bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: O1
Oral presentation
10th Benelux Bioinformatics Conference bbc 2015
O1. CELL TYPE-SELECTIVE DISEASE ASSOCIATION
OF GENES UNDER HIGH REGULATORY LOAD
Mafalda Galhardo 1 , Philipp Berninger 2 , Thanh-Phuong Nguyen 1 , Thomas Sauter 1 & Lasse Sinkkonen 1*.
Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg 1 ; Biozentrum, University of Basel
and Swiss Institute of Bioinformatics, Basel, Switzerland 2 . * lasse.sinkkonen@uni.lu
Identification of biomarkers and drug targets is a key task of biomedical research. We previously showed that diseaselinked
metabolic genes are often under combinatorial regulation (Galhardo et al. 2014). Here we extend this analysis to
include almost 100 transcription factors (TFs) and key histone modifications from over 100 samples to show that genes
under high regulatory load (HRL) are enriched for disease-association across cell types. Network and pathway analysis
suggests the central role of HRL genes in biological networks, under heavy regulation both at transcriptional and posttranscriptional
level, as a possible explanation for the observed enrichment. Thus, epigenomic mapping of enhancers
presents an unbiased approach for identification of novel disease-associated genes.
INTRODUCTION
Identification of disease-relevant genes and gene products
as biomarkers and drug targets is one of key tasks of
biomedical research. Still, a great majority of research is
focused on a small minority of genes while many remain
unstudied (Pandey et al. 2014). Unbiased prioritization
within these ignored genes would be important to harvest
the full potential of genomics in understanding diseases.
Many databases to catalog disease-associated genes have
been created, including DisGeNET that draws from
multiple sources (Bauer-Mehren et al. 2010). In addition,
large amounts of publicly available epigenomic data on
the cell type-selective regulation of these genes has been
produced. The importance of epigenetic regulation for
disease development is increasingly recognized, for
example in analysis of GWAS studies where causal SNPs
are mostly located within gene regulatory regions
(Maurano et al. 2012).
METHODS
Public ChIP-seq data produced by the ENCODE project
(Dunham et al. 2012), the BLUEPRINT Epigenome
project (Martens et al. 2013) and the NIH Epigenomic
Roadmap project (Kundaje et al. 2015) were downloaded
on May 2014. The data were used to rank active protein
coding genes (based on NCBI Entrez and marked by
H3K4me3) by their regulatory load based on the number
of associated TFs or enhancer (H3K27ac) regions using
GREAT tool. The enrichment of disease genes from
DisGeNET among HRL genes was tested using either
Matlab® hypergeometric cumulative distribution function
and adjusted for multiple testing with the Benjamini and
Hochberg methodology or normalized enrichment score.
Enriched diseases were clustered using R package
“blockcluster”. Peak calling for super-enhancers was done
using HOMER. A liver disease gene network was
constructed from HPRD based on liver diseases genes
from MeSH and genes from CTD and had 8278
interactions. Statistical analysis of KEGG pathway
enrichments and betweenness centrality was done using
random sampling tests. miRNA target predictions were
obtained from TargetScan6.2. Further details of the used
methods can be found in Galhardo et al. 2015.
RESULTS & DISCUSSION
Using ENCODE ChIP-Seq profiles for 93 transcription
factors (TFs) in nine cell lines, we show that HRL genes
are enriched for disease-association across cell types
(Figure 1). TF load correlates with the enhancer load of
the genes, allowing the identification of HRL genes by
epigenomic mapping of active enhancers marked by
H3K27ac modifications. Identification of the HRL genes
across 139 samples from 96 different cell and tissue types
reveals a consistent enrichment for disease-associated
genes in a cell type-selective manner.
The HRL genes are involved in more pathways than
expected by chance, exhibit increased betweenness
centrality in the interaction network of liver disease genes,
and carry longer 3’UTRs with more microRNA binding
sites than genes on average, suggesting a role as hubs
within regulatory networks.
Thus, epigenomic mapping of enhancers presents an
unbiased approach for identification of novel diseaseassociated
genes (Galhardo et al. 2015).
Transcription factor
binding sites
(93 TFs)
9 ENCODE cell lines
A549, GM12878, H1hESC, HCT116,
HeLaS3, HepG2, HUVEC, K562, MCF7
Gene ranking by
regulatory load
(Number of TFs or enhancers per gene)
ChIP-seq data (Human)
Active enhancers
(H3K27ac)
139 samples comprising
96 tissue or cell types
Disease genes
(min score 0.08)
High regulatory load genes are enriched
for disease association
FIGURE 1. Worflow of the disease-gene enrichment analysis.
Figure 1
REFERENCES
Pandey AK et al. PLoS One, 9:e88889 (2014).
Bauer-Mehren A et al. Nucleic Acids Res., 33:D514-D517 (2010).
Maurano et al. Science, 337:1190-1195 (2012).
Galhardo et al. Nucleic Asics Res. 42:1474-1496 (2014).
Dunham et al. Nature, 489:57-74 (2012)
Martens et al. Haematologica, 98:1487-1489 (2013)
Kundaje et al. Nature, 518:317-330 (2015).
Galhardo et al. Nucleic Acids Res. 10.1093/nar/gkv863 (2015).
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