How to Predict Molecular Interactions between Species?

Abstract

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.

Keywords: dual RNA-seq; dual transcriptomics; gene regulatory network; host-pathogen interaction; molecular inter-species interaction; network inference.

Figure 1

An example is provided how a molecular interaction between two species is detected. Panel (A) provides a schematic overview of possible interactions between organisms of two different species influencing each other's transcriptome. In (B) the processing of dual RNA-seq data extracted simultaneously from both interacting species is shown. In contrast, dotted lines represent simultaneous transcriptomics, where both transcriptomes are analyzed separately. In (C) an exemplary GRN resulting from the inference process is illustrated, including an indirect molecular inter-species interaction between two genes (red bar). In addition, molecular intra-species interactions (black) within each of the two species are shown. Arrowheads indicate activation and bars indicate repression.

Figure 2

Simplified overview of Candida albicans (red) interacting with an immune cell (blue) and its environment. C. albicans is stimulated by environmental factors (I) leading to a change of its transcriptome. Immune cells recognize the pathogen, e.g., via pattern recognition receptors (II), transmit the signal through the cell and adapt their transcriptional program (III). In turn, this stimulates C. albicans, e.g., by producing cellsurface or extracellular proteins (IV).