Molecular phylogenetic analysis indicated that diatom Lhcx proteins share a common ancestor with green algal Lhcsrs, suggesting acquisition via horizontal gene transfer. Knockout of the Lhcx1 gene in the diatom Chaetoceros gracilis almost eliminated non‑photochemical quenching and revealed that Lhcx1 mediates quenching in detached antenna complexes, while also influencing PSII quantum yield and carbon fixation under high‑light conditions. These findings elucidate the evolutionary origin and mechanistic role of Lhcx‑mediated photoprotection in diatoms.

The study employed computational approaches to characterize the SUMOylation (ULP) machinery in Asian rice (Oryza sativa), analyzing phylogenetic relationships, transcriptional patterns, and protein structures across the reference genome, a population panel, and wild relatives. Findings reveal an expansion of ULP genes in cultivated rice, suggesting selection pressure during breeding and implicating specific ULPs in biotic and abiotic stress responses, providing resources for rice improvement.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

Researchers isolated a fungal pathogen from a naturally infected Rumex crispus leaf in Japan and identified it as Teratoramularia rumicicola using morphological traits and phylogenetic analysis of ITS and LSU rDNA sequences. Host range tests showed the isolate (TR4) caused disease and reduced biomass in three Rumex species but was harmless to five tested forage crops, indicating its potential as a selective bioherbicide for pasture systems.

Large-scale bioinformatics identified a new class of transmembrane phosphotransfer proteins (TM‑HPt) across 61 plant species, showing conserved HPt motifs and potential activity in multistep phosphorelay signaling. Phylogenetic relationships were inferred via Bayesian DNA analysis, expression was validated by transcriptomics, and molecular modeling suggested possible membrane-associated structural arrangements.

The study identifies a novel C-terminal FR motif in Lotus japonicus NODULE INCEPTION (NIN) that expands DNA‑binding specificity by stabilizing the RWP‑RK dimer, and shows that loss of this motif impairs nodulation and nitrogen fixation. Comparative analysis reveals that Arabidopsis NLP2 also possesses a NIN‑type FR, and phylogenetic data suggest the motif originated in early gymnosperms, indicating it predates the evolution of root nodule symbiosis.

The study used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

The study reconstructed the evolutionary history of plant-specific GBF1-type ARF-GEFs by building phylogenetic trees and ortho‑synteny groups, identifying orthologs of AtGNOM and AtGNL1 across species. Functional analyses using transgenic Arabidopsis lines and yeast two‑hybrid assays revealed how duplication and loss events diversified GNOM paralogs, separating polar recycling from secretory trafficking functions.

The study used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.

The study introduces ENTRAP-seq, a high‑throughput in‑planta assay that couples protein‑coding libraries with a nuclear magnetic sorting‑based reporter to multiplexively assess transcriptional regulatory activity of thousands of protein variants. Using this platform and machine‑learning analysis, the authors screened 1,495 plant viral proteins, uncovering numerous novel regulatory domains, and applied machine‑guided, semi‑rational design to modify the activity of a plant transcription factor.