SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.
SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.
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DOI：
10.1093/nar/gkv1108
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发表时间：
2016-01-04
影响因子：
14.9
通讯作者：
Zheng J
中科院分区：
文献类型：
--
作者：
Guo J;Liu H;Zheng J
合成致死性（Synthetic lethality，SL）是两个基因之间的一种遗传相互作用，使得两个基因的同时扰动导致细胞死亡或细胞活力的显著降低，而单独的任一基因的扰动都不是致命的。SL反映了癌细胞和正常细胞之间的生物学内源性差异，因此抑制具有癌症特异性突变的基因的SL配偶体可以选择性地杀死癌细胞而不伤害正常细胞。因此，SL正在成为一种有前途的抗癌策略，可以通过减少严重的副作用来克服传统化疗的缺点。研究人员已经开发了实验技术和计算预测方法来识别人类和一些模式物种的SL基因对。然而，一直没有一个全面的数据库专门收集SL对和相关知识。在本文中，我们提出了一个全面的数据库，SynLethDB（http：//histone.sce.ntu.edu.sg/SynLethDB/），其中包含SL对收集的生化检测，其他相关数据库，计算预测和文本挖掘结果对人类和四个模式物种，即小鼠，果蝇，蠕虫和酵母。对于每个SL对，通过整合来自不同证据来源的个体评分来计算置信度评分。我们还开发了一个统计分析模块，基于1000多个癌细胞系的大规模基因组数据、基因表达谱和药物敏感性谱，估计癌细胞对靶向人类SL伴侣的药物治疗的可药性和敏感性。为了帮助用户访问和挖掘丰富的数据，我们开发了其他实用功能，如搜索和过滤，同源搜索，基因集富集分析。此外，还建立了一个方便用户的网络界面，以便利数据分析和解释。由于集成了数据集和分析功能，SynLethDB将成为生物医学研究社区和制药行业的有用资源。
Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.