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牛感染反应网络及其功能模块。

Cattle infection response network and its functional modules.

机构信息

Department of Animal Science, Iowa State University, Ames, IA, 50011, USA.

Department of Animal Science, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran.

出版信息

BMC Immunol. 2018 Jan 5;19(1):2. doi: 10.1186/s12865-017-0238-4.

Abstract

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

背景

加权基因共表达网络分析是一种从 mRNA 表达数据中提取共表达基因模式的强大技术,用于推断牛对五种不同细菌(大肠杆菌、禽分枝杆菌、牛分枝杆菌、沙门氏菌和金黄色葡萄球菌)和一种原生动物(刚果锥虫)的共同免疫策略,使用了 12 个牛感染实验中 604 个公开的基因表达微阵列。

结果

至少在三个不同感染实验中差异表达(DE)的 14999 个转录本被整合到 15 个模块中,这些模块包含 43 到 4441 个转录本。不同类型感染之间存在大量共同的 DE 转录本,表明可能存在共同的免疫策略来应对这些感染。识别模块中的转录本数量因感染类型而异。14 个模块显示出强烈的功能富集,与“免疫系统过程”(71%)、“代谢过程”(71%)、“生长和发育过程”(64%)和“信号通路”(50%)相关的特定 GO/途径术语,这表明这些生物途径在应对不同感染时密切相关。网络中最大的模块具有几个与不同脂质代谢方面相关的代表性 GO/途径术语,并且该模块中的基因在大多数情况下在各种感染中下调。该模块的特征基因值与网络中三个与免疫相关的模块之间存在显著的负相关,以及它们的枢纽基因之间的紧密相互连接,可能表明在牛的不同感染中这些模块的潜在共同调节。此外,根据邻接分析和相关基因之间的功能一致性,推断了 93 个具有无功能注释基因的潜在功能。在实验感染过程中,一些假设基因差异表达,这可能表明它们在牛对不同感染的反应中起着重要作用。

结论

我们鉴定了牛对不同感染的免疫反应涉及的几个生物学途径。这些发现为实验生物学家提供了丰富的信息,以设计实验、解释实验结果,并提出关于牛对不同感染的免疫反应的新假设。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bc6/5755453/458e352a1ebf/12865_2017_238_Fig1_HTML.jpg

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