Network analysis of gut microbial communities reveal key genera for a multiple sclerosis cohort with Mycobacterium avium subspecies paratuberculosis infection

• Published in: Gut pathogens 2024-07, Vol.16 (1), p.37-12, Article 37
• Main Authors: Ashraf, Hajra, Dikarlo, Plamena, Masia, Aurora, Zarbo, Ignazio R, Solla, Paolo, Ijaz, Umer Zeeshan, Sechi, Leonardo A
• Format: Article
• Language: English
• Subjects: Analysis; Antigens; Approximation; Binomial distribution; Cholesterol; Digestive system; Disease; Disease management; Ecosystems; Gastrointestinal diseases; Gastrointestinal tract; Generalized linear models; Gut microbiome; Health aspects; Hirsch index; Infections; Inflammatory bowel diseases; Integrated value of influence; Johne's disease; Microbiomes; Microbiota; Microorganisms; Multiple sclerosis; Mycobacterium avium; Network inference; Nodes; Paratuberculosis; RNA; rRNA; Variables
• ISSN: 1757-4749; 1757-4749
• DOI: 10.1186/s13099-024-00627-7

In gut ecosystems, there is a complex interplay of biotic and abiotic interactions that decide the overall fitness of an individual. Divulging the microbe-microbe and microbe-host interactions may lead to better strategies in disease management, as microbes rarely act in isolation. Network inference for microbial communities is often a challenging task limited by both analytical assumptions as well as experimental approaches. Even after the network topologies are obtained, identification of important nodes within the context of underlying disease aetiology remains a convoluted task. We therefore present a network perspective on complex interactions in gut microbial profiles of individuals who have multiple sclerosis with and without Mycobacterium avium subspecies paratuberculosis (MAP) infection. Our exposé is guided by recent advancements in network-wide statistical measures that identify the keystone nodes. We have utilised several centrality measures, including a recently published metric, Integrated View of Influence (IVI), that is robust against biases. The ecological networks were generated on microbial abundance data (n = 69 samples) utilising 16 S rRNA amplification. Using SPIEC-EASI, a sparse inverse covariance estimation approach, we have obtained networks separately for MAP positive (+), MAP negative (-) and healthy controls (as a baseline). Using IVI metric, we identified top 20 keystone nodes and regressed them against covariates of interest using a generalised linear latent variable model. Our analyses suggest Eisenbergiella to be of pivotal importance in MS irrespective of MAP infection. For MAP + cohort, Pyarmidobacter, and Peptoclostridium were predominately the most influential genera, also hinting at an infection model similar to those observed in Inflammatory Bowel Diseases (IBDs). In MAP- cohort, on the other hand, Coprostanoligenes group was the most influential genera that reduces cholesterol and supports the intestinal barrier. The identification of keystone nodes, their co-occurrences, and associations with the exposome (meta data) advances our understanding of biological interactions through which MAP infection shapes the microbiome in MS individuals, suggesting the link to the inflammatory process of IBDs. The associations presented in this study may lead to development of improved diagnostics and effective vaccines for the management of the disease.