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Ghafouri F, Dehghanian Reyhan V, Sadeghi M, Miraei-Ashtiani SR, Kastelic JP, Barkema HW, Shirali M. Integrated Analysis of Transcriptome Profiles and lncRNA-miRNA-mRNA Competing Endogenous RNA Regulatory Network to Identify Biological Functional Effects of Genes and Pathways Associated with Johne's Disease in Dairy Cattle. Noncoding RNA 2024; 10:38. [PMID: 39051372 PMCID: PMC11270299 DOI: 10.3390/ncrna10040038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/25/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Paratuberculosis or Johne's disease (JD), a chronic granulomatous gastroenteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP), causes huge economic losses and reduces animal welfare in dairy cattle herds worldwide. At present, molecular mechanisms and biological functions involved in immune responses to MAP infection of dairy cattle are not clearly understood. Our purpose was to integrate transcriptomic profiles and competing endogenous RNA (ceRNA) network analyses to identify key messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of peripheral blood mononuclear cells (PBMCs) for MAP infection in dairy cattle. In total, 28 lncRNAs, 42 miRNAs, and 370 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In this regard, we identified 21 hub genes (CCL20, CCL5, CD40, CSF2, CXCL8, EIF2AK2, FOS, IL10, IL17A, IL1A, IL1B, IRF1, MX2, NFKB1, NFKBIA, PTGS2, SOCS3, TLR4, TNF, TNFAIP3, and VCAM1) involved in MAP infection. Furthermore, eight candidate subnets with eight lncRNAs, 29 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 510, 22, and 11 significantly enriched GO terms related to MAP infection in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways related to MAP infection that were enriched included the immune system process, defense response, response to cytokine, leukocyte migration, regulation of T cell activation, defense response to bacterium, NOD-like receptor, B cell receptor, TNF, NF-kappa B, IL-17, and T cell receptor signaling pathways. Contributions of transcriptome profiles from MAP-positive and MAP-negative sample groups plus a ceRNA regulatory network underlying phenotypic differences in the intensity of pathogenicity of JD provided novel insights into molecular mechanisms associated with immune system responses to MAP infection in dairy cattle.
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Affiliation(s)
- Farzad Ghafouri
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - Vahid Dehghanian Reyhan
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - Mostafa Sadeghi
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - Seyed Reza Miraei-Ashtiani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj 77871-31587, Iran; (F.G.); (V.D.R.); (S.R.M.-A.)
| | - John P. Kastelic
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; (J.P.K.); (H.W.B.)
| | - Herman W. Barkema
- Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; (J.P.K.); (H.W.B.)
| | - Masoud Shirali
- School of Biological Sciences, Queen’s University Belfast, Belfast BT9 5AJ, UK
- Agri-Food and Biosciences Institute, Hillsborough BT26 6DR, UK
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Hasankhani A, Bahrami A, Mackie S, Maghsoodi S, Alawamleh HSK, Sheybani N, Safarpoor Dehkordi F, Rajabi F, Javanmard G, Khadem H, Barkema HW, De Donato M. In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection. Front Microbiol 2022; 13:1041314. [PMID: 36532492 PMCID: PMC9748370 DOI: 10.3389/fmicb.2022.1041314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 11/04/2022] [Indexed: 08/26/2023] Open
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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Affiliation(s)
- Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany
| | - Shayan Mackie
- Faculty of Science, Earth Sciences Building, University of British Columbia, Vancouver, BC, Canada
| | - Sairan Maghsoodi
- Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Kurdistan, Iran
| | - Heba Saed Kariem Alawamleh
- Department of Basic Scientific Sciences, AL-Balqa Applied University, AL-Huson University College, AL-Huson, Jordan
| | - Negin Sheybani
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Farhad Safarpoor Dehkordi
- Halal Research Center of IRI, FDA, Tehran, Iran
- Department of Food Hygiene and Quality Control, Faculty of Veterinary Medicine, University of Tehran, Tehran, Iran
| | - Fatemeh Rajabi
- Department of Agronomy and Plant Breeding, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Ghazaleh Javanmard
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Hosein Khadem
- Department of Agronomy and Plant Breeding, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcos De Donato
- Regional Department of Bioengineering, Tecnológico de Monterrey, Monterrey, Mexico
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Heidari M, Pakdel A, Bakhtiarizadeh MR, Dehghanian F. A framework for non-preserved consensus gene module detection in Johne's disease. Front Vet Sci 2022; 9:974444. [PMID: 35968017 PMCID: PMC9363878 DOI: 10.3389/fvets.2022.974444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 07/08/2022] [Indexed: 11/29/2022] Open
Abstract
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.
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Affiliation(s)
- Maryam Heidari
- Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
| | - Abbas Pakdel
- Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran
- *Correspondence: Abbas Pakdel
| | - Mohammad Reza Bakhtiarizadeh
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
- Mohammad Reza Bakhtiarizadeh
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Hasankhani A, Bahrami A, Sheybani N, Aria B, Hemati B, Fatehi F, Ghaem Maghami Farahani H, Javanmard G, Rezaee M, Kastelic JP, Barkema HW. Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic. Front Immunol 2021; 12:789317. [PMID: 34975885 PMCID: PMC8714803 DOI: 10.3389/fimmu.2021.789317] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 11/26/2021] [Indexed: 01/08/2023] Open
Abstract
Background The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches. Methods RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules. Results Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis. Conclusion This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.
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Affiliation(s)
- Aliakbar Hasankhani
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Abolfazl Bahrami
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
- Biomedical Center for Systems Biology Science Munich, Ludwig-Maximilians-University, Munich, Germany
| | - Negin Sheybani
- Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Tehran, Iran
| | - Behzad Aria
- Department of Physical Education and Sports Science, School of Psychology and Educational Sciences, Yazd University, Yazd, Iran
| | - Behzad Hemati
- Biotechnology Research Center, Karaj Branch, Islamic Azad University, Karaj, Iran
| | - Farhang Fatehi
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | | | - Ghazaleh Javanmard
- Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
| | - Mahsa Rezaee
- Department of Medical Mycology, School of Medical Science, Tarbiat Modares University, Tehran, Iran
| | - John P. Kastelic
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
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