Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Department of Statistics, Stanford University, Stanford, CA, USA.
Nat Methods. 2018 Dec;15(12):1067-1073. doi: 10.1038/s41592-018-0214-9. Epub 2018 Nov 26.
Cross-species differences form barriers to translational research that ultimately hinder the success of clinical trials, yet knowledge of species differences has yet to be systematically incorporated in the interpretation of animal models. Here we present Found In Translation (FIT; http://www.mouse2man.org ), a statistical methodology that leverages public gene expression data to extrapolate the results of a new mouse experiment to expression changes in the equivalent human condition. We applied FIT to data from mouse models of 28 different human diseases and identified experimental conditions in which FIT predictions outperformed direct cross-species extrapolation from mouse results, increasing the overlap of differentially expressed genes by 20-50%. FIT predicted novel disease-associated genes, an example of which we validated experimentally. FIT highlights signals that may otherwise be missed and reduces false leads, with no experimental cost.
跨物种差异构成了转化研究的障碍，最终阻碍了临床试验的成功，但物种差异的知识尚未被系统地纳入动物模型的解释中。在这里，我们介绍 Found In Translation (FIT; http://www.mouse2man.org )，这是一种统计方法，利用公共基因表达数据推断新的小鼠实验结果与人类等效条件下的表达变化。我们将 FIT 应用于 28 种不同人类疾病的小鼠模型数据中，并确定了 FIT 预测优于直接从小鼠结果进行跨物种外推的实验条件，使差异表达基因的重叠增加了 20-50%。FIT 预测了新的与疾病相关的基因，我们通过实验验证了其中一个例子。FIT 突出了可能被遗漏的信号，并减少了错误的线索，而无需进行实验。
