INTRODUCTION: The alterations in the gut microbial network in multiple system atrophy (MSA) remain poorly understood. This study aimed to identify key gut microbial interaction networks in MSA through comprehensive multimodal analyses. METHODS: Demographic information and frozen fecal specimens were collected from 119 participants [MSA, = 26; Parkinson's disease (PD), = 66; healthy control (HC), = 27]. Raw amplicons of the bacterial 16S rRNA V3-V4 gene region were processed using two methods: DADA2-denoising and clustering into operational taxonomic units. We conducted univariate and multivariable analyses to assess the differential abundance of bacterial genera and predicted metabolic pathways using four statistical methods: ANCOM, ANCOM-BC, ALDEx2, and MaAsLin 2. Interbacterial interactions were assessed using four correlation and two network analyses. RESULTS: We consistently observed lower levels of in MSA patients and lower levels of in PD patients compared with HCs (q < 0.05), both before and after adjusting for comorbidities, diet, and constipation status. The random forest classifiers effectively differentiated between MSA and PD, achieving high AUCs (0.75-0.78) in 5-fold cross-validation. A significant positive interbacterial interaction between group and was uniquely observed in MSA patients. Additionally, we identified an increase in the ARGORNPROST-PWY pathway (L-arginine degradation, q = 0.003) and a decrease in the PWY-6478 pathway (GDP-D-glycero-α-D-manno-heptose biosynthesis, q = 0.015) in MSA patients compared with HCs. CONCLUSION: Future studies are warranted to determine whether fecal microbiome-derived signatures can serve as reliable biomarkers for MSA.