Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Medicine 904
Genome-Wide Studies of
Transcriptional Regulation in
Human Liver Cells by Highthroughput Sequencing
MADHUSUDHAN REDDY BYSANI
ACTA
UNIVERSITATIS
UPSALIENSIS
UPPSALA
2013
ISSN 1651-6206
ISBN 978-91-554-8671-6
urn:nbn:se:uu:diva-198579
Dissertation presented at Uppsala University to be publicly examined in Rudbeck hall, The
Rudbeck Laboratory, Dag Hammarskjölds väg 20, Uppsala, Monday, June 10, 2013 at 09:15
for the degree of Doctor of Philosophy (Faculty of Medicine). The examination will be
conducted in English.
Abstract
Bysani, M. S. R. 2013. Genome-Wide Studies of Transcriptional Regulation in Human Liver
Cells by High-throughput Sequencing. Acta Universitatis Upsaliensis. Digital Comprehensive
Summaries of Uppsala Dissertations from the Faculty of Medicine 904. 50 pp. Uppsala.
ISBN 978-91-554-8671-6.
The human genome contains slightly more than 20 000 genes that are expressed in a tissue
specific manner. Transcription factors play a key role in gene regulation. By mapping the
transcription factor binding sites genome-wide we can understand their role in different
biological processes. In this thesis we have mapped transcription factors and histone marks
along with nucleosome positions and RNA levels. In papers I and II, we used ChIP-seq to map
five liver specific transcription factors that are crucial for liver development and function. We
showed that the mapped transcription factors are involved in metabolism and other cellular
processes. We showed that ChIP-seq can also be used to detect protein-protein interactions
and functional SNPs. Finally, we showed that the epigenetic histone mark studied in paper I
is associated with transcriptional activity at promoters. In paper III, we mapped nucleosome
positions before and after treatment with transforming growth factor β (TGFβ) and found that
many nucleosomes changed positions when expression changed. After treatment with TGFβ,
the transcription factor HNF4α was replaced by a nucleosome in some regions. In paper IV, we
mapped USF1 transcription factor and three active chromatin marks in normal liver tissue and
in liver tissue of patients diagnosed with alcoholic steatohepatitis. Using gene ontology, we as
expected identified many metabolism related genes as active in normal samples whereas genes
in cancer pathways were active in steatohepatitis tissue. Cancer is a common complication to
the disease and early signs of this were found. We also found many novel and GWAS catalogue
SNPs that are candidates to be functional. In conclusion, our results have provided information
on location and structure of regulatory elements which will lead to better knowledge on liver
function and disease.
Keywords: ChIP-seq, Transcription factors, Alcoholic steatohepatitis, Genome-wide, GWAS,
SNPs
Madhusudhan Reddy Bysani, Uppsala University, Department of Immunology, Genetics
and Pathology, Rudbecklaboratoriet, SE-751 85 Uppsala, Sweden.
© Madhusudhan Reddy Bysani 2013
ISSN 1651-6206
ISBN 978-91-554-8671-6
urn:nbn:se:uu:diva-198579 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-198579)
To my parents and well wishers
List of Papers
This thesis is based on the following papers, which are referred to in the text
by their Roman numerals.
I
Motallebipour M*, Ameur A*,Bysani MSR, Patra K, Wallerman O, Mangion J, Barker MA, McKernan KJ,Komorowski J,
Wadelius C. Differential binding and co-binding pattern of
FOXA1 and FOXA3 and their relation to H3K4me3 in HepG2
cells revealed by ChIP-seq. Genome Biology 2009, 10:R129
(17 November 2009).
II
Wallerman O*, Motallebipour M*, Enroth S, Patra K, Bysani
MS, Komorowski J, Wadelius C. Molecular interactions between HNF4a, FOXA2 and GABP identified at regulatory DNA
elements through ChIP-sequencing. (2009) Nucleic Acids Research, 37(22):7498-508.
III
Enroth S*, Andersson R*, Bysani MSR, Wallerman O, Tuch B
B, De la Vega F, Heldin C-H, Moustakas A, Komorowski J ,
Wadelius C. Nucleosome regulatory dynamics in response to
TGF-beta treatment in HepG2 cells (Manuscript).
IV
Bysani MSR, Wallerman O, Bornelöv S, Zatloukal K, Komorowski J Wadelius C. ChIP-seq in steatohepatitis and normal
liver tissue identifies candidate disease mechanisms related to
progression to cancer (Manuscript).
*These authors contributed equally in this work.
Reprints were made with permission from the respective publishers.
Contents
Introduction ................................................................................................... 11
Genetic variation ...................................................................................... 11
The ENCODE Project .............................................................................. 12
Sequencing technology ............................................................................ 13
Transcriptional regulation and RNA polymerases ................................... 13
The transcriptome ................................................................................ 15
Transcription factors ............................................................................ 16
Regulatory networks ............................................................................ 17
Transcription factors in disease ........................................................... 17
Transcription factors in reprogramming .............................................. 18
Epigenetics ............................................................................................... 20
Chromatin structure and organization ................................................. 20
Nucleosome positioning ...................................................................... 22
Post-translational histone modifications .............................................. 22
DNA methylation ................................................................................ 25
Imprinting and X-chromosome inactivation........................................ 27
TGFβ signaling......................................................................................... 28
Alcoholic steatohepatitis .......................................................................... 28
Materials and Methods.................................................................................. 30
Cells and Tissues ...................................................................................... 30
Methods .................................................................................................... 30
Chromatin Immunoprecipitation (ChIP) .................................................. 30
Verification of ChIP DNA by PCR .......................................................... 32
Library preparation for ChIP-sequencing................................................. 32
Next generation Sequencing..................................................................... 32
ChIP-reChIP ............................................................................................. 34
Co-Immunoprecipitation .......................................................................... 34
TGFβ treatment, nucleosome and RNA preparation................................ 34
Present Investigations ................................................................................... 35
Aims of the present studies ...................................................................... 35
Paper I ...................................................................................................... 36
Paper II ..................................................................................................... 37
Paper III .................................................................................................... 38
Paper IV.................................................................................................... 39
Concluding remarks and future perspectives ................................................ 41
Acknowledgements ....................................................................................... 43
References ..................................................................................................... 45
Abbreviations
3C
ASH
Bp
ChIA-PET
ChIP
ChIP-seq
CNV
Co-IP
CTCF
DHS
DNA
DNMT
ENCODE
FCHL
GABP
GTF
GWAS
H3K27ac
H3K4me1
H3K4me3
HCC
HCV
HepG2
HGP
HNF4α
ICR
LCR
lincRNA
lncRNA
miRNA
MNase1
MODY1
Chromosome Conformation Capture
Alcoholic Steatohepatitis
Base pair
Chromatin Interaction Analysis by Paired End Tags
Chromatin Immunoprecipitation
Chromatin Immunoprecipitation with Sequencing
Copy Number Variation
Co-Immunoprecipitation
CCCTC binding factor
DNase 1 Hyper Sensitivity
Deoxyribonucleic acid
DNA methyl transferase
ENCyclopedia of DNA Elements
Familal Combined Hyperlipedemia
GA Binding Protein
General Transcription Factor
Genome Wide Association Study
Histone 3 lysine 27 acetylation
Histone 3 lysine 4 mono-methylation
Histone 3 lysine 4 tri-methylation
Hepatocellular carcinoma
Hepatitis C Virus
Cell line from Hepatocellular Carcinoma
Human Genome Project
Hepatocyte Nuclear Factor 4α
Imprinted Control Region
Locus Control Region
long intervening noncoding RNA
long noncoding RNA
micro-RNA
Micrococcal Nuclease 1
Maturity Onset Diabetes of Young 1
mRNA
NASH
NGS
NRF
PCR
RNA
rRNA
SNP
TF
TGF
tRNA
TSS
USF1
UTR
messenger RNA
Non-alcoholic steatohepatitis
Next Generation Sequencing
Nuclear Respiratory Factor
Polymerase Chain Reaction
Ribonucleic acid
ribosomal RNA
Single Nucleotide Polymorphism
Transcription Factor
Transforming Growth Factor
transfer RNA
Transcription Start Site
Upstream Stimulatory Factor 1
Untranslated Region
Introduction
The decoding of a near complete human genome in 2001 is one of the largest
achievements in science since the discovery of the structure of deoxyribonucleic acid (DNA) by Watson and Crick in 1953 [1-3]. The human genome
project (HGP) revealed that there are approximately three billion nucleotides
organized in the 22 pairs of chromosomes and the sex chromosomes X and
Y (XX in women and XY in men). Only a small proportion 1.5%, of the
genome codes for mRNA. There are slightly more than 20 000 human protein coding genes. In addition, an unknown number of DNA loci are transcribed into non-coding RNAs such as ribosomal RNA (rRNA), transfer
RNA (tRNA), micro-RNA (miRNA), long non-coding RNA (lnc-RNA) or
transcripts of unknown function [4].
The successful completion of the HGP has provided a road map to many
large international and collaboration projects like the HapMap project, the
ENCyclopedia of DNA Elements (ENCODE) project and the 1000 genomes
project.
Genetic variation
The current estimate is that the human genome contains approximately 38
million single nucleotide polymorphisms (SNPs) and around 10 thousand of
common SNPs are associated with common diseases, whereas other variants
are rare and possible association to disease is not known. The HapMap project was aimed to identify common patterns of genetic variation in diverse
human populations [5]. The purpose of the 1000 genomes project was to
characterize the genetic variants with at least 1% frequency in the population
using high-throughput sequencing [6]. The second phase of this project used
1092 individuals to provide the current information map of 38 million SNPs,
1.4 million short indels and 14 000 large deletions [7] that have occurred at
different times during human evolution. Common variants are old and rare
variants are younger. A recent large scale exome-seq study of ~15 000 genes
in 6 515 individuals revealed that 86% of the SNPs that were predicted to be
harmful had arisen in the last 5 000 years [8]. Selection may not have had
time to act on these variants and remove them from the population. There are
a large number of copy number variations (CNV) varying in size from ~1 kb
to several mega base long in human genome. CNVs have been associated
11
with many diseases e.g. autism [9]. Genetic variants like SNPs and indels
may also affect the binding of transcription factors (TFs) [10]. Also others
have later shown that genetic variation changes the heritable chromatin status and TF binding [11, 12].
The ENCODE Project
The ENCODE project was started in 2003 to map the functional elements in
the human genome. The pilot phase of the project mapped TF binding sites,
histone marks, DNase 1 hypersensitive sites (DHS) etc. in 1% of the human
genome [13]. The next phase of the project was focused on continuing the
annotations of protein coding, non-protein coding genes and their transcripts
and transcription regulatory regions in the whole genome using massively
parallel next generation sequencing technology [14]. In September 2012,
ENCODE consortia published a series of papers in Nature, Science, Genome
Research and in Genome Biology to describe the results from 1 640 genomewide datasets (ChIP-seq, RNA-seq, DNase-seq, FAIRE-seq, 5C etc.) (Figure
1) from 147 cell types. The ENCODE data summary paper [15] claimed that
80.4% of the human genome is biochemically active with approximately
400 000 enhancer-like characters and 70 000 promoter-like characters. Their
findings will be further discussed in the coming sections.
The modENCODE project was started to identify the functional elements
in Caenorhabditis elegans and in Drosophila melanogaster. modENCODE
project has generated more than 700 datasets and they have identified protein
coding and non-coding genes, regulatory, replication and chromatin elements in Drosophila melanogaster. They have also identified new functions
of annotated genes, general and tissue-specific regulatory elements. The
complex modENCODE data generated today covers 82% of D
.melanogaster genome which is four times larger than the previously annotated protein coding exons [16]. The modENCODE project of C .elegans
was aimed at the transcriptome profiling during a developmental time
course, identification of TF binding sites and generation of chromatin organization maps. They have also focused on TF binding networks and miRNA
interactions [17].
12
Figure 1. The figure illustrates various methods used in the ENCODE project to
map the functional elements in the genome. The figure was obtained from the UCSC
genome browser webpage and the credit goes to Ian Dunham and Daryl Leja.
Sequencing technology
The launch of Next Generation Sequencing (NGS) technology, played a key
role in the ENCODE project, 1000 genomes project and many other projects.
Rapid development in NGS technology reduced the sequencing cost and
today the whole human genome can be sequenced in couple of hours.
For this thesis, we have used Illumina and Life technologies SOLiD sequencing platforms to map the regulatory regions, histone marks, nucleosome positioning etc. We have mapped liver specific TFs, using Chromatin
Immunoprecipitation in combination with NGS (ChIP-seq).
Transcriptional regulation and RNA polymerases
Transcription is the process where an equivalent copy of RNA is created
from a DNA sequence. Transcription is performed by three different multisubunit RNA polymerases. RNA polymerase I synthesizes 45S pre rRNA
which then matures into 28S, 18S and 5.8S rRNA [18]. RNA polymerase II
synthesizes pre-mRNAs, miRNAs and snRNAs [19]. RNA polymerase II
coordinates with RNA polymerase III to maintain gene expression globally
[20]. RNA polymerase II forms a holozyme along with several enzymes and
general TFs (GTFs). Transcription begins once the TATA binding protein
(TBP), subunit of TFIID, binds to the TATA box which allows the assembly
of GTFs and RNA polymerase II. A recent study showed that many promot13
ers lack a classical TATA box, but instead a more degenerate TATA-like
sequence has been found in Saccharomyces which might also exist in humans [21].
RNA polymerase III is involved in the synthesis of tRNAs, 5S rRNAs and
other small RNAs. RNA polymerase III transcription is needed for the generation of adapter tRNA molecules that are used for translation of the genetic
information in mRNA into protein. RNA polymerase III binds to only a few
hundreds of tRNAs and a study in six mammalian species showed that RNA
polymerase III is evolutionarily conserved and that RNA polymerase III
binding varies between species in terms of strength and location [22].
Promoters are necessary for transcription and are located upstream of the
transcription start site (TSS). Promoters have been divided into core promoters and proximal promoters. Core promoters are located within 50 bp of the
TSS and are important for the formation of the pre-initiation complex (PIC)
and the binding of GTFs. The TATA-box is located 20-30 bp upstream of
the TSS and is found in many promoters. Core promoters are usually silent
and need a proximal promoter to initiate transcription. Proximal promoter
starts where the core promoters end and are located +/-250 bp of the TSS
(Figure 2). The ENCODE project and others have studied GC-rich and ATrich promoters at the TSS of protein coding transcripts and found that these
promoters have distinct histone modification and TF patterns [15, 23]. GCrich promoters are most common and have the highest activity in all tissues.
Most of the housekeeping genes tend to be associated with GC-rich promoters [24].
In addition to promoters, gene activity is also regulated by the binding of
sequence specific activator and repressor proteins to DNA elements termed
enhancers, operators and silencers. Enhancers/silencers can be located many
kilo base pairs from the TSS and are involved in the activation or repression
of transcription [25, 26]. P300 is a well-studied enhancer protein whose effect on enhancer activity in different cells was measured by ChIP-seq [27]
and with other methods. In the ENCODE project the carbon copy of chromosome conformation capture (5C) method was used to map more than a
thousand long range interactions between the promoters and distal elements
in each cell type and to correlate their interaction with gene expression [28].
Using one of the recently developed methods, STARR-seq, many functional
enhancers have been identified in Drosophila [29]. In this method tentative
enhancers are cloned in a vector downstream of the TSS so that they are
transcribed. Their abundance reflects the enhancer activity and can be measured by RNA-seq.
Silencers are sequence specific DNA binding elements located close to or
far from genes and involved in the silencing of a target gene [30]. Locus
control regions (LCRs) have similar functions as enhancers/silencers but
have the ability to enhance at a greater distance and to regulate many genes
in tissue specific manner [31].
14
RNA polymerase II and GTFs are sufficient for promoter-specific transcription but additional factors such as mediators are important in order to
respond to the activators. The mediator complex plays an important role in
transcription and acts as a co-activator, co-repressor and sometimes as a
GTF during transcription (Figure 2) [32, 33].
Open chromatin sites hypersensitive to the enzyme deoxyribonuclease 1
(DNase 1) are found in all classes of regulatory elements. ENCODE project
has identified 2.9 million DHS sites from 129 cell and tissue types by using
DNase-seq [15, 34]. Most of the DHS sites they have identified showed cell
specific regulation. They also identified many novel TF binding motifs
(n=683) using DNase 1 footprints [35]. The majority of the disease associated GWAS SNPs are located at DHS sites and some of these affect TF binding, chromatin states and are involved in the formation of regulatory networks [36].
Figure 2.Schematic illustration of transcriptional regulation. The figure illustrates
part of the transcription machinery at core promoter, proximal promoter and TSS.
Figure adapted with the permission from Ong C.T & Corces V.G, Nature review
genetics, 2011 [37].
The transcriptome
RNA transcripts are involved in many cellular functions either directly or
indirectly. Technological advances in RNA sequencing has enabled the prediction of many RNA types, their location and function [38]. Only a small
portion of the transcripts code for protein and the remaining are non-coding.
There are many types of non-coding RNAs in the genome such as tRNA,
rRNA, miRNA and lncRNA. The main function of tRNA is to translate the
triplet amino acid code into amino acid sequence of proteins. rRNAs are
present in the ribosomes and play an important role in translation.
miRNAs are ~22 nucleotides long small non-coding RNA molecules that
control gene expression by degrading mRNA molecules or by inhibiting
translation [39-42]. miRNAs binds at 3’un-translated region (3’UTRs) of the
15
target gene [43]. According to the miRNA data base (miRBASE 19, August
2012; www.mirbase.org), there are about 21 264 miRNAs in 193 species.
Among them 2 019 were discovered in humans but the majority are not validated yet. Some of these miRNAs play an important role in organ development, for e.g. miR-127 in lung development and some of them are involved
in disease processes, e.g. miR-122 in HCV replication and miR-124 acts as a
tumor suppressor [44, 45].
Long non-coding RNAs (lnc-RNA) are at least 200 nucleotides long and
show less expression than protein coding genes. lnc-RNAs are expressed in a
cell specific manner [46] and can form networks to control gene expression
and function. They are also involved in the regulation of the three dimensional (3D) structure of chromosomes [47]. Most of the disease associated
SNPs are found in non-protein coding regions [48] and some of them may
affect the function of lnc-RNAs and thereby predispose to disease.
Some lnc-RNAs are called long intervening noncoding RNAs (linc-RNA)
and were discovered by Guttman and co-workers [49] by searching outside
of non-protein coding genes in regions marked by histone 3 lysine 4 trimethylation (H3K4me3) and Histone 3 lysine 36 tri-methylation
(H3K36me3). These linc-RNAs are involved in many biological processes
such as dosage compensation and imprinting [50] and many are associated
with chromatin remodeling complexes and regulate histone modification
patterns and gene expression [51, 52].
Transcription factors
TFs are a group of proteins that regulate the gene expression by binding to
DNA in a sequence specific manner. TFs affect transcription alone or in a
group by activating or repressing the recruitment of RNA polymerase II.
There are ~1 850 TFs in the human genome [30]. Based on their DNA binding motif they have been grouped into helix-loop-helix, leucine-zipper,
homeo domain, zinc finger and helix-turn-helix [53]. In a recent study, 830
TF binding motifs were identified by using high throughput SELEX and
ChIP-seq [54]. However, the DNA-binding motifs remain to be defined for
many TFs.
TFs can bind various regulatory regions, i.e. at promoters, enhancers/silencers, LCRs and insulators etc. TFs are modular proteins that contain
activator/repressor domains along with the DNA-binding domain [30]. TFs
also contain domains for protein-protein interactions. Some proteins called
co-activators or co-repressors regulate transcription by binding to TFs instead of DNA [55]. Transcription begins with the binding of one or several
TFs to their respected TF binding site and also the recruitment of co-factors.
The co-factors help to remove nucleosomes to allow binding of the transcription machinery to the promoters. Co-factors also modify the nucleosomes at
promoters and at distal regions.
16
Regulatory networks
The genomic information is almost the same for all multi-cellular organisms
but there is a lot of phenotypic variation. A recent technological advance in
the mapping of regulatory regions i.e. DHS sites and TF binding sites has
identified regulatory networks in humans and other species. The regulatory
networks are involved in the development, metabolism and hormonal stimulation etc. in a tissue specific manner [56]. To know the function of a specific TF, it is important to know which genes it regulates. Mapping of hepatocyte nuclear factors (HNF) in mouse embryonic stem cells showed that correct regulation of the HNF family network is required for differentiation and
metabolism. Promoter array analysis on three TFs in liver and in endocrine
pancreas showed that there is a transcriptional hierarchy between HNF1 and
HNF4α [57] suggesting that regulatory networks play a role in maintaining
different biological processes. ENCODE project has identified the regulatory
networks between 119 TFs and found that these factors co-bind in a combinatorial fashion. These networks are highly cell specific. They also found
that there is a significant difference between the networks for proximal and
distal regulatory elements. Strongly connected network elements are more
evolutionarily conserved than the weaker ones [15, 58].
Transcription factors in disease
TFs are involved in the control of gene expression and any abnormality of
their binding to regulatory regions may change the expression status and thus
lead to a disease. This may be caused by different types of mutations in TFs
like missense, nonsense, duplications or deletions. To date mutations in more
than 400 TFs and other nuclear proteins are known to cause human disease
(Wadelius C, personal communication). One example is mutations in
HNF4α described further below. Mutations in this protein change the affinity for its motif [59]. Mutations or variants at regulatory regions may affect
the binding of TFs which may lead to diseases such as cancer, diabetes, obesity, autoimmune and cardio vascular diseases etc. [60]. Many SNPs have
been associated to diseases or common traits and this information is collected in a catalogue at NIH (http://www.genome.gov/26525384). As of 19
April, 2013 the catalogue includes 1 571 publications and 9 906 SNPs. Most
of these SNPs are thought to affect gene regulation by changing the interaction with TFs. E.g. there has been studies showing that variants in the TF
gene TCF7L2 are associated with the risk of type 2 diabetes [61, 62]. CMYC is one of the well-studied TF in many tumors and it regulates many
genes involved in tumorigenesis.
17
Transcription factors in reprogramming
TFs also play a key role in the development and reprogramming of cells.
Induced pluripotent stems cells (iPS) were generated from adult human dermal fibroblasts using the TFs Oct3/4, Sox2, Klf4 and c-Myc (Figure 3). These factors could reprogram mouse embryonic or adult fibroblasts to iPS cells
[63, 64]. For this work, Professor, Yamanaka was awarded the Nobel Prize
in 2012. In a study by P.Huang et al, adult fibroblasts were directly converted into iHep cells by transduction of the TFs Gata4, Hnf1α and FoxA3 (Figure 3) [65]. In a parallel study, mouse embryonic and adult fibroblasts were
directly converted to cells that are closely resembled to hepatocytes. For this
study, they used combinations of Hnf4α plus any one of FoxA proteins
(FoxA1/FoxA2/FoxA3) in vitro (Figure 3) [66]. Thus, some defined factors
could soon be used to generate hepatocyte-like cells for therapeutic purposes. Further studies on these factors and processes could contribute to a breakthrough in tissue transplantation.
Figure 3.Illustration of the reprogramming of cells from one state to another by
selected transcription factors. Figure adapted with the permission from Lee Tong I
and Richard A. Young, Cell, 2013 [60].
We have studied the TFs FOXA1, FOXA2, FOXA3, HNF4α, GABP and
USF1 for this thesis. These factors are briefly described below.
FOXA: s
FOXA or hepatocyte nuclear factor 3 (HNF3) TFs belong to the forkhead
box/winged helix family. They are called pioneer factors since they can bind
to DNA that it is packed in nucleosomes and removes the nucleosomes to
make place for other TFs [67]. There are three FOXAs, namely FOXA1
(HNF3α), FOXA2 (HNF3β) and FOXA3 (HNF3γ). FOXA proteins play a
major role in early development, organogenesis and carbohydrate metabolism [68]. The forkhead box sequences of the three FOXAs are highly conserved and are 95% identical. All FOXA proteins share weaker homology
18
outside the fork head box, whereas there is higher degree of homology at the
N and C terminal ends. FOXA proteins interact with other TFs like USF1,
HNF1α, HNF4α and HNF6 for the regulation of target genes [69]. The
FOXA family gene expression during embryogenesis and combined with
liver specific FOXA target genes has been interpreted as evidence that the
FOXA TFs regulate hepatogenesis. There are ChIP-chip, ChIP-seq and EMSA studies on FOXA TFs in mouse, human tissues and in HepG2 but the
three factors have not been studied together in HepG2 cells previously.
HNF4α
Hepatocyte nuclear factor 4 alpha (HNF4α) is a TF that belongs to the zinc
finger protein family. HNF4α plays a key role in the formation of TF networks that regulate the development of vital organs such as the kidney, pancreas and liver [70, 71]. There are three members in the HNF4 family:
HNF4α, HNF4β and HNF4γ. HNF4α regulates genes involved in lipid and
carbohydrate metabolism [72]. HNF4α is also directly involved in the regulation of genes in glucose transport and in glycolysis. Any abnormality in
HNF4α regulation may lead to diabetes and cancer [73]. Mutations in
HNF4α lead to Maturity onset diabetes of young 1 (MODY1) which is an
autosomal dominant form of diabetes or to hyperinsulinaemic hypoglycemia
[74, 75]. A recent study found that mutations in the DNA binding domain of
HNF4α protein as well as in the nearby hinge region cause MODY1 by affecting the binding of HNF4α to DNA [59]. HNF4α is also associated to the
common form of diabetes [76].
GABP
GA binding protein (GABP) is a TF that belongs to the ETS family. It is
abundantly expressed in liver and muscle tissue. GABP binds to DNA as a
heterotetrameric complex and forms two GABPα and GABPβ subunits.
GABP mostly binds to GAA enriched sequences and its preferred motif is
CCGGAA. GABP regulates genes involved in the cell cycle, protein synthesis and metabolism. An alternative name for GABP is NRF2 since it regulates nuclear respiratory factors [77].
USF1
Upstream stimulatory factor 1 (USF1) belongs to the helix-loop-helix leucine zipper family and binds to the E-box sequence CA[C/T]GTG. USF1 can
interact with its targets as a homo-dimer or hetero-dimer. USF1 is along with
USF2 is involved in the recruitment of histone modifying enzymes and they
are enriched at the sites of H3K4me2 and H3ac [78]. USF1 is ubiquitously
expressed and involved in disorders of lipid and carbohydrate metabolism.
SNPs in the USF1 gene are strongly associated with the disease familial
combined hyperlipedemia (FCHL) which results from defects in the liver
and other organs. FCHL is phenotypically associated with type 2 diabetes
and the metabolic syndrome [79].
19
Epigenetics
After several decades of genetics research, some focus is now redirected
towards epigenetics. The definition of epigenetics is “the study of changes in
the gene function that are mitotically and/or meiotically heritable and that do
not entail a change in DNA sequence” [80]. This means that epigenetic modifications are heritable from cell to cell just like genetic modifications. There
are mainly two types of epigenetic mechanisms, DNA methylation and modification of histone tails.
Chromatin structure and organization
The eukaryotic nucleus harbors genetic information consisting of several
billion nucleotides. To maintain all this genetic information in the nucleus,
the DNA is packed tightly to form nucleosomes. A nucleosome consists of
approximately 147 bp of DNA wrapped twice around an octamer of eight
core histones, namely two of each histone H2A, H2B, H3 and H4. Due to
this packing DNA is 10 fold compacted [81, 82]. Nucleosomes are connected to each other by a ~20 bp long linker bound by histone H1, which seems
to regulate the distance between the nucleosomes (Figure 4).
Nucleosomes are further subjected to compaction to form higher order
chromatin fibers. However this tightly packed chromatin structure is virtually inaccessible for the transcriptional machinery. A prerequisite for the transcription of a certain gene segment is the structural reorganization of the
gene locus in question. Change in nucleosome positioning, post-translational
modification of histone tails and methylation of CpG dinucleotides constitute
various levels of chromatin modifications that affect DNA accessibility [83].
These alterations occur in a highly ordered fashion, where selective modifications allow for tissue-specific expression and heritability at cell division;
two hallmarks of epigenetic regulation.
Chromatin organization in the cell nucleus is not random. It is organized
in a spatial manner and plays an important role in gene regulation. Chromosome conformation capture (3C) based methods have been widely used to
study spatial chromatin organization and their interactions [84]. The new HiC method can be used to view the 3D interactions of the chromatin genomewide [85].
.
20
Figure 4. Compaction of DNA into chromosomes: A) DNA anti-parallel strands
base pair each other and form a double helical structure. The DNA double helix is
tightly wrapped around the histone core octamer to form a nucleosome. Nucleosomes are compacted and folded into 30 nm fibers for higher order of compaction
and for the chromosome formation. B) Nucleosome core particle: 3D image of nucleosome core particle showing that DNA is wrapped around the histone core octamer. Figure adapted with the permission from Felsenfeld & Groudine, Nature,
2003 [86].
21
Nucleosome positioning
Rapid advances in sequencing allowed mapping of nucleosome positions in
different species and in different cell types. At least in some genes nucleosomes are positioned in order at promoters while the positioning is more
random in the gene body (Figure 5) [87]. We have seen well-positioned nucleosomes downstream and upstream of a TSS and nucleosome free region
in between them. However, this average nucleosome positioning signal was
rarely observed at the TSS of individual genes. Some CTCF binding sites
contain well positioned nucleosomes but nucleosomes are not necessarily
well positioned on both sides of a TF bound region. This has been observed
in our data at JUND binding sites (Paper III). In addition, some nucleosome
positioning studies have shown that exons contain positioned nucleosomes
marked with specific histone modifications reflecting the expression level
[88, 89]. We have studied the nucleosome positioning pattern in HepG2 cells
after the treatment with transforming growth factor β (TGFβ) (Paper III).
Figure 5 Nucleosome positioning pattern. Figure illustrates the positioning pattern at
the TSS and at nucleosome free regions. Figure adapted with the permission from
Jiang & Pugh, Nature review genetics, 2009 [87].
Post-translational histone modifications
Histone tails undergo a wide range of post-translational modifications at
their N-terminal ends. Modified histones are involved in biological processes
such as transcription, replication and maintain the chromatin states. There
are at least eight known histone modifications. They are acetylation and
methylation of lysines (K) and arginines (R), ubiquitinylation of lysines (K)
and phosphorylation of serines (S) and threonines (T) (Figure 6) [82]. The
pattern of modifications on histone tails are complex and to further increase
the variation, many of the modifications can occur at different residues of the
histone tail. However, some histone modifications are frequently observed in
either active (euchromatin) or silent chromatin (heterochromatin) [90]. The
22
complex language of histone modifications is the subject of continuous research and great progress has been made in recent years.
Figure 6. Histone octamer and modification of their tails at N-terminal ends. Figure
adapted with the permission from Paul A. Marks et al, Nature reviews Cancer 2001
[91].
Acetylation and methylation of histones are the most commonly studied
chromatin marks. Acetylation is usually localized at the active regions of
chromatin; on the other hand methylation plays a dual role and is associated
with both transcriptional activation and silencing. As shown in Table 1, histone 3 lysine 27 tri-methylation (H3K27me3) is associated with transcriptional silencing. By contrast previous studies have shown that nucleosomes
with H3K4me3 are linked to transcriptional activity. High levels of
H3K4me3 is associated with 5’ regions of all active genes. There is a strong
positive correlation between this modification and RNA polymerase II activity, transcription rates, histone acetylation [92, 93].
Table 1. Different histone marks and their functional association in the genome.
Table obtained and modified from Mehdi Motallebipour PhD thesis (2008).
Histone
H2A
H2A
H2A
H2A
Residue
Ser1
Lys5
Lys119
Thr120
Modification
Phosphorylation
Acetylation
Ubiquitylation
Phosphorylation
Function
Mitosis
Activation
Spermatogenesis
Mitosis
23
H2A
H2A
H2B
H2B
H2B
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H3
H4
H4
H4
H4
H4
Ser139
Tyr142
Lys5,12,15, 20
Ser14
Lys120
Arg2, 8
Thr3
Lys4
Thr6
Arg8
Lys4, 9,14,18, 27, 36
Lys9
Ser10, 28
Thr11
Lys27
Lys27
Lys27
Lys36
Thr45
Lys56
Lys79
Ser1
Arg3
Lys5, 8, 12, 16
Lys20
Lys91
Phosphorylation
Phosphorylation
Acetylation
Phosphorylation
Ubiquitylation
Methylation
Phosphorylation
Methylation1-2-3
Phosphorylation
Methylation
Acetylation
Methylation
Phosphorylation
Phosphorylation
Methylation-1
Methylation-2
Methylation-3
Methylation1, 3
Phosphorylation
Acetylation
Methylation
Phosphorylation
Methylation
Acetylation
Methylation
Acetylation
Repair
Activation
Apoptosis
Elongation?
Activation
Mitosis
Activation
Activation
Activation
Activation
Silencing
Mitosis
Mitosis
Activation
More active than repression
Repression
Activation
Replication
Repair
Activation
Activation
Activation
Activation
Silencing
Repair
ChIP-seq studies suggest that histone modifications can predict the activity
state of chromatin. In two large scale ChIP-seq studies of 19 methylated
histone marks and 18 acetylated histone marks the localization of these
marks was determined in CD4+T cells and correlated to promoters and other
gene features (Table 1) [94, 95]. One can use the histone modification to
predict gene expression [96]. It is also possible to detect enhancer elements
using histone modifications. For example, by using acetylated histone 3 lysine 27 (H3K27ac) and mono-methyl histone 3 lysine 4 (H3K4me1), active
enhancers can be distinguished from inactive/poised enhancers [97].
24
DNA methylation
DNA methylation is a biological process in which a methyl group is added to
the 5’ positions of cytosine residues within CpG dinucleotide by DNA methyl transferases (DNMTs). CpGs occur in lower frequencies than expected
in the human genome and 70% of the CpGs are present at promoters. DNA
methylation is one of the important and widely studied epigenetic modifications that is involved in controlling cellular processes such as Xchromosome inactivation, transcription, embryonic development, cell differentiation, chromosome stability and genomic imprinting [98]. DNA methylation is mainly associated with gene silencing and heterochromatin formation
(Figure 7). DNA methylation along with histone modification plays a role in
gene activation, repression and nuclear structure. Two of the studies headed
by Dirk Schubeler showed that local DNA sequences such as TF binding
sequences (motifs) and respective TFs are the primary determinants of target
specification and regulation of DNA methylation in mammals [99, 100].
A study in colon cancer has identified methylation alterations at lower
CpG density sites called CpG island shores. CpG island shores are located
neither at promoters nor at CpG islands but are in the proximity of 2 kb from
CpG islands. Just like at CpG islands, methylation at CpG island shores is
strongly correlated with gene expression [101, 102]. Hypermethylation at
CpG island shores has been observed during normal tissue differentiation
[101, 103]. This is in line with data which shows that methylation with the
help of DNMTs plays a role in normal mammalian development [104].
DNA methylation has been associated with many diseases in addition to
cancer, e.g. systemic lupus erythematosis (SLE), mental retardation, Prader
Willi syndrome [98] and many other disorders. Prader Willi syndrome is due
to effect in imprinting discussed further below. DNA methylation suppresses
the expression of many genes and changes of methylation status i.e. hypo or
hypermethylation has been observed in many cancers. Mutations in
DNMT3A that reduce methylation have been observed in acute myeloid leukemia (AML). Differential methylation patterns have been observed in the
pancreatic islet cells of type 2 diabetic individuals suggesting that methylation also plays a role in type 2 diabetes [105]. A human methylation study in
rheumatoid arthritis patients revealed differential methylation in the major
histocompatibility complex region and it was suggested to mediate the genetic risk of rheumatoid arthritis [106].
25
Figure 7. Illustrates different methylation mechanisms in the genome. Figure
adapted with the permission from Smith Z.D & A. Meissner, Nature review genetics
2013 [104].
26
Imprinting and X-chromosome inactivation
Imprinting is an epigenetic phenomenon in which genes are expressed on the
basis of their parental origin. There are about 150 imprinted genes observed
in the mouse. Imprinted genes can form clusters of 20-3 700 kb of DNA.
The expression of these genes is regulated by an imprinted control region
(ICR). ICR is usually associated with parent origin specific methylation and
histone modifications. Notable imprinted clusters are H19/Igf2 and
Gtl2/Dlk1 in which the paternal alleles are methylated, and the non-coding
RNAs Kcnqot1 and Snprn, where the ICRs of the maternal allele are methylated [107]. One well-known locus in tumorigenesis is IGF2-H19 in which
loss of imprinting is associated with cancer [108] and in this region CTCF
regulates the methylation pattern (Figure 8).
In mammals, females have two X- chromosomes and males have one X
and one Y-chromosome. One of the X-chromosomes is inactivated in females by a 170 000 bp long non-coding RNA, X-inactivation specific transcript (XIST) [109, 110]. It plays an important role since XIST mediates the
epigenetic silencing of the genes on the X-chromosome. XIST is also involved in cancer and in reprogramming [107].
Figure 8. The mechanism of methylation and imprinting at H19/IGF2 loci in the
maternal and paternal allele. CTCF binds to the unmethylated maternal allele of the
ICR and prevents activation of IGF2. Modified figure adapted with the permission
from, Plass. C and P.D Soloway, European Journal of Human Genetics, 2002 [108].
27
TGFβ signaling
TGFβ is involved in many cellular processes such as differentiation, proliferation, apoptosis and development. There are three highly homologous
isoforms in the TGFβ family which are expressed in a tissue specific manner
[111]. TGFβ is involved in immune cell maturation and regulation of the
gene expression.
TGFβ signaling is complex and is mediated e.g. by the SMAD TFs. Abnormality in TGFβ signaling may contribute to cancer, diabetes, heart failure
etc. TGFβ can act as both tumor suppressor and tumor promoter and is associated to many cancers [112]. TGFβ is one of the main regulators of liver
disease and liver cancer and participates in liver inflammation, fibrosis and
cirrhosis in hepatocellular carcinoma (HCC) (Figure 9). Due to the effect of
the TGFβ signaling pathway in many diseases, it has been identified as a
promising drug target [113].
Figure 9. Roles of TGFβ in different cells and its positive and negative effects Figure adapted with the permission from Dooley. S & P. ten Dijke, 2012, Cell and tissue research [114].
Alcoholic steatohepatitis
Excess alcohol consumption may cause fatty liver disease or alcoholic steatohepatitis (ASH) which may lead to ballooned hepatocytes, mallory bodies,
inflammation, fibrosis, cirrhosis and malignant transformation to HCC.
There are similarities between ASH and the disease non-alcoholic steatohepatitis (NASH). One of the main sign of ASH is jaundice with fever and prox28
imal muscle loss [115]. Both genetic and environmental factors contribute to
this disease. Aldehyde dehydrogenase (ALDH) genes, alcohol dehydrogenase (ADH) genes and catalase genes participate in alcohol metabolism and
change in their function may contribute to the disease (Paper IV) and [116].
In hepatocytes alcohol is converted to acetaldehyde through oxidation and
then to acetate. Downstream in the pathway ethanol affects the lipid metabolism through the TFs PPAR-α, AMPK and SREBP1 leading to an increase in
fatty acid (FA) and steatosis. Lipopolysaccharides (LPS) in gram negative
bacteria may also contribute to inflammation in the liver (Figure 10).
ASH can be treated with proper diet control, using lactulose and gut
cleaning antibiotics. Corticosteroids can be used to inhibit the TFs AP-1and
NF-Kb. Other drugs such as Pentoxifylline and Infliximab antibodies that
inhibit the TFs that contribute to this disease can be used to treat ASH. In the
worst scenario, liver transplantation may be a final option.
Figure 10. An illustration of alcoholic steatohepatitis. Adapted with the permission
from Lucey. M .R et al, New England Journal of Medicine, 2009 [115].
29
Materials and Methods
Cells and Tissues
In this study we used the HepG2 cell line which is derived from a hepatocellular carcinoma in a Caucasian male. HepG2 cells were cultured in
RPMI1640 medium containing 10% fetal bovine serum (FBS), 1% Lglutamine at 37°C with 5% CO2. Human liver tissue from people diagnosed
with alcoholic steatohepatitis and normal liver tissue were obtained from the
Graz biobank, Medical University of Graz, Austria.
Methods
ChIP-seq is the main method used in this thesis. Apart from this, we performed Nucleosome-seq and RNA-seq for paper III. For the follow-up studies, we used semi-quantitative PCR, qPCR, ChIP-reChIP, coImmunoprecipitation (Co-IP) and Western blotting.
Chromatin Immunoprecipitation (ChIP)
Cells or finely chopped tissue was crosslinked with formaldehyde to keep
the protein-DNA interactions stable. Crosslinking was stopped by adding
glycine and the chromatin was sonicated to 100-300 bp fragments by using
Bioruptor. Sonication can be replaced by an enzyme called micrococcal nuclease 1 (MNase 1). MNase 1 cleaves the nucleosome free regions and
leaves the nucleosomes. The main advantage of using MNase 1 for shearing
is that chromatin marks can be mapped at the highest possible resolution and
assigned to individual nucleosomes. MNase 1 was used to map the histone
mark H3K4me3 in paper I and for mapping of the nucleosome positioning in
paper III.
Immunoprecipitation was performed using the antibodies against the TFs
or the histone marks of interest. The immunoprecipitated complex was attached to the solid phase of Protein-G agarose/sepharose beads or magnetic
beads. After the extensive washing to remove all unbound protein-DNA
complexes the protein-DNA complexes were eluted. Crosslinks were reversed the protein-bound DNA was treated with RNase A and proteinase K
30
and extracted with phenol, chloroform followed by ethanol precipitation. A
fraction of chromatin called input was not subject to immunoprecipitation
and was saved and purified after reverse-crosslinking the DNA. Input DNA
was later used to measure the background of the ChIP experiments. ChIP
enriched DNA can be used to verify known regions by PCR or can be used
in large scale sequencing.
Figure 11. Chromatin Immunoprecipitation. Chopped tissues or cells are crosslinked
then cells are lysed and chromatin is sheared into small fragments and immunoprecipitated and DNA is extracted. This DNA can be used in PCR, qPCR, Microarray
and in sequencing. Figure adapted with the permission of publishers, Philippe Collas, 2008 [117].
31
Verification of ChIP DNA by PCR
Enrichment in immunoprecipitated DNA can be verified by semiquantitative PCR or preferably real time quantitative PCR (qPCR) in known
target regions of the specific TF or histone modification. ChIP enrichment
by semi-quantitative PCR is usually evaluated by separating the PCR products on an agarose gel and visualizing under UV light. qPCR with SYBR
green is a more sensitive and accurate way of measuring ChIP enrichment as
it enables measurements to be done in the exponential phase of amplification. Serial dilutions of input DNA can be used to obtain a standard curve
and from this it is possible to quantify the ChIP enrichment. Enrichment can
be compared with that in a negative control ChIP using IgG as antiserum or
with the enrichment obtained from unbound regions of the TF or histone
mark.
Library preparation for ChIP-sequencing
ChIP combined with high-throughput sequencing is a powerful method to
map the regulatory elements across the genome. For ChIP-seq, ChIP-DNA is
end-polished with end-polishing enzymes T4 DNA polymerase, Klenow
DNA polymerase and T4 polynucleotide kinase to create blunt ends. The
end-repaired DNA fragments are A-tailed for the Illumina pipeline and ligated with adapters. The ligated fragments are amplified using adapter specific
primers and the number of cycles should be low to reduce duplicates. Since
the chemistry of the sequencing instruments only allows reading of smaller
fragments, the amplified PCR product is separated on an agarose gel and the
fragments are size selected and purified. It is also possible to use AMPure
XP beads to size select the fragments. The purified DNA fragment sizes
should be checked on a BioAnalyzer and can be used for large scale sequencing. The above protocol is an outline for different instruments but,
each NGS platforms have their own adapters and library protocols. There
may be changes between the steps for example; SOLiD adapters need to be
nick-translated before the amplification.
Next generation Sequencing
Sanger sequencing has been the main source for any type of sequencing until
the launch of the 454 sequencing instrument in 2005. The 454 technique is
based on pyro-sequencing and uses sequencing by synthesis [118]. The initial read length for 454 was 100-150 bp but, now it is up to 1000 bp. In 2006,
Illumina-Solexa released their Genome Analyzer which is also based on
sequencing by synthesis. After that Life technology’s SOLiD (Sequencing
by Oligo Ligation Detection) was launched into the market. Today there are
32
many other NGS instruments on the market, for example PGM and Proton
(Life tech/Ion torrent), RS (Pacific Biosciences) and ARGUS (Opgen) (Table 2). For all platforms the sequence reads come from the ends of the ChIPenriched fragments. The reads are aligned to the genome and peaks are
called using resources like MACS available on the internet or using in-house
technology.
Table 2. Different NGS platforms, their sequencing type, chemistry.
Platform
Sequencing Type
Amplification
Comments
Illumina
Synthesis
Bridge PCR
Short reads
SOLiD
Ligation
Emulsion PCR
High accuracy
Ion Torrernt
Synthesis, H+ detection
Emulsion PCR
short Time
454
Synthesis
Emulsion PCR
Long reads
Pac Bio
Synthesis
Ion Proton
Synthesis
Single molecule sequencing
Emulsion PCR
short Time
For this thesis, we have used Illumina and SOLiD platforms and I will briefly discuss their chemistry and current status.
Illumina sequencing system is one of the leaders in NGS. It is based on
sequencing by synthesis technology. In this system, denatured single stranded libraries with adapters are hybridized to the flow cell. Formamide is used
for denaturation and Bst polymerase is used for the extension and amplification to create clusters of individual molecules. The double stranded library is
made single stranded. Each base is labeled with a unique fluorophore and
one base at a time is incorporated and detected by a CCD camera. The HiSeq
2000 machine can generate up to 200 GB of data per run with the read length
2x100 bp.
SOLiD sequencing is based on ligation. Quantified SOLiD libraries are
added to paramagnetic beads and emulsion PCR is performed. Beads with
template are enriched and the selected beads undergo 3’ modification to
attach covalently to the slide or flow cell. During sequencing, a universal
sequencing primer hybridizes to the P1 adapter on the template beads and an
eight base labeled probe ligates to the sequencing primer. The first base of
this probe contains ligation site, a cleavage site at the 5th base and the last
base contains one of four fluorescent dyes. The images from the fluorescent
signal are recorded, the probe is cut at the cleavage site and the next probe is
ligated to generate a complex data in which each base is sequenced twice to
achieve an accurate sequence. SOLiD has recently launched the 5500 W
instrument in which a flow chip is used in place of emulsion PCR for the
amplification. Using this system we can get the data with 99.99% accuracy.
33
ChIP-reChIP
ChIP-reChIP is a method to detect two different proteins or histone marks
present at the same location in the genome. In this method, ChIP is performed with an antibody against the first protein. After the elution of the
protein-antibody-DNA complex from the solid phase; the complex is again
immunoprecipitated with a second antibody against the second protein. This
complex is eluted from the solid phase and DNA is extracted as described in
the ChIP method. Enrichment of ChIP-reChIP DNA can be verified by PCR
with known genomic loci to confirm that the studied proteins are located at
the same region in the genome. ChIP-reChIP enriched DNA could also be
used in sequencing to map all common target regions.
Co-Immunoprecipitation
Co-IP is a method used to find protein-protein interactions in vivo using
Western blotting. In this method, the extracted cell lysate is incubated with
an antibody on a solid phase and washed vigorously to remove the unbound
proteins. Later, the purified protein lysate is boiled and separated on a polyacrylamide gel and the separated proteins are transferred on to a PVDF
membrane. The membrane is probed with the antibody against the interacting protein of interest.
TGFβ treatment, nucleosome and RNA preparation
HepG2 cells were cultured as described above and serum starved overnight
and then treated with 2.5ng/mL of TGFβ for one hour. The cells were used
either for nucleosome preparation or RNA extraction. Nuclei were prepared
by homogenization with a Dounce homogenizer and the nuclear suspension
was slowly layered with the buffer containing 30% of sucrose. The nuclei
were collected and treated with 300U of MNase 1 at 37°C for 5 minutes.
Mono-nucleosome size DNA was prepared and DNA was gel purified before
the library preparation (Paper III).
Total RNA was extracted by using TRIzol-chloroform method according
to the manufacturer’s protocol. The quality of RNA was measured by using a
BioAnalyzer. The SOLiD transcriptome whole library preparation kit (Ambion) was used to prepare the strand-specific library.
34
Present Investigations
Aims of the present studies
The main aim of this thesis was to map regulatory elements in liver cells and
disease tissue by high-throughput sequencing. The specific aims are as follows:
Paper I and II
To investigate the TF binding sites for FOXA1, FOXA2, FOXA3, HNF4α,
GABP and their interaction in the human genome. The TFs were mapped
genome wide in the liver cell line HepG2 using ChIP-seq.
Paper III
To evaluate the effect of TGFβ on nucleosome positioning and RNA expression by nucleosome and RNA-sequencing.
Paper IV
To identify disease mechanisms of alcoholic steatohepatitis by mapping the
active chromatin marks H3K4me1, H3K4me3 and H3K27ac and the TF
USF1 in diseased and normal liver tissue.
35
Paper I
Differential binding and co binding pattern of FOXA1 and FOXA3 and their
relation to H3K4me3 in HepG2 cells revealed by ChIP-seq
We performed ChIP-seq for whole genome analysis of the TFs FOXA1,
FOXA3 and the histone mark, H3K4me3 on the SOLiD platform. We obtained FOXA2 data from paper II. From ChIP-seq data, we identified 8 175
FOXA1 peaks, 4 598 FOXA3 peaks and 41 480 H3K4me3 regions that correspond to 160 000 nucleosomes across the genome. Only a fraction of
FOXA1 (5.2%) and FOXA3 (12.2%) peaks were located within 1kb of TSS
and the majority of them were located more than 1 kb away from a TSS.
H3K4me3 signals were mainly localized downstream of the TSS. Of the
H3K4me3 regions 42% were located within 1 kb of a TSS. To identify promoter patterns, we compared H3K4me3 data with CAGE (Cap-analysis of
gene expression) tag data of HepG2 and found that 28% of H3K4me3 regions were not associated with any known transcript.
To study the relationship between FOXA proteins, we compared the
ChIP-seq data of the TFs FOXA1, FOXA2 and FOXA3 and found that these
three datasets had 2 304 regions in common. This suggested that the TFs not
only interact with DNA but also with each other. When we tested these interactions by Co-IP and ChIP-reChIP, we found that FOXA1 and FOXA3
bind to FOXA2, but there is no evidence of binding between FOXA1 and
FOXA3. Based on these results, we classified the binding sites of these factors pair-wise and found that 51% of FOXA2 and FOXA3 binding occurs
within 5kb of TSS whereas only 10% of FOXA2-FOXA1 bind within the
same distance.
We clustered H3K4me3 signals into seven clusters by K-means clustering
and each of these clusters has individual H3K4me3 signatures. All seven
clusters showed different levels of signals and expression. We found that the
genes with the highest expression had high levels of H3K4me3 enrichment
in HepG2 cells. Clusters I-III had the highest H3K4me3 enrichment upstream of the TSS and we found that it could be due to bidirectional promoters. When we looked at the CpG density at bidirectional promoters, we
found that there was high CpG density in all clusters except in cluster V.
Cluster V contains genes with TATA and CAAT boxes. Comparisons of the
clusters with CAGE tag data at TSSs of a gene revealed that 30% of genes in
each of these clusters had bidirectional promoters. Confirming of this bidirectionality, we also found an H3K4me3 double peak at FOXA1-2-3 binding sites. Another finding was that 11% of the genes in cluster-I had a
FOXA3 binding site within 1kb of TSS.
In summary, we showed that FOXA1 and FOXA3 TFs had differential
binding patterns in HepG2 cells and no verified interaction in vivo. Most of
the FOXA1, FOXA2 and FOXA3 bindings coincide with H3K4me3 and the
data suggested a directionality of this histone mark even though there is no
36
known TSS in close vicinity. We also showed that ChIP-seq can be used to
find mono-allelic binding of TFs or histone marks at SNPs. SNPs present
inside the regulatory regions have the capacity to differentially control the
activity of many genes and some of these SNPs could be associated with
disease. This tells us that ChIP-seq can also be used to identify diseaseassociated SNPs.
Paper II
Molecular interactions between HNF4a, FOXA2 and GABP identified at
regulatory DNA elements through ChIP-sequencing
In paper II we performed ChIP-seq of three TFs FOXA2, HNF4α and GABP
(NRF2) in HepG2 cells using the Solexa/Illumina platform. Before starting
this work, a paper was published by the group on USF1 and USF2 which
found that most of the USF2 binding was at distal positions and its binding
coincided with motifs for FOXA2 and HNF4α. They also found that there
are GABP binding motifs in close vicinity of TSSs bound by USF.
We found 18 693 HNA4α binding sites, 7 253 FOXA2 binding sites and
3 060 sites for GABP in HepG2 cells. Among these, 10% of HNF4α, 6.3%
of FOXA2 and 85.2% GABP binding sites were located within 500 bp of a
TSS so most of the HNF4α and FOXA2 binding sites are far from TSSs. We
also found that there was a large number of overlap between HNF4α and
GABP peaks at promoters. We have identified many variants of expected
motifs for FOXA2 and HNF4α and these motifs were highly similar to the
motifs already present in Transfac database. For GABP, we were able to
identify two motifs that were enriched equally for both GABP/NRF2 and for
NRF1. Although many GABP peaks contain motifs for both NRF1 and
NRF2, NRF1 motifs have a tight distribution in the peaks and some of the
GABP peaks contain an NRF1 motif only. Correlation of HepG2 expression
data with ChIP-seq data showed that the genes bound by HNF4α and
FOXA2 had higher expression than average expression in HepG2 cells but,
GABP did not show such an association in HepG2.
We also applied Co-IP studies as in paper I to see the possible interaction
between HNF4α, FOXA2 and GABP and identified that these factors were
interacting in vivo but, we did not see any interaction with USF2 except the
ChIP-seq overlap between the data sets.
In conclusion, we showed that ChIP-seq is a powerful method to map the
regulatory regions in the genome. Apart from this, ChIP-seq can be used to
identify SNPs associated with common diseases in TF binding regions.
37
Paper III
Nucleosome regulatory dynamics in response to TGF-beta treatment in
HepG2 cells.
In this paper, we performed high-throughput sequencing of nucleosomal
DNA and total RNA using the SOLiD platform before and after 1 hour of
TGFβ treatment. When we looked at the nucleosome positioning pattern
around the TSS, we found that there was a well-positioned nucleosome
downstream of TSS, another well positioned nucleosome upstream of the
TSS and a nucleosome free region between them. However, we did not see
any flanking positioned nucleosomes at 33% of TSSs.
We also observed a well-positioned nucleosome at exons as seen before
in CD4+ T cells. There was a higher density of nucleosomes in exons than in
introns and intergenic regions. When we separated the nucleosomes based on
their fuzzyness, exonic regions contained more fuzzy nucleosomes than
phased nucleosomes. This is compatible with the fact that there are AT-rich
sequences flanking exons which disfavor positioned nucleosomes.
When we looked the regulatory potential of TGFβ1 in HepG2 cells, 81%
of the TGFβ+ nucleosome positions matched with TGFβ- and a surprising
11% did not match with each other. After the strict filtering of the data, 24
318 nucleosome depleted loci were identified in TGFβ+ cells. When we
looked at the expression of genes with nucleosomes depleted in the vicinity,
after TGFβ1 treatment, 20% of those genes changed their expression.
To understand the role of TGFβ in regulation, we searched for TF motifs
using the JASPAR database at nucleosome depleted regions and found that
these regions were highly enriched with TF motifs. Some of these TFs, for
example SPI1, SMADs, FEV, ELF5, and HNF4α etc., are regulated by
TGFβ1. By coupling these results with the RNA-seq expression change after
TGFβ1 treatment it was found that many nucleosomes with TF motifs that
changed positions were related to the gene expression change. Of nucleosomes in introns that had TF motifs and that were depleted after treatment,
61% were associated with exon expression change. Out of 2 469 intronic
distal loci, 1 173 were associated with expression change in 306 genes.
When we compared the expression change after TGFβ1 treatment, 591
genes were up-regulated and 196 were down-regulated. Genes with neuronal
function, ion channels and ion transporters, neuro-transmitters, G-protein
coupled receptors were up-regulated and the TFs that were known to be regulated by TGFβ1 were among the most up-regulated genes.
Using HNF4α ChIP-seq data from paper II, we identified 37 candidate
HNF4α binding sites at nucleosome depleted regions in TGFβ- cells compared to TGFβ+ cells and the ChIP-qPCR validations supported our findings.
In summary, our data showed that 1 hour of TGFβ1 treatment depleted
many nucleosomes and some of these depletions were associated with
38
change in the expression of genes. In addition, chromatin is dynamic and
plays an important role in transcriptional regulation in general. TGFβ1 therefore has an important impact on chromatin organization and function.
Paper IV
ChIP-seq in steatohepatitis and normal liver tissue identifies candidate disease mechanism related to progression to cancer.
To learn more of disease mechanisms we mapped the active chromatin
marks H3K4me1, H3K4me3 and H3K27ac and the TF USF1 in ASH patients and in controls using ChIP-seq. We identified 2 054 USF1 peaks in
control and 1 766 for ASH and out of these 44% of peaks in control and 54%
in ASH contained a USF1 motif. Peak calling for chromatin marks gave
10 256 H3K4me1, 14 668 H3K4me3 and 11 656 H3K27ac peaks in the control sample and 10 674 for H3K4me1, 18 358 H3K4me3 and 12 385
H3K27ac in ASH sample. For each histone modification, the top 1 000 differentially enriched peaks were identified.
When we looked at the genomic localization of differentially enriched
histone modification peaks at +/-2 kb of a TSS, a majority of the H3K4me3
peaks were located at TSS as expected, whereas the peaks for H3K4me1 and
H3K27ac were distributed both at TSS and non-TSS regions. Surprisingly
there was an increased number of peaks in ASH patient compared to control.
There were 1 600 genes that contained histone modification peaks only in
control and 3 000 genes contain peaks only in patient. Comparison of differentially enriched USF1 peaks with histone modification peaks gave 7 overlaps which suggests that USF1 is just one among many factors that may contribute to differential gene regulation.
Gene ontology of genes associated with differentially enriched histone
modification peaks revealed that the genes from control sample were enriched with different metabolic process whereas ASH patient sample were
enriched for cancer associated genes. This suggests that the disease in this
patient is progressing towards cancer. We observed different genes associated with both ASH and NASH which indicates that common pathophysiological mechanisms exist. We looked at the pattern of histone modifications at
the genes involved in alcohol metabolism and disease (CD14, TLR4, TNF4α
and TGFβ) and found evidence that they contribute to disease in this case.
Using SNP calling in differentially enriched regions, we identified 178
potentially functional SNPs that are present in the GWAS catalogue and
additionally 1 237 GWAS SNPs with high LD were identified in the patient
and 456 in the control. The majority of these SNPs were located at the binding motifs of different TFs and DHS sites which suggest a regulatory potential of these SNPs. Some of these SNPs are within the binding motif of the
39
TFs associated with ASH and NASH. We also identified 383 novel SNPs
from ASH sample that might be involved in the etiology of the disease.
RNA expression with qPCR at differentially enriched USF1 sites did not
follow USF1 ChIP-seq pattern. We compared the histone modification ChIPseq data with RNA expression by qPCR and found that RNA expression at
majority of the sites followed the ChIP-seq pattern. In summary, our study
identified many genes associated with liver disorders and we also found
many genes associated with cancer pathways. This illustrates that analysis of
chromatin marks in disease tissue may provide useful insights into disease
mechanisms.
40
Concluding remarks and future perspectives
For this thesis, we used ChIP with next generation sequencing to map TFs,
histone marks and nucleosome positions and to determine gene expression.
Rapid advances in NGS technology have allowed the mapping of regions
in the genome in a short time span. Due to high competition, the price for
sequencing and reagents has dropped rapidly. At the time of our ChIP-seq
paper I and II [10, 119], very few genome-wide ChIP-seq studies had been
performed. Today there are thousands of ChIP-seq datasets, mainly from the
ENCODE project, and many additional have been published.
We have mapped FOXA TFs, HNF4α, GABP, USF1 and a few histone
marks by ChIP-seq. In paper I and II, we showed that ChIP-seq not only can
be used to map the TF binding sites, but also to identify protein-protein interactions genome-wide. We also developed a method to identify allelespecific binding of TF or histone modification and identified many functional SNPs. During the time of this thesis work, it was found that the TFs studied in paper I and II are sufficient to reprogram fibroblasts to liver cells
which illustrates the importance of these factors. In paper III, we mapped the
nucleosome positions across the HepG2 genome before and after TGFβ1
treatment and measured the nucleosome dynamics in response to TGFβ1.
We have seen a change in RNA expression of genes at nucleosome depleted
regions after treatment. Nucleosome positioning data is valuable for delineating gene regulatory regions. After the extensive studies in the liver cell line
HepG2, we focused on liver tissue from ASH patients and controls. We
identified differences in histone marks which provide insights into ASH
disease mechanisms and in this case its progression towards cancer. There
was a correlation between ChIP-seq of histone marks and RNA expression
which suggests that chromatin marks could be used to get further insights
into disease processes.
Advances in NGS technology has facilitated introduction of new ChIP
based methods. ChIA-PET was developed to map binding sites for nuclear
proteins and long way chromatin interactions globally and has been applied
to TFs and RNA Pol II [120, 121]. ChIP-exo was developed to map TFs at
single base-pair resolution [122] and will be useful to identify TF binding
sites more precisely.
There are ~1 850 TFs and it is not an easy task to map all TFs in one cell
and even more demanding to map them in all cell types and tissues. Consequently there is a need to develop high-throughput methods. Recently an
41
HT-ChIP method was developed for the preparation of sequencing library
samples using a pipetting robot [123]. Our group also developed a new HTChIP-library preparation method with which we can prepare many ChIPlibraries in a short time span and perhaps more efficiently than any other
method (Manuscript in preparation).
A lot of things have happened in genetics and genomics since the discovery of the structure of DNA exactly 60 years ago, but we still need answer to
many questions. Each and every regulatory element in the genome probably
has an important function and identification of their location and the gene(s)
they act on will provide useful information. We still need to understand the
epigenetic behavior in cancer cells and normal cells and high-throughput
mapping of several TFs in tumors by ChIP-seq may be a good strategy to
understand disease mechanisms. Mapping of regulatory regions, non-coding
RNAs, mRNA in dynamic conditions i.e. after treatment with different biological agents may provide clues for many diseases. NGS technology will
soon play a great role in disease diagnostics in patients since one can sequence the entire genome in one or a few days using novel bench-top instruments. Today interpretation of genomic sequences is limited to the protein coding regions, but soon we will also be able to deduce the functional
consequences of variation in gene regulatory regions.
42
Acknowledgements
This thesis work would not have been possible without following people. I
would like to express my deepest gratitude towards the people who helped
me directly or indirectly.
First of all, I would like to thank heartfully to my Supervisor, Professor
Claes Wadelius, who gave me the opportunity to pursue my PhD in his
group. I am very much thankful for your immense support, valuable discussions and suggestions.
I also would like to thank my co-supervisor Professor Jan Komorowski for
great collaboration and group meetings.
I am very much thankful to the former members of the group. Mehdi Motallebipour, for all your help during my early days in the lab. Ola Wallerman,
for taking care of large datasets and for all the help whenever needed. The
present members in the lab, Gang Pan, for all the discussions we have during
lunch and work. Marco Cavalli and Helena Nord, for all your help and fun in
lab.
Thanks to the present and former members of Komorowski’s lab especially,
Susanne, Stefan and Robin for all the data analysis, group meetings we have
together.
Professor Marta Alarcon, who gave me the opportunity to work in her lab.
Sergey Kozyrev, for all your suggestions and help. Special thanks to Hong
Yin, who introduced me to Claes.
Lena Åslund, for all the help during teaching period and for the discussions
in the journal club. Marcela Ferella, for being a good friend in the office.
I am grateful to the members of Uppsala Genome Center (Ulf, Inger,
Katherina and Cecelia) and Seq & SNP platform for the sequencing service.
Thanks to the people at Life technologies, California for the great collaboration.
43
Thanks to all other Rudbeck friends, Angelica, Ammar, Nikki, Hamid, Sara
E, Tanmoy, Jitendra and Kali.
Thanks to all Indian friends in Uppsala who made my stay more easy and
fun. Chandu-Geeta and Kiran K-Madhuri for always being there to help our
family in all aspects. Ravi-Sushma, good luck with your thesis and baby.
Ramesh-Laura, for your kindness. Srisailam-Swathi, for being good friends.
Kiran J-Bhavya, for your help to Akshara. Raghuveer, wish you good luck
with the new innings. Nallan-Gayatri, good luck with your thesis Nallan.
Sudhakar-Saritha, all the best in Canada. Rambabu-Lakshmi, thank you.
Thanks to all my friends in India, Vidya, Renuka, Aruna, C.V, Raja.B,
Raja.O and Ramana. Thank you so much Siva for always taking care of my
Tirumala trip.
Thanks to my sisters and brother-in-laws, Aruna-Nageswar Reddy and
Prameela-Prathap for all your help. Eswar Reddy mama for always being
there for anything. Peddnanna, Jayarami Reddy for all your support and Sesha Reddy anna for being a good friend than a brother. My wife’s parents,
Balarami Reddy mama and Ratna atha, for all your help and support. My
Padmavathi pinni, for all your care during my childhood and school days.
Thanks to my parents, Nagi Reddy and Lakshmidevi for your unconditional
support even my interests were beyond your limits. Simple thanks may not
enough.
Many thanks to my wife, Vijayalakshmi for all the support and love. Finally,
my little daughter Akshara, for all the joy you brought into our lives.
44
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Acta Universitatis Upsaliensis
Digital Comprehensive Summaries of Uppsala Dissertations
from the Faculty of Medicine 904
Editor: The Dean of the Faculty of Medicine
A doctoral dissertation from the Faculty of Medicine, Uppsala
University, is usually a summary of a number of papers. A few
copies of the complete dissertation are kept at major Swedish
research libraries, while the summary alone is distributed
internationally through the series Digital Comprehensive
Summaries of Uppsala Dissertations from the Faculty of
Medicine.
Distribution: publications.uu.se
urn:nbn:se:uu:diva-198579
ACTA
UNIVERSITATIS
UPSALIENSIS
UPPSALA
2013