Biology subjects

Shim, W. J.

Publications and source records attributed to Shim, W. J..

10 recordsLinked to original sources

bioRxiv2026

Combinatorial epigenomic patterns define regulatory programs underlying disease heterogeneity

Complex diseases exhibit substantial variation in clinical presentation and outcome despite shared diagnoses. Current genetic models typically represent inherited risk as a single additive liability, obscuring the diverse biological mechanisms through which variants influence disease. Here, we show that disease-associated variants are organized into recurrent regulatory programs that reveal latent disease mechanisms. Using genome-scale epigenomic maps across human tissues and cell states, we identify regulatory programs that partition disease-associated variants without phenotypic or disease-specific priors. Variants assigned to different programs exert distinct biological effects that translate into divergent clinical outcomes. In type 2 diabetes, these programs reveal previously unrecognized disease subtypes with opposing cardiometabolic profiles that stratify future risk of myocardial infarction and non-alcoholic fatty liver disease. Together, our findings establish that inherited disease risk is organized into latent regulatory programs, revealing a fundamental layer of biological heterogeneity underlying complex disease.

genomics↗

bioRxiv2026

Protocol-dependent cardiomyocyte states determine disease modelling capacity of human iPSCs

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are widely used to model cardiovascular disease, yet numerous differentiation protocols generate cardiomyocytes with heterogeneous molecular and functional properties, complicating experimental design. Here we systematically compare sixteen commonly used cardiomyocyte differentiation protocols and characterize their resulting cell states using single-nucleus RNA sequencing, functional phenotyping and computational integration with human genetic data. Despite similar cardiomyocyte yields, protocols produced distinct transcriptional programs, subtype compositions and physiological properties. By integrating protocol-specific gene expression signatures with genome-wide association studies of cardiovascular traits, we identify cardiomyocyte states enriched for genetic architectures underlying specific diseases. These analyses accurately predict protocols most suitable for modelling particular disease contexts, including electrophysiological defects associated with Brugada syndrome and metabolic vulnerability relevant to myocardial infarction. Our results demonstrate that differentiation protocols encode biologically distinct cardiomyocyte states with differential disease relevance and establish a framework for aligning stem-cell differentiation strategies with human complex trait genetics to guide model selection. This approach enables rational design of iPSC-based disease models and highlights how population-scale genetic data can inform experimental systems in stem cell biology.

systems biology↗

bioRxiv2026

Hierarchical control of cardiomyocyte maturation and ischaemia sensitivity by metabolic culture conditions

Ischaemic heart disease remains the leading cause of mortality worldwide, yet no therapies prevent cardiomyocyte death during acute ischaemia-reperfusion injury (IRI). Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide a platform for modelling cardiac injury, but their immature phenotype limits the physiological fidelity of in vitro models. Here, we systematically evaluated how experimental variables used during preparation of hiPSC-CM endpoint assays influence cardiomyocyte maturation and susceptibility to IRI. Integrating literature mining, molecular profiling, statistical genetics, and functional assays, we examined the effects of replating conditions, backbone media, metabolic substrates, and signalling modulators. We define the relationship between culture conditions and metabolic supplements in determining contractile maturation and sensitivity to IRI. Notably, we show that metabolic composition of the backbone medium establishes the baseline maturation state and determines responsiveness to additional maturation cues. These findings identify metabolic environment as a dominant regulator of injury susceptibility and provide a framework for improving the physiological fidelity of hiPSC-CM models of cardiac ischaemia.

cell biology↗

bioRxiv2025

Glycaemic variability underlies myocyte dysfunction and myocardial injury risk in diabetes

Heart disease is the leading cause of morbidity and mortality in individuals with diabetes, due largely to risks associated with ischaemic injuries such as myocardial infarction (MI). We use human population genetic data to demonstrate that current biomarkers of hyperglycaemia do not account for risk of post-MI mortality in diabetes patients. This study therefore systematically evaluates glycaemic stress underpinning cardiovascular risk in diabetes. Using in vivo and in vitro models, we demonstrate that glycaemic variability rather than hyperglycaemia alone is a dominant risk factor for heart muscle dysfunction and myocardial injury sensitivity in diabetes. These findings provide new preclinical models for mechanistic and drug discovery studies and inform strategies for managing cardiovascular outcomes in patients with diabetes.

cell biology↗

bioRxiv2024

Epigenetic constraint of cellular genomes evolutionarily links genetic variation to function

Cellular diversity is a product of evolution acting to drive divergent regulatory programs from a common genome. Here, we use cross-cell-type epigenetic conservation to gain insight into the impact of selective constraints on genome function and phenotypic variation. By comparing chromatin accessibility across hundreds of diverse cell-types, we identify 1.4% of the human genome safeguarded by conserved domains of facultative heterochromatin, which we term regions under "cellular constraint". We calculate single-base resolution cellular constraint scores and demonstrate robust prediction of functionally important coding and non-coding loci in a cell-type-, trait-, and disease-agnostic manner. Cellular constraint annotation enhances causal variant identification, drug discovery, and clinical diagnostic predictions. Furthermore, cell-constrained sequences share paradoxical evolutionary signals of positive and negative selection, suggesting a dynamic role in driving human adaptation. Overall, this study demonstrates that evolutionary chromatin dynamics can be leveraged to inform the translation of genetic discoveries into effective biological, therapeutic, and clinical outcomes.

genetics↗

bioRxiv2024

A community-oriented, data-driven resource to improve protocol design for cardiac modelling from human pluripotent stem cells

Protocol design and benchmarking is central to optimising model development using human pluripotent stem cell derived cardiomyocytes (hPSC-CMs). By applying data mining to decades of research and hundreds of peer reviewed studies, we evaluate how protocol variables associate with common properties of cardiac functional and physiological maturation. This resource is publicly accessible through CMPortal, a community-oriented website that provides data-driven tools for researchers to navigate leverage decades of knowledge for benchmarking protocol designs and outcomes for their dedicated applications in developmental biology, disease modelling, and drug screening.

developmental biology↗

bioRxiv2023

Habitat use of loggerhead (Caretta caretta) and green (Chelonia mydas) turtles in the Northwest Pacific Ocean, the northern limit of their distribution range

Verifying habitats, including the foraging and nesting areas for sea turtles, enables an understanding of their spatial ecology and successful planning of their conservation and management strategies. Recently, the observation frequency and bycatch of loggerhead (Caretta caretta) and green (Chelonia mydas) turtles have increased in the northern limit of their distribution range, in the northern part of the East China Sea and East Sea (Sea of Japan). We conducted satellite tracking to investigate the habitat use of seven loggerhead and eight green turtles from June 2016 to August 2022 in this area, where little is known about their spatial ecology. We applied a 50 percent volume contour method to determine their main foraging areas and analyzed 6 environmental variables to characterize their habitats. Loggerhead turtles mainly stayed in and used the East China Sea as a foraging area during the tracking period, while two individuals among them also used the East Sea as a seasonal foraging area. Most green turtles also used the East China Sea as a foraging area, near South Korea and Japan, with one individual among them using the southern part of the East Sea as a seasonal foraging area. Notably, one green turtle traveled to Hainan Island in the South China Sea, a historical nesting area. Our results showed that the two sea turtle species included the East Sea as a seasonal foraging area, possibly owing to the abundance of food sources available, despite its relatively lower sea temperature. Considering that loggerhead and green sea turtles were observed using the northern part of the East China Sea and East Sea more frequently than previously known and that the sea temperature gradually increases due to climate change, conservation and management activities are required for sea turtles in these areas.

zoology↗

bioRxiv2022

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across diverse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

bioinformatics↗

bioRxiv2022

An integrated cell barcoding and computational analysis pipeline for scalable analysis of differentiation at single-cell resolution

Pluripotent stem cells provide a scalable approach to analyse molecular regulation of cell differentiation across multiple developmental lineage trajectories. In this study, we engineered barcoded iPSCs to generate an atlas of multilineage differentiation from pluripotency, encompassing a time-course of WNT-induced differentiation perturbed using modulators of WNT, BMP, and VEGF signalling. Computational mapping of in vitro cell types to in vivo developmental lineages revealed a diversity of iPSC-derived cell types comprising mesendoderm lineage cell types including lateral plate and paraxial mesoderm, neural crest, and primitive gut. Coupling this atlas of in vitro differentiation with Summary data-based Mendelian Randomisation analysis of human complex traits, we identify the WNT-inhibitor protein TMEM88 as a putative regulator of mesendodermal cell types governing development of diverse cardiovascular and anthropometric traits. Using genetic loss of function models, we show that TMEM88 is required for differentiation of diverse endoderm and mesoderm cell lineages in vitro and that TMEM88 knockout in vivo results in a significant dysregulation of arterial blood pressure. This study provides an atlas of multilineage iPSC differentiation coupled with new molecular, computational, and statistical genetic tools to dissect genetic determinants of mammalian developmental physiology.

genomics↗

bioRxiv2019

Comparative analysis of diverse cell states establishes an epigenetic basis for inferring regulatory genes governing cell identity

Determining genes orchestrating cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive tri-methylation of histone 3 lysine 27 (H3K27me3) across hundreds of diverse cell types to identify genetic regulators of cell differentiation. We introduce a computational method, TRIAGE, that uses discordance between gene-repressive tendency and expression to identify genetic drivers of cell identity. We apply TRIAGE to millions of genome-wide single-cell transcriptomes, diverse omics platforms, and eukaryotic cells and tissue types. Using a wide range of data, we validate TRIAGEs performance for identifying cell-type specific regulatory factors across diverse species including human, mouse, boar, bird, fish, and tunicate. Using CRISPR gene editing, we use TRIAGE to experimentally validate RNF220 as a regulator of Ciona cardiopharyngeal development and SIX3 as required for differentiation of endoderm in human pluripotent stem cells. A record of this papers Transparent Peer Review process is included in the Supplemental Information.

cell biology↗