The study utilizes a large collection of fluorescently marked Ds-GFP insertional mutations in haploid maize pollen to link gene disruptions with quantitative fitness effects measured as transmission deviations. By integrating genome-derived features (e.g., codon usage) and expression profiling into interpretable machine learning models, they achieve high predictive performance (auROC >90%) for genes influencing pollen fitness, highlighting expression specificity as a key predictor.

The study examined how DNA methylation influences cold stress priming in Arabidopsis thaliana, revealing that primed plants exhibit distinct gene expression and methylation patterns compared to non-primed plants. DNA methylation mutants, especially met1 lacking CG methylation, showed altered cold memory and misregulation of the CBF gene cluster, indicating that methylation ensures transcriptional precision during stress recall.

The study used paired whole‑genome bisulphite sequencing and RNA‑seq on wheat landraces to investigate how DNA methylation patterns change during drought stress, revealing antagonistic trends across cytosine contexts and a key demethylation role for ROS1a family members. Gene‑body methylation correlated positively with expression but negatively with stress‑responsive changes, while drought‑induced hyper‑methylation of specific transposable elements, especially the RLX_famc9 LTR retrotransposon, appears to modulate downstream gene regulation via siRNA precursors.

The study examined DNA methylation dynamics in Arabidopsis thaliana shoots and roots under heat, phosphate deficiency, and combined stress using whole-genome bisulfite sequencing, small RNA‑seq, and RNA‑seq. Distinct stress‑specific methylation patterns were identified, with heat and combined stress causing CHH hypomethylation, phosphate deficiency causing hyper‑ and hypomethylation in shoots and roots respectively, and the combined stress exhibiting a unique signature independent of additive effects. Methylation changes were concentrated in transposable elements and regulatory regions, implicating RdDM and CMT2 pathways and suggesting a role in chromatin accessibility rather than direct transcriptional control.

The study examines how ectopic accumulation of methionine in Arabidopsis thaliana leaves, driven by a deregulated AtCGS transgene under a seed‑specific promoter, reshapes metabolism, gene expression, and DNA methylation. High‑methionine lines exhibit increased amino acids and sugars, activation of stress‑hormone pathways, and reduced expression of DNA methyltransferases, while low‑methionine lines show heightened non‑CG methylation without major transcriptional changes. Integrated transcriptomic and methylomic analyses reveal a feedback loop linking sulfur‑carbon metabolism, stress adaptation, and epigenetic regulation.

The study mapped the cis‑regulatory landscape of the winter rapeseed cultivar Express617, identifying thousands of novel regulatory elements and characterizing super‑enhancers that are asymmetrically enriched in the Cn subgenome of Brassica napus. An in‑silico pipeline combining population‑level expression data and machine‑learning models revealed that many SE‑associated genes are expressed above predicted levels, and structural variants disrupting SEs lead to reduced gene expression, highlighting their functional importance for gene regulation and breeding.

The study integrated 16 Arabidopsis thaliana whole‑genome bisulfite sequencing datasets from 13 stress experiments using a unified bioinformatic pipeline to map common and stress‑specific DNA methylation changes. Differentially methylated regions varied by stress type and methylation context, with CG DMRs enriched in gene bodies and CHG/CHH DMRs in transposable elements, some of which overlapped loci prone to stable epimutations. Gene ontology and TE enrichment analyses highlighted shared stress pathways and suggest environmental stress can generate heritable epigenetic variation.

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 examined short‑term and transannual drought memory in cambium tissues of two Populus genotypes and four epitypes with modified DNA‑methylation machinery, revealing persistent hormone, transcript, and methylation changes one week after stress relief. Trees previously stressed in Year 1 displayed distinct physiological and molecular responses to a second drought in Year 2, indicating long‑term memory linked to stable CG‑context DNA methylation, with genotype‑dependent differences in plasticity and stability. These findings position the cambium as a reservoir for epigenetic stress memory and suggest exploitable epigenetic variation for tree breeding under drought.

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.