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PubMed2025

Gene behaviors-based network enrichment analysis and its application to reveal immune disease pathways enriched with COVID-19 severity-specific gene networks.

MOTIVATION: Gene network analysis is essential for understanding the complex mechanisms underlying diseases, which often involve disruptions in molecular networks rather than individual genes. Despite the availability of large-scale omics datasets and computational tools for gene network analysis, interpretation of the biological relevance of these extensive networks remains challenging. RESULTS: We propose a novel computational strategy, gene behaviors-based network enrichment analysis, which systematically identifies functional pathways enriched in phenotype-specific gene networks. Our novel method incorporates comprehensive network characteristics, i.e. gene expression levels, edge strengths, and structural patterns of edges, to rank genes based on activity and assess pathway enrichment, effectively identifying functional pathways enriched within these networks. Through simulation studies, our strategy demonstrated superior performance compared with that of existing methods in identifying enriched pathways. We applied this strategy to whole-blood RNA-seq data from 1102 COVID-19 samples provided by the Japan COVID-19 Task Force. The analysis revealed immune disease pathways enriched with COVID-19 severity-specific gene networks, including "Systemic lupus erythematosus" in asymptomatic and severe samples and "Inflammatory bowel disease," "Primary immunodeficiency," and "Rheumatoid arthritis" in mild samples. Key biomarkers of COVID-19, such as CXCL8, S100A9, and HLA class I genes, have been identified as critical hub genes and the main players within these networks. AVAILABILITY AND IMPLEMENTATION: Code is available in Figshare (https://doi.org/10.6084/m9.figshare.29093648.v3).

COVID-19↗

PubMed2025

PathwayVote: an R package for robust pathway enrichment analysis for DNA methylation data using a consensus-based voting framework.

MOTIVATION: Pathway enrichment analysis is commonly used to interpret epigenomewide association studies, yet conventional methods often rely on arbitrary thresholds and simplified CpG-gene mappings, making them sensitive to analytical choices and unable to fully leverage CpG-gene relationships Recent advances in expression quantitative trait methylation (eQTM) studies offer a rich resource to refine these mappings, but are rarely utilized in DNA methylation enrichment pipelines. RESULTS: We developed PathwayVote, an R package that implements a voting-based consensus approach and leverages eQTM data to identify robustly enriched pathways. PathwayVote reduces dependence on arbitrary cutoffs and improves sensitivity and reproducibility of enrichment results. AVAILABILITY AND IMPLEMENTATION: PathwayVote is freely available on GitHub (https://github.com/YinanZheng/PathwayVote) under the GPL-3 license and CRAN: https://CRAN.R-project.org/package=PathwayVote. The version of the code corresponding to this manuscript has been archived on Zenodo (https://doi.org/10.5281/zenodo.17209507).

Humans↗

PubMed2025

Differential expression of plasma proteins and pathway enrichments in pediatric diabetic ketoacidosis.

BACKGROUND: In children with type 1 diabetes (T1D), diabetic ketoacidosis (DKA) triggers a significant inflammatory response; however, the specific effector proteins and signaling pathways involved remain largely unexplored. This pediatric case-control study utilized plasma proteomics to explore protein alterations associated with severe DKA and to identify signaling pathways that associate with clinical variables. METHODS: We conducted a proteome analysis of plasma samples from 17 matched pairs of pediatric patients with T1D; one cohort with severe DKA and another with insulin-controlled diabetes. Proximity extension assays were used to quantify 3072 plasma proteins. Data analysis was performed using multivariate statistics, machine learning, and bioinformatics. RESULTS: This study identified 214 differentially expressed proteins (162 upregulated, 52 downregulated; adj P < 0.05 and a fold change > 2), reflecting cellular dysfunction and metabolic stress in severe DKA. We characterized protein expression across various organ systems and cell types, with notable alterations observed in white blood cells. Elevated inflammatory pathways suggest an enhanced inflammatory response, which may contribute to the complications of severe DKA. Additionally, upregulated pathways related to hormone signaling and nitrogen metabolism were identified, consistent with increased hormone release and associated metabolic processes, such as glycogenolysis and lipolysis. Changes in lipid and fatty acid metabolism were also observed, aligning with the lipolysis and ketosis characteristic of severe DKA. Finally, several signaling pathways were associated with clinical biochemical variables. CONCLUSIONS: Our findings highlight differentially expressed plasma proteins and enriched signaling pathways that were associated with clinical features, offering insights into the pathophysiology of severe DKA.

Humans↗

PubMed2026

Study on the mechanism of SW inhibiting testosterone synthesis in mouse Leydig cells.

BACKGROUND: Swainsonine (SW), the main toxic component of locoweed, can cause livestock poisoning and reproductive damage in male animals; however, the mechanism by which it affects testosterone secretion remains unclear. METHODS: Ten-week-old male C57BL/6 mice were orally administered SW at doses of 0, 0.05, and 0.25 mg/(kg·d) for 28 days. TM3 mouse Leydig cells were treated with SW at concentrations of 0, 1, and 10 nM for 24 h. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on RNA-seq data from mouse testicular tissues to identify differentially enriched pathways between the control and SW-treated groups. Testosterone secretion levels were measured using an enzyme-linked immunosorbent assay (ELISA). The mRNA expression levels of steroidogenesis-related genes (StAR, Cyp11a1, Hsd3b2, and Hsd17b3) were detected by qPCR, while the expression of the steroidogenic acute regulatory (STAR) protein was detected by western blotting. AutoDock Vina molecular docking was used to predict the binding affinity between SW and the STAR protein. RESULTS: KEGG analysis revealed a significant enrichment of pathways related to steroid synthesis. In both the mouse model and TM3 cells, SW significantly inhibited testosterone secretion, downregulated the mRNA expression of StAR, Cyp11a1, Hsd3b2, and Hsd17b3, and reduced the protein expression of STAR. Molecular docking analysis revealed multiple potential hydrogen-bond interaction sites between SW and STAR. CONCLUSION: SW downregulates the expression of steroidogenesis-related genes and STAR protein, thereby suppressing testosterone secretion in male mice and TM3 cells.

Swainsonine↗

PubMed2026

Machine Learning and Metabolomics to Characterize Warburg-Like Metabolic Subtypes in Human Retinal Endothelial Cells Exposed to Risk Factors Associated With Proliferative Diabetic Retinopathy.

PURPOSE: High glucose (HG), hypoxia (Hyp), and their combination are major risk factors for proliferative diabetic retinopathy (PDR). Although these conditions induce features of the Warburg-like metabolic reprogramming in human retinal endothelial cells (HRECs), it remains unclear whether they produce distinct metabolic and angiogenic subtypes. This study aimed to characterize the Warburg-like-associated metabolic heterogeneity induced by these PDR-related risk factors and evaluate the ability of supervised machine-learning models to distinguish these subtypes. METHODS: HRECs were cultured under normoglycemic, HG, Hyp (2% O2), and combined HG-Hyp conditions. Untargeted LC-MS/MS metabolomics quantified metabolites spanning carbohydrates, amino acids, nucleotides, and lipids. Principal component analysis (PCA) assessed overall metabolic variation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified metabolic pathways associated with angiogenesis. In vitro angiogenesis assays measured endothelial tube formation and branching. Nine supervised classifiers (decision tree, logistic regression, naïve Bayes, random forest, K-Nearest Neighbors, neural network, gradient boosting, AdaBoost, and Support Vector Machine) were trained on the highest-ranked metabolites selected by the Information Gain Ratio feature-ranking approach. Model performance was evaluated using 10-fold cross-validation, leave-one-out cross-validation (LOOCV), permutation testing, and a classifier stability analysis under biologically meaningful distributional shift using an independent chemically induced hypoxia model (CoCl2). RESULTS: PCA revealed partial separation of metabolic profiles across conditions, indicating different Warburg-like metabolic subtypes. The combined HG-Hyp condition exhibited enhanced angiogenic potential relative to either HG or Hyp alone. KEGG pathway enrichment analysis identified fatty acid biosynthesis and elongation among the most significantly enriched pathways in HRECs under combined HG-Hyp conditions, alongside amino sugar and nucleotide sugar metabolism, glycerophospholipid metabolism, the pentose phosphate pathway, and glycolysis/gluconeogenesis. Supervised machine-learning classifiers distinguished these metabolic subtypes, with AdaBoost and gradient Boosting showing the most balanced, reproducible performance across 10-fold cross-validation, LOOCV, and permutation testing, and remaining the most reliable classifiers under domain-shift testing (area under the curve = 0.88, P = 0.0061). CONCLUSIONS: In this exploratory analysis, HG, Hyp, and their combination drive metabolically and functionally distinct subtypes of Warburg-like metabolic reprogramming in HRECs, with HG-Hyp in combination producing a highly angiogenic phenotype. Boosting-based ensemble classifiers provide a promising framework for detecting these subtypes even under domain-shift conditions, warranting validation in larger independent datasets. TRANSLATIONAL RELEVANCE: Integrating metabolomics with machine-learning classification offers a strategy to identify Warburg-like metabolic subtypes in retinal endothelial cells, providing insights into angiogenic mechanisms and guiding the development of targeted diagnostics or therapeutics for PDR.

Humans↗

PubMed2025

Association Analysis of the Circulating Proteome With Sarcopenia-Related Traits Reveals Potential Drug Targets for Sarcopenia.

BACKGROUND: Sarcopenia severely affects the physical health of the elderly. Currently, there is no specific drug available for sarcopenia. This study aims to identify pathogenic proteins and druggable targets for sarcopenia through Mendelian randomization (MR)-based analytical framework. METHODS: A sequential stepwise screening method that includes two-sample MR, Steiger filtering test and colocalization (MRSC) was applied to identify causal proteins associated with sarcopenia-related traits. In the MR analyses, 4372 circulating proteins with valid instrumental variables (IVs) from eight proteomic genome-wide association studies were utilized as exposures, and nine sarcopenia-related traits were utilized as outcomes. IVs were classified into cis-protein quantitative trait loci (pQTLs) and trans-pQTLs based on their positions. We conducted cis-only MRSC analyses and cis + trans MRSC analyses using cis-pQTLs and cis + trans pQTLs as IVs, respectively. Post-MRSC analyses were conducted on the prioritized findings of MRSC, including annotation of protein-altering variants (PAVs), assessment of overlap between pQTLs and expression quantitative trait loci (eQTLs), protein-protein interaction (PPI) analysis, pathway enrichment analysis and annotation of drug targets. Utilizing data from the UK Biobank, we performed an observational study to explore the associations between baseline circulating protein levels and the longitudinal changes in nine sarcopenia-related traits. RESULTS: A total of 181 causal associations for 65 proteins were prioritized by the cis-only MRSC analyses and 227 associations for 91 proteins were prioritized by the cis + trans MRSC analyses. Among the prioritized proteins, the majority of them employed non-PAVs as IVs and most of their cis-pQTLs overlapped with corresponding eQTLs and exhibited consistent directionality, with only one trans-pQTL overlapping with an eQTL. The PPI network of cis-only MRSC-prioritized proteins (p = 4.04 × 10-4) and cis + trans MRSC-prioritized proteins (p = 8.76 × 10-5) showed significantly more interactions than expected. Reactome, KEGG and GO pathway enrichment analyses for cis-only MRSC-prioritized proteins identified 52, 12 and 79 enriched pathways, respectively (adjusted p < 0.05). For proteins identified by cis + trans MRSC analyses, only 15 pathways were enriched through the GO pathway enrichment analyses. In the observational study, 197 circulating proteins were identified to be associated with one or more sarcopenia-related traits (p < 0.05/2923). Among them, the significant associations of CTSB (negative association) and ASGR1 (positive association) with sarcopenia-related traits were observed to have consistent directional associations in both MR-based studies and observational studies. Drug target annotations suggested that 52 MRSC-prioritized proteins and 145 biomarkers are drug targets or druggable. CONCLUSIONS: This study identified 89 potential pathogenic proteins and 197 candidate biomarkers for sarcopenia, providing valuable clues for the development of therapeutic drugs for sarcopenia.

Humans↗

PubMed2026

Integrative multi-omics analysis of metabolite-protein interaction networks across different stages of coronary heart disease.

To elucidate the molecular characteristics of synergistic interactions across the clinical stages of coronary heart disease (CHD)-specifically stable angina pectoris (SAP), unstable angina pectoris (UAP), and acute myocardial infarction (AMI)-through integrated metabolomic and proteomic analyses. Based on a cohort including SAP, UAP, AMI, and healthy controls, metabolomic and proteomic analyses were performed to identify differentially expressed molecules, followed by KEGG pathway enrichment analysis. Pathways co-enriched across both omics platforms were selected to construct metabolite-protein interaction networks. The number of pathways co-enriched in both metabolomic and proteomic analyses increased markedly with disease stage. Only two pathways (histidine metabolism and arginine and proline metabolism) were identified in the SAP stage; this number increased to five in the UAP stage (including ferroptosis and efferocytosis) and expanded to 25 in the AMI stage, encompassing three major functional modules: immune inflammation, metabolic reprogramming, and cell signaling. The core network exhibited a stepwise increase in connectivity, shifting from a sparse structure in the SAP stage to a highly interconnected architecture in the AMI stage, with L-glutamate and KNG1 identified as the central hubs in this cross-sectional network. In addition, CNDP1 exhibited a stage-dependent functional transition, shifting from downregulation in SAP to upregulation in AMI. In this cross-sectional analysis, metabolic dysregulation and immune activation exhibited stepwise increases in interconnectivity across the SAP, UAP, and AMI groups, with the most extensive crosstalk observed in the AMI stage-a network configuration consistent with a tightly coupled "molecular storm". These findings provide novel insights into stage-associated molecular signatures of CHD and identify candidate hub molecules for stage-oriented therapeutic investigation.

Humans↗

PubMed2026

Proteomic and Phosphoproteomic Signatures Link Molecular Remodeling to Behavioral Outcomes Following Elderberry and DHA Supplementation in Aging Mice.

Background: Aging is a risk factor for Alzheimer's disease and related dementias, which are associated with synaptic dysfunction and cognitive decline. Elderberry (Sambucus spp.) is rich in anthocyanins with antioxidant and anti-inflammatory properties. Docosahexaenoic acid (DHA), an essential fatty acid, plays a key role in neuronal membrane integrity during brain aging. However, it remains unclear whether elderberry and DHA exert overlapping or distinct effects on brain aging and how these relate to molecular signaling. This study aimed to characterize molecular signatures induced by dietary supplementation and to determine their relationships with behavioral outcomes. Methods: 44-week-old male C57BL/6J mice were randomly assigned to control, elderberry, DHA, or combined diets for 12 weeks. Behavioral testing assessed anxiety-like behavior, spatial learning and memory. Brain tissues underwent proteomic and phosphoproteomic profiling and fatty-acid analysis. Data were analyzed using Ingenuity Pathway Analysis to identify enriched pathways, upstream regulators, and functional associations. Results: Elderberry as well as DHA supplementation induced targeted remodeling of the proteome and phosphoproteome, with pathway enrichment involving synaptogenesis, glutamatergic signaling, and long-term potentiation. Upstream-regulator analysis predicted elderberry-associated CDK5 signaling, accompanied by reduced MAPT/Tau phosphorylation at selected sites, whereas DHA supplementation was associated with CAMK-related signaling. DHA supplementation altered fatty-acid composition, increasing the n-3/n-6 ratio. Elderberry reduced anxiety-like behavior and improved target-directed search during the Barnes maze probe test. Molecular signatures were examined in relation to the measured behavioral outcomes. Conclusions: Elderberry and DHA are associated with distinct molecular networks related to synaptic function and behavioral outcomes in the aging male mouse brain. These findings support further investigation of elderberry and DHA as dietary interventions targeting molecular and behavioral features of brain aging.

Animals↗

PubMed2026

In silico screening of anti-atherosclerotic compounds from Morus alba leaves by machine learning and network pharmacology.

OBJECTIVE: This study integrates machine learning with network pharmacology, molecular docking, and molecular dynamics simulations to screen bioactive compounds from Mulberry leaves and elucidate their potential mechanisms against atherosclerosis (AS). METHODS: A training dataset of anti-AS active compounds was compiled and encoded as Morgan fingerprints. Three machine learning classifiers, specifically Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XG-Boost), were constructed and evaluated using multiple performance metrics. Potential active components from Mulberry leaves and AS-related targets were retrieved, followed by protein-protein interaction network construction and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was then performed to evaluate binding affinities between core targets and candidate compounds, and the most stable complex was subjected to molecular dynamics simulations using GROMACS (2025). RESULTS: The RF model achieved superior performance (accuracy= 0.8354, F1 = 0.8408, AUC = 0.9119) with 100% external validation accuracy. Thirteen anti-AS candidates were prioritized from mulberry leaves, four of which have been previously documented. Network pharmacology revealed AKT1 and IL6 as core targets, enriched in pathways such as endocrine resistance. Molecular docking and dynamics simulations confirmed strong binding between oxysanguinarine and AKT1, with the complex exhibiting high stability. CONCLUSION: The RF model provides a reliable computational tool for prioritizing anti-AS compounds from Mulberry leaves. The integrated analysis reveals that Mulberry leaves exert anti-atherosclerotic effects through multi-target (e.g., AKT1, IL6) and multi-pathway (e.g., PI3K-Akt) mechanisms, offering a framework for further experimental validation.

Morus↗

PubMed2025

Identification of circulating miRNA alterations in diabetes patients excluding periodontitis effects: insights into target gene downregulation in diabetic complications.

BACKGROUND: Diabetes mellitus (DM) induces systemic complications through chronic metabolic dysregulation. Circulating exosomal microRNAs (miRNAs) are emerging as key regulators of post-transcriptional gene expression and may drive diabetes-associated pathologies. Although miRNAs have been widely studied in diabetes, the characterization of PD-independent miRNA signatures across tissues remains limited. This study aimed to identify DM-specific miRNA alterations and their contribution to systemic metabolic dysfunction independent of PD. METHODS: Exosomes were isolated from plasma samples, and small RNA sequencing was performed to identify differentially expressed miRNAs (DE-miRs) using the limma R package. Predicted target genes were identified using TargetScan and validated through bulk RNA sequencing datasets from four tissues-foot, kidney, pancreas, and retina. Differentially expressed genes (DEGs) were analyzed, followed by Gene Ontology Biological Process (GOBP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to elucidate diabetes-related mechanisms. RESULTS: We identified 9 upregulated and 6 downregulated DE-miRs specific to the diabetic group. TargetScan predicted 216 upregulated and 64 downregulated target genes. Functional validation revealed that these genes were enriched in pathways related to glucose metabolism, cellular stress response, and tissue repair. Notably, SREK1 and GLIPR1 were commonly detected across all four tissues, suggesting potential systemic regulators of diabetes-related complications. CONCLUSION: This study suggests that circulating exosomal miRNAs, independent of periodontitis, may function as systemic regulators in diabetes. Unlike previous studies, which did not distinguish co-morbid periodontitis, we specifically defined PD-independent miRNA signatures and validated their cross-organ regulatory effects on target genes. Our results revealed a cross-organ miRNA-mRNA regulatory network and identified common regulatory targets. These findings provide insights into both systemic and organ-specific mechanisms underlying diabetic complications and highlight the potential of miRNAs as biomarkers and therapeutic targets.

Humans↗

PubMed2021

RP-REP Ribosomal Profiling Reports: an open-source cloud-enabled framework for reproducible ribosomal profiling data processing, analysis, and result reporting.

Ribosomal profiling is an emerging experimental technology to measure protein synthesis by sequencing short mRNA fragments undergoing translation in ribosomes. Applied on the genome wide scale, this is a powerful tool to profile global protein synthesis within cell populations of interest. Such information can be utilized for biomarker discovery and detection of treatment-responsive genes. However, analysis of ribosomal profiling data requires careful preprocessing to reduce the impact of artifacts and dedicated statistical methods for visualizing and modeling the high-dimensional discrete read count data. Here we present Ribosomal Profiling Reports (RP-REP), a new open-source cloud-enabled software that allows users to execute start-to-end gene-level ribosomal profiling and RNA-Seq analysis on a pre-configured Amazon Virtual Machine Image (AMI) hosted on AWS or on the user's own Ubuntu Linux server. The software works with FASTQ files stored locally, on AWS S3, or at the Sequence Read Archive (SRA). RP-REP automatically executes a series of customizable steps including filtering of contaminant RNA, enrichment of true ribosomal footprints, reference alignment and gene translation quantification, gene body coverage, CRAM compression, reference alignment QC, data normalization, multivariate data visualization, identification of differentially translated genes, and generation of heatmaps, co-translated gene clusters, enriched pathways, and other custom visualizations. RP-REP provides functionality to contrast RNA-SEQ and ribosomal profiling results, and calculates translational efficiency per gene. The software outputs a PDF report and publication-ready table and figure files. As a use case, we provide RP-REP results for a dengue virus study that tested cytosol and endoplasmic reticulum cellular fractions of human Huh7 cells pre-infection and at 6 h, 12 h, 24 h, and 40 h post-infection. Case study results, Ubuntu installation scripts, and the most recent RP-REP source code are accessible at GitHub. The cloud-ready AMI is available at AWS (AMI ID: RPREP RSEQREP (Ribosome Profiling and RNA-Seq Reports) v2.1 (ami-00b92f52d763145d3)).

AMI↗

PubMed2026

[Effects and mechanisms of ethanol extract of Salvia miltiorrhiza on liver fibrosis in mice].

To identify clinically advantageous TCMs for anti-hepatic fibrosis and to elucidate the effects and molecular mechanisms of Salvia miltiorrhiza ethanol extract in the intervention of liver fibrosis, this study screened high-frequency anti-hepatic fibrosis TCMs through a review of clinical literature. The S. miltiorrhiza active components, potential targets, and liver fibrosis-related disease targets were obtained using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP), the GeneCards database, and other databases. Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses were performed on the shared targets between drugs and diseases. Molecular docking was conducted to evaluate the binding affinities between key components and core targets. In animal experiments, male Kunming mice were used to establish a liver fibrosis model induced by carbon tetrachloride(CCl_4). The mice were administered low, medium, and high doses of S. miltiorrhiza ethanol extract by gavage. The liver index, as well as serum aspartate aminotransferase(AST) and alanine aminotransferase(ALT) levels, were measured. Histopathological changes in liver tissue were observed using hematoxylin-eosin(HE) staining and Masson's trichrome staining. Western blot analysis was used to detect the protein expression levels of α-smooth muscle actin(α-SMA), Collagen Ⅰ, and heat shock protein 90 alpha family class A member 1(HSP90AA1) in liver tissue. The results showed that S. miltiorrhiza was the most frequently used TCM in clinical anti-hepatic fibrosis. A total of 65 active components and 135 potential targets were identified, and 109 common targets were obtained by intersecting these with liver fibrosis-related targets. The core targets included tumor protein p53(TP53), serine/threonine protein kinase AKT1(AKT1), Jun proto-oncogene(JUN), signal transducer and activator of transcription 3(STAT3), and HSP90AA1, which were mainly enriched in pathways related to cancer, hepatitis B, and the PI3K-AKT signaling pathway. Molecular docking indicated that the main active components of S. miltiorrhiza bound stably to the core targets, with the strongest binding affinity observed for HSP90AA1. Animal experiments demonstrated that the liver index, serum ALT and AST levels, and the expression of α-SMA, Collagen Ⅰ, and HSP90AA1 in liver tissue were significantly increased in the model group, accompanied by obvious pathological manifestations of fibrosis. Compared with the model group, different dose groups of S. miltiorrhiza ethanol extract reduced the liver index and serum ALT and AST levels to varying degrees, alleviated pathological damage and collagen deposition in liver tissue, and downregulated the protein expression of α-SMA, Collagen Ⅰ, and HSP90AA1. In conclusion, S. miltiorrhiza ethanol extract exerts a significant protective effect on CCl_4-induced liver fibrosis in mice, and its mechanisms may be related to the inhibition of HSP90AA1 expression and the regulation of liver fibrosis-related signaling pathways.

Animals↗

PubMed2026

A Computational Workflow for Prioritizing Microbial Metabolite-Associated Host Genes in Constipation-Predominant Irritable Bowel Syndrome.

No standardized computational pipeline exists for systematically prioritizing microbial metabolite-associated host genes and protein-ligand complexes from publicly available chemical, genomic, and structural databases. This article describes an eight-stage workflow that accepts a user-defined set of gut microbiota-derived metabolites and produces a ranked shortlist of candidate metabolite-associated host genes, enriched biological pathways, and structurally prioritized protein-ligand complexes for experimental follow-up. The pipeline integrates (i) chemoinformatic metabolite profiling; (ii) multi-database candidate target prediction using protein-chemical interaction and ligand-based target-prediction tool and a molecular docking program; (iii) differential gene expression analysis of publicly available transcriptomic data; (iv) target-differentially expressed gene overlap; (v) protein-protein interaction network construction and pathway enrichment; (vi) molecular docking with a molecular docking program; (vii) 200 ns molecular dynamics simulation using a molecular dynamics engine with a protein force field used for molecular dynamics simulations; and (viii) MM-PBSA binding free-energy estimation. As a worked example, nine gut microbiota-derived or microbiota-modified metabolites representing short-chain fatty acids, bile acids, tryptophan-derived metabolites, and urolithin A were processed using the public IBS-C rectal mucosal transcriptomic dataset GSE36701. The workflow ranked 17 unique predicted metabolite-associated genes that were differentially expressed in this dataset. Docking, molecular dynamics simulation, and MM-PBSA analyses structurally prioritized five metabolite-protein complexes: lithocholic acid-VDR, lithocholic acid-NR1H4/FXR, ursodeoxycholic acid-NR1H4/FXR, tryptamine-HTR2A (simulated in an explicit 1-Palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid bilayer), and urolithin A-CASP3. The protocol is designed to be adaptable to other metabolite sets, disease transcriptomic datasets, and target classes; all outputs are hypothesis-generating computational predictions that require independent transcriptomic replication, protein-level validation, and functional ligand-response assays before causal or therapeutic conclusions can be drawn.

Irritable Bowel Syndrome↗

PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗

PubMed2026

Comparative transcriptome analysis reveals ncRNA-mediated regulatory networks associated with muscle crispiness in grass carp.

Non-coding RNAs (ncRNAs) have been demonstrated to be involved in muscle development and to function as key regulators. However, the molecular mechanism underlying muscle crispiness in grass carp (GC) remains poorly understood, and whether these ncRNAs are involved in its regulation is still unknown. In the current investigation, differentially expressed (DE) RNAs (including lncRNAs, circRNAs, miRNAs, and mRNAs) were identified; concomitantly, target genes prediction was conducted, and functional and signaling pathway enrichment analyses were performed. Pathways related to muscle crispiness were identified, and the competitive endogenous RNA (ceRNA) (lncRNA/circRNA-miRNA-mRNA) regulatory network was further constructed. The results showed that a total of 126 DE-lncRNAs, 17 DE-circRNAs, 329 DE-miRNAs, and 442 DE-mRNAs were identified in muscle tissues of both the GC and crisp grass carp (CGC). GO and KEGG enrichment analyses revealed that target genes of DE-ncRNAs were significantly enriched in signaling pathways, including structural constituents of muscle, apoptosis, oxidative phosphorylation, and regulation of actin cytoskeleton, suggesting that these pathways may be involved in muscle texture remodeling. Subsequently, DE-RNAs enriched in related pathways were identified, and a core ceRNA regulation network comprising 3 lncRNAs, 4 circRNAs, 3 miRNAs, and 17 mRNAs was constructed. Additionally, 10 DE-RNAs from randomly selected groups were validated by qRT-PCR. Our findings not only provide scientific evidence elucidating the molecular mechanisms underlying muscle crispiness in GC but also establish a foundation for studying changes in muscle textural qualities across other fish species.

Animals↗

PubMed2026

Early Transcriptional Changes in Neutrophil-Mediated Processes Following Recanalization After Ischemic Stroke.

BACKGROUND: Ischemic stroke is a leading cause of death and long-term disability worldwide. Recanalization therapies, including thrombolysis and mechanical thrombectomy, restore blood flow, yet many patients experience poor outcomes, a phenomenon known as futile recanalization. Given the short therapeutic window for ischemic stroke, identifying early biomarkers to guide targeted interventions and improve outcomes is critical. METHODS: Using a murine middle cerebral occlusion model that mimics a large vessel occlusion with recanalization, a comprehensive microarray analysis from blood samples collected immediately and 3 hours after recanalization (N=44) was performed. Differentially expressed genes, enrichment pathways, immune cell proportions, enriched cell markers, predicted micro-RNAs, and transcription factors were identified using RStudio. Findings in mice were validated with rat middle cerebral artery occlusion (GSE21136) and patients with stroke (GSE16561) data sets to confirm transcriptional changes in peripheral blood postrecanalization. RESULTS: Il1r2, Cd55, Mmp8, Cd14, and Cd69 were early biomarkers poststroke and postrecanalization. Cross-validation revealed Vcan as a differentially expressed gene conserved across species, making it a novel ischemic marker detected as early as 3 hours postrecanalization (4 hours after middle cerebral artery occlusion) in mice, 24 hours after recanalization in rats (middle cerebral artery occlusion-thrombectomy), and within 24 hours from onset in humans receiving recombinant tissue plasminogen activator-thrombolysis. CIBERSORTx and ImmuCellAI-mouse deconvolution showed neutrophil elevation postrecanalization. Leukocyte and neutrophil activation pathways were enriched early after stroke in mice and humans, with stronger upregulation in the female sex. Several regulatory micro-RNAs were identified, and Nuclear Factor Erythroid 4 (NFE4) and Metal Regulatory Transcription Factor 1 (MTF1) emerged as key transcription factors. A coregulatory network underlying neutrophil activity was constructed, highlighting its central role in early responses to ischemia and recanalization, which was enriched in the female sex. CONCLUSIONS: We identified novel early genomic markers for ischemia and recanalization, including the conserved marker Vcan, and highlighted age- and sex-specific immune responses. Mapping a neutrophil-centered coregulatory network provides mechanistic insight into futile recanalization and supports the development of targeted therapies to improve clinical outcomes.

Animals↗

PubMed2026

Comparative proteomic analysis reveals the pathological mechanisms of overuse achilles tendinopathy and the therapeutic mechanisms of ESWT and PRP.

BACKGROUND: Achilles tendinopathy is a common musculoskeletal disorder with limited self-repair capacity. Although extracorporeal shock wave therapy (ESWT) and platelet-rich plasma (PRP) are widely used, their therapeutic mechanisms remain unclear. METHODS: A rat model of overuse Achilles tendinopathy was established by uphill treadmill running. Tendon morphology and structure were assessed by ultrasound and histology, and proteomic profiling was performed to identify differentially expressed proteins (DEPs) and enriched pathways. RESULTS: Ultrasound revealed subcutaneous edematous infiltration after overuse, and histology showed disorganized collagen fibers and altered cellular density. Compared with the normal group, the injury group showed 429 DEPs, which were enriched in pathways related to actin cytoskeleton and complement and coagulation cascades. Both ESWT and PRP treatments ameliorated these overuse-induced pathological changes. Compared with the rest group, the ESWT group showed 30 DEPs, while the PRP group showed 244, with 17 DEPs overlapping between the two comparisons. In the ESWT group, enriched pathways included actin cytoskeleton organization, protein stabilization, and sulfur metabolism. In the PRP group, enriched pathways included FcγR-mediated phagocytosis, lysosome, and endoplasmic reticulum protein processing. Compared with the normal group, the ESWT group showed 32 DEPs, whereas the PRP group showed only one (Serpina6), which was the only protein shared between the two comparisons. CONCLUSION: ESWT and PRP improve tendon healing in overuse Achilles tendinopathy through different molecular mechanisms. The PRP group showed a proteomic profile more similar to the normal group than the ESWT group. These findings provide a molecular basis for optimizing clinical treatment strategies.

Animals↗

PubMed2026

A CFH- and SPINT2-based prognostic signature for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant tumor with a poor prognosis, and reliable biomarkers for postoperative risk stratification remain limited. This study aimed to develop and validate a CFH- and SPINT2-based prognostic signature to support postoperative risk stratification and inform adjuvant therapy selection in CCA through integrative machine learning and single-cell transcriptomics. METHODS: Differentially expressed genes were screened from GSE26566. Integrative machine learning (least absolute shrinkage and selection operator-Cox, random forest, and univariate Cox regression) was performed in the training cohort (GSE89749; n=115) to construct a risk model, which was externally validated in two independent cohorts: cohort 1 (E-MTAB-6389; n=75) and cohort 2 [The Cancer Genome Atlas Cholangiocarcinoma (TCGA-CHOL) data set; n=36]. Systematic analysis was conducted and included examinations of immune infiltration [via single-sample gene set enrichment analysis (ssGSEA)], pathway enrichment (via hallmark GSEA), cellular localization (via single-cell RNA sequencing), and drug sensitivity (via the Genomics of Drug Sensitivity in Cancer 2 database). RESULTS: Two genes, CFH and SPINT2, were identified and incorporated into a prognostic risk score. High-risk patients in the training cohort had a significantly worse overall survival (log-rank P=0.02). External validation was performed in two independent cohorts. In validation cohort 1, the risk group was an independent prognostic factor [hazard ratio =2.27, 95% confidence interval (CI): 1.18-4.37; P=0.01]. In validation cohort 2, the model demonstrated acceptable discriminative ability (concordance index =0.721; 3-year area under the curve =0.692). The high-risk group exhibited an immunosuppressive microenvironment characterized by increased infiltration of macrophages and myeloid-derived suppressor cells, along with the activation of epithelial-mesenchymal transition, inflammatory response, and NF-κB signaling pathways. Single-cell analysis revealed a cell-type-specific expression pattern: CFH was predominantly expressed in fibroblasts, while SPINT2 was mainly expressed in malignant cells. Drug sensitivity analysis demonstrated that the high-risk group was more sensitive to gemcitabine, cisplatin, poly(ADP-ribose) polymerase (PARP) inhibitors, and mammalian target of rapamycin (mTOR) inhibitors, whereas the low-risk group was more sensitive to lapatinib. CONCLUSIONS: The CFH- and SPINT2-based prognostic signature may serve as an independent biomarker for postoperative risk stratification in CCA. High-risk patients, characterized by fibroblast-derived CFH enrichment and malignant-cell SPINT2 loss, exhibit an immunosuppressive microenvironment and may be more suitable for gemcitabine-based chemotherapy or PARP/mTOR inhibitors, whereas low-risk patients may benefit from less intensive adjuvant strategies or HER2/EGFR-targeted lapatinib. Prospective validation is warranted before clinical implementation.

Cholangiocarcinoma (CCA)↗