Lobo Daniel, Morokuma Junji, Levin Michael
Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.
Center for Regenerative and Developmental Biology Department of Biology, Tufts University, Medford, MA 02155, USA.
Bioinformatics. 2016 Sep 1;32(17):2681-5. doi: 10.1093/bioinformatics/btw299. Epub 2016 May 10.
Automated computational methods can infer dynamic regulatory network models directly from temporal and spatial experimental data, such as genetic perturbations and their resultant morphologies. Recently, a computational method was able to reverse-engineer the first mechanistic model of planarian regeneration that can recapitulate the main anterior-posterior patterning experiments published in the literature. Validating this comprehensive regulatory model via novel experiments that had not yet been performed would add in our understanding of the remarkable regeneration capacity of planarian worms and demonstrate the power of this automated methodology.
Using the Michigan Molecular Interactions and STRING databases and the MoCha software tool, we characterized as hnf4 an unknown regulatory gene predicted to exist by the reverse-engineered dynamic model of planarian regeneration. Then, we used the dynamic model to predict the morphological outcomes under different single and multiple knock-downs (RNA interference) of hnf4 and its predicted gene pathway interactors β-catenin and hh Interestingly, the model predicted that RNAi of hnf4 would rescue the abnormal regenerated phenotype (tailless) of RNAi of hh in amputated trunk fragments. Finally, we validated these predictions in vivo by performing the same surgical and genetic experiments with planarian worms, obtaining the same phenotypic outcomes predicted by the reverse-engineered model.
These results suggest that hnf4 is a regulatory gene in planarian regeneration, validate the computational predictions of the reverse-engineered dynamic model, and demonstrate the automated methodology for the discovery of novel genes, pathways and experimental phenotypes.
自动化计算方法可以直接从时空实验数据（如基因扰动及其产生的形态）中推断动态调控网络模型。最近，一种计算方法能够逆向工程出涡虫再生的首个机制模型，该模型可以概括文献中发表的主要前后模式形成实验。通过尚未进行的新实验来验证这个全面的调控模型，将增进我们对涡虫卓越再生能力的理解，并证明这种自动化方法的强大之处。
使用密歇根分子相互作用数据库和STRING数据库以及MoCha软件工具，我们将一个由涡虫再生逆向工程动态模型预测存在的未知调控基因鉴定为hnf4。然后，我们使用该动态模型预测在hnf4及其预测的基因途径相互作用因子β-连环蛋白和hh的不同单基因和多基因敲低（RNA干扰）情况下的形态学结果。有趣的是，该模型预测hnf4的RNA干扰将挽救被截断的躯干片段中hh的RNA干扰所导致的异常再生表型（无尾）。最后，我们通过对涡虫进行相同的手术和基因实验在体内验证了这些预测，获得了与逆向工程模型预测相同的表型结果。
这些结果表明hnf4是涡虫再生中的一个调控基因，验证了逆向工程动态模型的计算预测，并展示了用于发现新基因、途径和实验表型的自动化方法。
