Comparative Lipidomic Analysis Reveals Heat Stress Responses of Two Soybean Genotypes Differing in Temperature Sensitivity

Heat-induced changes in lipidome and their influence on stress adaptation are not well-defined in plants. We investigated if lipid metabolic changes contribute to differences in heat stress responses in a heat-tolerant soybean genotype DS25-1 and a heat-susceptible soybean genotype DT97-4290. Both genotypes were grown at optimal temperatures (OT; 30/20 °C) for 15 days. Subsequently, half of the plants were exposed to heat stress (38/28 °C) for 11 days, and the rest were kept at OT. Leaf samples were collected for lipid and RNA extractions on the 9th and 11th days of stress, respectively. We observed a decline in the lipid unsaturation level due to a decrease in the polyunsaturated linolenic acid (18:3) content in DS25-1. When examined under OT conditions, DS25-1 and DT97-4290 showed no significant differences in the expression pattern of the Fatty Acid Desaturase (FAD) 2-1A, FAD2-2B, FAD2-2C, FAD3A genes. Under heat stress conditions, substantial reductions in the expression levels of the FAD3A and FAD3B genes, which convert 18:2 lipids to 18:3, were observed in DS25-1. Our results suggest that decrease in levels of lipids containing 18:3 acyl chains under heat stress in DS25-1 is a likely consequence of reduced FAD3A and FAD3B expression, and the decrease in 18:3 contributes to DS25-1′s maintenance of membrane functionality and heat tolerance

Pattern of Protein Expression in Developing Wheat Grains Identified through Proteomic Analysis

Grain development is one of the biological processes, which contributes to the final grain yield. To understand the molecular changes taking place during the early grain development, we profiled proteomes of two common wheat cultivars P271 and Chinese Spring (CS) with large and small grains, respectively at three grain developmental stages (4, 8, and 12 days post anthesis). An iTRAQ (isobaric tags for relative and absolute quantitation) based proteomics approach was used for this purpose. More than 3,600 proteins were reported to accumulate during early grain development in both wheat cultivars. Of these 3,600 proteins, 130 expressed differentially between two wheat cultivars, and 306 exhibited developmental stage-specific accumulation in either or both genotypes. Detailed bioinformatic analyses of differentially expressed proteins (DEPs) from the large- and small-grain wheat cultivars underscored the developmental differences observed between them and shed light on the molecular and cellular processes contributing to these differences. In silico localization of either or both sets of DEPs to wheat chromosomes exhibited a biased genomic distribution with chromosome 4D contributing largely to it. These results corresponded well with the earlier studies, performed in common wheat, where chromosome 4D was reported to harbor QTLs for yield contributing traits specifically grain length. Collectively, our results provide insight into the molecular processes taking place during early grain development, a knowledge, which may prove useful in improving wheat grain yield in the future

Alterations in Growth Habit to Channel End-of-Season Perennial Reserves towards Increased Yield and Reduced Regrowth after Defoliation in Upland Cotton (<i>Gossypium hirsutum</i> L.)

Cotton (Gossypium spp.) is the primary source of natural textile fiber in the U.S. and a major crop in the Southeastern U.S. Despite constant efforts to increase the cotton fiber yield, the yield gain has stagnated. Therefore, we undertook a novel approach to improve the cotton fiber yield by altering its growth habit from perennial to annual. In this effort, we identified genotypes with high-expression alleles of five floral induction and meristem identity genes (FT, SOC1, FUL, LFY, and AP1) from an Upland cotton mini-core collection and crossed them in various combinations to develop cotton lines with annual growth habit, optimal flowering time, and enhanced productivity. To facilitate the characterization of genotypes with the desired combinations of stacked alleles, we identified molecular markers associated with the gene expression traits via genome-wide association analysis using a 63 K SNP Array. Over 14,500 SNPs showed polymorphism and were used for association analysis. A total of 396 markers showed associations with expression traits. Of these 396 markers, 159 were mapped to genes, 50 to untranslated regions, and 187 to random genomic regions. Biased genomic distribution of associated markers was observed where more trait-associated markers mapped to the cotton D sub-genome. Many quantitative trait loci coincided at specific genomic regions. This observation has implications as these traits could be bred together. The analysis also allowed the identification of candidate regulators of the expression patterns of these floral induction and meristem identity genes whose functions will be validated