Thomas J Hall1, Michael P Mullen2, Gillian P McHugo1, Kate E Killick1,3, Siobhán C Ring4, Donagh P Berry5, Carolina N Correia1, John A Browne1, Stephen V Gordon6,7, David E MacHugh8,9. 1. Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland. 2. Bioscience Research Institute, Athlone Institute of Technology, Dublin Road, Athlone, Westmeath, N37 HD68, Ireland. 3. Present address: Genuity Science, Cherrywood Business Park. Loughlinstown, Dublin, D18 K7W4, Ireland. 4. Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon, Cork, P72 X050, Ireland. 5. Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Cork, P61 C996, Ireland. 6. UCD School of Veterinary Medicine, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland. 7. UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland. 8. Animal Genomics Laboratory, UCD School of Agriculture and Food Science, UCD College of Health and Agricultural Sciences, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland. david.machugh@ucd.ie. 9. UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland. david.machugh@ucd.ie.
Abstract
BACKGROUND: Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. RESULTS: The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool-gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. CONCLUSIONS: The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.
BACKGROUND:BovineTB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. RESULTS: The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool-gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. CONCLUSIONS: The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.
Authors: Thierry Garnier; Karin Eiglmeier; Jean-Christophe Camus; Nadine Medina; Huma Mansoor; Melinda Pryor; Stephanie Duthoy; Sophie Grondin; Celine Lacroix; Christel Monsempe; Sylvie Simon; Barbara Harris; Rebecca Atkin; Jon Doggett; Rebecca Mayes; Lisa Keating; Paul R Wheeler; Julian Parkhill; Bart G Barrell; Stewart T Cole; Stephen V Gordon; R Glyn Hewinson Journal: Proc Natl Acad Sci U S A Date: 2003-06-03 Impact factor: 11.205
Authors: Francisco Olea-Popelka; Adrian Muwonge; Alejandro Perera; Anna S Dean; Elizabeth Mumford; Elisabeth Erlacher-Vindel; Simona Forcella; Benjamin J Silk; Lucica Ditiu; Ahmed El Idrissi; Mario Raviglione; Ottorino Cosivi; Philip LoBue; Paula I Fujiwara Journal: Lancet Infect Dis Date: 2016-09-30 Impact factor: 25.071