Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.
Networks Underpinning Symbiosis Revealed Through Cross-Species eQTL Mapping.
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DOI：
10.1534/genetics.117.202531
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发表时间：
2017-08
期刊：
影响因子：
3.3
通讯作者：
Nielsen DM
中科院分区：
文献类型：
--
作者：
Guo Y;Fudali S;Gimeno J;DiGennaro P;Chang S;Williamson VM;Bird DM;Nielsen DM
物种之间的相互作用在植物、动物和微生物中普遍存在，识别涉及的分子信号是一个活跃的研究领域。生物体参与了广泛的跨物种分子对话，然而潜在的分子参与者只知道几个相互作用。已经设计了许多技术来发现涉及生物体之间信号传递的基因。通常，这些只关注其中一个合作伙伴。我们开发了一种基于表达数量性状基因定位(EQTL)的方法来识别来自两个参与种间交互作用的伙伴的基因之间的因果关系。我们通过对98株等基因植物(Medicago Truncatula)的表达进行了分析，每一株植物都接种了二倍体寄生线虫单倍体线虫的一个遗传株系。通过这种设计，寄主植物之间基因表达的系统性差异可以映射到它们感染寄生虫的遗传多态。寄生基因对植物基因表达的影响往往很大，由单个寄生基因座引起的表达水平变化高达90倍(P=3.2x10−52)。绘制的基因座包括许多多效性位点，其中包括一个87kb的寄生虫基因座，它调节了>；60宿主基因的表达。已鉴定的213个宿主基因对转录因子有很大的富集性。我们通过网络推理从两个物种中提取了基因和多态之间的高阶联系。为了复制我们的结果，并测试影响是否在更广泛的寄主范围内保持不变，我们用感染了番茄根结线虫的番茄进行了验证性实验。这表明同源基因也受到了类似的影响。最后，为了验证跨物种eQTL定位的更广泛的实用性，我们将这一策略应用于沙门氏菌感染研究的数据，成功地识别了影响细菌表达的人类基因组的多态。
Interactions between species are pervasive among plants, animals, and microbes, and identifying the molecular signals involved is an active area of research.. Organisms engage in extensive cross-species molecular dialog, yet the underlying molecular actors are known for only a few interactions. Many techniques have been designed to uncover genes involved in signaling between organisms. Typically, these focus on only one of the partners. We developed an expression quantitative trait locus (eQTL) mapping-based approach to identify cause-and-effect relationships between genes from two partners engaged in an interspecific interaction. We demonstrated the approach by assaying expression of 98 isogenic plants (Medicago truncatula), each inoculated with a genetically distinct line of the diploid parasitic nematode Meloidogyne hapla. With this design, systematic differences in gene expression across host plants could be mapped to genetic polymorphisms of their infecting parasites. The effects of parasite genotypes on plant gene expression were often substantial, with up to 90-fold (P = 3.2 × 10−52) changes in expression levels caused by individual parasite loci. Mapped loci included a number of pleiotropic sites, including one 87-kb parasite locus that modulated expression of >60 host genes. The 213 host genes identified were substantially enriched for transcription factors. We distilled higher-order connections between polymorphisms and genes from both species via network inference. To replicate our results and test whether effects were conserved across a broader host range, we performed a confirmatory experiment using M. hapla-infected tomato. This revealed that homologous genes were similarly affected. Finally, to validate the broader utility of cross-species eQTL mapping, we applied the strategy to data from a Salmonella infection study, successfully identifying polymorphisms in the human genome affecting bacterial expression.