Drought and salinity are major abiotic stresses that restrict crop productivity by disrupting water relations, ion balance, cellular metabolism, and gene regulation. This study investigated the molecular and genetic responses associated with these stresses using the publicly available rice transcriptomic dataset GSE21651 from the NCBI Gene Expression Omnibus. The analysis included 16 samples representing control, drought, and salinity conditions and 57,381 probe sets. Genome-wide differential-expression analysis revealed that 23,506 probe sets (40.96%) were significant at adjusted P < 0.05, indicating extensive transcriptional reprogramming under stress. Exploratory expression profiling and UMAP analysis further demonstrated distinct global expression patterns among control, drought, and salinity groups. Functional interpretation of significant genes identified major stress-responsive components involved in water transport, osmotic protection, ABA-mediated signalling, antioxidant defence, metabolic adjustment, and transcriptional regulation. Important candidate genes included aquaporins, late embryogenesis abundant proteins, dehydrins, antioxidant enzymes, and WRKY, NAC, MYB, and bZIP transcription factors. The findings demonstrate that drought and salinity tolerance in rice is governed by coordinated molecular networks involving both shared and stress-specific responses. These results highlight the value of transcriptomic analysis for identifying candidate genes that can support marker-assisted breeding, genetic engineering, and genome-editing strategies aimed at developing climate-resilient crops.
Biju Sidharthan, P. V. Pulate, Misha Yadav et al.· Genetics and Molecular Resea...· 0 citations
Irrigated agriculture consumes the largest share of global freshwater withdrawals, and this dependence is intensifying as rainfall variability and evaporative demand increase under a warming climate. Deficit irrigation, defined as the deliberate application of water below full crop evapotranspiration requirements at selected growth stages, has emerged as a central strategy for reconciling food production with water scarcity. This review synthesises evidence on the physiological basis of crop responses to water deficit, the modelling frameworks used to predict yield and water productivity outcomes, and the performance of deficit irrigation across field crops, orchard and vine systems, and vegetables. The economic and behavioural dimensions of farmer adoption, the interaction between deficit irrigation and soil salinisation under changing climatic conditions, and emerging genetic and computational tools that support precision water management are also examined. Findings indicate that regulated and sustained deficit irrigation strategies can improve water productivity substantially, often between eight and thirty per cent relative to full irrigation, although yield penalties vary widely by crop, growth stage sensitivity and environmental context. Climate change is projected to alter the reliability of these gains, particularly where warming erodes the compensatory physiological mechanisms that underpin moderate water stress benefits. Economic viability depends strongly on relative water and commodity prices, and farmer adoption remains constrained by risk aversion and limited technical support. The review concludes that deficit irrigation should be understood not as a fixed prescription but as an adaptive, crop- and context-specific component of climate-risk management, requiring closer integration of physiological monitoring, crop modelling and economic decision support.
Biju Sidharthan, B. Sushmitha, B. Santhosh et al.· Journal of Experimental Agri...· 0 citations
The increasing complexity of disease pathogenesis and the limitations to the traditional approaches to drug discovery suggests that it is necessary to move to more effective and integrative approaches. In this study, a genome-wide genetic pathway analysis system has been built, and an artificial intelligence (AI)-assisted framework to identify the drug targets based on a genetic pathway analysis has been developed. The used dataset was the Genomics of Drug Sensitivity in Cancer (GDSC) data that included both measures of drug response and genomic features (gene expression, copy number alterations, and methylation data). The use of machine learning models like Random Forest, XGBoost, and Artificial Neural Networks were implemented in drug sensitivity prediction. The XGBoost is the best in predictive accuracy among them. Importance of feature analysis and pathway enrichment analysis revealed that key determinants of drug response are key signalling pathways, with PI3K-Akt and MAPK pathways being the most important. Moreover, a drug-target pathway network was built to explain the intricate biological interactions and to find out possible therapeutic targets. The results showed the utility of the combination of AI with pathway-level bioinformatics analysis to not only provide predictive accuracy but also provide biological interpretability. Though the use of in vitro data is limited in the study, the study provides a scalable, robust framework of drug discovery based on AI assistance. The results can be used to further the field of precision medicine through the identification of biologically relevant targets as well as optimization of therapeutic approaches.
Vikram R. Patil, Taru Gupta, Indu Melkani et al.· Genetics and Molecular Resea...· 0 citations
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