bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P11. IDENTIFICATION OF TRANSCRIPTION FACTOR CO-ASSOCIATIONS
IN SETS OF FUNCTIONALLY RELATED GENES
Pieter De Bleser 1,2,4* , Arne Soetens 1,2,4 & Yvan Saeys 1,3,4 .
VIB Inflammation Research Center 1 ; Department of Biomedical Molecular Biology 2 , Department of Respiratory
Medicine 3 , Ghent University 4 . * pieterdb@irc.vib-ugent.be
Co-associations between transcription factors (TFs) have been studied genome-wide and resulted in the identification of
frequently co-associated pairs of TFs. Co-association of TFs at distinct binding sites is contextual: different combinations
of TFs co-associate at different genomic locations, producing a condition-dependent gene expression profile for a cell.
Here, we present a novel method to identify these condition-dependent co-associations of TFs in sets of functionally
related genes.
INTRODUCTION
The functional expression of genes is achieved by
particular interactions of regulatory transcription factors
(TFs) operating at specific DNA binding sites of their
target genes. Dissecting the specific co-associations of TFs
that bind each target gene represent a difficult challenge.
Co-associations of transcription factor pairs have been
studied genome-wide and resulted in the identification of
frequently co-associated pairs of TFs (ENCODE Project
Consortium, 2012). It was found that TFs co-associate in a
context-specific fashion: different combinations of TFs
bind different target sites and the binding of one TF might
influence the preferred binding partners of other TFs. Here,
we present a tool to identify these condition-dependent coassociations
of TFs in sets of functionally related genes
(e.g. metabolic pathways, tissues, sets of TF target genes,
sets of differentially regulated genes).
METHODS
In a first step, we determine the set of regulatory TFs for
each gene (Tang et al., 2011) in the set using the ChIP-Seq
binding data for 237 TFs from the ReMap database
(Griffon et al., 2015). This results in a number of
regulatory ChIP-Seq binding regions per TF per gene,
represented as a matrix in which each row corresponds to
a gene while the columns correspond to the used TF. In a
next step, this matrix is used as input to the distance
difference matrix (DDM) algorithm, modified to
accommodate this data. The DDM algorithm is a method
that simultaneously integrates statistical over
representation and co-association of TFs (De Bleser et al.,
2007). The result matrix is subsequently reduced, retaining
only the columns of over-represented and co-associated
TFs. Visualization is done by (1) hierarchical clustering of
the reduced result matrix and reordering of the columns
and (2) conversion of the reduced result matrix into a SIF
(simple interaction file format) file, summarizing the
regulator-regulated relationships between transcription
factors and target genes. This SIF file can be imported into
CytoScape for visualization of the regulatory network.
RESULTS & DISCUSSION
FOXF1, TBX3, GATA6, IRX3, PITX2, DLL1 and
NKX2-5 are experimentally verified target genes of the
EZH2 transcription factor (Grote et al., 2013).
Running the transcription factor co-association analysis
method on this data set results in the clustering solution
plot shown in Figure 1.
The strongest associations between TFs are found between
EZH2, POU5F1, SUZ12 and CTBP2. A secondary cluster
of transcription factor associations is composed of
EOMES, SMAD2+3 and NANOG.
The finding of SUZ12 as a cofactor can be accounted for:
EZH2 and SUZ12 are subunits of Polycomb repressive
complex 2 (PRC2), which is responsible for the repressive
histone 3 lysine 27 trimethylation (H3K27me3) chromatin
modification (Yoo and Hennighausen, 2012). CTBP2 is a
known transcriptional repressor (Turner and Crossley,
2001).
The method has been applied previously for the
identification of TFs associated with both high tissuespecificity
and high gene expression levels (Rincon et al.,
2015). The method will be made available as a web tool.
FIGURE 1. Transcription factor co-associations in the EZH2 data set.
Note the tendency of EZH2 to co-localize with POU5F1, SUZ12 and
CTBP2.
REFERENCES
De Bleser,P. et al. (2007) A distance difference matrix approach to identifying
transcription factors that regulate differential gene expression. Genome Biol., 8,
R83.
ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements
in the human genome. Nature, 489, 57–74.
Griffon,A. et al. (2015) Integrative analysis of public ChIP-seq experiments reveals
a complex multi-cell regulatory landscape. Nucleic Acids Res., 43, e27.
Grote,P. et al. (2013) The tissue-specific lncRNA Fendrr is an essential regulator of
heart and body wall development in the mouse. Dev. Cell, 24, 206–214.
Rincon,M.Y. et al. (2015) Genome-wide computational analysis reveals
cardiomyocyte-specific transcriptional Cis-regulatory motifs that enable
efficient cardiac gene therapy. Mol. Ther. J. Am. Soc. Gene Ther., 23, 43–52.
Tang,Q. et al. (2011) A comprehensive view of nuclear receptor cancer cistromes.
Cancer Res., 71, 6940–6947.
Turner,J. and Crossley,M. (2001) The CtBP family: enigmatic and enzymatic
transcriptional co-repressors. BioEssays News Rev. Mol. Cell. Dev. Biol., 23,
683–690.
Yoo,K.H. and Hennighausen,L. (2012) EZH2 methyltransferase and H3K27
methylation in breast cancer. Int. J. Biol. Sci., 8, 59–65.
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