Plant and Environmental Sci
Non Technical Summary
Soybean is the most important oilseed and one of the most important and affordable protein sources worldwide. High temperature is a major factor limiting soybean yield. Incomplete knowledge of the molecular changes underlying soybean heat tolerance has hindered progress in developing heat-tolerant varieties for sustainable production. The goals of the proposed project are to identify soybean genes associated with lipid metabolic changes and with physiological mechanisms contributing to heat tolerance and to develop molecular markers for high-throughput screening of large germplasm collections for heat tolerance. The specific objectives of the project are to (1) characterize the heat tolerance of members of a soybean recombinant inbred line population (derived from a genetic cross between a heat-tolerant and a susceptible genotype) and elite soybean genotypes by measuring physiological responses, (2) elucidate lipid metabolic changes in tolerant and susceptible genotypes under heat stress, and identify changes associated with tolerance, and (3) determine genomic regions (QTLs) and molecular markers associated with heat-induced lipid metabolic changes and other heat tolerance-related traits in soybean. Molecular markers of heat tolerance will be the key deliverable from this project, which will advance soybean breeding programs in developing heat-tolerant varieties. The project will generate foundational knowledge on the genetic basis of lipid biosynthetic pathways and other physiological mechanisms related with heat tolerance, which will be valuable for crop improvement programs in developing climate-resilient varieties.
Animal Health Component
Research Effort Categories
Goals / Objectives
The long-term goal of the proposed work is to enhance soybean's efficiency for producing stable yields under heat stress conditions to improve its climate resilience. The project uses an interdisciplinary (crop ecophysiology, lipidomics, genomics, and breeding) approach to improve plant genetics, enhancing the inherent potential of plants to produce yields under threatening temperature conditions associated with climate change. Cutting-edge lipidomics and genomics techniques are integrated in this work to forge an innovative pathway to sustainable agriculture.Specific Objectives:Characterize the heat tolerance of members of a soybean recombinant inbred line (RIL) population (derived from a genetic cross between a heat-tolerant and a susceptible genotype) and elite soybean genotypes by measuring physiological responses.Elucidate lipid metabolic changes in tolerant and susceptible genotypes under heat stress, and identify changes associated with tolerance.Identify genomic regions (QTLs) and molecular markers associated with heat-induced lipid metabolic changes and other heat tolerance-related traits in soybean.Completing the above objectives will allow the identification of soybean genes and enzymes contributing to lipid metabolic changes associated with heat tolerance and the development of molecular markers for high-throughput screening of large germplasm collections for heat tolerance. An added value from this study will be in identifying non-lipid-related QTLs for the heat stress-related physiological traits and genomic localization of heat-stress induced differentially expressed genes (DEGs) between soybean genotypes with contrasting levels of heat tolerance. Studying correspondence among the genomic locations of lipid-QTLs [bothcis(mapping proximal to known lipid biosynthesis/metabolism gene) andtrans(mapping distal to the known lipid genes)], QTLs for physiological traits, and DEGs will allow identification of heat stress-associated markers and genes, which after necessary validation will have great utility in breeding for heat tolerance. Additionally, this analysis has the potential to shed light on the molecular functions of some of the hundreds of genes putatively involved in lipid metabolism, but whose biochemical functions in plant lipid metabolism are not fully understood. Hence, this study will significantly improve our basic understanding of the regulation of plant lipid metabolism, especially under heat stress.
The soybean germplasm tested in this project.A heat-tolerant genotype DS25-1, heat-susceptible genotype DT97-4290, and the F6-derived population of 200 recombinant inbred lines (RILs) developed from a cross between DS25-1 and DT97-4290.Additional genotypes which exhibit either unique lipid profiles or tolerance/susceptibility to various abiotic stresses. SC17-8001,SC18-8005, SC18-8006, USDA-N8002, NC-ROY, DS49-142, DS1169-333, DS34-1, LG01-5087-5, Jake, and OsageGroups 'i' and 'ii' will be characterized for their lipid profiles and physiological responses under heat stress, and used for identifying molecular markers.A F5-derived Recombinant Inbred Line (RIL) population of 301 lines developed from a cross between heat tolerant and susceptible parents, DS34-1 and LD00-3309, respectively. This population, developed by Dr. James Smith, will be used as the DNA marker validation panel.MethodsFor Objective-1Field study [Years 1&2]: The F6-derived population of 200 RILs (DS25-1/DT97-4290) (group-i above), the parental genotypes (DS25-1 and DT97-4290), and the 12 selected genotypes (group-ii above) will be grown at Clemson, SC in two years (summers of 2022 and 2023). In both years, the plants will be grown at ambient temperatures until flowering. Thereafter, two treatments - ambient and high temperature - will be applied for two weeks to evaluate heat stress impacts during flowering [one of the most heat sensitive stages in soybean (Djanaguiraman et al. 2013a)]. Each genotype will have four replicated plots (single row per plot; row length ~ 9 m) under both control (ambient temperature) and heat stress treatments. The control treatment will be realized by open field conditions.Measurements to test Obj. 1 [Years 1-3]: Photosynthesis, chlorophyll fluorescence, and chlorophyll index will be measured ~1 week before treatment, on days 1, 7, and 14 of the treatment (start, middle, and end of the treatment), and one week after treatment (during recovery) on the 3rd trifoliate from the main-stem apex of the plants (Djanaguiraman et al. 2013b). A Li-Cor 6800 photosynthesis system will measure leaf photosynthetic rates. A fluorometer will measure chlorophyll fluorescence. A chlorophyll meter (Soil Plant Analyzer Development, "SPAD") will measure chlorophyll index. Reactive oxygen species (ROS) content and lipid peroxidation will be measured on leaf samples collected on days 1, 7, and 14 of the treatment. ROS content will be measured using OxiSelect ROS kits (Narayanan et al. 2016a). Lipid peroxidation will be estimated by measuring malondialdehyde content using thiobarbituric acid reactive substances assay kits (Narayanan et al. 2016a). Pollen grains will be collected on the last day of stress to measure viability. Pollen viability will be measured using triphenyl tetrazolium chloride staining (Djanaguiraman et al. 2013b). Plant height will be measured on the last day of stress. Plants from a one-meter row will be harvested from each plot to determine biomass and yield (seed numbers and weight) at maturity. Each of these traits will be measured on four replications of all 214 genotypes under control and heat stress treatments in both years.For Objective-2Leaf samples will be collected on the last day of stress for lipid extraction and profiling from 214 genotypes [200 RILs (group i), their parental genotypes (DS25-1 and DT97-4290), and 12 selected genotypes (group ii)] [2568 samples per year (2 types of tissue x 2 treatments x 214 genotypes x 3 reps)]. Lipids will be extracted as described by Shiva et al. (2018).For Objective-3 Sample collection for DNA & RNA extractions from field-grown plants [Years 1&2]: Leaf samples for DNA extractions (for QTL analysis) will be collected on ice before the heat stress treatment to avoid the impact of stress on DNA quality in sensitive genotypes. The leaf samples will be collected for RNA extraction [for gene expression analysis via droplet digital (dd) PCR] on dry ice before the onset, on the 9th day, and 2 days after the stress treatment.Gene expression analysis [Years 3&4]: The RNA samples obtained from the leaves of field-grown plants (described above) will be converted to cDNA and used to profile the expression of differentially expressed genes [DEGs; determined by RNA sequencing (RNAseq) analysis of the parental lines; given under 'Controlled environmental study-1' below] using ddPCR. The gene expression data from the field-grown plants will be used to (1) validate the DEGs, (2) map expression QTLs (eQTLs), and (3) study correspondence in the genomic locations of the lipid-QTLs, eQTLs, and QTLs for physiological traits (QTL analysis is given in the following section).QTL analysis [Years 3&4]: A complete set of RILs with the parental genotypes (sequenced in triplicate) was sequenced via LGC Genomics' established protocols (https://www.biosearchtech.com/) for normalized GBS (nGBS) with Ms1I and Illumina NextSeq paired-end sequencing (2×150 bp) (Elshire et al. 2011).For identification of recombination breakpoints in the mapping population, an 18?bp sliding window approach will be used (Patil et al. 2018). This information will be used to develop the recombination bins, which will be used as genetic markers for the construction of a linkage map using QTL IciMapping Version 3.3 software (Meng et al. 2015).Controlled environmental study-1 [Years 3&4]: Genotypes DS25-1 and DT97-4290 (parents for the RIL population used for the identification of markers) will be grown in growth chambers for an RNAseq experiment. This controlled environmental study will allow precise identification of heat-stress induced genes over genes triggered via other stresses. Plants will be grown at optimal temperatures (30/20°C) until flowering. Thereafter, two treatments - optimal and high temperature (38/28°C) - will be applied for two weeks. Leaf samples will be collected before the onset, the 9th day, and 2 days after the stress treatment to determine the expression level differences between the genotypes under control (30/20°C) and heat stress conditions (38/28°C). The results of the RNAseq will be analyzed in the following steps. (1) After removal of the low-quality sequences, sequence reads from all 12 treatments (i.e., 2 treatments x 2 genotypes x 3 time points) will be pooled and assembled using Trinity package to construct a reference sequence set. (2) The DEGs in each treatment will be identified by aligning sequences to the reference sequence set at the false discovery rate (FDR) of <0.05 and log2FC (fold change) value of >1. (3) All unique gene sequences identified in each combination will be annotated by aligning them to protein sequences available in the public domain using the BLASTX program; gene ontology (GO) terms will be assigned to each unigene using Blast2GO program. (4) Kyoto Encyclopedia of Genes and Genomes will be used for the functional categorization and pathway visualization of DEGs.Controlled environmental study-2 [Years 4 & 5]: The F5-derived population of 301 RILs and their parental genotypes (DS34-1 and LD00-3309) will be grown in plant growth chambers to validate the molecular markers. This RIL population will be the QTL validation panel. Plants will be grown at optimal temperatures (30/20°C) until flowering. Thereafter, two treatments - optimal temperature and high temperature (38/28°C) - will be applied for two weeks. Heat responses of plants will be assessed based on physiological traits (photosynthesis, chlorophyll fluorescence, chlorophyll index, pollen viability) (measured on the last day of stress), and yield (measured at maturity). All traits will be measured on four replicated plants under optimal and high temperature treatments for each RIL. Leaf samples from 301 RILs and their parents (each with 4 replications) will be collected before stress treatment for DNA extraction.