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The accumulation of triacylglycerol (TAG) in vegetative tissues is necessary to adapt to changing temperatures. It has been hypothesized that TAG accumulation is required as a storage location for maladaptive membrane lipids. The TAG acyltransferase family has five members (DIACYLGLYCEROL ACYLTRANSFERSE1/2/3 and PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE1/2), and their individual roles during temperature challenges have either been described conflictingly or not at all. Therefore, we used Arabidopsis (Arabidopsis thaliana) loss of function mutants in each acyltransferase to investigate the effects of temperature challenge on TAG accumulation, plasma membrane integrity, and temperature tolerance. All mutants were tested under one high- and two low-temperature regimens, during which we quantified lipids, assessed temperature sensitivity, and measured plasma membrane electrolyte leakage. Our findings revealed reduced effectiveness in TAG production during at least one temperature regimen for all acyltransferase mutants compared to the wild type, resolved conflicting roles of pdat1 and dgat1 by demonstrating their distinct temperature-specific actions, and uncovered that plasma membrane integrity and TAG accumulation do not always coincide, suggesting a multifaceted role of TAG beyond its conventional lipid reservoir function during temperature stress. 

Abstract Chilling stress threatens plant growth and development, particularly affecting membrane fluidity and cellular integrity. Understanding plant membrane responses to chilling stress is important for unraveling the molecular mechanisms of stress tolerance. Whereas core transcriptional responses to chilling stress and stress tolerance are conserved across species, the associated changes in membrane lipids appear to be less conserved, as which lipids are affected by chilling stress varies by species. Here, we investigated changes in gene expression and membrane lipids in response to chilling stress during one 24 h cycle in chilling-tolerant foxtail millet (Setaria italica), and chilling-sensitive sorghum (Sorghum bicolor) and Urochloa (browntop signal grass, Urochloa fusca, lipids only), leveraging their evolutionary relatedness and differing levels of chilling stress tolerance. We show that most chilling-induced lipid changes are conserved across the three species, while we observed distinct, time-specific responses in chilling-tolerant foxtail millet, indicating the presence of a finely orchestrated adaptive mechanism. We detected rhythmicity in lipid responses to chilling stress in the three grasses, which were also present in Arabidopsis thaliana, suggesting the conservation of rhythmic patterns across species and highlighting the importance of accounting for time of day. When integrating lipid datasets with gene expression profiles, we identified potential candidate genes that showed corresponding transcriptional changes in response to chilling stress, providing insights into the differences in regulatory mechanisms between chilling-sensitive sorghum and chilling-tolerant foxtail millet. 

Although genome-sequence assemblies are available for a growing number of plant species, gene-expression responses to stimuli have been cataloged for only a subset of these species. Many genes show altered transcription patterns in response to abiotic stresses. However, orthologous genes in related species often exhibit different responses to a given stress. Accordingly, data on the regulation of gene expression in one species are not reliable predictors of orthologous gene responses in a related species. Here, we trained a supervised classification model to identify genes that transcriptionally respond to cold stress. A model trained with only features calculated directly from genome assemblies exhibited only modest decreases in performance relative to models trained by using genomic, chromatin, and evolution/diversity features. Models trained with data from one species successfully predicted which genes would respond to cold stress in other related species. Cross-species predictions remained accurate when training was performed in cold-sensitive species and predictions were performed in cold-tolerant species and vice versa. Models trained with data on gene expression in multiple species provided at least equivalent performance to models trained and tested in a single species and outperformed single-species models in cross-species prediction. These results suggest that classifiers trained on stress data from well-studied species may suffice for predicting gene-expression patterns in related, less-studied species with sequenced genomes.