JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs’ structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

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.

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 hypothesised 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.

MYB plays a key role in gene regulation throughout the hematopoietic hierarchy and is critical for the maintenance of normal hematopoietic stem cells (HSC). Acquired genetic dysregulation of MYB is involved in the etiology of a number of leukemias, although inherited noncoding variants of the MYB gene are a susceptibility factor for many hematological conditions, including myeloproliferative neoplasms (MPN). The mechanisms that connect variations in MYB levels to disease predisposition, especially concerning age dependency in disease initiation, are completely unknown. Here, we describe a model of Myb insufficiency in mice that leads to MPN, myelodysplasia, and leukemia in later life, mirroring the age profile of equivalent human diseases. We show that this age dependency is intrinsic to HSC, involving a combination of an initial defective cellular state resulting from small effects on the expression of multiple genes and a progressive accumulation of further subtle changes. Similar to previous studies showing the importance of proteostasis in HSC maintenance, we observed altered proteasomal activity and elevated proliferation indicators, followed by elevated ribosome activity in young Myb-insufficient mice. We propose that these alterations combine to cause an imbalance in proteostasis, potentially creating a cellular milieu favoring disease initiation.

The cholesterol-sensing nuclear receptor liver X receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in regulating glucose and lipid metabolism in the liver. More knowledge of their mechanistic interplay is needed to understand their role in pathological conditions like fatty liver disease and insulin resistance. In the current study, LXR and ChREBP co-occupancy was examined by analyzing ChIP-seq datasets from mice livers. LXR and ChREBP interaction was determined by Co-immunoprecipitation (CoIP) and their transactivity was assessed by real-time quantitative polymerase chain reaction (qPCR) of target genes and gene reporter assays. Chromatin binding capacity was determined by ChIP-qPCR assays. Our data show that LXRα and ChREBPα interact physically and show a high co-occupancy at regulatory regions in the mouse genome. LXRα co-activates ChREBPα and regulates ChREBP-specific target genes in vitro and in vivo. This co-activation is dependent on functional recognition elements for ChREBP but not for LXR, indicating that ChREBPα recruits LXRα to chromatin in trans. The two factors interact via their key activation domains; the low glucose inhibitory domain (LID) of ChREBPα and the ligand-binding domain (LBD) of LXRα. While unliganded LXRα co-activates ChREBPα, ligand-bound LXRα surprisingly represses ChREBPα activity on ChREBP-specific target genes. Mechanistically, this is due to a destabilized LXRα:ChREBPα interaction, leading to reduced ChREBP-binding to chromatin and restricted activation of glycolytic and lipogenic target genes. This ligand-driven molecular switch highlights an unappreciated role of LXRα in responding to nutritional cues that was overlooked due to LXR lipogenesis-promoting function.