Chemogenomic profiling on a genomewide scale using reverse-engineered gene networks
Chemogenomic profiling on a genomewide scale using reverse-engineered gene networks
复制标题
DOI：
10.1038/nbt1075
复制
发表时间：
2005-03-01
影响因子：
46.9
通讯作者：
Collins, JJ
中科院分区：
文献类型：
--
作者：
di Bernardo, D;Thompson, MJ;Collins, JJ
药物发现中的一个主要挑战是将生物活性化合物的分子靶标与数百至数千种间接对靶标活性变化做出反应的额外基因产物区分开来(1-8)。在这里，我们提出了一种综合的计算-实验方法来计算基因产物和相关途径成为化合物靶标的可能性。这是通过使用细胞基因调控网络的反向工程模型过滤暴露于化合物的细胞的mRNA表达谱来实现的。我们将该方法应用于一组515个全基因组酵母表达谱，这些表达谱来自各种处理(化合物、敲除和诱导表达)，并正确地丰富了大多数被检查的化合物中的已知靶标和相关途径。我们通过预测和验证硫氧还蛋白和硫氧还蛋白还原酶作为其靶标，以PTSB为例展示了我们的方法，PTSB是一种以前未知的作用模式的生长抑制化合物。
A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets(1-8). Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.