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The bovine alveolar macrophage DNA methylome is resilient to infection with Mycobacterium bovis.

ABSTRACT: DNA methylation is pivotal in orchestrating gene expression patterns in various mammalian biological processes. Perturbation of the bovine alveolar macrophage (bAM) transcriptome, due to Mycobacterium bovis (M. bovis) infection, has been well documented; however, the impact of this intracellular pathogen on the bAM epigenome has not been determined. Here, whole genome bisulfite sequencing (WGBS) was used to assess the effect of M. bovis infection on the bAM DNA methylome. The methylomes of bAM infected with M. bovis were compared to those of non-infected bAM 24?hours post-infection (hpi). No differences in DNA methylation (CpG or non-CpG) were observed. Analysis of DNA methylation at proximal promoter regions uncovered >250 genes harbouring intermediately methylated (IM) promoters (average methylation of 33-66%). Gene ontology analysis, focusing on genes with low, intermediate or highly methylated promoters, revealed that genes with IM promoters were enriched for immune-related GO categories; this enrichment was not observed for genes in the high or low methylation groups. Targeted analysis of genes in the IM category confirmed the WGBS observation. This study is the first in cattle examining genome-wide DNA methylation at single nucleotide resolution in an important bovine cellular host-pathogen interaction model, providing evidence for IM promoter methylation in bAM.

SUBMITTER: O'Doherty AM

PROVIDER: S-EPMC6365515 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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The bovine alveolar macrophage DNA methylome is resilient to infection with Mycobacterium bovis.

O'Doherty Alan Mark AM Rue-Albrecht Kevin Christophe KC Magee David Andrew DA Ahting Simone S Irwin Rachelle Elizabeth RE Hall Thomas Jonathan TJ Browne John Arthur JA Nalpas Nicolas Claude NC Walsh Colum Patrick CP Gordon Stephen Vincent SV Wojewodzic Marcin Włodzimierz MW MacHugh David Evan DE

Scientific reports 20190206 1

DNA methylation is pivotal in orchestrating gene expression patterns in various mammalian biological processes. Perturbation of the bovine alveolar macrophage (bAM) transcriptome, due to Mycobacterium bovis (M. bovis) infection, has been well documented; however, the impact of this intracellular pathogen on the bAM epigenome has not been determined. Here, whole genome bisulfite sequencing (WGBS) was used to assess the effect of M. bovis infection on the bAM DNA methylome. The methylomes of bAM i ...[more]

PMID: 30728374

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Project description:BACKGROUND: Mycobacterium bovis, the causative agent of bovine tuberculosis, is an intracellular pathogen that can persist inside host macrophages during infection via a diverse range of mechanisms that subvert the host immune response. In the current study, we have analysed and compared the transcriptomes of M. bovis-infected monocyte-derived macrophages (MDM) purified from six Holstein-Friesian females with the transcriptomes of non-infected control MDM from the same animals over a 24 h period using strand-specific RNA sequencing (RNA-seq). In addition, we compare gene expression profiles generated using RNA-seq with those previously generated by us using the high-density Affymetrix® GeneChip® Bovine Genome Array platform from the same MDM-extracted RNA. RESULTS: A mean of 7.2 million reads from each MDM sample mapped uniquely and unambiguously to single Bos taurus reference genome locations. Analysis of these mapped reads showed 2,584 genes (1,392 upregulated; 1,192 downregulated) and 757 putative natural antisense transcripts (558 upregulated; 119 downregulated) that were differentially expressed based on sense and antisense strand data, respectively (adjusted P-value ? 0.05). Of the differentially expressed genes, 694 were common to both the sense and antisense data sets, with the direction of expression (i.e. up- or downregulation) positively correlated for 693 genes and negatively correlated for the remaining gene. Gene ontology analysis of the differentially expressed genes revealed an enrichment of immune, apoptotic and cell signalling genes. Notably, the number of differentially expressed genes identified from RNA-seq sense strand analysis was greater than the number of differentially expressed genes detected from microarray analysis (2,584 genes versus 2,015 genes). Furthermore, our data reveal a greater dynamic range in the detection and quantification of gene transcripts for RNA-seq compared to microarray technology. CONCLUSIONS: This study highlights the value of RNA-seq in identifying novel immunomodulatory mechanisms that underlie host-mycobacterial pathogen interactions during infection, including possible complex post-transcriptional regulation of host gene expression involving antisense RNA.

Project description:ObjectiveBovine 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.MethodsRNA-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).ResultsAs 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.ConclusionThe 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.

Dataset Information

The bovine alveolar macrophage DNA methylome is resilient to infection with Mycobacterium bovis.

Publications

The bovine alveolar macrophage DNA methylome is resilient to infection with Mycobacterium bovis.

Similar Datasets

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