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 authors used a bottom‑up thermodynamic modelling framework to investigate how plants decode calcium signals, starting from Ca2+ binding to EF‑hand proteins and extending to higher‑order decoding modules. They identified six universal Ca2+-decoding modules that can explain variations in calcium sensitivity among kinases and provide a theoretical basis for interpreting calcium signal amplitude and frequency in plant cells.

The study reveals that each individual plant possesses a statistically unique leaf appearance that can be discriminated using convolutional neural network (CNN) based deep learning, enabling a "plant face" recognition concept. Applications demonstrated include distinguishing leaves from the same species/cultivar, analyzing leaflet positional patterns on compound leaves, assessing bilateral symmetry, and detecting morphological differences linked to stem chirality, highlighting the encoding of genetic, environmental, and developmental information in leaf morphology.

Four barley genotypes were examined under simultaneous Fusarium culmorum infection and drought, revealing genotype-dependent Fusarium Head Blight severity and largely additive transcriptomic responses dominated by drought. Co‑expression and hormone profiling linked ABA and auxin to stress‑specific gene modules, and a multiple linear regression model accurately predicted combined‑stress gene expression from single‑stress data, suggesting modular regulation.

The study examined nitrogen use strategies in the model alga Chlamydomonas reinhardtii by comparing growth on ammonium, nitrate, and urea, finding similar molar nitrogen utilization efficiency under saturating conditions. Rapid nitrogen uptake and storage were demonstrated through pulse experiments, and source‑specific transcriptome analysis revealed distinct regulation of assimilation pathways and transporters, supporting a model of flexible nitrogen acquisition and storage.

The study investigates how maternal environmental conditions, specifically temperature and light intensity, influence seed longevity in eight Arabidopsis thaliana natural accessions. Seeds developed under higher temperature (27 °C) and high light showed increased longevity, with transcriptome analysis of the Bor-4 accession revealing dynamic changes in stored mRNAs, including upregulation of antioxidant defenses and raffinose family oligosaccharides. These findings highlight the genotype‑dependent modulation of seed traits by the maternal environment.

The study evaluated whether integrating genomic, transcriptomic, and drone-derived phenomic data improves prediction of 129 maize traits across nine environments, using both linear (rrBLUP) and nonlinear (SVR) models. Multi-omics models consistently outperformed single-omics models, with transcriptomic data especially enhancing cross‑environment predictions and capturing genotype‑by‑environment interactions. The results highlight the added value of combining transcriptomics and phenomics with genotypes for more accurate and generalizable trait prediction in maize.

The study investigates the evolutionary shift from archegonial to embryo‑sac reproduction by analyzing transcriptomes of Ginkgo reproductive organs and related species. It reveals that the angiosperm pollen‑tube guidance module MYB98‑CRP‑ECS is active in mature Ginkgo archegonia and that, while egg cell transcription is conserved, changes in the fate of other female gametophyte cells drove the transition, providing a molecular framework for this major reproductive evolution.

A comparative physiological study of persimmon cultivars with flat (Hiratanenashi) and round (Koushimaru) fruit shapes revealed that differences in cell proliferation, cell shape, and size contribute to shape variation. Principal component analysis of elliptic Fourier descriptors tracked shape changes, while histology and transcriptome profiling identified candidate genes, including a WOX13 homeobox gene, potentially governing fruit shape development.

The study examines how the SnRK1 catalytic subunit KIN10 integrates carbon availability with root growth regulation in Arabidopsis thaliana. Loss of KIN10 reduces glucose‑induced inhibition of root elongation and triggers widespread transcriptional reprogramming of metabolic and hormonal pathways, notably affecting auxin and jasmonate signaling under sucrose supplementation. These findings highlight KIN10 as a central hub linking energy status to developmental and environmental cues in roots.