Predicting transcriptional responses to cold stress across plant species

Abstract

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.

Understanding Heterosis, Genetic Effects, and Genome Wide Associations for Forage Quantity and Quality Traits in Multi-Cut Pearl Millet

“…The genes Pgl_GLEAN_10034437, Pgl_GLEAN_10012611, Pgl_GLEAN_10027418, Pgl_GLEAN_ 10024973, and Pgl_GLEAN_10037615 were found coding for functions, such as Coatomer beta C-terminal region, Methyltransferase domain/Hen1 La-motif C-terminal domain, NHL domain containing protein, Coatomer epsilon subunit, and CCT motif, respectively. Furthermore, some of the genes, such as Pgl_GLEAN_10029543, Pgl_GLEAN_10034437, Pgl_GLEAN_10012611, Pgl_GLEAN_10027418, Pgl_GLEAN_ 10024973, Pgl_GLEAN_10037615, and Pgl_GLEAN_10032186 were also responsible for cold tolerance ( Meng et al, 2021 ).…”

“…One SNP was found closely associated with CP and nine SNPs were found associated with IVOMD (Table 5). The SNP for CP corresponded to the gene Pgl_GLEAN_10029543 were also responsible for cold tolerance (Meng et al, 2021).…”

Deep‐learning power and perspectives for genomic selection

“…Transfer learning applications in plant breeding are just starting. For example, Meng et al (2021) used it for predicting transcriptional responses to cold stress across plant species; they found that 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.…”

Coping with cold: Sorghum cold stress from germination to maturity

“…However, the total number of differentially expressed genes does not necessarily correlate with chilling tolerance or susceptibility (Marla et al., 2017). Recent work suggests that a trained model can predict genes that will transcriptionally respond to low temperature stress, even when the comparison species have different degrees of cold or chilling tolerance, which may prove useful in understanding low temperature stress responses across sorghum varieties and in other species (Meng et al., 2021).…”