Transcriptomic analysis of Musa acuminata cv. Mas reveals salinity stress-responsive small open reading framesAbstract
Soil salinity severely limits crop productivity, especially in fruit crops such as banana. While most studies focus on protein-coding genes, small open reading frames (sORFs) are emerging as potential regulators of salinity stress responses. This study aimed to investigate the transcriptional response of sORFs in M. acuminata cv. Mas under salinity stress using RNA sequencing and NanoString validation. Transcriptomic analysis was performed on banana plantlets treated with 0 mM and 300 mM NaCl to assess gene expression under control and high salinity conditions. A total of 136 differentially expressed sORFs were identified, including 131 annotated and 5 novel sORFs. Gene Ontology analysis showed their enrichment in oxidoreductase activity, ion transport, and photosynthesis, while KEGG analysis highlighted roles in tryptophan metabolism and MAPK signaling. These findings reveal the potential regulatory functions of sORFs in banana salinity stress responses and underscore their potential as targets for developing salinity-tolerant cultivars.
Keywords:
Banana; differentially expressed sORFs; abiotic stress; RNA sequencing; salinity response
INTRODUCTION
Abiotic stress is a major factor that greatly impacts global plant productivity and crop yield. Elevated soil salinity is one of the most significant abiotic stresses that affecting plant growth by disrupting plant cells’ water and nutrient uptake (Hasanuzzaman and Nahar 2024). Recent studies have revealed the global distribution of salinity-affected areas, with Australia being the largest salinity-affected region, followed by North and Central Asia, South America, and South Asia (Shokat and Großkinsky 2019). Moreover, an annual global loss of approximately $27.3 billion in the agricultural sector has been estimated due to increased soil salinity (Qadir et al. 2014). Understanding the molecular mechanisms of salinity stress tolerance in crops is crucial for improving agricultural productivity and global food security.
Bananas are climacteric fruits with high nutritional value that belong to the monocotyledonous herbaceous plant group. According to FAOSTAT (2020), the annual global banana production has reached nearly 120 million tonnes, cultivated across 5.2 million hectares of agricultural land. However, banana research has progressed more slowly than other major food crops. Bananas are mainly cultivated as a staple food in the least developed countries, particularly in Africa (Sreedharan et al. 2013). Recent advances in high-throughput sequencing technologies, such as RNA sequencing, have accelerated the discovery of the molecular mechanisms underlying plant abiotic stress tolerance, including pathways related to MAPK signaling, secondary metabolite biosynthesis, and plant hormone signal transduction (Alamholo and Alireza 2023). Small open reading frames (sORFs) have emerged as a new frontier in functional genomics, encoding short peptides (<100 amino acids) that participate in various physiological and stress responses (Hanada et al. 2013). A previous transcriptomic study examined banana responses to salinity stress (Hu et al. 2017), but the transcriptional landscape of sORFs in M. acuminata cv. Mas remains unexplored. Therefore, this study provides the first sORF transcriptional landscape in cv. Mas using RNA sequencing, providing new insights into their potential roles and associated biological pathways in salinity stress adaptation.
MATERIAL AND METHODS
Plant material and growth conditions
In this study, cv. Mas was chosen because of the lack of prior research on its salinity response and its importance as a major commercial cultivar in Malaysia (DOA 2009). Tissue culture of plantlets of cv. Mas were purchased from Horus Green Nursery Banana Tissue Culture. Banana plantlets at the five-leaf stage with approximately equal height (8 cm) were selected for the salinity stress treatment. Morphologically similar banana seedlings were acclimatized in black polybags with drainage holes (12 cm × 8 cm × 9.5 cm) containing organic black soil under a controlled environment for 7 days (16 h light/8 h dark cycle; 60% relative humidity; 28 °C).
Treatment with salinity stress
After 7 days of environmental adaptation, the bananas were subjected to 0 mM (0 dS m−1), 100 mM (10.7 dS m−1), 200 mM (19.8 dS m−1), and 300 mM (28.1 dS m−1) of NaCl (R&M Chemical, Biolution). For the salinity stress treatment, each banana plantlet received 50 mL of saline water twice weekly for 14 days. Control banana plantlets were irrigated with an equal volume of distilled water. Each treatment was conducted in triplicate.
Total RNA extraction
After 7 days of treatment, only the second youngest leaf from the plantlets treated with 300 mM and 0 mM NaCl was extracted for RNA-Seq. The 300 mM NaCl treatment was chosen because it produced clear salinity stress symptoms, indicating a sufficient stress level for transcriptomic analysis. The time point for RNA sequencing was selected based on preliminary observations (Supplementary Figure 1) and a previous study (Hu et al. 2017). Each treatment included three biological replicates, resulting in a total of 6 RNA samples for sequencing. A Nanophotometer (Implen GmbH, Germany) was used to measure RNA concentration, while agarose gel electrophoresis was used to assess RNA integrity.
Overview of the DEGs and DE sORFs identified in cv. Mas under salinity stress. A) Volcano plot of the DEGs. B) Summary of the identified DE sORFs. The X-axis represents the up- or downregulation of sORFs. The Y-axis represents the number of sORFs. C) Summary of the identified SignalP_sORFs (sORFs that contain the predicted signal peptide-like sequences). The X-axis represents the up- or downregulation of sORFs. The Y-axis represents the number of sORFs. D) Summary of identified SWP_sORFs (sORFs that shared sequence homology with small peptides in the SWISS-PROT database). The X-axis represents the up- or downregulation of sORFs. The Y-axis represents the number of sORFs.
Identifying sORFs by transcriptomic sequencing and bioinformatics analysis
The RNA samples were sent to the Beijing Genomics Institute (BGI) in China for RNA sequencing. Biological replicates from the 0 mM NaCl treatment were designated as MR 1-3, while those from the 300 mM NaCl treatment were labeled as MR 4-6. Trimmomatic was used to preprocess the raw RNA-Seq data to remove adapters and low-quality bases. The cleaned reads were then aligned using the HISAT2 aligner to the Musa_acuminata_pahang_v4 banana reference genome (https://banana-genome-hub.southgreen.fr/filebrowser/download/1440408). StringTie (Pertea et al. 2015) was used to assemble and merge the RNA-Seq alignments into a core transcript set. Cuffcompare, a tool within Cufflinks, was used to identify novel transcripts by focusing on those transcripts classified under the “u,” “i,” “o,” and “j” class codes. The coding potential of these novel transcripts was then evaluated using the Coding Potential Calculator (CPC). Novel genes were identified from the set of predicted coding transcripts by excluding those with exact matches or overlaps with previously annotated genes. These novel transcripts and genes were then integrated with reference transcripts to establish a comprehensive dataset for downstream analysis. Gene expression levels were determined using RNA-Seq by Expectation-Maximization (RSEM) (Li and Dewey 2011), with normalization based on Fragments per Kilobase per Million mapped fragments (FPKM). Differentially expressed genes (DEGs) were identified using DESeq2 (Love et al. 2014), applying a minimum fold change (FC) of >1 or < −1, and an adjusted p-value (Padj) ≤ 0.05.
To identify differentially expressed sORFs (DE sORFs), both annotated and novel sORFs with coding sequences (CDSs) shorter than 360 base pairs (bp), a minimum FC > 1 or < −1, and a Padj ≤ 0.05 were selected. The identified DE sORFs were further categorized into two groups: those predicted to contain signal peptides (SignalP_sORF) and those that shared sequence homology to small peptides in the SWISS-PROT database (SWP_sORF). Signal peptide prediction was performed using SignalP 6.0 (https://services.healthtech.dtu.dk/services/SignalP-6.0/) with “Eukarya” and “Short output” as the parameters. The sORF sequences were also subjected to a local BLASTP search against the SWISS-PROT small peptide dataset (https://www.uniprot.org/help/downloads) to identify homology with known peptides using the following parameters: -evalue 1e-05 -max_target_seqs 1 -outfmt 6.
Functional categories of DEGs and DE sORFs
Gene Ontology (GO) classification and KEGG functional enrichment analyses were performed to determine the functions of DEGs and DE sORFs. The identified DEGs and DE sORFs were annotated using the GO database via AmiGo 2 (https://amigo.geneontology.org/amigo/landing). The GO classification results were then organized into three main ontologies: biological process (BP), molecular function (MF), and cellular component (CC). Additionally, KEGG Orthology (KO) annotation and KEGG pathway mapping for DEGs and DE sORFs were conducted using BlastKOALA (https://www.kegg.jp/blastkoala/). To further investigate their function, GO and KEGG analyses were performed using the phyper function in R. The p-values for the hypergeometric test were calculated using the following formula:
Only GO and KEGG terms with adjusted p-values ≤ 0.05 were considered significantly enriched. SRPlot, an open-access data visualization and graphing platform (https://www.bioinformatics.com.cn/en), was used to visualize the final results.
Validation of RNA-Seq data via NanoString analysis
To confirm the expression levels of the selected sORFs identified in RNA-Seq under salinity stress, NanoString analysis was conducted following the standard protocol provided by NanoString Technologies (https://nanostring.com/). For the NanoString analysis, a 1 µg aliquot of total RNA was used for each sample. Seven CodeSets were utilized, with two serving as housekeeping genes, namely eEF-1-alpha (eEF-1-alpha) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH). The remaining five CodeSets were designed for specific sORFs of interest. Five sORFs were selected based on their adjusted p-value and log2FoldChange. The generated data were analyzed using the nSolver 4.0 Analysis Software (https://nanostring.com/wp-content/uploads/nSolver_setup_windows_x64_4.0.70.zip). Finally, the correlation analysis between the NanoString and RNA-Seq results was performed using the Pearson correlation coefficient in SRPlot.
RESULTS AND DISCUSSION
A comprehensive transcriptome analysis was performed on the young leaves of cv. Mas after 7 days of exposure to salinity stress. Although the transcriptomes of other banana cultivars under salinity conditions have been previously reported (Hu et al. 2017), the present study offers novel insights into the expression patterns of sORFs in bananas in high salinity environments, which have not yet been thoroughly examined.
The Cuffcompare analysis revealed an average of 674 novel genes and 27,287 known genes per sample, resulting in a total of 27,961 genes identified across all samples (Table 1). The prediction of novel genes indicates the existence of previously unannotated transcripts that can enhance banana salinity stress tolerance by encoding stress-responsive peptides. Meanwhile, the identified genes provide a comprehensive view of the core functional elements in bananas grown in saline conditions. Several salinity stress-induced sORFs unique to bananas were identified among these known and novel genes. These sORFs require further analysis to elucidate their expression patterns and functional roles in the response to salinity stress.
Summary of the number of known and novel genes discovered in the control (MR1-3) and salinity-stressed banana samples (MR4-6)
Transcriptome data analysis showed that approximately 77% (25,341 genes) of the genes in cv. Mas showed no significant difference in expression between the control and treatments (Figure 1A). Of the 5,156 DEGs identified, 2,663 genes showed higher expression and 2,493 showed lower expression in the salinity-treated samples (Figure 1A). Approximately 3% of DEGs were homologous to genes previously well-characterized as abiotic stress-responsive, including those encoding late embryogenesis abundant (LEA) proteins, calmodulin-binding proteins (CaMBP), and plasma membrane intrinsic proteins (PIPs) (Trono and Pecchioni 2022). These findings confirm that the salinity stress treatment effectively triggered an expected physiological stress response in cv. Mas. The DEG profile of cv. Mas was predominantly characterized by upregulation, in contrast to the downregulation trend observed in M. acuminata cv. BD (Wei et al. 2022). This divergence suggests that cv. Mas may activate a distinct set of transcriptional responses, potentially prioritizing gene induction over suppression under salinity stress.
Despite the identification of over 5,156 DEGs, a relatively smaller proportion of sORFs (2.6%) were differentially expressed under saline stress. In total, 136 DE sORFs were identified in cv. Mas, comprising 131 annotated sORFs and 5 novel sORFs (Table 2). Of these, 86 were downregulated and 50 were upregulated (Figure 1B). These sORFs responded to high salinity either directly or indirectly, although their specific functions remain uncharacterized. Out of the 136 DE sORFs, 18 were predicted to encode peptides with the characteristics of signal peptides (Table 2). These included 11 upregulated and 7 downregulated DE sORFs, suggesting that their encoded peptides may be transmembrane and have a potential role in protein secretion and targeting processes in banana plants under salinity stress (SignalP_sORF, Figure 1C).
A total of 62 of the 136 DE sORFs, 62 shared homology with small peptide sequences documented in the SWISS-PROT database (Table 2). Among these sORFs, 21 were upregulated and 41 were downregulated under salinity stress conditions (SWP_sORF, Figure 1D). Their change in expression under the stress condition suggests that their encoded peptides could improve plant salinity tolerance. Increasing evidence supports that sORFs function as regulators of various developmental processes (Xiao et al. 2025) and mediate responses to environmental stress (Zhou et al. 2022). These findings support the importance of the DE sORFs identified in this study, particularly those homologous to small peptides previously reported to be involved in abiotic stress mitigation. The discovery of 62 DE sORFs with homology to known small peptides suggests that they encode functional peptides rather than transcriptional noise. This study also highlights the differential expression of some sORFs that contain predicted signal peptide-like sequences, with most of these sORFs (11/18) showing higher expression levels under high salinity conditions. Signal peptides are short sequences found at the N-terminus of proteins that direct their secretion by providing targeting information (Owji et al. 2018). Interestingly, 2 DE sORFs shared sequence homology with phytosulfokines (PSKs) and were predicted to have the characteristics of signal peptides using SignalP 6.0. PSKs are well-known peptides involved in plant signaling and ROS homeostasis maintenance by modulating peroxidase activity (Kim et al. 2021). Both sORFs were significantly upregulated under salinity stress, suggesting the involvement of small signaling peptides in the regulation of oxidative stress responses. Overall, these DE sORFs provide a promising foundation for future functional characterization and serve as good candidates for enhancing banana salinity tolerance.
DE sORFs in cv. Mas could be categorized into various groups, with “photosynthesis” (GO:0015979), “photosynthesis, light reaction” (GO:0019684), and “generation of precursor metabolites and energy” (GO:0006091) being the most enriched GO terms in the BP category (Figure 2A). For the CC ontology, the most enriched terms were “thylakoid” (GO:0009579), “photosystem” (GO:0009521), and “photosynthetic membrane” (GO:0034357). The most enriched MF terms were “oxidoreductase activity” (GO:0016491), “tetrapyrrole binding” (GO:0046906), and “monooxygenase activity” (GO:0004497). The “biosynthesis of secondary metabolites” pathway was the most enriched KEGG pathway, followed by “photosynthesis,” “metabolic pathways,” “carbon metabolism,” and “plant hormone signal transduction” (Figure 2B). Notably, several DE sORFs were annotated to plant hormone signaling pathways, particularly tryptophan metabolism. Within this pathway, four DE sORFs were differentially expressed in the small auxin upregulated RNA (SAUR) protein, with two being upregulated and two downregulated (Figure 2C). For the wounding signaling that was involved in the MAPK signaling pathway, a DE sORF associated with calcium-binding proteins such as calmodulins (CaMs) (K14492) was found to be downregulated under high salinity conditions (Figure 2D).
Functional enrichment and validation of DE sORFs in cv. Mas under salinity stress. (A) GO enrichment of DE sORFs. The x-axis represents the enrichment factor, and the y-axis represents the GO term name. B) KEGG functional enrichment of the DE sORFs. The x-axis represents the enrichment factor, while the y-axis represents the pathway name. C) Tryptophan metabolism of the plant hormone signal transduction pathway, in which two DE sORFs were downregulated and two DE sORFs were upregulated. D) The wounding - MAPK signal pathway, in which one DE sORF was downregulated in the CaM4. E) Correlation plot of the expression profile between RNA sequencing and NanoString, with a correlation coefficient of 0.87.
The enrichment of terms such as “thylakoid,” “photosynthetic membrane,” and “photosystem” in the CC category indicates that these sORFs may function within chloroplast-related compartments, which are responsible for photosynthesis. In the BP category, key enriched terms such as “photosynthesis,” “photosynthesis, light reaction,” and “generation of precursor metabolites and energy” further support their involvement in photosynthesis, consistent with Song et al. (2020)’s findings. Thus, the enrichment of these terms suggests that even under salinity stress conditions, bananas rely heavily on photosynthesis as a fundamental process for energy production and growth. Additionally, the enrichment of the terms “oxidoreductase activity” and “monooxygenase activity” in the MF category suggests that sORFs have potential enzymatic roles in redox regulation. Ion homeostasis imbalance caused by salinity stress can lead to oxidative stress and ROS generation (Yang and Guo 2018). Enzymes with oxidoreductase activities facilitate electron transfer to ROS and induce ROS scavenging (Martemucci et al. 2022). The enrichment of the terms “ion transport” and “transporter activity” suggests that these DE sORFs may play a role in maintaining ion homeostasis under salinity stress. Plants can counteract the impaired cellular homeostasis by activating ion transport mechanisms that regulate the uptake of ions (Balasubramaniam et al. 2023). Together, these findings underscore the potential role of sORFs in photosynthesis, ion regulation, and oxidative stress responses, although they only represent a small fraction of the banana transcriptome.
Among the enriched pathways of DE sORFs, the “biosynthesis of secondary metabolites” pathway was the most enriched, underscoring its importance in the salinity stress response. To put that into perspective, the enhanced accumulation of flavonoids in rice has been shown to improve tolerance to combined salinity and heat stress by influencing physiological, biochemical, and molecular responses (Jan et al. 2021). The involvement of sORFs in plant hormone signal transduction pathways has also been increasingly documented, underscoring their potential roles as hormone-mediated response regulators (Ong et al. 2023, Ng et al. 2025). During tryptophan metabolism, most DEGs related to the SAUR protein were downregulated. A similar pattern of SAUR gene downregulation under abiotic stress was reported by Teoh et al. (2022). This pattern also extended to the DE sORFs, with two upregulated and two downregulated in response to salinity stress. SAUR proteins modulate adaptive growth and stress responses (Stortenbeker and Bemer 2019). For instance, TaSAUR78 overexpression in Arabidopsis enhanced salinity tolerance by reducing ROS accumulation (Yuan et al. 2019). In addition to auxin, the interaction among other phytohormones, such as strigolactones, abscisic acid, ethylene, and gibberellin, plays a crucial role in salinity adaptation. The interplay between abscisic acid and strigolactones contributes to drought tolerance in both mycorrhizal and non-mycorrhizal plants (Khan et al. 2024). These findings suggest that the regulation of SAURs by both DEGs and sORFs may indirectly influence hormone-mediated stress adaptation in bananas under salinity stress. Moreover, most DEGs and DE sORFs associated with CaM4 were downregulated in the MAPK signaling pathway. CaM4 acts as a negative regulator of ROS production in Arabidopsis by suppressing the expression of respiratory burst oxidase homologs (RBOHs) (Takahashi et al. 2011). Elevated ROS levels activate OXI1 kinase, which in turn triggers MAPK cascade activation (Rentel et al. 2004). MAPK signaling is crucial for maintaining ROS homeostasis and enhancing plant tolerance to abiotic stress (Cristina et al. 2010). These findings suggest that sORFs may function downstream of ROS signaling or contribute to redox homeostasis, aligning with their GO enrichment in oxidoreductase activity. From a practical perspective, these identified sORFs could serve as potential gene targets for functional validation and genetic improvement programs aimed at enhancing salinity stress tolerance in cv. Mas.
DE sORFs associated with salinity stress were analyzed using the NanoString technology to validate the RNA-Seq findings. The results demonstrated a strong positive correlation between the two datasets, with a correlation coefficient (R) of 0.87 (Figure 2E). This finding underscores the high reliability of our RNA-Seq data, which is consistent with the findings of previous studies that validated RNA-Seq using NanoString (Ong et al. 2023, Ng et al. 2025). This agreement further confirms that these DE sORFs play an important role in the responses of bananas to salinity stress, especially in the pathways related to hormone signaling and redox regulation. Although the NanoString results provide strong validation of the transcriptional profiles, further studies integrating proteomic and functional analyses are needed to further clarify their biological roles.
CONCLUSION
This study provides the first comprehensive transcriptomic analysis and annotation of sORFs in cv. Mas under high salinity stress, highlighting their potential involvement in stress adaptation. Although sORFs represented a relatively small proportion of the differentially expressed transcripts, they were significantly enriched in functional categories and pathways associated with photosynthesis, ion transport, redox homeostasis, secondary metabolite biosynthesis, and plant hormone signaling. In particular, several DE sORFs shared homology with known small peptides, such as phytosulfokines, previously reported to function in abiotic stress mitigation. The differential regulation of SAUR and CaM4-associated sORFs further supports their involvement in plant hormone signal transduction and MAPK signaling pathways, respectively. These findings contribute to a better understanding of sORF-related tolerance mechanisms in bananas and support the development of salinity-tolerant cultivars. Future studies should analyze and compare banana transcriptomes under different abiotic stress conditions to identify shared stress-responsive sORFs.
ACKNOWLEDGEMENTS
This work was supported by Universiti Malaya, under grant number (UMG002-2022). Supplementary files are available upon request to the corresponding author.
Data Availability
The datasets generated and/or analyzed during the current research are accessible in the NCBI BioProject repository, available at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1299623
REFERENCES
- Alamholo M, Alireza T2023 Molecular mechanism of drought stress tolerance in barley (Hordeum vulgare L.) via a combined analysis of the transcriptome dataCzech Journal of Genetics and Plant Breeding 59:76-94
- Balasubramaniam T, Shen G, Esmaeili N, Zhang H2023 Plants’ response mechanisms to salinity stressPlants 12:2253
- Cristina M S, Petersen M, Mundy J2010 Mitogen-activated protein kinase signaling in plantsAnnual Review of Plant Biology 61:621-649
-
DOA - Department of Agriculture, Malaysia2009 Pakej teknologi pisang jabatan pertanian. Available at <Available at https://www.doa.gov.my >. Accessed on October 19, 2025.
» https://www.doa.gov.my -
FAOSTAT - Food and Agriculture Organization Corporate Statistical Database2020. Available at <Available at https://www.fao.org/faostat/en/#home >. Accessed on October 19, 2025
» https://www.fao.org/faostat/en/#home - Hanada K, Higuchi-Takeuchi M, Okamoto M, Yoshizumi T, Shimizu M, Nakaminami K, Nishi R, Ohashi C, Iida K, Tanaka M, Horii Y, Kawashima M, Matsui K, Toyoda T, Shinozaki K, Seki M, Matsui M2013 Small open reading frames associated with morphogenesis are hidden in plant genomesProceedings of the National Academy of Sciences 110:2395-2400
- Hasanuzzaman M, Nahar K2024 Salinity stress in plants: Challenges in view of physiological aspects. In Majidian P and Ghorbani H (eds) Abiotic stress in crop plants-ecophysiological responses and molecular approaches. IntechOpen, London, 318p.
- Hu W, Ding Z, Tie W, Yan Y, Liu Y, Wu C, Liu J, Wang J, Peng M, Xu B, Jin Z2017 Comparative physiological and transcriptomic analyses provide integrated insight into osmotic, cold, and salt stress tolerance mechanisms in bananaScientific Reports 7:43007
- Jan R, Kim N, Lee SH, Khan M A, Asaf S, Lubna Lubna, Park JR, Asif S, Lee IJ, Kim KM2021 Enhanced flavonoid accumulation reduces combined salt and heat stress through regulation of transcriptional and hormonal mechanismsFrontiers in Plant Science 12:796956
- Khan MK, Pandey A, Hamurcu M, Vyhnánek T, Zargar SM, Kahraman A, Topal A, Gezgin S2024 Exploring strigolactones for inducing abiotic stress tolerance in plantsCzech Journal of Genetics and Plant Breeding 60:55-69
- Kim J S, Jeon B W, Kim J2021 Signaling peptides regulating abiotic stress responses in plantsFrontiers in Plant Science 12:704490
- Li B, Dewey CN2011 RSEM: accurate transcript quantification from RNA-seq data with or without a reference genomeBMC Bioinformatics 12:1-16
- Love MI, Huber W, Anders S2014 Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2Genome Biology 15:1-21
- Martemucci G, Costagliola C, Mariano M, D’andrea L, Napolitano P, D’Alessandro AG2022 Free radical properties, source and targets, antioxidant consumption and healthOxygen 2:48-78
- Ng WQ, Rejab NA, Taheri S, Teo CH2025 Elucidating the roles of small open reading frames towards drought stress in Solanum lycopersicumCrop Breeding and Applied Biotechnology 25:e510625112
- Ong SN, Tan BC, Hanada K, Teo CH2023 Unearth of small open reading frames (sORFs) in drought stress transcriptome of Oryza sativa subsp. IndicaGene 878:147579
- Owji H, Nezafat N, Negahdaripour M, Hajiebrahimi A, Ghasemi Y2018 A comprehensive review of signal peptides: Structure, roles, and applicationsEuropean Journal of Cell Biology 97:422
- Pertea M, Pertea GM, Antonescu CM, Chang Chang, TC TC, Mendell JT, Salzberg SL2015 StringTie enables improved reconstruction of a transcriptome from RNA-seq readsNature Biotechnology 33:290
- Qadir M, Quillérou E, Nangia V, Murtaza G, Singh M, Thomas RJ, Drechsel P, Noble AD2014 Economics of salt‐induced land degradation and restorationNatural Resources Forum 38:282-295
- Rentel MC, Lecourieux D, Ouaked F, Usher SL, Petersen L, Okamoto H, Knight H, Peck SC, Grierson CS, Hirt H, Knight MR2004 OXI1 kinase is necessary for oxidative burst-mediated signalling in ArabidopsisNature 427:858-861
- Shokat S, Großkinsky DK2019 Tackling salinity in sustainable agriculture - What developing countries may learn from approaches of the developed worldSustainability 11:4558
- Song Q, Joshi M, Joshi V2020 Transcriptomic analysis of short-term salt stress response in watermelon seedlingsInternational Journal of Molecular Sciences 21:6036
- Sreedharan S, Shekhawat U KS, Ganapathi TR2013 Transgenic banana plants overexpressing a native plasma membrane aquaporin MusaPIP1;2 display high tolerance levels to different abiotic stressesPlant Biotechnology Journal 11:942-952
- Stortenbeker N, Bemer M2019 The SAUR gene family: the plant’s toolbox for adaptation of growth and developmentJournal of Experimental Botany 70:17-27
- Takahashi F, Mizoguchi T, Yoshida R, Ichimura K, Shinozaki K2011 Calmodulin-dependent activation of MAP kinase for ROS homeostasis in ArabidopsisMolecular Cell 41:649-660
- Teoh EY, Teo CH, Baharum NA, Pua TL, Tan BC2022 Waterlogging stress induces antioxidant defense responses, aerenchyma formation, and alters metabolisms of banana plantsPlants 11:2052
- Trono D, Pecchioni N2022 Candidate genes associated with abiotic stress response in plants as tools to engineer tolerance to drought, salinity and extreme temperatures in wheat: an overviewPlants 11:3358
- Wei J, Liu D, Liu Y, Wei S2022 Physiological analysis and transcriptome sequencing reveal the effects of salt stress on banana (Musa acuminata cv. BD) leafFrontiers in Plant Science 13:822838
- Xiao F, Zhou H, Lin H2025 Decoding small peptides: Regulators of plant growth and stress resilienceJournal of Integrative Plant Biology 67:596-631
- Yang Y, Guo Y2018 Unraveling salt stress signaling in plantsJournal of Integrative Plant Biology 60:796-804
- Yuan G, Xu CB, Sun XJ, Hu Z, Fan SJ, Jiang Q, Zhang H2019 TaSAUR78 enhances multiple abiotic stress tolerance by regulating the interacting gene TaVDAC1Journal of Integrative Agriculture 18:2682-2690
- Zhou Y, Zhai H, Xing S, Wei Z, He S, Zhang H, Gao S, Zhao N, Liu Q2022 A novel small open reading frame gene, IbEGF, enhances drought tolerance in transgenic sweet potatoFrontiers in Plant Science 13:965069
