Biology subjects

Dashti, H.

Publications and source records attributed to Dashti, H..

11 recordsLinked to original sources

bioRxiv2026

Metabolic disease-relevant stimuli unmask context-dependent genetic regulation of cardiometabolic loci in human adipocytes

Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiometabolic disease, yet translating these associations into regulatory mechanisms, effector genes, and cellular programs remains a major challenge. A key limitation is that genetic effects are often highly context dependent, varying across cell states and environmental conditions that are difficult to model at scale. Here, we leverage CellGenBank, a population-scale biobank of primary human adipose-derived mesenchymal stem cells (AMSCs), to implement a multi-donor cell village in vitro system to map cardiometabolic disease genetic variation across adipocyte differentiation and metabolic stress conditions. We pooled AMSCs from 118 donors into multiplexed villages, differentiated them toward adipocytes, and profiled chromatin accessibility and gene expression using single-nucleus multiome sequencing under four disease-relevant conditions: basal, elevated free fatty acids, low glucose, and hypoxia. By combining with genetic demultiplexing, we quantify how regulatory element activity, gene expression, and higher-order cellular programs are modulated by both genotype and environmental context. Across conditions, we identify widespread context-specific cis-regulatory effects, including expression and chromatin accessibility quantitative trait loci that are masked in baseline states. Genetic effects frequently converge on coordinated transcriptional programs linked to lipid metabolism, insulin responsiveness, and stress adaptation, enabling the identification of cellular program QTLs that bridge variants, genes, and disease-relevant phenotypes. Integration with cardiometabolic GWAS reveals enhanced colocalization in condition- and state-resolved analyses, highlighting the importance of modeling environmental context to resolve disease mechanisms. Together, our study establishes large-scale adipocyte cell villages as a powerful and generalizable framework to map the context-dependent regulatory architecture of cardiometabolic disease and provides a resource linking human genetic variation to adipocyte cellular programs.

genomics↗

bioRxiv2025

The 9p21.3 Coronary Artery Disease Risk Locus Modulates Vascular Cell-State Transitions via Enhancer-Driven Regulation of MTAP

The 9p21.3 locus is the strongest genetic association with coronary artery disease (CAD), yet its causal mechanisms remain unresolved. We map the regulatory architecture of 9p21.3 in disease-relevant vascular cells, identifying 12 enhancers within the CAD risk haplotype that respond dynamically to inflammatory and metabolic stress in fibroblasts and smooth muscle cells. These activated states are enriched for CAD heritability, implicating stress-responsive vascular wall cells in disease pathophysiology. Dense CRISPRi tiling integrated with fine-mapping and genomic constraint across >500,000 individuals nominates MTAP as the effector gene, with rs1537371 as a likely causal variant. Perturbation and multi-modal analyses show that MTAP loss induces pro-fibrotic and angiogenic programs and sensitizes vascular cells to TGF-{beta}-driven pathological transitions. Our findings reveal a vascular-specific enhancer network through which noncoding variation at 9p21.3 modulates CAD risk via MTAP--a previously unrecognized regulator of vascular remodeling located 269 kb from the risk haplotype. HighlightsO_LI9p21.3 CAD risk locus harbors 12 functional enhancers active in vascular wall cell types C_LIO_LICAD heritability is enriched in stressed vascular fibroblasts and smooth muscle cells C_LIO_LIHigh-resolution CRISPRi-MAC-seq maps 9p21.3 enhancer-gene and SNP-gene interactions C_LIO_LIMTAP identified as a causal effector gene regulated by 9p21.3 fine-mapped and constrained variants C_LIO_LIMTAP modulates TGF-{beta}-driven pathological vascular cell transitions C_LI

genomics↗

bioRxiv2025

Climate-Driven Ecosystem Productivity Changes Restructure Food Systems

Climate change affects food systems through multiple pathways, yet the isolated effects of climate-driven ecosystem productivity changes remain poorly understood. While field studies translate productivity changes to crop yields, at larger scales yield conflates productivity with management and technology, and critically, productivity changes occur across all vegetation types, not just agricultural lands. We isolate ecosystem productivity changes to trace their propagation through food systems across three socioeconomic scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) by coupling the CLASSIC land surface model with GCAM integrated assessment model. Productivity changes lead to crops systematically shifting from human consumption to animal feed and bioenergy (up to 15% reallocation), while there is a significant transition of managed agricultural lands to unmanaged lands by up to 6 million km2 as productivity gains enable less intensive land use. These changes drive extreme regional divergence, with food production volume changing from +35% to -28% and substantial portfolio reorganization. Disruptions peak under regional rivalry (SSP3-7.0), revealing how fragmented governance amplifies biophysical feedbacks beyond climate forcing alone. While how much we eat may remain stable, ecosystem productivity fundamentally reshapes what we eat and where to source it.

ecology↗

bioRxiv2025

Rare missense variants of the leukocyte common antigen related receptor (LAR) display reduced activity in transcellular adhesion and synapse formation

The leukocyte common antigen related receptor (LAR) is a member of the LAR receptor protein tyrosine phosphatase (RPTP) family of synaptic adhesion molecules that contribute to the proper alignment and specialization of synaptic connections in the mammalian brain. LAR-RPTP members have been genetically associated with neuropsychiatric disorders, but the molecular consequences of genetic perturbations of LAR remain unstudied. Using exome sequencing data from psychiatric patients and controls, we identify rare missense variants of LAR that render the extracellular domain (ECD) unstable and susceptible to proteolytic cleavage. Using recombinant and cellular systems, we describe three variants that cause disruption of the LAR:NGL-3 interaction, which results in loss of transcellular adhesion and synaptogenic effects. Furthermore, we show that overexpression of two of these variants elicit altered morphological phenotypes in an imaging-based morphological profiling assay compared to wild type LAR, suggesting that destabilization of the LAR ECD has broad effects on LAR function. In conclusion, our study identifies three rare, missense variants in LAR that could provide insights into LAR involvement with psychiatric pathobiology.

neuroscience↗

bioRxiv2024

Loss of RREB1 reduces adipogenesis and improves insulin sensitivity in mouse and human adipocytes

There are multiple independent genetic signals at the Ras-responsive element binding protein 1 (RREB1) locus associated with type 2 diabetes risk, fasting glucose, ectopic fat, height, and bone mineral density. We have previously shown that loss of RREB1 in pancreatic beta cells reduces insulin content and impairs islet cell development and function. However, RREB1 is a widely expressed transcription factor and the metabolic impact of RREB1 loss in vivo remains unknown. Here, we show that male and female global heterozygous knockout (Rreb1+/-) mice have reduced body length, weight, and fat mass on high-fat diet. Rreb1+/- mice have sex- and diet-specific decreases in adipose tissue and adipocyte size; male mice on high-fat diet had larger gonadal adipocytes, while males on standard chow and females on high-fat diet had smaller, more insulin sensitive subcutaneous adipocytes. Mouse and human precursor cells lacking RREB1 have decreased adipogenic gene expression and activated transcription of genes associated with osteoblast differentiation, which was associated with Rreb1+/- mice having increased bone mineral density in vivo. Finally, human carriers of RREB1 T2D protective alleles have smaller adipocytes, consistent with RREB1 loss-of-function reducing diabetes risk.

physiology↗

bioRxiv2021

A single cell atlas of human and mouse white adipose tissue

White adipose tissue (WAT), once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, heterogenous, and involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense1. High fat feeding and other metabolic stressors cause dramatic changes in adipose morphology, physiology, and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and type 2 diabetes (T2D)2. Here, we provide detailed cellular atlases of human and murine subcutaneous and visceral white fat at single cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells (ASPCs), vascular, and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease, and we provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits, and cell types in the function of WAT across species, depots, and nutritional conditions.

physiology↗

bioRxiv2021

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler

A primary obstacle in translating genetics and genomics data into therapeutic strategies is elucidating the cellular programs affected by genetic variants and genes associated with human diseases. Broadly applicable high-throughput, unbiased assays offer a path to rapidly characterize gene and variant function and thus illuminate disease mechanisms. Here, we report LipocyteProfiler, an unbiased high-throughput, high-content microscopy assay that is amenable to large-scale morphological and cellular profiling of lipid-accumulating cell types. We apply LipocyteProfiler to adipocytes and hepatocytes and demonstrate its ability to survey diverse cellular mechanisms by generating rich context-, and process-specific morphological and cellular profiles. We then use LipocyteProfiler to identify known and novel cellular programs altered by polygenic risk of metabolic disease, including insulin resistance, waist-to-hip ratio and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse phenotypic profiling in lipid-storing cells, and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

genomics↗

bioRxiv2020

Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the world. It has been reported that [~]10%-15% of individuals with colorectal cancer experience a causative mutation in the known susceptibility genes, highlighting the importance of identifying mutations for early detection in high risk individuals. Through extensive sequencing projects such as the International Cancer Genome Consortium (ICGC), a large number of somatic point mutations have been identified that can be used to identify cancer-associated genes, as well as the signature of mutational processes defined by the tri-nucleotide sequence context (motif) of mutated sites. Mutation is the hallmark of cancer genome, and many studies have reported cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes, however, none of these cancer subtyping methods consider these features simultaneously. This highlights the need for a better and more inclusive subtype classification approach to enable biomarker discovery and thus inform drug development for CRC. In this study, we developed a statistical pipeline based on a novel concept gene-motif, which merges mutated gene information with tri-nucleotide motif of mutated sites, to identify cancer subtypes, in this case CRCs. Our analysis identified for the first time, 3,131 gene-motif combinations that were significantly mutated in 536 ICGC colorectal cancer samples compared to other cancer types, identifying seven CRC subtypes with distinguishable phenotypes and biomarkers. Interestingly, we identified several genes that were mutated in multiple subtypes but with unique sequence contexts. Taken together, our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers.

cancer biology↗

bioRxiv2018

Robust nomenclature and software for enhanced reproducibility in molecular modeling of small molecules

Computational molecular dynamics, energy minimization, and modeling of molecular interactions are widely used in studies involving natural products, metabolites, and drugs. Manually directed computational steps commonly utilize an evolving collection of experimental and computational data, to which new data sources are added or modified as needed. Several software packages capable of incorporating sources of data are available, but the process remains error prone owing to the complexities of preparing and maintaining a consistent set of input files and the proper post-processing of derived data. We have devised a methodology and implemented it using an extensible software pipeline called RUNER (for Robust and Unique Nomenclature for Enhanced Reproducibility) that creates a robust and standardized computational process. The pipeline combines a web service and a graphical user interface (GUI) to enable seamless modifications and verified maintenance of atom force field parameters. The GUI provides an implementation for the widely used molecular modeling software package Xplor-NIH. We describe the RUNER software and demonstrate the rationale for the pipeline through examples of structural studies of small molecules and natural products. The software, pipeline, force field parameters, and file verification data for more than 4,100 compounds (including FDA-approved drugs and natural products) are freely accessible from [http://runer.nmrfam.wisc.edu].\n\nAuthor SummaryWe describe an automated and verifiable computational pipeline for calculating the force field parameters of small molecules. The pipeline integrates several software tools and guarantees reproducibility of the parameters by utilizing a standard nomenclature across multiple computational steps and by maintaining file verification identifiers. We demonstrate the application of this pipeline to (a) processing of more than 4,100 compounds in high-throughput mode, and (b) structural studies of natural products. The graphical user interface (GUI) associated with the pipeline facilitates the manually tedious steps of force field parameters adjustments and supports visualization of the process.

bioinformatics↗

bioRxiv2018

GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates

Sleep is an essential homeostatically-regulated state of decreased activity and alertness conserved across animal species, and both short and long sleep duration associate with chronic disease and all-cause mortality1,2. Defining genetic contributions to sleep duration could point to regulatory mechanisms and clarify causal disease relationships. Through genome-wide association analyses in 446,118 participants of European ancestry from the UK Biobank, we discover 78 loci for self-reported sleep duration that further impact accelerometer-derived measures of sleep duration, daytime inactivity duration, sleep efficiency and number of sleep bouts in a subgroup (n=85,499) with up to 7-day accelerometry. Associations are enriched for genes expressed in several brain regions, and for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission, catecholamine production, synaptic plasticity, and unsaturated fatty acid metabolism. Genetic correlation analysis indicates shared biological links between sleep duration and psychiatric, cognitive, anthropometric and metabolic traits and Mendelian randomization highlights a causal link of longer sleep with schizophrenia.

genetics↗

bioRxiv2018

Biological and clinical insights from genetics of insomnia symptoms

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.\n\nOne Sentence SummaryWe identify 57 genomic regions associated with insomnia pointing to the involvement of phototransduction and ubiquitination and potential causal links to CAD and depression.

genomics↗