Machine learning extracts marks of thiamine's role in cold acclimation in the transcriptome of Vitis vinifera

Unveiling Iso- and Aniso-Hydric Disparities in Grapevine-A Reanalysis by Transcriptome Portrayal Machine Learning

Mechanisms underlying grapevine responses to water(-deficient) stress (WS) are crucial for viticulture amid escalating climate change challenges. Reanalysis of previous transcriptome data uncovered disparities among isohydric and anisohydric grapevine cultivars in managing water scarcity. By using a self-organizing map (SOM) transcriptome portrayal, we elucidate specific gene expression trajectories, shedding light on the dynamic interplay of transcriptional programs as stress duration progresses. Functional annotation reveals key pathways involved in drought response, pinpointing potential targets for enhancing drought resilience in grapevine cultivation. Our results indicate distinct gene expression responses, with the isohydric cultivar favoring plant growth and possibly stilbenoid synthesis, while the anisohydric cultivar engages more in stress response and water management mechanisms. Notably, prolonged WS leads to converging stress responses in both cultivars, particularly through the activation of chaperones for stress mitigation. These findings underscore the importance of understanding cultivar-specific WS responses to develop sustainable viticultural strategies in the face of changing climate.

Special Issue on "Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics 2.0"

The analysis of molecular mechanisms underlying plant adaptation to environmental changes and stress response is crucial for plant biotechnology [...].