The study identifies the circadian clock component ELF3 as a temporal gatekeeper that limits hormone‑induced pericycle proliferation and lateral root development in Arabidopsis thaliana. Time‑resolved transcriptomics, imaging, and genetic analyses show that ELF3 maintains rhythmic expression of key regulators via LNK1 and MADS‑box genes, and that loss of ELF3 disrupts this rhythm, enhancing callus growth and accelerating root organogenesis.

The study reveals that the microtubule-associated protein MAP70-2 integrates mechanical and biochemical signals to guide division plane orientation during early lateral root primordium formation in Arabidopsis thaliana. Dynamic MAP70-2 localization to cell corners and the cortical division zone precedes cytokinesis, and loss of MAP70-2 results in misoriented divisions and malformed lateral roots, highlighting its role in three‑dimensional differential growth under mechanical constraints.

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 uncovers how arbuscular mycorrhizal (AM) fungi induce lateral root formation in maize by activating ethylene‑responsive transcription factors (ERFs) that regulate pericycle cell division and reshape flavonoid metabolism, lowering inhibitory flavonols. It also shows that the rhizobacterium Massilia collaborates with AM fungi, degrading flavonoids and supplying auxin, thereby creating an integrated ethylene‑flavonoid‑microbe signaling network that can be harnessed to improve nutrient uptake and crop sustainability.

The study examined drought tolerance across nine provenances of the conifer Pinus nigra using high‑throughput phenotyping combined with metabolomic and transcriptomic analyses under controlled soil‑drying conditions. Drought tolerance, measured by the decline in Fv/Fm, varied among provenances but was not linked to a climatic gradient and was independent of growth, with tolerant provenances showing distinct flavonoid and diterpene profiles and provenance‑specific gene expression patterns. Integrating phenotypic and molecular data revealed metabolic signatures underlying drought adaptation in this non‑model conifer.

The study shows that the membrane lipids PI4P, PI(4,5)P2, and phosphatidylserine have distinct spatial and temporal dynamics during lateral root primordium formation in Arabidopsis thaliana, with PI4P acting as a stable basal lipid, PI(4,5)P2 serving as a negative regulator of initiation, and phosphatidylserine increasing after founder cell activation. Using live-cell biosensors, genetic mutants, and an inducible PI(4,5)P2 depletion system, the authors demonstrate that reducing PI(4,5)P2 enhances lateral root initiation and development.

The study engineers Type‑B response regulators to alter their transcriptional activity and cytokinin sensitivity, enabling precise modulation of cytokinin‑dependent traits. Using tissue‑specific promoters, the synthetic transcription factors were deployed in Arabidopsis thaliana to reliably increase or decrease lateral root numbers, demonstrating a modular platform for controlling developmental phenotypes.

The study characterizes the tomato class B heat shock factor SlHSFB3a, revealing its age‑dependent expression in roots and its role in enhancing lateral root density by modulating auxin homeostasis. Overexpression of SlHSFB3a increases lateral root emergence, while CRISPR‑mediated knockouts produce the opposite phenotype, indicating that SlHSFB3a regulates auxin signaling through repression of auxin repressors and activation of the ARF7/LOB20 pathway.

The Global Wheat Dataset Consortium released a comprehensive semantic segmentation dataset (GWFSS) of wheat organs across developmental stages, comprising 1,096 fully annotated images and 52,078 unannotated images from 11 institutions. Models based on DeepLabV3Plus and Segformer were trained, with Segformer achieving ≈90% mIoU for leaves and spikes but lower precision (54%) for stems, while also enabling weed exclusion and discrimination of necrotic, senescent, and residue tissues.