Jagadish, S. V. K. | bioRxiv Science

bioRxiv2023

Time of day and genotype sensitivity adjust molecular responses to temperature stress in sorghum

Sorghum is one of the four major C4 crops that are considered to be tolerant to environmental extremes. Sorghum shows distinct growth responses to temperature stress depending on the sensitivity of the genetic background. About half of the transcripts in sorghum exhibit diurnal rhythmic expressions emphasizing significant coordination with the environment. However, an understanding of how molecular dynamics contribute to genotype-specific stress responses in the context of the time of day is not known. We examined whether temperature stress and the time of day impact the gene expression dynamics in cold-sensitive and tolerant and heat-sensitive and tolerant sorghum genotypes. We found that time of day is highly influencing the temperature stress responses, which can be explained by the rhythmic expression of most thermo-responsive genes. This effect is more pronounced in thermo-tolerant genotypes, suggesting a stronger regulation of gene expression by the time of day and/or by the circadian clock. Genotypic differences were mostly observed on average gene expression levels, but we identified groups of genes regulated by temperature stress in a time-of-day and genotype-specific manner. These include transcriptional regulators and several members of the Ca2+-binding EF-hand protein family. We hypothesize that expression variation of these genes between genotypes may be responsible for contrasting sensitivities to temperature stress in tolerant vs susceptible sorghum varieties. These findings offer a new opportunity to selectively target specific genes in efforts to develop climate-resilient crops based on their time of day and genotype variation responses to temperature stress.

plant biology↗

bioRxiv2018

Root Anatomy based on Root Cross-Section Image Analysis with Deep Learning

Aboveground plant efficiency has improved significantly in recent years, and the improvement has led to a steady increase in global food production. The improvement of belowground plant efficiency has potential to further increase food production. However, belowground plant roots are harder to study, due to inherent challenges presented by root phenotyping. Several tools for identifying root anatomical features in root cross-section images have been proposed. However, the existing tools are not fully automated and require significant human effort to produce accurate results. To address this limitation, we use a fully automated approach, specifically, the Faster Region-based Convolutional Neural Network (Faster R-CNN), to identify anatomical traits in root cross-section images. By training Faster R-CNN models on root cross-section images, we can detect objects such as root, stele and late metaxylem, and predict rectangular bounding boxes around such objects. Subsequently, the bounding boxes can be used to estimate the root diameter, stele diameter, late metaxylem number, and average diameter. Experimental evaluation using standard object detection metrics, such as intersection-over-union and mean average precision, has shown that the Faster R-CNN models trained on rice root cross-section images can accurately detect root, stele and late metaxylem objects. Furthermore, the results have shown that the measurements estimated based on predicted bounding boxes have small root mean square error when compared with the corresponding ground truth values, suggesting that Faster R-CNN can be used to accurately detect anatomical features. A webserver for performing root anatomy using the Faster R-CNN models trained on rice images is available at https://rootanatomy.org, together with a link to a GitHub repository that contains a copy of the Faster R-CNN code. The labeled images used for training and evaluating the Faster R-CNN models are also available from the GitHub repository.

bioinformatics↗