1 Introduction

Plants inhabit dynamic environments, subject to continuous fluctuations that can be detrimental to their growth and development (Zhu et al., 2016). Environmental stressors can be broadly classified into biotic and abiotic stress (Debnath et al., 2013). Biotic stress refers to adverse effects resulting from herbivore attacks and pathogen infections, while abiotic stress pertains to unfavourable environmental conditions such as cold, heat, drought, and excessive salt and calcium concentrations. Plants possess inherent physiological processes to cope with these stresses, relying on diverse genetic expressions regulated by multiple transcription factors (Atkinson et al., 2012). The ability to withstand stress varies among plant species, with different genes being responsible for stress tolerance against specific stressors (Seth et al., 2020). Rabadanova et al., 2018 emphasize the importance of enhancing plant stress tolerance for increased productivity and environmental sustainability, as crops lacking stress tolerance require excessive inputs like fertilizers, pesticides, and irrigation. Understanding plant stress response necessitates examining stress signals, which facilitate communication between various molecular responses related to stress resistance. This study will specifically focus on the abiotic stress response of rice (Oryza sativa).

Plants employ various strategies (Fig. 1) to tolerate or avoid abiotic stresses, including reduced photosynthesis, stomatal closure, increased reactive oxygen species (ROS) scavenging, stunted leaf growth, and elongated roots (Cohen and Leach, 2019). Whereas, biotic stress factors, such as pathogens, can also induce stomatal closure and decrease photosynthesis. Defensive mechanisms against biotic stress include secretion of phytotoxins (e.g., ROS, phytoalexins, and secondary metabolites) and localized cell death. Phytohormones, such as salicylic acid (SA), jasmonic acid (JA), and ethylene, are crucial for plant immunity against pathogenic factors (Lata et al., 2011). Transcription factor families involved in abiotic stress response include abscisic acid (ABA)-dependent and ABA-independent transcription factors. Kim et al., 2020 identified stress-responsive genes in O. sativa using expressed sequence tags (ESTs) generated from drought-stressed seedlings, revealing distinct gene families responsible for abiotic stress response.

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Fig. 1

Strategies employed by plants against environmental stressors (Source: Author’s creation with PlantText UML Editor).

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According to Zhao et al. (2010), dehydration-responsive element-binding (DREB) genes confer resistance to drought, low temperature, and high salinity stress in rice. Most DREB genes are thought to regulate downstream stress-responsive genes by binding directly to drought-responsive elements (DRE) and cis-elements (GCC box) (Fig. 2). Studies by Zhang et al. (2013) and Ranawake et al., 2012 investigated the expression patterns of DREB genes and identified numerous stress-responsive genes in rice, respectively. Rabbani et al., 2003 used a cDNA microarray technique to profile rice gene expression under various environmental stresses, such as drought, cold, high salinity, and abscisic acid. Sevanthi et al. (2021) identified six highly heat-sensitive genes in rice, while Riccio-Rengifo et al. (2021) categorized stress-induced proteins into functional and regulatory proteins.

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Fig. 2

Functions of DREB gene in rice combating different abiotic stresses (Source: Author’s creation with PlantText UML Editor).

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Mondini et al. (2015) suggested significant overlap between gene expression and mechanisms of action in response to abiotic and external stress stimuli. Consequently, abiotic stress response genes play a critical role in stress regulation and assisting rice plants in coping with adverse conditions induced by abiotic stress. Throughout their evolution, plants have developed various strategies and processes to deal with stressors such as dryness, heat, cold, and excessive salinity (Debnath et al., 2022).

Plant stress responses are crucial not only for their economic value but also for maintaining environmental homeostasis. If rice plants lose their stress resistance capacity, they will require more water and fertilizers, which is neither cost-effective nor environmentally sustainable, given the immense agricultural significance of rice (Seth et al., 2020). Shi and Chan (2014) proposed enhancing plant stress responses through modulation of gene expression and post-translational modification. Gaining a comprehensive understanding of the genes responsible for abiotic stress response in rice plants and their expression patterns in response to abiotic stress can contribute valuable insights for modulating genetic expression. Additionally, studying the roles of different abiotic stress-responsive genes can help in understanding their importance in developing stress-tolerant rice varieties.

Despite the wealth of information on regulating various stress-responsive genes in rice, there remains a significant gap in the literature regarding a comprehensive study that investigates the expressions of abiotic stress-responsive genes and their interplay within different stress response pathways in rice plants. Numerous primary articles found in electronic databases offer insight into the expression patterns of the aforementioned genes through systematic reviews. However, considering the lack of a comprehensive study on this topic, it is essential to formulate a hypothesis for a comprehensive analysis of the expression patterns of abiotic stress-responsive genes in O. sativa. This will enable researchers to identify key factors and mechanisms that can be harnessed to improve stress tolerance in rice plants, ultimately enhancing productivity and minimizing negative environmental impacts to achieve sustainable development goal.

2 Materials and methods

2.5

2.5 In silico expression study

The electronic fluorescence pictogram (eFP) program is a widely recognized tool for visualizing transcriptome data and has been employed in various model organisms. To understand the expression levels of the six identified genes under stress conditions, their expression profiles were analyzed using the Rice eFP Browser (https://bar.utoronto.ca/transcriptomics/efp_rice/cgi-bin/efpWeb.cgi?dataSource = rice_leaf_gradient). Developed at the University of Toronto, this tool facilitates the visual analysis of gene expression by providing illustrative graphics for different tissue types, with distinct colors representing the expression levels of a specific gene in each tissue (Jung et al., 2011).Bioinformatic analysis revealed that six out of the fourteen genes, namely AOX1a, AOX1b, ALDH2a, ALDH2b, OsNAC6 and OsDHN1, exhibited the potential to be expressed under all abiotic stresses investigated. Consequently, these six genes were further screened using the Rice eFP Browser to evaluate their expression potential through in silico analysisfor exploring a more exhaustive understanding of the stress-responsive expression patterns of the identified genes in O. sativa.

These methodological approaches followed in this study (Fig. 3) collectively allows for a comprehensive examination of the characterization, evolutionary relationship, functional associations, potential roles and expression patterns of the identified abiotic stress-sensitive genes in O. sativa.

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Fig. 3

Methodological approaches followed in this study to identify abiotic stress-responsive genes in O. sativa. (Source: Author’s creation with PlantText UML Editor).

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3 Results

3.3

3.3 Multiple sequence alignment and phylogenetic analyses

The multiple sequence alignment (MSA) of the translated proteins indicates an average protein length of 416 aa and a pairwise identity of 9.2% (Supplementary file 4). The MSA revealed that the proteins have an average molecular weight of 45.353 kDa and an average isoelectric point of 6.88. The percentage similarity matrix (Fig. 4) was constructed using the BLOSUM62 matrix to assess the amino acid substitution rates in clusters of the relevant protein translated by these genes. The matrix primarily highlights the evolutionary relationships among the genes. In terms of evolutionary divergence, genes sharing higher percentages of similarities are more closely related, as confirmed by the subsequent phylogenetic analysis.

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Fig. 4

Percentage similarity matrix of 14 identified abiotic-stress responsive genes in O. sativa. The colour scheme indicates the heatmap of similarities among the genes (Source: Author’s creation with OriginPro 2022 v.9.9.0.225).

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The phylogenetic analysis, based on the Maximum Likelihood method, resulted in four clusters of genes (Fig. 5). The resulting dataset had 948 positions from the 14 amino acid sequence analyses. In agreement with the similarity matrix constructed, ALDH2a, ALDH2b, AOX1a and AOX1b formed cluster 1 in the phylogenetic tree, as they showed the highest values of percentage similarities among them, ranging from 82.7 to 91.9%. Cluster 2 is formed by OsERF28, OsDREB1B and OsDREB1G, corresponding to percentage similarities ranging from 41.6 to 57.9%. OsNAC6 and OsDHN1 formed cluster 3, showing a 22.2% similarity. Lastly, cluster 4 is formed by OsSTLK, OsNCA1A and OsNIN6, expressing percentage similarities ranging from 14.9 to 30%. OsSADR1 and OsPP2Care not clustered in the phylogenetic tree, although they show a percentage similarity of 18% per the matrix constructed.

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Fig. 5

Phylogenetic tree of 14 identified abiotic-stress responsive genes in Oryza sativa made in MEGA11 (20). The Maximum Likelihood (ML) phylogenetic tree is computed with the highest log likelihood value of −14428.59. Above the branches is the branch length and below is the proportion of replicate trees in which the related taxa grouped together in the bootstrap test with 1000 repetitions.

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3.4

3.4 Protein-protein interaction and gene co-occurrence study

The phylogenetic analysis suggests that these genes share homology, potentially evolving from a single ancestral gene. The protein–protein interaction (PPI) network, based on high confidence values as defined by the STRING software, revealed 19 nodes in the network of proteins located in functional subsystems, with an average node degree of 1.68, an average local clustering coefficient of 0.458, and a PPI enrichment p-value of 0.000639 (Fig. 6). In addition to the 14 proteins in the search query (Supplementary file 5), five more proteins (OsJ_04113, OsJ_24269, OS03T0297600-01, OS07T0188800-01, and OsJ_06966) were predicted as functional partners by the network. The STRING software recorded 17 interactions among the query proteins (Supplementary file 6). The k-means clustering identified 3 clusters of interactions at the protein level with a high level of interaction confidence. Cluster 1 groups 6 genes (AOX1a, AOX1b, DHN1, DREB1G, OS11T0175500-02, OsJ_33156), Cluster 2 groups 7 genes (DREB1H, NAC6, OS01T0104100-01, OS03T0297600-01, OS05T0305900-01, OS08T0545500-00, OsJ_027745), and Cluster 3 groups 6 genes (Aldh2a, Aldh2b, OS07T0188800-01, OsJ_04113, OsJ_06966, OsJ_24269). The KEGG pathway analysis identified that the query proteins are involved in 14 potential KEGG pathways (Table 2). The co-occurrence patterns of the 14 identified abiotic-stress responsive genes in rice and 5 predicted functional protein partners are also examined across various taxa (Supplementary file 7). These genes were further subjected to a co-expression study in STRING, which provided an initial understanding of the pattern of expression of these genes under various abiotic stresses (Supplementary file 8).

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Fig. 6

Protein-protein interaction among 14 identified abiotic-stress responsive genes in O. Sativa along with 5 predicted functional protein partners made in STRING.

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Table 2 KEGG pathway analysis for the query proteins.

KEGG Pathways	Observed gene count	Background gene count	Strength	False discovery rate	Matching proteins in network
beta-Alanine metabolism	4	55	2.14	3.97E-06	Aldh2a, Aldh2b, OS07T0188800-01, OsJ_24269
Fatty acid degradation	3	60	1.97	0.00026	OsJ_04113, Aldh2a, Aldh2b
Valine, leucine and isoleucine degradation	3	65	1.94	0.00026	Aldh2a, Aldh2b, OS07T0188800-01
Pantothenate and CoA biosynthesis	3	52	2.03	0.00026	Aldh2a, Aldh2b, OsJ_24269
Limonene and pinene degradation	2	9	2.62	0.00046	Aldh2a, Aldh2b
Pyruvate metabolism	3	110	1.71	0.00075	OsJ_06966, Aldh2a, Aldh2b
Histidine metabolism	2	24	2.19	0.0019	Aldh2a, Aldh2b
Glycolysis / Gluconeogenesis	3	179	1.5	0.0023	OsJ_06966, Aldh2a, Aldh2b
Propanoate metabolism	2	42	1.95	0.0043	OsJ_06966, OS07T0188800-01
Ascorbate and aldarate metabolism	2	65	1.76	0.009	Aldh2a, Aldh2b
Arginine and proline metabolism	2	80	1.67	0.0122	Aldh2a, Aldh2b
Lysine degradation	2	88	1.63	0.0135	Aldh2a, Aldh2b
Tryptophan metabolism	2	104	1.56	0.0172	Aldh2a, Aldh2b
Glycerolipid metabolism	2	110	1.53	0.0178	Aldh2a, Aldh2b

3.5

3.5 In silico expression study

The expression potential of each of the six genes was examined individually using an electronic fluorescent pictogram (eFP) to better understand how these genes may respond under stressful conditions (Fig. 7). The expression potential of each gene was calculated through the GCOS expression signal (Supplementary file 9). ALDH2a showed the highest and lowest expression potential during inflorescence P3 and lowest in young leaves ALDH2b showed the highest expression potential in young leaves and the lowest in SAM Meanwhile, AOX1ashowed the highest expression potential in seed S1 and the lowest in SAM.AOX1b showed the highest expression potential in seed S1 and the lowest in inflorescence P2. OsDHN1 showed the highest expression potential in young inflorescence and the lowest in seed S5. OsNAC6 showed the highest expression potential in the mature leaves and the lowest in SAM.

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Fig. 7

Expression profiling of 6 identified abiotic-stress responsive genes in Oryza sativa, analysis done in Rice eFP browser: (a) ALDH2a, (b) ALDH2b, (c) AOX1a, (d) AOX1b, (e) OsDHN1, (f) NAC6.

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In summary, the comprehensive analysis of 14 abiotic stress-responsive genes in rice, including their expression patterns, evolutionary relationships, protein–protein interactions, and co-expression, provides valuable insights into their potential roles in abiotic stress response. Further experimental validation and functional characterization of these genes will contribute to a better understanding of their roles in stress tolerance and may help develop strategies for improving rice crop resilience.

4 Discussion

The 14 abiotic-stress-responsive genes in O. sativa identified for this study are distributed across different chromosomes, suggesting that multiple interconnected cascades regulate the abiotic-stress-response. The presence of more than one gene on a single chromosome further indicates the prominent cross-talk in the response pathways due to the prevalence of abiotic stress (Zarattini et al., 2021). The transcript length, CDS length, and translated amino acid length (Supplementary file 10) of these genes varied significantly, as demonstrated by the analysis carried out in this study. This variation implies individual gene occupancy in the abiotic stress response pathways.

A pairwise similarity matrix is a fundamental but effective tool for understanding the co-expression of various genes. The matrix revealed a wide range of variation in percentage similarity among the genes of interest, ranging from 3.2 to 91.9%. The broad range of similarity percentages is well supported by the phylogenetic analysis conducted in this study.

The base tree(s) for the heuristic search were automatically generated during the development of the phylogenetic tree using the Neighbor-Join and BioNJ algorithms and then determining the topology with the highest log likelihood value. The branch lengths of the phylogenetic trees were inferred by computing the number of substitutions per site, providing abundant information on the evolutionary development of the genes involved in the analysis. The phylogenetic tree was constructed using the Maximum Likelihood (ML) approach, which employs various substitution models to account for multiple changes at the exact sequence location along the evolutionary timeline. The Jones-Taylor-Thornton (JTT) substitution model (Jones et al., 1992) was considered while constructing the phylogenetic tree, as this model best fits the phylogeny by corresponding to the lowest Bayesian Information Criterion (BIC) score calculated. Bootstrapping was performed using 1000 replicates (Felsenstein, 1985; Hillis and Bull, 1993). The clusters in the phylogenetic tree indicate the similar potential ancestry of genes within the same cluster. Although each cluster is somehow interrelated according to the phylogenetic tree, genes within the same cluster showed significant percentage similarity in the pairwise similarity matrix, justifying the phylogenetic tree.

The protein–protein interaction analysis revealed three prominent clusters of interactions. The most robust interactions were found among Aldh2a, Aldh2b, OS07T0188800-01 and OsJ_04113 in one cluster and between AOX1a and AOX1b in another cluster. Notably, the KEGG pathway analysis identified two genes, ALDH2a and ALDH2b, to participate in every pathway except propanoate metabolism. The profiles of potential expression levels of the analyzed six genes indicated that resistance or tolerance mechanisms under various abiotic stress stimuli gradually develop throughout the plant's lifetime. Each gene demonstrated peaks of expression at different stages of plant growth. These findings underscore the significant roles of these genes in the abiotic-stress response in O. sativa and suggest intense cross-talk of response mechanisms.

From an agricultural perspective, understanding the abiotic stress-responsive genes in O. sativa is crucial for improving crop resilience and maintaining yield stability under adverse environmental conditions (Paul et al., 2023). Rice is a staple food crop for more than half of the world's population, and its production must keep pace with the growing demand. However, the increasing frequency and severity of abiotic stress factors, such as drought, salinity, and cold, due to climate change pose significant challenges to rice production worldwide. The findings of this study on the 14 abiotic-stress responsive genes, their interactions, and their potential expression levels under various stress conditions provide valuable insights for developing rice varieties with enhanced tolerance to multiple abiotic stresses (Fig. 8). Identifying and characterizing these genes and their roles in abiotic stress response pathways can help plant breeders and biotechnologists to design targeted breeding strategies, including marker-assisted selection and genetic engineering approaches, for developing stress-tolerant rice cultivars (Sun et al., 2022). The resulting improved rice varieties would enhance agricultural productivity and ensure food security in the face of climate change.

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Fig. 8

Key findings of the study conducted on the genes playing significant regulatory roles in abiotic stress responses in O. Sativa (Source: Author’s creation with PlantText UML Editor).

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Furthermore, understanding the cross-talk of response mechanisms among these abiotic stress-responsive genes can contribute to the development of rice cultivars with broad-spectrum stress tolerance (Husaini, 2022). This can be achieved by manipulating multiple genes or regulatory elements involved in the cross-talk, thereby enabling the plant to withstand multiple stress factors simultaneously. Future research should focus on validating the roles of these identified genes through functional genomics approaches, such as gene knockout or over-expression studies, in rice plants grown under controlled stress conditions. Moreover, the study of gene regulatory networks and the identification of transcription factors involved in the regulation of these stress-responsive genes will provide a comprehensive understanding of the molecular mechanisms underlying abiotic stress responses in rice. Therefore, the insights gained from this study on the abiotic stress-responsive genes in O. sativa have significant implications for agriculture, especially in the context of climate change. By harnessing the potential of these genes and understanding their interactions and cross-talk, researchers and plant breeders can develop new rice varieties with enhanced tolerance to multiple abiotic stresses, thus ensuring sustainable rice production and global food security.

5 Conclusion

The findings of the present study suggest that six key genes (AOX1a, AOX1b, ALDH2a, ALDH2b, OsNAC6, OsDHN1) play significant regulatory roles in abiotic stress responses in O. sativa. The expression profiles of these genes were found to vary across different stages of the rice life cycle, such as young and mature leaves, young inflorescence, and seed development stages. These results indicate that these genes participate in interconnected cascades throughout the plant's life cycle, providing protection against abiotic stresses. The cross-talk between response mechanisms of these abiotic-stress responsive genes was also corroborated by phylogenetic analysis, protein–protein interaction, and gene co-expression studies. Therefore, using these genes as targets for developing gene-based molecular markers could enable the breeding of rice cultivars with enhanced resistance to challenging environmental conditions. Furthermore, these findings have the potential to contribute to the successful application of computational biology in plant breeding by reducing the expense, complexity, and time required for traditional biological studies.

CRediT authorship contribution statement

Sandip Debnath: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Shaik Aisha: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing - original draft, Writing - review & editing. Ayushman Malakar: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing - original draft, Writing - review & editing. Kahkashan Perveen: Manuscript preparation, reviewing, and editing. Alanoud Alfagham: Manuscript preparation, reviewing, and editing. Mehrun NishaKhanam: Manuscript preparation, reviewing, and editing. Biswajit Pramanik: Manuscript preparation, reviewing, and editing. Ahmed Mohammed: Manuscript preparation, reviewing, and editing.