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A framework for non-preserved consensus gene module detection in Johne's disease
Johne's disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) is a major concern in dairy industry. Since, the pathogenesis of the disease is not clearly known, it is necessary to develop an approach to discover molecular mechanisms behind this disease with high confidence. Biolog...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363878/ https://www.ncbi.nlm.nih.gov/pubmed/35968017 http://dx.doi.org/10.3389/fvets.2022.974444 |
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author | Heidari, Maryam Pakdel, Abbas Bakhtiarizadeh, Mohammad Reza Dehghanian, Fariba |
author_facet | Heidari, Maryam Pakdel, Abbas Bakhtiarizadeh, Mohammad Reza Dehghanian, Fariba |
author_sort | Heidari, Maryam |
collection | PubMed |
description | Johne's disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) is a major concern in dairy industry. Since, the pathogenesis of the disease is not clearly known, it is necessary to develop an approach to discover molecular mechanisms behind this disease with high confidence. Biological studies often suffer from issues with reproducibility. Lack of a method to find stable modules in co-expression networks from different datasets related to Johne's disease motivated us to present a computational pipeline to identify non-preserved consensus modules. Two RNA-Seq datasets related to MAP infection were analyzed, and consensus modules were detected and were subjected to the preservation analysis. The non-preserved consensus modules in both datasets were determined as they are modules whose connectivity and density are affected by the disease. Long non-coding RNAs (lncRNAs) and TF genes in the non-preserved consensus modules were identified to construct integrated networks of lncRNA-mRNA-TF. These networks were confirmed by protein-protein interactions (PPIs) networks. Also, the overlapped hub genes between two datasets were considered hub genes of the consensus modules. Out of 66 consensus modules, 21 modules were non-preserved consensus modules, which were common in both datasets and 619 hub genes were members of these modules. Moreover, 34 lncRNA and 152 TF genes were identified in 12 and 19 non-preserved consensus modules, respectively. The predicted PPIs in 17 non-preserved consensus modules were significant, and 283 hub genes were commonly identified in both co-expression and PPIs networks. Functional enrichment analysis revealed that eight out of 21 modules were significantly enriched for biological processes associated with Johne's disease including “inflammatory response,” “interleukin-1-mediated signaling pathway”, “type I interferon signaling pathway,” “cytokine-mediated signaling pathway,” “regulation of interferon-beta production,” and “response to interferon-gamma.” Moreover, some genes (hub mRNA, TF, and lncRNA) were introduced as potential candidates for Johne's disease pathogenesis such as TLR2, NFKB1, IRF1, ATF3, TREM1, CDH26, HMGB1, STAT1, ISG15, CASP3. This study expanded our knowledge of molecular mechanisms involved in Johne's disease, and the presented pipeline enabled us to achieve more valid results. |
format | Online Article Text |
id | pubmed-9363878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93638782022-08-11 A framework for non-preserved consensus gene module detection in Johne's disease Heidari, Maryam Pakdel, Abbas Bakhtiarizadeh, Mohammad Reza Dehghanian, Fariba Front Vet Sci Veterinary Science Johne's disease caused by Mycobacterium avium subsp. paratuberculosis (MAP) is a major concern in dairy industry. Since, the pathogenesis of the disease is not clearly known, it is necessary to develop an approach to discover molecular mechanisms behind this disease with high confidence. Biological studies often suffer from issues with reproducibility. Lack of a method to find stable modules in co-expression networks from different datasets related to Johne's disease motivated us to present a computational pipeline to identify non-preserved consensus modules. Two RNA-Seq datasets related to MAP infection were analyzed, and consensus modules were detected and were subjected to the preservation analysis. The non-preserved consensus modules in both datasets were determined as they are modules whose connectivity and density are affected by the disease. Long non-coding RNAs (lncRNAs) and TF genes in the non-preserved consensus modules were identified to construct integrated networks of lncRNA-mRNA-TF. These networks were confirmed by protein-protein interactions (PPIs) networks. Also, the overlapped hub genes between two datasets were considered hub genes of the consensus modules. Out of 66 consensus modules, 21 modules were non-preserved consensus modules, which were common in both datasets and 619 hub genes were members of these modules. Moreover, 34 lncRNA and 152 TF genes were identified in 12 and 19 non-preserved consensus modules, respectively. The predicted PPIs in 17 non-preserved consensus modules were significant, and 283 hub genes were commonly identified in both co-expression and PPIs networks. Functional enrichment analysis revealed that eight out of 21 modules were significantly enriched for biological processes associated with Johne's disease including “inflammatory response,” “interleukin-1-mediated signaling pathway”, “type I interferon signaling pathway,” “cytokine-mediated signaling pathway,” “regulation of interferon-beta production,” and “response to interferon-gamma.” Moreover, some genes (hub mRNA, TF, and lncRNA) were introduced as potential candidates for Johne's disease pathogenesis such as TLR2, NFKB1, IRF1, ATF3, TREM1, CDH26, HMGB1, STAT1, ISG15, CASP3. This study expanded our knowledge of molecular mechanisms involved in Johne's disease, and the presented pipeline enabled us to achieve more valid results. Frontiers Media S.A. 2022-07-27 /pmc/articles/PMC9363878/ /pubmed/35968017 http://dx.doi.org/10.3389/fvets.2022.974444 Text en Copyright © 2022 Heidari, Pakdel, Bakhtiarizadeh and Dehghanian. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Veterinary Science Heidari, Maryam Pakdel, Abbas Bakhtiarizadeh, Mohammad Reza Dehghanian, Fariba A framework for non-preserved consensus gene module detection in Johne's disease |
title | A framework for non-preserved consensus gene module detection in Johne's disease |
title_full | A framework for non-preserved consensus gene module detection in Johne's disease |
title_fullStr | A framework for non-preserved consensus gene module detection in Johne's disease |
title_full_unstemmed | A framework for non-preserved consensus gene module detection in Johne's disease |
title_short | A framework for non-preserved consensus gene module detection in Johne's disease |
title_sort | framework for non-preserved consensus gene module detection in johne's disease |
topic | Veterinary Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9363878/ https://www.ncbi.nlm.nih.gov/pubmed/35968017 http://dx.doi.org/10.3389/fvets.2022.974444 |
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