Phenotype inference in an Escherichia coli strain panel.
Phenotype inference in an Escherichia coli strain panel.
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DOI：
10.7554/elife.31035
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发表时间：
2017-12-27
期刊：
影响因子：
7.7
通讯作者：
Beltrao P
中科院分区：
文献类型：
--
作者：
Galardini M;Koumoutsi A;Herrera-Dominguez L;Cordero Varela JA;Telzerow A;Wagih O;Wartel M;Clermont O;Denamur E;Typas A;Beltrao P
了解遗传变异如何导致表型差异是生物学中的一个基本问题。将高通量基因功能测定与遗传变异影响的机制模型相结合是全基因组关联研究的一个有前途的替代方案。在这里，我们组装了一个由696种大肠杆菌菌株组成的大型面板，我们对它们进行了基因分型，并在214种生长条件下测量了它们的表型特征。我们整合了变异效应预测因子，以获得所有菌株中每个基因功能丧失的基因水平概率。最后，我们将这些概率与参考K-12菌株中的条件基因重要性信息相结合，以计算每个菌株的生长缺陷。我们不仅可以在高达38%的测试条件下可靠地预测这些缺陷，而且还可以直接识别通过互补试验验证的致病变体。我们的工作证明了前瞻性预测模型的力量和精确遗传干预的可能性。
Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.