This project aims to improve the tools for making models for networks of interacting molecules in the small mustard plant, Arabidopsis. To demonstrate the effectiveness of these methods, the resources developed in this research will be used to study the plant's immunity to a virus infection under several conditions. Understanding how and when the virus overcomes the plants defenses gives us a handle on controlling an important pathogen of crop plants in the Southern US, with benefits such as better yield and less pesticide use. While biologists can measure the presence and amounts of tens of thousands of molecules in a cell all at once, understanding how they are connected, the 'networks' or 'systems' that lead to function, is a much harder problem. The huge amounts of measurements have to be properly managed so they are usable, and additional information has to be correctly added: it is important to track how amounts of one type of molecule change over time and not mix up different things. It is also important to understand which parts of the cell affect one another: those that belong to a functionally connected pathway (or network), and which are independent of each other. For example, plants have complicated mechanisms to defend themselves against biological and environmental stresses. Signaling pathways cause the plant's response, and they are influenced by internal genetic factors as well as the external ones that are more easily observed. If every protein (or other molecule) inside the cell that plays a part in carrying and interpreting the signal is known then very effective predictions about the final response are possible. However, plant researchers don't know nearly as much about the molecules in their organisms as is available to many researchers studying animals, which means there are a lot of missing nodes. That makes it hard to come up with a specific prediction that can be tested: this research aims to overcome this problem for selected plant pathways, to showcase what is possible when there is sufficient information. This project will actively engage students in interdisciplinary research, with a particular focus on recruiting underrepresented groups. The University of Texas at San Antonio (UTSA) is a Hispanic-Serving Institution.
This project has the following four specific aims: 1) to construct genome-wide transcriptional regulatory network in Arabidopsis with validation in immune-responsive genes; 2) to improve the prediction of protein-protein interactions and identification of defense subnetworks in Arabidopsis; 3) to perform network-based analysis of Arabidopsis immune-responsive network in order to decipher the role of plant viral RNA silencing suppressors in plant immunity; and 4) to provide online databases and analytic tools for network-based plant systems biology studies. This project promises to significantly improve network-based analysis with several innovative ideas. First, the proposed approaches focus on improving accuracy of predictions for individual genes by defining a network neighborhood for each node and testing for enrichment in the neighborhood for each node. This is in contrast to most existing approaches that make predictions on gene modules (within- or cross-species) and therefore lack quality control on an individual gene level. Second, combining protein-protein interaction network, gene co-expression network, and sample-sample network, this research provides an example to analyze such networks in a dynamic context automatically defined by the global transcriptomic landscape; as such, this study is expected to provide more specific predictions that can be experimentally tested. In addition, integration of computational tools to characterize defense-related network structure in this work will significantly improve the ability to study the role of co-regulated networks of genes in any number of processes, including but not limited to genes implicated in both plant and animal disease, cancer or stem cell biology, or tissue specificity of gene expression. The results of the project can be found at http://cs.utsa.edu/~jruan/plantnet/.
