Performing Department
(N/A)
Non Technical Summary
Leaf microbiomes are affected by the plants they inhabit and the environment, and even small changes in the microbiome composition can impact plant fitness. However, little is known about how plants control their microbiome during disease or defense responses, or how the assembled microbiome influences plant health. This knowledge is paramount for promoting crop resilience in the face of increasing environmental stresses. Our long-term goal is to understand how the plant immune system regulates assembly of the leaf microbiome, and how that microbiome, in turn, modulates the host immune system to resist pathogens. In this proposal, we use the rice/bacterial blight (BB) system as a model to address how plants shape their microbiomes during biotic stresses. Our aims are to (1) identify rice genes that influence leaf microbiome assemblage during susceptible and resistant interactions; (2) validate the function of candidate genes orchestrating the assembly of the rice microbiome, and (3) identify plant and microbial markers for enhanced plant resistance. Overall, the proposed research will provide in-depth understanding of how plants undergoing biotic stresses guide leaf microbiome assembly and how the leaf microbiome can help plants defend themselves from disease stresses. Ultimately, our study will pave the way for improved rice breeding to ensure a healthy microbiome that will maximize rice yield and minimize losses due to diseases, ensuring food security for millions globally.
Animal Health Component
25%
Research Effort Categories
Basic
75%
Applied
25%
Developmental
0%
Goals / Objectives
Leaf microbiomes are affected by plant genotypes and the environment, and even small changes in the microbiome composition can impact plant fitness. However, little is known about if and how plants control their microbiome during disease or defense responses, or how the assembled microbiome influences plant health. This knowledge is paramount for promoting crop resilience in the face of increasing environmental stresses. Our long-term goal is to understand the role and mechanisms of the plant immune system in regulating assembly of the leaf microbiome, and how that assemblage, in turn, modulates the host immune system to resist pathogens. In this proposal, we use the rice/bacterial blight (BB) system as a model to address mechanisms by which plants can shape their microbiomes during biotic stresses. Our aims are to (1) identify rice genes that influence leaf microbiome assemblage during susceptible and resistant interactions using modified Transcriptome-wide association studies (TWAS) linked to microbiome analysis; (2) validate the function of candidate genes orchestrating the assembly of the rice microbiome through gene-editing studies, and (3) identify plant and microbial markers for enhanced plant resistance by integrating machine learning and genome-scale modeling. Overall, the proposed research will provide in-depth understanding of how plants undergoing biotic stresses guide leaf microbiome assembly and how the leaf microbiome can help plants defend themselves from disease stresses. Ultimately, our study will pave the way for improved rice breeding to ensure a healthy microbiome that will maximize rice yield and minimize losses due to diseases, ensuring food security for millions globally.Our long-term goal is to decipher the role and mechanisms of the plant immune system in regulating microbiota assembly processes and how the assembly of microbiota in turn modulates the host immune system to resist pathogens. To approach this goal, we will:Aim 1: Identify rice genes that influence leaf microbiome structure during susceptible and resistant interactions with the BB pathogen Xoo using modified Transcriptome-wide association studies (TWAS) linked to microbiome analysis (MWAS) and microbiome transcriptome-wide studies (mTWAS)].Aim 2: Validate the function of the candidate genes in orchestrating the assembly of the rice microbiome through rice genome-editing studies linked with disease/resistance phenotyping and microbiome analysis.Aim 3: Integrate machine learning and genome scale metabolic modeling to a) elucidate the mechanistic role of host mediated genetic control of phyllosphere microbiome in regulating pathogen establishment; and b) identify multi-omics host and microbiota biomarkers related to disease resistance.
Project Methods
Aim 1: Identify rice genes influencing leaf microbiome structure during susceptible and resistant interactions with the bacterial blight pathogenXoo.Transcriptome-wide association studies (TWAS) are a powerful approach to identify genes functioning in complex traits. Unlike traditional GWAS, which focuses on SNPS and their associations with traits, TWAS incorporates transcriptomic data to identify genes whose expression levels are associated with a specific phenotype, in our case, disease susceptibility/resistance and microbial community structure. In addition, TWAS is accommodating of small sample sizes and extreme phenotypes following the framework of Li et al. In this study, we will integrate gene expression data from rice AILs undergoing susceptibility or resistance with our existing GWAS results to identify candidate genes whose altered expression contributes to susceptibility or resistance. We will also identify microbial groups whose abundance is associated with susceptibility or resistance and determine the impact of identified candidate genes on abundance of those microbial groups.Aim 2: Validate the function of the candidate genes in orchestrating the assembly of the rice microbiome through rice genome-editing studies linked with disease/resistance phenotyping and microbiome analysis.We seek to understand the molecular mechanisms by which disease-resistance genes modulate the composition and diversity of the rice microbiome. Candidate genes identified from analysis of previous work and from the analyses in Aim 1 will be used to investigate their effect on the leaf microbial structure and disease resistance. First, we will utilize existing IR64 lines, both edited and unedited, in promoters of SWEET14 (not activated by PXO86 TALE AvrXa7 due to mutation in promoter = no disease) and SWEET11 and OsSWEET13 (susceptible due to activation of wild type SWEET14 promoter by PXO86 TALE AvrXa7 = disease) to analyze the impact of edited sugar transporters on the leaf microbiome under active resistant and disease interactions. By preventing expression of these sugar transporters, we will be able to discern how changes in sugar availability can affect the structure and diversity of the microbiome during resistant vs susceptible interactions and infected vs noninfected conditions.Second, we will use existing IR64 rice lines edited in the promoters of three DR genes (OsPAL4, OsGLP8-4, OsOXO4) (Table 2) that were confirmed to play a role in rice defense when activated by pathogen attack. In the DR-edited lines where DR gene activation by pathogen infection is absent and disease susceptibility increases, we will determine if changes in DR-gene regulated defenses and associated metabolites or structures affect the structure and diversity of the microbiome under infected vs noninfected conditions.Third, we will use CRISPR/Cas gene editing to knock out function of two candidate genes identified from the Roman-Reyna study,and evaluate the impact of the knock-outs on the rice microbiome and rice resistance to Xoo. The candidate genes Os02g019000 and Os04g235400 have tentatively been selected due to their colocalization with known disease and pest-resistance QTLs such as Xa2 (chr04[29644621-33083265]) for bacterial blight resistance, qSB-2 (chr02[23457248-30711734]) for sheath blight resistance and qGRH-2 chr02[23191143-31166188] for green rice leafhopper (GRH) resistance.Finally, we will knock out four candidate genes identified in Aim 1 using CRISPR/Cas gene editing, and evaluate the impacts on the rice leaf microbiome community and rice resistance to Xoo. The candidates will be selected based on the confidence of their prediction, and their putative functions. We will compare metagenome profiles between resistant and susceptible genotypes with and without infection, with the goal of understanding how disease-resistance genes may shape the microbiome's composition, particularly in response to the presence of disease-causing agents. This comparison will give us valuable insights into the role of predicted genes in microbiome assembly and help us uncover potential mechanisms of disease resistance.Aim 3: Integrate machine learning and genome scale metabolic modeling to a) elucidate the mechanistic role of host mediated genetic control of phyllosphere microbiome in regulating pathogen establishment; and b) identify multi-omics host and microbiota biomarkers related to disease resistance.Omics tools have revolutionized our ability to elucidate the functions of individual microbes in simple microbial communities.Yet, it remains challenging to predict microbial function under different environmental conditions and in response to interactions among different microbial taxa in more complex settings like those typical of agricultural ecosystems. Currently, we lack the knowledge, technologies, and computational and modeling approaches to predict how microbial interactions will affect community function. Here we will develop a predictive framework that can be used to provide tangible recommendations for exploiting the benefits that plant-associated microbiome provides to their hosts under stress conditions.