Field experiments combined with RNA sequencing revealed that wheat ploidy influences heat stress resilience, with tetraploid T. turgidum showing the smallest yield loss and hexaploid T. aestivum mounting the largest transcriptional response. Ploidy-dependent differences were observed in differential gene expression, alternative splicing—including hexaploid-specific exon skipping of NF‑YB—and co‑expression networks linked to grain traits, highlighting candidate pathways for breeding heat‑tolerant wheat.

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 identifies the AP2/ERF transcription factor GEMMIFER (MpGMFR) as essential for asexual reproduction in the liverwort Marchantia polymorpha, showing that loss of MpGMFR via genome editing or amiRNA abolishes gemma and gemma cup formation, while dexamethasone‑induced activation triggers their development. Transient strong activation of MpGMFR initiates gemma initial cells at the meristem, which mature into functional gemmae, indicating MpGMFR is both necessary and sufficient for meristem‑derived asexual propagule formation.

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 created a system that blocks root‑mediated signaling between wheat varieties in a varietal mixture and used transcriptomic and metabolomic profiling to reveal that root chemical interactions drive reduced susceptibility to Septoria tritici blotch, with phenolic compounds emerging as key mediators. Disruption of these root signals eliminates both the disease resistance phenotype and the associated molecular reprogramming.

The study employed ultra large‑scale 2D clinostats to grow tomato (Solanum lycopersicum) plants beyond the seedling stage under simulated microgravity and upright control conditions across five sequential trials. Simulated microgravity consistently affected plant growth, but the magnitude and direction of the response varied among trials, with temperature identified as a significant co‑variant; moderate heat stress surprisingly enhanced growth under simulated microgravity. These results highlight the utility of large‑scale clinostats for dissecting interactions between environmental factors and simulated microgravity in plant development.

The study demonstrates that the interaction between spliceosomal proteins STA1 and DOT2 controls nuclear speckle organization, pre‑mRNA splicing efficiency, and heat‑stress tolerance in Arabidopsis thaliana. A missense mutation in DOT2 restores the weakened STA1‑DOT2 interaction in the sta1‑1 mutant, linking interaction strength to speckle formation and transcriptome‑wide intron retention under heat stress, while pharmacological inhibition of STA1‑associated speckles reproduces the mutant phenotypes. These findings reveal a heat‑sensitive interaction node that couples spliceosome assembly to nuclear speckle dynamics and splicing robustness.

The study establishes Aeschynomene evenia as a new model for dissecting legume immunity against the soilborne pathogen Aphanomyces euteiches and its relationship with Nod factor-independent symbiosis. Quantitative resistance was assessed through inoculation assays, phenotypic and cytological analyses, and RNA‑seq identified thousands of differentially expressed genes, highlighting immune signaling and specialized metabolism, with mutant analysis confirming dual‑function kinases that modulate resistance. Comparative transcriptomics with Medicago truncatula revealed conserved and unique immune responses, positioning the A. evenia–A. euteiches system as a valuable platform for exploring quantitative resistance and symbiosis integration.

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 the chloroplast‑localized protein AT4G33780 in Arabidopsis thaliana using CRISPR/Cas9 knockout and overexpression lines, revealing tissue‑specific expression and context‑dependent effects on seed germination, seedling growth, vegetative development, and root responses to nickel stress. Integrated transcriptomic (RNA‑seq) and untargeted metabolomic analyses show extensive transcriptional reprogramming—especially of cell‑wall genes—and altered central energy metabolism, indicating AT4G33780 coordinates metabolic state with developmental regulation rather than controlling single pathways.