Biology subjects

Zhang, M. J.

Publications and source records attributed to Zhang, M. J..

10 recordsLinked to original sources

bioRxiv2026

A Fragment Screen Identifies Acrylamide Covalent Inhibitors of the TEAD/YAP Protein-Protein Interaction

TEA domain (TEAD) proteins bind co-activator Yes-associated protein (YAP) to regulate the expression of target genes of the Hippo pathway. The TEAD*YAP protein-protein interaction is not druggable, but TEADs possess a unique and deep palmitate pocket with a highly conserved cysteine located outside the TEAD*YAP protein-protein interaction interface. Here, we screen a fragment library of acrylamide electrophiles and identify a fragment that forms an adduct with the conserved palmitate pocket cysteine and inhibits TEAD4 binding to YAP. Synthesis of a focused set of derivatives and time- and concentration-dependent studies with four TEADs provide reaction rates and binding constants. Co-crystal structures of fragments bound to TEAD2 and TEAD3 reveal reaction at the conserved palmitate pocket cysteine but also at another less conserved cysteine located in the palmitate pocket of TEAD2 closer to the TEAD*YAP interface. These fragments provide a starting point for the development of allosteric acrylamide small-molecule covalent TEAD*YAP inhibitors.

biochemistry↗

bioRxiv2025

DALE-Eval: A comprehensive cell type-specific expression deconvolution benchmark for transcriptomics data

Deconvolution of bulk transcriptomic data unlocks rich, cell type-specific insights from complex tissue samples. While cell type fraction deconvolution has been extensively developed and bench-marked, the next frontier--cell type-specific expression (CTSE) deconvolution--remains largely underexplored. Here, we introduce DALE-Eval, a comprehensive benchmark of CTSE methods, featuring novel evaluation metrics, identification of key drivers of performance, and practical recommendations for maximizing the value of CTSE in downstream applications.

bioinformatics↗

bioRxiv2024

Myocardial Endoglin Regulates Cardiomyocyte Proliferation and Cardiac Regeneration

The mammalian heart loses almost all its regenerative potential in the first week of life due to the cessation of the ability of cardiomyocytes to proliferate. In recent years, a number of regulators of cardiomyocyte proliferation have been identified. Despite this, a clear understanding of the regulatory pathways that control cardiomyocyte proliferation and cardiac regeneration is lacking, and there are likely additional regulators to be discovered. Here, we performed a genome-wide screen on fetal murine cardiomyocytes to identify potential novel regulators of cardiomyocyte proliferation. Endoglin was identified as an inhibitor of cardiomyocyte proliferation in vitro. Endoglin knock-down resulted in enhanced DNA synthesis, cardiomyocyte mitosis and cytokinesis in mouse, rat and human cardiomyocytes. Using gene-targeted mice, we confirmed myocardial Endoglin to be important in cardiomyocyte proliferation and cardiac regeneration using gene-targeted mice. Mechanistically, we show that Smad signaling is required for the endoglin-mediated anti-proliferative effects. Our results identify the TGF-{beta} coreceptor Endoglin as a regulator of cardiac regeneration and cardiomyocyte proliferation. SummaryHigh-content function screening is used to identify a novel inhibitor of cardiomyocyte proliferation which can promote mammalian cardiac regeneration.

genetics↗

bioRxiv2024

Free fatty acid receptor 4 in cardiac myocytes ameliorates ischemic cardiomyopathy

AimsFree fatty acid receptor 4 (Ffar4) is a receptor for long-chain fatty acids that attenuates heart failure driven by increased afterload. Recent findings suggest that Ffar4 prevents ischemic injury in brain, liver, and kidney, and therefore, we hypothesized that Ffar4 would also attenuate cardiac ischemic injury. Methods and ResultsUsing a mouse model of ischemia-reperfusion (I/R), we found that mice with systemic deletion of Ffar4 (Ffar4KO) demonstrated impaired recovery of left ventricular systolic function post-I/R with no effect on initial infarct size. To identify potential mechanistic explanations for the cardioprotective effects of Ffar4, we performed bulk RNAseq to compare the transcriptomes from wild-type (WT) and Ffar4KO infarcted myocardium 3-days post-I/R. In the Ffar4KO infarcted myocardium, gene ontology (GO) analyses revealed augmentation of glycosaminoglycan synthesis, neutrophil activation, cadherin binding, extracellular matrix, rho signaling, and oxylipin synthesis, but impaired glycolytic and fatty acid metabolism, cardiac repolarization, and phosphodiesterase activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated impaired AMPK signaling and augmented cellular senescence in the Ffar4KO infarcted myocardium. Interestingly, phosphodiesterase 6c (PDE6c), which degrades cGMP, was the most upregulated gene in the Ffar4KO heart. Further, the soluble guanylyl cyclase stimulator, vericiguat, failed to increase cGMP in Ffar4KO cardiac myocytes, suggesting increased phosphodiesterase activity. Finally, cardiac myocyte-specific overexpression of Ffar4 prevented systolic dysfunction post-I/R, defining a cardioprotective role of Ffa4 in cardiac myocytes. ConclusionsOur results demonstrate that Ffar4 in cardiac myocytes attenuates systolic dysfunction post-I/R, potentially by attenuating oxidative stress, preserving mitochondrial function, and modulation of cGMP signaling.

physiology↗

bioRxiv2024

Spatial transcriptomics elucidates medulla niche supporting germinal center response in myasthenia gravis thymoma

Myasthenia gravis (MG) is known to be epidemiologically associated with abnormalities of the thymus, an organ that maintains central tolerance. However, due to the complexity of the thymus, specific characteristics related to the pathogenesis of MG remain elusive. In our study, we attempted to narrow down the features associated with MG using spatial transcriptome analysis of thymoma and thymic hyperplasia samples. We found that the majority of thymomas were constituted by the cortical region, whereas the medullary region was localized in comparatively restricted areas. Moreover, the medullary region contained polygenic enrichment, MG-specific germinal center structures, and a supporting immune microenvironment. Additionally, neuromuscular medullary thymic epithelial cells (nmTECs), previously identified as MG-specific autoantigen-producing cells, were situated at the cortico-medullary junction. The immune microenvironment in the medulla was characterized by a specific chemokine pattern and specific immune cells, such as CCR7+ migratory dendritic cells (migDCs) and effector regulatory T (Treg) cells. Furthermore, similar germinal center structures and immune microenvironments were observed in the medulla during thymic hyperplasia. This study indicates that the medulla and junction areas are related to the pathology of MG, suggesting that these areas should be the focus of future studies on MG pathogenesis and drug targeting.

immunology↗

bioRxiv2022

Epigenomic and chromosomal architectural reconfiguration in developing human frontal cortex and hippocampus

The human frontal cortex and hippocampus play critical roles in learning and cognition. We investigated the epigenomic and 3D chromatin conformational reorganization during the development of the frontal cortex and hippocampus, using more than 53,000 joint single-nucleus profiles of chromatin conformation and DNA methylation (sn-m3C-seq). The remodeling of DNA methylation predominantly occurs during late-gestational to early-infant development and is temporally separated from chromatin conformation dynamics. Neurons have a unique Domain-Dominant chromatin conformation that is different from the Compartment-Dominant conformation of glial cells and non-brain tissues. We reconstructed the regulatory programs of cell-type differentiation and found putatively causal common variants for schizophrenia strongly overlap with chromatin loop-connected, cell-type-specific regulatory regions. Our data demonstrate that single-cell 3D-regulome is an effective approach for dissecting neuropsychiatric risk loci.

genomics↗

bioRxiv2021

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution without the need for annotation of individual cells to cell types; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =346K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

bioinformatics↗

bioRxiv2019

Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures

Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. All together, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

bioinformatics↗

bioRxiv2018

AdaFDR: a Fast, Powerful and Covariate-Adaptive Approach to Multiple Hypothesis Testing

Multiple hypothesis testing is an essential component of modern data science. Its goal is to maximize the number of discoveries while controlling the fraction of false discoveries. In many settings, in addition to the p-value, additional information/covariates for each hypothesis are available. For example, in eQTL studies, each hypothesis tests the correlation between a variant and the expression of a gene. We also have additional covariates such as the location, conservation and chromatin status of the variant, which could inform how likely the association is to be due to noise. However, popular multiple hypothesis testing approaches, such as Benjamini-Hochberg procedure (BH) and independent hypothesis weighting (IHW), either ignore these covariates or assume the covariate to be univariate. We introduce AdaFDR, a fast and flexible method that adaptively learns the optimal p-value threshold from covariates to significantly improve detection power. On eQTL analysis of the GTEx data, AdaFDR discovers 32% and 27% more associations than BH and IHW, respectively, at the same false discovery rate. We prove that AdaFDR controls false discovery proportion, and show that it makes substantially more discoveries while controlling FDR in extensive experiments. AdaFDR is computationally efficient and can process more than 100 million hypotheses within an hour and allows multi-dimensional covariates with both numeric and categorical values. It also provides exploratory plots for the user to interpret how each covariate affects the significance of hypotheses, making it broadly useful across many applications.

bioinformatics↗

bioRxiv2018

One read per cell per gene is optimal for single-cell RNA-Seq

An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? A mathematical framework reveals that, for estimating many important gene properties, the optimal allocation is to sequence at the depth of one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plugin estimator but one developed via empirical Bayes.

bioinformatics↗