Transcriptome-based meta-analysis of drought stress regulatory genes in tomato
Abstract
Plants possess various molecular defense systems to ward off biotic and abiotic stresses and adjust to environmental changes. The RNA-seq technology assists biologists in quickly identifying genes responding to abiotic stresses. Thereby, using meta-transcriptomic data from the GEO NCBI public database, we have attempted to reach a consensus on gene variations. This approach identified a global set of 18 drought-responsive genes by Bonferroni-adjusted proportional test on one sample, p 2 (biological process), XH/XS domain-containing protein (molecular function & cellular component), alpha 1,4-glycosyltransferase family protein (KEGG) and Histone superfamily protein (plant ontology). qRT-PCR-based gene expression analyses elaborated the similar trends in gene expression in both local genotypes under drought stress conditions covering the ratio of six to four as per down and upregulated meta-genes, respectively. This could indicate that these genes are part of a putative set of stress-responsive genes that are crucial for survival and adaptation under drought conditions thereby, they may serve as potential targets for future functional studies and breeding applications that will be helpful to enhance tomato production in the future.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Quercetin Influences Quorum Sensing in Food Borne Bacteria: In-Vitro and In-Silico Evidence
- Phenolic-protein interactions: insight from in-silico analyses – a review
- In Silico Discovery of Candidate Drugs against Covid-19
- Neuropharmacological Effects of Quercetin: A Literature-Based Review
- In vitro and in silico anti-dengue activity of compounds obtained from Psidium guajava through bioprospecting
- Effect of Quercetin Rich Onion Extracts on Bacterial Quorum Sensing
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.