In the study, researchers explored the genetic mechanisms by which plants protect themselves from environmental stressors and adapt to changing conditions. The investigation involved the analysis of RNA-Seq data from various tomato genotypes, tissue types, and drought durations. A time series approach was employed to identify gene modules responsive to drought at both early and late stages. Machine learning techniques were applied to pinpoint the most responsive genes to drought stress.
The analysis revealed six candidate genes in tomatoes: Fasciclin-like arabinogalactan protein 2 (FLA2), Amino acid transporter family protein (ASCT), Arginine decarboxylase 1 (ADC1), Protein NRT1/PTR family 7.3 (NPF7.3), BAG family molecular chaperone regulator 5 (BAG5), and Dicer-like 2b (DCL2b) that demonstrated responsiveness to drought. Gene association networks were constructed to identify potential interacting partners, confirming their involvement in drought response.
These candidate genes offer valuable insights into the adaptation of tomato plants to drought conditions. Furthermore, they have the potential to significantly impact molecular breeding and genome editing efforts in the field of tomato cultivation, shedding light on the molecular mechanisms underlying drought adaptation. This research underscores the critical role of genetics in plant adaptation, particularly in the context of changing climates and growing populations.
Chowdhury, R.H., Eti, F.S., Ahmed, R. et al. Drought-responsive genes in tomato: meta-analysis of gene expression using machine learning. Sci Rep 13, 19374 (2023). https://doi.org/10.1038/s41598-023-45942-2