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.
Bioscience Research Institute, Athlone Institute of Technology, Dublin Road, Athlone, Westmeath, N37 HD68, Ireland.
BMC Genomics. 2021 May 12;22(1):343. doi: 10.1186/s12864-021-07643-w.
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.
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.
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.
牛型结核（bTB）是一种由感染牛分枝杆菌（Mycobacterium bovis）引起的主要地方性疾病，严重影响全球的牛生产业。肺泡巨噬细胞是最早接触病原体的关键固有免疫细胞，先前的研究表明，在受到病原体的细胞内感染时，肺泡巨噬细胞的功能会发生实质性的重新编程。在这里，我们使用差异表达以及基于相关性和交互作用的网络方法，从转录组水平分析宿主对牛分枝杆菌感染的反应，以确定核心感染反应途径和基因模块。然后，我们将这些结果与全基因组关联研究（GWAS）数据集进行整合，以增强对牛分枝杆菌感染易感性/抗性的基因组变异的检测。
宿主基因表达数据由感染牛分枝杆菌 24 小时和 48 小时后与非感染对照肺泡巨噬细胞（bAM）相比的牛肺泡巨噬细胞（bAM）的 RNA-seq 数据组成。使用三种不同的计算管道对这些 RNA-seq 数据进行分析，产生了六个独立的基因集：1）使用严格的倍数变化和 P 值阈值过滤的差异表达基因（DEG-24：378 个基因，DEG-48：390 个基因）；2）从表达相关性网络中获得的基因（CON-24：460 个基因，CON-48：416 个基因）；3）从差异表达网络中获得的基因（DEN-24：339 个基因，DEN-48：495 个基因）。通过使用新的基因组学数据整合工具-gwinteR，将这六个基因集与三个牛型结核品种的 GWAS 数据集进行整合。使用 GWAS 汇总统计数据，这种方法分别检测到夏洛莱牛、利木赞牛和荷斯坦-弗里森牛与牛分枝杆菌感染易感性/抗性相关的 36、102 和 921 个优先 SNP。
三种平行分析的结果表明，三种计算方法都可以识别与牛分枝杆菌感染易感性/抗性相关的 SNP 显著富集的基因。结果表明 SNP 发现之间存在明显而显著的重叠，表明基于网络的生物学相关转录组学数据的整合可以利用 GWAS 数据集的大量附加信息。这些分析还表明不同品种之间存在显著差异，荷斯坦-弗里森品种的 GWAS 通过数据整合对 SNP 进行优先排序最为有效。由于该功能基因组学数据是使用来自该群体的 bAM 生成的，这表明牛型结核抵抗力性状的基因组结构可能比以前假设的更为特定于品种。
