Evidence map›Paper›PMID 38987816›Full record

ArticleGut pathogens2024

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

Hajra Ashraf, Plamena Dikarlo, Aurora Masia, Ignazio R Zarbo, Paolo Solla, Umer Zeeshan Ijaz, Leonardo A Sechi

Abstract read
In one paragraph

Article in Gut pathogens, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hajra AshrafDepartment of Biomedical Sciences, University of Sassari, Sassari, Italy.
Plamena DikarloBIOMES NGS GmbH, Schwartzkopffstraße 1, 15745, Halle 21, Wildau, Germany.
Aurora MasiaDepartment of Medicine and Pharmacy, Neurology, University of Sassari, Sassari, Italy.
Ignazio R ZarboDepartment of Medicine and Pharmacy, Neurology, University of Sassari, Sassari, Italy.
Paolo SollaDepartment of Medicine and Pharmacy, Neurology, University of Sassari, Sassari, Italy.
Umer Zeeshan IjazWater & Environment Research Group, Mazumdar-Shaw Advanced Research Centre, University of Glasgow, Glasgow, UK. Umer.Ijaz@glasgow.ac.uk.
Leonardo A SechiDepartment of Biomedical Sciences, University of Sassari, Sassari, Italy. sechila@uniss.it.

Funding

Engineering and Physical Sciences Research Council EP/V030515/1
6 · The paper itself

Abstract

backgroundIn 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Gut microbiomeIntegrated value of influenceMultiple sclerosisNetwork inference

Identifiers

PMID38987816
PMCPMC11238521

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.