Rozzi, B. | bioRxiv Science

bioRxiv2025

A field-focused systems approach reveals mRNA covalent modifications linked to sorghum growth and development under drought stress

RNA Covalent Modifications (RCMs) are post-transcriptional chemical alterations that influence RNA stability and translation efficiency, thus play critical roles in eukaryotic growth and development. However, their role in regulating plant performance under abiotic stress remain largely unexplored. Here, we integrated multi-omics data in six Sorghum bicolor accessions under water-limiting conditions in the field to explore the relationship between RCMs and drought response. Within a stress and photosynthesis-associated gene co-expression module, we identified SbDUS2, a member of family of enzymes, conserved across eukaryotes, which catalyzes the reduction of uracil to dihydrouridine (DHU) on RNA molecules. DHU-modified transcripts in this module were enriched for photosynthetic functions and showed strong correlation with photosynthetic traits. To elucidate the function of this RCM, we characterized loss of function dus2 mutants in the genetic model, Arabidopsis thaliana. Under control conditions, these DHU-deficient mutants exhibited impaired germination and delayed development. Furthermore, when exposed to heat or water-limiting conditions, these mutants showed significantly reduced net CO2 assimilation and survival. Using multiple transcriptome-wide RNA stability assays, we demonstrated that transcripts associated with lower DHU level in a dus2 background generally exhibited increased stability compared to Col-0 controls. Particularly, lack of DUS2 led to the hyperstability of photosynthesis-related transcripts, impeding their turnover and likely preventing proper photosynthetic acclimation during stress. We propose a model based on these data where DHU acts as a critical post-transcriptional regulator marking mRNAs for rapid turnover under stress, highlighting an overlooked regulatory layer contributing to plant resilience.

plant biology↗

bioRxiv2024

Quantifying Leaf Symptoms of Sorghum Charcoal Rot in Images of Field-Grown Plants 9 Using Deep Neural Networks

Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red-green-blue (RGB) images of sorghum plants exhibiting symptoms of infection. EfficientNet-B3 and a fully convolutional network (FCN) emerged as the top-performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet-B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models validation scores increased linearly, and their processing time decreased exponentially. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a base for drone-based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web-based application where users can easily analyze their own images. Core ideasO_LIAutomated phenotyping tools are required for the efficient detection and quantification of charcoal rot of sorghum. C_LIO_LIClassification and segmentation models can distinguish between concurrent plant stresses with similar symptoms. C_LIO_LILarger image patch sizes generally improve model performance and reduce processing time. C_LI

plant biology↗