Journal
FRONTIERS IN MICROBIOLOGY
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2022.979320
Keywords
16S rRNA gene sequencing; microbiome; metaproteome; influenza A virus infection; community detection
Categories
Funding
- Federal Excellence Initiative of Mecklenburg-Western Pomerania
- European Social Fund (ESF) [825903]
- European Union
- DFG
- Open Access Publication Fund of the University of Greifswald
- [ESF/14-BM-A55-0014/16]
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This study utilizes pig gut microbiome data to uncover the roles of microbial families such as Ruminococcaceae, Lachnospiraceae, Spirochaetaceae, and Prevotellaceae in the gut microbiome following Influenza A Virus (IAV) infection. The integration of metaproteomic data also helps identify microbial biomarkers and elucidate their functional roles in protecting the host after IAV infection.
Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.
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