Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria

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.

Common protein-coding variants influence the racing phenotype in galloping racehorse breeds

“…The R software package gwinteR 77 was used to determine whether genomic regions containing SNPs that are proximal to genes within the DEG sets were enriched for significance in the CSS analysis for Racing versus non-Racing breeds. The numbers of statistically significant SNPs pre- and post-data integration are summarised in Supplementary Data 7 .…”

“…To integrate the SNP data arising from the CSS analyses with gene sets generated from functional genomics data analyses 24 we used an R software package, gwinteR 77 as follows: (1) a set of significant and non-significant SNPs (named the target SNP set) was collated across all genes in each gene set at increasing genomic intervals upstream and downstream from each gene inclusive of the coding sequence (e.g., ±10 kb, ±20 kb, ±30 kb… …±100 kb); (2) for each genomic region, a null distribution of 1000 SNP sets, each of which contains the same number of total significant and non-significant combined SNPs as the target SNP set, was generated by resampling with replacement from the search space of the total population of SNPs in the CSS SNP data set; (3) the nominal (uncorrected) CSS P values for the target SNP set and the null distribution SNP sets were converted to local FDR-adjusted P values ( P adj. ) using the fdrtool R package (version 1.2.15) 134 ; (4) to test the primary hypothesis for each observed genomic interval target SNP set a permuted P value ( P perm. )…”

Integrative genomics analysis highlights functionally relevant genes for equine behaviour

“…An r software package, gwinteR (Hall et al., 2021), was used to integrate the SNP data arising from the CSS analyses with HEG sets generated from RNA‐seq data. The gwinteR software tool works as follows: (1) a set of significant and non‐significant SNPs (named the target SNP set) is collated across all genes in a specific gene set at increasing genomic intervals upstream and downstream from each gene inclusive of the coding sequence (e.g, ±10 kb, ±20 kb, ±30 kb, …, ± 100 kb); (2) for each genomic region, a null distribution of 1000 SNP sets, each of which contains the same number of total significant and non‐significant combined SNPs as the target SNP set, is generated by resampling with replacement from the search space of the total population of SNPs in the CSS data set; (3) the nominal (uncorrected) CSS p ‐values for the target SNP set and the null distribution SNP sets are converted to local false discovery rate‐adjusted p ‐values ( p adj. )…”

Erchen Decoction regulates AMPK pathway in the treatment of metabolic syndrome induced by second-generation antipsychotics based on network pharmacology and molecular docking strategy

“…To search for the terms "clozapine" and "olanzapine", the GeneCards Database (https://www.genecards.org/, Version 5.13) was used with a Relevance score[? ]1 as the standard screening target [28].…”