Aug 2026· Computational biology and chemistry· Vol 125, pp.
109324
· 0 citations
Medicine
TL;DR
This study integrates transcriptomic, genetic-causal, immunological, and structural analyses to prioritize STAT6 as a potentially druggable MS-associated target and strengthen the computational plausibility of STAT6-ligand interactions.
Abstract
Background
AND
Objectives
Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system characterized by inflammation, demyelination, and neurodegeneration. This study aimed to identify key genetic and immune-related biomarkers involved in MS pathogenesis and to explore structurally plausible therapeutic targets.
Materials And Methods
Publicly available gene-expression datasets were analyzed using an integrated computational framework. This framework comprised differential expression analysis, Mendelian randomization, immune-cell infiltration profiling, functional enrichment analysis, and drug-gene interaction screening. Candidate STAT6-ligand complexes were then examined by CB-Dock2 molecular docking, followed by 100 ns molecular dynamics (MD) simulation, MM-PBSA decomposition, per-residue energy analysis, and PCA-based free-energy landscape (FEL) analysis.
Results
Three MS-associated genes were prioritized: MS4A3, STAT6, and UBE2O. MS4A3 and STAT6 showed positive associations with MS risk, whereas UBE2O showed a protective association. Enrichment analyses implicated immune-regulatory and inflammatory pathways, including Th17 differentiation, Th1/Th2 differentiation, and ubiquitin-mediated proteolysis. Drug-gene interaction screening and CB-Dock2 docking identified STAT6-ligand interactions, particularly with methylprednisolone and Doxorubicin hydrochloride. Subsequent 100 ns MD simulations supported continued ligand association with the predicted STAT6 pocket. MM-PBSA and PCA-based free-energy analyses indicated favorable non-bonded interactions and distinct low-energy conformational ensembles, with methylprednisolone providing the more clinically interpretable STAT6-associated result.
Conclusions
This study integrates transcriptomic, genetic-causal, immunological, and structural analyses to prioritize STAT6 as a potentially druggable MS-associated target. The additional MD, MM-PBSA, and PCA/FEL analyses strengthen the computational plausibility of STAT6-ligand interactions, especially for the clinically relevant STAT6-methylprednisolone complex. Nevertheless, all structural findings remain computational predictions and require experimental validation before clinical translation.
INTRODUCTION
To evaluate the causal effects of circulating inflammatory proteins on the development of Multiple Sclerosis (MS) and to discover potential therapeutic targets for the disease.
METHODS
Two-sample Mendelian randomization analysis was used to evaluate causal relationships between levels of 91 inflammatory proteins in serum and the risk of MS. For each circulating protein, genetic variation data were from a genome-wide association study of 14,736 individuals of European ancestry. The MS dataset included 47,429 cases and 68,374 controls. Additional sensitivity, colocalization, functional enrichment, and protein-protein interaction analyses were performed to assess the robustness and biological relevance of the findings. Soluble proteins causally associated with MS were assessed for their therapeutic target potentials using the Drug-Gene Interaction Database.
RESULTS
The analysis identified 9 circulating inflammatory proteins with nominal evidence of association with MS risk. Among them, CD40 (Odds Ratio [OR]: 0.907) and VEGFA (OR: 0.971) appear protective, while IL1A (OR: 1.139), CD6 (OR: 1.047), TNF (OR: 1.058), LIF-R (OR: 1.052), CCL25 (OR: 1.032), CD244 (OR: 1.057) and CXCL10 (OR: 1.061) were associated with higher risks of MS. After correcting for multiple testing, associations with MS risks remained significant only for CD40. Supportive analyses highlighted immune- and inflammationrelated pathways, particularly cytokine signaling, as mediators of the risks of MS. Six circulating proteins were identified as candidates in an exploratory assessment of druggability.
DISCUSSION
These results offered new insight into the inflammatory landscape of MS, reinforcing the pathogenic relevance of immune signaling pathways. The robust statistical significance of CD40 highlighted it as a primary candidate for further investigation, while the additional eight proteins provided a foundation for future hypothesis-generating studies. While the findings strengthened the biological plausibility of inflammation-driven disease mechanisms, the presence of nominal significance and potential pleiotropy for certain markers necessitated a cautious interpretation.
CONCLUSION
Genetic evidence linking inflammatory proteins to MS pathogenesis was uncovered, offering clues for novel therapeutic targets for this condition.
Runyang Xu, Ancha Baranova, H. Cao et al.· Current Neurovascular Resear...· 0 citations
Aim: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterised by progressive motor neuron degeneration. Despite identification of multiple susceptibility loci, the genetic and molecular basis of ALS pathogenesis remains incompletely understood. The present study aimed to integrate functional and metabolic analyses with GWAS-derived variants to investigate key genes, pathways, and cellular mechanisms underlying ALS.
Methodology: Genetic variants associated with ALS were retrieved from published GWAS datasets and analysed through integrative bioinformatic approaches, including Gene Ontology (GO) enrichment and KEGG pathway mapping. Cell Marker enrichment assessed immune cell involvement, whilst metabolomic profiling examined lipid metabolism alterations. Unsupervised machine learning, encompassing clustering and principal component analysis (PCA), identified patterns across susceptibility loci on chromosomes 9, 12 and 19.
Results: Core susceptibility genes identified included C9orf72, UNC13A and ITPR2. Enrichment analyses revealed disruptions in synaptic vesicle docking, neurotransmitter release, calcium homeostasis, oxidative stress, and neuroinflammation. Metabolomic profiling implicated disturbed lipid metabolism, whilst chromosomal clustering highlighted a genetic basis for disease heterogeneity.
Interpretation: These findings underscore the multifactorial nature of ALS across genetic, molecular and metabolic dimensions, identifying potential molecular targets to guide future therapeutic development.
Key words: ALS, Calcium signalling, GWAS, Neurodegeneration, Synaptic dysfunction
U. Adiga, P. Supriya, S. Adiga et al.· Journal of environmental bio...· 0 citations
INTRODUCTION
The pathogenesis of Alzheimer's disease (AD) is complex, with immune system dysregulation playing a critical role. However, the specific molecular mechanisms linking peripheral immune responses to central pathologies in AD remain unclear. This study aims to systematically screen for reliable plasma biomarkers of AD by integrating transcriptomics, Mendelian randomization (MR) of plasma proteomics, and bioinformatics analyses, and to explore their potential pathogenic mechanisms and therapeutic drugs.
MATERIALS AND METHODS
Transcriptomic sequencing of plasma samples from three AD patients and three healthy controls was first performed to identify differentially expressed genes (DEGs) and perform functional enrichment analyses. The aim of this study is to provide a preliminary indication of gene expression changes based on real patient samples for subsequent MR and bioinformatics analyses, rather than serving as confirmatory evidence. Following this, two-sample MR was performed to explore the potential causal relationship between plasma proteins and AD in genetic prediction, and MR-positive results were intersected with transcriptome DEGs to identify highconfidence targets. After that, protein-protein interaction (PPI) analysis, functional enrichment (GO/KEGG), and transcription factor (TF) target network analysis were conducted. Based on the KEGG pathway analysis, the causal association between antibodies related to Epstein-Barr virus and AD in genetic prediction was further evaluated. In the end, the diagnostic power of core biomarkers was validated in the GEO dataset. Potential therapeutic drugs were screened in the CTD database, followed by verification through molecular docking and molecular dynamics simulation.
RESULT
Our transcriptomic enrichment analysis of DEGs indicates that AD is significantly correlated with viral infection and immune and inflammatory pathways. According to the results of MR analyses, 36 plasma proteins have a causal effect on AD in genetic prediction. Among these 36 targets, two pathways are identified as enriched: "EBV Infection" and "Efferocytosis". Seven core targets are CR2, ICAM1, TAPBP, TNFAIP3, THBS1, SCARF1, and SIRPG. Also, the concentration of antibodies against EBV EBNA-1 and VCA p18 was confirmed by MR analyses to be risk factors for AD. According to drug predictions, molecular docking, and molecular dynamics simulations, resveratrol can stabilize CR2.
DISCUSSION
This study systematically identifies major plasma immune biomarkers associated with AD and proposes a mechanism by which EBV infection regulates plasma proteins CR2, TNFAIP3, and THBS1, which may affect AD risk. Resveratrol is thought to have preventive and protective effects, as predicted computationally.
CONCLUSION
This study systematically identified key plasma markers associated with AD. Resveratrol is likely to become a potentially effective preventive and protective drug in the prevention and treatment of AD, providing new ideas and targets for immune intervention of AD.
Xu Wang, Jinjian Li, Zhen Wei et al.· Current Alzheimer Research· 0 citations
Plasma proteins are promising biomarkers and potential drug targets in psoriasis. We conducted a two-sample Mendelian randomisation analysis integrating protein quantitative trait loci from UK Biobank and deCODE genetics with a psoriasis GWAS meta-analysis of 36,466 cases. To strengthen causal inference, we performed colocalisation analyses to evaluate shared genetic signals and applied summary data-based MR (SMR) with HEIDI testing using expression quantitative trait loci to exclude linkage-driven associations. After correction for multiple testing, 78 circulating proteins showed genetically predicted associations with psoriasis, with 27 demonstrating strong colocalisation (PPH4>80%). Triangulation prioritised 12 Tier 1 proteins, STX4, FLT3, NFKB1, IL18, PRSS53, SPAG1, SGSH, PLAT, RALB, TNFSF11, SPHK2, and STAT3, supported by consistent effects and no heterogeneity. Network profiling and Genome for REPositioning analyses assessed biological connectivity and druggability, revealing enrichment in anatomical therapeutic chemical groups L and B. Single-cell RNA sequencing confirmed cell-type-specific expression and modulation following IL-23 blockade.
D. Meena, C. V. Chalitsios, J. Huang et al.· medRxiv· 0 citations
Rheumatoid Arthritis (RA) is a common autoimmune disease
with complex pathogenesis and high prevalence, severely affecting patients’ quality of
life. Multi-omics analysis has emerged as a powerful tool in RA research, providing insights
into cell heterogeneity, genetic mechanisms, and immune microenvironment characteristics.
This research looks into important genes linked to rheumatoid arthritis(RA)
using single-cell data and Mendelian randomization analysis, while also uncovering the
characteristics of the immune microenvironment and its mechanisms linking to these
key genes.
Gene expression, eQTL, and GWAS data were collected. Analyses included
single-cell data processing (quality control, dimensionality reduction, clustering, cell annotation,
and cell subset contribution assessment), subgroup non-negative matrix factorization,
Mendelian randomization, co-localization analysis, immune infiltration analysis,
and GSEA/GSVA.
This study analyzed 95,036 single-cell samples and found that alterations in B cells are closely associated with disease progression. Further analysis identified seven distinct B cell subpopulations, with immune responses and the tumor microenvironment exerting significant influence on their dynamics. Mendelian randomization analysis revealed key genes, CD83 and CRIP2, that are linked to the risk of RA. Subsequent investigation demonstrated strong associations between these genes and immune cell populations. GSEA and GSVA analyses showed that CD83 is involved in pathways related to allograft rejection and antigen processing, while CRIP2 is associated with interactions of extracellular matrix receptors and the IL-17 signaling pathway. Additionally, immunometabolism pathway analysis highlighted potential therapeutic targets and underlying mechanisms.
This study analyzed 95,036 single-cell samples and found that alterations in B
cells are closely associated with disease progression. Further analysis identified seven
distinct B cell subpopulations, with immune responses and the tumor microenvironment
exerting significant influence on their dynamics. Mendelian randomization analysis revealed
key genes, CD83 and CRIP2, that are linked to the risk of RA. Subsequent investigation
demonstrated strong associations between these genes and immune cell populations.
GSEA and GSVA analyses showed that CD83 is involved in pathways related to
allograft rejection and antigen processing, while CRIP2 is associated with interactions
of extracellular matrix receptors and the IL-17 signaling pathway. Additionally, immunometabolism
pathway analysis highlighted potential therapeutic targets and underlying
mechanisms.
The findings suggest that B cells play a major role in the RA immune microenvironment
and identify CD83 and CRIP2 as potential biomarkers and therapeutic
targets. CD83 may contribute to RA through immune regulation, antigen presentation,
and inflammatory signaling, whereas CRIP2 may be involved in metal ion homeostasis,
cellular stress responses, and immune-related pathological processes. Immune infiltration
analyses further indicate that these genes are closely associated with immune-cell
activity in RA. Overall, the study provides new insight into RA pathogenesis and supports
the potential clinical relevance of CD83 and CRIP2, although further experimental
and clinical validation is required.
This study offers valuable understanding about RA pathogenesis and identifies
probable diagnostic biomarkers as well as therapeutic targets (CD83 and CRIP2),
which could improve our awareness of the illness and help with formulating more successful
care plans.
Shi-Kai Chen, Hanyu Wang, Cheng Wang et al.· Current Medicinal Chemistry· 0 citations
OBJECTIVE
Early diagnosis of seronegative rheumatoid arthritis (SNRA) is often challenging due to lack of reliable serological markers. The aim of this study was to explore protein biomarkers of SNRA and identify their potential as therapeutic targets.
METHODS
Third-eight seropositive (SPRA) patients and 20 SNRA patients were enrolled in our omics study. Differentially expressed proteins (DEPs) between the two groups were identified via the Astral mass Analyzer for quantitative proteomics using data-independent acquisition (DIA) mass spectrometry, and the dataset of GSE93272 was used for the validation test. A variety of bioinformatics algorithms such as random forest, support vector machine, nomogram construction, consensus clustering were applied to identify key regulators, expression patterns, and molecular subtypes. We also performed functional enrichment analysis of differentially expressed genes (DEGs) across clusters and evaluated immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). Mendelian randomization (MR) analyses were conducted to identify druggable targets, and colocalization analyses were performed to determine whether RA risk and the expression of these druggable genes were influenced by shared SNPs. Additionally, we validated these findings through both in vitro and in vivo experiments, using clinical peripheral blood samples, RAW264.7 cells, and the collagen-induced arthritis (CIA) rat model to assess the functions of key genes in RA.
RESULTS
Using quantitative proteomics, we identified 22 DEPs between SPRA and SNRA, including 12 upregulated and 10 downregulated proteins. Notably, CAMK2G protein was positively correlated with inflammation indicators in RA patients. Differential expression analysis between RA samples and controls identified nine key DEPs as potential biomarkers. A random forest classifier and a nomogram incorporating these nine biomarkers were constructed, and their clinical utility was supported by decision curve analysis. In addition, consensus clustering based on these hub genes stratified RA patients into distinct subgroups characterized by differential immune cell infiltration patterns and polygenic risk scores. Significant association between NR1H3 and disease by colocalization analysis in MR suggests its potential as druggable target. Notably, our experiments have confirmed that CAMK2G expression is associated with enhanced osteoclastogenic capacity, and enhanced CAMK2G expression accompanies joint damage in CIA rats.
CONCLUSION
Our research on polygenic risk model indicates CAMK2G protein could serve as a promising biomarker and NR1H3 might be a potential candidate for targeting therapy for RA.
Peng Zhang, Youbang Liang, Xin Li et al.· Journal of Autoimmunity· 0 citations
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