Results for “Brassica”

PubMed2026

Identification of the BrSK gene family in flowering Chinese cabbage and functional characterization of BrSK2 subfamily involvement in heat stress.

Glycogen synthase kinase 3 (GSK3) kinases are evolutionarily conserved regulators of plant development and stress signaling, yet their contributions to thermotolerance in cool-adapted Brassica crops remain poorly understood. Here, we identified 16 BrSK genes in the Caixin (Brassica rapa ssp. chinensis var. parachinensis) genome, all harboring intact catalytic motifs indicative of functional kinase activity. Spatiotemporal expression profiling revealed preferential accumulation of BrSK transcripts in stem apices and floral organs during reproductive transition, while promoter analysis identified abundant heat- and abiotic stress-responsive cis-elements. Under heat stress, BrSK21, BrSK22, and BrSK23 displayed striking genotype-specific expression dynamics. BrSK21/22/23 transcripts were stably suppressed in the heat-tolerant cultivar '49-19' but transiently declined before rapidly rebounding in the heat-sensitive 'Liuye 50', mirroring RNA-seq profiles. Protein-protein interaction assays (Y2H, BiFC, and LCI) demonstrated specific associations between BrSK kinases and BrHSFA1. Functional validation via VIGS revealed that silencing of BrSK21 significantly enhanced thermotolerance, with triple silencing of BrSK21/22/23 conferring additive protection, indicating functional redundancy within the BrSK2 subfamily. Collectively, these findings establish the BrSK2 subfamily as negative regulators of heat tolerance in Caixin, likely via modulation of BrHSFA1 expression. This work identifies high-priority targets for molecular breeding of climate-resilient Brassica vegetables.

Plant Proteins↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗