Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal infections (PANDAS) are characterized by neuropsychiatric symptoms linked to immune dysregulation. Emerging evidence highlights the role of host-microbiome interactions in modulating neuro-immune functions via gut-brain axis signaling; however, its contribution to PANDAS pathophysiology remains poorly understood.We conducted microbiome analysis from samples collected across multiple sites of PANDAS patients including nasal, throat and stool. We performed an integrated multi-omics analysis of stool samples from pediatric PANDAS cases and healthy controls, including discordant twin pairs. Microbial composition and function were assessed using 16S rRNA gene sequencing, shotgun metagenomics, while untargeted metabolomic profiling was performed using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS).PANDAS cases exhibited reduced alpha diversity and significantly altered beta diversity compared to controls, indicating shifts in gut microbial composition. Shotgun metagenomic analysis revealed differential enrichment of functional pathways, including diminished quorum sensing, altered gamma-aminobutyric acid (GABA) biosynthesis, and microbial degradation processes. Multiple gut-brain modules (GBMs) and gut metabolic modules (GMMs) associated with neurotransmission, transport activities and metabolism were significantly perturbed in PANDAS. Metabolomic profiling showed reduced functional diversity and distinct clustering of metabolic profiles, with differential abundance of amino acids, bile acids, and neuroactive compounds. Integrative analysis further identified disrupted microbe-metabolite networks allied to gut-brain signaling.Our findings reveal significant functional shifts in gut microbiota composition, functional capacity and metabolite profile in PANDAS, suggesting dysregulation of the gut-brain axis signaling. This study provides a foundation for development of microbiome-based biomarkers and therapeutic strategies for pediatric neuropsychiatric disorders. Background: Methods: Results: Conclusions:
