Large-scale Transcriptional Data Analyses Of Plant Immune Responses

In nature,plants are frequently exposed to various biotic stresses that limit crop yields.There is an urgent need to know how plants response to various biotic stresses,in order to develop disease-resistant crops.The availability of large-scale omics data,such as transcriptome and interactome,provides abundant data for systemically analyzing plant immunity.Plant immunity is complex and involves massive transcriptional reprogramming.Systematical analysis of plant immunity based on large-scale omics data will help us to better understand plant immunity.In order to achieve the above purpose,this thesis performed a series of transcriptome data analyses to investigate plant immunity.According to their lifestyles,plant pathogens can be generally divided into biotrophs,necrotrophs and hemibiotrophs.It has been proved that plants activate distinct defense responses to biotrophic and necrotrophic pathogens.A comprehensive exploration of common and specific plant responses to biotrophs and necrotrophs is necessary for a better understanding of plant immunity.In this work,the author comparatively analyzed Arabidopsis defense responses evoked by the biotrophic fungus Golovinomyces orontii and the necrotrophic fungus Botrytis cinerea.Two time-course transcriptional datasets were integrated with an Arabidopsis protein-protein interaction(PPI)network to construct a G.orontii conditional PPI sub-network(gCPIN)and a B.cinerea conditional PPI sub-network(bCPIN).The author found that hubs in gCPIN and bCPIN played important roles in disease resistance.Moreover,hubs from two conditional networks had different evolutionary rate.Network module analysis identified two functional modules,and they were namely DevRC annotated as "development"and DefRC annotated as "defense".Although DevRC and DefRC were included in gCPIN and bCPIN,expression correlation of interactions connecting DevRC and DefRC were opposite in two conditions.Further analysis showed that auxin might involve in the regulation of the opposite correlations.Based on the current results,the author speculated that auxin participated in regulating the trade-off between plant development and defense.Based on high-resolution time-course transcriptional data,the author first showed that differential coexpression was a common phenomenon in plant immunity.Moreover,the author identified 1,315 differentially coexpressed genes,which were significantly enriched on plant defense-related genes.Further analysis showed that differential coexpression analysis was commentary to differential expression analysis,which provided a new strategy for analyzing plant defense response-related transcriptional data.By integrating transcriptional regulatory network,the author identified 30 TFs,which regulated the phenomenon of differential coexpression in plant immunity.By analyzing the changes of metabolic gene expression correlation within a common metabolic pathway,the author identified 36 metabolic pathways.Some of these pathways played vital roles in plant immunity.These analyses systemically analyzed the phenomenon of differential coexpression in plant immunity and provided a new analyzing strategy for understanding plant immunity.Plant metabolisms are not only closely related to plant growth and development,but also play important roles in plant immunity.Primary metabolisms provide energy for plant immunity and secondary metabolisms produce antimicrobial secondary metabolites.In this work,the author attempted to analyze plant immunity by comparing the expression of metabolic pathways during Arabidopsis response to 14 different pathogens based on large-scale transcriptome data.At the metabolic gene level,the author found that metabolic genes were significantly differentially expressed when responding to all 14 pathogens,and identified 23 frequently differentially expressed metabolic genes.At metabolic pathway level,the author identified 26 metabolic pathways which were frequently differentially expressed when responding to 14 pathogens by gene set enrichment analysis.Clustering analysis as well as comparison with abiotic stresses proved the importance and and specificity of the 26 pathways in plant immunity.Based on the CLR algorithm,the author predicted 353 regulatory relationships between 171 TFs and 26 pathways,which contributed to a better understanding of how plant regulates plant metabolism during plant immunity.In summary,the thesis systemically investigates plant immunity by analyzing transcriptome.Integrative analyses of transcriptome and interactome characterized the difference in hub proteins and modules during plant defense response to two different lifestyle pathogens.Differential coexpression analysis first showed that differential coexpression was a common phenomenon in plant immunity and identified some candidate genes for further verification.Metabolic pathway analyses identified 26 pathways which were frequently differentially expressed in response to 14 different pathogens.It is possible that fine-tuning of these 26 frequently influenced metabolic pathways by modifying activity or expression of key enzymes might result in the enhancement of plant resistance without compromising crop yield.In this thesis,the author adopts new methods to analyze plant immunity by integrating transcriptome data,and it is hoped that these analyses will contribute to a better understanding of plant-pathogen interactions.The methods used in this thesis can also be applied to answer other important biological questions.