Genetius

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.

Phosphite (Phi) and phosphate (Pi) share the same root uptake system, but Phi acts as a biostimulant that modulates plant growth and disease resistance in a species‑ and Pi‑dependent manner. In Arabidopsis, Phi induces hypersensitive‑like cell death and enhances resistance to Plectosphaerella cucumerina, while in rice it counteracts Pi‑induced susceptibility to Magnaporthe oryzae and Fusarium fujikuroi, accompanied by extensive transcriptional reprogramming.

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 examined how elevated atmospheric CO₂ (550 ppm) affects immunity in the C₄ cereal maize (Zea mays L.) by exposing plants grown under ambient and elevated CO₂ to a range of pathogens. Elevated CO₂ increased susceptibility to sugarcane mosaic virus, decreased susceptibility to several bacterial and fungal pathogens, and left susceptibility to others unchanged, with reduced bacterial disease linked to heightened basal immune responses. These findings provide a baseline for future investigations into CO₂‑responsive defense mechanisms in C₄ crops.

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 characterizes the plant spliceosomal protein AtSF1 in Arabidopsis thaliana, using iCLIP and RNA‑seq to map its in vivo branch point binding sites and demonstrate that loss of AtSF1 causes widespread 3' splice‑site mis‑selection. Structural comparison reveals a plant‑specific domain architecture, and the identified AtSF1 targets are enriched for circadian and defense genes, linking splicing regulation to timing and immunity.

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.