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一个全基因组范围的必需共模块图谱为未表征基因赋予功能。

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes.

作者信息

Wainberg Michael, Kamber Roarke A, Balsubramani Akshay, Meyers Robin M, Sinnott-Armstrong Nasa, Hornburg Daniel, Jiang Lihua, Chan Joanne, Jian Ruiqi, Gu Mingxin, Shcherbina Anna, Dubreuil Michael M, Spees Kaitlyn, Meuleman Wouter, Snyder Michael P, Bassik Michael C, Kundaje Anshul

机构信息

Department of Genetics, Stanford University, Stanford, CA, USA.

Department of Computer Science, Stanford University, Stanford, CA, USA.

出版信息

Nat Genet. 2021 May;53(5):638-649. doi: 10.1038/s41588-021-00840-z. Epub 2021 Apr 15.

Abstract

A central question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into 'co-essential' pathways, but this approach has been limited by ubiquitous false positives. In the present study, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and protein complexes, and predicts the functions of 108 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, a key enzyme for plasmalogen synthesis. We also show that C15orf57 encodes a protein that binds the AP2 complex, localizes to clathrin-coated pits and enables efficient transferrin uptake. Finally, we provide an interactive webtool for the community to explore our results, which establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of new gene functions.

摘要

后基因组时代的一个核心问题是基因如何相互作用以形成生物途径。通过对数百种细胞系的基因依赖性进行测量,已将基因聚类为“共同必需”途径,但这种方法一直受到普遍存在的假阳性的限制。在本研究中,我们开发了一种统计方法,能够可靠地识别基因的共同必需性,并产生一组全基因组的功能模块。该图谱概括了多种途径和蛋白质复合物,并预测了108个未表征基因的功能。通过验证顶级预测结果,我们发现TMEM189编码磷脂酰乙醇胺去饱和酶,这是缩醛磷脂合成的关键酶。我们还表明,C15orf57编码一种与AP2复合物结合的蛋白质,定位于网格蛋白包被小窝,并能实现高效的转铁蛋白摄取。最后,我们为社区提供了一个交互式网络工具,以探索我们的结果,这些结果将共同必需性分析确立为生物途径识别和新基因功能发现的强大资源。

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