Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Schmidt, Florian; Hebel, Marie; Wegner, Martin; Baumgarten, Nina; Goeke, J.; Vreeken, Jilles; Schulz, Marcel H.

doi:10.1101/585125

Cited by 9 publications

(7 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We associated emCpGs with target genes by relying on (i) the STITCHIT database of regulatory elements to gene links [47] or (ii) genomic distance. It is well known that cis-regulatory elements may regulate distal genes, which are not necessarily the closest ones [70].…”

Section: Discussionmentioning

confidence: 99%

“…To assess the functional relevance of the identified emCpGs in these different cellular contexts, we estimated the enrichment for biological processes and pathways in the list of genes linked to emCpGs for each pair of emTF-cancer cohort. We linked emCpGs to genes using gene-specific regulatory elements defined by the STITCHIT algorithm, which relies on an integrative analysis of epigenetic and transcriptomic data [47]. This method allows to assign emCpGs lying in distal cis-regulatory elements to their potential target genes.…”

Section: Emcpgs Are Predicted To Regulate Genes Involved In Immune Response Cell Fate Determination and Cancer Pathwaysmentioning

confidence: 99%

See 1 more Smart Citation

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Lemma

Fleischer

Martinsen

et al. 2021

Preprint

View full text Add to dashboard Cite

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesized that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer cohorts. We performed emQTL (expression-methylation quantitative trait loci) analyses in each cohort and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding site in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF-binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumour-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Emcpgs Are Predicted To Regulate Genes Involved In Immune Response Cell Fate Determination and Cancer Pathwaysmentioning

confidence: 99%

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Lemma

Fleischer

Martinsen

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…While approaches like JEME [27], FOCS [28] or STITCHIT [29] offer the linkage of regulatory elements on a gene-specific level, these methods require the availability of large data sets for the considered species and tissues, which is generally not the case. In practice, the established window and nearest gene-based linkage paradigms are still being used [25].…”

Section: Introductionmentioning

confidence: 99%

Integrative prediction of gene expression with chromatin accessibility and conformation data

Schmidt

Kern

Schulz

2020

Epigenetics & Chromatin

Self Cite

View full text Add to dashboard Cite

Background: Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter-enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organization of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability. Results: We have extended our TEPIC framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We designed a novel machine learning approach that allows the prioritization of TFs binding to distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines. Conclusion: We present a novel approach that can be used to prioritize TFs involved in distal and promoter-proximal regulatory events by integrating chromatin accessibility, conformation, and gene expression data. We show that the integration of chromatin conformation data can improve gene expression prediction and aids model interpretability.

show abstract

“…While approaches like FOCS [27] or STITCHIT [28] offer the linkage of regulatory elements on a genespecific level, these methods require the availability of large data sets for the considered species and tissues, which is generally not the case. In practice, the established window and nearest gene based linkage paradigms are still being used [25].…”

Section: Introductionmentioning

confidence: 99%

Integrative prediction of gene expression with chromatin accessibility and conformation data

Schmidt

Kern

Schulz

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Enhancers play a fundamental role in orchestrating cell state and development. Although several methods have been developed to identify enhancers, linking them to their target genes is still an open problem. Several theories have been proposed on the functional mechanisms of enhancers, which triggered the development of various methods to infer promoter enhancer interactions (PEIs). The advancement of high-throughput techniques describing the three-dimensional organisation of the chromatin, paved the way to pinpoint long-range PEIs. Here we investigated whether including PEIs in computational models for the prediction of gene expression improves performance and interpretability. Results: We have extended our Tepic framework to include DNA contacts deduced from chromatin conformation capture experiments and compared various methods to determine PEIs using predictive modelling of gene expression from chromatin accessibility data and predicted transcription factor (TF) motif data. We found that including long-range PEIs deduced from both HiC and HiChIP data indeed improves model performance. We designed a novel machine learning approach that allows to prioritize TFs in distal loop and promoter regions with respect to their importance for gene expression regulation. Our analysis revealed a set of core TFs that are part of enhancer-promoter loops involving YY1 in different cell lines. Conclusion: We show that the integration of chromatin conformation data improves gene expression prediction, underlining the importance of enhancer looping for gene expression regulation. Our general approach can be used to prioritize TFs that are involved in distal and promoter-proximal regulation using accessibility, conformation and expression data.

show abstract

Integrative analysis of epigenetics data identifies gene-specific regulatory elements

Cited by 9 publications

References 65 publications

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Integrative prediction of gene expression with chromatin accessibility and conformation data

Integrative prediction of gene expression with chromatin accessibility and conformation data

Contact Info

Product

Resources

About