Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus 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 pla...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2022.979320/full |
| _version_ | 1811340330311614464 |
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| author | Anirban Bhar Laurin Christopher Gierse Alexander Meene Haitao Wang Claudia Karte Theresa Schwaiger Charlotte Schröder Thomas C. Mettenleiter Tim Urich Katharina Riedel Lars Kaderali |
| author_facet | Anirban Bhar Laurin Christopher Gierse Alexander Meene Haitao Wang Claudia Karte Theresa Schwaiger Charlotte Schröder Thomas C. Mettenleiter Tim Urich Katharina Riedel Lars Kaderali |
| author_sort | Anirban Bhar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:39:26Z |
| format | Article |
| id | doaj.art-f92d34b9e1fd45a7b3b9ba755750a107 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-13T18:39:26Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-f92d34b9e1fd45a7b3b9ba755750a1072022-12-22T02:34:46ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2022-10-011310.3389/fmicb.2022.979320979320Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infectionAnirban Bhar0Laurin Christopher Gierse1Alexander Meene2Haitao Wang3Claudia Karte4Theresa Schwaiger5Charlotte Schröder6Thomas C. Mettenleiter7Tim Urich8Katharina Riedel9Lars Kaderali10Institute of Bioinformatics, University Medicine Greifswald, Greifswald, GermanyInstitute of Microbiology, University of Greifswald, Greifswald, GermanyInstitute of Microbiology, University of Greifswald, Greifswald, GermanyInstitute of Microbiology, University of Greifswald, Greifswald, GermanyFriedrich-Loeffler-Institut, Greifswald-Insel Riems, Greifswald, GermanyFriedrich-Loeffler-Institut, Greifswald-Insel Riems, Greifswald, GermanyFriedrich-Loeffler-Institut, Greifswald-Insel Riems, Greifswald, GermanyFriedrich-Loeffler-Institut, Greifswald-Insel Riems, Greifswald, GermanyInstitute of Microbiology, University of Greifswald, Greifswald, GermanyInstitute of Microbiology, University of Greifswald, Greifswald, GermanyInstitute of Bioinformatics, University Medicine Greifswald, Greifswald, GermanyInfluenza 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.https://www.frontiersin.org/articles/10.3389/fmicb.2022.979320/full16S rRNA gene sequencingmicrobiomemetaproteomeinfluenza A virus infectioncommunity detection |
| spellingShingle | Anirban Bhar Laurin Christopher Gierse Alexander Meene Haitao Wang Claudia Karte Theresa Schwaiger Charlotte Schröder Thomas C. Mettenleiter Tim Urich Katharina Riedel Lars Kaderali Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection Frontiers in Microbiology 16S rRNA gene sequencing microbiome metaproteome influenza A virus infection community detection |
| title | Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection |
| title_full | Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection |
| title_fullStr | Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection |
| title_full_unstemmed | Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection |
| title_short | Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection |
| title_sort | application of a maximal clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza a virus infection |
| topic | 16S rRNA gene sequencing microbiome metaproteome influenza A virus infection community detection |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2022.979320/full |
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