed with PMH (A) and PMM (B). Primers were chosen within the region of enrichment of indicated genes. Results were expressed as percentage of input. Four control regions were included as negative controls. The first bar of each gene represents an IgG control ChIP, the second bar HIF1α ChIP with cells under normoxic conditions and the third bar HIF1α ChIP with 0.5% hypoxia treated cells. The level of significance of the comparison between negative control targets and ChIP-chip results is indicated by an asterisk. P-values were calculated using an unpaired, two-tailed students t-test. The level of average hypoxic enrichment levels in negative control HIF1α ChIP is indicated by the red line.

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9.2.4. Hif1α binds to a distinct subset of genes in PMM and PMH

The binding data of PMH and PMM overlaps by about 20% (46 genes) between the two

cell types (Figure 9) indicating a strong cell type specificity of Hif1α. I next compared

genes, that show modulated expression in PMH and PMM under hypoxic conditions

9.2.5. Expression levels differ in PMM and PMH exposed to 16h of hypoxia

Genome-wide expression levels of PMM and PMH in response to 16h of exposure to

hypoxia were measured. Commercially available mouse whole-genome expression

arrays (Agilent) were used. A transcript was termed ´up regulated or down regulated´ if

raw intensity values exceeded a mean value of 500 and significant if the p-value was

lower than 0.02. Expression levels of at least ten randomly chosen transcripts were

validated by quantitative RT-PCR (data not shown). The estimated FDR of the PMM and

the PMH dataset is 10%. In total 377 and 1217 genes were more than 1.5 fold up

regulated in PMH and PMM, respectively (Figure 10). The list of the top 300 up

regulated genes is provided in the appendix.

Figure 9: Overlap between PMH and PMM binding data. All group III gene sets of the primary cell ChIP-chip experiments were represented with Venn diagrams and compared.

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9.2.6. Bound genes marginally overlap with differentially expressed genes in PMM

and PMH

To determine the frequency, at which Hif1α binding resulted in differential expression,

the expression and the binding data sets of PMH and PMM were compared. Of the 176

significantly bound genes in the PMM dataset, only 44 genes were enhanced upon

hypoxia (Figure 11A). Within the PMH dataset, only 48 genes were induced upon

hypoxia. Reduced expression upon hypoxia was observed only for one gene, within the

PMM dataset and for a total of 11 genes within the PMH dataset (Figure 11B). These

data suggest that Hif1α binds a large portion of genes that are not differentially

expressed upon hypoxia and most genes that show altered expression upon hypoxia are

not bound by HIF

As already presented, the binding data of PMH and PMM overlaps by about 20% (46

genes) between the two cell types (Figure 9) indicating a significant overlap (24.6 fold

enrichment compared to a random gene set) but also a strong cell type specificity of

Hif1α. I next compared genes, that show modulated expression and are bound by Hif1α

in PMH and PMM under hypoxic conditions. I found a 43% (20 genes) overlap of genes

that were differentially expressed and that were directly bound by Hif1α in both cell types

(Figure 11C). A complete list of these genes including expression data and significance

of the binding event is presented in (Table 2).

Figure 10: Overlap between expression data of PMH and PMM. Venn diagrams represent the amount of differentially expressed genes in PMH and PMM as indicated, after 16h of exposure to hypoxic conditions.

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Taken together, these two Hif1α location studies show a clear enrichment in common

targets in different cell types. However, the majority of Hif1α target genes within each

data set do not overlap, suggesting a strong cell-type specific promoter binding of Hif1α.

Figure 11: Overlap between expression data and binding data of PMH and PMM. Green (up regulated) and red (down regulated) Venn diagrams represent the amount of differentially expressed genes in PMM (A) and PMH (B) as indicated, after 16h of exposure to hypoxic conditions. Blue Venn diagrams indicate group III binding data. (C) Comparison of the intersection of (A) and (B)

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9.2.7. Functional clustering reveals common and specific biological roles for Hif1α in

PMM and PMH

To classify biological processes associated with enriched genes, the genes of group II of

both PMH and PMM cells were clustered into overrepresented Gene Ontological

Clusters of Biological Processes using DAVID Bioinformatics Resource

(http://david.abcc.ncifcrf.gov/) (Table 3). The principal category, which was significantly

overrepresented in all tested cell types, was glycolysis (highest p-value < 0.0013).

Additionally, vasculature development could be associated to PMH only, whereas

transcription and regulation of transcription could be associated to PMM.

These findings confirm that Hif1α is essential in the regulation of glycolysis under

hypoxic conditions in hepatocytes as well as macrophages. This highlights the

importance of anaerobic glycolysis for both cell types. However, these results also imply

cell type-specific regulation of biological processes by Hif1α. Angiogenesis is a more

important response to hypoxia in PMH compared to PMM. Instead, PMM regulate a high

number of genes associated to regulation of transcription.

Table 2: Genes bound by HIF1a and induced by hypoxia in both, PMM and PMH. The genes listed in this table are satisfying statistical criteria for significant binding and transcriptional up regulation in both, PMM and PMH. Fold change levels are color coded with low fold change (yellow) to high fold change (red). Down regulated genes are color coded in blue.

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9.2.8. HIF1α binding occurs preferably in close proximity of the TSS

To check for preferred binding sites of HIF1α within promoter regions, all group II

sequences were summed up in groups of 100bp and plotted according to their distance

to the TSS within the range from -5000bp to +3000bp. Indeed, a strong bias of the

binding events towards the TSS could be observed in PMM (Figure 12A) and PMH

(Figure 12B) and no strong bias could be observed in IgG control ChIP samples (Figure

12C).

Additionally, the binding events occurred preferably before the TSS in all cell types.

Within the PP region, no binding site within enhancer regions was allocated. Together

these results demonstrate that the binding pattern of Hif1α in all tested cell types follows

the typical binding pattern of a transcription factor with a strong bias towards the TSS

(Xia et al., 2009). A preference for enhancers at specific sites could not be observed.

Additionally, a higher number of simultaneous binding events could be observed among

genes with higher binding scores.

Table 3: The majority of HIF1αααα targets can be associated to glycolysis using DAVID and GO categories. The complete group II genes of PMH and PMM were analyzed by DAVID and clustered into GO categories of biological processes (category BP4). The top 10 overrepresented clusters are presented along with the respective count of Genes, pvalue, fold enrichment within the cluster and False Discovery Rate as computed by DAVID.

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Figure 12: HIF1a binding occurs preferentially in close proximity to the TSS. Group II peaks of PMM (A), PMH (B), and IgG control (C) were clustered into groups of 100bp and plotted according to their relative distance to the TSS and against the frequency of total genes within the whole PP region.

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9.2.9. Binding peaks of differentially expressed genes show a bias towards the TSS

In order to test whether the proportion of Hif1α binding events that caused alteration of

transcript levels shows a bias towards a preferred location on the respective promoter

region, the frequency of bound and at the same time differential expressed genes was

plotted against their relative location on the promoter. Therefore, I clustered the binding

events into groups of 1kpb or 500bp and within each group, the frequency of bound

genes with differential expression was calculated and all groups with less than 15 genes

were neglected (Figure 13). In PMH and PMM, it was evident that the amount of bound

genes with differential expression is almost twice as high within the range of 1000bp

around the TSS, compared to groups that were located elsewhere. Furthermore, within

the IgG control ChIP-chip dataset, no such bias could be observed. This analyses

demonstrate, that genes proximally bound by Hif1α with regard to the TSS, are more

likely differentially expressed as compared to the distally bound genes

Results

Figure 13: Peaks of differentially expressed genIgG group III datasets were clustered into groups of 1000bp and genes of the PMH dataset were clustered into groups of 500bp. The groups were plotted according their relative location to the TSS and agaiof total genes within the whole PP region that showed differential expression.

: Peaks of differentially expressed genes show a bias towards the TSS. Genes of the PMM and IgG group III datasets were clustered into groups of 1000bp and genes of the PMH dataset were clustered into groups of 500bp. The groups were plotted according their relative location to the TSS and against the frequency of total genes within the whole PP region that showed differential expression.

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Genes of the PMM and IgG group III datasets were clustered into groups of 1000bp and genes of the PMH dataset were clustered into

nst the frequency

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9.3. Genome wide binding study using the murine leukemic monocyte-

macrophage cell line (Raw.264) and ChIP-Seq

Hif1α has been demonstrated to regulate a vast majority of genes that are induced by

hypoxia (ref). The relatively small amount of significant binding events in the ChIP-chip

experiments for primary cells as opposed to changes in expression of many more genes

may indicate that the latter technique and tools attached to it do not quite provide a full

picture about HIF binding. In any case, the number of genes revealed by ChIP-chip does

not contain sufficient sequence information to perform valuable in silico analysis of

promoters that are bound by Hif1α. In order to complement the promoter-biased binding

data of PMM with genome-wide data and to validate the results with a different method,

ChIP-Seq was performed with Raw.264 cells (Figure 14). As explained above, this

assay is not dependent on the resolution of the promoter arrays.

Figure 14: Experimental design based on promoter occupancy and expression patterns. Raw.264 cells will be exposed to 0.5% and 21% Oxygen levels. ChIP will be performed for each condition individually with hypoxic conditions of 3h. Resulting ChIP fragments will be sequenced and reads will be mapped to reference genome.

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9.3.1. Hif1α rapidly accumulates upon hypoxia in Raw.264 cells

As described for primary cells, I first aimed at determining ideal time points and

conditions for subsequent chromatin-immunoprecipitation of Hif1α in Raw.264 cells.

Protein abundance of Hif1α in Raw.264 cells was assessed during a time course of

hypoxia exposure using Western blotting. Nuclear Hif1α in Raw.264 cells was detectable

at 1.5h after exposure to hypoxia and gradually increased during following indicated time

points (Figure 15).

To determine whether nuclear HIF1α accumulation correlates with its promoter

occupancy, I analyzed three established HIF1α-targeted promoters at different time

points of hypoxia by ChIP-QPCR. All three tested genes showed a significant increase

of HIF1α promoter binding after 3h of hypoxic exposure (Figure 16A). For all three

tested genes, promoter occupancy was maintained after 16h of hypoxic exposure at

levels seen upon 3h of hypoxic conditioning. These results indicate that

HIF1α stabilization leads to its binding to promoters in Raw.264 cells.

Figure 15: HIF1αααα protein is stabilized upon hypoxia in Raw.264 cells. Western Blot analysis with 150µg of nuclear protein extracts from Raw.264 cells. Protein levels of HIF1α were assessed under normoxic conditions and after four time points of exposure to 0.5% oxygen. Lamin A was used as a loading control.

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9.3.2. Expression of the aforementioned HIF1α target genes was enhanced several

hours after the initial binding event

To assess whether promoter occupancy correlates with changes in expression of

respective genes, transcript levels of genes were measured for Raw.264 cells (Figure

16B), at different time points after hypoxic exposure using quantitative RT-PCR. As

expected, induction of transcripts was observed several hours after HIF1α promoter

occupancy for all three tested transcripts and in all tested cells.

These experiments clearly demonstrated that expected hypoxic responses occurs in

Raw.264 cells under conditions used making it to a suitable system for subsequent

experiments. As for the primary cells tested earlier, based on the pattern of promoter

occupancy and subsequent transcription, we decided to take the 3h time point for

chromatin-IPs.

Figure 16: The Expression of HIF1αααα target genes was induced several hours after the initial binding event.

A: ChIP-QPCR from Raw.264 cells of three established HIF1α binding locations at the promoters of GAPDH, LDHA and JMJD1A and at different time points of exposure to hypoxic conditions (0.5% O2) were performed. Ct values were normalized to percent of input and afterwards to normoxia. B: RT-PCR was performed of total mRNA from Raw.264 cells after different time points of exposure to hypoxic conditions (0.5% O2).

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9.4. Promoters that are occupied by HIF1α α α α in Raw.264 cells

9.4.1. Cell-specific binding of targets by Hif1α under hypoxic conditions

To determine the amount of significant binding events, I applied stringent statistical

criteria to the ChIP-Seq data set. Comparison of the individual hypoxia- to normoxia

control experiments revealed that within all three normoxia data sets, the amount of

significantly bound genes was less than 25% of the binding events observed under

hypoxia. Additionally, around 16% of these binding events were found in cells under

normoxic conditions suggesting nonspecific enrichment for most of the genes.

Figure 17: HIF1αααα targets distinct genes in different cell types. Significant binding events of HIF1α can be divided into three subclasses. Group I peaks are peaks that are uniquely found and that can be associated to a unique position within the genome. Group II peaks are peaks that can be associated to a maximum of two genes (one upstream and one downstream within either the whole genome or the PP region) or minimal to one gene. All peaks that cannot be associated to a unique gene within the respective region are neglected from Group II. On the other hand a peak can be found twice if a peak is associated to a gene up- and downstream (e.g. at bicistronic promoters). Since Agilent expression arrays cover only a limited amount of ENSEMBL annotated transcripts, Group II peaks can be subdivided to a group of peaks that have are covered by the Agilent expression arrays and have therefore present expression data (Group III). The overlap between each dataset is represented by Venn Diagrams on the right.

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The amount of all statistically significant binding events under hypoxic conditions of each

experimental set are indicated in groups as described for ChIP-chip studies described

above (Figure 17). Group II and III genes are subdivided into A and B, where A stands

for binding events associated to genes within an extended promoter region (-100kbp to

+100kbp) and B for binding events associated to genes that are located within the limits

of the PP region. In total 3320 genes of in total 27078 genes within the database, could

be associated to 8245 peaks of the Raw.264 cell dataset within the PP region. A

complete list of the first 300 group III genes is provided in the appendix.

9.4.2. ChIP-Seq data validation

In order to determine the significance of the Raw-264 cell ChIP-Seq experiment

considering all 8245 binding events referred to as group I, I calculated mean

conservation of the area under the peak and compared the data to location- and size

matched, randomized control region (LSC) derived sequences (Figure 18A). Sequences

were ranked according to their binding scores (amount of reads) and the mean

conservation score was calculated for groups of 250 sequences. Throughout the dataset

and until the last group, the mean conservation score was significantly elevated

compared to the one of the LSC sequences (pvalue<0.001).

Another data consistency assessment was performed by analysis of the peak

sequences. Sequences of the ChIP-Seq experiment were ranked according to their

binding scores (amount of reads) and the occurrence of the classical HRE within a group

of 50 sequences was traced (Figure 18B). Finally, enrichment levels for each motif were

calculated compared to a LSC sequence. Again, significant (pvalue<0.001) and more

than 10 fold enrichment was observed for HREs considering the whole ChIP-Seq

dataset, whereas no enrichment was observed for the repeat like motifs which were

used as controls.

Taken together, conservation- and motif analysis revealed that 8245 peaks were

statistically significant (pvalue<0.001).

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Figure 18: ChIP-Seq data validation. (A) Raw.264 HIF1α ChIP-Seq data were ranked according to their score and clustered into groups of 300 peak. Average PhasCons score was plotted against the ranked groups. The green line and the blue line represent the ChIP-Seq data and LSC sequences respectively. The black line represents a sixth order polynomial regression curve through each dataset. The dashed red line represents a widely accepted cut off for significantly enhanced PhasCons conservation scores. (B) Raw.264 HIF1α ChIP-Seq data were ranked according to their score and clustered into groups of 50 peaks. Four highly overrepresented motifs within the ChIP-Seq dataset of Raw.264 cells were computed by Gibbs-Motif-Sampler. The average fold overrepresentation compared to an LSC sequence set was computed and plotted against the ranked groups. The blue line represents the overrepresentation of an HRE like motif, which was one of the top motifs given by Gibbs-motif-Sampler. The black line represents a sixth order polynomial regression curve through each dataset.

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9.4.3. Functional clustering of the ChIP-Seq data

To characterize biological processes associated with enriched genes, the top 500 genes

of group IIB were clustered into overrepresented Gene Ontological Clusters of Biological

Processes using DAVID Bioinformatics Resource (http://david.abcc.ncifcrf.gov/) (Table

4). The principal category, which was significantly overrepresented in all tested cell

types, was glycolysis.

These findings confirm that Hif1α is essential in the regulation of glycolysis under

hypoxic conditions in Raw.264 cells. Additionally, a high number of genes clustered to

regulation of transcription. Comparisons of single genes and associated functions are

provided below.

9.4.4. HIF1α binding occurs preferably in close proximity of the TSS

To check for the preferred binding sites of HIF1α within the genome, all group II genes

were summed up in groups of 100bp and plotted according to their distance to the TSS

within the range from -5000bp to +3000bp (Figure 19A). Raw.264 cells showed a strong

bias towards the TSS, with 35% of all binding events being located within a -100bp to

+100bp window. This bias was even more pronounced at an extended window from -

100kbp to +100kbp in groups of 1kbp (Figure 19B).

Within the PP region, no striking enhancer like, preferred binding site could be located.

The genome wide peak distribution of HIF1α in Raw.264 cells revealed, that a significant

proportion of binding events occurs within the CDS (Figure 20). The distribution between

intergenic and intragenic associated peaks was not significantly different compared to a

randomized dataset.

Table 4: The majority of HIF1αααα targets in Raw.264 cells can be associated to glycolysis using DAVID and GO categories. The top 500 genes derived by the ChIP-Seq data of Raw.264 cells were analyzed by DAVID and clustered into GO categories of biological processes (category BP4). The top 10 overrepresented clusters are presented along with the respective count of Genes, pvalue, fold enrichment within the cluster and False Discovery Rate as computed by DAVID.

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Apart from the bias towards the TSS as described above, a two times

overrepresentation compared to a randomized dataset of simultaneous binding to the 5’

and the 3’ end of genes was observed (Figure 20). Although, the 314 genes showing

this pattern among the group II genes of the Raw.264 dataset did not show any bias in

expression levels, the mean binding score was clearly higher.

The genome wide peak distribution of HIF1α in Raw.264 cells revealed, that a significant

proportion of binding events occurs within the CDS (Figure 21). The distribution between

intergenic and intragenic associated peaks was not significantly different compared to a

randomized dataset.

Together these results demonstrate that the binding pattern of Hif1α in Raw.264 cells

follows the typical binding pattern of a transcription factor with a strong bias towards the

TSS. A preference for enhancers at specific sites could not be observed. Additionally,

among the genes with higher binding scores, a higher number of simultaneous binding

events could be observed.

Figure 19: HIF1αααα binding occurs preferentially in close proximity to the TSS in Raw.264 cells. (A) Group IIA peaks and Group IIB peaks (B) of Raw.264 cells were clustered into groups of 100bp and plotted according to their relative distance to the TSS and against the frequency of total genes within the whole PP region.

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Figure 21: Genome wide HIF1αααα binding distribution relative to functional genomics elements in Raw.264 cells. Group I peaks were annotated and associated to functional genomics elements. The frequency of the overall amount of peaks was calculated and the significantly overrepresented elements compared to the general genomic element distribution within the mouse genome, is marked in red.

Figure 20: Example of the overrepresented pattern showing simultaneous HIF1αααα binding to the 5’ and the 3’ end. The ChIP-Seq results of the gene TFRC within the Raw.264 cell dataset were plotted according to its genomic location. The black line as well as the heat plot represents the Hypoxia ChIP-Seq reads and the red line represents the Normoxia HIF1α ChIP-Seq experiment. Conservation scores as well as genomic region are shown below.

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9.5. Characterization of the Hypoxia Response Element

9.5.1. Hif1α binds preferably to the extended core HRE -CGTACGTGC- motif.

To characterize motifs that are targeted by HIF1α, Weeder, an algorithm, that was

shown to predict overrepresented motifs in mammalian genome-wide association

studies with high accuracy (Linhart et al., 2008), was used. In all tested cell types and for

both ChIP-chip and ChIP-Seq the canonical HRE -ACGTG- was found as the most

overrepresented motif (Figure 23A). Importantly, the published consensus motif could be

refined. At position -3 to -1 to the core HRE a -CGT- was clearly overrepresented in all

three experiments. Based on ChIP-Seq results, at position +1 of the core HRE, an

additional -C- appears to defiine the HIF1α binding site. Conclusively, the published core

HRE -CGTG- was confirmed and could be expanded to a more precise -CGTACGTGC-.

9.5.2. HRE harboring Peaks are preferably localized close to the TSS

In order to visualize localization of the HRE within the promoter region in relation to

binding sites, I plotted the promoter region against the location-sorted peaks. HREs

were highlighted show that the HRE positive sequences display a clear bias towards the

TSS across all tested cell types (Figure 22). This observation was especially true for

PMM; and Raw.264 cells showed an even more pronounced TSS-biased distribution of

HRE harboring peaks. Analysis of IgG control ChIP data revealed a randomized

distribution of peaks and HRE motifs relative to the peaks.

Altogether, these data show that HREs occur less frequently in peaks distant to the TSS

(>2kbp6). Since I showed above that the overall peak distribution is strongly TSS-biased

in all tested cell types (Figure 12 and Figure 19), peaks without a clear HRE and more

distant localization to the TSS are most likely non-specific.

In order to visualize localization of the HRE within the promoter region in relation to

binding sites, I plotted the promoter region against the location-sorted peaks. HREs

were highlighted show that the HRE positive sequences display a clear bias towards the

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Figure 22: Peaks harboring HRE elements are preferably localized close to the TSS. In order to visualize the general HRE and peak distribution, each peak of each dataset was plotted sorted according to the distance to the TSS against the genomic location. The genomic region is represented by a black line, whereas a peak region is represented by a green line. A HRE consensus site is represented as a blue spot and red if it is located within a peak region. Once a peak is harboring a HRE, the green line is represented in yellow instead. (A): Raw.264, (B): PMM, (C): PMH, (D): IgG

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Figure 23: The most overrepresented motifs detected by Weeder and Gibbs-Motif-Sampler are closely related to the established HRE. All datasets were tested for overrepresented motifs by Weeder (A) and Gibbs-Motif-Sampler (B). In the first Row, the well-established HRE is shown (Wenger et al., 2005). In the following rows the top motifs of each analysis are represented with a Weblogo Plot expressed in bits of likelihood. Additionally, for the motifs detected by Weeder, the pvalue of the probability comparison to a matched, randomized region is given.

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9.5.3. Tandem HREs are commonly found across all tested cell types

Since Gibbs Motif Sampler allows for larger candidate motif searches compared to

Weeder, it was used as an additional tool to detect overrepresented motifs. Additionally,

it allows for better identification of flanking sequences that are potentially occupied by

other transcription factors (Figure 23B). The resulting motifs of this algorithm are much

less redundant compared to the ones resulting from the Weeder analyses. However, to

assess the significance of each motif, the input sequences have to be compared to a

reference genome. The references used in this study were LSC sequences. Within the

PMH and PMM dataset, the core HRE frequently occurred as a duplet: -

CGTGNNNNACGTG-. Analysis of the Raw.264 sequences resulted in a smaller, single

HRE similar to the motif resulting from the analysis by Weeder. One reason that Gibbs

Motif Sampler failed to detect tandem HREs as one of the most common Hif1α target

motifs within the Raw.264 dataset, can be the in average 10 times smaller peak size. To

test this hypothesis, I analyzed broadened ChIP-Seq peaks (+50bp) of the Raw.264

dataset with Gibbs Motif sampler for overrepresented motifs. Indeed, one of the most

overrepresented consensus sites compared to LSC sequences was the tandem HRE

consensus site -CGTGNNNNNCGTG- (Figure 23B). As the single HREs, the tandem

HRE harboring peaks, which are around 15% of all peaks in all datasets, show a bias

towards the TSS (Figure 24B). However, due to the 10 times smaller peak size, within

the Raw.264 dataset the tandem HRE frequency per base pair is far higher than within

the primary cell datasets.

Conclusively, these data suggest that tandem HREs are frequently observed in all tested

cell types and broaden the view on the sequence preference of Hif1α-mediated hypoxia

regulated genes.

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Figure 24: Tandem HREs increase ChIP efficiency and are commonly found across all tested cell types. (A) Raw.264 HIF1α ChIP-Seq data were ranked according to their score and clustered into 50 peak bins. Three highly overrepresented motifs within the ChIP-Seq dataset of Raw.264 cells were computed by Gibbs-Motif-Sampler. The average fold overrepresentation compared to an LSC sequence set was computed and plotted against the ranked bins. The green line represents the overrepresentation of an tandem HRE motif, which was one of the top motifs given by Gibbs-motif-Sampler. The black line represents a sixth order polynomial regression curve through each dataset. (B) In order to visualize the tandem HRE distribution, each peak of each dataset was plotted and sorted according to the distance to the TSS against the genomic location. Due to the bigger size of the tandem HRE, peak regions of the ChIP-Seq region were broadened by 50bp to each side. The genomic region is represented by a black line, whereas a peak region is represented by a green line. A tandem HRE site is represented as a blue spot and red if it is located within a peak region. Once a peak is harboring a tandem HRE, the green line is represented in yellow instead.

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9.6. Transcription factors interacting with Hif1αααα

9.6.1. AP1 transcription factor motifs are overrepresented at enhancer regions bound

by Hif1α in Raw.264 cells

As mentioned above, motif search results by Gibbs Motif Sampler offer a variety of non-

redundant candidate consensus sites. To detect overrepresented motifs, I checked

several of those candidate consensus sites within the datasets and compared them to

LSC sequences. One motif that showed a consistent overrepresentation throughout the

whole dataset of Raw.264 cells was the canonical AP1 transcription factor target site -

TGANTCA- (Figure 25A). A comparative blot with other candidate motifs showed a

significant enrichment throughout the ranked and clustered peaks (Figure 25A).

Interestingly, the highest enrichment scores for the AP1 consensus are found within

groups larger then rank 1000. A location plot of a 788 peaks window underlined this

observation (Figure 25B). Unlike the HRE positive peaks, the Hif1α-bound peaks that

harbor an AP1 side are located far away from the TSS.

9.6.2. Potential interplay of HIF1α and AP1 may underlie developmental and

differentiation processes

In order to assess functional aspects of AP1 and Hif1α co-regulation, I clustered the AP1

positive and HIF1α bound peaks into GO Categories (biological processes). The

significantly enriched categories were predominantly associated with developmental

functions and apoptosis (Figure 26A). The main developmental categories that I found

were angiogenesis and hematopoiesis. The mostly anticipated HIF1α-associated

cluster, glycolysis, was not significantly enriched. Additionally, the BioCarta graphical

database revealed significant association with terminal differentiation of macrophages in

the Raw.264 dataset (Figure 26B). About half of the genes required for this process

were bound by HIF1α in Raw.264 cells and at the same time contained AP1 binding

sites. Together, these results demonstrate an important co-regulatory function of AP1

and Hif1α. Co-regulation might be limited to genes with developmental and angiogenic

functions and preferably occur at enhancer regions far away of the TSS.

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Figure 25: AP1 transcription factors are adjacent to HREs. (A) Raw.264 HIF1α ChIP-Seq data were ranked according to their score and clustered into 50 peak bins. Three highly overrepresented motifs within the ChIP-Seq dataset of Raw.264 cells were computed by Gibbs-Motif-Sampler. The average fold overrepresentation compared to an LSC sequence set was computed and plotted against the ranked bins. The green line represents the overrepresentation of a tandem HRE motif, which was one of the top motifs given by Gibbs-motif-Sampler. The black line represents a third order polynomial regression curve through each dataset. (B) In order to visualize the TRE distribution, each peak of each dataset was plotted sorted according to the distance to the TSS against the genomic location. The genomic region is represented by a black line, whereas a peak region is represented by a green line. A TRE site is represented as a blue spot and red if it is located within a peak region. Once a peak is harboring a TRE site, the green line is represented in yellow instead.

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Figure 26: HIF1αααα and AP1 co-regulated genes that were associated with development and differentiation. The TRE positive complete Group II-A ChIP-Seq data of Raw.264 cells (in total 917 genes), were analyzed by DAVID (A) and BioCarta (B) and clustered into GO categories of biological processes. In (A) the top 20 overrepresented clusters are presented along with the respective count of Genes, pvalue, fold enrichment within the cluster and False Discovery Rate as computed by DAVID. In (B) the only significantly (pvalue < 0.0078) overrepresented pathway of the analysis, terminal differentiation of macrophages, is shown as an illustration. All bound target genes are marked by a red star.

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9.6.3. The transcription factors SP1 and AP2 are candidates that might regulate

hypoxia-induced genes in PMH independently of HIF1α

By far the largest part of promoters of differentially expressed genes was not found to be

bound by HIF1α in primary cells. Therefore, promoters of differentially expressed genes

that were not associated with HIF1α binding were analyzed and screened for

overrepresented motifs of known transcription factors. The analysis of the promoter

region from -950 to +50bp with regard to the TSS was done by PScan (Zambelli et al.,

2009) using Transfac as a library of known position weight matrices of transcription

factors. In total, 1169 promoters of genes that were found to be more than 1.5 fold up

regulated in PMH, were screened for overrepresented motifs. The most overrepresented

motif was a -GC- rich box of the transcription factor SP1 (p-value < 1.16615e-27 for

Transfac consensus V$SP1_Q6 and p-value < 3.49648e-22 for Transfac consensus

V$SP1_01). The second strongly overrepresented consensus site was a binding site of

the transcription factor family AP2 (p-value < 9.01831e-22 for Transfac consensus

V$AP2_Q6).

I subsequently assessed whether candidate transcription factors were induced upon

hypoxia and whether they are direct Hif1α targets. On the transcriptional level, SP1

showed a minor up-regulation after 3h of hypoxia of 1.31 fold and a moderate induction

of 1.65 fold after 16h of hypoxia in PMH. However, the only significant binding event of

HIF1α was found within the Raw.264 dataset. For AP2, only AP2 delta showed a

significant binding event in PMM and on the transcriptional level none of the tested cell

types showed expression of AP2 isoforms.

These data suggest a possible role of SP1 and AP2 in the regulation of genes that are

differentially expressed upon long term hypoxia.

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9.7. Downstream regulatory mechanism regulated by Hif1αααα

9.7.1. The Hif1α target JMJD3 mediates chromatin remodeling at the ADM promoter

upon hypoxia

I next screened datasets for overrepresented GO categories that were involved in

transcriptional regulation. A major class of genes, which is directly targeted and

differentially expressed in all tested cells, is the JmjC domain containing protein class

(Table 5). In Raw.264, more than half of all known JmjC domain-containing proteins

were significantly bound at the PP region. Also within the 176 significant binding events

of the PMM, six JmjC domain-containing proteins could be associated. However, at the

expression level, only the PMH dataset showed a marked differential expression of

many of the various JmjC domain-containing proteins.

Table 5: JMJC domain containing proteins are master regulators of the hypoxic response. All known JmjC domain-containing genes are represented in a table. For each dataset the rank within the ChIP datasets are provided and, if differentially expressed, the fold up regulation is provided.

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Figure 27: JMJD3 is enhanced on the transcriptional and on the protein level in PMH upon hypoxia. (A) RT-PCR was performed of total mRNA from PMH after different time points of exposure to hypoxic conditions (0.5% O2). Primer pairs of three genes encoding JmjC domain containing proteins were chosen (JMJD1A, JMJD2B and JMJD3). (B) Western Blot analysis with 150µg of nuclear extracts from PMH. Protein levels of HIF1αwere traced under normoxic conditions and after four time points of exposure to 0.5% oxygen. Lamin A was used as a control. Relative protein levels of JMJD3 as calculated by densitometric analysis are provided.

In order to assess the functional impact of HIF1α binding to JmjC domain containing

proteins, a targeted approach for enzyme activity was performed with one candidate of

the bound histone demethylases, JMJD3. First, it was shown that JMJD3 is induced on

mRNA level in PMH by more than two fold over the time course of hypoxia (Figure 27A).

On the protein level JMJD3 accumulates approximately after 3h of hypoxic exposure

and peaks after 16h with a 3.5 fold induction compared to Lamin A (Figure 27B). JMJD3

is demethylating H3K27me3, which is a Polycomb mediated repressor mark. I next

sought for a classical target of Polycomb and JMJD3 that was induced and bound by

Hif1α upon hypoxia in PMH and then assessed its chromatin status. Adrenomedullin

(ADM) was one candidate that fulfilled these criteria. I analyzed the whole promoter

region for H3K27me3 using ChIP-QPCR. The significant binding event of HIF1α occurs -

880bp in front of the ADM promoter (pvalue < 0.0004). In PMM no binding was observed

at the ADM promoter region, however, in Raw.264 cells, a highly significant binding

event was observed at the 3’ end of the gene (Figure 28A).

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Under normoxic conditions, a peak for H3K27me3 was observed at around 500bp

downstream of the TSS of ADM. After 3h of hypoxia this peak was decreasing

background levels and after 16h of hypoxia the whole promoter was devoid of

H3K27me3 marks (Figure 28B). Transcriptionally, ADM was found 16 times induced

after 16h of exposure of the PMH cells to hypoxic conditions whereas after 3h only a

minor induction (1.8 fold) occurred (Figure 28C).

Figure 28: JMJD3 is actively derepressing the ADM promoter upon exposure to hypoxia in PMH. (A) The ChIP-Seq results of the gene ADM within the Raw.264 cell dataset were plotted according to its genomic location. The black line as well as the heat plot represents the Hypoxia ChIP-Seq reads and the black line represents the Normoxia HIF1α ChIP-Seq experiment. Conservation scores as well as genomic region are shown below. (B) ChIP-QPCR was performed with PMH and primer pairs designed to cover most of the PP region of ADM were used. The fold enrichment of the H3K27me3 mark after different time points of exposure to hypoxic conditions was plotted against the genomic location. (C) RT-PCR of ADM was performed of total mRNA from PMH after different time points of exposure to hypoxic conditions (0.5% O2).

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It was previously shown that LPS induces JMJD3 through the transcription factor NFκB.

Data in Raw.264 cells show a significant binding event of Hif1α to a region

approximately 5000bp upstream of the TSS (Figure 29A). In order to check whether

HIF1α induces, or contributes to induction of JMJD3, protein levels were assessed in

Raw.264 cells after 2 and 8h of hypoxic exposure (Figure 29B). Hypoxia induced JMJD3

protein abundance 5.2 fold after 2h and 6.9 fold after 8h of exposure to hypoxia.

However, the significance of this result has to be taken with care as this potentiating can

only be seen after normalizing to Lamin A that markedly decreased upon hypoxia.

Conclusively, these data suggest that JMJD3 is bound and regulated by Hif1α and

mediates demethylation of H3K27me3 at the ADM promoter upon hypoxia.

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Figure 29: JMJD3 is enhanced on the protein level and bound by HIF1αααα in Raw.264 cells. (A) The ChIP-Seq results of the gene JMJD3 within the Raw.264 cell dataset were plotted according to its genomic location. The black line as well as the heat plot represents the Hypoxia ChIP-Seq reads and the black line represents the Normoxia HIF1α ChIP-Seq experiment. Conservation scores as well as genomic region are shown below. (B) Western Blot analysis with 150µg of nuclear extracts from Raw.264 cells. Protein levels of HIF1a were traced under normoxic conditions and after two time points of exposure to 0.5% oxygen. Lamin A was used as a control. Relative protein levels of HIF1α and JMJD3 as calculated by densitometric analysis are provided.

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10. Discussion

It has been a major scientific goal to characterize HIF1α-mediated transcriptional

responses and to assign global functions to HIF1α in the past. However, a

comprehensive view on important transcriptional characteristics like cell specific binding

and cooperative regulation with other transcription factors was still lacking.

Here I present my integrative approach using genome wide binding and expression

studies to analyze HIF1α-mediated transcriptional responses to hypoxia.

10.1. Binding of HIF1αααα is cell type specific

With primary mouse macrophages and primary hepatocytes, two cell types were used in

this study that employ HIF1α to adopt to low oxygen levels as previously shown (Cramer

et al., 2003; Kim et al., 2006).

To complement and validate the promoter-biased data of the ChIP-chip approach of

PMM with a genome-wide approach, ChIP-Seq was performed using the murine

leukaemic monocyte macrophage cell line, Raw.264.

Comparison of the amount of binding events in both cell types revealed that HIF1α

binding is cell-type specific with only ~25% of overlap between PMM and PMH. The only

genome-wide binding study comparing a transcription factor in different primary cells

was described by (Odom et al., 2004). The study was performed with three transcription

factors of the HNF family focusing only on PP regions. It revealed an overlap of

approximately 50% of binding events between primary, pancreatic beta islets and

primary hepatocytes. Therefore, despite using a FDRs of 20% and 14% for PMH and

PMM, respectively, the overlap in these cell types is about half of the one in the HNF

study indicating that HIF binding is strongly cell type-dependent.

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One reason for this may be that the epigenetic status and maintenance by histone

modifiers is markedly affected upon exposure to hypoxia and only few differentially

expressed factors in PMM compared to PMH can change the transcriptional pattern of

the cell (chapter 6.2.1). Indeed, several histone modifiers of the JmjC family were bound

and differentially expressed between the two cell types (Table 5) and the cell type-

specific methylation status of the DNA may further contribute to the cell type-specific

accessibility of HIF1α target sites.

However, the dynamics of binding events was not addressed during a time course and it

could well be that HIF1α promoter occupation occurs at different time points in different

cell types. Finally also technical limitations should be considered since the ChIP-chip

approach involves several critical steps until a final binding pattern can be proposed.

First the purity of the primary cell populations can vary among replicates. Thioglycollate-

elicited macrophages might be contaminated with an unknown proportion of other cell

types of the myeloid lineage (e.g. neutrophils) and hepatocytes elicited by liver perfusion

may contain Kupffer cells. Additionally, although stringently monitored, I observed

unavoidable differences in cross-linking- and shearing efficiency in the two cell types.

10.2. One out of five genes that are bound by HIF1αααα are differentially

expressed in PMH and PMM

Integration of the expression data after 16h of hypoxia into the binding dataset at 3h of

hypoxia revealed that 19% and 25% of the genes that are bound in PMH and PMM

respectively, are also significantly up-regulated. This finding is well in line with previous

studies integrating binding into expression data. In fact, only one ChIP-chip study with

FoxP3 (Zheng et al., 2007) was showing a higher overlap between both datasets.

The overlap of down-regulated genes was about five to ten times lower, which is in line

with previous studies in which HIF1α has been demonstrated to constitute a

transcriptional activator rather than a repressor.

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It is important to mention that a stringent cut off of raw intensities of 500 was chosen to

exclude genes that are only marginally expressed. This set-up avoids false positive

differentially expressed genes due to the lack of sensitivity of the arrays. On the other

hand, low sensitivity and limitations in the design of ESTs in these arrays may also

contribute to false negative results. In any case, genes which passed these criteria were

considered to be actively transcribed. For both cell types, these were about 50% of

genes spotted on the whole genome expression arrays. Interestingly, if only binding

events of genes that are actively transcribed, are considered for comparison to the

expression data, the overlap of differentially expressed and bound genes can be

increased to more than 60% between PMM and PMH. Technically, this could be due to

the limitations in sensitivity and design of the expression array.

Another ex0planation could be that probably many genes bound by HIF1α are missing

important prerequisites for active transcription such as DNA and histone modifications

as well as co-factor recruitment. The limited overlap between PMM and PMH also

suggests that these differences may be cell type specific.

10.3. One out of twenty-five hypoxia responsive genes are bound by HIF1αααα in

PMH

In total, about 8 and 25 times more genes were significantly up regulated upon exposure

to hypoxia than bound by HIF1α in PMM and PMH, respectively suggesting that most of

the transcriptional changes induced by hypoxia occurred either secondary to- or

independent of HIF1α. Since physiological changes upon adaptation to hypoxic

conditions are profound (chapter 6.1.1), a significant change of the expression pattern in

the hypoxic cell is expected. These changes in response to cellular stress can induce

other transcription factors such as for example AP1. Furthermore, the epigenetic pattern

is dramatically changing during hypoxia (chapter 6.2.3). My work proposes several

candidates for secondary regulation of differentially expressed genes (see below) These

candidates however need to be confirmed in the future.

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Another interpretation of the high amount of differentially expressed genes that are not

directly targeted by HIF1α can be the highly dynamic process of transcriptional

regulation. Although, we and others (Ohnishi et al., 2007; Xia et al., 2009) showed that

HIF1α binding is best reflected by differential expression after 12-24 hours of exposure

to 0.5% oxygen, several genes might have been differentially expressed at early time

points after HIF1α binding, whereas others are differentially expressed upon long term

hypoxia only. Therefore, to address the regulatory impact of HIF1α binding, a high

resolution assessment of HIF1α binding during a time course would have to be

integrated into a high resolution time-dependent analysis of transcript levels upon

hypoxia.

10.4. ChIP-Seq reveals markedly more HIF1αααα binding events during hypoxia in

Raw.264 cells

Using a genome-wide ChIP-Seq approach in Raw.264 cells, a more than 10 fold

increase in binding events within the PP could be detected compared to ChIP-chip in

primary cells. Many previously published genome-wide binding studies revealed

significant binding events of transcription factors in the amount of thousands to ten

thousands (Table 1). However, two recent genome wide binding studies with HIF1α

using ChIP-chip proposed significant binding of about 150 – 600 genes.

The reason for the enormous difference in the amounts of direct HIF1α targets between

our or others’ ChIP-chip-based association studies and our ChIP-Seq-derived data can

be explained from a technical or biological point of view. The technical difference can be

due to the different methods used for binding site detection after ChIP. It has been

demonstrated that ChIP-Seq provides an enhanced sensitivity as well as an increased

resolution compared to ChIP-chip used in the other studies (Kharchenko et al., 2008),

the differences can be well explained.

On the other hand, it can be speculated that HIF1α levels are enhanced in Raw.264

cells compared to primary cells and other cell types used in the respective studies.

Indeed, it has been shown, that increased abundance of transcription factors is

correlating with increased binding. However, to exclude one or the other hypothesis,

further experiments would have to be demonstrated.

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10.5. HIF1αααα directly binds to genes associated to glycolysis, angiogenesis and

regulation of transcription, depending on the cell type.

Functional clustering of the binding events in both cell types revealed striking similarities

as well as differences. Among all tested datasets, glycolysis was the top

overrepresented cluster. However, unlike PMM, PMH showed an overrepresentation of

genes involved in angiogenesis and blood vessel development. The two cell type

specific clusters in PMMs were anion transport and general, DNA-dependent

transcription. Similar analysis using the top 500 genes bound in Raw.264 cells confirmed

the enrichment for genes involved in regulation of transcription. Therefore it can be

speculated that hepatocytes more likely react to low oxygen levels by transcription of

angiogenic factors compared to macrophages. In macrophages, HIF1α seems to

specifically induce anion transporters. This would be an important contribution of HIF1α

in this cell type, in order to maintain physiological pH levels under hypoxia. It has been

shown that macrophages are relying on anaerobic glycolysis to maintain energy levels

under hypoxia and even upon normoxic conditions (Cramer et al., 2003), the end

product of anaerobic glycolysis, lactate, which is in fact a natural occurring anion has to

be removed constantly.

However, it has to be noted, that the interpretation of biological processes,

overrepresented in a set of genes has to be taken with care, since one major factor of a

cluster can be biologically more meaningful than a whole battery of genes

overrepresented and assigned to the same cluster (Rhee et al., 2008).

10.6. HIF1αααα preferentially binds close to the TSS

Assessing localization of binding events, I showed a strong bias towards the TSS.

Approximately more than 60% of the binding events within the PP occurred in close

proximity to the TSS (+-1kbp) in all tested cell types. The fraction of peaks located within

the +-1kbp window is more than 35% of all group IIB peaks after using an extended

window of +-100kbp around the TSS in the whole genome Raw.264 cell dataset. This

finding confirms the observations of other genome wide binding studies of other

transcription factors (Xia et al., 2009).

Discussion

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This effect was even more pronounced by analysis of the group of genes that were

differentially expressed in each primary cell type. The percentages of differentially

expressed genes with a HIF1α binding event within the first 1kbp surrounding the TSS,

were approximately 2 times higher than the ones located 1kbp more distant to the TSS.

One interpretation of these results may be that transcription factor binding to the TSS is

directly linked to transcriptional initiation in response to hypoxia, while binding to

enhancer regions might have some modulatory effects that are not necessarily

dependent on hypoxia. It has to be explored in the future what the physiologic triggers of

enhancer binding might be and what the consequences of these modifications might be.

A similar effect was seen if peaks where filtered for HRE containing sequences in all

tested cell types. The fraction of genes that were bound by HIF1α lacking an HRE

showed decreased binding scores and a localization more distant to the TSS. The lower

binding scores seen in the HRE negative fraction of peaks might either underscore the

decreased statistical reliability of these peaks or the decreased affinity of the

transcriptional complex involving HIF1α. Apart from the bias to the TSS no preferred

binding site could be located. However, an overrepresentation of a characteristic binding

pattern, which could be observed simultaneously at the 5´- and at the 3´ end was found

by analysis of the Raw.264 dataset. This suggests a common mechanism of

transcriptional regulation by HIF1α, however the functional impact of this binding pattern

has to be determined by expression analysis.

10.7. HIF1αααα preferentially binds to an HRE consisting of nine base pairs or an

tandem core HRE

Using a well-established motif search algorithm for mammalian sequences, all peak

sequences were analyzed in order to characterize the motif targeted by HIF1α. The

resulting data strongly indicate that HIF1α favorably targets a 9bp long motif (5´-

CGTACGTGC-3´), 4bp longer than the established core HRE.

Furthermore a fraction of approximately 15% of the significant peak sequences of all

datasets showed a strong overrepresentation of tandem HRE (5´-CGTGNNNNNCGTG-

3´), consisting of two core HREs (5´-CGTG-3´) and a nonspecific, 5bp long intervening

sequence.

Discussion

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Together these data suggest that HIF1α favors CpG rich consensus sites. This enables

the cell to persistently modulate HIF1α binding by methylation of the CpG (Wenger et

al., 2005). Furthermore, HIF1α seems to bind cooperatively to promoters by preferable

binding to tandem HREs. However, it has to be shown whether two molecules of HIF1α

can bind simultaneously to each core HRE within the tandem HRE.

10.8. The TRE consensus motif is overrepresented at enhancer regions

targeted by HIF1αααα

Further sequence analysis using a different algorithm that filters for redundant motifs,

revealed a strong and consistent overrepresentation of AP1 consensus sites. I could

show that these motifs are preferentially found at peak sequences far away from the

TSS in Raw.264 cells. Gene ontological clustering for biological processes of the genes

associated to these AP1 positive peaks revealed a strong overrepresentation in clusters

associated to small GTPase-mediated signal transduction, blood vessel development

and apoptosis, which are among the anticipated ontological clusters regulated by AP1

transcription factors (Eychene et al., 2008; Jochum et al., 2001).

The enrichment of AP1 consensus motifs may be either due to indirect enrichment

through long distance interactions with the HIF1α associated transcriptional complex or

by direct interaction of HIF1α and AP1 at enhancer sites.

However it is difficult to estimate the specific factor of the AP1 family that might be a

primary candidate for this interaction since AP1 transcription factors are composed

heterodimers belonging to the c-Fos, c-Jun, ATF and JDP families. Furthermore AP1

factors are induced by a wide range of stimuli e.g. cytokines, growth factors, stress, and

bacterial and viral infections (Eferl and Wagner, 2003; Shaulian and Karin, 2001).

Therefore, conclusively it can be suggested that HIF1α might be not sufficient to

regulate the expression of the genes associated to enriched sequences by HIF1α ChIP

harboring an AP1 target site. Since both transcription factors are activator transcription

factors, the presence of AP1 factors might be necessary to modulate developmental and

apoptotic functions upon hypoxia.

Discussion

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Furthermore clustering of the TRE positive HIF1α bound genes in gene ontological

categories by the BioCarta graphical database revealed a significant association to the

process of terminal macrophage differentiation. Especially the two ETS transcription

factors, ETS1 and ETS2 which are known to interact with HIF1α (Salnikow et al., 2008),

were among these genes and are involved in a variety of differentiation processes such

as hematopoiesis (Sharrocks, 2001). Since developmental processes often take place at

sites of low oxygen levels (chapter 6.1.5), it might well be that HIF1α, together with AP1

family members induce ETS transcription factors in order to regulate the progression of

cellular differentiation.

10.9. SP1 is a potential HIF1αααα target and might regulate genes in response to

hypoxia independent of HIF1αααα

Using the same algorithm that filters for redundant motifs, but analyzing only the

promoters of genes, which are not bound, but differentially expressed, revealed

overrepresentation of a consensus motif targeted by families of two well described

transcription factors, SP1 and AP2.

The ubiquitous transcription factor SP1 is known to constitutively activate housekeeping

genes lacking a classical TATA-Box but was recently also associated to a variety of

other processes such as differentiation (Wierstra, 2008). Genes that were found

differentially expressed but not bound by HIF1α upon hypoxia may be regulated by SP1

since the latter transcription factor is transcriptionally induced in response to hypoxia.

However, since no binding of HIF1α to SP1 could be observed, this induction is either

secondary or even HIF-independent.

Therefore SP1 might be induced by hypoxia and subsequently enhance expression of

housekeeping genes lacking a TATA-Box and leading to a differential expression of an

unknown subset of genes measured by expression analysis in PMH upon 16h of

hypoxia.

Discussion

Page 85

The second motif overrepresented on the promoters of genes differentially expressed

but not bound by HIF1α under hypoxia was the AP2 consensus motif. The transcription

factors of the AP2 family are mainly associated to regulation of developmental

processes (Eckert et al., 2005). Although not expressed in PMM and PMH, AP2 might

be of important function in other cell types to further enhance expression of

developmental genes upon exposure to hypoxia independently of HIF1α.

10.10. Transcriptional regulation of chromatin modifiers by HIF1αααα

An intriguing link of HIF1α to the regulation of chromatin modifiers of the JmjC family

was previously reported, however the functional impact of this link has been questioned

by the authors due to the common assumption that histone demethylation is oxygen-

dependent (Xia et al., 2009). Although a recent study showed decreased methylation

under hypoxia, the mechanism of this process is currently unknown (chapter 6.2.3).

I could confirm this link by showing that HIF1α binds to several JmjC family members in

all tested cell types. Moreover, I tested one member of the JmjC family, JMJD3, which is

significantly bound in PMM and Raw.264 cells and almost significantly bound in PMH

and differentially expressed in all tested cell types, for its functional impact during

hypoxia. The results showed, that the promoter of ADM, a gene that is targeted by

HIF1α only at the 3´end, clearly loses its repressive H3K27me3 mark during the course

of hypoxia in PMH and this loss strongly correlates with the up regulation of the ADM

transcript.

Together, these results suggests an important link between the dynamic, epigenetic

regulation of genes by JMJD3 and possibly other demethylases, and transcriptional

changes observed under hypoxia. A similar link has been previously shown specifically

for JMJD3 in response to inflammatory stimuli (De Santa et al., 2007).

Discussion

Page 86

10.11. Comparison to previous genome wide HIF1αααα binding studies

Two very recent studies discovered novel functions of HIF1α by ChIP-chip using DNA

tiling arrays in combination with expression analysis. Kung et al utilized HepG2, a

human hepatoma cell line, for a genome wide binding and expression study and

integrated expression data of U87 and MBA-231 cells, a human glioblastoma and a

human breast cancer cell line respectively, into the HepG2 data set (Xia et al., 2009).

The data revealed that 50% of a total of 283 detected significant binding events were

linked to promoters of known genes.

Interestingly and in line with my study, four JmjC domain containing proteins were found

to be direct targets and 17 JmjC family members had significantly increased mRNA

abundance under hypoxic conditions. One JmjC family member, Jarid1B, was

functionally assessed. It was concluded, that induction of JmjC family members under

hypoxia was to compensate for their lower enzymatic activity at low oxygen levels and

thus to maintain the methylation levels found at normoxic conditions which is

contradictory to the data I observed by analysis of JMJD3 (chapter 9.7.1).

Furthermore, analysis with two de novo motif search algorithms, including Weeder, also

used in this thesis, revealed the classical HRE (5’-RCGTG-3’) as the most

overrepresented motif in the dataset without any preference to extended motifs.

However, it has to be noted that according to internal recalculations with the methods

used in this thesis, significantly less genes could be associated to the peaks (less than

150 in the PP) detected in this study. This was presumably due to filtering for duplicate

peak-gene associations and a more stringent gene definition used in my dataset. The

overlap between this dataset to my data was only about 15% to Raw.264 cells, PMM

and PMH.

Discussion

Page 87

The second binding study of HIF1α and HIF2a using MCF7 cells, a human breast

cancer-derived cell line, was a comparative study using promoter arrays (sequences

from -7.5kbp to +2.5kbp with regard to the TSS). In total 546 and 143 binding events

could be mapped to 394 and 134 genes in case of HIF1α and HIF2a, respectively. The

overlap of the HIF2a data to my data were marginally; the overlap of the MCF7 HIF1α

ChIP-chip data to my dataset were less than 20% compared to Raw.264 cells, PMM and

PMH. As outlined above, only the classical HRE (5’-RCGTG-3’) was found to be the

most overrepresented motif in the dataset without any preference to extended motifs.

Subsequent comparison of the binding data to the expression data revealed that only

20.8% of the HIF1α bound genes were at the same time differentially expressed by

exposure to either Hypoxia or DMOG. Moreover, siRNA-directed knockdown of HIF2a

revealed, that only one out of ninety differentially expressed genes that were bound by

both isoforms lost its differential expression suggesting HIF1α to be the crucial isoform

to regulate hypoxia induced transcriptional responses.

Together, these studies revealed and confirmed the finding in primary cells, that HIF1α

binds approximately to 150-400 genes, depending on the cell type. However, the

overlaps between all studies are not exceeding 20%, highlighting the cell type specific

function of HIF1α

Outlook

Page 88

11. Outlook

Using proximal promoter arrays, the HIF1α binding data in PMH and PMM suggested

that HIF1α binds in a cell type-specific manner. This conclusion was based on the

finding of relatively low overlaps (~25%). It would be interesting to see whether ChIP-

Seq derived data - by combining it with high resolution expression analysis - would be

equally different assuming the higher resolution and specificity of this method. Target

motifs could be refined and genome wide observations, far from the promoter region,

could be provided.

Moreover, Agilent genome wide expression arrays are covering only 1 to 3 exons per

gene. With integration of RNA-Seq, a method that uses sequencing to analyze genome

wide transcript levels and therefore accounts for all spliced isoforms expressed, a

genome wide comparison between different cell types could be performed.

The genome wide location study of HIF1α in Raw.264 cells revealed several intriguing

results including binding patterns, motif refinements and proposal of new cooperating

candidate transcription factors. However, due to the lack of expression data, no

functional information was provided, following HIF1α binding. Therefore, a RNA-Seq

experiment would extend the view on the current findings.

Additionally the huge amount of bound target genes in Raw.264 cells was measured by

one ChIP-Seq experiment. To confirm the data at different score groups and at different

loci, ChIP-QPCR will be performed in the near future. In addition, a solid FDR could be

estimated for this dataset.

Analysing the sequences, an extended 9bp core HRE could be proposed. To determine,

whether HIF1α shows an increased affinity to this site, luciferase assays should be

performed.

Furthermore, it was speculated that HIF1α might interact with AP1 transcription factors

in order to regulate developmental genes by interaction at enhancer sites. However, until

now it is not clear whether this interaction is cooperative or due to long-range interaction.

Therefore, the TRE+ HIF1α bound sequences should be further analyzed and ChIP-

QPCR experiments with HIF1α and ideally, AP1 members should reveal more of the

characteristics of this interaction.

Outlook

Page 89

Furthermore, it would be interesting to see how expression levels of selected HIF1α

TRE+ genes behave on the transcriptional level if either HIF1α and / or AP1 member are

inactivated. A limited siRNA screen with AP1 members, tracing a few HIF1α bound

TRE+ candidate transcripts, would give a good insight at the significance of our

hypothesis.

Similar approaches could be followed to study the importance of SP1 and AP2 members

in order to differentially express genes that are not directly bound by HIF1α. Therefore, a

knockdown of the AP2 members or the SP1 members and subsequent measurments of

transcript levels of genes being associated to the respective consensus site during

hypoxia should reveal the significance of these factors under hypoxic conditions.

Finally yet importantly the unexpected functional activity of JMJD3 under hypoxia

provided an intriguing link between hypoxia and dynamic epigenetic changes mediated

by JmjC family members. However, several follow up experiments have to be performed

to validate these results. First of all, knock-down of either HIF1α or JMJD3 in response

to hypoxia should abolish the demethylation of H3K27me3. Furthermore, more

promoters with known Polycomb interaction and known induction under hypoxic

conditions have to be tested for H3k27me3 levels. A ChIP-Seq experiment of JMJD3

and H3K27me3 under normoxia compared to long-term hypoxia and, ideally, RNA-Seq

studies should complete the picture of this unexpected finding.

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13. Supplements

13.1. Top 300 up regulated genes in PMH and PMM

Top 300 Genes >1.5 fold upregulated in PMM

Top 300 Genes >1.5 fold upregulated in PMH

Gene Name p-value Fold Change

Gene Name p-value

Fold Change

Egln3 0.000100 20.74

Cyp2s1 0.000036 136.00

Ankrd37 0.002185 13.58

1190002H23Rik 0.000077 81.61

Adm 0.000998 11.62

Chdh 0.000780 36.81

Ak3l1 0.002748 11.54

Anxa8 0.000141 35.44

Ndrg1 0.000983 9.08

Dok7 0.000683 33.70

Bnip3 0.005619 7.67

Stmn4 0.003565 27.56

P4ha2 0.001501 6.02

Kbtbd11 0.001028 23.07

Ero1l 0.003712 5.82

Plcxd1 0.001679 21.67

Slc16a3 0.001855 5.70

Sema7a 0.001440 20.62

Agpat9 0.003343 5.02

Iqck 0.000439 18.49

Rcor2 0.015189 4.76

Gprc5b 0.000439 18.49

Gys1 0.001811 4.68

Tgm1 0.002479 17.64

F13a1 0.002372 4.24

Smtnl2 0.002474 17.52

Aldoc 0.000872 4.22

Prr15 0.000617 17.45

Supt6h 0.005340 5.60

Plod2 0.001114 17.27

Prelid2 0.001560 4.04

4930583H14Rik 0.000578 16.17

Pgk1 0.000961 4.31

Adm 0.000295 16.15

Selenbp1 0.009057 3.94

Il12rb1 0.001063 14.36

Slc2a1 0.000894 3.89

Egln3 0.001547 14.25

Tmem45a 0.005783 3.88

Tmcc3 0.001685 13.72

Tpi1 0.001413 3.78

Grhl1 0.001005 13.68

AC161410.3 0.002375 3.77

Amz1 0.000214 13.01

Prr15 0.003668 3.77

Vegfa 0.002778 14.22

Slc7a2 0.000578 3.70

Ankrd37 0.004703 12.23

Rgs11 0.000252 3.68

Ptges 0.000715 11.66

1190002H23Rik 0.009236 3.68

H2-Ab1 0.000138 12.62

Selenbp2 0.009305 3.65

Rcor2 0.000992 11.05

2310056P07Rik 0.000712 3.60

Pkp3 0.000171 10.92

Pfkfb3 0.000108 3.56

Pgf 0.000769 10.18

Pgm2 0.004730 3.45

Cidea 0.000486 10.00

Cxcr7 0.004420 3.40

5730557B15Rik 0.010427 9.97

Hyal1 0.000750 3.49

Vldlr 0.000958 9.62

P4ha1 0.011339 3.37

Ndrg4 0.001505 9.58

Sox7 0.007087 3.15

Pfkfb3 0.001431 9.48

Mif 0.001107 3.09

Pim1 0.008687 9.29

Mthfd1l 0.016038 3.06

CT010467.6 0.012259 9.20

Ldha 0.003130 3.48

BC031353 0.003555 9.20

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AL929165.9 0.004132 3.03

Ndrg1 0.000188 9.12

AL845449.4 0.004132 3.03

Pfkp 0.006706 9.74

AC153577.2 0.004132 3.03

Tmeff1 0.005535 9.01

Serpinb1c 0.012104 3.02

Ccbp2 0.004816 8.95

AL663049.8 0.005295 2.98

Epb4.1l4a 0.000213 8.83

AV249152 0.005295 2.98

Bnip3 0.000146 8.79

AL671335.12 0.002440 2.97

4933402N22Rik 0.009261 8.70

AC163215.4 0.001356 2.96

Car2 0.002617 8.51

Pglyrp3 0.001356 2.96

AC138119.5 0.008692 8.43

Grhpr 0.001607 2.95

AC164410.5 0.007620 8.43

Mid1 0.003817 2.91

Unc13a 0.001381 8.02

Serpinb1a 0.009044 2.85

Slc16a3 0.000209 8.01

Kit 0.011188 2.81

St8sia3 0.001047 8.29

AL845256.3 0.008215 2.80

Nppb 0.004399 7.87

AL672249.6 0.002740 2.79

AC132468.4-202 0.002043 7.75

Rasgef1a 0.009598 2.78

EG432825 0.002043 7.75

2600010E01Rik 0.001525 2.75

AC087117.9-203 0.013922 7.89

Jun 0.007707 2.68

Gpr120 0.000449 7.51

Tmem42 0.005763 2.67

AC136642.4-203 0.002649 7.23

Jmjd1a 0.004449 2.97

Fbxo10 0.000287 7.08

Mamdc2 0.012989 2.66

Polr1e 0.000287 7.08

Pdk1 0.010072 2.65

Adora2b 0.001050 7.07

Kcna7 0.003409 2.60

1700025G04Rik 0.001071 6.92

Tph1 0.007339 2.59

Cldn1 0.002996 6.90

AC160757.3-201 0.003932 2.58

Foxk1 0.001295 6.87

Cav1 0.005121 3.08

Arg1 0.004818 6.86

BX005181.5 0.002848 2.58

Psmd1 0.000895 6.81

Eno1 0.002848 2.58

Htr2b 0.000895 6.81

AC150274.2 0.002848 2.58

AC136642.4-202 0.003007 6.80

Htra3 0.000757 2.56

Sdcbp2 0.008005 6.74

Fzd7 0.000512 2.51

Pdk4 0.006252 6.72

Pkm2 0.005857 2.48

Speer3 0.005640 6.69

Pgam1 0.000612 2.47

Syce2 0.002415 6.68

Zfyve28 0.001480 2.46

1700001L05Rik 0.002801 6.68

Jmjd2b 0.001686 2.45

Krt19 0.006360 6.67

Dgkg 0.005519 2.42

Hk1 0.009403 7.70

Spata13 0.008979 2.37

Lrrc58 0.000747 6.56

Serpine1 0.004829 2.36

Enah 0.000706 6.51

Eif4ebp1 0.004476 2.36

AC141567.4-202 0.006021 6.40

Mxi1 0.003631 2.35

Lce1d 0.003657 6.34

Gpi1 0.000400 2.32

Slc2a1 0.003617 6.31

Tec 0.013569 2.31

Icam2 0.006975 6.28

AL731692.8 0.007518 2.30

Gys1 0.007887 6.20

Mast4 0.009726 2.30

Tbl2 0.003026 6.19

Zfp395 0.015158 2.29

Colec12 0.008947 7.66

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Tcp11l2 0.002484 2.28

Wdfy1 0.007536 6.16

Pafah1b3 0.004994 2.26

Ero1l 0.001062 6.13

Mmp13 0.011935 2.24

Ugcg 0.001655 6.12

Trim13 0.010468 2.20

9130404D14Rik 0.003173 6.08

Pfkl 0.003313 2.19

Klf4 0.003683 6.03

Il15 0.003473 2.19

Ablim1 0.002940 5.90

Arhgap22 0.015659 2.18

Prelid2 0.003602 5.88

Btg1 0.000469 2.17

Fabp4 0.005920 5.88

AC133650.4 0.003186 2.17

Nupr1 0.001645 5.86

Traf6 0.001618 2.16

Gsn 0.007575 5.79

E230019M04Rik 0.003262 2.15

Tmem189 0.000123 5.68

AL672270.12 0.003262 2.15

Lce1f 0.003013 14.64

Syne2 0.009858 2.13

AC127247.3 0.003993 5.68

Foxk2 0.011923 2.12

Flt1 0.015077 5.65

EG277333 0.001558 2.11

1810015C04Rik 0.002336 5.63

EG624367 0.001347 2.11

Atf3 0.002957 5.59

Peli2 0.001347 2.11

Kcne3 0.002047 5.56

AC170187.2 0.001490 2.11

Erg 0.010678 5.53

2310016C08Rik 0.005198 2.11

AL691472.6 0.000383 5.41

AL672180.11 0.002359 2.10

Tspan18 0.000383 5.41

Pcnx 0.001762 2.34

Jarid2 0.001511 5.39

AL805906.7 0.002607 2.10

Arl4d 0.000574 5.38

Cntln 0.002607 2.10

Adamtsl5 0.011697 5.33

Kbtbd11 0.003125 2.09

Krt17 0.002863 5.32

5330426P16Rik 0.001330 2.51

Fzd1 0.001821 5.27

Mtss1 0.003298 2.09

Slc20a1 0.001641 5.22

Il1rl1 0.007920 2.08

Cryab 0.000223 5.20

Appl2 0.001204 2.07

Laptm4b 0.000855 5.13

Stard9 0.015959 2.07

4931428F04Rik 0.006380 12.62

Cdan1 0.015959 2.07

Nol3 0.012542 5.02

Maff 0.019152 2.05

Rragd 0.000610 4.96

EG545052 0.003161 2.05

Dusp4 0.001779 4.96

Higd1a 0.008220 2.22

P4ha2 0.003607 4.93

1190002N15Rik 0.004045 2.04

Lonrf3 0.010236 4.93

AC168279.3 0.000638 2.03

2310047D13Rik 0.000279 4.89

Bckdhb 0.000262 2.03

Dusp9 0.003173 4.87

Spsb4 0.010982 2.02

Eif4a2 0.002359 5.13

OTTMUSG00000003456 0.013907 2.01

Ppfibp2 0.001512 4.87

Nt5e 0.003961 2.01

Gpsm1 0.007336 4.78

Pfkp 0.001488 1.99

P4ha1 0.004661 4.75

Rora 0.012614 2.07

Hmox1 0.003084 4.72

Vegfa 0.003964 2.35

Kdelr3 0.000115 4.66

Stk24 0.009957 1.98

Cugbp2 0.002054 4.65

Col6a3 0.005629 1.97

Fgf1 0.013113 4.60

AC115124.6 0.002253 1.96

Krt23 0.002195 4.55

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AC156551.5 0.002253 1.96

Mapk13 0.001578 4.53

Tmem189 0.007558 1.96

Myd116 0.003441 4.52

3000002C10Rik 0.002170 1.96

Rassf4 0.016532 4.51

A430107O13Rik 0.013185 1.95

8430408G22Rik 0.016532 4.51

Il13ra1 0.003975 1.94

2310040A07Rik 0.007960 4.41

Cox7a1 0.004536 1.94

Col12a1 0.000631 4.41

Samd9l 0.018739 1.94

St3gal1 0.002460 4.37

Vim 0.000895 1.93

Aff4 0.002111 4.35

Trib3 0.001585 1.93

Nampt 0.004369 4.33

AL604043.11 0.000895 1.92

Zfand2a 0.002281 4.32

Jmjd6 0.012132 1.92

Plekha2 0.005762 5.95

AL731648.6 0.000556 1.91

Creb3l3 0.000928 4.24

Asph 0.005434 1.91

Tnfrsf10b 0.000302 4.34

Ccng2 0.001269 1.91

Tiparp 0.002012 4.23

Rlf 0.006235 1.97

Arl5b 0.000687 4.21

2610024E20Rik 0.004993 1.90

Lce1g 0.004045 4.19

Scd2 0.006105 1.90

4632417N05Rik 0.000832 4.17

Ahnak 0.018101 1.89

Lce1i 0.006496 4.16

Sdc4 0.002789 1.89

RP23-256J17.1 0.006666 4.14

Snx25 0.007885 1.88

Rod1 0.001337 4.11

Arrdc3 0.019577 1.88

Bex2 0.007300 4.11

Crebl2 0.005065 1.87

A230050P20Rik 0.002395 4.09

Npepps 0.006056 1.86

Angptl6 0.002395 4.09

Golph3l 0.002904 2.00

Lamb3 0.003981 4.09

AL954636.9 0.001712 1.85

Dnmt3a 0.001629 4.06

AL929407.16 0.001211 1.90

Arid5a 0.001281 4.06

Neurl2 0.000488 1.84

Rab11fip1 0.002660 4.06

AC087117.9-201 0.010452 1.84

EG385328 0.006321 4.04

Hspa1b 0.010452 1.84

Mef2a 0.000163 4.03

AC087117.9-203 0.003961 2.09

Rab15 0.010421 4.02

AC156499.2 0.015068 1.84

Folr2 0.006442 4.02

Glb1 0.015068 1.84

Dcn 0.004189 4.02

Map2k1 0.008357 1.83

Cdh11 0.001474 4.00

Ier3 0.007942 1.83

Kif21b 0.001924 4.00

AL929132.9 0.002834 1.83

Me2 0.003572 4.00

Dppa3 0.019514 1.83

AC116557.30 0.000242 3.99

Ptpro 0.003290 1.81

Selenbp1 0.002516 3.98

Rcbtb2 0.009412 1.84

2610005L07Rik 0.000910 3.97

Lmo2 0.011616 1.81

Srgn 0.003207 3.96

Errfi1 0.001025 1.81

Cxcr7 0.004409 3.96

Aldoa 0.003651 1.93

Wnt9a 0.003457 3.95

Tspan9 0.001550 1.79

Esam 0.009152 3.88

Alkbh5 0.006630 1.79

Flrt3 0.013098 3.88

Kif21b 0.001917 1.79

Macrod2 0.013098 3.88

Hipk3 0.003252 1.78

AL928700.7 0.013098 3.88

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Ppm1b 0.015795 1.78

Trp53i13 0.001796 3.87

Iqck 0.012069 1.78

RP24-388B10.2 0.002295 3.87

Gprc5b 0.012069 1.78

OTTMUSG00000016789 0.002295 3.87

A230051G13Rik 0.004901 1.77

RP24-388B10.4 0.002295 3.87

Wsb1 0.009613 1.77

Xk 0.002813 3.93

Anxa2 0.005736 1.83

OTTMUSG00000016790 0.002295 3.87

BC031353 0.010128 1.77

RP24-388B10.6 0.002295 3.87

4930431B09Rik 0.005091 1.77

RP24-388B10.8 0.002295 3.87

Dapp1 0.006563 1.76

RP24-388B10.10 0.002295 3.87

Cpa3 0.007797 1.76

OTTMUSG00000016779 0.002295 3.87

Zfp503 0.000259 1.75

1700012L04Rik 0.002813 3.93

Sorbs1 0.011197 1.75

RP24-388B10.9 0.002295 3.87

Pgp 0.000932 1.75

Pfkl 0.002021 3.87

Hk2 0.011760 1.75

Mtss1 0.003469 3.84

Tmem71 0.001182 1.82

1700027L20Rik 0.001138 3.82

E130012A19Rik 0.001915 1.74

Jmjd1a 0.009279 4.02

Slc37a4 0.008871 1.74

Tsc1 0.001516 3.72

Samd10 0.016554 1.74

5430407P10Rik 0.000243 3.72

Hk1 0.002591 1.73

Higd1a 0.000715 3.72

Prnp 0.000048 1.73

Inhbb 0.011098 3.71

Unc13b 0.001955 1.73

Prdx4 0.000231 3.70

Bhlhb2 0.001694 1.72

AI413582 0.000815 3.70

Tagln 0.000542 1.72

AC123048.4 0.000518 3.97

CT030181.13 0.002139 1.72

AC152164.15 0.000518 3.97

AL627074.11 0.008353 1.71

Grpel2 0.004134 3.69

Prelid1 0.008576 1.71

9130227C08Rik 0.000409 3.66

Fbxl15 0.002580 1.71

Lpin2 0.007459 3.65

Csnk1d 0.006571 1.83

Atp13a4 0.000227 3.64

AL662901.18 0.006628 1.71

Timp3 0.001583 3.64

Lancl1 0.003391 1.70

Syn3 0.001583 3.64

Slc38a2 0.008158 1.70

Crybb3 0.002848 3.63

Fbxo21 0.002222 1.70

Cd68 0.001851 3.59

Pde4b 0.014739 1.69

Stk17b 0.007264 3.56

Dsel 0.006893 1.69

Fhdc1 0.004671 3.55

Lamp2 0.001983 1.69

Hspa4 0.006617 3.53

Zfp292 0.000444 1.69

2310016C08Rik 0.004683 3.57

Mll5 0.002080 1.68

Smox 0.009979 3.51

Tmem159 0.004957 1.68

2310008H04Rik 0.007934 3.51

Arntl 0.003473 1.68

Rybp 0.008200 3.50

Rnf113a1 0.001955 1.68

AC192334.1-201 0.008200 3.50

Myo1d 0.008041 1.68

Fgd6 0.004245 3.49

Ppp1r14c 0.014465 1.68

Odf2 0.002109 3.47

Lrrk2 0.012514 1.67

Alkbh5 0.014473 3.47

Cma1 0.013874 1.67

Selenbp2 0.002733 3.46

Nampt 0.011947 1.67

Uchl1 0.004002 3.46

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Casp6 0.002202 1.67

Appl2 0.003909 3.45

AL671990.5 0.001073 1.67

Rpl22 0.001675 3.44

Olfr1153 0.000551 1.66

Gm22 0.000327 3.44

Hyal3 0.007373 1.65

Dcps 0.003240 3.43

Nat6 0.007373 1.65

4930581F22Rik 0.003240 3.43

Cysltr1 0.010446 1.65

1700113O17Rik 0.014687 3.43

Galnt7 0.005874 1.88

Egln1 0.002466 3.43

Bbc3 0.010635 1.65

Rnf12 0.019883 3.43

Txnip 0.009473 1.65

Dedd2 0.003120 3.42

OTTMUSG00000021867 0.009473 1.65

Serpine1 0.001827 3.41

2310022B05Rik 0.004306 1.65

Camk2d 0.003365 5.45

Psme3 0.003825 1.65

Arrdc4 0.008732 3.38

Cd14 0.000237 1.64

Smurf1 0.006538 3.37

AL808132.5 0.001015 1.64

Apln 0.015273 3.58

AC150660.4 0.001015 1.64

Cd72 0.015602 3.37

AL627070.16 0.001015 1.64

Lcp1 0.001361 3.34

AC142167.4 0.001015 1.64

Ankrd23 0.002915 3.31

AL935328.14 0.001015 1.64

Dgkh 0.007591 3.31

AC156283.6 0.001015 1.64

Krt16 0.002117 3.30

AC124113.9 0.001015 1.64

Samd8 0.001048 3.30

AL669829.11 0.001015 1.64

Map3k1 0.001098 3.29

BX005163.13 0.001015 1.64

Ak3l1 0.007155 3.29

Gapdh 0.001634 1.96

Gpr137b 0.001824 3.28

AC150744.2 0.001015 1.64

Ing5 0.000241 3.28

AC118474.10 0.001015 1.64

Btg2 0.009577 3.32

AL805956.22 0.001015 1.64

Tnfrsf1b 0.004937 3.26

AC163335.6 0.001015 1.64

Apoa4 0.002135 3.25

AC147142.2-201 0.001015 1.64

Pdk1 0.004947 3.25

AL954370.3 0.001015 1.64

Mir16 0.001383 3.25

OTTMUSG00000005300 0.001015 1.64

Sap30 0.006207 3.23

AC134337.3 0.001015 1.64

Mxi1 0.002749 3.22

AC125070.4 0.001015 1.64

Cd109 0.002915 3.20

AL845308.11 0.001015 1.64

Lilrb3 0.014610 3.20

AC121279.7 0.001015 1.64

Adamts1 0.002815 3.96

AL807395.8 0.001015 1.64

Snx8 0.003064 3.20

AC166075.2 0.000862 1.90

Mt1 0.012268 3.55

AC148327.3-203 0.001015 1.64

Ugp2 0.002507 3.17

AC102196.7 0.001015 1.64

Sox9 0.006937 3.16

AL732526.8 0.001015 1.64

Rbm35b 0.001967 3.16

OTTMUSG00000017911 0.001015 1.64

Polr3g 0.009804 3.14

AC134918.5 0.001015 1.64

Klhl24 0.004343 3.12

AC125407.4 0.001015 1.64

Pcgf5 0.003896 3.12

AL671988.15 0.001015 1.64

Fcna 0.000252 3.12

BX679665.3 0.001015 1.64

OTTMUSG00000012511 0.000252 3.12

CT009534.18 0.001015 1.64

Aldoa 0.010994 3.09

Supplements

Page 103

AC121959.3 0.001015 1.64

Stx3 0.003397 3.09

AL807790.15 0.001015 1.64

Adarb1 0.002488 3.06

AC166827.2-201 0.001015 1.64

AC140331.2-201 0.009936 3.06

AC107864.11 0.001015 1.64

Tram2 0.002412 3.05

AL772328.13 0.001015 1.64

Wdr33 0.002226 3.70

AL607064.16 0.001015 1.64

Zfp655 0.006051 3.05

AC158396.2-202 0.001015 1.64

1190002N15Rik 0.000845 3.04

AC132147.4 0.001015 1.64

Zxdc 0.000162 3.03

1700027N10Rik 0.001787 1.64

Ptpn14 0.002864 3.03

Casp4 0.002021 1.64

2310021P13Rik 0.011891 3.02

Zfp7 0.010802 1.64

Mt2 0.010777 3.01

Adipor2 0.012738 1.63

Bnip3l 0.005236 3.01

Spn 0.000793 1.63

CT030242.6 0.009613 3.01

Pde4a 0.015422 1.63

Ormdl3 0.007867 3.01

Gata2 0.005682 1.63

Oxct1 0.008969 3.08

Cdkn1a 0.012854 1.63

1500032D16Rik 0.000676 2.99

Map3k6 0.005080 1.63

Ralgds 0.013324 2.98

Narf 0.007097 2.12

AC159809.2-201 0.004543 2.98

Cdkn1b 0.001577 1.62

Ccng2 0.001966 2.97

AC122193.5 0.001577 1.62

Centd3 0.004149 2.97

Cd3eap 0.014366 1.62

Rnf144b 0.002449 2.96

D12Ertd553e 0.005049 1.62

Myo5a 0.000140 2.95

Rest 0.003287 1.85

Eif1b 0.015867 2.95

Cep170 0.002740 1.62

Slc46a3 0.004098 2.94

Cav2 0.002684 1.62

RP24-302M3.2 0.000945 2.94

Gadd45a 0.009031 1.62

Gadd45a 0.003676 2.94

Polr3g 0.010965 1.61

Arrb1 0.014854 2.94

Cadm1 0.004915 1.61

Phf3 0.004922 2.93

Klhl6 0.016141 1.61

Mysm1 0.001263 2.93

Tle1 0.003876 1.71

2310056P07Rik 0.000516 2.93

Frmd6 0.001563 1.60

Hook2 0.005067 2.93

Pnrc1 0.001282 1.60

Akap2 0.002921 2.92

Man1a 0.002474 1.60

Mapk7 0.011942 2.91

Heca 0.000048 1.59

Inhba 0.009922 2.91

Rchy1 0.006711 1.59

Fabp1 0.011055 2.90

Trappc6a 0.008213 1.59

Zfp654 0.016651 2.90

AL929226.7 0.000620 1.59

Xirp2 0.014524 2.90

Prdx4 0.000115 1.59

Pkm2 0.002890 2.90

Supplements

Page 104

13.2. Group III genes of PMM, PMH and Raw.264 cells (Top 300)

Group III genes in PMM

Group III genes in PMH

Group III genes in Raw.264 (Top 300)

Gene Name p-value Distance

Gene Name p-value Distance

Gene Name Reads Distance

2310016C08Rik 4.89E-05 -97

1110058L19Rik 0.000156867 597

Pdk1 740 -24

2310044G17Rik 2.86E-05 -189

1500031H01Rik 7.31E-05 1626

Atxn7l2 676 -63

2310056P07Rik 1.27E-06 -247

1810063B05Rik 0.000519906 2188

Hjurp 624 58

4930583H14Rik 4.59E-07 -105

2010012O05Rik 0.000583294 130

Eno1 610 -356

5830415F09Rik 1.06E-05 -29

2310016C08Rik 2.15E-05 -692

Ankrd37 608 -463

6030408C04Rik 0.000422619 -42

2310056P07Rik 2.01E-07 -426

Gbe1 568 359

9030624J02Rik 0.000596294 -4668

2610510H03Rik 0.000254009 1743

D030013I16Rik 564 -390

Abca4 0.000444898 240

2810405K02Rik 0.000343609 1552

Gipr 543 -93

Ablim1 0.000265013 -3876

2810422J05Rik 0.00040683 580

Plod2 525 -169

AC133650.4 0.000505192 -3

2810428I15Rik 0.00040683 -3048

Narf 514 -526

AC139884.8 0.00013712 -106

4833442J19Rik 3.65E-05 -7

Gapdh 510 -166

Adamts20 0.000447399 -1433

4930519F16Rik 0.000263597 -3935

Efna1 497 -203

Agpat5 0.000417623 280

4930583H14Rik 0.000152161 -204

Ier3 495 -260

AI317395 0.000368012 1563

4932418E24Rik 0.000465378 -4030

Kcnab2 493 121

Alg11 0.000351884 0

6030408C04Rik 4.33E-05 20

Sfi1 489 -4221

Alkbh5 3.9E-08 -609

6430517E21Rik 0.000329079 -2823

Seh1l 467 -90

Ankrd23 0.000435355 -807

8430408G22Rik 0.000159231 -41

Gpi1 454 -3935

Ankrd37 8.13E-10 -478

A930104D05Rik 0.000279238 -447

Pgam1 443 -269

Ankzf1 6.21E-05 -24

AC091531.9 3.81E-05 163

Asph 441 -158

Anxa2 4.74E-06 -116

AC107671.7 3.2E-05 -1533

Bsg 435 -115

Arg1 2.17E-06 -2898

AC165946.4 0.000566094 -235

Pkm2 420 558

Asb1 0.000470373 -4509

Acox1 0.000152246 -275

2310016C08Rik 411 -1439

Atp2b3 5.9E-05 1307

Adam22 0.000161433 1678

Pgm2 398 317

Atpbd4 1.39E-06 -17

Adm 0.000459532 -880

Rsbn1 396 -321

B3galnt1 0.000291925 -746

Adm2 0.000130401 605

Ccdc58 393 -445

Bat5 0.000281291 -3675

Aff3 0.000543129 -1194

2310056P07Rik 393 -118

Bhlhb2 0.000326481 -211

Agpat4 0.00018956 153

Pnrc1 392 853

Birc3 0.000108541 3

Alkbh5 2.54E-06 -1022

AC116557.30 389 -1149

Blm 0.000222553 -148

Ankrd1 0.000447919 -3529

Pkp2 387 -189

Bnip3 1.77E-07 -411

Ankrd37 6.93E-06 -711

Gys1 376 -342

Bnip3l 0.000106903 169

Ankzf1 0.000430117 -206

Ruvbl2 376 -250

Btg2 0.000360406 -4307

Anp32a 0.000450142 1017

Map3k1 375 -763

C1qb 0.000464954 -602

Anxa2 6.65E-05 -264

Arrb1 375 873

Car8 0.000563025 390

Aoc2 0.000420004 -4439

Jmjd6 373 562

Ccdc126 0.000210067 -146

Ap3b2 0.000352235 -810

Fzd7 369 -494

Ccdc58 1.27E-06 -316

Ascl1 5.09E-05 2349

CT009708.6 368 -2483

Supplements

Page 105

Ccng2 0.000256376 -271

Astn1 0.000329079 -3271

6030408C04Rik 367 -191

Cenpl 0.000518533 -83

Atf7ip 0.000434322 584

Rnf19a 365 -156

Chaf1a 0.000234755 -3638

Atp12a 0.000562777 1633

Park7 364 -4096

Chrm1 0.000273685 -4363

Atp1a2 0.000435616 2144

1600014C23Rik 362 -2958

Clca3 3.81E-05 -2471

Atpbd4 7.4E-05 -188

Zfp292 349 -23

Clcnkb 0.000210258 1690

Atr 3.81E-05 67

4930583H14Rik 345 -258

Cnga1 0.000508985 2401

B230317F23Rik 0.000454662 -124

Pfkl 343 370

Cope 0.00039855 -216

B3galt5 0.000289985 -3930

Bhlhb2 339 -201

Csdc2 0.000211307 1889

Bbs5 0.000539171 -4799

Rnmt 334 -179

CT009708.6 4.74E-06 -2545

Bhlhb2 5.89E-05 -297

4933403F05Rik 334 -14

Ctsa 4.6E-06 -338

Bnc2 0.000130663 -901

Tpi1 328 547

D130059P03Rik 1.19E-05 -276

Bnip3 1.64E-06 2090

Neud4 326 -139

Dars2 0.000518533 -52

Bnip3l 0.000546102 478

Ero1l 319 -319

Ddit4 8.45E-06 -35

Btn2a2 0.000598022 -1873

Neurl2 317 25

Ddx49 0.00039855 -125

C130057D23Rik 0.000584781 -3811

Ctsa 317 530

Dhcr24 0.000216807 -2846

Capn6 9.48E-05 598

Phospho1 316 -196

Disc1 0.000555922 -4145

Ccdc58 2.01E-07 -137

AC120398.10 315 -23

Dnajc5 0.000364843 -177

Cd200r1 9.92E-05 -1675

P4ha2 314 -124

E230015B07Rik 0.000224817 2996

Cdk2ap2 0.000410758 2362

9130227C08Rik 314 -210

Ehf 0.000448706 -396

Cdx2 0.000106753 -3319

Cbln3 314 -503

Eif4ebp1 1.66E-05 747

Chrnb3 0.000199967 695

Hmga1 308 -3731

Eno1 1.72E-09 -252

Clcn3 0.000454662 -13

Bnip3 308 -120

Ero1l 2.99E-06 -294

Cplx4 0.00023064 -460

Ldha 307 -70

F10 4.45E-06 -375

Cpxm2 0.000312883 1538

B230317F23Rik 306 23

F3 0.000265988 -1661

Csmd1 0.000160835 -1148

Jarid1b 305 -3804

Fbln2 0.000241778 -868

CT009708.6 6.65E-05 -2397

Car12 304 2

Fcamr 0.000363412 209

Ctnnal1 2E-05 -3567

Mrpl18 302 -325

Fgfrl1 0.000373886 2829

Cyfip2 0.000162875 -2271

Tcp1 302 -49

Fkbp15 0.000323032 -37

Cyp26a1 5.83E-06 -2193

Nos2 298 -211

Foxg1 0.000236391 -3765

D10Wsu102e 0.00023258 -562

Alkbh5 294 -843

Fzd7 1.45E-06 -418

D16Ertd472e 0.000467519 333

Mettl11a 293 -159

Gapdh 1.29E-09 -196

D830014E11Rik 0.000309408 -4403

Gabpb2 293 -64

Gata1 0.000570003 -2982

D930020E02Rik 0.000471988 62

Eif4ebp1 292 730

Gbe1 5.47E-05 525

Dars 0.000110062 361

Adm 291 2897

Gmppa 0.000221067 -29

Ddit4 1.08E-06 -324

Pfkp 291 1485

Grid1 0.000402777 515

Ddx3y 0.000434088 -4657

Slc16a3 290.6666667 -22

Grin2a 0.000318681 -1260

Dedd 0.000132478 -1166

2310008H04Rik 289 96

Gys1 8.52E-09 -107

Def8 0.000135857 -1991

Eno2 288 -5

Gzf1 0.000256361 -104

Defb36 0.000463888 -2110

Rbpj 287 -3225

Hils1 0.000359148 973

Dnaja2 0.000110259 -2309

Fosl2 287 -2061

Hk2 2.75E-05 -245

Dync1h1 0.000591415 -3857

Me2 285 -203

Hsp90ab1 1.32E-05 -860

Dzip1l 0.000191363 -3606

Dennd4b 285 -734

Ier3 1.99E-06 -101

Eda2r 0.000372587 -4723

Stc1 285 366

Inhbb 0.000174645 -807

Ehbp1 0.000268342 -4812

Wsb1 280 -85

Isca1 0.000270342 -1827

Emr4 2.49E-05 1552

Rnf126 278 -1

Supplements

Page 106

Jarid1b 0.000167022 -3773

Eno1 5.09E-08 -83

Gtf2e2 275 -108

Jarid1c 2.84E-05 -52

Enox1 0.000464633 1811

1700104B16Rik 275 -1122

Jmjd1a 1.74E-09 -637

Enpep 0.000108422 -562

Bhlhb3 271.5 -808

Jmjd2b 3.76E-07 -454

Epm2a 0.000204509 1825

Anxa2 270 -178

Jmjd6 5.11E-06 313

Epyc 0.00036961 -4711

Nampt 269 -264

Krt13 0.000441895 -2581

Errfi1 0.000346055 -61

Bnip3l 268.5 -148

Krt15 0.00010803 -1805

Fads2 0.000305306 -3191

Safb2 267 416

Lace1 0.000442202 -2207

Fcer1a 0.000145694 -3992

Selenbp1 266 -170

Ldha 1.88E-05 -149

Fcrla 9.48E-05 1048

Triobp 266 -3289

Lgals3 0.000227677 -4827

Fdx1 9.5E-05 2100

Atp11b 264 -264

Lpar4 0.000327807 -1020

Ferd3l 0.000355863 -404

Mif 259 -43

Ly6g6f 0.000281291 -45

Fgf3 0.000217148 1329

Hyal1 258 624

Mettl9 4.21E-07 -10

Fhl2 0.000513895 -3934

Nat6 258 -656

Mrpl45 5.5E-05 -239

Fkbp14 0.000135422 -1674

Ticam2 252 85

Mrpl54 7.28E-05 30

Fntb 0.000408548 3

Rcor2 251 -71

Mthfd1l 0.000323064 577

Folr4 1.32E-05 1168

F10 247 -672

Myt1 0.000324867 -1309

Foxp3 2.13E-05 -471

F3 245 -1736

Nampt 0.000555601 -187

Fyb 0.000468949 2085

1700112E06Rik 245 -410

Narf 5.54E-07 -166

Gabrb3 3.15E-06 1685

Arid2 245 -787

Ncln 0.000339303 -534

Gapdh 2.87E-07 -341

Srgn 243 -2965

Neurl2 4.6E-06 -3

Gas6 0.000361564 -1122

Hk2 242 -216

Nobox 0.000234398 905

Gc 0.00034019 -2800

Narg1l 241 -182

Obfc2a 0.000198978 127

Ggh 0.00014476 2356

Pcgf5 240 -244

Otog 0.00018548 -2250

Gjc1 0.000566796 679

Tnfrsf9 236 -2502

Oxsr1 0.0001923 -615

Gpr1 5.12E-05 -2963

Mel13 236 -21

P2ry4 0.000190337 -4599

Gramd3 0.000532832 -1556

Higd1a 234 -134

P4ha1 3.1E-06 30

Gtf2f1 3.45E-05 -372

5830415F09Rik 233 -44

P4hb 1.16E-05 -552

Gys1 3.64E-06 -178

Prelid2 231 -172

Pcdha10 0.000543374 -3126

Hdhd1a 0.000369122 -3076

Sap30 230 -742

Pcm1 0.000381192 -136

Hint2 0.000493374 1274

Rnf7 230 -10

Pcsk9 0.000384107 1142

Hivep1 0.000554647 -1465

Cep170 228 650

Pde1c 2.99E-05 2472

Hoxd9 0.000547429 -2058

BC030867 227 -3540

Pfkfb3 2.42E-06 -1157

Ict1 0.000591056 -4782

Fgf11 227 609

Pfkl 1.13E-05 144

Isca1 0.00025836 -2177

Rusc2 226 2267

Pgd 5.17E-05 -62

Iscu 6.05E-05 -889

Dnajc5 225 -41

Pgk1 0.00013486 3

Isg20 1.12E-05 -306

Rabggta 224 -2453

Pgm2 0.000409109 242

Isyna1 5.63E-05 -1128

6330569M22Rik 223 -165

Piga 0.000380997 -311

Iyd 0.000257197 463

Pfkfb3 220.6666667 -2442

Pkm2 4.63E-09 503

Jmjd1a 5.78E-07 -552

Klhl35 218 2835

Pkp3 0.000301651 905

Jmjd2b 0.000207 -208

2900016B01Rik 217 907

Pnrc1 7.32E-05 641

Kcnh7 0.00012726 -2505

Ccng2 217 -268

Polr2d 0.000285688 56

Kctd6 0.000222827 1087

Mthfd1l 217 582

Ppp2r2b 0.000460329 1748

Khk 9.04E-05 -456

Isca1 216 -1725

Prelid1 4.66E-05 123

Kif11 0.000175903 -66

Rlf 213 8

Prr15 0.000479882 1177

Klf7 0.000517891 -715

Clca5 212 -3228

Supplements

Page 107

Pygl 5.17E-06 -70

Klhl1 0.000588366 -1672

Prr12 212 -968

Rasl10b 0.000447551 -2120

Klhl9 0.000185321 216

Stt3b 212 -515

Rbm3 9.32E-05 116

Krt84 0.000346879 -542

Ahnak 212 -4463

Rgma 0.000486255 -816

Lactb2 1.77E-05 -121

P4hb 211.5 -579

Rnf145 8.18E-06 999

Ldha 8.19E-05 -1187

l7Rn6 210 -148

Rnf152 0.000538257 -3189

Lmln 5.27E-05 -1719

Amz1 210 -131

Rps6kl1 0.000470054 -1299

Lrp1 0.000228891 -1335

P4ha1 209 -110

Rusc2 1.37E-05 2221

Map2k7 2.69E-05 -99

Gmppa 209 -8

Ruvbl2 8.52E-09 -485

Map3k1 4.91E-06 -1005

Jmjd1a 209 -526

S1pr4 0.000339303 2740

March1 0.000509414 -3608

Map4k4 203 933

Safb2 0.000481919 377

Mcl1 0.000411434 68

Nktr 202 -205

Sall3 0.000291988 -2215

Mrpl45 6.14E-05 -239

Lonp1 201 -25

Sf3b1 4.54E-06 -206

Mrps28 0.000274517 37

Galk1 199 347

Sfrs1 0.000524429 -130

Ms4a13 0.000292547 -492

Jarid2 199 -675

Sfrs11 0.00022596 -1694

Mtcp1 0.000508149 -4302

Lgals3 198 544

Sfxn5 0.000204439 1081

Narf 3.34E-06 -166

Pygl 196 -100

Sh3gl1 0.000234755 -201

Ndfip1 0.000313729 819

Helb 195 -136

Slc16a3 1.18E-07 1990

Ndrg4 0.000383702 522

Cd47 195 -171

Slc26a5 0.000473083 -2202

Noxo1 0.000399622 99

Tas2r119 194 -2473

Slc31a1 0.000323032 -135

Npat 0.000336362 1744

Arhgdig 193 -1576

Smtnl2 0.000472642 -272

Nrxn3 6.59E-05 1272

Zfp335 193 -582

Snca 5.39E-05 -315

Obfc2a 4.74E-05 127

EG624866 193 -406

Sntb1 0.000581609 -4881

Ogt 6.76E-05 -130

Rgs11 193 -13

Spcs3 0.000186706 -132

Olfr1121 0.000435625 -2797

Slc2a1 192 -2871

Spo11 0.000137378 -474

Olfr1140 0.000374575 1003

Hdac7a 192 -2006

Srebf1 0.000150093 86

Olfr1261 0.000290036 -325

Stc2 192 -1313

Stx18 0.000270225 -13

Olfr134 0.000484949 -913

Pim3 190 -623

Sytl2 0.000474651 -151

Olfr1419 0.000593748 -2773

2810008M24Rik 190 -2851

Tcap 0.000591625 -4582

Olfr1469 5.34E-05 -2356

AC116591.4-202 188 13

Tcfap4 1.69E-05 900

Olfr1471 0.000421478 -3750

Dock8 187 -81

Tfrc 5.4E-05 43

Olfr173 0.000464473 -1766

Pgk1 186 -186

Thbs2 0.000409987 -1835

Olfr178 0.000438918 -1311

Aldoc 186 -124

Tmem112 3.15E-05 72

Olfr340 0.000281199 -92

Bbs5 185 52

Tmem194 1.14E-07 616

Olfr494 0.000194191 -1175

Tcfap4 182 1118

Tmem42 0.000505192 -79

Olfr622 0.00013418 -827

Kif21b 182 -4395

Tnfaip3 0.000399453 -2909

Olfr640 8.05E-05 1108

Sf3b1 182 16

Tpi1 8.94E-08 660

Olfr731 0.000404882 -774

Ddit4 181 -171

Trappc2 0.000179996 -1380

Olfr788 0.000318105 -281

Atr 180 -159

Trappc6a 8.27E-07 88

Olfr794 0.000496575 -2834

Epm2a 179 -43

Trim29 0.00042545 1936

Olfr851 0.000335301 1942

Myom3 179 1514

Ttll3 0.000320873 -4644

Olfr961 0.000595807 1623

Sertad1 178 -469

Ube2g1 2.57E-06 1399

Oxsr1 9.97E-05 -615

Foxo3 178 -51

Usf2 5.5E-07 -1007

P4ha1 1.64E-05 -141

Vwa1 177 -112

Ush1c 0.00018548 -247

Pak3 0.000557225 -1848

1700029J07Rik 176 -53

Vdac1 0.000274455 -236

Pax1 0.000380726 -269

Tgif1 176 -39

Supplements

Page 108

Vgll4 8.58E-05 1202

Pcyt1b 0.000448898 486

Tusc3 176 469

Wdr69 0.000154401 1303

Pdk1 0.000208794 -36

Ufsp2 176 -228

Zbtb33 0.000348424 -3751

Pfkfb3 1.56E-05 -1288

Colec12 175 1083

Zfp280c 0.000454488 -365

Pfkl 0.000140087 43

Pcm1 175 -229

Zfp62 8.14E-05 309

Pgd 0.000156156 -211

Micall2 175 -3921

Pgk1 0.000211405 -39

Mt1 174 -197

Pgm2 0.0003883 242

Agrp 173 1473

Phka1 0.000140741 -1570

Atp6v0d1 173 -816

Pitpnm1 0.000410758 -337

C030039L03Rik 172 -2961

Pkm2 5.17E-05 734

AC160757.3-201 172 203

Pkp3 0.000579347 527

Setd5 172 -1142

Plch1 8.92E-05 581

Igf2bp2 172 2315

Plekha8 0.000135422 -260

R3hdm1 171 644

Pnrc1 0.000111904 771

Glt1d1 171 658

Pole 0.000260098 2853

Orai2 171 -604

Ppp1r12a 0.00012602 1269

Spesp1 171 -2292

Ppp1r3f 2.13E-05 -4957

Vdac1 170 -504

Praf2 0.00034833 -846

Egln1 169 -247

Prelid1 0.00031609 183

Bcl7b 168 -15

Prok2 0.000588081 1752

Fblim1 168 -55

Prox1 0.00028482 -2091

Lgmn 167 1596

Prph2 0.000128936 383

Acvrl1 166 472

Pth2r 0.000382335 -1675

Pvr 166 -59

Ptma 0.000405631 -98

Plekha2 166 34

Puf60 0.000164542 -134

Eef2k 165 -79

Qk 0.000531373 1115

Ube2g1 164 1231

Rabac1 0.000430796 -1395

Sap18 163 -140

Rabggta 1.49E-05 -2696

Fusip1 160 -115

Rbpj 0.000103609 -1102

Rab39 159 118

Rel 0.000531225 315

Ube2q1 159 -283

Rnf145 6.24E-05 860

4632404H12Rik 159 -93

Rps11 0.000303783 -68

2510039O18Rik 159 -1998

Rps6ka6 1.87E-05 -4945

Plod1 159 -2172

Ruvbl2 3.64E-06 -414

Aldoa 159 -136

Sall1 2.22E-05 962

Artn 157 2710

Sart3 6.05E-05 -265

Bnip1 157 -144

Sele 0.000312375 -1212

Tuba1b 156 348

Serpina10 0.000521693 2805

Tnfsf9 155 24

Serpinb11 0.000562937 -1091

EG665044 154 255

Serpine1 1.55E-05 -226

Fosb 154 1316

Sf3b1 9.28E-05 178

Klf13 154 -843

Shf 0.000107294 639

Hspb7 154 -2109

Slc16a3 2.78E-06 1990

Braf 154 -175

Slc2a1 6.78E-07 -2523

Clcnkb 154 -4078

Slc36a4 0.000449738 -2584

Tpd52 153 -76

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Page 109

Slc39a12 0.000562912 -325

Mettl9 153 -26

Slc41a3 0.00026551 -893

Cry2 152 2900

Smad2 0.000278453 1425

Gbf1 152 810

Snap25 0.000311153 16

Fes 151 -102

Snapc1 0.000570455 811

AI848100 151 -201

Snx30 0.000428566 1857

Serpine1 151 -182

Spata19 0.000505916 682

Pgp 151 -509

Spats1 0.000565605 1169

Cdkn1a 150 -2850

Spink6 0.00034762 1332

Dynll1 150 -208

Stx6 0.000107616 -2478

Ttc14 149 -145

Sync 0.00056425 732

Suv420h1 148 589

Tacr3 0.000459486 880

Dap 145 -210

Taf13 0.000257534 -1619

Exoc1 145 -4544

Tbc1d25 6.11E-05 760

Erp29 145 -34

Tcf4 0.000192441 -20

Zfp446 145 -91

Tessp2 0.000442568 673

AC168063.3 145 -3382

Tfrc 0.000469868 43

Tlr6 145 -1164

Tifa 3.9E-05 -2910

C79267 145 -1283

Tiprl 0.000458386 -72

Map2k1 144 442

Tmem147 0.000436318 -299

Lrba 144 -580

Tmsb4x 0.000348767 -861

4930543L23Rik 143 -45

Tnfrsf19 0.000173121 1448

Spg21 143 276

Tnrc18 0.000235095 1188

Tbc1d4 143 226

Tpi1 0.000374024 660

A230051G13Rik 143 380

Trib3 3.55E-05 -93

Fbxl11 142 -4

Trpc7 0.000547418 -1948

Haao 141 -4149

Tsga14 0.000370265 -3595

Tbkbp1 141 -531

U2af2 0.000523532 -521

1110020G09Rik 140 108

Ube2g1 0.000475695 1604

Llgl1 140 -19

Ubp1 0.00037749 1788

Dars 140 498

Usf2 0.000135198 -890

Adssl1 140 -193

Usp54 0.000474489 -209

Utp11l 140 -179

Wdr23 5.28E-05 521

Cd3eap 139 -1734

Wdr40a 0.000140001 -22

AC139884.8 139 -248

Wwp2 0.000367019 -141

AC142098.2 139 -2521

Zdhhc16 0.000513009 2497

Ing3 139 288

Zfp384 3.37E-05 -3804

C130050O18Rik 139 -2391

Zscan18 0.000553094 2061

Ppp1r13l 139 -63

Dixdc1 138 -260

Ccdc126 138 -127

Hook2 137 -4655

Usp28 137 -309

Hddc2 137 266

Tnfaip3 137 -2811

Jmjd2b 136 -510

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Page 110

Agl 136 -48

Raver1 136 247

Exoc7 135 -149

Blm 135 -104

Slc41a2 135 -37

Ubl7 135 -119

Maz 134 32

BC031781 134 -589

Ttc25 134 2710

Oxsr1 133 -491

5330426P16Rik 133 446

4933426M11Rik 133 -2634

Mrpl45 133 -249

Mxi1 133 2581

Hk1 132.5 -202

Alg11 132 19

Agpat5 132 331

Ankrd24 131 -1001

Sirt6 131 -77

Rapgef1 131 1578

Prkcbp1 131 -4478

Ralb 131 1864

1110039B18Rik 131 -89

Gzf1 131 -162

Cstf2 131 -82

AC087780.10 130 181

Gpr68 130 82

Ppp1cb 130 -127

Snx21 129 -111

Zfp652 129 -510

Lemd2 129 1257

Tmem112 128 -7

Stk33 128 -4900

Atf3 128 -2616

Ache 127 -1105

Sh3gl1 127 -222

Usf2 127 -893

Gls 127 -273

Kdelr2 127 -19

Rights / License: Research Collection In Copyright - Non ......mammalian cells. Most importantly,...

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Transcript of Rights / License: Research Collection In Copyright - Non ......mammalian cells. Most importantly,...