Biology subjects

Mathelier, A.

Publications and source records attributed to Mathelier, A..

17 recordsLinked to original sources

bioRxiv2025

HiFIseek: gene-specific enrichment of high-impact mutations in associated genomic regions

Transcription is regulated through the sequence-specific binding of transcription factors (TFs) to cis-regulatory regions (CRRs). Although scattered along the genome, multiple CRRs are brought in close spatial proximity to the genes they regulate through the formation of DNA loops. The sequence component of transcriptional regulation suggests that DNA variants in CRRs could disrupt TF-DNA interactions and gene regulatory networks through a cascading effect. To date, only a few cases of recurrent cis-regulatory variants have been described. As an alternative to variant recurrence, some methods utilize genomic annotations that indicate the functional impact (FI) of a variant and the CRRs of each gene to detect the enrichment of high-impact cis-regulatory variants (CRVs). However, the agreement across the different associations and FI scoring methods remains unexplored. This work demonstrates that gene-CRR and FI scoring methods exhibit little consensus, highlighting the impact of the choice of a specific CRR-score combination in a given analysis. In addition, we demonstrate that cancer genes exhibit a higher frequency of FI variants compared to non-cancer genes. Based on this, we developed HiFIseek, a Snakemake pipeline exploring the enrichment of FI variants in associated genomic regions using all region-score combinations and two enrichment detection methods. We apply HiFIseek to detect genes exhibiting high FI CRVs in eight cancer cohorts and a set of breast cancer risk-associated single-nucleotide polymorphisms (SNPs). In the cancer cohorts, we observe a small degree of agreement across CRR-score combinations. Although HiFIseek detected known cancer-related genes with FI CRVs, most of them did not show significant changes in their expression. In the SNPs cohort, HiFIseek found small consensus across CRR-score combinations. However, one of the combinations returned 18 known cancer-related genes, including BRCA1, showing an enrichment of high-impact CRVs.

bioinformatics↗

bioRxiv2025

SUMOylation Restricts Adaptive Thermogenesis by Suppressing Beiging Gene Networks

SUMOylation regulates chromatin states and transcriptional programs that preserve cellular identity, yet how transient perturbation of the SUMO pathway impacts adipocyte plasticity remains unclear. Here we show that brief pharmacologic inhibition of SUMO conjugation in human pre-adipocytes using TAK-981 primes stable de novo beige differentiation in the presence of the PPARG agonist rosiglitazone. Transient TAK-981 exposure before adipogenic induction produces long-lasting changes in the transcriptome and metabolism of mature adipocytes, including robust induction of canonical beiging markers like UCP1 and increased mitochondrial respiration. Mechanistically, ATAC-seq and transcription factor footprinting revealed immediate and durable chromatin remodeling and early mobilization of CEBP family members, followed by stable activation of CEBPA and PPARG regulatory networks. ChIP experiments demonstrated loss of H3K27me3 and gain of H3K27ac at PPAR response elements within key thermogenic enhancers, with increased PPARG occupancy across the UCP1 regulatory unit. This mechanism is enforced by enhanced cAMP-PKA-p38 signaling, and stabilization of beiging transcriptional activators. Our data support a model in which transient relief of SUMO-mediated repression unlocks dominant regulatory units, notably the UCP1 enhancer cluster, producing a monomorphic, cell type specific reprogramming toward adaptive thermogenesis. These findings identify SUMOylation as a reversible epigenetic barrier to adipocyte beiging and suggest that temporally controlled SUMO pathway inhibition combined with PPARG activation could be exploited to modulate adipose tissue thermogenic capacity. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/642034v3_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@9efd29org.highwire.dtl.DTLVardef@1fecda3org.highwire.dtl.DTLVardef@16eb784org.highwire.dtl.DTLVardef@1d25683_HPS_FORMAT_FIGEXP M_FIG C_FIG SUMOylation restricts de novo differentiation of pre-adipocytes into beige adipocytes by repressing cAMP signaling and preventing epigenetic and transcriptional reprogramming of white adipocytes.

molecular biology↗

bioRxiv2024

Gene regulatory network integration with multi-omics data enhances survival predictions in cancer

The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. The role of gene regulation as a core driver of biological processes and its role in the development and progression of cancer has been well established. Transcriptional regulation, a crucial aspect of gene regulation, can be represented as a network of interactions between regulators (such as transcription factors) and their target genes. These networks are known as gene regulatory networks (GRNs). Several joint dimensionality reduction (JDR) methods and tools for integrating multi-modal data have been developed in recent years. This study presents a new approach to integrate multi-modal data of different dimensions to consider sample-specific GRNs with multi-omics data in ten cancer datasets from The Cancer Genome Atlas (TCGA). We compare JDR models that include GRNs with those that do not, assessing their association with patient survival. We find that including GRNs can improve associations with patient survival across several cancer types. Focusing on liver cancer, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression.

systems biology↗

bioRxiv2023

Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis; specifically, we identified three types of emQTL networks associated with alterations linked to the regulation of proliferation, metabolism, and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in transcription factor binding regions (TFBRs) located in enhancers. The underlying CpGs were topologically linked to upregulated genes through chromatin loops. Loss of enhancer methylation and target genes were exemplified in pancreatic cancer. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL. Taken together, our integrative pan-cancer analysis reveals a common architecture of aberrant DNA demethylation that illustrates a convergence of pathological regulatory mechanisms across cancer types.

cancer biology↗

bioRxiv2023

Identification of transcription factor co-binding patterns with non-negative matrix factorization

Transcription factor (TF) binding to DNA is critical to transcription regulation. Although the binding properties of numerous individual TFs are well-documented, a more detailed comprehension of how TFs interact cooperatively with DNA, forming either complex or co-binding to the same region, is required. Indeed, the combinatorial binding of TFs is essential to cell differentiation, development, and response to external stimuli. We present COBIND, a novel method based on non-negative matrix factorization (NMF) to identify TF co-binding patterns automatically. COBIND applies NMF to one-hot encoded regions flanking known TF binding sites (TFBSs) to pinpoint enriched DNA patterns at fixed distances. We applied COBIND to 8,293 TFBS datasets from UniBind for 404 TFs in seven species. The method uncovered already established co-binding patterns (e.g., between POU5F1 and SOX2 or SOX17) and new co-binding configurations not yet reported in the literature and inferred through motif similarity and protein-protein interaction knowledge. Our extensive analyses across species revealed that 84% of the studied TFs share a co-binding motif with other TFs from the same structural family. The co-binding patterns captured by COBIND are likely functionally relevant as they harbor higher evolutionarily conservation than isolated TFBSs. Open chromatin data from matching human cell lines further supported the co-binding predictions. Finally, we used single-molecule footprinting data from mouse embryonic stem cells to confirm that the co-binding events captured by COBIND were likely occurring on the same DNA molecules.

bioinformatics↗

bioRxiv2022

MoDLE: High-performance stochastic modeling of DNA loop extrusion interactions

DNA loop extrusion emerges as a key process establishing genome structure and function. We introduce MoDLE, a computational tool for fast, stochastic modeling of molecular contacts from DNA loop extrusion capable of simulating realistic contact patterns genome wide in a few minutes. MoDLE accurately simulates contact maps in concordance with existing molecular dynamics approaches and with Micro-C data, and does so orders of magnitude faster than existing approaches. MoDLE runs efficiently on machines ranging from laptops to high performance computing clusters, and opens up for exploratory and predictive modeling of 3D genome structure in a wide range of settings.

bioinformatics↗

bioRxiv2021

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

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 types. We performed emQTL (expression-methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites 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 tumor-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 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.

cancer biology↗

bioRxiv2020

UniBind: maps of high-confidence direct TF-DNA interactions across nine species

Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. Hence, it is critical to locate these TF-DNA interactions to understand transcriptional regulation. The availability of datasets generated by chromatin immunoprecipitation followed by sequencing (ChIP-seq) empowers our efforts to predict the specific locations of TFBSs with greater confidence than previously possible by fusing computational and experimental approaches. In this work, we processed ~10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~56 million TFBSs with experimental and computational evidence for direct TF-DNA interactions for 644 TFs in >1,000 cell lines and tissues. These TFBSs were used to predict >198,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface (https://unibind.uio.no/), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

genomics↗

bioRxiv2020

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of the gene regulatory program in cancers

BackgroundMost cancer alterations occur in the noncoding portion of the human genome, which contains important regulatory regions acting as genetic switches to ensure gene expression occurs at correct times and intensities in correct tissues. However, large scale discovery of noncoding events altering the gene expression regulatory program has been limited to a few examples with high recurrence or high functional impact. ResultsWe focused on transcription factor binding sites (TFBSs) that show similar mutation loads than what is observed in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluated the combined effects of transcriptional and post-transcriptional alteration on the dysregulation of the regulatory programs in cancer. The analysis of seven cancer cohorts culminated with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that were associated with a cascading trans-effect deregulation on the cells regulatory program. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predicted 17 miRNAs as pan-cancer-associated through deregulation of their target gene networks. Overall, our predictions were enriched for protein-coding and miRNA genes previously annotated as cancer drivers. Functional enrichment analyses highlighted that cis-regulatory mutations are associated with the dysregulation of key pathways associated with carcinogenesis ConclusionsThese pan-cancer results suggest that our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients. It highlights how the gene regulatory program is disrupted in cancer cells by combining transcriptional and post-transcriptional regulation of gene expression.

genomics↗

bioRxiv2020

Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

BackgroundAbnormal DNA methylation is observed as an early event in breast carcinogenesis. However, how such alterations arise is still poorly understood. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and have been shown to play key roles in various biological processes. Here, we integrate miRNA expression and DNA methylation at CpGs to study how miRNAs may affect the breast cancer methylome and how DNA methylation may regulate miRNA expression. ResultsmiRNA expression and DNA methylation data from two breast cancer cohorts were subjected to genome-wide correlation analysis. Clustering of the miRNA expression-DNA methylation association pairs significant in both cohorts identified distinct clusters of miRNAs and CpGs. These clusters recapitulated important biological processes associated with breast cancer pathogenesis. Notably, two major clusters were related to immune or fibroblast infiltration, hence identifying miRNAs associated with cells of the tumor microenvironment, while another large cluster was related to estrogen receptor (ER) signaling. Studying the chromatin landscape surrounding the CpGs associated with the estrogen-signaling cluster, we found that miRNAs from this cluster are likely to be regulated through DNA methylation of enhancers bound by FOXA1, GATA2 and ER-alpha. Further, at the hub of the estrogen-cluster, we identified hsa-miR-29c-5p as negatively correlated with the mRNA and protein expression of the DNA methyltransferase DNMT3A, a key enzyme regulating DNA methylation. We found deregulation of hsa-miR-29c-5p already in pre-invasive breast lesions and postulate that hsa-miR-29c-5p may trigger early event abnormal DNA methylation in ER positive breast cancer. ConclusionsWe describe how miRNA expression and DNA methylation interact and associate with distinct breast cancer phenotypes.

cancer biology↗

bioRxiv2019

Copy number motifs expose genome instability type and predict driver events and disease outcome in breast cancer

Tumor evolution is dependent on and constrained by the genotypes emerging from genome instability. We hypothesized that non-site-specific copy number motifs would correlate with underlying replication defects and also with tumor and patient fate. Six feature detectors were defined to characterize and score the local spatial behaviour of a copy number profile. By accumulating scores across genomic regions, a low-dimensional representation of the tumor genome was obtained. The proposed Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to 2384 breast tumors from three breast cancer cohorts, revealing distinct copy number motifs in established molecular subtypes. A prognostic index combining the features predicted breast cancer specific survival better than both the genomic instability index (GII) and all commonly used clinical stratifications. CARMA offers effective comparison of tumor subgroups and extracts biologically and clinically relevant features from allele-specific copy number profiles.

bioinformatics↗

bioRxiv2018

Beware the Jaccard: the choice of metric is important and non-trivial in genomic colocalisation analysis.

BackgroundThe generation and systematic collection of genome-wide data is ever-increasing. This vast amount of data has enabled researchers to study relations between a variety of genomic and epigenomic features, including genetic variation, gene regulation, and phenotypic traits. Such relations are typically investigated by comparatively assessing genomic co-occurrence. Technically, this corresponds to assessing the similarity of pairs of genome-wide binary vectors. A variety of metrics have been proposed for this problem in other fields like ecology. However, while several of these metrics have been employed for assessing genomic co-occurrence, their appropriateness for the genomic setting has never been investigated.\n\nResultsWe show that the choice of metric may strongly influence results and propose two alternative modelling assumptions that can be used to guide this choice. On both simulated and real genomic data, the Jaccard index is strongly affected by dataset size and should be used with caution. The Forbes coefficient (fold change) and tetrachoric correlation are less affected by dataset size, but one should be aware of increased variance for small datasets.\n\nAvailabilityAll results on simulated and real data can be inspected and reproduced at: https://hyperbrowser.uio.no/sim-measure

bioinformatics↗

bioRxiv2018

A map of direct TF-DNA interactions in the human genome

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF-DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF-DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF-DNA interactions. Our work culminated with predicted interactions covering >4% of the human genome, obtained by uniformly processing 1,983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF-DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis-regulatory modules enriched for disease- and trait-associated SNPs. Finally, we provide this collection of direct TF-DNA interactions and cis-regulatory modules in the human genome through the UniBind web-interface (http://unibind.uio.no).

bioinformatics↗

bioRxiv2018

Super-enhancers are transcriptionally more active and cell-type-specific than stretch enhancers

BackgroundSuper-enhancers and stretch enhancers represent classes of transcriptional enhancers that have been shown to control the expression of cell identity genes and carry disease- and trait-associated variants. Specifically, super-enhancers are clusters of enhancers defined based on the binding occupancy of master transcription factors (TFs), chromatin regulators, or chromatin marks, while stretch enhancers are large chromatin-defined regulatory regions of at least 3,000 base pairs. Several studies have characterized these regulatory regions in numerous cell types and tissues to decipher their functional importance. However, the differences and similarities between these regulatory regions have not been fully assessed.\n\nResultsWe integrated genomic, epigenomic, and transcriptomic data from ten human cell types to perform a comparative analysis of super and stretch enhancers with respect to their chromatin profiles, cell-type-specificity, and ability to control gene expression. We found that stretch enhancers are more abundant, more distal to transcription start sites, cover twice as much the genome and are significantly less conserved than super-enhancers. In contrast, super-enhancers are significantly more enriched for active chromatin marks and cohesin complex and transcriptionally active than stretch enhancers. Importantly, a vast majority of superenhancers (85%) overlap with only a small subset of stretch enhancers (13%), which are enriched for cell-type-specific biological functions, and control cell identity genes.\n\nConclusionsThese results suggest that super-enhancers are transcriptionally more active and cell-type-specific than stretch enhancers, and importantly, most of the stretch enhancers that are distinct from superenhancers do not show an association with cell identity genes, are less active, and more likely to be poised enhancers.

genomics↗

bioRxiv2018

MirGeneDB2.0: the curated microRNA Gene Database

Small non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are unique because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguish bona fide miRNAs from the myriad other small RNAs generated by cells. However, making this separation is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database MirGeneDB to 45 organisms that represent the full taxonomic breadth of Metazoa. By consistently annotating and naming more than 10,900 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked more than 2,100 bona fide microRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct Metazoan evolution. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs but also biomedical and biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.

genomics↗

bioRxiv2017

JASPAR RESTful API: accessing JASPAR data from any programming language

JASPAR is a widely used open-access database of curated, non-redundant transcription factor binding profiles. Currently, data from JASPAR can be retrieved as flat files or by using programming language-specific interfaces. Here, we present a programming language-independent application programming interface (API) to access JASPAR data using the Representational State Transfer (REST) architecture. The REST API enables programmatic access to JASPAR by most programming languages and returns data in seven widely used formats. Further, it provides an endpoint to infer the TF binding profile(s) likely bound by a given DNA binding domain protein sequence. Additionaly, it provides an interactive browsable interface for bioinformatics tool developers. The REST API is implemented in Python using the Django REST Framework. It is accessible at http://jaspar.genereg.net/api/ and the source code is freeiy available at https://bitbucket.org/CBGR/jaspar under GPL v3 iicense.

bioinformatics↗

bioRxiv2017

Intervene: a tool for intersection and visualization of multiple gene or genomic region sets

BackgroundA common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited.\n\nResultsTo address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets.\n\nConclusionsIntervene and its web application companion provide an easy command line, and an interactive web interface to compute intersections of multiple genomic and list sets. They also have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene, with the web application available at https://asntech.shinyapps.io/intervene.

bioinformatics↗