Biology subjects

zhou, x.

Publications and source records attributed to zhou, x..

10 recordsLinked to original sources

bioRxiv2026

Isthmin-1 is a Key Regulator of Induced Pluripotent Stem Cell-Derived Cardiomyocytes Maturation through Activation of p53 Signaling

AimsHuman induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) exhibit an immature structural and metabolic phenotype that limits their utility for cardiac disease modeling and regenerative therapy. Although multiple extrinsic strategies have been proposed to enhance iPSC-CM maturation, intrinsic molecular regulators governing this process remain incompletely defined. This study aimed to identify and characterize a novel molecular factor that promotes cardiomyocyte maturation. Methods and ResultsCross-species transcriptomic analyses comparing adult versus fetal hearts and hypertrophic cardiomyopathy versus healthy myocardium identified Isthmin-1 (ISM1) as a conserved maturation-associated gene. In human iPSC-CMs, ISM1 overexpression enhanced sarcomere organization, mitochondrial oxidative phosphorylation, ATP production, and calcium-handling properties, whereas ISM1 knockdown impaired these maturation features. RNA sequencing revealed global transcriptional reprogramming toward an adult-like state, characterized by suppression of cell-cycle-related gene programs and activation of oxidative metabolic pathways. KEGG pathway enrichment, GSVA, and GSEA consistently identified p53 signaling as the most significantly activated pathway in ISM1-overexpressing iPSC-CMs. Mechanistically, ISM1 directly interacted with p53, enhanced its protein stability, promoted nuclear localization, and increased transcription of p53 downstream targets involved in metabolic remodeling and cell-cycle exit. Pharmacological inhibition of p53 abolished ISM1-induced structural and metabolic maturation, demonstrating that ISM1 promotes cardiomyocyte maturation in a p53-dependent manner. ConclusionsISM1 is a previously unrecognized molecular driver of cardiomyocyte maturation that promotes structural, metabolic, and functional maturation of iPSC-CMs through activation of p53-dependent transcriptional programs. These findings provide new mechanistic insight into cardiomyocyte maturation and identify ISM1 as a promising target for improving the maturity of stem cell-derived cardiomyocytes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/698535v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@15daca3org.highwire.dtl.DTLVardef@f1396dorg.highwire.dtl.DTLVardef@f13400org.highwire.dtl.DTLVardef@18d0b41_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2025

Rapid and Complete Inactivation of Enveloped Viruses by Electrochemical Disinfection: Unraveling the Contribution of Reactive Chlorine Species

Waterborne viruses pose significant threats to global water safety. Electrochemical (EC) disinfection is considered a promising next-generation technology to address this challenge. However, its efficacy against enveloped viruses remains inadequately under-stood. In this study, we employed bacteriophage Phi6 as a surrogate to systematically eval-uate the inactivation efficiency and damage mechanisms of enveloped viruses in a flow-through EC reactor. Experimental results indicated that Phi6 was more susceptible to EC disinfection than common bacterial surrogates. Comprehensive damage was observed across all major viral components, including structural proteins, the lipid envelope, and the RNA genome. The electrogenerated reactive chlorine species (RCS) were identified as the primary agent responsible for this inactivation, exhibiting higher reactivity than conven-tional free chlorine. Furthermore, radical chlorine species were also confirmed to be pro-duced during EC disinfection, contributing to rapid and extensive viral inactivation. This study demonstrates the feasibility of EC disinfection for inactivating enveloped viruses and provides deeper mechanistic insights into its highly efficient viral inactivation performance. SYNOPSISOur results elucidate the rapid inactivation and thorough damage of the enveloped virus surrogate Phi6 by electrogenerated reactive chlorine species during electrochemical disinfection. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/688411v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@7e8c29org.highwire.dtl.DTLVardef@23fcedorg.highwire.dtl.DTLVardef@b26312org.highwire.dtl.DTLVardef@1864ab1_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

S3R: Spatially Smooth and Sparse Regression Reveals High-Dimensional Regulatory Networks in Spatial Transcriptomics

Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that estimates location-specific coefficients linking a response feature to high-dimensional spatial predictors. S3R unites structured sparsity with a minimum-spanning-tree-guided smoothness penalty, yielding coefficient fields that are coherent within neighborhoods yet permit sharp boundaries. In synthetic data, S3R accurately recovers spatially varying effects, selects relevant predictors, and preserves known boundaries. Applied to Visium-based ST data, S3R recapitulates layer-specific target-TF associations in human dorsolateral prefrontal cortex with concordant layer-wise correlations in matched single-cell data. In acute Haemophilus ducreyi skin infection, S3R converts spot-level gene expression mixtures into cell type-attributed expression fields, revealing per-cell type spatial gradients, and improving concordance of spatially variable gene calls when tests are applied to these demixed fields. In pancreatic ductal adenocarcinoma, S3R builds cross-cell-type, cross-gene co-variation tensors that quantify cell-cell interaction strength at gene-pair resolution and nominate interacting genes whose pathway enrichments align with established stromal-epithelial and immune crosstalk. An efficient implementation scales to large assays, and on a Xenium-based breast cancer dataset, S3R delineates the contributions at gene-gene, local neighborhood, and global context-level to target gene expression. Because responses and predictors in S3R are user-defined, it could flexibly address diverse biological questions within a single, scalable, and interpretable regression framework.

bioinformatics↗

bioRxiv2025

TRiC-assisted folding of class I HDAC family proteins regulated by distinct co-chaperone and cofactor networks

Class I histone deacetylases (HDACs), including HDAC1, HDAC2, HDAC3, and HDAC8, are essential for diverse cellular processes. Although the chaperonin TRiC is implicated in the activation of class I HDACs, the underlying mechanisms remain elusive. Using cryo-electron microscopy (cryo-EM), cross-linking mass spectrometry (XL-MS), and biochemistry analyses, we established class I HDACs as novel TRiC substrates and elucidate TRiC-assisted folding of HDAC1 and HDAC3 during its ATPase cycle, orchestrated by distinct co-chaperone and cofactor networks. In the closed TRiC chamber, both HDAC1 and HDAC3 adopt near-native states with shared binding interfaces. However, their open-state configurations diverge: Hsp70 and PDCD5 engage atop and within TRiC, respectively, for HDAC3, whereas prefoldin (PFD) binds atop TRiC for HDAC1, suggesting roles in substrate delivery and folding modulation. Furthermore, an unexpected bent conformation of CCT4, detected in TRiC-HDAC1 complex, may facilitate co-chaperone dissociation from TRiC. In contrast, HDAC8 folds independently of TRiC. Our study reveals the mechanism governing TRiC-assisted folding of class I HDACs in orchestration of dynamic co-chaperone/cofactor network, shielding new lights on the sophisticated regulatory landscape of TRiC, and open promising avenues for designing peptides or small molecules to selectively modulate TRiC-assisted folding of class I HDACs and other substrates.

molecular biology↗

bioRxiv2023

Predicting the Trend of SARS-CoV-2 Mutation Frequencies Using Historical Data

As the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral variations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies in the coming months. In this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. To improve upon the accuracy of traditional time-series models, we transformed the prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling the first-order derivative of the mutation frequency. We predicted the final derivative in each segment based on the preceding derivatives, employing various machine learning methods, including random forest, XGBoost, support vector machine, and neural network models, in this supervised learning setting. Empowered by the novel transformation strategy and the high capacity of machine learning models, we witnessed low prediction error that is confined within 0.1% and 1% when making predictions for future 30 and 80 days respectively. In addition, the method also led to a notable increase in prediction accuracy compared to traditional time-series models, as evidenced by lower MAE, and MSE for predictions made within different time horizons. To further assess the methods effectiveness and robustness in predicting mutation patterns for unforeseen mutations, we categorized all mutations into three major patterns. The model demonstrated its robustness by accurately predicting unseen mutation patterns when training on data from two pattern categories while testing on the third pattern category, showcasing its potential in forecasting a variety of mutation trajectories. To enhance accessibility and utility, we built our methodology into an R-shiny app (https://swdatpredicts.shinyapps.io/rshiny_predict/), a tool with potential applicability in studying other infectious diseases, thus extending its relevance beyond the current pandemic.

bioinformatics↗

bioRxiv2021

Functional Anatomy of the TDP-43 Redox Sensor

TAR binding protein 43 (TDP-43) is an RNA binding protein that assists in the maturation, export and sub-cellular localization of mRNA. The carboxyl terminal 153 residues of TDP-43 are of low sequence complexity and allow for self-association of the protein in a manner leading to its phase separation from an aqueous environment. These interactions assist TDP-43 in forming cytoplasmic RNA granules involved in the transport of mRNA for localized translation. Self-association of the TDP-43 low complexity (LC) domain is facilitated by a region of twenty five residues that are of extreme evolutionary conservation. The molecular basis for self-adherence of the protein through this region has been illuminated by a combination of structural and biochemical studies, allowing definition of a morphologically specific cross-{beta} structure predicted to be weakly assembled by main chain hydrogen bonds. In this study we have investigated the importance of individual, Pauling hydrogen bonds hypothesized to facilitate self-adherence of the TDP-43 LC domain.

biochemistry↗

bioRxiv2021

Tracing the genetics of neurological disease to the mutation-directed addition of single hydrogen bonds

Mutations causative of neurological and neurodegenerative disease can occur in coding regions that specify protein domains of low sequence complexity. These autosomal dominant mutations can be idiosyncratic in their recurrent appearance at the same amino acid. Here we report studies of recurrent mutations in proline residues located within low complexity (LC) domains associated with the neurofilament light chain protein, the microtubule-associated tau protein, and the heterogeneous nuclear RNPA2 protein. All such mutations manifest their effects by directing formation of variant proteins endowed with the addition of a single, main chain hydrogen bond specified by the variant amino acid replacing proline. Here we show that methylation of the peptide backbone nitrogen atom associated with these variant amino acids eliminates the aberrant hydrogen bond and restores normal protein function.

biochemistry↗

bioRxiv2020

Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Single-cell RNA sequencing (scRNA-seq) technologies promise to characterize the transcriptome of genes at cellular resolution, which shed light on unfolding cell heterogeneity and diversity. Fast-growing scRNA-seq profiles require efficient clustering algorithms to identify the same type of cells. Although many methods have been developed for cell clustering, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed GraphSCC, a robust graph artificial intelligence model to cluster single cells by accounting for structural relations between cells. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. The experimental results indicate that GraphSCC model outperforms state-of-the-art methods in terms of various evaluation metrics on both simulated and real datasets. Further visualizations show that GraphSCC provides representations for better intra-cluster compactness and inter-cluster separability.

bioinformatics↗

bioRxiv2020

Red-light is an environmental effector for mutualism between begomovirus and its vector whitefly

Environments such as light condition influence the spread of infectious diseases by affecting insect vector behavior. However, whether and how light affects the host defense which further affect insect preference and performance, remains unclear, nor has been demonstrated how pathogens co-adapt light condition to facilitate vector transmission. We previously showed that begomoviral βC1 inhibits MYC2-mediated jasmonate signaling to establish plant-dependent mutualism with its insect vector. Here we show red-light as an environmental catalyzer to promote mutualism of whitefly-begomovirus by stabilizing βC1, which interacts with PHYTOCHROME-INTERACTING FACTORS (PIFs) transcription factors. PIFs positively control plant defenses against whitefly by directly binding to the promoter of terpene synthase genes and promoting their transcription. Moreover, PIFs integrates light and jasmonate signaling by interaction with MYC2, and co-regulation the transcription of terpene synthase genes. However, begomovirus encoded βC1 inhibit PIFs’ and MYC2’ transcriptional activity via disturbing their dimerization, thereby impairing plant defenses against whitefly-transmitted begomoviruses. Our results thus describe how a viral pathogen hijacks host light signaling to enhance the mutualistic relationship with its insect vector.Author summary Climate change is driving disease rapidly spread, esp. for global distribution of insect-borne diseases. This paper reports red-light as an environmental factor to promote insect vector olfactory orientation behavior and increase viral disease transmission. Plant virus adapts the supplemental red lighting practice in modern agricultural greenhouse production under protection, therefore enhancing disease spreading globally.View Full Text

plant biology↗

bioRxiv2020

Transiently structured head domains control intermediate filament assembly

Low complexity (LC) head domains 92 and 108 residues in length are, respectfully, required for assembly of neurofilament light (NFL) and desmin intermediate filaments (IFs). As studied in isolation, these IF head domains interconvert between states of conformational disorder and labile, {beta}-strand-enriched polymers. Solid state nuclear magnetic resonance (ss-NMR) spectroscopic studies of NFL and desmin head domain polymers reveal spectral patterns consistent with structural order. A combination of intein chemistry and segmental isotope labeling allowed preparation of fully assembled NFL and desmin IFs that could also be studied by ss-NMR. Assembled IFs revealed spectra overlapping with those observed for {beta}-strand-enriched polymers formed from the isolated NFL and desmin head domains. Phosphorylation and disease causing mutations reciprocally alter NFL and desmin head domain self-association, yet commonly impede IF assembly. These observations show how facultative structural assembly of LC domains via labile, {beta}-strand-enriched self-interactions may broadly influence cell morphology.

biochemistry↗