Schulze Sylvie, Schleicher Jana, Guthke Reinhard, Linde Jörg
Research Group Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute Jena, Germany.
Front Microbiol. 2016 Mar 31;7:442. doi: 10.3389/fmicb.2016.00442. eCollection 2016.
Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We conclude that the application of network inference on dual-transcriptomics data is a promising approach to predict molecular inter-species interactions.
生物体通过物理接触不断与其他物种相互作用，这会导致分子水平上的变化，例如转录组的变化。借助RNA测序或微阵列等高通量实验，可以监测所有基因的这些变化。细胞内基因表达对环境变化的适应是通过复杂的基因调控网络介导的。通常，我们对这些网络的了解并不完整。网络推断基于转录组数据预测基因调控相互作用。高通量转录组研究的一个新兴应用是双转录组学实验。在此，同时测量两个或更多相互作用物种的转录组。基于感染真菌病原体白色念珠菌的小鼠树突状细胞的双RNA测序数据集，应用软件工具NetGenerator预测种间基因调控网络。为了促进对分子种间相互作用的进一步研究，我们最近讨论了宿主-病原体相互作用的双RNA测序实验，并扩展了应用工具NetGenerator（舒尔茨等人，2015年）。NetGenerator的更新版本在算法过程中利用测量方差，并接受具有缺失值的基因表达时间序列数据。此外，我们测试了关于基因调控网络刺激功能的多种建模方案。在这里，我们总结了舒尔茨等人（2015年）的工作，并将其置于更广泛的背景中。我们回顾了利用双转录组学方法研究相互作用物种分子基础的各种研究。除了应用于宿主-病原体相互作用外，双转录组学数据还用于研究互利共生和共生相互作用。此外，我们简要介绍了预测基因调控网络的其他方法，并讨论了它们在双转录组学数据中的应用。我们得出结论，对双转录组学数据应用网络推断是预测分子种间相互作用的一种有前途的方法。
