TL;DR
Findings suggest that metabolic alterations in AN directly influence immune regulation, and a novel metabolic-immune pathway with therapeutic potential in AN is revealed.
Primary Sjögren’s syndrome (pSS) and type 1 diabetes mellitus (T1DM) share immune-inflammatory features, yet conserved pathogenic signatures linking these autoimmune disorders remain incompletely understood. The present research sought to uncover common molecular markers and dissect the underlying immune-metabolic cross-talk underlying pSS and T1DM. Gene expression profiles of patients with pSS and T1DM were retrieved from the Gene Expression Omnibus database, normalized, and corrected for batch effects prior to downstream analyses. Overlapping potential biomarkers were screened by integrating differential expression analysis, weighted gene co-expression network analysis and least absolute shrinkage and selection operator regression. Functional enrichment based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases was implemented to interpret gene biological properties, and a protein–protein interaction network was further established afterwards. Diagnostic performance was evaluated using receiver operating characteristic analysis. Experimental validation was conducted in non-obese diabetic (NOD) mice using quantitative PCR, immunohistochemistry, and flow cytometry. The CIBERSORT algorithm was adopted to quantify immune cell infiltration levels. ZBTB16 was identified as a shared hub biomarker in both pSS and T1DM and exhibited favorable diagnostic performance. Experimental validation confirmed significantly reduced ZBTB16 expression in peripheral blood mononuclear cells, salivary gland tissues, and pancreatic tissues of NOD mice. Gene Set Enrichment Analysis indicated that ZBTB16 -associated signatures were enriched in mitochondrial-related processes, neuroactive ligand-receptor interactions, and ribosome-related pathways. Immune infiltration analysis revealed that resting natural killer (NK) cells were positively correlated with ZBTB16 expression in both diseases. Flow cytometric analysis further confirmed a reduced proportion of resting NK cells in peripheral blood of NOD mice, consistent with the CIBERSORT-based prediction. This study identifies ZBTB16 as a shared biomarker linking pSS and T1DM. Reduced resting NK-cell abundance was consistently observed in both computational and experimental analyses, and bioinformatic correlation analysis suggested a positive association with ZBTB16 expression. These findings provide evidence for shared molecular and immunological signatures underlying the two autoimmune disorders and support further investigation of the biological role and diagnostic value of ZBTB16 in pSS and T1DM.
Chronic low-grade inflammation induced by bacterial lipopolysaccharide has been implicated in the pathogenesis of polycystic ovary syndrome; however, the genetic mechanisms linking lipopolysaccharide signaling to immune and metabolic dysregulation remain insufficiently elucidated. In the present study, transcriptomic datasets and single-cell sequencing data related to polycystic ovary syndrome were analyzed in combination with lipopolysaccharide-related genes retrieved from a toxicogenomics database. Differential expression analysis, weighted gene co-expression network analysis, clustering analysis, and machine learning algorithms were integrated to identify candidate biomarkers. Subsequently, functional enrichment analysis, immune cell infiltration analysis, regulatory network construction, and drug prediction analyses were conducted, while single-cell sequencing analysis was employed to identify key cellular populations and characterize gene expression dynamics. Two genes, C11orf68 and EVI5L, were identified as potential biomarkers and were significantly downregulated in patients with polycystic ovary syndrome. Functional analyses associated these genes with iron metabolism and immune regulation, whereas immune infiltration profiling identified T lymphocytes as key effector cells involved in disease progression. These findings suggest a potentially previously unrecognized association among lipopolysaccharide-related genes, iron metabolism imbalance, and immune dysregulation in polycystic ovary syndrome, thereby providing a potential framework for future mechanistic investigations and the development of diagnostic and therapeutic targets.
Hyperuricemia (HUA) is a major risk factor for gout and multiple metabolic disorders. Although serum uric acid (UA) is the gold standard for HUA diagnosis, it fails to reflect early metabolic disturbances and shows limited predictive value for asymptomatic HUA. This study sought to elucidate the pathological mechanisms underlying HUA and identify novel diagnostic biomarkers beyond UA. This study enrolled 195 patients with HUA and 98 healthy controls. Global metabolomics and proteomics profiling were performed to characterize molecular alterations underlying HUA. Based on the biological relevance of the shared dysregulated pathways, a pathway correlation network was constructed to elucidate the pathological mechanisms driving HUA initiation and progression. Furthermore, diagnostic biomarkers for HUA were identified using machine learning algorithms, and were validated with an external cohort. HUA patients exhibited distinct metabolic and proteomic profiles compared with healthy controls. Integrated multi-omics pathway analysis revealed that peroxisome proliferators-activated receptor signaling pathway, arachidonic acid metabolism, purine metabolism, pyrimidine metabolism and sphingolipid signaling pathway were significantly dysregulated in HUA. Among them, arachidonic acid metabolism was identified as a hub pathway involved in HUA progression. Furthermore, a metabolite panel consisting of cysteine-S-sulfate, glycerophosphocholine and 4-hydroxyphenylpyruvic acid was screened by machine learning and validated in an independent cohort, which showed slightly higher diagnostic performance for HUA than UA. This study reveals the core metabolic and protein regulatory networks of HUA, and identifies a novel serum metabolite panel for the diagnosis of HUA. These findings provide new insights for improved clinical diagnosis and management.
Background Alzheimer’s disease (AD) develops through complex interactions between the central nervous system and peripheral systems. The microbiota-metabolite-immune axis has emerged as an important focus of AD research. However, the coordinated mechanisms that regulate this axis remain poorly understood. Methods We used a multi-stage, multi-omics strategy to systematically investigate peripheral–central interactions in AD. The analytical framework integrated Mendelian randomization (MR), summary-data-based Mendelian randomization (SMR), differential expression analysis, machine learning, single-cell and spatial transcriptomics, and quantitative real-time polymerase chain reaction (qPCR) trend confirmation. Results Exploratory MR analyses identified multiple microbial taxa, metabolites, and immune cell phenotypes showing associations consistent with potential causal effects on AD. Integrating the SMR and MR findings with differential expression analysis led to the identification of 31 core genetically associated genes. A five-gene predictive model comprising ATF7IP2, TWSG1, PTPRN2, ASCC3 and IGF1R was then developed using machine learning. The diagnostic potential of the individual feature genes was further evaluated in an external validation dataset. Spatial transcriptomic analyses revealed clear cell type-specific expression patterns in brain tissue, with IGF1R, ASCC3and TWSG1 showing potential co-localization in oligodendrocytes. qPCR trend confirmation in pooled samples produced expression trends consistent with the directions inferred from eQTL-based MR. Conclusions This study mapped a regulatory network underlying the AD microbiota–metabolite-immune-brain axis and identified core genes with potential diagnostic and therapeutic value. The spatial transcriptomic findings, while primarily based on in situ co-localization analysis, highlight a biologically plausible but provisional working hypothesis regarding an active role for oligodendrocytes in AD pathology. Overall, this study supports a systems-level view of AD that may inform precision medicine strategies.
Background Irritable bowel syndrome (IBS) is a prevalent functional gastrointestinal disorder with an elusive pathophysiology. Although immune dysregulation and mild mucosal inflammation are recognized as important factors in IBS, the specific contribution of immunosenescence remains unclear. Prior MR studies on IBS focused on single molecular traits; however, an integrated multi−omics framework for aging−immune genes has not been applied. Here, we integrate cis−eQTL/pQTL/mQTL with machine learning and in vivo validation to identify putatively causal biomarkers and unravel the aging−immune axis. Methods Aging- and immune-related genes were curated from published databases and analyzed using genome-wide cis-expression quantitative trait loci (cis-eQTL), cis-protein quantitative trait loci (cis-pQTL), and cis-methylation quantitative trait loci (cis-mQTL) datasets. All QTL data were derived from blood or plasma samples. Candidate genes were identified through integrative multi-omics analysis, followed by functional annotation, machine learning-based feature selection, immune infiltration profiling, and drug-target prediction. The expression of key biomarkers was validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR) in colonic tissues from a rat model of diarrhea-predominant IBS (IBS-D). Results Integrative multi-omics analysis initially identified 34 high-confidence candidate genes. Through multi-algorithm machine learning such as least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest, these candidates were refined to a core panel of five biomarkers: the antioxidant enzyme catalase (CAT), acyl-CoA dehydrogenase very long chain (ACADVL), chemokine (C-C motif) ligand 4 (CCL4), cyclin E1 (CCNE1), and Jagged canonical Notch ligand 1 (JAG1). These biomarkers demonstrated strong diagnostic performance for CAT, CCNE1, and JAG1 in the validation cohort (AUC: 0.935–0.951), while ACADVL and CCL4 showed only moderate diagnostic potential (AUC: 0.645–0.649). The combined AUC range in the training cohort was 0.898–0.959. However, in the IBS-D rat model, only JAG1 was significantly upregulated in colonic tissue, whereas CAT, ACADVL, CCL4, and CCNE1 showed no significant changes. Among the five markers, only JAG1 was significantly upregulated in the IBS-D rat colon, confirming its local gut relevance. CAT and CCNE1 showed strong diagnostic performance (AUC >0.9), whereas ACADVL and CCL4 performed modestly (AUC <0.7). Given the blood-derived QTL data and colonic validation, cross-tissue heterogeneity is a key caveat: JAG1 is robustly validated in colon, while the remaining four markers—especially ACADVL and CCL4—require further evaluation in blood, PBMCs, or other relevant tissues. Functional enrichment analysis revealed their involvement in critical biological processes, including oxidative stress, fatty acid metabolism, immune cell recruitment, cell cycle progression, and epithelial-immune crosstalk via Notch signaling. Immune infiltration analysis uncovered a distinct immune landscape in IBS, with significant correlations between biomarker expression and immune cell populations. Notably, only JAG1 was significantly upregulated in the colonic tissues of the IBS-D rat model, confirming its relevance to gut pathophysiology. No significant changes were detected for CAT, ACADVL, CCL4, or CCNE1 in the same tissue samples, highlighting the tissue-specific nature of these candidate biomarkers. The SMR analysis demonstrated consistent causal directions between genetically predicted expression and disease risk for these five markers, with no discordance relative to their upregulation in IBS patients. All biomarker validation was performed at the transcriptomic level (RT−qPCR); no protein−level assays (e.g., Western blot, IHC, or ELISA) were conducted. Conclusions This study delineates the genetic architecture linking aging and immunity to IBS through an integrative multi−omics and machine learning approach, providing novel putatively causal evidence and identifying JAG1 as a robustly validated biomarker with diagnostic and therapeutic potential. The remaining candidates warrant further investigation in appropriate biological contexts.
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder in which immune dysregulation has emerged as an important component of disease pathogenesis; however, the contribution of circulating proteins and their cellular context remains incompletely understood. Here, we performed an integrative multi-omics analysis combining Mendelian randomization (MR), bulk transcriptomics, single-cell RNA sequencing, and peripheral blood validation to systematically identify plasma proteins associated with AD. Proteome-wide MR analysis identified multiple circulating proteins associated with AD risk. Integration with transcriptomic data identified AIF1 (allograft inflammatory factor 1) as a shared candidate supported by both genetic prioritization and differential expression analysis. Although bulk transcriptomic data showed reduced AIF1 expression in AD, single-cell analysis revealed distinct cell type-specific expression patterns, with predominant enrichment in monocytes and other innate immune populations. PBMC-based qPCR further confirmed an overall reduction in AIF1 expression in AD. Further analyses suggested that AIF1-associated immune alterations were linked to changes in inflammatory signaling pathways, including STAT, IRF, and NF-κB-related activity, as well as differences in intercellular communication involving MIF, GALECTIN, ANNEXIN, and CypA-related signaling. Peripheral immune cell composition analysis indicated differences between AD and control samples, characterized by relative changes in innate immune cell proportions. Collectively, these findings identify AIF1 as an immune-associated factor linked to genetic and transcriptional alterations in AD and suggest its association with monocyte-related immune states and altered immune signaling patterns. This study provides a multi-layered framework for investigating peripheral immune involvement in AD and highlights potential directions for understanding immune-related alterations and biomarker discovery.
