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      In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

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          Abstract

          Objective

          Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

          Methods

          RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39  M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein–protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

          Results

          As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs- M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

          Conclusion

          The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

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          Most cited references333

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                30 November 2022
                2022
                : 13
                : 1041314
                Affiliations
                [1] 1Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran , Karaj, Iran
                [2] 2Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University , Munich, Germany
                [3] 3Faculty of Science, Earth Sciences Building, University of British Columbia , Vancouver, BC, Canada
                [4] 4Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences , Kurdistan, Iran
                [5] 5Department of Basic Scientific Sciences, AL-Balqa Applied University, AL-Huson University College , AL-Huson, Jordan
                [6] 6Department of Animal and Poultry Science, College of Aburaihan, University of Tehran , Tehran, Iran
                [7] 7Halal Research Center of IRI, FDA , Tehran, Iran
                [8] 8Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran , Tehran, Iran
                [9] 9Department of Agronomy and Plant Breeding, College of Agriculture and Natural Resources, University of Tehran , Karaj, Iran
                [10] 10Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary , Calgary, AB, Canada
                [11] 11Regional Department of Bioengineering, Tecnológico de Monterrey , Monterrey, Mexico
                Author notes

                Edited by: Bernat Pérez de Val, Centre for Research on Animal Health, Spain

                Reviewed by: Cristina Lourdes Vazquez, Instituto Nacional de Tecnología Agropecuaria (INTA), Oliveros, Argentina; Hugo Esquivel-Solís, CONACYT Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco (CIATEJ), Mexico

                *Correspondence: Aliakbar Hasankhani, A.hasankhani74@ 123456ut.ac.ir
                Abolfazl Bahrami, A.Bahrami@ 123456ut.ac.ir
                Farhad Safarpoor Dehkordi, F.safarpoor@ 123456ut.ac.ir

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Infectious Agents and Disease, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.1041314
                9748370
                36532492
                99a2df98-eb83-4510-afe1-f6f1e7336faa
                Copyright © 2022 Hasankhani, Bahrami, Mackie, Maghsoodi, Alawamleh, Sheybani, Safarpoor Dehkordi, Rajabi, Javanmard, Khadem, Barkema and De Donato.

                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.

                History
                : 10 September 2022
                : 04 November 2022
                Page count
                Figures: 9, Tables: 1, Equations: 0, References: 334, Pages: 32, Words: 25611
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                bovine tuberculosis,hub-central gene,maximal clique centrality,mycobacterium bovis,rna-seq,systems biology,weighted gene co-expression network analysis

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