Current IssueEditorial BoardOnline First ArticlesArchive
Current IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Genetic Basis and Identification of Candidate Genes for Drought Tolerance in Oryza sativa L. by DNA Molecular-assisted Selection

A
Arshad Naji Alhasnawi1,2,*
https://orcid.org/0000-0003-2817-8807
1Department of Biology, College of Education for Pure Sciences, Al-Muthanna University, Al-Samawah, 66001, Iraq.
2College of Engineering, Al-Ayen University, Thi-Qar, Nasiriyah,  64011, Iraq.

ABSTRACT

Background: Drought stress represents a major obstacle to ensuring worldwide agricultural output and food availability. As a primary dietary staple, rice is highly vulnerable to insufficient water, suffering significant productivity declines under drought pressure. Gaining insight into plant mechanisms of coping with drought is essential for creating future cultivars adapted to limited water access. 

Methods: This investigation focuses on large-scale discovery and assessment of ten drought-resilient genes in Oryza sativa L. Information on these genes was gathered from publicly accessible platforms, including the National Center for Biotechnology Information (NCBI) and examined through bioinformatics approaches such as the basic local alignment search tool (BLAST) searches and protein interaction mapping. 

Result: Findings confirmed the presence of ten genes actively engaged in modulating drought-associated biological responses. Additional transcription factors were pinpointed, highlighting their critical role in amplifying adaptation to water deficit. The research highlights the utility of computational genomics for rapidly identifying candidate drought-resilient genes, thus presenting a framework for targeted breeding strategies aimed at producing rice varieties better adapted to climate variability and water limitation.

INTRODUCTION

Oryza sativa L. (rice) is recognised as the most vital staple in the developing world (Mandal et al., 2019) and source of dietary fibre, minerals, vitamins, proteins and carbohydrates (Olivya et al., 2025), though its overall productivity and yield reliability remain highly vulnerable to environmental stresses (Chengqi et al., 2024). Feeding more than half of humanity, rice is indispensable, yet its production is drastically reduced under drought conditions. While traditional landraces display wide-ranging morphological, physiological and molecular traits that confer resilience to water scarcity, many modern high-yielding cultivars show pronounced sensitivity to drought stress (Yadav et al., 2023). Drought represents one of the most critical environmental challenges, with nearly 50% of global rice output estimated to be directly affected by insufficient water availability (Sircar and Parekh, 2019). Furthermore, drought prompts the build-up of osmo-protective compounds such as proline, sugars, polyamines and antioxidants, while also modifying gene expression patterns, particularly those of transcription factors and stress-related proteins, ultimately diminishing crop productivity. To withstand such effects, rice plants employ strategies including drought avoidance, drought escape and dought tolerance-mechanisms aimed at mitigating damage caused by water stress (Kumar et al., 2022).

The ongoing shifts in climate are intensifying land degradation, advancing desert expansion and increasing soil salinity-processes which are severely diminishing cultivable areas and agricultural productivity. This issue is especially urgent given the steadily rising global population (González, 2023).

Abiotic stresses like salinity, drought and temperature are expected to get worse because of climate change. This will have a negative impact on crop plants, especially rice plants and lower their output (Mohapatra and Saho, 2025). Among abiotic stressors, drought ranks as a major constraint on crop yields across the globe and its severity is escalating with changing climate patterns. At any point in a plant’s life cycle, drought exerts harmful and yield-suppressing effects, ultimately threatening survival. Like other environmental challenges, exposure to water deficit provokes a cascade of molecular, physiological and biochemical adjustments that attempt to counteract drought pressure (Oguz et al., 2022). Droughts have become more common because of global warming and big changes in the climate (Xu et al., 2025).

Plants have gradually developed a broad spectrum of defensive strategies for mitigating the damage caused by biotic and abiotic constraints (Alhasnawi, 2019). Of these, drought stands out as the most destructive abiotic factor, generating severe reductions in productivity worldwide. Water deficit induces osmotic stress, which lowers chlorophyll and relative water levels while increasing osmolyte build-up, epicuticular wax deposition, antioxidant enzyme function, reactive oxygen species formation, secondary metabolite accumulation, lipid membrane oxidation and abscisic acid activity (Ahmad et al., 2022).

It must be emphasised that these adaptive strategies enabling plants to withstand drought stress are controlled by numerous genes. Thus, pinpointing the specific genes which improve drought resilience is essential for maintaining yield stability within global crop production systems (Hura et al., 2022).

For these reasons, it is imperative to investigate the molecular genetic framework underlying drought resistance in rice through advanced bioinformatics methods. The stress-responsive genes are classified as genes encoding proteins engaged in osmolyte biosynthesis (proline, mannitol, glycine betaine, trehalose, etc.), protective proteins (heat shock proteins and late embryogenesis abundant proteins, etc.), transcription factors (WRKY, DREB, ERF, MYC, MYB, bZIP, NAC, GRAS, etc.), antioxidant enzymes (superoxide dismutase, ascorbate peroxidase, catalase, glutathione S-transferases, glutathione reductase, etc.) and phytohormones (auxin, abscisic acid, jasmonic acid, ethylene, etc.) (Srivastava et al., 2022). Genetic diversity is important for breeding projects that want to create new types of rice (Kumar et al., 2025). The different genotypes’ genetic diversity is an important tool for breeding programs because it helps create new farming systems and increase output diversity (Rai et al., 2025).

Advances in transcriptome profiling, along with improved computational resources, have enabled bioinformatics-driven identification of rice genes potentially linked to drought resistance by comparison with previously characterised drought-associated genes. Using a sub-network extraction algorithm combined with gene co-expression data, we constructed an integrated interaction network encompassing both well-identified drought-responsive rice genes and putative ones (Gao et al., 2020).

In the present work, a variety of computational platforms and omics-based approaches were employed to explore molecular factors and biological pathways that underlie drought tolerance in rice. The primary aim was to analyse and contrast drought-resilient genes across diverse rice cultivars through bioinformatics pipelines in order to pinpoint critical genetic determinants contributing to drought adaptation. These findings are expected to provide a valuable resource for gene discovery relevant to drought tolerance. Moreover, this analytical framework can clarify functional interconnections among genes involved in the multifaceted drought stress response in rice, while also serving as a comparative reference point for extending similar investigations in other agriculturally important crop species.

MATERIALS AND METHODS

Data collection
 
The research was conducted at Department of Biology, College of Education for Pure Sciences, Al-Muthanna University in 2025 to identify genes associated with drought tolerance in Oryza sativa L. using different drought-responsive genes from the National Center for Biotechnology Information (NCBI) database. The study included molecular responses using indicators and genes related to drought stress.

Known drought-responsive genes contributing to tolerance in rice, specifically Oryza sativa L., were identified and retrieved from the National Center for Biotechnology Information (NCBI) database. The collected data were then used to compile essential details regarding these rice genes, while homology-based comparisons allowed assessment of their chromosomal distribution across ten gene symbols.

Utilising these data, we have summarised the generic information regarding the genes in Oryza sativa L. and on the basis of the homology information, we have compared the location of the ten gene symbols for drought tolerance, including LOC4323843, LOC4337576, LOC4343219, LOC4347708, LOC4346460, LOC4345942, LOC4327045, LOC4325652, LOC4334350 and LOC4326935. Ten genomic context datasets and sequences remain distinct from specific genome assemblies. Both messenger ribonucleic acid (mRNA) and protein sequences are widely applied in computational studies to derive evolutionary insights into drought tolerance in Oryza sativa L.
 
Sequence alignment
 
Sequence comparisons of analysed clones with drought-related candidate genes in Oryza sativa L. were carried out through multiple sequence alignment using NCBI resources. The NCBI platform provides the basic local alignment search tool (BLAST) utilities that enable gene sequence queries, serving as a key bioinformatics method for assessing protein similarities. This approach highlights conserved domains and sequence variations, thereby offering insights into evolutionary linkages as well as functional stability among proteins implicated in drought tolerance mechanisms.
 
Phylogenetic analysis
 
Phylogenetic relationships were inferred using the distance-matrix neighbour-joining approach. Gene sequences associated with drought tolerance in Oryza sativa L. were first aligned and evolutionary trees were then generated through molecular evolutionary genetics analysis (MEGA) software (Version 11.013, 1993-2025) to examine divergence and conservation patterns among drought-responsive genes.

In addition, homologs of experimentally verified salinity tolerance genes were identified. Corresponding nucleotide sequences were retrieved either from GenBank or directly from contributing authors, followed by BLAST analysis to detect homologous counterparts. This process enabled recognition of related sequences, expanding the genetic framework for understanding stress adaptation mechanisms in rice.

RESULTS AND DISCUSSION

Bioinformatics analysis was employed to identify drought-resilient genes, focusing on a selected group of ten gene symbols in Oryza sativa L. The findings summarised in Table 1 highlight several rice genes critically associated with tolerance to water deficit. The table lists 10 specific genes demonstrated to play significant roles in mediating the plant’s adaptive responses to drought-induced stress. The identified gene symbols include LOC4323843, LOC4337576, LOC4343219, LOC4347708, LOC4346 460, LOC4345942, LOC4327045, LOC4325652, LOC4 334350 and LOC4326935. All identified genes were classified as protein-coding and verified under the Reference Sequence (RefSeq) database’s categories (validated or model), ensuring the accuracy of their reference sequence information.

Table 1: A gene symbol provides information about gene description, locus tag, gene typ, RefSeq status and organism in Oryza sativa L. for improved drought tolerance.



Each gene occupies a unique chromosomal position and is annotated with an individual locus tag, such as OSNPB_010971100 for LOC4323843, OSNPB_ 0501 07900 for LOC4337576, OSNPB_070476900 for LOC43 43219, OSNPB_090537700 for LOC4347708, OSNPB_ 090133600 for LOC4346460, OSNPB_ 080499800 for LOC4345942, OSNPB_010785700 for LOC4327045, OSN PB_010184900 for LOC4325652, OSNPB_030786400 for LOC4334350 and OSNPB_ 010702500 for LOC4326935. All analysed genes originate from the Oryza sativa Japonica group and each is associated with several alternative designations recorded across scientific references and databases, such as DI19-5, OsDi19-5, OsJ_004810 and P0518C01.8 for LOC4323843; DI1, DI19-6, OsDi19-6 and OsJ_016081 for LOC4337576; cdsp32, OsCDSP32 and OsJ_24223 for LOC4343219; PAP2 for LOC4346460; OJ1118_A06.9 for LOC4345942; R1G1A, bip104, SSRP1-A and OsJ_00664 for LOC4325652; and rDRP1 and rab25 for LOC4326935; signifying the terminological variations in gene naming for drought tolerance (Table 1).

The genomic context analysis of Chromosome 1 - NC_089035.1, Chromosome 5 - NC_089039.1, Chromos ome 7 - NC_089041.1, Chromosome 9 - NC_089043.1, Chromosome 9 - NC_089043.1, Chromosome 8 - NC_ 089042.1, Chromosome 1 - NC_089035.1, Chromosome 1 - NC_089035.1, Chromosome 3 - NC_089037.1 and Chromosome 1 - NC_089035.1 indicated a high density of drought-responsive loci distributed along proximal as well as distal regions. Multiple candidate genes were identified, notably including transcription factor family members, which show strong associations with tolerance to abiotic stresses (Fig 1A-1J).

Fig 1: Genomic context sections of the chromosomes.



The observed clustering of stress-responsive genes within syntenic chromosomal regions indicates the presence of potential regulatory hubs supporting adaptive functions. Furthermore, conserved promoter motifs and cis regulatory sequences were identified upstream of these candidate loci, underscoring their likely involvement in transcriptional control during water-deficit stress. Collectively, chromosome 1 emerges as a pivotal genomic region governing several drought-resilience pathways in rice.

Ten rice genes encoding proteins implicated in drought stress resistance were further characterised by mapping gene identifiers to corresponding symbols and compiling details regarding their gene sequences and protein products. Transcript sizes ranged from 994 nucleotides in LOC4323843 to 2529 nucleotides in LOC4325652. At the protein level, polypeptide lengths spanned from 202 amino acids (LOC4323843) to 641 amino acids (LOC4325652). These variations demonstrate extensive structural and functional diversity among drought-associated genes and their encoded proteins, with findings summarised in Table 2.

Table 2: List of gene transcripts and proteins in Oryza sativa L. for improved drought tolerance.



The genomic context analysis of genomic sequences LOC4323843, LOC4337576, LOC4343219, LOC4347708, LOC4346460, LOC4345942, LOC4327045, LOC4325652, LOC4334350 and LOC4326935 revealed that these loci are positioned within drought-responsive regions and encode transcripts containing predicted regulatory and functional domains linked to stress adaptation. The constructed gene models display distinct exon–intron structures, generating transcripts that are translated into proteins carrying conserved motifs associated with abiotic stress signalling pathways. Functional annotation further indicates that these products may operate as transcriptional regulators or stress-responsive proteins, thereby contributing to cellular stability under conditions of water scarcity. Additionally, surrounding genomic regions harbour other stress-related genes, indicating potential co-regulation or coordinated expression patterns during drought exposure (Fig 2A-2J).

Fig 2: Genomic regions, transcripts and products.



The examined gene data were cross-referenced with standard databases to retrieve complete mRNA and protein sequences, along with source sequence details and UniProt accession numbers. Analyses demonstrated that each gene possesses dual identifiers corresponding to both its mRNA transcript and encoded protein, such as NP_001359124.1, NP_001406959.1, P_001409084.1, P_015651317.1, NP_001390665.1, XP_015648389.1, XP_015622164.1, NP_001388399.1, XP_015628802.1 and NP_001393222.1. This highlights the reliability of molecular characterisation for these genes within genomic repositories (Table 3).

Table 3: These reference sequences exist independently of genome builds mRNA and protein(s).



The results in Table 4 and Table 5 depict that the source sequence for every gene is also documented, for instance, AK069516 and AU057067 for LOC4323843; AP014961, AY335486 and EG709698 for LOC4337576; AU172639, CB653462, CI167083, CI286584 and CI688566 for LOC4343219; A0A0P0XPV8, A2Z3I6, A3C107, Q69JX7 and Q940D3 for LOC4347708; CA754409, CB678401 and CI199149 for LOC4346460; AB117991, AK066832 and AK068959 for LOC4325652; and AY333185 for LOC43 26935. The UniProt Knowledgebase’s (UniProtKB) TrEMBL database also contains corresponding entries, which are automatically generated protein sequences, thereby broadening the scope of the analysis and facilitating monitoring of predicted protein models. Such integration improves the dependability of datasets used in functional annotation and diagnostic assessments of genes. Moreover, these findings represent a critical step in clarifying links between nucleotide sequence and protein architecture, ultimately supporting the development of computational diagnostic tools for drought stress resilience.

Table 4: These reference sequences exist independently of genome builds genomic.



Table 5: Contextual information includes genomics, genomic assembly and chromosomal location of the genes under study.




A phylogenetic tree was generated using alignment scores for NC_089035.1, NC_089039.1, NC_089041.1, NC_089043.1, NC_089043.1, NC_089042.1, NC_08903 5.1, NC_089035.1, NC_089037.1 and NC_089035.1, revealing a close evolutionary association between the genomic sequences. The clustering arrangement demonstrates strong sequence conservation, indicating probable functional similarity and shared evolutionary origins. Branch length measurements displayed limited divergence, reinforcing the hypothesis that these loci trace back to a common ancestral gene. Furthermore, the grouping of these sequences within the same phylogenetic clade emphasises their likely contribution to preserving conserved biological roles, potentially linked to mechanisms of drought tolerance. This conserved evolutionary signature offers additional support for the functional relevance of these genomic loci in Oryza sativa L. (Fig 3A-3J).

Fig 3: A phylogenetic tree analysis was constructed based on the alignment scores.



Plants adapt to environmental challenges by modulating their physiological processes and developmental programs through genome-wide changes in gene expression. Epigenetic regulators, including deoxyribonucleic acid (DNA) methylation and demethylation, are thought to exert critical influence in this process (Wang et al., 2011). The degree of drought tolerance exhibited by any plant species is largely determined by the presence and efficiency of genetic mechanisms underlying adaptation  (Kim et al., 2020). Bioinformatics-based platforms and analytical tools are highly valuable for evaluating plant responses to abiotic stressors (Neelapu and Chaitanya, 2024). 

High-throughput technologies, such as RNA sequencing (RNA-seq), enable detailed profiling of differential gene expression, offering insights into genes central to stress resilience. The integration of bioinformatics, genomics and next-generation sequencing   provides a deeper understanding of molecular pathways responsible for tolerance to diverse stress conditions. Such knowledge can be strategically applied to accelerate the breeding of stress-resilient crops and to enhance both yield performance and crop quality (Mu et al., 2022).

This research applied an integrative bioinformatics framework to uncover multiple drought-associated candidate genes, including regulatory transcription factors and functional enzymes, using publicly accessible rice transcriptome datasets. The outcomes support the prevailing model in which drought resilience arises from a complex genetic network controlling traits such as stomatal regulation, deep root systems and cellular equilibrium  (Kumar et al., 2015). The capacity to interrogate multi-omics datasets computationally facilitates systematic dissection of this multifactorial trait.

A key strength of the approach was the incorporation of co-expression network methodologies, notably weighted gene co-expression network analysis (WGCNA). This enabled identification of complete gene modules strongly associated with drought-related traits, extending beyond traditional differential expression analysis. Central hub genes within these modules, such as protein kinases mediating signalling events, were recognised as high-priority targets due to their potential to orchestrate the expression of numerous downstream genes (Ambrosino et al., 2020).

In addition, exploitation of rice pangenome datasets made it possible to detect allelic diversity among these candidate loci across drought-tolerant and drought-susceptible cultivars. This step is especially important, as it connects genetic polymorphisms with functional adaptation, thereby equipping plant breeders with precise molecular markers for targeted selection (Gao et al., 2019).

Thus, while bioinformatics offers a powerful hypothesis-generating engine, the functional validation of predicted genes remains essential. The candidates identified here should be meticulously tested by utilising clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 gene editing to generate knockouts and/or overexpression lines in pertinent genetic backgrounds for substantiating their role in drought resilience by means of exhaustive phenotypic and physiological evaluation (Razzaq et al., 2021). 

Future studies should aim to integrate additional layers of biological data to enhance prediction accuracy. This encompasses incorporating epigenomic data to capture how DNA methylation and histone modifications control stress memory, as well as metabolomic profiles for linking genetic regulation with physiological outcomes (Zitnik et al., 2019). Moreover, machine learning models trained on multi-omics data from field-grown plants under diverse drought regimes hold strong potential for estimating the most effective gene combinations for breeding (Crossa et al., 2025).

Bioinformatics tools enable the identification of stress-responsive genes, molecular markers and regulatory elements, whereas AI techniques strengthen predictive modelling, inference of gene regulatory networks and actual plant monitoring. Together, these innovations are vital for formulating stress-resilient plant varieties capable of thriving under increasingly extreme environmental conditions caused by global climate change and anthropogenic pressures. Practical applications encompass estimating drought-resistant gene variants, determining salt-tolerant crop cultivars and enabling real-time monitoring of plant health under extreme temperatures by means of AI-driven phenomics platforms (Zhang et al., 2024).

CONCLUSION

In conclusion, this thorough bioinformatics-driven study was successful in identifying a strong and trustworthy group of candidate genes that are essential for giving rice drought tolerance. The study created a comprehensive and potent framework for gene discovery and functional prioritization under water-limited conditions by methodically combining transcriptome profiling, co-expression network analysis and whole-genome-scale techniques. In addition to improving the precision of candidate gene identification, this integrative approach offered a deeper understanding of the intricate biochemical processes and regulatory networks that underlie rice’s responses to drought stress. Thorough functional validation employing cutting-edge molecular and physiological assays will be crucial to the successful conversion of these molecular discoveries into useful and sustainable agricultural uses. To fully utilize the genetic potential identified in this work, it will also be necessary to strategically choose advantageous alleles using marker-assisted selection, genomic selection, or precision genetic engineering techniques. The development of high-yielding, drought-tolerant and climate-resilient rice cultivars is ultimately anticipated to be greatly accelerated by the research’s findings, contributing to global food security and strengthening rice production systems’ ability to adapt to rising environmental variability and climate change.

CONFLICT OF INTEREST

The authors declare no conflict of interest.
 

REFERENCES


  1. Ahmad, H.M., Fiaz, S., Hafeez, S., Zahra, S., Shah, A.N., Gul, B. and Aziz, O. (2022). Plant growth-promoting rhizobacteria eliminate the effect of drought stress in plants: A review. Frontiers in Plant Science. 13: 875774. www.frontiersin. org (July 5, 2025).

  2. Alhasnawi, A.N. (2019). β-glucan-mediated alleviation of NaCl stress in Ocimum basilicum L. in relation to the response of antioxidant enzymes and assessment DNA marker. Journal of Ecological Engineering. 20(8): 90-99.

  3. Ambrosino, L.,  Colantuono, C., Diretto, G., Fiore , A. and Chiusano, M.L. (2020). Bioinformatics resources for plant abiotic stress responses: State of the art and opportunities in the fast evolving-omics era. Plants. 9(5): 591. https:// www.mdpi.com/2223-7747/9/5/591/htm (September 4, 2025).

  4. Chengqi, Z., Yuxuan, Y., Tian, Q., Yafan, H., Jifeng, Y. and Zhicheng, S. (2024). Drought-tolerant rice at molecular breeding eras: An emerging reality. Rice Science. 31(2): 179- 189. https://www.sciencedirect.com/science/article/pii/ S1672630823001130 (July 5, 2025).

  5. Crossa, J., Montesinos-Lopez, O.A., Costa-Neto, G., Vitale, P., J.W.R. Martini, J.W.R., Runcie, D., Fritsche-Neto, R. et al. (2025). Machine learning algorithms translate big data into predictive breeding accuracy. Trends in Plant Science. 30(2): 167- 184. https://www.cell.com/action/showFullText?pii= S13 60138524002590 (September 4, 2025).

  6. Gao, L., Gonda, I., Sun, H., Ma, Q., Bao, K., Tieman, D.M., Burzynski- Chang, E.A., Fish, T.L. et al. (2019). The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nature Genetics. 51(6): 1044-1051. https:/ /www.nature.com/articles/s41588-019-0410-2 (September 4, 2025).

  7. Gao, Y., Chen, Y., Ma, Z., Zeng, T., Ahmad, I., Zhang, Y. and Yue, Z. (2020). Identification of Rice Drought-Resistant Gene Based on Gene Expression Profiles and Network Analysis Algorithm. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, Cham, 290- 301. https://link.springer.com/chapter/10.1007/978-3- 030-60802-6_26 (July 6, 2025).

  8. González, E.M. (2023). Drought stress tolerance in plants. International Journal of Molecular Sciences. 24(7): 6562. https:// www.mdpi.com/1422-0067/24/7/6562 (July 5, 2025).

  9. Hura, T., Hura, K. and Agnieszka, O. (2022). Drought-stress induced physiological and molecular changes in plants. International Journal of Molecular Sciences. 23(9): 4698. https://www.mdpi.com/1422-0067/23/9/4698/htm (July 5, 2025).

  10. Kim, Y., Chung, Y.S., Lee, E., Tripathi, P., Heo, S. and Kyung-Hwan, K. (2020). Root response to drought stress in rice (Oryza sativa L.). International Journal of Molecular Sciences. 21(4): 1513. https://www.mdpi.com/1422- 0067/21/4/1513/htm (September 4, 2025).

  11. Kumar, A., Pathak , R.K.,  Gupta, S.M., Gaur, V.S. and Pandey, D. (2015). Systems biology for smart crops and agricultural innovation: Filling the gaps between genotype and phenotype for complex traits linked with robust agricultural productivity and sustainability. OMICS A Journal of Integrative Biology. 19(10): 581-601. https://pubmed.ncbi. nlm.nih.gov/26484978/ (September 4, 2025).

  12. Kumar, A., Sengar, R.S., Pathak, R.K. and Singh, A.K. (2022). Integrated approaches to develop drought-tolerant rice: Demand of era for global food security. Journal of Plant Growth Regulation. 42(1): 96-120. https://link.springer. com/article/10.1007/s00344-021-10561-6 (July 5, 2025).

  13. Kumar, S., Jayasudha S.,  Rajani and Singh, S.K. (2025). Genetic variability, heritability and genetic advance for yield and quality traits in rice (Oryza Sativa L.). Indian Journal of Agricultural Research. 60(2): 183-90. doi: 10.18805/ IJARe.A-6402. 

  14. Mandal, A., Alhasnawi, A., Jasim, H. and Mohamad, A. (2019). Evaluation of salt stress and molecular analysis of genetic variation of iraqi rice cultivars. Biodiversitas Journal of Biological Diversity. 20(11): 3309-3314. https:/ /smujo.id/biodiv/article/view/4365 (March 8, 2023).

  15. Mohapatra, M.D. and Saho, R.K. (2025). Antioxidant and agronomical screening of Oryza sativa L. in response to abiotic stress from local landraces. Indian Journal of Agricultural Research. 59(12): 1809-1818. doi: 10.18805/IJARe.A- 6468. 

  16. Mu, H., Wang, B. and Fang, Y. (2022). Bioinformatics in plant breeding and research on disease resistance. Plants. 11(22): 3118. https://www.mdpi.com/2223-7747/11/22/ 3118/htm (September 4, 2025).

  17. Neelapu, N.R.R. and Chaitanya, K.V. (2024). Bioinformatics Tools for Assessing Drought Stress Tolerance in Crops. In: Bioinformatics for Plant Research and Crop Breeding, Singapore: Wiley. 233-249. https://onlinelibrary.wiley.com/ doi/full/10.1002/9781394209965.ch9 (September 4, 2025).

  18. Oguz, M.C., Aycan, M., Oguz, E., Poyraz, I.  and Yildiz, M.  (2022). Drought stress tolerance in plants: Interplay of molecular, biochemical and physiological responses in important development stages. Physiologia. 2(4): 180-197. https:/ /www.mdpi.com/2673-9488/2/4/15 (July 5, 2025).

  19. Olivya, S.R., Dijee, B., Rose, F.M., Rashmi, C.R. and Biju S. (2025). An assessment of cosplay of microbes treated seeds on storability of rice seeds. Indian Journal of Agricultural Research. 60(1): 76-81. doi: 10.18805/IJARe.A-6355. 

  20. Rai, K.N., Rajesh, R.Y., Amit, Kavita, Kumar, P. and Dhaniya, R. (2025). Genetic variation and character association studies for seed yield and its attributes in diverse panel of mungbean genotypes [Vigna radiata (L.) Wilczek]. Legume Research. 49(2): 190-199. doi: 10.18805/LR- 5481. 

  21. Razzaq, A., Wani, S.H., Saleem, O., Yu , M., Zhou, M. and Shabala, S.  (2021). Rewilding crops for climate resilience: Economic analysis and de novo domestication strategies. Journal of Experimental Botany. 72(18): 6123-6139. https:// dx.doi.org/10.1093/jxb/erab276 (September 4, 2025).

  22. Sircar, S. and Nita, P. (2019). Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach. Plos One. 14(5): e0216068. https://pmc.ncbi.nlm.nih.gov/ articles/PMC6502313/ (July 5, 2025).

  23. Srivastava, D., Verma, G., Chawda, K., Chauhan, A.S., Pande, V. and Chakrabarty, D. (2022). Overexpression of Asr6, abscisic acid stress-ripening protein, enhances drought tolerance and modulates gene expression in rice (Oryza sativa L.). Environmental and Experimental Botany. 202: 105005. https://www.sciencedirect.com/science/article/ abs/pii/S0098847222002271 (July 5, 2025).

  24. Wang , W.S., Ya-Jiao, P., Xiu-Qin, Z., Dwivedi, D., Ling-Hua, Z., Ali, J., Bin-Ying, F. and Zhi-Kang, L. (2011). Drought- induced site-specific dna methylation and its association with drought tolerance in rice (Oryza sativa L.). Journal of Experimental Botany. 62(6): 1951-1960. https:// dx.doi.org/10.1093/jxb/erq391 (September 4, 2025).

  25. Xu, B., Yanhua, W., Rina, W., Yujing, L., Shiyu, Z. and Xiuli, Z. (2025). Drought resistance under PEG-6000 stress during germination of onobrychis viciifolia (Sainfoin) from different regions of China. Legume Research. 48(10): 1663-1667. doi: 10.18805/LRF-862.  

  26. Yadav, N., Sevanthi, A.M.,  Pandey, R., Chinnusamy, V., Singh, A.K. and Singh, N.K. (2023). Physiological response and agronomic performance of drought tolerance mutants of aus rice cultivar nagina 22 (Oryza sativa L). Field Crops Research. 290: 108760. https://www. sciencedirect.com/science/article/abs/pii/S0378429 022003318 (July 5, 2025).

  27. Zhang, X., Ibrahim, Z., M.B. Khaskheli, H. Raza, Zhou, F.  and Shamsi, I.H. (2024). Integrative approaches to abiotic stress management in crops: Combining bioinformatics educational tools and artificial intelligence applications. Sustainability (Switzerland). 16(17): 7651. https://www. mdpi.com/2071-1050/16/17/7651/htm (September 4, 2025).

  28. Zitnik, M., Nguyen, F., Wang, W., Leskovec, J., Goldenberg, A. and Hoffman, M.M. (2019). Machine learning for integrating data in biology and medicine: Principles, practice and opportunities. Information Fusion. 50: 71-91. https:// pmc.ncbi.nlm.nih.gov/articles/PMC6242341/(September 4, 2025).
Published In
Agricultural Science Digest
Article Metrics
Metrics Icon
0
Views
0
Citations
Reviewed By
Share Article
Download Article
In this Article
    Contact us
    Follow us

    Full Research Article

    Genetic Basis and Identification of Candidate Genes for Drought Tolerance in Oryza sativa L. by DNA Molecular-assisted Selection

    A
    Arshad Naji Alhasnawi1,2,*
    https://orcid.org/0000-0003-2817-8807
    1Department of Biology, College of Education for Pure Sciences, Al-Muthanna University, Al-Samawah, 66001, Iraq.
    2College of Engineering, Al-Ayen University, Thi-Qar, Nasiriyah,  64011, Iraq.

    ABSTRACT

    Background: Drought stress represents a major obstacle to ensuring worldwide agricultural output and food availability. As a primary dietary staple, rice is highly vulnerable to insufficient water, suffering significant productivity declines under drought pressure. Gaining insight into plant mechanisms of coping with drought is essential for creating future cultivars adapted to limited water access. 

    Methods: This investigation focuses on large-scale discovery and assessment of ten drought-resilient genes in Oryza sativa L. Information on these genes was gathered from publicly accessible platforms, including the National Center for Biotechnology Information (NCBI) and examined through bioinformatics approaches such as the basic local alignment search tool (BLAST) searches and protein interaction mapping. 

    Result: Findings confirmed the presence of ten genes actively engaged in modulating drought-associated biological responses. Additional transcription factors were pinpointed, highlighting their critical role in amplifying adaptation to water deficit. The research highlights the utility of computational genomics for rapidly identifying candidate drought-resilient genes, thus presenting a framework for targeted breeding strategies aimed at producing rice varieties better adapted to climate variability and water limitation.

    INTRODUCTION

    Oryza sativa L. (rice) is recognised as the most vital staple in the developing world (Mandal et al., 2019) and source of dietary fibre, minerals, vitamins, proteins and carbohydrates (Olivya et al., 2025), though its overall productivity and yield reliability remain highly vulnerable to environmental stresses (Chengqi et al., 2024). Feeding more than half of humanity, rice is indispensable, yet its production is drastically reduced under drought conditions. While traditional landraces display wide-ranging morphological, physiological and molecular traits that confer resilience to water scarcity, many modern high-yielding cultivars show pronounced sensitivity to drought stress (Yadav et al., 2023). Drought represents one of the most critical environmental challenges, with nearly 50% of global rice output estimated to be directly affected by insufficient water availability (Sircar and Parekh, 2019). Furthermore, drought prompts the build-up of osmo-protective compounds such as proline, sugars, polyamines and antioxidants, while also modifying gene expression patterns, particularly those of transcription factors and stress-related proteins, ultimately diminishing crop productivity. To withstand such effects, rice plants employ strategies including drought avoidance, drought escape and dought tolerance-mechanisms aimed at mitigating damage caused by water stress (Kumar et al., 2022).

    The ongoing shifts in climate are intensifying land degradation, advancing desert expansion and increasing soil salinity-processes which are severely diminishing cultivable areas and agricultural productivity. This issue is especially urgent given the steadily rising global population (González, 2023).

    Abiotic stresses like salinity, drought and temperature are expected to get worse because of climate change. This will have a negative impact on crop plants, especially rice plants and lower their output (Mohapatra and Saho, 2025). Among abiotic stressors, drought ranks as a major constraint on crop yields across the globe and its severity is escalating with changing climate patterns. At any point in a plant’s life cycle, drought exerts harmful and yield-suppressing effects, ultimately threatening survival. Like other environmental challenges, exposure to water deficit provokes a cascade of molecular, physiological and biochemical adjustments that attempt to counteract drought pressure (Oguz et al., 2022). Droughts have become more common because of global warming and big changes in the climate (Xu et al., 2025).

    Plants have gradually developed a broad spectrum of defensive strategies for mitigating the damage caused by biotic and abiotic constraints (Alhasnawi, 2019). Of these, drought stands out as the most destructive abiotic factor, generating severe reductions in productivity worldwide. Water deficit induces osmotic stress, which lowers chlorophyll and relative water levels while increasing osmolyte build-up, epicuticular wax deposition, antioxidant enzyme function, reactive oxygen species formation, secondary metabolite accumulation, lipid membrane oxidation and abscisic acid activity (Ahmad et al., 2022).

    It must be emphasised that these adaptive strategies enabling plants to withstand drought stress are controlled by numerous genes. Thus, pinpointing the specific genes which improve drought resilience is essential for maintaining yield stability within global crop production systems (Hura et al., 2022).

    For these reasons, it is imperative to investigate the molecular genetic framework underlying drought resistance in rice through advanced bioinformatics methods. The stress-responsive genes are classified as genes encoding proteins engaged in osmolyte biosynthesis (proline, mannitol, glycine betaine, trehalose, etc.), protective proteins (heat shock proteins and late embryogenesis abundant proteins, etc.), transcription factors (WRKY, DREB, ERF, MYC, MYB, bZIP, NAC, GRAS, etc.), antioxidant enzymes (superoxide dismutase, ascorbate peroxidase, catalase, glutathione S-transferases, glutathione reductase, etc.) and phytohormones (auxin, abscisic acid, jasmonic acid, ethylene, etc.) (Srivastava et al., 2022). Genetic diversity is important for breeding projects that want to create new types of rice (Kumar et al., 2025). The different genotypes’ genetic diversity is an important tool for breeding programs because it helps create new farming systems and increase output diversity (Rai et al., 2025).

    Advances in transcriptome profiling, along with improved computational resources, have enabled bioinformatics-driven identification of rice genes potentially linked to drought resistance by comparison with previously characterised drought-associated genes. Using a sub-network extraction algorithm combined with gene co-expression data, we constructed an integrated interaction network encompassing both well-identified drought-responsive rice genes and putative ones (Gao et al., 2020).

    In the present work, a variety of computational platforms and omics-based approaches were employed to explore molecular factors and biological pathways that underlie drought tolerance in rice. The primary aim was to analyse and contrast drought-resilient genes across diverse rice cultivars through bioinformatics pipelines in order to pinpoint critical genetic determinants contributing to drought adaptation. These findings are expected to provide a valuable resource for gene discovery relevant to drought tolerance. Moreover, this analytical framework can clarify functional interconnections among genes involved in the multifaceted drought stress response in rice, while also serving as a comparative reference point for extending similar investigations in other agriculturally important crop species.

    MATERIALS AND METHODS

    Data collection
     
    The research was conducted at Department of Biology, College of Education for Pure Sciences, Al-Muthanna University in 2025 to identify genes associated with drought tolerance in Oryza sativa L. using different drought-responsive genes from the National Center for Biotechnology Information (NCBI) database. The study included molecular responses using indicators and genes related to drought stress.

    Known drought-responsive genes contributing to tolerance in rice, specifically Oryza sativa L., were identified and retrieved from the National Center for Biotechnology Information (NCBI) database. The collected data were then used to compile essential details regarding these rice genes, while homology-based comparisons allowed assessment of their chromosomal distribution across ten gene symbols.

    Utilising these data, we have summarised the generic information regarding the genes in Oryza sativa L. and on the basis of the homology information, we have compared the location of the ten gene symbols for drought tolerance, including LOC4323843, LOC4337576, LOC4343219, LOC4347708, LOC4346460, LOC4345942, LOC4327045, LOC4325652, LOC4334350 and LOC4326935. Ten genomic context datasets and sequences remain distinct from specific genome assemblies. Both messenger ribonucleic acid (mRNA) and protein sequences are widely applied in computational studies to derive evolutionary insights into drought tolerance in Oryza sativa L.
     
    Sequence alignment
     
    Sequence comparisons of analysed clones with drought-related candidate genes in Oryza sativa L. were carried out through multiple sequence alignment using NCBI resources. The NCBI platform provides the basic local alignment search tool (BLAST) utilities that enable gene sequence queries, serving as a key bioinformatics method for assessing protein similarities. This approach highlights conserved domains and sequence variations, thereby offering insights into evolutionary linkages as well as functional stability among proteins implicated in drought tolerance mechanisms.
     
    Phylogenetic analysis
     
    Phylogenetic relationships were inferred using the distance-matrix neighbour-joining approach. Gene sequences associated with drought tolerance in Oryza sativa L. were first aligned and evolutionary trees were then generated through molecular evolutionary genetics analysis (MEGA) software (Version 11.013, 1993-2025) to examine divergence and conservation patterns among drought-responsive genes.

    In addition, homologs of experimentally verified salinity tolerance genes were identified. Corresponding nucleotide sequences were retrieved either from GenBank or directly from contributing authors, followed by BLAST analysis to detect homologous counterparts. This process enabled recognition of related sequences, expanding the genetic framework for understanding stress adaptation mechanisms in rice.

    RESULTS AND DISCUSSION

    Bioinformatics analysis was employed to identify drought-resilient genes, focusing on a selected group of ten gene symbols in Oryza sativa L. The findings summarised in Table 1 highlight several rice genes critically associated with tolerance to water deficit. The table lists 10 specific genes demonstrated to play significant roles in mediating the plant’s adaptive responses to drought-induced stress. The identified gene symbols include LOC4323843, LOC4337576, LOC4343219, LOC4347708, LOC4346 460, LOC4345942, LOC4327045, LOC4325652, LOC4 334350 and LOC4326935. All identified genes were classified as protein-coding and verified under the Reference Sequence (RefSeq) database’s categories (validated or model), ensuring the accuracy of their reference sequence information.

    Table 1: A gene symbol provides information about gene description, locus tag, gene typ, RefSeq status and organism in Oryza sativa L. for improved drought tolerance.



    Each gene occupies a unique chromosomal position and is annotated with an individual locus tag, such as OSNPB_010971100 for LOC4323843, OSNPB_ 0501 07900 for LOC4337576, OSNPB_070476900 for LOC43 43219, OSNPB_090537700 for LOC4347708, OSNPB_ 090133600 for LOC4346460, OSNPB_ 080499800 for LOC4345942, OSNPB_010785700 for LOC4327045, OSN PB_010184900 for LOC4325652, OSNPB_030786400 for LOC4334350 and OSNPB_ 010702500 for LOC4326935. All analysed genes originate from the Oryza sativa Japonica group and each is associated with several alternative designations recorded across scientific references and databases, such as DI19-5, OsDi19-5, OsJ_004810 and P0518C01.8 for LOC4323843; DI1, DI19-6, OsDi19-6 and OsJ_016081 for LOC4337576; cdsp32, OsCDSP32 and OsJ_24223 for LOC4343219; PAP2 for LOC4346460; OJ1118_A06.9 for LOC4345942; R1G1A, bip104, SSRP1-A and OsJ_00664 for LOC4325652; and rDRP1 and rab25 for LOC4326935; signifying the terminological variations in gene naming for drought tolerance (Table 1).

    The genomic context analysis of Chromosome 1 - NC_089035.1, Chromosome 5 - NC_089039.1, Chromos ome 7 - NC_089041.1, Chromosome 9 - NC_089043.1, Chromosome 9 - NC_089043.1, Chromosome 8 - NC_ 089042.1, Chromosome 1 - NC_089035.1, Chromosome 1 - NC_089035.1, Chromosome 3 - NC_089037.1 and Chromosome 1 - NC_089035.1 indicated a high density of drought-responsive loci distributed along proximal as well as distal regions. Multiple candidate genes were identified, notably including transcription factor family members, which show strong associations with tolerance to abiotic stresses (Fig 1A-1J).

    Fig 1: Genomic context sections of the chromosomes.



    The observed clustering of stress-responsive genes within syntenic chromosomal regions indicates the presence of potential regulatory hubs supporting adaptive functions. Furthermore, conserved promoter motifs and cis regulatory sequences were identified upstream of these candidate loci, underscoring their likely involvement in transcriptional control during water-deficit stress. Collectively, chromosome 1 emerges as a pivotal genomic region governing several drought-resilience pathways in rice.

    Ten rice genes encoding proteins implicated in drought stress resistance were further characterised by mapping gene identifiers to corresponding symbols and compiling details regarding their gene sequences and protein products. Transcript sizes ranged from 994 nucleotides in LOC4323843 to 2529 nucleotides in LOC4325652. At the protein level, polypeptide lengths spanned from 202 amino acids (LOC4323843) to 641 amino acids (LOC4325652). These variations demonstrate extensive structural and functional diversity among drought-associated genes and their encoded proteins, with findings summarised in Table 2.

    Table 2: List of gene transcripts and proteins in Oryza sativa L. for improved drought tolerance.



    The genomic context analysis of genomic sequences LOC4323843, LOC4337576, LOC4343219, LOC4347708, LOC4346460, LOC4345942, LOC4327045, LOC4325652, LOC4334350 and LOC4326935 revealed that these loci are positioned within drought-responsive regions and encode transcripts containing predicted regulatory and functional domains linked to stress adaptation. The constructed gene models display distinct exon–intron structures, generating transcripts that are translated into proteins carrying conserved motifs associated with abiotic stress signalling pathways. Functional annotation further indicates that these products may operate as transcriptional regulators or stress-responsive proteins, thereby contributing to cellular stability under conditions of water scarcity. Additionally, surrounding genomic regions harbour other stress-related genes, indicating potential co-regulation or coordinated expression patterns during drought exposure (Fig 2A-2J).

    Fig 2: Genomic regions, transcripts and products.



    The examined gene data were cross-referenced with standard databases to retrieve complete mRNA and protein sequences, along with source sequence details and UniProt accession numbers. Analyses demonstrated that each gene possesses dual identifiers corresponding to both its mRNA transcript and encoded protein, such as NP_001359124.1, NP_001406959.1, P_001409084.1, P_015651317.1, NP_001390665.1, XP_015648389.1, XP_015622164.1, NP_001388399.1, XP_015628802.1 and NP_001393222.1. This highlights the reliability of molecular characterisation for these genes within genomic repositories (Table 3).

    Table 3: These reference sequences exist independently of genome builds mRNA and protein(s).



    The results in Table 4 and Table 5 depict that the source sequence for every gene is also documented, for instance, AK069516 and AU057067 for LOC4323843; AP014961, AY335486 and EG709698 for LOC4337576; AU172639, CB653462, CI167083, CI286584 and CI688566 for LOC4343219; A0A0P0XPV8, A2Z3I6, A3C107, Q69JX7 and Q940D3 for LOC4347708; CA754409, CB678401 and CI199149 for LOC4346460; AB117991, AK066832 and AK068959 for LOC4325652; and AY333185 for LOC43 26935. The UniProt Knowledgebase’s (UniProtKB) TrEMBL database also contains corresponding entries, which are automatically generated protein sequences, thereby broadening the scope of the analysis and facilitating monitoring of predicted protein models. Such integration improves the dependability of datasets used in functional annotation and diagnostic assessments of genes. Moreover, these findings represent a critical step in clarifying links between nucleotide sequence and protein architecture, ultimately supporting the development of computational diagnostic tools for drought stress resilience.

    Table 4: These reference sequences exist independently of genome builds genomic.



    Table 5: Contextual information includes genomics, genomic assembly and chromosomal location of the genes under study.




    A phylogenetic tree was generated using alignment scores for NC_089035.1, NC_089039.1, NC_089041.1, NC_089043.1, NC_089043.1, NC_089042.1, NC_08903 5.1, NC_089035.1, NC_089037.1 and NC_089035.1, revealing a close evolutionary association between the genomic sequences. The clustering arrangement demonstrates strong sequence conservation, indicating probable functional similarity and shared evolutionary origins. Branch length measurements displayed limited divergence, reinforcing the hypothesis that these loci trace back to a common ancestral gene. Furthermore, the grouping of these sequences within the same phylogenetic clade emphasises their likely contribution to preserving conserved biological roles, potentially linked to mechanisms of drought tolerance. This conserved evolutionary signature offers additional support for the functional relevance of these genomic loci in Oryza sativa L. (Fig 3A-3J).

    Fig 3: A phylogenetic tree analysis was constructed based on the alignment scores.



    Plants adapt to environmental challenges by modulating their physiological processes and developmental programs through genome-wide changes in gene expression. Epigenetic regulators, including deoxyribonucleic acid (DNA) methylation and demethylation, are thought to exert critical influence in this process (Wang et al., 2011). The degree of drought tolerance exhibited by any plant species is largely determined by the presence and efficiency of genetic mechanisms underlying adaptation  (Kim et al., 2020). Bioinformatics-based platforms and analytical tools are highly valuable for evaluating plant responses to abiotic stressors (Neelapu and Chaitanya, 2024). 

    High-throughput technologies, such as RNA sequencing (RNA-seq), enable detailed profiling of differential gene expression, offering insights into genes central to stress resilience. The integration of bioinformatics, genomics and next-generation sequencing   provides a deeper understanding of molecular pathways responsible for tolerance to diverse stress conditions. Such knowledge can be strategically applied to accelerate the breeding of stress-resilient crops and to enhance both yield performance and crop quality (Mu et al., 2022).

    This research applied an integrative bioinformatics framework to uncover multiple drought-associated candidate genes, including regulatory transcription factors and functional enzymes, using publicly accessible rice transcriptome datasets. The outcomes support the prevailing model in which drought resilience arises from a complex genetic network controlling traits such as stomatal regulation, deep root systems and cellular equilibrium  (Kumar et al., 2015). The capacity to interrogate multi-omics datasets computationally facilitates systematic dissection of this multifactorial trait.

    A key strength of the approach was the incorporation of co-expression network methodologies, notably weighted gene co-expression network analysis (WGCNA). This enabled identification of complete gene modules strongly associated with drought-related traits, extending beyond traditional differential expression analysis. Central hub genes within these modules, such as protein kinases mediating signalling events, were recognised as high-priority targets due to their potential to orchestrate the expression of numerous downstream genes (Ambrosino et al., 2020).

    In addition, exploitation of rice pangenome datasets made it possible to detect allelic diversity among these candidate loci across drought-tolerant and drought-susceptible cultivars. This step is especially important, as it connects genetic polymorphisms with functional adaptation, thereby equipping plant breeders with precise molecular markers for targeted selection (Gao et al., 2019).

    Thus, while bioinformatics offers a powerful hypothesis-generating engine, the functional validation of predicted genes remains essential. The candidates identified here should be meticulously tested by utilising clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 gene editing to generate knockouts and/or overexpression lines in pertinent genetic backgrounds for substantiating their role in drought resilience by means of exhaustive phenotypic and physiological evaluation (Razzaq et al., 2021). 

    Future studies should aim to integrate additional layers of biological data to enhance prediction accuracy. This encompasses incorporating epigenomic data to capture how DNA methylation and histone modifications control stress memory, as well as metabolomic profiles for linking genetic regulation with physiological outcomes (Zitnik et al., 2019). Moreover, machine learning models trained on multi-omics data from field-grown plants under diverse drought regimes hold strong potential for estimating the most effective gene combinations for breeding (Crossa et al., 2025).

    Bioinformatics tools enable the identification of stress-responsive genes, molecular markers and regulatory elements, whereas AI techniques strengthen predictive modelling, inference of gene regulatory networks and actual plant monitoring. Together, these innovations are vital for formulating stress-resilient plant varieties capable of thriving under increasingly extreme environmental conditions caused by global climate change and anthropogenic pressures. Practical applications encompass estimating drought-resistant gene variants, determining salt-tolerant crop cultivars and enabling real-time monitoring of plant health under extreme temperatures by means of AI-driven phenomics platforms (Zhang et al., 2024).

    CONCLUSION

    In conclusion, this thorough bioinformatics-driven study was successful in identifying a strong and trustworthy group of candidate genes that are essential for giving rice drought tolerance. The study created a comprehensive and potent framework for gene discovery and functional prioritization under water-limited conditions by methodically combining transcriptome profiling, co-expression network analysis and whole-genome-scale techniques. In addition to improving the precision of candidate gene identification, this integrative approach offered a deeper understanding of the intricate biochemical processes and regulatory networks that underlie rice’s responses to drought stress. Thorough functional validation employing cutting-edge molecular and physiological assays will be crucial to the successful conversion of these molecular discoveries into useful and sustainable agricultural uses. To fully utilize the genetic potential identified in this work, it will also be necessary to strategically choose advantageous alleles using marker-assisted selection, genomic selection, or precision genetic engineering techniques. The development of high-yielding, drought-tolerant and climate-resilient rice cultivars is ultimately anticipated to be greatly accelerated by the research’s findings, contributing to global food security and strengthening rice production systems’ ability to adapt to rising environmental variability and climate change.

    CONFLICT OF INTEREST

    The authors declare no conflict of interest.
     

    REFERENCES


    1. Ahmad, H.M., Fiaz, S., Hafeez, S., Zahra, S., Shah, A.N., Gul, B. and Aziz, O. (2022). Plant growth-promoting rhizobacteria eliminate the effect of drought stress in plants: A review. Frontiers in Plant Science. 13: 875774. www.frontiersin. org (July 5, 2025).

    2. Alhasnawi, A.N. (2019). β-glucan-mediated alleviation of NaCl stress in Ocimum basilicum L. in relation to the response of antioxidant enzymes and assessment DNA marker. Journal of Ecological Engineering. 20(8): 90-99.

    3. Ambrosino, L.,  Colantuono, C., Diretto, G., Fiore , A. and Chiusano, M.L. (2020). Bioinformatics resources for plant abiotic stress responses: State of the art and opportunities in the fast evolving-omics era. Plants. 9(5): 591. https:// www.mdpi.com/2223-7747/9/5/591/htm (September 4, 2025).

    4. Chengqi, Z., Yuxuan, Y., Tian, Q., Yafan, H., Jifeng, Y. and Zhicheng, S. (2024). Drought-tolerant rice at molecular breeding eras: An emerging reality. Rice Science. 31(2): 179- 189. https://www.sciencedirect.com/science/article/pii/ S1672630823001130 (July 5, 2025).

    5. Crossa, J., Montesinos-Lopez, O.A., Costa-Neto, G., Vitale, P., J.W.R. Martini, J.W.R., Runcie, D., Fritsche-Neto, R. et al. (2025). Machine learning algorithms translate big data into predictive breeding accuracy. Trends in Plant Science. 30(2): 167- 184. https://www.cell.com/action/showFullText?pii= S13 60138524002590 (September 4, 2025).

    6. Gao, L., Gonda, I., Sun, H., Ma, Q., Bao, K., Tieman, D.M., Burzynski- Chang, E.A., Fish, T.L. et al. (2019). The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nature Genetics. 51(6): 1044-1051. https:/ /www.nature.com/articles/s41588-019-0410-2 (September 4, 2025).

    7. Gao, Y., Chen, Y., Ma, Z., Zeng, T., Ahmad, I., Zhang, Y. and Yue, Z. (2020). Identification of Rice Drought-Resistant Gene Based on Gene Expression Profiles and Network Analysis Algorithm. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, Cham, 290- 301. https://link.springer.com/chapter/10.1007/978-3- 030-60802-6_26 (July 6, 2025).

    8. González, E.M. (2023). Drought stress tolerance in plants. International Journal of Molecular Sciences. 24(7): 6562. https:// www.mdpi.com/1422-0067/24/7/6562 (July 5, 2025).

    9. Hura, T., Hura, K. and Agnieszka, O. (2022). Drought-stress induced physiological and molecular changes in plants. International Journal of Molecular Sciences. 23(9): 4698. https://www.mdpi.com/1422-0067/23/9/4698/htm (July 5, 2025).

    10. Kim, Y., Chung, Y.S., Lee, E., Tripathi, P., Heo, S. and Kyung-Hwan, K. (2020). Root response to drought stress in rice (Oryza sativa L.). International Journal of Molecular Sciences. 21(4): 1513. https://www.mdpi.com/1422- 0067/21/4/1513/htm (September 4, 2025).

    11. Kumar, A., Pathak , R.K.,  Gupta, S.M., Gaur, V.S. and Pandey, D. (2015). Systems biology for smart crops and agricultural innovation: Filling the gaps between genotype and phenotype for complex traits linked with robust agricultural productivity and sustainability. OMICS A Journal of Integrative Biology. 19(10): 581-601. https://pubmed.ncbi. nlm.nih.gov/26484978/ (September 4, 2025).

    12. Kumar, A., Sengar, R.S., Pathak, R.K. and Singh, A.K. (2022). Integrated approaches to develop drought-tolerant rice: Demand of era for global food security. Journal of Plant Growth Regulation. 42(1): 96-120. https://link.springer. com/article/10.1007/s00344-021-10561-6 (July 5, 2025).

    13. Kumar, S., Jayasudha S.,  Rajani and Singh, S.K. (2025). Genetic variability, heritability and genetic advance for yield and quality traits in rice (Oryza Sativa L.). Indian Journal of Agricultural Research. 60(2): 183-90. doi: 10.18805/ IJARe.A-6402. 

    14. Mandal, A., Alhasnawi, A., Jasim, H. and Mohamad, A. (2019). Evaluation of salt stress and molecular analysis of genetic variation of iraqi rice cultivars. Biodiversitas Journal of Biological Diversity. 20(11): 3309-3314. https:/ /smujo.id/biodiv/article/view/4365 (March 8, 2023).

    15. Mohapatra, M.D. and Saho, R.K. (2025). Antioxidant and agronomical screening of Oryza sativa L. in response to abiotic stress from local landraces. Indian Journal of Agricultural Research. 59(12): 1809-1818. doi: 10.18805/IJARe.A- 6468. 

    16. Mu, H., Wang, B. and Fang, Y. (2022). Bioinformatics in plant breeding and research on disease resistance. Plants. 11(22): 3118. https://www.mdpi.com/2223-7747/11/22/ 3118/htm (September 4, 2025).

    17. Neelapu, N.R.R. and Chaitanya, K.V. (2024). Bioinformatics Tools for Assessing Drought Stress Tolerance in Crops. In: Bioinformatics for Plant Research and Crop Breeding, Singapore: Wiley. 233-249. https://onlinelibrary.wiley.com/ doi/full/10.1002/9781394209965.ch9 (September 4, 2025).

    18. Oguz, M.C., Aycan, M., Oguz, E., Poyraz, I.  and Yildiz, M.  (2022). Drought stress tolerance in plants: Interplay of molecular, biochemical and physiological responses in important development stages. Physiologia. 2(4): 180-197. https:/ /www.mdpi.com/2673-9488/2/4/15 (July 5, 2025).

    19. Olivya, S.R., Dijee, B., Rose, F.M., Rashmi, C.R. and Biju S. (2025). An assessment of cosplay of microbes treated seeds on storability of rice seeds. Indian Journal of Agricultural Research. 60(1): 76-81. doi: 10.18805/IJARe.A-6355. 

    20. Rai, K.N., Rajesh, R.Y., Amit, Kavita, Kumar, P. and Dhaniya, R. (2025). Genetic variation and character association studies for seed yield and its attributes in diverse panel of mungbean genotypes [Vigna radiata (L.) Wilczek]. Legume Research. 49(2): 190-199. doi: 10.18805/LR- 5481. 

    21. Razzaq, A., Wani, S.H., Saleem, O., Yu , M., Zhou, M. and Shabala, S.  (2021). Rewilding crops for climate resilience: Economic analysis and de novo domestication strategies. Journal of Experimental Botany. 72(18): 6123-6139. https:// dx.doi.org/10.1093/jxb/erab276 (September 4, 2025).

    22. Sircar, S. and Nita, P. (2019). Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach. Plos One. 14(5): e0216068. https://pmc.ncbi.nlm.nih.gov/ articles/PMC6502313/ (July 5, 2025).

    23. Srivastava, D., Verma, G., Chawda, K., Chauhan, A.S., Pande, V. and Chakrabarty, D. (2022). Overexpression of Asr6, abscisic acid stress-ripening protein, enhances drought tolerance and modulates gene expression in rice (Oryza sativa L.). Environmental and Experimental Botany. 202: 105005. https://www.sciencedirect.com/science/article/ abs/pii/S0098847222002271 (July 5, 2025).

    24. Wang , W.S., Ya-Jiao, P., Xiu-Qin, Z., Dwivedi, D., Ling-Hua, Z., Ali, J., Bin-Ying, F. and Zhi-Kang, L. (2011). Drought- induced site-specific dna methylation and its association with drought tolerance in rice (Oryza sativa L.). Journal of Experimental Botany. 62(6): 1951-1960. https:// dx.doi.org/10.1093/jxb/erq391 (September 4, 2025).

    25. Xu, B., Yanhua, W., Rina, W., Yujing, L., Shiyu, Z. and Xiuli, Z. (2025). Drought resistance under PEG-6000 stress during germination of onobrychis viciifolia (Sainfoin) from different regions of China. Legume Research. 48(10): 1663-1667. doi: 10.18805/LRF-862.  

    26. Yadav, N., Sevanthi, A.M.,  Pandey, R., Chinnusamy, V., Singh, A.K. and Singh, N.K. (2023). Physiological response and agronomic performance of drought tolerance mutants of aus rice cultivar nagina 22 (Oryza sativa L). Field Crops Research. 290: 108760. https://www. sciencedirect.com/science/article/abs/pii/S0378429 022003318 (July 5, 2025).

    27. Zhang, X., Ibrahim, Z., M.B. Khaskheli, H. Raza, Zhou, F.  and Shamsi, I.H. (2024). Integrative approaches to abiotic stress management in crops: Combining bioinformatics educational tools and artificial intelligence applications. Sustainability (Switzerland). 16(17): 7651. https://www. mdpi.com/2071-1050/16/17/7651/htm (September 4, 2025).

    28. Zitnik, M., Nguyen, F., Wang, W., Leskovec, J., Goldenberg, A. and Hoffman, M.M. (2019). Machine learning for integrating data in biology and medicine: Principles, practice and opportunities. Information Fusion. 50: 71-91. https:// pmc.ncbi.nlm.nih.gov/articles/PMC6242341/(September 4, 2025).
    In this Article
      Contact us
      Follow us
      Published In
      Agricultural Science Digest

      Editorial Board

      View all (0)