Monocyte Activation Drives Atrial Fibrillation in Hypertension.
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Atrial fibrillation↗
Biomedical subjects
Wei Zhang
Publications and source records attributed to Wei Zhang.
FPR2/ALX stimulation modulates microglia and natural killer cells to restrict autoimmune astrocytopathy.
Autoantibody- and complement-mediated cytotoxicity can cause autoimmune astrocytopathy that leads to CNS inflammatory demyelination. Formyl peptide receptor 2 (FPR2/ALX) governs the activation and propagation of immune response. However, the precise role of FPR2/ALX in neuroinflammation and the effect of FPR2/ALX stimulation on autoimmune astrocytopathy are poorly understood. Using a mouse model of autoimmune astrocytopathy induced by AQP4-IgG- and complement-mediated cytotoxicity, we found that the stimulation of FPR2/ALX with the small-molecule agonist Quin-C1 led to reduced brain lesion volume, astrocyte loss and demyelination. This was accompanied by enhanced anti-inflammatory activity of microglia and reduced infiltration of lymphocytes in the brain. FPR2/ALX stimulation also led to increased phosphorylation of SYK and AKT in mice with autoimmune astrocytopathy. Notably, the benefits of FPR2/ALX stimulation were attenuated in mice with autoimmune astrocytopathy after microglial depletion using the CSF1R inhibitor PLX5622 or natural killer (NK) cell depletion using an anti-NK1.1 monoclonal antibody. Additionally, the protective effects of FPR2/ALX stimulation were diminished in mice with autoimmune astrocytopathy that received the SYK inhibitor R406. Collectively, our findings demonstrate that FPR2/ALX stimulation may represent a promising therapeutic strategy to attenuate detrimental neuroinflammation in autoimmune astrocytopathy by modulating microglia and NK cells. FPR2/ALX stimulation suppresses autoimmune astrocytopathy: Using a mouse model of autoimmune astrocytopathy, we demonstrated that FPR2/ALX stimulation with the small molecule Quin-C1 reduces the CNS infiltration of lymphocytes and augments the anti-inflammatory activity of microglia, leading to attenuated astrocyte pathology induced by AQP4-IgG and complement-mediated attacks. Mechanistically, the benefits of FPR2/ALX stimulation using Quin-C1 involve microglia, natural killer (NK) cells, and SYK-AKT signaling.
Animals↗
Nitrogen sources and concentrations shape algal odor compounds: Key drivers of β-cyclocitral and β-ionone in water bodies of the lower Yangtze River.
Taste and odor (T&O) compounds derived from cyanobacterial blooms pose escalating threats to freshwater security worldwide, yet the drivers of specific T&O metabolites remain poorly constrained. Here, we investigated the dual effects of nitrogen (N) sources and concentrations on the production of β-cyclocitral and β-ionone, two algal-derived T&O compounds, through integrated field surveys (54 sites across lakes and rivers) in the eutrophic lower Yangtze River, China, and laboratory cultivation of typical cyanobacteria (Microcystis aeruginosa and Pseudanabaena cinerea). Our field data revealed that the concentrations of β-cyclocitral and β-ionone in lakes and rivers were not significantly different, but increased with the trophic level index. Redundancy analysis and Mantel analysis showed that Microcystis and Pseudanabaena were potentially dominant contributors to β-cyclocitral and β-ionone in the water column. Structural equation modeling and variation partitioning analysis showed that enhanced nitrate (NO3--N) significantly promoted the production of these compounds. Laboratory experiments demonstrated that inorganic N (NaNO₃) maximized total T&O yields by promoting algal biomass, whereas organic N (urea and glutamic acid) elevated the T&O production per unit biomass by 1.5- to 9.5-fold. Notably, Pseudanabaena exhibited a 2.3-fold higher β-ionone yield than Microcystis, with greater sensitivity to N concentrations. Our study highlights the critical role of nitrogen pollution, both source and concentration, in the production of T&O compounds by phytoplankton and provides reference data for managing T&O issues in rivers and shallow lakes.
Norisoprenoids↗
Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.
BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.
Humans↗
X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.
MOTIVATION: The rapid accumulation of multi-omics data presents a valuable opportunity to advance our understanding of complex diseases and biological systems, driving the development of integrative computational methods. However, the complexity of biological processes, spanning multiple molecular layers and involving intricate regulatory interactions, requires models that can capture both intra- and cross-omics relationships. Most existing integration methods primarily focus on sample-level similarities or intra-omics feature interactions, often neglecting the interactions across different omics layers. This limitation can result in the loss of critical biological information and suboptimal performance. To address this gap, we propose X-intNMF, a network-regularized non-negative matrix factorization (NMF) framework that simultaneously integrates intra- and cross-omics feature interactions into a shared low-dimensional representation (see Fig. 1). By modeling these multi-layered relationships, X-intNMF enhances the representation of biological interactions and improves integration quality and prediction accuracy. RESULTS: For evaluation, we applied X-intNMF to predict breast cancer phenotypes and classify clinical outcomes in lung and ovarian cancers using mRNA expression, microRNA expression, and DNA methylation data from TCGA. The results show that X-intNMF consistently outperforms state-of-the-art methods. Ablation studies confirm that incorporating both cross-omics and intra-omics interactions contributes significantly to the model's improved performance. Additionally, survival analysis on 25 TCGA cancer datasets demonstrates that the integrated multi-omics representation offers strong prognostic value for both overall survival and disease-free status. These findings highlight X-intNMF's ability to effectively model multi-layered molecular interactions while maintaining interpretability, robustness, and scalability within the NMF framework. AVAILABILITY AND IMPLEMENTATION: The source code and datasets supporting this study are publicly available at GitHub (https://github.com/compbiolabucf/X-intNMF) and archived on Zenodo (https://doi.org/10.5281/zenodo.18238385).
Multiomics↗
Comprehensive transcriptomics and proteomics analysis of neointima formation in human saphenous vein: implications for bypass graft disease.
Human saphenous veins (SVs) are widely used as grafts in coronary artery bypass (CABG) surgery but often fail due to neointima formation. Little is known, however, regarding the cellular, transcriptomic, and proteomic dynamics of neointima formation in human veins. Here, we performed transcriptomics and proteomics analysis in an ex vivo tissue culture model of neointima formation in human SVs procured for CABG surgery. Histological examination demonstrated significant elastin degradation and neointima formation (indicated by increased neointima area and neointima-to-media ratio) in SVs subjected to tissue culture. Analysis of data from 72 patients suggests that the progression of SV remodeling and neointima formation differs according to sex and body mass index, which is negatively associated with neointima formation in males only. RNA sequencing demonstrated upregulation of proinflammatory and proliferation-related genes during neointima formation and identified novel processes, including increased cellular stress and DNA damage responses, reflecting tissue trauma associated with vein harvesting. Proteomic analysis identified upregulated extracellular matrix-related and coagulation/thrombosis proteins and downregulated metabolic proteins. Spatial transcriptomics, used to infer regionally enriched gene expression, suggested dynamic alterations in fibroblast and vascular smooth muscle cell (VSMC) states during neointima formation. Specifically, we identified the emergence of HES1+ and matrix metalloproteinase 2- and 14-positive (MMP2+/MMP14+) expression in VSMCs and fibroblasts, respectively, during neointima formation. Furthermore, our data suggest that MIR647, identified through screening, maintains VSMC contractile gene expression. Our findings suggest dynamic transcriptomic and proteomic changes during neointima formation in human veins and provide useful mechanistic information for the pathogenesis of SV graft disease.NEW & NOTEWORTHY Using multiomics and spatial transcriptomics, we uncover dynamic molecular and cellular changes driving neointima proliferation in human saphenous veins, the most common conduit for bypass surgery. Our study highlights sex- and body mass index-associated differences, novel fibroblast and smooth muscle cell states, and a role for microRNA-647 in preserving vascular contractile phenotype. These findings provide new insight into the mechanisms of vein graft failure and may guide future strategies to improve coronary bypass outcomes.
Humans↗
Novel serum small extracellular vesicle miRNAs with multi-target RCA-CRISPR sensor for liver cancer detection.
BACKGROUND: Detecting liver cancer (LC) remains a significant challenge in clinical practice. Small extracellular vesicle (sEV) miRNAs show promise as non-invasive biomarkers for LC detection, yet their diagnostic potential remains largely unexplored. This study aimed to identify specific sEV miRNA signatures for LC detection and develop a novel synchronized multi-miRNA detection platform to enhance diagnostic efficiency and sensitivity. METHODS: High-throughput sequencing was conducted across four distinct cohorts: normal controls (NC), hepatitis B virus (HBV) patients, liver cirrhosis patients, and LC patients. This sequencing process identified miRNAs with differential expression, followed by RT-qPCR validation in serum sEV miRNAs from LC patients and NC. An innovative detection method, RCA-CRISPR, was introduced, combining rolling circle amplification (RCA) with CRISPR/Cas12a (RCA-CRISPR) for quick and sensitive miRNAs detection. RESULTS: Sequencing results showed a consistent elevation of hsa-miR-203b-5p, hsa-miR-4661-5p, and hsa-miR-219a-2-3p across all cohorts. RT-qPCR validations confirmed significant upregulation of these miRNAs in serum sEVs from LC patients, and the combined three-miRNA panel exhibited high diagnostic accuracy (p = 0.0003; AUC = 0.81). The RCA-CRISPR method demonstrated a detection limit of 3.12 pM for simultaneous multi-target miRNA detection, highlighting its exceptional sensitivity. CONCLUSIONS: Our study identifies hsa-miR-203b-5p, hsa-miR-4661-5p, and hsa-miR-219a-2-3p as promising sEV miRNA biomarkers for LC detection. The developed RCA-CRISPR sensor provides a robust tool for multi-miRNA analysis, potentially advancing non-invasive LC diagnostics. Future validation in larger, prospectively collected cohorts is essential to establish the clinical utility and performance of this biomarker panel and RCA-CRISPR sensor.
MicroRNAs↗
Immune-Like Malignant Epithelial Programs Shape Tumor-Immune Interactions and Inform Prognostic Stratification in Lung Adenocarcinoma.
Lung adenocarcinoma (LUAD) is characterized by marked cellular heterogeneity, yet how malignant epithelial states contribute to immune regulation and clinical outcomes remains incompletely defined. We integrated single-cell RNA-sequencing data to map the cellular landscape of LUAD and identify malignant epithelial cells based on inferred copy-number alterations. Epithelial states were further examined through trajectory inference, transcription factor analysis, and cell-cell communication profiling. Single-cell-derived genes were subsequently integrated with TCGA and independent GEO cohorts to construct and validate a machine learning-based prognostic signature. Malignant epithelial cells displayed distinct functional programs, including an immune-like state associated with genomic instability, immune-related transcriptional activity, tumor-immune communication, and patient outcomes. The resulting immune-like malignant epithelial cell signature (IMEC-Sig) consistently stratified survival across multiple cohorts. Low IMEC-Sig scores were accompanied by greater immune infiltration, higher immune checkpoint expression, and increased immunophenoscore, whereas high scores were linked to a comparatively immunosuppressive phenotype. Pan-cancer analyses further identified KRT8 as a gene associated with unfavorable prognosis, and functional experiments showed that KRT8 silencing suppressed proliferation, migration, invasion, and colony formation in LUAD cells. Together, these findings connect malignant epithelial heterogeneity with the immune context and clinical outcomes, support IMEC-Sig as a biologically informed prognostic tool, and nominate KRT8 as a potential therapeutic target in LUAD.
Humans↗
The Multi-Omics Landscape of Enzymatic Alterations in Systemic Lupus Erythematosus.
OBJECTIVE: Systemic lupus erythematosus (SLE) is an autoimmune disease closely associated with enzyme dysfunction, yet its underlying molecular mechanisms remain incompletely understood. This study aims to characterize enzyme-network alterations associated with SLE status and disease activity and to identify candidate molecules with potential clinical relevance. METHODS: We integrated proteomic and phosphoproteomic data from peripheral blood mononuclear cells (PBMCs) of 130 SLE patients and 90 healthy controls (HC), along with transcriptomic data from 1461 SLE patients. Through systematic analysis of key enzyme phosphorylation sites, upstream transcription factors (TFs), and computationally prioritized candidate compounds, we sought to characterize enzyme-centered regulatory associations. RESULTS: Integrated proteomic and phosphoproteomic analyses revealed significant metabolic and signaling pathway disturbances, along with distinct phosphorylation patterns in SLE immune cells. Multiple SLE-associated and disease-activity-associated candidate molecules were identified. Regulatory network analysis uncovered an upstream transcription factor cluster centered around STAT1. Computational drug screening identified computationally prioritized candidate compounds with multi-gene DSigDB associations, which require further clinical safety evaluation and experimental validation. CONCLUSIONS: This study constructs a molecular map of SLE, highlighting associations between enzyme-network alterations, catalytic dysregulation, and SLE-related immune molecular signatures, and identifies candidate molecules for future clinical and functional evaluation.
Humans↗
Characterisation of the Novel HLA-DRB3*01:127 Allele by Next-Generation Sequencing.
HLA-DRB3*01:127 differs from the HLA-DRB3*01:01:02:01 allele by one nucleotide substitution in the exon 3.
Humans↗
Identification of Two Novel HLA-DQB1 Alleles, HLA-DQB1*02:239 and HLA-DQB1*02:271.
Compared with HLA-DQB1*02:02:01:01, the alleles HLA-DQB1*02:239 and HLA-DQB1*02:271 each show one nucleotide substitution, respectively.
Humans↗
Characterisation of the Novel HLA-A*02:1205 and HLA-A*02:07:27 Alleles by Next-Generation Sequencing.
Compared with HLA-A*02:07:01:01, the alleles HLA-A*02:1205 and HLA-A*02:07:27 each show one nucleotide substitution, respectively.
Humans↗
The Novel HLA-A*02:1229 Allele Was Identified by Next-Generation Sequencing.
HLA-A*02:1229 differs from HLA-A*02:07:01:01 by a single nucleotide substitution at position 1014 T>A.
Humans↗
Metabolomics Reveals Metabolic Characteristics of Functional Cure in Chronic Hepatitis B Treated With Entecavir Combined With Pegylated Interferon Alpha.
BACKGROUND: Entecavir (ETV) combined with pegylated interferon alpha (PEG-IFNα) improves chronic hepatitis B (CHB) functional cure rates, but therapeutic heterogeneity and underlying metabolic mechanisms remain unclear. This study used untargeted metabolomics to identify metabolic signatures, mechanisms, and predictive biomarkers of functional cure with ETV-PEG-IFNα. METHODS: Thirty-eight CHB patients were grouped into ETV monotherapy (Group E, n = 12) and ETV-PEG-IFNα combination therapy (Group Z, n = 26); Group Z was subdivided into cured (Group A, n = 13) and noncured (Group B, n = 13). Serum metabolomic profiling, multivariate statistics, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified differential metabolites. A random forest model was built using key metabolites. RESULTS: Three hundred eighty-eight metabolites were identified. Four differential metabolites distinguished Group A and B (upregulated guanidinoacetic acid, uracil 5-carboxylate; downregulated L-methionine S-oxide, oleamide), enriching amino acid metabolism pathways. Nine differential metabolites between Group E and Z implicated amino acid, immune, and fatty acid pathways. The random forest model based on the four Group A/B metabolites showed 88.5% cross-validation accuracy (AUC = 0.920), with L-methionine S-oxide and oleamide as key predictors. CONCLUSIONS: This study reveals metabolic rewiring in CHB functional cure via ETV-PEG-IFNα therapy, involving energy metabolism, oxidative stress, and immunomodulation, based on which we propose a tentative metabolism-immunity synergy model to guide future research. Key metabolites, especially L-methionine S-oxide and oleamide, show exploratory predictive potential for functional cure that warrants further validation in independent cohorts.
Humans↗
Multi-omics Investigations of Immune Microenvironment of Human Colorectal Cancer.
BACKGROUND/AIM: Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. Although immunotherapy has improved outcomes for a subset of patients, its limited efficacy in many cases highlights the need for a more comprehensive understanding of the CRC immune microenvironment. This study aimed to characterize the molecular landscape of the CRC immune microenvironment using an integrated multi-omics approach and to identify candidate regulatory molecules associated with immune remodelling. MATERIALS AND METHODS: We integrated structural variation, DNA methylation, chromatin accessibility, proteomic, and phosphoproteomic data generated from an in-house CRC cohort with transcriptomic data from The Cancer Genome Atlas (TCGA). Analyses focused on 1,539 immune-related genes (IRGs) associated with CD4+ T cells, B cells, and natural killer (NK) cells. Multi-layered genomic and proteomic analyses were performed to identify altered immune-related pathways, hub genes, candidate transcription factors, and upstream kinases. RESULTS: Higher infiltration of CD4+ T cells, B cells, and NK cells was associated with CRC. IRGs exhibited widespread alterations across genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic levels. IL10, LEP, ITGAM, and EGFR emerged as candidate hub genes. EGFR phosphorylation at S991 and T693 was significantly decreased in CRC. STAT2 and HSF1 were identified as candidate upstream transcription factors, while CDK2 emerged as a candidate upstream kinase associated with immune infiltration and immune checkpoint expression. CONCLUSION: This study provides a systematic multi-omics characterization of immune microenvironment remodelling in CRC and identifies candidate molecular regulators that may serve as potential targets for future immunotherapy research.
Humans↗
MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.
In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.
Humans↗
soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.
The rapid advancement of spatial multi-omics technologies has opened new avenues for dissecting tissue architecture with unprecedented resolution. However, inherent disparities across omics modalities, such as differences in biological hierarchy and resolution, pose significant challenges for integrative analysis. To address this, we present soFusion, a method for representation learning on spatial multi-omics data that enables automated identification of tissue compartmentalization. soFusion employs a graph convolutional network (GCN) to extract latent embeddings from spatial omics profiles. To simultaneously capture both cross-modality relationships and modality-specific features, we introduce a novel strategy for intra- and inter-omics feature learning. Moreover, modality-specific decoders are designed to preserve the unique information embedded in each omics type. We evaluated soFusion on multiple datasets including gene expression, protein expression, and epigenetic features. Across all benchmarks, soFusion consistently outperformed existing methods in delineating anatomical structures and identifying spatial domains with improved continuity and reduced noise. Collectively, soFusion offers an effective solution for spatial multi-omics integration, substantially enhancing the robustness of spatial domain identification.
Humans↗
Heme oxygenase 1 (HO-1) is a drug target for reversing cisplatin resistance in non-small cell lung cancer.
INTRODUCTION: Platinum-based drugs, the most widely used chemotherapeutic drugs in clinical oncology, have long faced the problem of drug resistance, which is urgently in need of resolution. Identifying biomarkers of drug resistance may help reduce platinum resistance and improve therapeutic efficacy. OBJECTIVES: This study aims to identify potential biomarkers associated with the development of cisplatin resistance in non-small cell lung cancer (NSCLC) and explore mechanisms to overcome chemoresistance. METHODS: NSCLC cisplatin resistance cell lines were constructed, and transcriptome sequencing was performed. Results were validated using Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Molecular docking, proteomics sequencing, and in vitro and in vivo experiments were conducted to evaluate the role of Heme Oxygenase 1 (HO-1) in cisplatin resistance. RESULTS: NSCLC cisplatin resistance cell lines, GEO and TCGA data identified HMOX1, downstream of Nrf2, as a key drug resistance gene induced by cisplatin. Activation of the Nrf2/HO-1 pathway was found to induce ferroptosis resistance, a critical mechanism of cisplatin resistance. Candidate compounds SB 202190 and Nordihydroguaiaretic acid (NDGA) effectively reactivated ferroptosis by inhibiting HO-1, thereby increasing cisplatin sensitivity. CONCLUSION: The Nrf2/HO-1 pathway is a significant contributor to cisplatin resistance in NSCLC. Targeting HO-1 with SB 202190 and NDGA presents a promising strategy to overcome resistance and improve chemotherapy outcomes.
Cisplatin↗