식물-미생물 상호작용의 생물학적 네트워크 동정을 위한 시스템 생물학적 연구

Abstract

Plants are constantly in contact with a huge number of microorganisms including pathogenic or symbiotic bacteria and viruses. Interactions with these microbes can affect plant growth and development by causing diseases or by providing essential nutrients. Understanding of molecular mechanisms of plant-microbe interactions is important for preventing agricultural damage by pathogens and for improving crop productivity, as well as for decoding a fundamental basis of interspecies communication. Plant-microbe interactions lead to changes in gene expression and phytohormone balance in host plants; these changes have important functions in defense responses to pathogens and in symbiotic responses to beneficial microbes. Previous studies have focused on the identification of signaling pathways and potential key regulators that mediate these microbe-induced changes. However, knowledge about the coordination of the host responses is still limited. To depict an integrated view of plant responses to pathogenic or symbiotic microbes, I have attempted to understand the biological networks of plant-microbe interactions by studying 1) resistance mechanisms of rice Oryza sativa to rice stripe virus (RSV), 2) interplays among phytohormones and biotic stresses in Arabidopsis thaliana, and 3) hormone-dependent regulation of nodule development in Medicago truncatula. Using integrative analyses of transcriptomes and interactomes, I constructed network models that illustrate the relationships among genes and processes associated with major changes in each biological context, and identified potential key regulators in the networks. Several identified regulators were experimentally confirmed. The objective of the first study was to identify RSV resistance factors by comparing gene expression profiles of two near-isogenic lines (NILs), NIL22 and NIL37 of rice, which differ in susceptibility to RSV. Among 237 differentially expressed genes (DEGs) between the two NILs, 11 DEGs located within a quantitative trait locus (QTL) associated with RSV resistance were selected as RSV resistance candidates. In addition, 417 DEGs between RSV-infected and RSV-uninfected conditions were identified as RSV infection-related genes. Based on degree of the interactions among the two sets of DEGs, 20 DEGs with significant numbers of interactions were also selected as RSV resistance candidates. Among 31 RSV resistance candidates, 21 potential RSV resistance factors were identified by using quantitative real-time PCR (qPCR) to validate their expressions. I finally constructed a network model to describe the relationships among the 21 potential factors and RSV resistance-related processes. The objective of the second study was to develop a web-based platform for identifying Network models for Interplays among Developmental signaling in Arabidopsis (iNID). iNID provides 1) transcriptomes related to a broad spectrum of developmental and environmental factors such as phytohormones and abiotic/biotic stresses, and 2) interactome including protein-protein, protein-RNA, protein-DNA, and genetic interactions in Arabidopsis. Moreover, a series of tools for identifying potential key regulators and network models related to interplays among the factors are provided. The utility of iNID was examined in two case studies related to the interplays of 1) auxin, brassinosteroid (BR), and blue light, and 2) eight phytohormones and biotic stresses. In these case studies, 34 potential key regulators were suggested to be involved in the interplays. Among these regulators, involvement of BME3 and TEM1 in the auxin-BR-blue light interplays was validated experimentally using mutant analysis and qPCR. The case study of the interplays among phytohormones and biotic stresses showed that a systems approach using iNID could reveal differential coordination of phytohormones in responses to diverse pathogens. In this case study, two network models for elucidating 1) the interplays among salicylic acid (SA) and biotic stresses associated with systemic acquired resistance and 2) abscisic acid (ABA)-biotic stress interplay were proposed. The objective of the third study was to identify potential regulatory modules in hormonal regulation of nodule development in M. truncatula. Time-course gene expression profiling of nodules in exogenous phytohormone treatments was performed using mRNA-seq. Transcriptional changes during nodule development were then measured by comparing gene expressions between nodules in early and mature stages in each condition. To clarify the coordination of phytohormones during nodule development, 30 differential expression patterns (DEPs) across the transcriptional changes were identified. Among the DEPs, four were closely related to the cooperative action of two nodule-promoting phytohormones, auxin and cytokinin (CK). Network models for elucidating the interplays between auxin-CK in nodulation showed that novel regulatory modules related to peptide processing/transport, transmembrane proteins, and calcium signaling might control nodulation.