Genome-wide identification of sORFs in indica rice and their comparative transcriptomic analysis under stress

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This study aimed to identify drought-responsive sORFs in Oryza sativa subsp. indica and to evaluate their potential roles in drought stress adaptation, addressing the question of whether sORFs contribute to physiological and molecular drought tolerance mechanisms in rice. Stress-responsive sORFs were identified through in silico prediction combined with transcriptomic analysis across multiple abiotic stress conditions. Functional annotation was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and signal peptide prediction was used to infer potential secretory functions. Three indica rice varieties (MR 219, MR 220, and MR 297) were subjected to drought stress for physiological assessment. Oxidative stress markers and antioxidant gene expression were analyzed, and candidate sORF expression was validated using semi-quantitative RT-PCR. Differentially expressed sORFs (DE sORFs) were significantly enriched in GO terms related to structural molecule activity and binding, while KEGG analysis highlighted MAPK signaling and antioxidant defense pathways. Several sORFs were predicted to encode secretory peptides. Among the tested varieties, MR 297 showed enhanced drought tolerance with reduced leaf rolling and stable chlorophyll content, whereas MR 219 exhibited higher oxidative damage, indicated by elevated malondialdehyde and proline levels. Antioxidant genes were strongly upregulated under drought stress, particularly in MR 219. Expression of selected sORFs ( OsisORF_0050 , OsisORF_3394 , and OsisORF_3007 ) was confirmed under drought conditions. This study reveals sORFs as previously overlooked contributors to drought stress responses in indica rice and provides a foundation for their functional characterization and potential application in crop improvement. Intergenic sORF drought stress heat stress salt stress cold stress indica rice Figures Figure 1 Figure 2 Introduction Rice, a staple food crop, consists of multiple subspecies, including japonica , temperate japonica , and javanica (Kim, 2019 ). Indica rice, derived from japonica and O. rufipogon crosses, retains cold tolerance traits and exhibits resilience to challenging environments, such as acidic soils (Zhang et al., 2018 ; Kang et al., 2010 ). Nevertheless, abiotic stresses, including drought, salinity, and extreme temperatures, continue to limit growth and productivity, prompting the evolution of intricate defense mechanisms in rice (Lau et al., 2021 ; Lau et al., 2023 ; Mohd Amnan et al., 2023 ). Despite these adaptations, the precise molecular mechanisms underlying rice’s resilience to environmental stresses remain unclear. Small open reading frames (sORFs) are short DNA sequences that encode peptides of fewer than 100 amino acids (Saghatelian & Couso, 2015 ). Increasing evidence suggests that sORFs play important roles in plant stress responses and other physiological processes. Despite their potential significance, sORFs were historically overlooked due to limitations in bioinformatics tools, weak sequence conservation, and low expression levels (Cabrera-Quio et al., 2016 ; Hanada et al., 2013 ). The first systematic identification of sORFs in plants was reported by Hanada et al. ( 2007 ), who predicted approximately 8,000 coding sORFs in the Arabidopsis thaliana genome. Advances in sequencing technologies and computational tools have since enabled the discovery of sORFs across both coding and previously annotated non-coding regions, revealing their contributions to plant morphology, growth, and stress responses (Hanada et al., 2013 ; Takahashi et al., 2019 ). Functional studies have demonstrated that sORFs regulate diverse aspects of plant biology. In Arabidopsis , they influence plant morphology and stress responses (Takeda et al., 2023 ), whereas in maize, Zm401 and Zm908p11 are involved in pollen development and tube growth (Wang et al., 2009 ; Dong et al., 2013 ). Additionally, Higuchi-Takeuchi et al. ( 2020 ) reported that certain sORFs participate in circadian rhythm signaling under elevated CO₂, highlighting the versatile roles of these small peptides in plants. Despite increasing recognition of sORFs’ roles, intergenic sORFs in indica rice remain largely unexplored. Identifying these sORFs presents an opportunity to uncover novel regulatory pathways and traits relevant to crop improvement. This study aimed to identify intergenic sORFs in the indica genome and explore their potential roles in abiotic stress responses (cold, drought, heat, and salt). Transcriptomic analyses were conducted to predict sORFs associated with stress pathways, and semi-qPCR was used to validate their expression in drought-stressed seedlings. Collectively, these findings provide new insights into the functional significance of sORFs in plant adaptation and their potential contribution to enhancing stress tolerance in rice. Materials and methods In silico prediction and expression profiling of sORFs from indica rice Intergenic sORFs prediction from indica genome The intron, exon, coding sequence (CDS), and intergenic regions of the indica genome were retrieved using gff2sequence (Camiolo & Porceddu, 2013 ) from EnsemblPlants. sORFs were predicted using sORFfinder2 (Hanada et al., 2010 ), with repeat sequences masked by RepeatMasker. Redundant sequences were clustered using CD-HIT-EST (Li & Godzik, 2006 ) at 95% identity, with all tools run using default parameters. Data retrieval of indica transcriptomes Thirteen RNA-Sequencing libraries covering five abiotic stresses were downloaded from NCBI SRA and raw reads retrieved from the European Nucleotide Archive (Table 1 ). Table 1 List of transcriptome datasets for indica rice downloaded from online database Study NCBI Variety Treatments Reference Drought PRJNA248474 H471ck 1 day Huang et al. ( 2014 ) 3 days HHZ 1 day 3 days P28ck 1 day 3 days - MR 219 30 soil moisture content Ong et al. ( 2023 ) 40 soil moisture content Salt PRJNA732136 Chao2R 3 days Kong et al. ( 2021 ) 7 days Heat PRJNA917024 IR64 0.5 hour Wang et al. ( 2023 ) 1 hour 2 hours 4 hours 8 hours 24 hours Cold PRJNA506503 IR64 2 hours Dasgupta et al. ( 2020 ) Transcriptome analysis of sORFs from published datasets Strand specificity was checked using infer_experiment.py (Wang et al., 2012 ). HISAT2 (Kim et al., 2015 ) was used for read alignment, and FeatureCounts (Liao et al., 2014 ) quantified reads, followed by DESeq2 (Love et al., 2014 ) for differential expression (DE) analysis in R. To identify common stress-responsive genes, DEGs from different varieties under the same stress condition were merged into a single DEG list. Similarly, DE sORFs were compiled into a combined list for downstream analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using TBtools (Chen et al., 2023 ). All data visualization, including plots for DEGs, GO enrichment, and KEGG pathways, was conducted in RStudio (version 4.2.2). For both DEGs and DE sORFs, only GO terms consistently enriched across all four stress conditions (cold, drought, heat, and salt) were retained for analysis. This approach highlights the common functional responses shared under multiple abiotic stresses. Identifying signal peptides from DE sORFs Signal peptides in DE sORFs were predicted using SignalP 6.0 ( https://services.healthtech.dtu.dk/services/SignalP-6.0/ ) (Teufel et al., 2022 ). Drought treatment Seeds of three indica rice varieties (MR 219, MR 220, and MR 297) were obtained from the Malaysian Agricultural Research and Development Institute, Malaysia. After 10 days of soaking for germination, seedlings were grown in the GP-BSL2 Plant Biotechnology Facility, Universiti Malaya, under controlled conditions (12-h photoperiod, 28°C). At the four-leaf stage, seedlings were divided into well-watered and drought-stressed groups (n = 3 per variety). Drought stress was imposed by withholding water for nine days, and three experimental sets were prepared for morphological, physiological, biochemical, and molecular analyses. Determination of morpho-physiological traits Chlorophyll content was quantified following Lichtenthaler ( 1987 ). Briefly, ~ 25 mg of freeze-dried leaf powder was extracted with 80% (v/v) acetone, incubated in the dark, centrifuged, and the supernatant was diluted before measurement. Absorbance was recorded using a nanophotometer (Implen GmbH, Germany) at 470, 647, and 663 nm, and chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids were calculated accordingly. Malondialdehyde (MDA) content was determined as described by Heath and Packer ( 1968 ), where 100 mg of leaf powder was extracted with trichloroacetic acid (TCA), reacted with thiobarbituric acid (TBA), heated, cooled, centrifuged, and the absorbance of the supernatant was measured at 532 and 600 nm to estimate lipid peroxidation. Proline content was assessed based on Bates et al. ( 1973 ), with leaf extracts prepared in ethanol, reacted with acid ninhydrin and acetic acid, extracted with toluene, and the absorbance of the toluene fraction measured at 520 nm; proline concentration was determined using a standard calibration curve. Semi-quantitative PCR analysis RNA was extracted from aboveground samples using the CTAB method (Poon et al., 2020 ), treated with DNase I (Thermo Scientific, USA), and reverse-transcribed to cDNA (Nextscript, Taiwan). Quantitative RT-PCR was performed for four antioxidant-related genes using the 2^−ΔΔCt method (Livak & Schmittgen, 2001 ) (Table 2 ). Semi-quantitative PCR validated three selected stress-responsive sORFs ( OsisORF_0050 , OsisORF_3007 , and OsisORF_3394 ) across indica varieties using the following conditions: 30 cycles of 98°C for 10 s, 60°C for 30 s, and 72°C for 20 s. Table 2 Primers used in qRT-PCR Gene Sense/Antisense Reference OsActin 5’CTGCGATAATGGAACTGGT 3’ Chen et al. ( 2014 ) 5’ACAATGCTGGGGAAGACA 3’ OsCAT -β 5’GACAAGGAGAACAATTTCCAACAG 3’ Rosatto et al. (2017) 5’AGTAGGAGATCCAGATGCCAC 3’ OsAPX 5’TCAGGACATTGTTGCCCTC 3’ 5’GTCACCACTCAGAAGCTCC 3’ OsGR2 5’CACCTGTTGCACTGATGGAG 3’ 5’GTTCACTCAAGCCCACTACTG 3’ OsSOD -Fe 5’CAAGTCACAAACCCAGAGTCAT 3’ 5’GGAATACAAGATGTCAGGCTCA 3’ Statistical analyses Statistical analyses were conducted using SAS (Windows version, NC State University). Two-tailed t-tests assessed significance, while Duncan’s Multiple Range Test ( p ≤ 0.05) was used for multiple comparisons. Results In silico prediction of sORFs from indica rice A total of 3,627,065 sORFs were predicted from the indica genome using the sORFfinder2 prediction pipeline (Hanada et al., 2010 ). Of these, 195,522 were classified as coding sORFs (Table 3 ). Following CD-HIT-EST clustering, the number of coding sORFs was reduced to 175,424. Subsequently, after repeat masking, the resulting set comprised of 135,673 unique and repeat-free coding sORFs (Table 3 ). Table 3 Summary of sORFs found in O. sativa subsp. indica Description of sORF Number of sORF sORF Coding sORFs Unique coding sORFs Repeat free unique coding sORFs 3,627,065 195,522 175,424 135,673 Transcriptome analysis Differential expression of sORFs across stresses and time points Genome-wide analysis of sORF expression revealed highly variable responses across abiotic stresses and time points (Table 1 ). Cold stress had the smallest impact, with only seven DE sORFs (five upregulated, one downregulated) (Fig. 1 A). Drought treatments showed moderate responses, ranging from 1,086 DE sORFs in HHZ3D to 1,910 in H471ck1D, with a nearly balanced distribution of up- and downregulated transcripts. Salt stress triggered a progressive increase in DE sORFs, from 964 at 3 days to 2,544 at 7 days. Heat stress induced the most pronounced transcriptomic shifts, with early exposure (0.5–2 h) producing 1,977–2,871 DE sORFs, intermediate durations (4–8 h) yielding 1,319–3,886, and 24 h peaking at 6,124 DE sORFs, including 4,291 upregulated and 1,833 downregulated (Fig. 1 A). Overall, heat and prolonged salt exposure exerted the strongest regulatory influence on sORFs, whereas cold stress had a negligible effect. Overlap of DEGs and DE sORFs Among Stresses To further assess stress-specific and shared responses, we performed an overlap analysis of DEGs and DE sORFs across four major stress types: heat, salt, drought, and cold (Figs. 1 B, 1 C). Among DEGs, heat-responsive genes dominated with 26,020 DEGs, followed by cold (25), salt (9493), and drought (14589) (Fig. 1 B). Only 10 genes were common across all four stresses, indicating highly stress-specific transcriptional responses. Similarly, for DE sORFs, the largest DE set was detected under heat stress (9,070), with smaller numbers under salt (3,026), drought (4,061), and cold (7) (Fig. 1 C). No sORF was shared among all four stresses, and only three were common between salt and heat. These findings suggest that while sORFs are widely involved in stress responses, their regulation is largely stress-specific, with limited overlap across stress types. Functional annotation of DEGs and DE sORFs To identify core functional categories shared across stress responses, we performed an overlap-based selection of enriched GO terms from DEGs and DE sORFs across cold, drought, heat, and salt stress conditions (Figs. 1 D, E). In DEGs, enriched terms were primarily associated with biological processes (BP), including GO:0019725 and GO:0006091, as well as stress-related processes such as GO:0040029. Cellular component (CC) enrichment revealed terms such as GO:0005739 (mitochondrion), GO:0005737 (cytoplasm), GO:0005730 (nucleolus), GO:0005840 (ribosome), GO:0005622 (intracellular), and GO:0009579 (thylakoid). In contrast, DE sORFs showed a broader distribution across molecular function (MF) categories. Highly enriched terms included GO:0005198 (structural molecule activity), GO:0005488 (binding), GO:0030234 (enzyme regulator activity), GO:0003677 (DNA binding), GO:0008289 (lipid binding), and GO:0003682 (chromatin binding). Some overlap with CC categories was also observed, particularly in GO:0005840 (ribosome) and GO:0005739 (mitochondrion). KEGG pathway enrichment analysis of DEGs and DE sORFs under stress KEGG enrichment further distinguished the regulatory roles of DEGs and DE sORFs (Figs. 1 F, G). DEGs were broadly enriched across pathways associated with metabolism (A09100), genetic information processing, and MAPK signaling pathway—plant (04016), reflecting a systemic transcriptional response to stress (Fig. 1 F). In contrast, DE sORFs exhibited a narrower but more targeted enrichment pattern, primarily within signal transduction (B09132), environmental information processing (A09180), and MAPK signaling (04016), especially under heat and salt stress (Fig. 1 G). These results indicate that sORFs may function as specialized regulatory modules in key signaling pathways rather than as components of broad metabolic processes. Identifying signal peptides To explore the signaling potential of stress-responsive sORFs, we predicted signal peptides among DE sORFs identified under salt, drought, and heat stress (Fig. 1 H). A total of 572 DE sORFs were predicted. Of these, 105 were unique to salt stress, 54 to drought, and 89 to heat, while 68 were common across all three stresses. Additional overlaps included 38 sORFs shared between salt and drought and 77 between drought and heat. This distribution suggests that a subset of sORF-derived peptides may act as universal regulators of stress adaptation, whereas others likely serve stress-specific functions in signaling networks. Drought stress tolerance assay and validation of transcriptome-derived sORFs in indica varieties To validate transcriptome-based findings, a drought stress tolerance assay was conducted on three indica rice varieties (MR 219, MR 220, and MR 297). Drought stress was imposed at the four-leaf stage by withholding water for nine days, resulting in progressive leaf rolling and reduced soil moisture (< 10%). Morphological assessments at 9 dps revealed varietal differences in drought response, with MR 297 showing the lowest leaf rolling score (2.50) (Table 4 ) and minimal reduction in RWC (12.19%), while MR 219 exhibited the greatest RWC decline but maintained the tallest stature (Figs. 2 A, B). Biomass and root-to-shoot ratios declined under stress, with MR 220 experiencing the highest biomass reduction (80%) (Figs. 2 D-I). Stress markedly increased MDA and proline in all varieties, with MR 219 showing the highest levels (MDA: 5.2 nM/g FW; proline: 28.17 µM/g FW) (Figs. 2 N, O), whereas chlorophyll and carotenoid contents decreased in MR 219 and MR 220 but increased in MR 297 (Figs. 2 J-M). Molecular responses corroborated the physiological trends, as antioxidant-related genes ( CAT-β , APX 1, GR 2, and SOD- Fe) were strongly upregulated under drought, particularly in MR 219 ( GR 2: log₂ fold-change 3.63) (Figs. 2 P-S). Importantly, semi-quantitative RT-PCR confirmed expression of three transcriptome-predicted drought-responsive sORFs: OsisORF_0050 and OsisORF_3394 were detected exclusively under drought stress, whereas OsisORF_3007 was expressed under both control and stress conditions, with enhanced expression in stressed samples (Fig. 2 T). These findings reinforce the transcriptomic prediction of stress-inducible sORFs and suggest their potential involvement in drought adaptation, possibly through regulation of antioxidant pathways and membrane stability mechanisms. Table 4 Leaf symptoms and their corresponding drought and leaf rolling scores Cultivars Tiller number Drought score Leaf rolling score (mean value) MR 219 1 2 4.75 MR 220 1 3 3.83 MR 297 1 2 2.50 Discussion sORFs were identified not only within coding transcripts, including the 3'UTR, CDS, and 5'UTR, but also in non-coding regions such as mitochondrial RNAs, circular RNAs, and long noncoding RNAs (Orr et al., 2020 ; Kute et al., 2022 ). The detection of sORFs in plant genomes remains challenging due to their polyploid or paleopolyploid nature and repetitive sequences (Feng et al., 2023 ). Despite these difficulties, advancements have made the identification of sORFs in plant genomes possible. In this study, a total of 135,673 coding sORFs were identified in the indica rice genome. This number is notably higher than that reported in other plant species. For example, Liang et al. ( 2021 ) reported 9,388 sORF-encoding peptides in maize, while Chieng et al. ( 2023 ) identified 50,191 coding sORFs in Cucumis sativus variety hardwickii . The high abundance of coding sORFs in indica rice may be attributed to its larger genome size, which is approximately 390 Mbp (Zhou et al., 2020 ). In contrast, the genome of C. sativus is slightly smaller, around 342 Mbp (Turek et al., 2023 ), which may partly explain the lower number of sORFs identified in cucumber. In leguminous plants, Guillen et al. (2013) identified 6170, 10,461, 30,521, and 23,599 of putative sORFs in the genomes of Phaseolus vulgaris, Glycine max, Medicago truncatula , and Lotus japonicus genomes, respectively. Ahmad et al. ( 2024 ) identified 416,873 intergenic sORFs from cucumber Chinese long inbred line 9930. Such considerable variation in the number of coding sORFs may be attributed to the difference in prediction pipeline opted. sORFs are not only play a crucial role in plant biological processes such as growth regulation, morphogenesis, and cell signaling but are also involved in responses to abiotic stresses (reviewed in Ong et al., 2022 ). Building on this, the intergenic sORFs identified in the indica genome were analyzed to investigate their functions and expression profiles across different indica varieties under various abiotic stresses, using transcriptomic data from publicly available RNA-seq datasets. In the transcriptome of Populus deltoides , 1,282 high-confidence sORFs were identified (Yang et al., 2011 ), whereas Hanada et al. ( 2013 ) identified 2,099 coding sORFs in A. thaliana expressed under different experimental conditions. In rice, 36 and 1,076 sORFs were found to be regulated during conditions of iron excess and deficiency, respectively (Bashir et al., 2014 ). Chieng et al. ( 2022 ) identified 14,799 transcribed sORFs from RNA-seq datasets of var. sativus . Additionally, Ong et al. ( 2023 ) reported 122 and 143 sORFs regulated in indica rice under 40% and 30% soil moisture content, respectively. In our results, the number of DE sORFs ranged from 7 to 9,000 across different abiotic stresses and rice varieties, indicating their potential role as the initial step in the protein-coding response to abiotic stresses. Plants reprogram their transcriptome in response to unfavorable environmental conditions (Teoh et al., 2022 ; Norhafizah et al., 2025 ). Despite plants exhibiting distinct responses to each stress, DE sORFs were similarly associated with various GO enrichments. Transcriptome analysis revealed overlapping GO terms enriched in MF terms such as GO:0005198 (structural molecule activity), GO:0005488 (binding), GO:0003677 (DNA binding), GO:0008289 (lipid binding), and GO:0003682 (chromatin binding), which was similarly enriched in japonica rice under 10 abiotic stress conditions (Kawahara et al., 2015). The KEGG enrichment analysis highlights a striking difference between the functional distribution of DEGs and DE sORFs under stress. While DEGs were broadly enriched across diverse pathways related to metabolism, genetic information processing, and environmental signaling, DE sORFs exhibited a more selective enrichment pattern, primarily in pathways associated with signal transduction (B09132), environmental information processing (A09180), and the MAPK signaling pathway (04016). These pathways were also enriched in apple plant under abiotic stress conditions (Li et al., 2019 ). This suggests that sORFs may act as specialized regulatory components rather than general responders to stress. The high enrichment of DE sORFs in signaling-related pathways, particularly under heat and salt stress, implies their potential role in signal perception and transduction during stress adaptation (Li et al., 2019 ). The interplay between various signaling pathways, transcription factors, hormones, and stress-responsive genes is essential for modulating cell growth during abiotic stress conditions in plants. For instance, WRKY transcription factors play a significant role in plant responses to abiotic stresses such as drought, salinity, and temperature extremes (Li et al., 2020 ). Genes from the JAZ family have been found to have a significant role in granting resistance to abiotic stressors, enhancing cell growth under conditions like high temperature, salinity, and osmotic stresses (Shen et al., 2020 ). These findings highlight the importance of these pathways in stress tolerance, thus the involvement of DE sORFs in abiotic stress-related ontologies. The importance of DE sORFs was further validated through signal peptide prediction. Signal peptides have been shown to contribute to adaptation and tolerance during abiotic stress (Kim et al., 2021 ), suggesting that DE sORFs play a role in abiotic stress responses. CLAVATA3 (CLV)/EMBRYO-SURROUNDING REGION RELATED 25 ( CLE25 ) peptides respond to water deficiency by inducing stomatal closure (Takahashi et al., 2018 ). Similarly, the C-terminally encoded peptide 5 ( CEP5 ) enhances drought tolerance in Arabidopsis . In contrast, CAP-derived peptide 1 ( CAPE1 ) negatively impacts salt stress tolerance, while plant elicitor peptides (Peps) contribute to salinity stress tolerance (Nakaminami et al., 2018 ). The validation of DE sORFs through signal peptide prediction suggests their role in abiotic stress responses, as various peptides, such as CLE25 and CEP5 , enhance drought tolerance, while others like CAPE1 negatively affect stress adaptation. Physiological and biochemical assays of three indica rice varieties under drought stress provided further context to sORF predictions. MR 297 exhibited superior drought tolerance with minimal leaf rolling and stable chlorophyll retention, whereas MR 219 displayed severe oxidative stress, indicated by higher malondialdehyde and proline levels. Elevated expression of antioxidant genes (Os CAT-β , Os APX , and Os GR 2) under drought stress, particularly in MR 219, reflects an active detoxification response. Importantly, semi-quantitative RT-PCR validated transcriptomic predictions by confirming drought-induced expression of candidate sORFs ( OsisORF_0050 and OsisORF_3394 ), while OsisORF_3007 showed constitutive but enhanced expression under stress. Notably, several validated sORFs were predicted to encode signal peptides, supporting their functional involvement in stress mitigation. These findings suggest that sORFs may either interact with ROS scavenging machinery or act as signaling peptides to trigger systemic tolerance mechanisms. The integrated computational and experimental framework presented here highlights stress-responsive sORFs as promising targets for functional genomics and crop improvement. Their small size, rapid translation potential, and involvement in critical signaling pathways offer unique advantages for engineering stress tolerance with minimal metabolic burden. Conclusion This study provides the first comprehensive genome-wide prediction and functional analysis of sORFs in indica rice under multiple abiotic stresses. In indica , 135,673 unique coding sORFs were identified, and transcriptome profiling revealed highly stress-specific regulation, with heat and prolonged salt stress exerting the strongest effects. Overlap-based enrichment analysis of DE sORFs and protein-coding genes highlighted a central role for sORFs in signaling, stress perception, and oxidative stress regulation, as supported by GO and KEGG pathway associations with MAPK signaling and signal transduction pathways. The detection of signal peptides in a subset of sORFs suggests potential involvement in intercellular communication and systemic stress responses. Physiological and biochemical validation in indica rice varieties demonstrated contrasting drought tolerance strategies, corroborated by antioxidant gene upregulation and differential metabolite accumulation. Expression of transcriptome-predicted drought-responsive sORFs was confirmed by semi-quantitative RT-PCR, reinforcing their functional relevance in stress adaptation. Collectively, these findings propose that sORFs act as specialized regulators within stress signaling networks, complementing canonical gene functions to enhance resilience. Future functional characterization of these sORFs will be critical to elucidate their mechanistic roles and exploit them as novel targets for breeding stress-tolerant rice varieties. Future work should focus on in planta functional validation using overexpression, CRISPR-based knockout, and exogenous peptide assays to elucidate their roles in signaling and metabolism. Prioritizing sORFs with predicted signal peptides or those enriched in pathways such as MAPK signaling could accelerate the identification of master regulators of stress adaptation. Ultimately, leveraging sORFs as molecular tools holds significant potential for developing climate-resilient rice varieties and ensuring food security under increasing environmental challenges. Declarations Acknowledgements This work was funded by the Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme (FRGS), Grant No: FRGS/1/2019/STG05/UM/02/3. Authors’ contributions CHT conceived the idea. SNO conducted the experiment and drafted the manuscript. CHT, BCT and KH assisted in data analysis, reviewed the manuscript and provided suggestions for its improvement. Ethics approval Not applicable Data availability statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Declaration of Competing of Interest The authors declare that there was no conflict of interest. References Ahmad, E. M., Abdelsamad, A., El-Shabrawi, H. M., El‐Awady, M. A., Aly, M. A., and El‐Soda, M. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9148773","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610553430,"identity":"3ecb973d-fdb8-4162-8351-676d32c2027f","order_by":0,"name":"SHEUE NI ONG","email":"","orcid":"","institution":"Sanya Research Institute, Chinese Academy of Tropical Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"SHEUE","middleName":"NI","lastName":"ONG","suffix":""},{"id":610553438,"identity":"e1b2d258-a16c-4b03-98c2-d955fc05bf45","order_by":1,"name":"Boon Chin Tan","email":"","orcid":"","institution":"Centre for Research in Biotechnology for Agriculture (CEBAR), Universiti Malaya","correspondingAuthor":false,"prefix":"","firstName":"Boon","middleName":"Chin","lastName":"Tan","suffix":""},{"id":610553439,"identity":"6ce4afe6-97c7-444a-919a-0dc40f9459c3","order_by":2,"name":"Kousuke Hanada","email":"","orcid":"","institution":"Department of Bioscience and Bioinformatics, Kyushu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kousuke","middleName":"","lastName":"Hanada","suffix":""},{"id":610553443,"identity":"a79feabc-0e6d-4204-93cd-85a36145471d","order_by":3,"name":"Hui Zhao","email":"","orcid":"","institution":"Sanya Research Institute, Chinese Academy of Tropical Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhao","suffix":""},{"id":610553453,"identity":"d23a723d-a498-4596-99ed-626e93861f9f","order_by":4,"name":"Chee How Teo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCQYGZiiT8QGYOkCsFh4gbUCyFjYJorTwz24++Lig4jCDPXvzsWreHAY5vhsJrJt58Fly51iy8Ywzhxl4eI6l3ebdxmAseSOB7TY+LQYSOWbSvG23GXiADJCWxA2EteR//w3WIv/GrBiopZ4ILTlszBBbeMyYgVoSDAhpkbiRZizNc+Y/D8+ZtGTJudskDGeeedh2cw4eLfwzkh9+5qlIk2NvP3zww9ttNvJ8x5OP3XiDRwsMwFwCihrGBiZ8DsMOGH+QrGUUjIJRMAqGMQAApFZIRjYsnB4AAAAASUVORK5CYII=","orcid":"","institution":"Centre for Research in Biotechnology for Agriculture (CEBAR), Universiti Malaya","correspondingAuthor":true,"prefix":"","firstName":"Chee","middleName":"How","lastName":"Teo","suffix":""}],"badges":[],"createdAt":"2026-03-17 12:44:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9148773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9148773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105416646,"identity":"66fae107-e130-4096-a271-95afaacd0f8f","added_by":"auto","created_at":"2026-03-25 19:10:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80055,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression and functional enrichment of DEGs and DE sORFs under abiotic stress conditions. (A) Bar graph showing the number of DE sORFs under heat, cold, salt, and drought stress. Upregulated and downregulated transcripts are color-coded. (B–C) Venn diagrams illustrating the overlap of DEGs (B) and DE sORFs (C) across the four stress conditions, highlighting shared and unique expression profiles. (D) GO enrichment heatmap of DEGs expression levels across stress treatments, with color intensity representing relative transcript abundance. (E) GO enrichment for DE sORFs (F) Dot plots of KEGG pathway enrichment for DEGs, where dot size indicates gene count and color gradient reflects enrichment score. (G) Comparative KEGG enrichment analysis of DE sORFs across stress conditions, showing gene ratio and enrichment score. (H) Venn diagram depicting the distribution of predicted signal peptides among DE sORFs under different stress treatments.\u003c/p\u003e","description":"","filename":"Online0303PlantBiosystemsFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9148773/v1/e4eec02e88fd4c1b641a58f4.png"},{"id":105416647,"identity":"ac14199f-fa8a-422e-ae2f-3707cf6982cc","added_by":"auto","created_at":"2026-03-25 19:10:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62394,"visible":true,"origin":"","legend":"\u003cp\u003eMorphological, physiological, and molecular responses of three \u003cem\u003eindica\u003c/em\u003e rice varieties (MR219, MR220, MR297) under well-watered and drought-stressed conditions. (A–J) Morphological traits: (A) Relative water content, (B) plant height, (C) root length, (D) root/shoot length ratio, (E) Plant biomass, (F) Shoot fresh weight, (G) root fresh weight, (H) shoot dry weight, (I) root dry weight, and (J–O) Physiological traits: (J) total chlorophyll, (K) carotenoid content, (L) chlorophyll a content, (M) chlorophyll b content, (N) MDA content, and (O) proline content. (P–S) Relative expression (log₂ fold change) of drought-responsive genes under well-watered (blue) and drought-stressed (orange) conditions: (P) \u003cem\u003eOsCAT\u003c/em\u003e-β, (Q) \u003cem\u003eOsAPX\u003c/em\u003e, (R) \u003cem\u003eOsGR2\u003c/em\u003e, and (S) \u003cem\u003eOsSOD\u003c/em\u003e-Fe. Data represent mean ± SE (n = 3). Asterisks indicate significant differences between treatments (p ≤ 0.05, Student’s t-test). Different letters indicate significant differences among varieties (p ≤ 0.05, Duncan’s test). (T) RT-PCR validation of drought-responsive sORFs (\u003cem\u003eOsisORF_0050\u003c/em\u003e, \u003cem\u003eOsisORF_3394\u003c/em\u003e, \u003cem\u003eOsisORF_3007\u003c/em\u003e) in MR219, MR220, and MR297 under well-watered and drought-stressed conditions. Actin was used as an internal control. Lane description: 1 – 100 bp ladder, 2–4 – well-watered replicates, 5–7 – drought-stressed replicates.\u003c/p\u003e","description":"","filename":"Online0304PlantBiosystemsFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9148773/v1/81aa08eae6c4f0bd36c0d408.png"},{"id":105566249,"identity":"722714b5-f13b-4876-940a-c6fc63e09e22","added_by":"auto","created_at":"2026-03-27 12:55:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1239519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9148773/v1/5ba832d9-1c97-4118-a882-52c45a3e4548.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide identification of sORFs in indica rice and their comparative transcriptomic analysis under stress","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRice, a staple food crop, consists of multiple subspecies, including \u003cem\u003ejaponica\u003c/em\u003e, temperate \u003cem\u003ejaponica\u003c/em\u003e, and \u003cem\u003ejavanica\u003c/em\u003e (Kim, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eIndica\u003c/em\u003e rice, derived from \u003cem\u003ejaponica\u003c/em\u003e and \u003cem\u003eO. rufipogon\u003c/em\u003e crosses, retains cold tolerance traits and exhibits resilience to challenging environments, such as acidic soils (Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Nevertheless, abiotic stresses, including drought, salinity, and extreme temperatures, continue to limit growth and productivity, prompting the evolution of intricate defense mechanisms in rice (Lau et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lau et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mohd Amnan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these adaptations, the precise molecular mechanisms underlying rice\u0026rsquo;s resilience to environmental stresses remain unclear.\u003c/p\u003e \u003cp\u003eSmall open reading frames (sORFs) are short DNA sequences that encode peptides of fewer than 100 amino acids (Saghatelian \u0026amp; Couso, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Increasing evidence suggests that sORFs play important roles in plant stress responses and other physiological processes. Despite their potential significance, sORFs were historically overlooked due to limitations in bioinformatics tools, weak sequence conservation, and low expression levels (Cabrera-Quio et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hanada et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The first systematic identification of sORFs in plants was reported by Hanada et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who predicted approximately 8,000 coding sORFs in the \u003cem\u003eArabidopsis thaliana\u003c/em\u003e genome.\u003c/p\u003e \u003cp\u003eAdvances in sequencing technologies and computational tools have since enabled the discovery of sORFs across both coding and previously annotated non-coding regions, revealing their contributions to plant morphology, growth, and stress responses (Hanada et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Takahashi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Functional studies have demonstrated that sORFs regulate diverse aspects of plant biology. In \u003cem\u003eArabidopsis\u003c/em\u003e, they influence plant morphology and stress responses (Takeda et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas in maize, \u003cem\u003eZm401\u003c/em\u003e and \u003cem\u003eZm908p11\u003c/em\u003e are involved in pollen development and tube growth (Wang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, Higuchi-Takeuchi et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that certain sORFs participate in circadian rhythm signaling under elevated CO₂, highlighting the versatile roles of these small peptides in plants.\u003c/p\u003e \u003cp\u003eDespite increasing recognition of sORFs\u0026rsquo; roles, intergenic sORFs in \u003cem\u003eindica\u003c/em\u003e rice remain largely unexplored. Identifying these sORFs presents an opportunity to uncover novel regulatory pathways and traits relevant to crop improvement. This study aimed to identify intergenic sORFs in the \u003cem\u003eindica\u003c/em\u003e genome and explore their potential roles in abiotic stress responses (cold, drought, heat, and salt). Transcriptomic analyses were conducted to predict sORFs associated with stress pathways, and semi-qPCR was used to validate their expression in drought-stressed seedlings. Collectively, these findings provide new insights into the functional significance of sORFs in plant adaptation and their potential contribution to enhancing stress tolerance in rice.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eprediction and expression profiling of sORFs from\u003c/b\u003e \u003cb\u003eindica\u003c/b\u003e \u003cb\u003erice\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eIntergenic sORFs prediction from\u003c/b\u003e \u003cb\u003eindica\u003c/b\u003e \u003cb\u003egenome\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe intron, exon, coding sequence (CDS), and intergenic regions of the \u003cem\u003eindica\u003c/em\u003e genome were retrieved using gff2sequence (Camiolo \u0026amp; Porceddu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) from EnsemblPlants. sORFs were predicted using sORFfinder2 (Hanada et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), with repeat sequences masked by RepeatMasker. Redundant sequences were clustered using CD-HIT-EST (Li \u0026amp; Godzik, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) at 95% identity, with all tools run using default parameters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData retrieval of\u003c/b\u003e \u003cb\u003eindica\u003c/b\u003e \u003cb\u003etranscriptomes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThirteen RNA-Sequencing libraries covering five abiotic stresses were downloaded from NCBI SRA and raw reads retrieved from the European Nucleotide Archive (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of transcriptome datasets for \u003cem\u003eindica\u003c/em\u003e rice downloaded from online database\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCBI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePRJNA248474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eH471ck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHuang et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHHZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP28ck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMR 219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 soil moisture content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOng et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 soil moisture content\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePRJNA732136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChao2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKong et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 days\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eHeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePRJNA917024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eIR64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWang et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 hour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRJNA506503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIR64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDasgupta et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analysis of sORFs from published datasets\u003c/h2\u003e \u003cp\u003eStrand specificity was checked using infer_experiment.py (Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). HISAT2 (Kim et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was used for read alignment, and FeatureCounts (Liao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) quantified reads, followed by DESeq2 (Love et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) for differential expression (DE) analysis in R. To identify common stress-responsive genes, DEGs from different varieties under the same stress condition were merged into a single DEG list. Similarly, DE sORFs were compiled into a combined list for downstream analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using TBtools (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All data visualization, including plots for DEGs, GO enrichment, and KEGG pathways, was conducted in RStudio (version 4.2.2). For both DEGs and DE sORFs, only GO terms consistently enriched across all four stress conditions (cold, drought, heat, and salt) were retained for analysis. This approach highlights the common functional responses shared under multiple abiotic stresses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentifying signal peptides from DE sORFs\u003c/h3\u003e\n\u003cp\u003eSignal peptides in DE sORFs were predicted using SignalP 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/services/SignalP-6.0/\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/services/SignalP-6.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Teufel et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDrought treatment\u003c/h3\u003e\n\u003cp\u003eSeeds of three \u003cem\u003eindica\u003c/em\u003e rice varieties (MR 219, MR 220, and MR 297) were obtained from the Malaysian Agricultural Research and Development Institute, Malaysia. After 10 days of soaking for germination, seedlings were grown in the GP-BSL2 Plant Biotechnology Facility, Universiti Malaya, under controlled conditions (12-h photoperiod, 28\u0026deg;C). At the four-leaf stage, seedlings were divided into well-watered and drought-stressed groups (n\u0026thinsp;=\u0026thinsp;3 per variety). Drought stress was imposed by withholding water for nine days, and three experimental sets were prepared for morphological, physiological, biochemical, and molecular analyses.\u003c/p\u003e\n\u003ch3\u003eDetermination of morpho-physiological traits\u003c/h3\u003e\n\u003cp\u003eChlorophyll content was quantified following Lichtenthaler (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Briefly, ~\u0026thinsp;25 mg of freeze-dried leaf powder was extracted with 80% (v/v) acetone, incubated in the dark, centrifuged, and the supernatant was diluted before measurement. Absorbance was recorded using a nanophotometer (Implen GmbH, Germany) at 470, 647, and 663 nm, and chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids were calculated accordingly. Malondialdehyde (MDA) content was determined as described by Heath and Packer (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), where 100 mg of leaf powder was extracted with trichloroacetic acid (TCA), reacted with thiobarbituric acid (TBA), heated, cooled, centrifuged, and the absorbance of the supernatant was measured at 532 and 600 nm to estimate lipid peroxidation. Proline content was assessed based on Bates et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), with leaf extracts prepared in ethanol, reacted with acid ninhydrin and acetic acid, extracted with toluene, and the absorbance of the toluene fraction measured at 520 nm; proline concentration was determined using a standard calibration curve.\u003c/p\u003e\n\u003ch3\u003eSemi-quantitative PCR analysis\u003c/h3\u003e\n\u003cp\u003eRNA was extracted from aboveground samples using the CTAB method (Poon et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), treated with DNase I (Thermo Scientific, USA), and reverse-transcribed to cDNA (Nextscript, Taiwan). Quantitative RT-PCR was performed for four antioxidant-related genes using the 2^\u0026minus;ΔΔCt method (Livak \u0026amp; Schmittgen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Semi-quantitative PCR validated three selected stress-responsive sORFs (\u003cem\u003eOsisORF_0050\u003c/em\u003e, \u003cem\u003eOsisORF_3007\u003c/em\u003e, and \u003cem\u003eOsisORF_3394\u003c/em\u003e) across \u003cem\u003eindica\u003c/em\u003e varieties using the following conditions: 30 cycles of 98\u0026deg;C for 10 s, 60\u0026deg;C for 30 s, and 72\u0026deg;C for 20 s.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers used in qRT-PCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSense/Antisense\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOsActin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;CTGCGATAATGGAACTGGT 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChen et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;ACAATGCTGGGGAAGACA 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOsCAT\u003c/em\u003e-β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;GACAAGGAGAACAATTTCCAACAG 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eRosatto et al. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;AGTAGGAGATCCAGATGCCAC 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOsAPX\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;TCAGGACATTGTTGCCCTC 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;GTCACCACTCAGAAGCTCC 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOsGR2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;CACCTGTTGCACTGATGGAG 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;GTTCACTCAAGCCCACTACTG 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eOsSOD\u003c/em\u003e-Fe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;CAAGTCACAAACCCAGAGTCAT 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026rsquo;GGAATACAAGATGTCAGGCTCA 3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SAS (Windows version, NC State University). Two-tailed t-tests assessed significance, while Duncan\u0026rsquo;s Multiple Range Test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) was used for multiple comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eprediction of sORFs from\u003c/b\u003e \u003cb\u003eindica\u003c/b\u003e \u003cb\u003erice\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 3,627,065 sORFs were predicted from the \u003cem\u003eindica\u003c/em\u003e genome using the sORFfinder2 prediction pipeline (Hanada et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Of these, 195,522 were classified as coding sORFs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Following CD-HIT-EST clustering, the number of coding sORFs was reduced to 175,424. Subsequently, after repeat masking, the resulting set comprised of 135,673 unique and repeat-free coding sORFs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of sORFs found in \u003cem\u003eO. sativa\u003c/em\u003e subsp. \u003cem\u003eindica\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDescription of sORF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of sORF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esORF\u003c/p\u003e \u003cp\u003eCoding sORFs\u003c/p\u003e \u003cp\u003eUnique coding sORFs\u003c/p\u003e \u003cp\u003eRepeat free unique coding sORFs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,627,065\u003c/p\u003e \u003cp\u003e195,522\u003c/p\u003e \u003cp\u003e175,424\u003c/p\u003e \u003cp\u003e135,673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTranscriptome analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression of sORFs across stresses and time points\u003c/h2\u003e \u003cp\u003eGenome-wide analysis of sORF expression revealed highly variable responses across abiotic stresses and time points (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cold stress had the smallest impact, with only seven DE sORFs (five upregulated, one downregulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Drought treatments showed moderate responses, ranging from 1,086 DE sORFs in HHZ3D to 1,910 in H471ck1D, with a nearly balanced distribution of up- and downregulated transcripts. Salt stress triggered a progressive increase in DE sORFs, from 964 at 3 days to 2,544 at 7 days. Heat stress induced the most pronounced transcriptomic shifts, with early exposure (0.5\u0026ndash;2 h) producing 1,977\u0026ndash;2,871 DE sORFs, intermediate durations (4\u0026ndash;8 h) yielding 1,319\u0026ndash;3,886, and 24 h peaking at 6,124 DE sORFs, including 4,291 upregulated and 1,833 downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eOverall, heat and prolonged salt exposure exerted the strongest regulatory influence on sORFs, whereas cold stress had a negligible effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eOverlap of DEGs and DE sORFs Among Stresses\u003c/h2\u003e \u003cp\u003eTo further assess stress-specific and shared responses, we performed an overlap analysis of DEGs and DE sORFs across four major stress types: heat, salt, drought, and cold (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Among DEGs, heat-responsive genes dominated with 26,020 DEGs, followed by cold (25), salt (9493), and drought (14589) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Only 10 genes were common across all four stresses, indicating highly stress-specific transcriptional responses.\u003c/p\u003e \u003cp\u003eSimilarly, for DE sORFs, the largest DE set was detected under heat stress (9,070), with smaller numbers under salt (3,026), drought (4,061), and cold (7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). No sORF was shared among all four stresses, and only three were common between salt and heat. These findings suggest that while sORFs are widely involved in stress responses, their regulation is largely stress-specific, with limited overlap across stress types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation of DEGs and DE sORFs\u003c/h2\u003e \u003cp\u003eTo identify core functional categories shared across stress responses, we performed an overlap-based selection of enriched GO terms from DEGs and DE sORFs across cold, drought, heat, and salt stress conditions (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, E). In DEGs, enriched terms were primarily associated with biological processes (BP), including GO:0019725 and GO:0006091, as well as stress-related processes such as GO:0040029. Cellular component (CC) enrichment revealed terms such as GO:0005739 (mitochondrion), GO:0005737 (cytoplasm), GO:0005730 (nucleolus), GO:0005840 (ribosome), GO:0005622 (intracellular), and GO:0009579 (thylakoid).\u003c/p\u003e \u003cp\u003eIn contrast, DE sORFs showed a broader distribution across molecular function (MF) categories. Highly enriched terms included GO:0005198 (structural molecule activity), GO:0005488 (binding), GO:0030234 (enzyme regulator activity), GO:0003677 (DNA binding), GO:0008289 (lipid binding), and GO:0003682 (chromatin binding). Some overlap with CC categories was also observed, particularly in GO:0005840 (ribosome) and GO:0005739 (mitochondrion).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eKEGG pathway enrichment analysis of DEGs and DE sORFs under stress\u003c/h2\u003e \u003cp\u003eKEGG enrichment further distinguished the regulatory roles of DEGs and DE sORFs (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, G). DEGs were broadly enriched across pathways associated with metabolism (A09100), genetic information processing, and MAPK signaling pathway\u0026mdash;plant (04016), reflecting a systemic transcriptional response to stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). In contrast, DE sORFs exhibited a narrower but more targeted enrichment pattern, primarily within signal transduction (B09132), environmental information processing (A09180), and MAPK signaling (04016), especially under heat and salt stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). These results indicate that sORFs may function as specialized regulatory modules in key signaling pathways rather than as components of broad metabolic processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying signal peptides\u003c/h2\u003e \u003cp\u003eTo explore the signaling potential of stress-responsive sORFs, we predicted signal peptides among DE sORFs identified under salt, drought, and heat stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). A total of 572 DE sORFs were predicted. Of these, 105 were unique to salt stress, 54 to drought, and 89 to heat, while 68 were common across all three stresses. Additional overlaps included 38 sORFs shared between salt and drought and 77 between drought and heat. This distribution suggests that a subset of sORF-derived peptides may act as universal regulators of stress adaptation, whereas others likely serve stress-specific functions in signaling networks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDrought stress tolerance assay and validation of transcriptome-derived sORFs in\u003c/b\u003e \u003cb\u003eindica\u003c/b\u003e \u003cb\u003evarieties\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo validate transcriptome-based findings, a drought stress tolerance assay was conducted on three \u003cem\u003eindica\u003c/em\u003e rice varieties (MR 219, MR 220, and MR 297). Drought stress was imposed at the four-leaf stage by withholding water for nine days, resulting in progressive leaf rolling and reduced soil moisture (\u0026lt;\u0026thinsp;10%).\u003c/p\u003e \u003cp\u003eMorphological assessments at 9 dps revealed varietal differences in drought response, with MR 297 showing the lowest leaf rolling score (2.50) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and minimal reduction in RWC (12.19%), while MR 219 exhibited the greatest RWC decline but maintained the tallest stature (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Biomass and root-to-shoot ratios declined under stress, with MR 220 experiencing the highest biomass reduction (80%) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-I).\u003c/p\u003e \u003cp\u003eStress markedly increased MDA and proline in all varieties, with MR 219 showing the highest levels (MDA: 5.2 nM/g FW; proline: 28.17 \u0026micro;M/g FW) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN, O), whereas chlorophyll and carotenoid contents decreased in MR 219 and MR 220 but increased in MR 297 (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ-M). Molecular responses corroborated the physiological trends, as antioxidant-related genes (\u003cem\u003eCAT-β\u003c/em\u003e, \u003cem\u003eAPX\u003c/em\u003e1, \u003cem\u003eGR\u003c/em\u003e2, and \u003cem\u003eSOD-\u003c/em\u003eFe) were strongly upregulated under drought, particularly in MR 219 (\u003cem\u003eGR\u003c/em\u003e2: log₂ fold-change 3.63) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eP-S). Importantly, semi-quantitative RT-PCR confirmed expression of three transcriptome-predicted drought-responsive sORFs: \u003cem\u003eOsisORF_0050\u003c/em\u003e and \u003cem\u003eOsisORF_3394\u003c/em\u003e were detected exclusively under drought stress, whereas \u003cem\u003eOsisORF_3007\u003c/em\u003e was expressed under both control and stress conditions, with enhanced expression in stressed samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eT). These findings reinforce the transcriptomic prediction of stress-inducible sORFs and suggest their potential involvement in drought adaptation, possibly through regulation of antioxidant pathways and membrane stability mechanisms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeaf symptoms and their corresponding drought and leaf rolling scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiller number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrought score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeaf rolling score (mean value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR 219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR 220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR 297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003esORFs were identified not only within coding transcripts, including the 3'UTR, CDS, and 5'UTR, but also in non-coding regions such as mitochondrial RNAs, circular RNAs, and long noncoding RNAs (Orr et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kute et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The detection of sORFs in plant genomes remains challenging due to their polyploid or paleopolyploid nature and repetitive sequences (Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these difficulties, advancements have made the identification of sORFs in plant genomes possible.\u003c/p\u003e \u003cp\u003eIn this study, a total of 135,673 coding sORFs were identified in the \u003cem\u003eindica\u003c/em\u003e rice genome. This number is notably higher than that reported in other plant species. For example, Liang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported 9,388 sORF-encoding peptides in maize, while Chieng et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified 50,191 coding sORFs in \u003cem\u003eCucumis sativus\u003c/em\u003e variety \u003cem\u003ehardwickii\u003c/em\u003e. The high abundance of coding sORFs in \u003cem\u003eindica\u003c/em\u003e rice may be attributed to its larger genome size, which is approximately 390 Mbp (Zhou et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, the genome of \u003cem\u003eC. sativus\u003c/em\u003e is slightly smaller, around 342 Mbp (Turek et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which may partly explain the lower number of sORFs identified in cucumber.\u003c/p\u003e \u003cp\u003eIn leguminous plants, Guillen et al. (2013) identified 6170, 10,461, 30,521, and 23,599 of putative sORFs in the genomes of \u003cem\u003ePhaseolus vulgaris, Glycine max, Medicago truncatula\u003c/em\u003e, and \u003cem\u003eLotus japonicus\u003c/em\u003e genomes, respectively. Ahmad et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified 416,873 intergenic sORFs from cucumber Chinese long inbred line 9930. Such considerable variation in the number of coding sORFs may be attributed to the difference in prediction pipeline opted.\u003c/p\u003e \u003cp\u003esORFs are not only play a crucial role in plant biological processes such as growth regulation, morphogenesis, and cell signaling but are also involved in responses to abiotic stresses (reviewed in Ong et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Building on this, the intergenic sORFs identified in the \u003cem\u003eindica\u003c/em\u003e genome were analyzed to investigate their functions and expression profiles across different \u003cem\u003eindica\u003c/em\u003e varieties under various abiotic stresses, using transcriptomic data from publicly available RNA-seq datasets. In the transcriptome of \u003cem\u003ePopulus deltoides\u003c/em\u003e, 1,282 high-confidence sORFs were identified (Yang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), whereas Hanada et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified 2,099 coding sORFs in \u003cem\u003eA. thaliana\u003c/em\u003e expressed under different experimental conditions. In rice, 36 and 1,076 sORFs were found to be regulated during conditions of iron excess and deficiency, respectively (Bashir et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Chieng et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified 14,799 transcribed sORFs from RNA-seq datasets of var. \u003cem\u003esativus\u003c/em\u003e. Additionally, Ong et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported 122 and 143 sORFs regulated in \u003cem\u003eindica\u003c/em\u003e rice under 40% and 30% soil moisture content, respectively. In our results, the number of DE sORFs ranged from 7 to 9,000 across different abiotic stresses and rice varieties, indicating their potential role as the initial step in the protein-coding response to abiotic stresses.\u003c/p\u003e \u003cp\u003ePlants reprogram their transcriptome in response to unfavorable environmental conditions (Teoh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Norhafizah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite plants exhibiting distinct responses to each stress, DE sORFs were similarly associated with various GO enrichments. Transcriptome analysis revealed overlapping GO terms enriched in MF terms such as GO:0005198 (structural molecule activity), GO:0005488 (binding), GO:0003677 (DNA binding), GO:0008289 (lipid binding), and GO:0003682 (chromatin binding), which was similarly enriched in \u003cem\u003ejaponica\u003c/em\u003e rice under 10 abiotic stress conditions (Kawahara et al., 2015).\u003c/p\u003e \u003cp\u003eThe KEGG enrichment analysis highlights a striking difference between the functional distribution of DEGs and DE sORFs under stress. While DEGs were broadly enriched across diverse pathways related to metabolism, genetic information processing, and environmental signaling, DE sORFs exhibited a more selective enrichment pattern, primarily in pathways associated with signal transduction (B09132), environmental information processing (A09180), and the MAPK signaling pathway (04016). These pathways were also enriched in apple plant under abiotic stress conditions (Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This suggests that sORFs may act as specialized regulatory components rather than general responders to stress. The high enrichment of DE sORFs in signaling-related pathways, particularly under heat and salt stress, implies their potential role in signal perception and transduction during stress adaptation (Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The interplay between various signaling pathways, transcription factors, hormones, and stress-responsive genes is essential for modulating cell growth during abiotic stress conditions in plants. For instance, WRKY transcription factors play a significant role in plant responses to abiotic stresses such as drought, salinity, and temperature extremes (Li et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Genes from the JAZ family have been found to have a significant role in granting resistance to abiotic stressors, enhancing cell growth under conditions like high temperature, salinity, and osmotic stresses (Shen et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings highlight the importance of these pathways in stress tolerance, thus the involvement of DE sORFs in abiotic stress-related ontologies.\u003c/p\u003e \u003cp\u003eThe importance of DE sORFs was further validated through signal peptide prediction. Signal peptides have been shown to contribute to adaptation and tolerance during abiotic stress (Kim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggesting that DE sORFs play a role in abiotic stress responses. \u003cem\u003eCLAVATA3 (CLV)/EMBRYO-SURROUNDING REGION RELATED 25\u003c/em\u003e (\u003cem\u003eCLE25\u003c/em\u003e) peptides respond to water deficiency by inducing stomatal closure (Takahashi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, the \u003cem\u003eC-terminally encoded peptide 5\u003c/em\u003e (\u003cem\u003eCEP5\u003c/em\u003e) enhances drought tolerance in \u003cem\u003eArabidopsis\u003c/em\u003e. In contrast, \u003cem\u003eCAP-derived peptide 1\u003c/em\u003e (\u003cem\u003eCAPE1\u003c/em\u003e) negatively impacts salt stress tolerance, while plant elicitor peptides (Peps) contribute to salinity stress tolerance (Nakaminami et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The validation of DE sORFs through signal peptide prediction suggests their role in abiotic stress responses, as various peptides, such as \u003cem\u003eCLE25\u003c/em\u003e and \u003cem\u003eCEP5\u003c/em\u003e, enhance drought tolerance, while others like \u003cem\u003eCAPE1\u003c/em\u003e negatively affect stress adaptation.\u003c/p\u003e \u003cp\u003ePhysiological and biochemical assays of three \u003cem\u003eindica\u003c/em\u003e rice varieties under drought stress provided further context to sORF predictions. MR 297 exhibited superior drought tolerance with minimal leaf rolling and stable chlorophyll retention, whereas MR 219 displayed severe oxidative stress, indicated by higher malondialdehyde and proline levels. Elevated expression of antioxidant genes (Os\u003cem\u003eCAT-β\u003c/em\u003e, Os\u003cem\u003eAPX\u003c/em\u003e, and Os\u003cem\u003eGR\u003c/em\u003e2) under drought stress, particularly in MR 219, reflects an active detoxification response. Importantly, semi-quantitative RT-PCR validated transcriptomic predictions by confirming drought-induced expression of candidate sORFs (\u003cem\u003eOsisORF_0050\u003c/em\u003e and \u003cem\u003eOsisORF_3394\u003c/em\u003e), while \u003cem\u003eOsisORF_3007\u003c/em\u003e showed constitutive but enhanced expression under stress. Notably, several validated sORFs were predicted to encode signal peptides, supporting their functional involvement in stress mitigation. These findings suggest that sORFs may either interact with ROS scavenging machinery or act as signaling peptides to trigger systemic tolerance mechanisms.\u003c/p\u003e \u003cp\u003eThe integrated computational and experimental framework presented here highlights stress-responsive sORFs as promising targets for functional genomics and crop improvement. Their small size, rapid translation potential, and involvement in critical signaling pathways offer unique advantages for engineering stress tolerance with minimal metabolic burden.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first comprehensive genome-wide prediction and functional analysis of sORFs in \u003cem\u003eindica\u003c/em\u003e rice under multiple abiotic stresses. In \u003cem\u003eindica\u003c/em\u003e, 135,673 unique coding sORFs were identified, and transcriptome profiling revealed highly stress-specific regulation, with heat and prolonged salt stress exerting the strongest effects. Overlap-based enrichment analysis of DE sORFs and protein-coding genes highlighted a central role for sORFs in signaling, stress perception, and oxidative stress regulation, as supported by GO and KEGG pathway associations with MAPK signaling and signal transduction pathways. The detection of signal peptides in a subset of sORFs suggests potential involvement in intercellular communication and systemic stress responses.\u003c/p\u003e \u003cp\u003ePhysiological and biochemical validation in \u003cem\u003eindica\u003c/em\u003e rice varieties demonstrated contrasting drought tolerance strategies, corroborated by antioxidant gene upregulation and differential metabolite accumulation. Expression of transcriptome-predicted drought-responsive sORFs was confirmed by semi-quantitative RT-PCR, reinforcing their functional relevance in stress adaptation. Collectively, these findings propose that sORFs act as specialized regulators within stress signaling networks, complementing canonical gene functions to enhance resilience. Future functional characterization of these sORFs will be critical to elucidate their mechanistic roles and exploit them as novel targets for breeding stress-tolerant rice varieties.\u003c/p\u003e \u003cp\u003eFuture work should focus on in planta functional validation using overexpression, CRISPR-based knockout, and exogenous peptide assays to elucidate their roles in signaling and metabolism. Prioritizing sORFs with predicted signal peptides or those enriched in pathways such as MAPK signaling could accelerate the identification of master regulators of stress adaptation. Ultimately, leveraging sORFs as molecular tools holds significant potential for developing climate-resilient rice varieties and ensuring food security under increasing environmental challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme (FRGS), Grant No: FRGS/1/2019/STG05/UM/02/3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHT conceived the idea. SNO conducted the experiment and drafted the manuscript. CHT, BCT and KH assisted in data analysis, reviewed the manuscript and provided suggestions for its improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there was no conflict of interest.\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad, E. M., Abdelsamad, A., El-Shabrawi, H. M., El‐Awady, M. A., Aly, M. A., and El‐Soda, M. 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(2020). \u0026ldquo;A platinum standard pan-genome resource that represents the population structure of Asian rice.\u0026rdquo; \u003cem\u003eScientific Data\u003c/em\u003e, 7(1), 113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-020-0438-2\u003c/span\u003e\u003cspan address=\"10.1038/s41597-020-0438-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-biosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Biosystems](https://link.springer.com/journal/44473)","snPcode":"44473","submissionUrl":"https://submission.springernature.com/new-submission/44473/3?","title":"Plant Biosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Intergenic sORF, drought stress, heat stress, salt stress, cold stress, indica, rice","lastPublishedDoi":"10.21203/rs.3.rs-9148773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9148773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall open reading frames (sORFs) represent an underexplored source of functional peptides in plant genomes. This study aimed to identify drought-responsive sORFs in \u003cem\u003eOryza sativa\u003c/em\u003e subsp. \u003cem\u003eindica\u003c/em\u003e and to evaluate their potential roles in drought stress adaptation, addressing the question of whether sORFs contribute to physiological and molecular drought tolerance mechanisms in rice.\u003c/p\u003e \u003cp\u003eStress-responsive sORFs were identified through \u003cem\u003ein silico\u003c/em\u003e prediction combined with transcriptomic analysis across multiple abiotic stress conditions. Functional annotation was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and signal peptide prediction was used to infer potential secretory functions. Three indica rice varieties (MR 219, MR 220, and MR 297) were subjected to drought stress for physiological assessment. Oxidative stress markers and antioxidant gene expression were analyzed, and candidate sORF expression was validated using semi-quantitative RT-PCR.\u003c/p\u003e \u003cp\u003eDifferentially expressed sORFs (DE sORFs) were significantly enriched in GO terms related to structural molecule activity and binding, while KEGG analysis highlighted MAPK signaling and antioxidant defense pathways. Several sORFs were predicted to encode secretory peptides. Among the tested varieties, MR 297 showed enhanced drought tolerance with reduced leaf rolling and stable chlorophyll content, whereas MR 219 exhibited higher oxidative damage, indicated by elevated malondialdehyde and proline levels. Antioxidant genes were strongly upregulated under drought stress, particularly in MR 219. Expression of selected sORFs (\u003cem\u003eOsisORF_0050\u003c/em\u003e, \u003cem\u003eOsisORF_3394\u003c/em\u003e, and \u003cem\u003eOsisORF_3007\u003c/em\u003e) was confirmed under drought conditions.\u003c/p\u003e \u003cp\u003eThis study reveals sORFs as previously overlooked contributors to drought stress responses in indica rice and provides a foundation for their functional characterization and potential application in crop improvement.\u003c/p\u003e","manuscriptTitle":"Genome-wide identification of sORFs in indica rice and their comparative transcriptomic analysis under stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 19:10:32","doi":"10.21203/rs.3.rs-9148773/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-15T19:46:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T15:16:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T14:18:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173654122939462250727240039122735040705","date":"2026-03-26T15:07:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123847722415396630375842352196551155071","date":"2026-03-23T09:27:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T10:05:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T03:49:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T03:49:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Biosystems","date":"2026-03-17T12:07:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-biosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Biosystems](https://link.springer.com/journal/44473)","snPcode":"44473","submissionUrl":"https://submission.springernature.com/new-submission/44473/3?","title":"Plant Biosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bc0f5bfa-7b3c-4226-9b52-63bca93a8dc3","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T09:55:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 19:10:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9148773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9148773","identity":"rs-9148773","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}