Proteomic, Transcriptomic, Mutational, and Functional Assays Reveal the Involvement of Both THF and PLP Sites at the GmSHMT08 in Resistance to Soybean Cyst Nematode

Genome-wide identification of SHMT family genes in alfalfa (Medicago sativa) and its functional analyses under various abiotic stresses

BACKGROUND

Alfalfa (Medicago sativa L.) is the most widely planted legume forage and one of the most economically valuable crops in the world. Serine hydroxymethyltransferase (SHMT), a pyridoxal phosphate-dependent enzyme, plays crucial roles in plant growth, development, and stress responses. To date, there has been no comprehensive bioinformatics investigation conducted on the SHMT genes in M. sativa.

RESULTS

Here, we systematically analyzed the phylogenetic relationship, expansion pattern, gene structure, cis-acting elements, and expression profile of the MsSHMT family genes. The result showed that a total of 15 SHMT members were identified from the M. sativa genome database. Phylogenetic analysis demonstrated that the MsSHMTs can be divided into 4 subgroups and conserved with other plant homologues. Gene structure analysis found that the exons of MsSHMTs ranges from 3 to 15. Analysis of cis-acting elements found that each of the MsSHMT genes contained different kinds of hormones and stress-related cis-acting elements in their promoter regions. Expression and function analysis revealed that MsSHMTs expressed in all plant tissues. qRT-PCR analysis showed that MsSHMTs induced by ABA, Salt, and drought stresses.

CONCLUSIONS

These results provided definite evidence that MsSHMTs might involve in growth, development and adversity responses in M. sativa, which laid a foundation for future functional studies of MsSHMTs.

Rice Serine Hydroxymethyltransferases: Evolution, Subcellular Localization, Function and Perspectives

In rice, there is a lack of comprehensive research on the functional aspects of the members of the serine hydroxymethyltransferase (SHMT) gene family. This study provides a comprehensive investigation of the SHMT gene family, covering phylogeny, gene structure, promoter analysis, expression analysis, subcellular localization, and protein interaction. Remarkably, we discovered a specific gene loss event occurred in the chloroplast-localized group IIa SHMTs in monocotyledons. However, OsSHMT3, which originally classified within cytoplasmic-localized group Ib, was found to be situated within chloroplasts in rice protoplasts. All five OsSHMTs are capable of forming homodimers, with OsSHMT3 being the only one able to form dimers with other OsSHMTs, except for OsSHMT1. It is proposed that OsSHMT3 functions as a mobile protein, collaborating with other OsSHMT proteins. Furthermore, the results of cis-acting element prediction and expression analysis suggested that members of the OsSHMT family could be involved in diverse stress responses and hormone regulation. Our study aims to provide novel insights for the future exploration of SHMTs.

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage

Soybean (Glycine max (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide use, leading to reduced yields. Identifying the level of leaf damage aids yield projections, pesticide, and fertilizer decisions. Deep learning models (DLMs) and neural networks mastering tasks from abundant data have been used for binary healthy/unhealthy leaf classification. However, no DLM predicts and categorizes soybean leaf damage severity (five levels) for tailored pesticide use and yield forecasts. This paper introduces a novel DLM for accurate damage prediction and classification, trained on 2930 near-field soybean leaf images. The model quantifies damage severity, distinguishing healthy/unhealthy leaves and offering a comprehensive solution. Performance metrics include accuracy, precision, recall, and F1-score. This research presents a robust DLM for soybean damage assessment, supporting informed agricultural decisions based on specific damage levels and enhancing crop management and productivity.