The study used transcriptomic profiling to compare tomato (Solanum lycopersicum) responses to three evolutionarily distant pathogens—nematodes, aphids, and oomycetes—during compatible interactions, identifying differentially expressed genes and key host hubs. Integrating public datasets and performing co‑expression and GO enrichment analyses, the authors mapped shared dysregulation clusters and employed Arabidopsis interactome data to place tomato candidates within broader networks, highlighting potential targets for multi‑pathogen resistance.

The study generated the first single‑nucleus RNA‑sequencing dataset of tomato (Solanum lycopersicum) roots colonized by the arbuscular mycorrhizal fungus Rhizophagus irregularis, revealing distinct transcriptional programs in epidermal and cortical cells across stages of arbuscule development. Using unsupervised subclustering and a Motif‑Informed Network Inference (MINI‑EX) approach, the authors identified candidate transcription factors that may coordinate cell‑cycle reactivation and nutrient integration during symbiosis, offering a resource for future functional genetics.

The study assessed three savory essential oil–based formulations for controlling early blight caused by Alternaria solani in tomato, finding that formulation CC2020 most effectively reduced disease severity in both in vitro and greenhouse trials. CC2020 also helped maintain tomato fruit vitamin C levels and lowered fungal melanin production, indicating dual benefits for disease suppression and fruit quality.

The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

The study generated a dataset of 420 sgRNAs targeting promoters, exons, and introns of 137 tomato genes in protoplasts, linking editing efficiency to chromatin accessibility, genomic context, and sequence features. Open chromatin sites showed higher editing rates, while transcriptional activity had little effect, and a subset of guides produced near‑complete editing with microhomology‑mediated deletions. Human‑trained prediction models performed poorly, highlighting the need for plant‑specific guide design tools.

The study characterizes a radiation‑induced root‑suppressed (Rs) mutant in tomato that displays dwarfism and pleiotropic defects in leaves, flowers, and fruits. Metabolite profiling and rescue with H2S donors implicate disrupted sulfur metabolism, and whole‑genome sequencing identifies promoter mutations in two root‑meristem‑specific sulfotransferase genes as likely contributors to the root phenotype.

The study identifies the extended NT‑C2 domain of Plastid Movement Impaired 1 (PMI1) as the main membrane‑binding module that interacts with PI4P and PI(4,5)P2, requiring basic residues for plasma‑membrane association. Calcium binding by the NT‑C2 domain modulates its phosphoinositide preference, and cytosolic Ca2+ depletion blocks blue‑light‑induced PMI1 redistribution, indicating that both the NT‑C2 domain and adjacent intrinsically disordered regions are essential for PMI1’s role in chloroplast movement.

The study shows that nitrogen deficiency markedly elevates the exudation of the triterpenoid Solanoeclepin A (SolA) from tomato roots, a process that requires non‑sterile soil and involves the rhizosphere microbiota. Transient silencing of two candidate biosynthetic genes (CYP749A19 and CYP749A20) reduced SolA levels and impaired recruitment of beneficial Massilia spp., which promote plant growth under nitrogen limitation, indicating that SolA acts as a microbe‑mediated recruitment signal that was co‑opted by cyst nematodes.

The study used untargeted metabolomics and QTL mapping in a tomato recombinant inbred line population to characterize root exudate composition and identify genetic loci controlling specific metabolites. It reveals domestication-driven changes in exudate profiles and links metabolic QTLs with previously reported microbial QTLs, suggesting a genetic basis for shaping the root microbiome.

The study cloned the resistance genes Rmo2 and Rwt7 from barley and wheat, revealing them as orthologous tandem kinase proteins (TKPs) with an N‑terminal heavy metal‑associated (HMA) domain. Domain‑swapping experiments indicated that the HMA domain dictates effector specificity, supporting a model of TKP diversification into paralogs and orthologs that recognize distinct pathogen effectors.