Publications

IRX3 is linked to predisposition to obesity through the FTO locus and is upregulated during early adipogenesis in risk-allele carriers, shifting adipocyte fate toward fat storage. However, how this elevated IRX3 expression influences later developmental stages remains unclear. Here we show that IRX3 regulates adipocyte fate by modulating epigenetic reprogramming. ChIP-sequencing in preadipocytes identifies over 300 IRX3 binding sites, predominantly at promoters of genes involved in SUMOylation and chromatin remodeling. IRX3 knockout alters expression of SUMO pathway genes, increases global SUMOylation, and inhibits PPARγ activity and adipogenesis. Pharmacological SUMOylation inhibition rescues these effects. IRX3 KO also reduces SUMO occupancy at Wnt-related genes, enhancing Wnt signaling and promoting osteogenic fate in 3D cultures. This fate switch is partially reversible by SUMOylation inhibition. We identify IRX3 as a key transcriptional regulator of epigenetic programs, acting upstream of SUMOylation to maintain mesenchymal identity and support adipogenesis while suppressing osteogenesis in mouse embryonic fibroblasts.

UNLABELLED: In the last two decades, significant progress has been made toward understanding the genetic basis of type 2 diabetes. An important supporter of this research has been the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), most recently through the Accelerating Medicines Partnership Program for Type 2 Diabetes (AMP T2D) and Accelerating Medicines Partnership Program for Common Metabolic Diseases (AMP CMD). These public-private partnerships of the National Institutes of Health, multiple biopharmaceutical and life sciences companies, and nonprofit organizations, facilitated and managed by the Foundation for the National Institutes of Health, were designed to improve understanding of therapeutically relevant biological pathways for type 2 diabetes. On the occasion of NIDDK's 75th anniversary, we review the history of NIDDK support for these partnerships, which saw the convergence of research directions prioritized by academic consortia, the pharmaceutical industry, and government funders. Although the NIDDK was not the sole originator or funder of these efforts, its support and leadership have been pivotal to the partnerships' success and have enabled their research to be broadly accessible through the AMP Common Metabolic Diseases Knowledge Portal (CMDKP) and the AMP Common Metabolic Diseases Genome Atlas (CMDGA). Findings from AMP CMD align with NIDDK's mission to conduct research and share results with the goal of improving health and quality of life.

ARTICLE HIGHLIGHTS: The Accelerating Medicines Partnership Program for Type 2 Diabetes (AMP T2D) and Accelerating Medicines Partnership Program for Common Metabolic Diseases (AMP CMD) were created to accelerate the translation of genetic and genomic data into knowledge about the biology of disease. Their goal was to gain a better understanding of the mechanisms underlying types 1 and 2 diabetes and prediabetes, obesity, cardiovascular disease, kidney disease, and nonalcoholic steatohepatitis. This work identified multiple genes and pathways underlying these diseases. The findings of AMP T2D and AMP CMD have implications for drug development and improved risk prediction, diagnosis, and treatment for common metabolic diseases.

Isocitrate dehydrogenase (IDH) mutants define a class of gliomas that are initially slow-growing but inevitably progress to fatal disease. To characterize their malignant cell hierarchy, we profiled chromatin accessibility and gene expression across single cells from low-grade and high-grade IDH-mutant gliomas and ascertained their developmental states through a comparison to normal brain cells. We provide evidence that these tumors are initially fueled by slow-cycling oligodendrocyte progenitor cell-like cells. During progression, a more proliferative neural progenitor cell-like population expands, potentially through partial reprogramming of 'permissive' chromatin in progenitors. This transition is accompanied by a switch from methylation-based drivers to genetic ones. In low-grade IDH-mutant tumors or organoids, DNA hypermethylation appears to suppress interferon (IFN) signaling, which is induced by IDH or DNA methyltransferase 1 inhibitors. High-grade tumors frequently lose this hypermethylation and instead acquire genetic alterations that disrupt IFN and other tumor-suppressive programs. Our findings explain how these slow-growing tumors may progress to lethal malignancies and have implications for therapies that target their epigenetic underpinnings.

Sequence-based deep learning models have emerged as powerful tools for deciphering the cis-regulatory grammar of the human genome but cannot generalize to unobserved cellular contexts. Here, we present EpiBERT, a multi-modal transformer that learns generalizable representations of genomic sequence and cell type-specific chromatin accessibility through a masked accessibility-based pre-training objective. Following pre-training, EpiBERT can be fine-tuned for gene expression prediction, achieving accuracy comparable to the sequence-only Enformer model, while also being able to generalize to unobserved cell states. The learned representations are interpretable and useful for predicting chromatin accessibility quantitative trait loci (caQTLs), regulatory motifs, and enhancer-gene links. Our work represents a step toward improving the generalization of sequence-based deep neural networks in regulatory genomics.

Obesity strongly increases the risk of cardiometabolic diseases, yet the underlying mediators of this relationship are not fully understood. Given that obesity strongly influences circulating protein levels, we investigated proteins mediating the effects of obesity on coronary artery disease, stroke and type 2 diabetes. By integrating two-step proteome-wide Mendelian randomization, colocalization, epigenomics and single-cell RNA sequencing, we identified five mediators and prioritized collagen type VI α3 (COL6A3). COL6A3 levels were strongly increased by body mass index and increased coronary artery disease risk. Notably, the carboxyl terminus product of COL6A3, endotrophin, drove this effect. COL6A3 was highly expressed in disease-relevant cell types and tissues. Finally, we found that body fat reduction could reduce plasma levels of COL6A3-derived endotrophin, indicating a tractable way to modify endotrophin levels. In summary, we provide actionable insights into how circulating proteins mediate the effects of obesity on cardiometabolic diseases and prioritize endotrophin as a potential therapeutic target.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.