Biology subjects

Palpant, N.

Publications and source records attributed to Palpant, N..

At least 19 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

Cardiomyocyte caveolae govern myocardial function and sex-dependent regulation of ventricular compliance and resilience via cavin-1

AimsCaveolae are plasmalemmal microdomains regulating stretch-dependent, nitric oxide (NO), and other signalling pathways governing myocardial structure, function and resilience. We have reported that global deletion of the scaffold protein cavin-1 disrupts caveolar biogenesis and impairs ventricular compliance and tolerance to ischaemic injury. However, cardiomyocyte-specific and sex-dependent roles of cavin-1 and caveolar complexes remain unresolved. Methods and ResultsWe generated a floxed Cavin-1 transgenic mouse, enabling cardiomyocyte-specific knockdown via adeno-associated virus (AAV) mediated expression of iCre recombinase driven by a cardiac-specific troponin T promoter. Knockdown was confirmed by RNA, protein, and immunofluorescence analyses, and cardiac function was assessed via echocardiography, left ventricular pressure-volume (PV) catheterisation, and ex vivo PV analysis of perfused hearts. Conditionally deleted hearts and myocytes exhibited up to 50% knockdown of Cavin-1 mRNA together with 15% deficiency in muscle-specific Caveolin-3, 70% depletion of caveolae, and mislocalisation of NO synthase (NOS) within cardiomyocytes. This was associated with elevated heart rate and shortened PR interval; reduced intraventricular and systolic blood pressures and peripheral resistance; and sex-dependent impairment of ventricular filling (females only). Diastolic dysfunction was detectable ex vivo, to a greater extent in male vs. female hearts. Mechanisms were sex-dependent, linked to interstitial fibrosis in females and NOS overactivity (inhibited by 100 {micro}M L-NAME) in males. Female hearts also exhibited increased susceptibility to ischaemia-reperfusion injury. Coronary function appeared preserved in both sexes, with intact reactive hyperaemic responses. ConclusionThis model identifies cardiomyocyte caveolae and cavin-1 as key determinants of myocardial function and compliance, involving sex-dependent remodelling and NOS signalling. By linking cardiomyocyte disruption to whole-organ and -body dysfunction, this model provides mechanistic insight into impaired function in heart failure and ageing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/717104v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@51bfe4org.highwire.dtl.DTLVardef@10d4323org.highwire.dtl.DTLVardef@1b2baa7org.highwire.dtl.DTLVardef@fc5f21_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

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

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

Comprehensive molecular impact mapping of common and rare variants at GWAS loci

Deep learning sequence to function models can predict the molecular effects of genetic variants, but their predictions are limited to the cell types and assays they are trained on. Here we describe DNACipher, a deep learning model that predicts the effects of genetic variants across diverse biological contexts--including those not directly measured. DNACipher takes 196 kb of genome sequences as input and imputes variant effects across 38,582 cell type-assay combinations. DNACipher generates predictions for >7 times as many contexts as Enformer, which allows for better detection of variant effects at expression quantitative trait loci (eQTLs). We also introduce DNACipher Deep Variant Impact Mapping (DVIM), a method to identify variants with molecular effects at genome-wide association study (GWAS) loci. Application of DVIM to type 1 diabetes (T1D) reduced the mean fine-mapping credible set size from 24 to 1.4 variants per signal. DVIM variants had significantly higher fine-mapping posterior probabilities, and their predicted effects were supported by single-nucleus ATAC-seq and luciferase assays. DVIM also detected 6547 rare variants with molecular effects at 96% of GWAS T1D loci, and these were enriched for associations with immune traits. In summary, DNACipher DVIM prioritises common and rare variants at GWAS loci by predicting molecular effects across a broad range of contexts.

genetics↗

bioRxiv2025

Multilingual model improves zero-shot predictionof disease effects on proteins

Predicting variant effects remains a central challenge in genomics. Protein language models (PLMs) capture amino-acid-level sequence constraints, whereas codon language models operate on coding sequences and may retain information that is lost upon translation. Here, we tested whether scores from the codon language model CaLM provide predictive information beyond protein-level representations for missense-variant interpretation. Across 71,436 ClinVar missense variants from 11,554 genes, adding CaLM to PLM baselines produced modest but reproducible improvements under gene-held-out cross-validation. PLM-only ensemble controls and explicit mutational-context analyses indicated that this improvement could not be explained solely by generic ensembling or simple codon-substitution features. Aggregating CaLM probabilities across synonymous codons attenuated codon-degeneracy-associated discordance while preserving most of the broader differences between CaLM and PLM scores. Gene-level analyses further showed that CaLM contribution varied continuously across genes and depended partly on the protein-model background. Across ClinMAVE functional assays, however, improvements were less consistent, indicating that codon-protein complementarity is context dependent rather than universal. Together, these results identify a modest but reproducible component of variant-effect information in CaLM-derived codon-level scores that is not fully captured by protein-level language-model representations.

bioinformatics↗

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↗

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↗

bioRxiv2024

Cardiovascular symptoms of PASC are associated with trace-level cytokines that affect the function of human pluripotent stem cell derived cardiomyocytes

Globally, over 65 million individuals are estimated to suffer from post-acute sequelae of COVID-19 (PASC). A large number of individuals living with PASC experience cardiovascular symptoms (i.e. chest pain and heart palpitations) (PASC-CVS). The role of chronic inflammation in these symptoms, in particular in individuals with symptoms persisting for >1 year after SARS-CoV-2 infection, remains to be clearly defined. In this cross-sectional study, blood samples were obtained from three different sites in Australia from individuals with i) a resolved SARS-CoV-2 infection (and no persistent symptoms i.e. Recovered), ii) individuals with prolonged PASC-CVS and iii) SARS-CoV-2 negative individuals. Individuals with PASC-CVS, relative to Recovered individuals, had a blood transcriptomic signature associated with inflammation. This was accompanied by elevated levels of pro-inflammatory cytokines (IL-12, IL-1{beta}, MCP-1 and IL-6) at approximately 18 months post-infection. These cytokines were present in trace amounts, such that they could only be detected with the use of novel nanotechnology. Importantly, these trace-level cytokines had a direct effect on the functionality of pluripotent stem cell derived cardiomyocytes in vitro. This effect was not observed in the presence of dexamethasone. Plasma proteomics demonstrated further differences between PASC-CVS and Recovered patients at approximately 18 months post-infection including enrichment of complement and coagulation associated proteins in those with prolonged cardiovascular symptoms. Together, these data provide a new insight into the role of chronic inflammation in PASC-CVS and present nanotechnology as a possible novel diagnostic approach for the condition.

microbiology↗

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

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↗

bioRxiv2021

Village in a dish: a model system for population-scale hiPSC studies

The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and under different conditions. Human induced pluripotent stem cells (hiPSCs) are uniquely suited to study these context-dependent effects, but to do so requires cell lines from hundreds or potentially thousands of individuals. Village cultures, where multiple hiPSC lines are cultured and differentiated together in a single dish, provide an elegant solution for scaling hiPSC experiments to the necessary sample sizes required for population-scale studies. Here, we show the utility of village models, demonstrating how cells can be assigned back to a donor line using single cell sequencing, and addressing whether line-specific signaling alters the transcriptional profiles of companion lines in a village culture. We generated single cell RNA sequence data from hiPSC lines cultured independently (uni-culture) and in villages at three independent sites. We show that the transcriptional profiles of hiPSC lines are highly consistent between uni- and village cultures for both fresh (0.46 < R < 0.88) and cryopreserved samples (0.46 < R < 0.62). Using a mixed linear model framework, we estimate that the proportion of transcriptional variation across cells is predominantly due to donor effects, with minimal evidence of variation due to culturing in a village system. We demonstrate that the genetic, epigenetic or hiPSC line-specific effects on gene expression are consistent whether the lines are uni- or village-cultured (0.82 < R < 0.94). Finally, we identify the consistency in the landscape of cell states between uni- and village-culture systems. Collectively, we demonstrate that village methods can be effectively used to detect hiPSC line-specific effects including sensitive dynamics of cell states.

genomics↗

bioRxiv2021

Muscle-specific Cavin4 interacts with Bin1 to promote T-tubule formation and stability in developing skeletal muscle

The cavin proteins are essential for caveola biogenesis and function. Here, we identify a role for the muscle-specific component, Cavin4, in skeletal muscle T-tubule development by analyzing two vertebrate systems: mouse and zebrafish. In both models Cavin4 localized to T-tubules and loss of Cavin4 resulted in aberrant T-tubule maturation. In zebrafish, which possess duplicated cavin4 paralogs, Cavin4b was shown to directly interact with the T-tubule-associated BAR domain protein, Bin1. Loss of both Cavin4a and Cavin4b caused aberrant accumulation of interconnected caveolae within the T-tubules, a fragmented T-tubule network enriched in Caveolin-3, and an impaired Ca2+ response upon mechanical stimulation. We propose a role for Cavin4 in remodeling the T-tubule membrane early in development by recycling caveolar components from the T-tubule to the sarcolemma. This generates a stable T-tubule domain lacking caveolae that is essential for T-tubule function.

cell biology↗

bioRxiv2019

Pharmacological inhibition of acid sensing ion channel 1a protects the heart from ischemia-reperfusion injury

The proton-gated acid-sensing ion channel 1a (ASIC1a) is implicated in the injury response to cerebral ischemia but little is known about its role in cardiac ischemia. We provide genetic evidence that ASIC1a is involved in myocardial ischemia-reperfusion injury (IRI) and show that pharmacological inhibition of ASIC1a yields robust cardioprotection in rodent and human models of cardiac ischemia, resulting in improved post-IRI cardiac viability and function. Consistent with a key role for ASIC1a in cardiac ischemia, we show that polymorphisms in the ASIC1 genetic locus are strongly associated with myocardial infarction. Collectively, our data provide compelling evidence that ASIC1a is a key target for cardioprotective drugs to reduce the burden of disease associated with myocardial ischemia.

physiology↗

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↗

bioRxiv2017

Cardiac directed differentiation using small molecule Wnt modulation at single-cell resolution

Differentiation into diverse cell lineages requires the orchestration of gene regulatory networks guiding diverse cell fate choices. Utilizing human pluripotent stem cells, we measured expression dynamics of 17,718 genes from 43,168 cells across five time points over a thirty day time-course of in vitro cardiac-directed differentiation. Unsupervised clustering and lineage prediction algorithms were used to map fate choices and transcriptional networks underlying cardiac differentiation. We leveraged this resource to identify strategies for controlling in vitro differentiation as it occurs in vivo. HOPX, a non-DNA binding homeodomain protein essential for heart development in vivo was identified as dys-regulated in in vitro derived cardiomyocytes. Utilizing genetic gain and loss of function approaches, we dissect the transcriptional complexity of the HOPX locus and identify the requirement of hypertrophic signaling for HOPX transcription in hPSC-derived cardiomyocytes. This work provides a single cell dissection of the transcriptional landscape of cardiac differentiation for broad applications of stem cells in cardiovascular biology.

developmental biology↗