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Yan Zhou

Publications and source records attributed to Yan Zhou.

6 recordsLinked to original sources
PubMed2026

A germline KDM3C polymorphism impairs DNA repair and sensitizes to chemoradiotherapy.

Chemoradiotherapy (CRT) is the standard-of-care therapy for many solid malignancies, yet predictive biomarkers of treatment response remain limited. We identified a germline single nucleotide polymorphism (SNP) in an intrinsically disordered region of the lysine demethylase KDM3C/JMJD1C (p.S464T) that is associated with CRT outcomes in locally advanced rectal cancers (LARC) and head and neck squamous cell carcinoma (LA-HNSCC). In silico modeling with AlphaFold predicted S464T substitution influenced interaction between phosphorylated KDM3C and RNF8 FHA domain. In cellular models, conversion of S464 to T464 increased sensitivity to DNA-damaging agents. S464T substitution impaired damage-induced MDC1-RAP80 signaling and downstream RAP80-BRCA1 colocalization. SNP carrying cells impaired DNA repair causing genotoxic stress that is associated with increased cGAS-cGAMP innate immune signaling and increased apoptosis. Population analyses with the SNP highlighted an increase incidence of UV-induced skin and other cancers, linking inherited variation in the chromatin regulatory gene KDM3C to genome instability, cancer risk, and therapeutic vulnerability.

Journal Article↗
PubMed2026

Integrative TWAS and multi-omics analyses prioritize HSPE1 as a candidate risk gene for bipolar disorder with immune cell-specific regulatory evidence.

BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder associated with substantial disability. Although genome-wide association studies have identified multiple BD-associated loci, the underlying genes and mechanisms remain incompletely understood. METHODS: We integrated a European-ancestry BD genome-wide association dataset with cross-tissue and tissue-specific transcriptome-wide association studies (TWAS) and complementary gene-based analysis. Candidate genes were further evaluated using differential expression analysis, consensus clustering, immune infiltration analysis, machine learning, summary-data-based Mendelian randomization, Mendelian randomization using single-cell expression quantitative trait locus data, single-nucleus transcriptomics, phenome-wide association analysis, and virtual screening. RESULTS: The integrative analyses prioritized 37 candidate genes. Peripheral-blood differential-expression analysis identified 14 genes that remained significant after FDR correction, and their expression profiles separated BD samples into two expression-defined clusters. Machine-learning analysis selected UNC50, LMAN2L, LYG2, HSPE1, and KANSL3 for an exploratory classification nomogram. SMR associated genetically predicted higher HSPE1 expression with increased BD risk in two blood eQTL datasets. Cell-type-specific analyses indicated HSPE1-related associations in T-cell and natural killer cell subsets, while single-nucleus analysis descriptively showed higher HSPE1 expression in medial thalamic T cells from BD samples. PheWAS identified no genome-wide significant associations for HSPE1, whereas virtual screening identified candidate compounds with favorable predicted docking scores against the HSPE1 structure. CONCLUSION: This integrative multi-omics study identified HSPE1 as a candidate BD risk gene with immune-cell-related regulatory evidence, providing insight into BD pathogenesis and supporting functional validation.

Humans↗
PubMed2026

Biological characterization and genome analysis of Bacillus thuringiensis GX0003935 with biocontrol activity against Meloidogyne enterolobii.

Meloidogyne enterolobii is a highly aggressive root-knot nematode, and reduced availability of chemical nematicides increases the need for effective biocontrol alternatives. We characterized Bacillus thuringiensis GX0003935 in terms of nematicidal activity, stability, biocontrol efficacy, and genome features. The culture broth and filtrate caused more than 97% corrected mortality of second-stage juveniles within 48 h, whereas bacterial suspension showed limited activity, suggesting that extracellular factors substantially contribute to nematicidal activity. The culture filtrate retained high nematicidal activity after exposure to UV irradiation, heat treatment, broad pH range, and prolonged storage, and the strain maintained stable activity during serial passaging. Furthermore, protease sensitivity assays, ammonium sulfate precipitation, and polarity characterization collectively suggested a composite active system involving proteinaceous and non-proteinaceous components. In pot trials, culture broth and filtrate reduced galling by approximately 74%. Genome sequencing combined with ANI/dDDH analyses confirmed GX0003935 as B. thuringiensis. Multiple candidates (proteases, chitinases, and toxin proteins) and secondary metabolite biosynthetic gene clusters were revealed, while known nematicidal Cry toxins were not detected. RT-qPCR results confirmed that the expression of these candidate genes at different growth stages. B. thuringiensis GX0003935 exhibits stable, extracellular-factor-associated nematicidal activity and effectively suppresses M. enterolobii in water spinach, indicating its potential as a biocontrol candidate.

Bacillus thuringiensis↗
PubMed2025

CAFs activated by YAP1 upregulate cancer matrix stiffness to mediate hepatocellular carcinoma progression.

BACKGROUND: The stiffness of the matrix is closely related to the progression of hepatocellular carcinoma (HCC). Although direct targeting of stromal rigidity in HCC remains a clinical challenge, cancer-associated fibroblasts (CAFs) are considered key contributors to this process. Given the heterogeneity of CAFs, this study explored the relationship between specific CAF subsets and liver cancer matrix stiffness, aiming to identify novel therapeutic targets for HCC patients. METHODS: Single-cell sequencing datasets were leveraged to identify cell types within liver cancer and characterize the transcriptomic profiles of CAFs. Prognostic analysis, utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) liver cancer datasets, assessed the correlation between matrix stiffness-related genes and HCC patient outcomes. Pseudo-time analysis was applied to trace the developmental trajectories of CAFs. By calculating intercellular communication probabilities and analyzing transcription factor activity, the functions and interactions of different CAF subsets were elucidated. Gene Ontology (GO) analysis was used to explore the functional roles of CAFs in distinct Yes-associated protein 1 (YAP1) groups. Finally, cellular experiments and animal experiments were further conducted to validate the hypotheses of this study. RESULTS: This study identified CAF subpopulations based on single-cell sequencing data and analyzed transcriptional changes within these subpopulations. Key findings include the identification of collagen type I alpha 1 (COL1A1), collagen type III alpha 1 (COL3A1), and lysyloxidase (LOX) as pivotal node genes during CAF development. Moreover, the expression of matrix stiffness-related genes was inversely correlated with the prognosis of HCC patients. Notably, the YAP1-positive CAF subpopulation emerged as the primary contributor to matrix stiffness in liver cancer. This subpopulation upregulates the expression of matrix stiffness-related genes and promotes tumor progression by activating signaling pathways such as autophagy and GTPase activity regulation. Cellular experiments and animal studies further validated this conclusion. CONCLUSION: This single-cell analysis uncovered the functional roles of CAFs in liver cancer. The YAP1-positive CAF subpopulation, in particular, was shown to contribute to matrix stiffness by upregulating the expression of relevant genes and promoting tumor progression through the activation of specific signaling pathways.

Carcinoma, Hepatocellular↗
PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗