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 members of the REMORIN protein family as inhibitors of plasma membrane H⁺‑ATPases, leading to extracellular pH alkalinization that modulates cell surface processes such as steroid hormone signaling and coordinates root developmental transitions in Arabidopsis thaliana. This inhibition represents an ancient mechanism predating root evolution, suggesting that extracellular pH patterning has shaped plant morphogenesis.

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 authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.

PlantCV v4 is an open-source Python framework that simplifies image-based plant phenotyping by providing extensive tutorials and streamlined installation, enabling users with limited coding skills to automate trait extraction. The release adds support for fluorescence, thermal, and hyperspectral imaging and introduces a new subpackage for morphological measurements such as leaf angle, which is validated against manual data collection methods.

The study combined high-throughput image-based phenotyping with genome-wide association studies to uncover the genetic architecture of tolerance to the spittlebug Aeneolamia varia in 339 interspecific Urochloa hybrids. Six robust QTL were identified for plant damage traits, explaining up to 21.5% of variance, and candidate genes linked to hormone signaling, oxidative stress, and cell‑wall modification were highlighted, providing markers for breeding.

The study benchmarked several sampling approaches for simultaneous profiling of root exudates and rhizosphere microbiota in soil-grown barley, revealing consistent exudate chemistry across methods but variation in root morphology and nitrogen exudation. High‑throughput amplicon sequencing and quantitative PCR showed protocol‑specific impacts on microbial composition, yet most rhizosphere-enriched microbes were captured by all approaches. The authors conclude that different protocols provide comparable integrated data, though methodological differences must be aligned with experimental objectives.

The study combined ecometabolomics of root exudates with fungal community profiling to assess how abiotic (soil moisture, temperature legacy) and biotic (microbial inoculum, plant density) treatments shape metabolite diversity and fungal assemblages in Guarea guidonia seedlings. While soil microbial legacy and moisture drove metabolite diversity, antimicrobial treatments altered metabolite composition, and fungal community structure was linked to metabolite profiles, revealing metabolite‑fungal associations as early indicators of plant response to disturbance.

The study introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.

The study profiled root transcriptomes of Arabidopsis wild type and etr1 gain-of-function (etr1-3) and loss-of-function (etr1-7) mutants under ethylene or ACC treatment, identifying 4,522 ethylene‑responsive transcripts, including 553 that depend on ETR1 activity. ETR1‑dependent genes encompassed ethylene biosynthesis enzymes (ACO2, ACO3) and transcription factors, whose expression was further examined in an ein3eil1 background, revealing that both ETR1 and EIN3/EIL1 pathways regulate parts of the network controlling root hair proliferation and lateral root formation.