Haixia Wu | PubMed Health

PubMed2026

Genome-wide analysis of the plant-specific PLATZ gene family in Taraxacum kok-saghyz and its roles in response to drought and salt tolerance.

Abiotic stress severely limits plant growth and productivity. Taraxacum kok-saghyz Rodin (TKS), known for its environmental resilience, represents a valuable resource for identifying stress-tolerant genes to improve stress-adaptive crops. Plant AT-rich protein and zinc-binding protein (PLATZ) transcription factors serve as core regulators of plant growth, developmental processes, and adaptive responses to various stress conditions; however, they remain uncharacterized in TKS. Here, we identified 10 TksPLATZ genes through a whole-genome analysis. Phylogenetically, these genes were grouped into five distinct evolutionary branches. Promoter sequence analysis revealed multiple types of cis-acting regulatory elements that are connected with hormonal signal responses and environmental stress adaptation. Integrated analysis of transcriptome datasets and RT-qPCR validation demonstrated that TksPLATZ genes display tissue-specific expression profiles and show distinct responsive patterns to drought and salt stress treatments. Among them, TksPLATZ1, TksPLATZ2 and TksPLATZ7 were markedly induced under both stressors and were selected for further functional study. We demonstrated that TksPLATZ1, TksPLATZ2 and TksPLATZ7 localize to the cell nucleus and act as transcriptional activators and repressors, respectively. Phenotypic data from overexpression experiments in plants confirm that heterologous expression of TksPLATZ1, TksPLATZ2, and TksPLATZ7 enhances the tolerance of Arabidopsis to salt and osmotic stress. These findings provide valuable genetic resources for improving plant tolerance to environmental stresses.

Salt Tolerance↗

PubMed2025

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of immune recognition and vaccine development. In this study, an evolutionary scale model (ESM) combined with parallel CNN blocks and a cross attention mechanism was used to construct a novel ESMpHLA model for predicting HLA class I binding peptides. Based on the 91,560 binding peptides of 41 HLA-A alleles, 56,731 of 50 HLA-B alleles and 2444 of 10 HLA-C alleles, the ESMpHLA model was successfully established and achieved satisfying prediction performances with the overall accuracy and AUC values of 0.874 and 0.938 for the test dataset. The results indicate that the ESMpHLA model performs well in dealing with different HLA class I 2-field alleles as well as the peptides with different lengths. Then, the generalisation ability of the ESMpHLA model was validated by an independent test dataset compiled from recent IEDB weekly benchmark datasets. The results showed that the ESMpHLA model achieved the highest ROC-AUC and PR-AUC values when compared with the latest BVMHC, CapsNet-MHC, STMHCpan and BVLSTM models. In addition, two ensemble models were also established by integrating the above 5 deep learning models using soft-voting and hard-voting strategies.

Humans↗