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Cattle infection response network and its functional modules

机译:牛感染反应网络及其功能模块

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Weighted Gene Co-expression Network analysis, a powerful technique used to extract co-expressed gene pattern from mRNA expression data, was constructed to infer common immune strategies used by cattle in response to five different bacterial species (Escherichia coli, Mycobacterium avium, Mycobacterium bovis, Salmonella and Staphylococcus aureus) and a protozoa (Trypanosoma Congolense) using 604 publicly available gene expression microarrays from 12 cattle infection experiments. A total of 14,999 transcripts that were differentially expressed (DE) in at least three different infection experiments were consolidated into 15 modules that contained between 43 and 4441 transcripts. The high number of shared DE transcripts between the different types of infections indicated that there were potentially common immune strategies used in response to these infections. The number of transcripts in the identified modules varied in response to different infections. Fourteen modules showed a strong functional enrichment for specific GO/pathway terms related to “immune system process” (71%), “metabolic process” (71%), “growth and developmental process” (64%) and “signaling pathways” (50%), which demonstrated the close interconnection between these biological pathways in response to different infections. The largest module in the network had several over-represented GO/pathway terms related to different aspects of lipid metabolism and genes in this module were down-regulated for the most part during various infections. Significant negative correlations between this module’s eigengene values, three immune related modules in the network, and close interconnection between their hub genes, might indicate the potential co-regulation of these modules during different infections in bovine. In addition, the potential function of 93 genes with no functional annotation was inferred based on neighbor analysis and functional uniformity among associated genes. Several hypothetical genes were differentially expressed during experimental infections, which might indicate their important role in cattle response to different infections. We identified several biological pathways involved in immune response to different infections in cattle. These findings provide rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on immune response to different infections in cattle.
机译:加权基因共表达网络分析是一种用于从mRNA表达数据中提取共表达基因模式的强大技术,旨在推断牛对五种不同细菌种类(大肠杆菌,鸟分枝杆菌,牛分枝杆菌)的常用免疫策略。 ,沙门氏菌和金黄色葡萄球菌)和原生动物(刚果锥虫)使用来自12个牛感染实验的604种公开可用的基因表达微阵列。在至少三个不同的感染实验中,总共14999个差异表达(DE)的转录本被整合为15个模块,其中包含43到4441个转录本。在不同类型的感染之间共享的DE转录物数量很高,表明对这些感染有潜在的通用免疫策略。所识别的模块中的转录物数量响应于不同的感染而变化。十四个模块显示出与“免疫系统过程”(71%),“代谢过程”(71%),“生长与发育过程”(64%)和“信号通路”相关的特定GO /途径术语的强大功能丰富性( 50%),这表明这些生物途径之间对不同感染的反应紧密相关。网络中最大的模块具有多个与脂质代谢不同方面相关的GO /通路术语,这些模块在大多数感染期间都被下调了。该模块的本征基因值,网络中的三个免疫相关模块及其中心基因之间紧密的相互联系之间存在显着的负相关关系,这可能表明这些模块在牛的不同感染中可能存在潜在的共调节作用。此外,根据邻居分析和相关基因之间的功能一致性,推断了93个没有功能注释的基因的潜在功能。在实验性感染期间,几种假设的基因差异表达,这可能表明它们在牛对不同感染的反应中起重要作用。我们确定了几种对牛的不同感染的免疫反应涉及的生物学途径。这些发现为实验生物学家设计实验,解释实验结果以及发展关于对牛不同感染的免疫反应的新假设提供了丰富的信息。

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