The study reveals that in the liverwort Marchantia polymorpha, the UV‑B photoreceptor MpUVR8 forms homodimers that monomerize and accumulate in the nucleus upon UV‑B exposure, activating COP1‑dependent growth inhibition, gene expression reprogramming, and UV‑absorbing metabolite production. MpRUP promotes redimerization of MpUVR8, acting as a negative regulator, while MpSPA also negatively modulates UVR8 signaling, indicating lineage‑specific diversification of UV‑B signaling components that originated over 400 Myr ago.

The study investigates the role of the chromatin regulator MpSWI3, a core subunit of the SWI/SNF complex, in the liverwort Marchantia polymorpha. A promoter mutation disrupts male gametangiophore development and spermiogenesis, causing enhanced vegetative propagation, and transcriptomic analysis reveals that MpSWI3 regulates genes controlling reproductive initiation, sperm function, and asexual reproduction, highlighting its ancient epigenetic role in balancing vegetative and reproductive phases.

The study investigates the role of two ATP-binding cassette transporters, MpABCG1 and MpABCG36, in the sequestration of specialized metabolites within oil bodies of the liverwort Marchantia polymorpha. Loss‑of‑function mutants displayed reduced accumulation of sesquiterpenes and, specifically for MpABCG1, decreased levels of bis‑bibenzyls, while oil‑body formation remained largely unaffected, indicating these transporters are essential for metabolite accumulation rather than organelle biogenesis.

The study benchmarked over 20 web‑based gRNA on‑target efficiency prediction tools against an experimental plant CRISPR editing dataset, finding several machine‑learning based tools whose scores strongly correlated with observed InDel frequencies. Additionally, the performance of popular platforms such as CRISPOR and CRISPR‑P was assessed, offering guidance for improved gRNA design in plant genome editing.

The study evaluates the use of single-cell RNA sequencing (scRNA-seq) data to predict plant metabolic pathway genes (MPGs) in Arabidopsis thaliana, comparing five multi-label machine‑learning algorithms against traditional bulk RNA‑seq approaches. scRNA‑seq generated co‑expression networks that, while different, yielded significantly higher MPG classification accuracy, especially when data were split by genetic background or tissue type, and deep learning outperformed classical methods. The authors conclude that scRNA‑seq offers superior predictive power and should be incorporated into future MPG discovery pipelines.

The study characterizes the single-copy S-nitrosoglutathione reductase 1 (MpGSNOR1) in the liverwort Marchantia polymorpha, showing that loss-of-function mutants generated via CRISPR/Cas9 exhibit marked morphological defects and compromised SNO homeostasis and immune responses. These findings indicate that GSNOR-mediated regulation of S‑nitrosylation is an ancient mechanism linking development and immunity in early land plants.

The study examines how proteasomal degradation of A‑class and B‑class Auxin Response Factors (ARFs) is regulated in the bryophyte Marchantia polymorpha, identifying a key residue required for MpARF2 degradation that is also conserved in MpARF1. While disruption of MpARF2 degradation impairs development across life‑cycle stages, blocking MpARF1 degradation has minimal phenotypic impact, indicating divergent functional integration despite a shared degradation mechanism.

The study integrated weekly morphophysiological measurements with high-density genotyping-by-sequencing data and a machine‑learning pipeline to dissect flowering time variation in diverse Cannabis sativa landraces. By applying mutual information, recursive feature elimination, random forest, and support vector machine classifiers to over 234,000 combined genetic, phenotypic, and environmental features, the authors identified 53 key markers that classify early, medium, and late flowering types with 96.6% accuracy. Notable loci, including CsFT3 and CsCFL1, were highlighted as promising targets for breeding and smart‑crop strategies.

The study used extensive gravimetric load‑cell and ambient sensor data collected over seven years from hundreds of greenhouse-grown crops to train machine‑learning models for predicting daily whole‑plant transpiration. Random Forest and XGBoost achieved the highest accuracy (R² up to 0.89), with ambient temperature identified as the dominant driver. These results highlight the promise of ML‑based tools for precise agricultural water management.

The study used high-resolution chemical imaging to map cell-type specific metabolic changes in plant roots inoculated with a nine-strain endophyte consortium under drought, revealing that endophytes differentially alter root metabolomes across spatial domains. Machine learning identified metabolites and exudates predictive of drought and endophyte treatment, and correlation analyses showed dynamic endophyte–metabolite relationships under stress.