Integrative Cross-Cohort Meta-Analysis Reveals a Conserved Dysbiotic Signature of Streptococcaceae and Lachnospiraceae in Multiple Sclerosis
Arif, A.; Garg, P.; Srivastava, P.
Show abstract
BackgroundMultiple Sclerosis (MS) is a chronic autoimmune disorder characterized by inflammation and demyelination in central nervous system (CNS). Although increasing evidence suggests that gut microbial dysbiosis contributes to MS pathogenesis through the microbiota-gut-brain axis, reproducible microbial signatures associated with disease progression across independent clinical cohorts remain incompletely characterized. ObjectiveThis study aimed to identify conserved gut microbial alterations associated with Multiple Sclerosis by integrating publicly available human gut microbiome datasets and characterizing disease-associated microbial signatures linked to immune dysregulation. DesignHuman gut metagenomic 16S rRNA sequencing data from MS patients and healthy controls obtained from publicly available repositories (NCBI, Bioproject). Raw sequencing reads were processed using a standardized microbiome analysis workflow, including quality control, denoising, taxonomic assignment, phylogenetic reconstruction, diversity analyses, and differential abundance testing. Microbial community structure was evaluated using alpha- and beta-diversity analyses, while statistically significant differences between study groups were assessed using PERMANOVA, Kruskal-Wallis, and ANCOM to identify disease-associated bacterial taxa. ResultsIntegration of independent cohorts revealed consistent alterations in the gut microbial composition of MS patients compared with healthy controls. Significant reductions in microbial diversity and distinct microbial community structures were observed in MS. Differential abundance analysis demonstrated enrichment of the pro-inflammatory family Streptococcaceae, whereas beneficial short-chain fatty acid-producing taxa, particularly Lachnospiraceae, were significantly depleted in MS patients. These conserved microbial alterations indicate disruption of immune-regulatory bacterial communities and support the involvement of gut microbial dysbiosis in MS-associated neuroinflammation. ConclusionThis study identifies a reproducible gut microbial dysbiosis signature associated with Multiple Sclerosis, characterized by expansion of pro-inflammatory bacterial taxa and depletion of beneficial SCFA-producing microorganisms. These findings strengthen the evidence supporting the microbiota-gut-brain axis in MS pathogenesis and highlight microbial community signatures that may contribute to future biomarker development and microbiome- based therapeutic strategies.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Predictive Metagenomic Analysis of Autoimmune Disease Identifies Robust Autoimmunity and Disease Specific Microbial Signatures 93%
- Nasopharyngeal microbial communities of patients infected with SARS-COV-2 that developed COVID-19. 90%
- Antibiotic prophylaxis and hospitalization of horses subjected to median laparotomy: gut microbiota trajectories and abundance increase of Escherichia 90%
Similar papers in this journal
- Gut microbiota analyses of Saudi populations for type 2 diabetes-related phenotypes reveals significant association 91%
- Long-term impact of fecal transplantation in healthy volunteers 90%
- Machine learning-based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases 90%
Similar papers in this journal
- Olfactory Dysfunction in Patients with Multiple Sclerosis; A Systematic Review and Meta-Analysis 93%
- Masitinib Limits Neuronal Damage, as Measured by Serum Neurofilament Light Chain Concentration, in a Model of Neuroimmune-Driven Neurodegenerative Disease 92%
- Herpes Simplex Virus Infection, Acyclovir and IVIG Treatment All Independently Cause Gut Dysbiosis. 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.