Anthony Mathelier

• Nothnagel, Patrizia Maria Christiane; Meslin, Paul-Arthur Richard; Wik, Jonas Aakre; Dufour, Damien; Strøm, Yunna Erika & Bjørås, Magnar [Show all 11 contributors for this article] (2026). Transient SUMOylation inhibition in human pre-adipocytes stably imprints a transcriptional beiging fate. Nucleic Acids Research (NAR). ISSN 0305-1048. 54(6), p. 1–22. doi: 10.1093/nar/gkag232. Full text in Research Archive Show summary

  SUMOylation regulates chromatin states and transcriptional programs that preserve cellular identity, yet how perturbation of the SUMOylation pathway impacts adipocyte plasticity remains unclear. Here, we show that brief pharmacologic inhibition of SUMOylation 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 produces 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 RNA-sequencing revealed immediate 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 at thermogenic enhancers, and increased PPARG occupancy across the UCP1 regulatory unit. This mechanism is enforced by enhanced cAMP-PKA-p38 signaling and stabilization of beiging transcription activators. We propose that transient relief of SUMO-mediated repression unlocks dominant regulatory units, notably the UCP1 enhancer cluster, producing a monomorphic 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.

• Ferenc, Katalin Terezia; Martini, Lorenzo; Rauluseviciute, Ieva; Sandve, Geir Kjetil Ferkingstad & Mathelier, Anthony (2026). inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btag026. Full text in Research Archive Show summary

  The accurate development, assessment, interpretation, and benchmarking of bioinformatics frameworks for analyzing transcriptional regulatory grammars rely on controlled simulations to validate the underlying methods. However, existing simulators often lack end-to-end flexibility or ease of integration, which limits their practical use. We present inMOTIFin, a lightweight, modular, and user-friendly Python-based software that addresses these gaps by providing versatile and efficient simulation and modification of DNA regulatory sequences. inMOTIFin enables users to simulate or modify regulatory sequences efficiently for the customizable generation of motifs and insertion of motif instances with precise control over their positions, co-occurrences, and spacing, as well as direct modification of real sequences, facilitating a comprehensive evaluation of motif-based methods and interpretation tools. We demonstrate inMOTIFin applications for the assessment of de novo motif discovery, the analysis of transcription factor cooperativity, and the support of explainability analyses for deep learning models. inMOTIFin ensures robust and reproducible analyses for studying transcriptional regulatory grammars.

• Kumar, Vipin; Rossini, Roberto; Paulsen, Jonas & Mathelier, Anthony (2025). BHiCect 2.0: Multi-resolution clustering of Hi-C data. EarthArXiv preprint platform. doi: 10.48550/ARXIV.2512.17512. Full text in Research Archive
• Puig, Rafael Riudavets; Brorson, Ina Skaara; O’Mahony, Denise; Kristensen, Vessela N. & Mathelier, Anthony (2025). HiFIseek: gene-specific enrichment of high-impact mutations in associated genomic regions. bioRxiv (preprint article). doi: 10.1101/2025.06.12.659305. Full text in Research Archive Show summary

  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.

• Nothnagel, Patrizia Maria Christiane; Meslin, Paul-Arthur Richard; Wik, Jonas Aakre; Strøm, Yunna Erika; Bjørås, Magnar & Enserink, Jorrit [Show all 10 contributors for this article] (2025). Transient SUMOylation Inhibition In Human Pre-adipocytes Stably Imprints a Transcriptional Beiging Fate. bioRxiv (preprint article). 54(6). doi: 10.1101/2025.03.07.642034. Full text in Research Archive Show summary

  Abstract 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 SUMOylation restricts de novo differentiation of pre-adipocytes into beige adipocytes by repressing cAMP signaling and preventing epigenetic and transcriptional reprogramming of white adipocytes.

• Zeitlinger, Julia; Roy, Sushmita; Ay, Ferhat; Mathelier, Anthony; Medina-Rivera, Alejandra & Mahony, Shaun [Show all 8 contributors for this article] (2025). Perspective on recent developments and challenges in regulatory and systems genomics. Bioinformatics Advances. 5(1). doi: 10.1093/bioadv/vbaf106. Full text in Research Archive Show summary

  Abstract Summary: Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges toward solving this fundamental problem. We describe how cis-regulatory elements are mapped with various genomics assays and how studies of the 3D chromatin organization could help identifying long-range regulatory effects. We discuss how the cis-regulatory sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also describe current methods for mapping gene regulatory networks to describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals.

• Baydar, Damla Övek; Rauluseviciute, Ieva; Aronsen, Dina Ruud; Blanc-Mathieu, Romain; Bonthuis, Ine & de Beukelaer, Herman [Show all 34 contributors for this article] (2025). JASPAR 2026: expansion of transcription factor binding profiles and integration of deep learning models. Nucleic Acids Research (NAR). ISSN 0305-1048. 54(D1), p. D184–D193. doi: 10.1093/nar/gkaf1209. Full text in Research Archive Show summary

  Abstract JASPAR (https://jaspar.elixir.no/) is an open-access database that has provided high-quality, manually curated, and non-redundant DNA binding profiles for transcription factors (TFs) as position frequency matrices (PFMs) for over 20 years. We expanded the CORE (306 new profiles, 12% increase) and UNVALIDATED (433, 60% increase) collections with new PFMs and updated 13 existing profiles. We updated the TF binding site predictions and genome tracks for eight species. TF binding profile clusters and familial TF binding sites were updated accordingly. We integrate the inMOTIFin software to easily simulate regulatory sequences using JASPAR PFMs. To enrich TFs’ annotations, we provide scientific literature-based human TF target information. Notably, this release features a deep learning (DL) collection, providing a paradigm shift in modeling and characterizing TF–DNA interactions with 1259 BPNet models trained on Homo sapiens ENCODE chromatin immunoprecipitation followed by sequencing (ChIP-seq) datasets from 240 TFs and interpreted to reveal predictive motif patterns for the models. The motifs associated with the same TF were clustered to provide a summary of the binding properties, resulting in 240 primary and 113 alternative motif patterns in the DL collection. The JASPAR 2026 collections lay a foundation for future endeavors in genomic research, serving the scientific community in uncovering the mechanisms of gene regulation.

• Pop, Romana Tabita; Hsieh, Ping-Han; Belova, Tatiana; Mathelier, Anthony & Kuijjer, Marieke (2025). Gene regulatory network integration with multi-omics data enhances survival predictions in cancer. Briefings in Bioinformatics. ISSN 1467-5463. 26(4). doi: 10.1093/bib/bbaf315. Full text in Research Archive Show summary

  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. To fully harness these data, integrative approaches have emerged as essential tools, enabling the combination of multiple omics modalities to uncover disease mechanisms. However, many such approaches overlook gene regulatory mechanisms, which play a central role in the development and progression of cancer. Patient-specific gene regulatory networks (GRNs), representing interactions between regulators (such as transcription factors) and their target genes in each individual tumour, offer a powerful framework to bridge this gap and investigate the regulatory landscape of cancer. In this study, we introduce a novel approach for integrating patient-specific GRNs with multi-omic data and assess whether their inclusion in joint dimensionality reduction models improves survival prediction across multiple cancer types. By applying our method on ten cancer datasets from The Cancer Genome Atlas, we demonstrate that incorporating GRNs enhances associations with patient survival in several cancer types. Focusing on liver cancer, with validation in independent data, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression. These were linked to dysregulated fatty acid metabolism, and identified JUND as a potential novel transcriptional regulator driving these processes. Our findings highlight the value of network-based multi-omics integration for uncovering clinically relevant regulatory mechanisms and improving our understanding of cancer biology at the patient-specific level.

• Ferenc, Katalin Terezia; Rauluseviciute, Ieva; Hovan, Ladislav; Kumar, Vipin; Kuijjer, Marieke & Mathelier, Anthony (2024). Improving bioinformatics software quality through teamwork. Bioinformatics. ISSN 1367-4803. 40(11). doi: 10.1093/bioinformatics/btae632. Full text in Research Archive Show summary

  Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers’ work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

• Ankill, Jørgen; Zhao, Zhi; Tekpli, Xavier; Kure, Elin Wenche Hegland; Kristensen, Vessela N. & Mathelier, Anthony [Show all 7 contributors for this article] (2024). Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation. PLoS Computational Biology. ISSN 1553-734X. 20:e1012565(11), p. 1–33. doi: 10.1371/journal.pcbi.1012565. Full text in Research Archive Show summary

  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 including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

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