Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious.
Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious.
复制标题
细胞代谢成本的计算评估成功地预测了表达有害的基因。
DOI：
10.1073/pnas.1312361110
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发表时间：
2013
影响因子：
11.1
通讯作者：
Ruppin,Eytan
中科院分区：
文献类型：
--
作者：
Wagner,Allon;Zarecki,Raphy;Reshef,Leah;Gochev,Camelia;Sorek,Rotem;Gophna,Uri;Ruppin,Eytan
基因抑制和过表达都是模式生物中基因型与表型联系的基本工具。计算方法在研究和预测基因缺失的有害影响方面已被证明是非常宝贵的，但仍然缺乏用于过表达的并行计算方法。在这里，我们提出了表达依赖性基因效应（EDGE），一种可以预测天然或外源代谢基因过表达所产生的有害影响的计算机方法。我们首先通过结合我们进行的小规模生长实验和对现有大规模数据集的分析来测试和验证EDGE在细菌中的预测能力。其次，从微生物到多种植物和人类组织的广泛的跨物种分析表明，EDGE预测的过度表达时有害的基因确实通常被下调。这反映了一种普遍的选择力量，它控制着潜在有害基因的表达。第三，基于EDGE的分析表明，癌症遗传重编程特异性地抑制了过度表达阻碍增殖的基因。这种抑制的幅度足够大，仅基于EDGE结果就可以几乎完美地区分正常组织和癌组织。我们希望EDGE能促进我们对与特定转录物上调相关的人类病理学的理解，并促进基因过表达在代谢工程中的利用。
Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE’s predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically down-regulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGE-based analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.