Back

Characterization of the novel SmsHSP24.1 Promoter: Unveiling its abiotic stress-inducible expression in transgenic Eggplant (Solanum melongena L.)

Khatun, M. M.; Khan, I.; Borphukan, B.; Das, K. C.; Khan, H.; Islam, M. R.; Reddy, M. K.; Salimullah, M.

2024-04-22 plant biology
10.1101/2024.04.20.589358 bioRxiv
Show abstract

Promoters play a pivotal role in regulating gene expression, orchestrating vital processes in plants, including their responses to various environmental stresses. In this study, we focus on the comprehensive characterization of the SmsHSP24.1 promoter, a novel cis-acting element, within the context of transgenic Eggplant (Solanum melongena L.). This detailed analysis shed light on the intricate mechanisms governing SmsHSP24.1 promoter-driven gene regulation, particularly in response to adverse environmental challenges such as heat, salt and drought stressors offering valuable insights into its role in plant stress adaptation. The advances in our understanding of promoter-driven gene regulation also contribute to the broader goal of enhancing crop resilience to abiotic stresses, positioning the SmsHSP24.1 promoter as a promising tool in agricultural biotechnology applications. HighlightsO_LIDemonstrated that the full-length 2.0 kb SmsHSP24.1 promoter significantly enhances gene expression under heat stress, with an observable decline in expression with promoter truncation. C_LIO_LIIdentified specific regulatory elements within the SmsHSP24.1 promoter that are decisive for inducible expression in response to abiotic stresses such as heat, salt, and drought. C_LIO_LIHighlighted the utility of the SmsHSP24.1 promoter in crop improvement programs, offering a tool for developing transgenic plant tolerance to combined stresses. C_LI

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.