Biology subjects

Mizikovsky, D.

Publications and source records attributed to Mizikovsky, D..

8 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↗

bioRxiv2025

Unsupervised Variant Clustering Identifies Genetic Subtypes of Disease

Current approaches to identify disease subtypes rely on prior knowledge or external phenotypic references that limit their generalisability across traits. This study presents an unsupervised, phenotype- free framework that analyses the genetic co-occurrence of variants across individuals to identify variant clusters underpinning disease heterogeneity. The approach is developed and validated using simulated combinations of real phenotypes then applied to dissect disease subtypes of type 2 diabetes and asthma. The approach captures clinically established mechanistic profiles in type 2 diabetes and asthma without requiring any reference phenotype data and enables identification of plasma proteins for novel subtype- specific biomarkers and drug targets. Lastly, we demonstrate that variants with conflicting effects on disease-relevant traits are resolved into distinct clusters, identifying subtypes with opposing phenotypic profiles that are lost by the aggregated genetic risk. Co-occurrence-based clustering provides a scalable strategy for understanding disease heterogeneity across diverse populations by identifying biologically meaningful subtype structure from genotype data alone.

genomics↗

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 robust unsupervised clustering approach for high-dimensional biological imaging data reveals shared drug-induced morphological signatures

Modern biology increasingly relies on large-scale screening to generate high dimensional datasets with potential to accelerate discovery. However, analysing these complex datasets remains challenging, particularly in applications where the underlying structure and groupings are unknown, and high dimensionality introduces noise and artifacts that make follow up studies difficult to prioritise. Here, we present an unsupervised consensus clustering tool that quantifies biologically meaningful patterns based on multi-scale data organisation to guide decision-making in high-throughput screening. Using large-scale drug screening data in cancer cell lines and bacterium model, we demonstrate its ability to use diverse data inputs to prioritize robust drug clusters with shared biological mechanisms and conserved drug responses. This method addresses key limitations associated with prioritising robust, actionable hits from scalable screening data.

bioinformatics↗

bioRxiv2022

HOPX governs a molecular and physiological switch between cardiomyocyte progenitor and maturation gene programs

This study establishes the homeodomain only protein, HOPX, as a determinant controlling the molecular switch between cardiomyocyte progenitor and maturation gene programs. Time-course single-cell gene expression with genome-wide footprinting reveal that HOPX interacts with and controls core cardiac networks by regulating the activity of mutually exclusive developmental gene programs. Upstream hypertrophy and proliferation pathways compete to regulate HOPX transcription. Mitogenic signals override hypertrophic growth signals to suppress HOPX and maintain cardiomyocyte progenitor gene programs. Physiological studies show HOPX directly governs genetic control of cardiomyocyte cell stress responses, electro-mechanical coupling, proliferation, and contractility. We use human genome-wide association studies (GWAS) to show that genetic variation in the HOPX-regulome is significantly associated with complex traits affecting cardiac structure and function. Collectively, this study provides a mechanistic link situating HOPX between competing upstream pathways where HOPX acts as a molecular switch controlling gene regulatory programs underpinning metabolic, signaling, and functional maturation of cardiomyocytes.

developmental biology↗

bioRxiv2022

Organisation of gene programs revealed by unsupervised analysis of diverse gene-trait associations

Genome wide association studies provide statistical measures of gene-trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organises 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein-protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.

genomics↗