Plant microRNA networks in abiotic stress pathways: Meta-analyses of sRNA data associated with drought, heat and salt stress responses in Arabidopsis thaliana

Plant microRNA networks in abiotic stress pathways: Meta-analyses of sRNA data associated with drought, heat and salt stress responses in Arabidopsis thaliana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Plant microRNA networks in abiotic stress pathways: Meta-analyses of sRNA data associated with drought, heat and salt stress responses in Arabidopsis thaliana Tabassum Jehan, Harshita Tripathi, Stuti Sehrawat, Geetansh Gautam, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7401744/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract MicroRNAs (miRNAs) are a diverse group of small regulatory RNAs in plants that modulate gene expression at the post-transcriptional or translational level. This study focuses on identifying novel miRNAs and their target pathways involved in responses to drought, salt, and heat stress. A small RNA (sRNA) sequencing dataset was analysed to identify mature miRNAs expressed in 15-day-old Arabidopsis thaliana seedlings subjected to abiotic stress conditions. Differential expression analysis was performed using log 2 FC-based filtering (stress vs. control), which led to the identification of significantly upregulated and downregulated miRNAs, including novel families such as miR3932b, miR5630b, and miR169j. Target genes of these miRNAs were predicted using the psRNA target tool (pmiREN), and protein-protein interaction (PPI) analysis was conducted using STRING. Additionally, analyses with the Find Individual Motif Sites (FIM) algorithm showed significant enrichment of crucial transcription factor families, including AP2 (APETALA2), WRKY, BBR (BARLEY B RECOMBINANT), etc. The identified miRNA targets were also found to be involved in signal transduction pathways other than stress, including most significant developmental and growth processes. Our findings therefore offer meaningful insights into the intricacies of miRNA–target networks and their modulation of transcriptional pathways during plant acclimation to abiotic stresses. miRNA abiotic stress Arabidopsis thaliana psRNAtarget STRING transcription factors protein-protein interaction (PPI) gene ontology stress response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction MicroRNAs (miRNAs) are small, non-coding RNA molecules that play a crucial role in post-transcriptional gene regulation by binding to target messenger RNAs (mRNAs), leading to their degradation or translational repression. These molecules, approximately 22 nucleotides in length, have been found to be highly conserved across plant species, underscoring their evolutionary significance in regulating gene expression in response to various physiological and environmental stimuli (Jones-Rhoades et al. 2006 ). Recent breakthroughs have demonstrated that miRNAs are fundamental in modulating stress responses in plants, highlighting their potential as key regulatory elements in plant adaptation to abiotic stresses such as drought, salinity, and temperature fluctuations (Sunkar et al. 2007 ; Zhang et al. 2022 ). In plants, miRNAs were first identified in the model organism Arabidopsis thaliana in 2002, paving the way for extensive research into their roles in plant development and stress adaptation (Reinhart et al. 2002 ). Since then, plant miRNAs have been extensively studied, revealing their critical functions in various biological processes, including leaf morphogenesis, root architecture, reproductive development, and defense mechanisms against pathogens (Jones-Rhoades and Bartel 2004 ; Mallory and Vaucheret 2006 ). In response to different developmental cues, environmental signals, and stress conditions, plant cells tightly regulate protein production through miRNA-mediated gene silencing(Chen 2009 ). The process begins with the transcription of primary miRNA (pri-miRNA) molecules by RNA polymerase II from specific MICRORNA (MIR) genes. These precursors adopt a characteristic stem-loop structure and are processed into mature miRNAs, which are subsequently incorporated into the miRNA-induced silencing complex (miRISC) to direct target gene regulation via mRNA cleavage or translational repression(Voinnet, 2009 ). This dynamic regulatory mechanism allows plants to tweak gene expression in response to external stimuli, thereby enhancing their resilience to adverse environmental conditions. Among various model organisms, Arabidopsis thaliana has emerged as a powerful system for studying miRNA function due to its small genome size, short life cycle, and ease of genetic manipulation(Meyers et al. 2008 ). Over the past two decades, research on Arabidopsis miRNAs has revealed their roles in abiotic stress adaptation, including responses to drought, salinity, and temperature extremes. Several miRNA families, such as miR393, miR398, and miR169, have been shown to modulate stress-responsive genes and signaling pathways, enhancing plant tolerance to unfavorable conditions (Sunkar and Zhu 2004 ; Liu et al. 2008 ). This study aims to identify novel miRNAs and their target pathways involved in Arabidopsis thaliana’s response to drought, salinity, and heat stress. To achieve this, a small RNA (sRNA) sequencing dataset was utilized to analyze differentially expressed mature miRNAs in 15-day-old Arabidopsis seedlings subjected to these stress conditions(Pegler et al. 2019 ). By integrating bioinformatics analyses such as miRNA target prediction and functional annotation, the study seeks to uncover key miRNA-mediated regulatory networks that contribute to plant adaptation to environmental stresses. The findings from this study will not only enhance our understanding of plant stress biology but may also offer valuable insights for developing stress-resilient crops through genetic engineering or miRNA-based biotechnological approaches. 2. Materials and Methods 2.1 Study design and data acquisition This study sought to identify significant changes in the biological environment of Arabidopsis thaliana by analysing the expression levels of microRNAs (miRNAs) and their target genes in response to abiotic stress conditions, specifically heat, drought & salinity. For this purpose, the required dataset was extracted from Pegler et. al. ( 2019 ), which dealt with sRNA sequencing of 15-day old stressed Arabidopsis thaliana seedlings. The extracted Log 2 Fold Changes (FC) value for miRNA expression under each stress condition were sorted from smallest to largest to identify miRNAs with significant up- and down- regulation. MicroRNA selection was based on differential expression levels determined using Log 2 fold change (FC) values. MicroRNAs with a Log 2 FC greater than 1.0 were classified as up-regulated, while those with a Log 2 FC less than − 1.0 were considered down-regulated. These thresholds were selected due to their biological relevance in identifying miRNAs with significant differential expression, ensuring a robust distinction between up- and down-regulated candidates. 2.2 Obtaining targets of miRNA The pMIREN tool was used for target identification. This comprehensive plant miRNA database contains over 20,000 annotated miRNAs from various species, including 20,388 miRNA loci, 5,757 families, and 141,327 predicted miRNA-target pairs. It provides a valuable resource for data mining and miRNA functional studies(Guo et al. 2020 ). 2.3 Target Gene Identification Target gene IDs for each microRNA were retrieved from the PmiREN database using the corresponding microRNA identifier or relevant keywords for each stress condition. Predicted target gene names were then cross-referred using TAIR database (Guo et al., 2020 : Swarbreck et al., 2007 ). 2.4 Transcription factor binding site analyses After this, a statistical analysis was done using TFBS-TDT Hub for transcription factor binding sites. Plant species ( Arabidopsis thaliana ) was selected for which TFBS analysis was required and all target gene IDs extracted using PmiREN were entered one target gene ID per line. Quick heat maps were generated, using the FIMS algorithm (Find Individual Motif Sites) to visualize miRNA expression pattern. Key parameters such as p-value (-log10), were used to assist statistical significance which was prevalent from the strength of the blue color (Grau and Franco-Zorrilla, 2022 : Yu et al., 2021 ). 2.5 Protein-Protein Interaction Analysis and Statistical Analysis The STRING database was employed to study about the protein-protein interaction for miRNA target genes. The protein coded by each target IDs were extracted using TAIR database and were recorded in the excel sheet. After, this the protein names were entered in STRING database together one protein per line for multiple protein dataset and a protein-protein interaction map was generated. This included network analysis to identify interaction hubs and clusters, which may represent critical regulatory components under abiotic stress condition (Szklarczyk et al. 2025 ). 2.6. Gene ontology analyses All the target IDs obtained for each miRNA for each of the abiotic stress, were analysed for GO (gene ontology) enrichment terms. This was achieved through the shinyGO tool, which estimated the GO enrichment through fold enrichment and FDR value (Ge et al., 2020 ). 3. Results In this study, we conducted a comprehensive meta-analysis of differentially expressed microRNAs (miRNAs) in Arabidopsis thaliana under various abiotic stresses, including heat, drought, and salinity. Categorization of miRNA targets enabled us to investigate regulatory mechanisms and transcriptional pathways associated with each stress type. Under heat stress, we observed a significant upregulation of miRNAs, including miR156, miR159, and miR398, while miR164 and miR169 were downregulated. These miRNAs have been previously associated with stress adaptation, including the modulation of ROS scavenging (miR398) (Chowdhury et al. 2025 ) and the regulation of flowering time (Wang, 2014 ) (miR156). Transcription factor (TF) family enrichment analysis using the TDT hub revealed an overrepresentation of AP2 and BBR/BPC TFs, implying that these families may act as key regulators of heat-responsive miRNA targets (Fig. 1a). STRING-based protein-protein interaction analysis of the miRNA target genes under heat stress identified several core proteins, including BCAT3 (BRANCHED-CHAIN-AMINO-TRANSFERASE-3) and BCAT5 (BRANCHED-CHAIN-AMINO-TRANSFERASE-5), which are involved in branched-chain amino acid metabolism and potentially aid in osmo-protection and energy balance during heat exposure (Diebold et al. 2002). Additionally, SERK (SOMATIC EMBRYOGENESIS RECEPTOR KINASE) and NAC054, both known for their roles in developmental reprogramming and stress signal transduction (Santos et al. 2009, Xu et al. 2024 ), were found as core components emphasizing their functional relevance in heat-stress adaptation (Fig. 1b). miRNAs such as miR851, miR771, and miR157b were upregulated under drought stress, potentially contributing to protective gene regulation (Camargo-Ramirez et al. 2018, Shikata et al. 2012 ), while miR4228, miR172, and miR5020 were downregulated, possibly removing repression on stress-adaptive targets. TF family enrichment of the targets was distinctively lower compared to other stress conditions. However, BBR/BPC remained the most enriched, suggesting a selective regulatory response (Fig. 2a). The STRING PPI network for drought-responsive miRNA targets highlighted key proteins, including LBD37 (LOB-DOMAIN-CONTAINING-37) (Chai et al. 2022 ), a negative regulator of nitrogen-responsive genes; GPAT9 (GLYCEROL-3-PHOSPHATE-ACYL-TRANSFERASE-9), involved in membrane lipid remodelling (Gong et al. 2023 ); and PGD1 (PLASTID-GALACTOLIPID-DEGRADATION-1), which contributes to redox balance through the oxidative pentose phosphate pathway (Du et al. 2018 ). These targets perhaps indicate metabolic adjustments and signalling events required during drought tolerance. Under salt stress, miR156a (Wang et al. 2023 ) and miR169e showed marked upregulation, aligning with their known roles in developmental plasticity (Sorin et al. 2014 ), whereas miR857 and miR778 were downregulated, suggesting a fine-tuned modulation of lignin biosynthesis and transcriptional repression pathways under salinity (Zhao et al. 2015 , Bennet et al. 2022). The transcriptional landscape of the targets was visibly wider, with extensive enrichment across multiple TF families (Fig. 3a). Families such as WRKY, YABBY, and zf-HD displayed the most substantial enrichment (with the highest –log10[FDR] values), indicating widespread transcriptional rewiring under salinity conditions. The PPI network highlighted several well-characterized stress-responsive proteins (Fig. 3b), including NADP-ME2 (NADP-MALIC-ENZYME-2), which regulates malate metabolism and cellular pH homeostasis (Badia et al. 2015); ACO1 (1-AMINOCYCLOPROPANE-1-CARBOXYLATE-OXIDASE), a key enzyme in ethylene biosynthesis (Houben et al. 2019); and MYB101 and MYB33 (Wang et al. 2025 ), both transcription factors involved in ABA signalling and the osmotic stress response. Additionally, LOX2 (LIPOXYGENASE-2) (Yang et al. 2020 ) and TIFY family proteins, which are repressors of jasmonate signalling (Chung et al. 2009), were observed as network hubs, suggesting that both ethylene and jasmonate pathways are highly active during salt stress responses. Our study also indicates that these pathways also interact with miRNA regulation to shape a plant’s response towards the stress. Gene ontology enrichment analyses of the miRNA targets revealed a striking and consistent overrepresentation of developmental processes across all three abiotic stresses: heat (Fig. 4), drought (Fig. 5) and salt (Fig. 6). In the Biological Process category (sub part a ), terms such as embryonic meristem development, floral organ development, were prominently enriched, indicating that stress-responsive miRNAs may intersect with developmental reprogramming pathways (Yang et al. 2023) The Cellular Component (subpart b) showed a concentration of targets in compartments mostly associated with transcriptional activity and intercellular communication, such as the secretory vesicles, and plasmodesmata. In the Molecular function category (subpart c), key terms like DNA-binding transcription factor activity, mRNA binding, and ligase activity suggest regulatory flexibility at both transcriptional and post-transcriptional levels. KEGG (Kyoto Encyclopedia of genes and genomes ) analyses (subpart d) of the targets specifically from heat stress highlighted enriched terms including biosynthesis of secondary metabolites and amino acid metabolism, pointing toward active metabolic adjustments in response to stress. Taken together, these results suggest that stress-induced miRNAs not only modulate canonical stress pathways but may also regulate quintessential developmental and metabolic functions, likely to optimize growth and survival under adverse stressors. 4. Discussion and Conclusion The meta-analyses of differentially expressed miRNAs under abiotic stress conditions in Arabidopsis thaliana reveal the intricate and stress-specific regulatory landscapes regulated by miRNA–target interactions. The identification of unique miRNA expression profiles under heat, drought, and salt stress, along with distinct enrichments in transcription factor families and PPI network configurations, highlights the efficiency with which plants modulate gene expression in response to environmental perturbations. The enrichment of developmental GO terms across stresses points to the fact that stress acclimation juxtaposes with developmental reprogramming. The involvement of TF families such as AP2, WRKY, and BBR/BPC, and PPI network hubs like SERK, LBD37, and MYB101 further emphasizes the integration of hormonal, metabolic, and transcriptional signalling cascades in miRNA-mediated responses. Interestingly, the relatively less TF enrichment of the targets of drought induced miRNAs, contrasted with the extensive TF and pathway from the targets enriched from miRNAs differentially expressed under salt stress. This highlights differential levels of regulatory plasticity among different stress conditions. Collectively, our analysis reveals how stress-responsive miRNAs do not act alone but form dynamic, condition-specific regulatory hubs that bridge environmental signals with developmental and metabolic processes. These insights not only deepen our understanding of plant stress biology but also point toward potential genetic nodes for engineering enhanced stress resilient plants. Our analysis of the already published Pegler et al. data set showed that miRNA-mediated regulation of abiotic stress responses in Arabidopsis is highly stressor-specific, with each stress condition leading to a unique set of TF enrichments and PPI network configurations. The present findings therefore offer meaningful insights into the intricacies of miRNA–target networks and their modulation of transcriptional and post-transcriptional pathways during the adaptation to abiotic stress in plants. Declarations Authors Contributions TJ contributed to investigation, analysis, writing and reviewing and editing. HT, SS and GG contributed to data mining and analysis. RV contributed to conceptualization, supervision and analysis. SB contributed to investigation, analysis and writing, reviewing and editing. All authors contributed to the article and approved the submitted version. 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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-7401744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544225322,"identity":"5c02c3c2-e866-4421-a617-6670c76b626e","order_by":0,"name":"Tabassum Jehan","email":"","orcid":"","institution":"Zakir Husain Delhi College","correspondingAuthor":false,"prefix":"","firstName":"Tabassum","middleName":"","lastName":"Jehan","suffix":""},{"id":544225323,"identity":"280f25d7-c198-4d9a-b56e-b877793ce536","order_by":1,"name":"Harshita Tripathi","email":"","orcid":"","institution":"Zakir Husain Delhi 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10:37:30","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76870,"visible":true,"origin":"","legend":"","description":"","filename":"ACPPD25006550structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/ba25fbf72ff33503d38232c8.xml"},{"id":96708891,"identity":"97bcb8f9-06fd-41c2-aceb-c55e686fc247","added_by":"auto","created_at":"2025-11-25 10:05:59","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84508,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/43ed71f94574166e5c4a4459.html"},{"id":96618142,"identity":"7948ab65-0cc0-4d1c-bc66-1a622636bf3b","added_by":"auto","created_at":"2025-11-24 10:37:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":356853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeciphering the targets oftop upand downregulated miRNAs obtainedupon treatment with heatstress in 15 day old \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA.thaliana \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eseedlings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Heat map representing enriched TF families from the targets of heatstress induced miRNAs. (b) PPI interactionnetwork obtained withSTRING db, perfomed with the various targets of miRNAs. The heatmap has been obtained from TDThub, by using FIM algorithm, and the intensity of the blue colour represents the strength of –log10(FDR).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/9884b1eef2afa3b681afe9b3.png"},{"id":96618143,"identity":"68436188-834f-459f-8511-837dfbaaee85","added_by":"auto","created_at":"2025-11-24 10:37:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeciphering the targets of top up and downregulated miRNAs obtained upon treatment with drought stress in 15 day old \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA.thaliana \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eseedlings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Heat map representing enriched TF families from the targets of droughtstress induced miRNAs. (b) PPI interactionnetwork obtained withSTRING db, perfomed with the various targets of miRNAs. The heatmap has been obtained from TDThub, by using FIM algorithm, and the intensity of the blue colour represents the strength of –log10(FDR).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/414889532087d12ad6973efc.png"},{"id":96618146,"identity":"f228a626-0855-4b54-b807-8105ffd1272e","added_by":"auto","created_at":"2025-11-24 10:37:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":152841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDeciphering the targets of top up and downregulated miRNAs obtained upon treatment with salt stress in 15 day old \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eA.thaliana \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eseedlings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Heat map representing enriched TF families from the targets of saltstress induced miRNAs. (b) PPI interactionnetwork obtained withSTRING db, perfomed with the various targets of miRNAs. The heatmap has been obtained from TDThub, by using FIM algorithm, and the intensity of the blue colour represents the strength of –log10(FDR).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/472f49e7e10370f89157518a.png"},{"id":96618144,"identity":"abeddea5-3d16-480e-b28f-000591f624ee","added_by":"auto","created_at":"2025-11-24 10:37:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96115,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eObtaining top gene ontology terms associated with the target gene Ids for the heat stress induced miRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Biological Process(b) Cellular Component and (c) Molecular function and (d)top enriched terms associated with KEGG pathways. FDR for this analyses was set to0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/0f80c689661934a0eea326d2.png"},{"id":96618149,"identity":"59e6f1aa-6a81-4c34-bd55-61c01c6c5a08","added_by":"auto","created_at":"2025-11-24 10:37:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":243556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eObtainingtop gene ontology terms associated with the target gene Idsfor the droughtstress induced miRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Biological Process(b) Cellular Component and (c) Molecular function. FDR for this analyses was set to0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/c33a77c03ae90de5e2287346.png"},{"id":96708737,"identity":"89074ca9-45ba-456f-a44c-6344e10b0f58","added_by":"auto","created_at":"2025-11-25 10:05:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":253105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eObtainingtop gene ontology terms associated with the target gene Idsfor the saltstress induced miRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Biological Process(b) Cellular Component and (c) Molecular function. FDR for this analyses was set to0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/b16f7043e3e6f3f6d48f3a16.png"},{"id":107487550,"identity":"d17ae534-8156-462b-85a5-e639b2d4a323","added_by":"auto","created_at":"2026-04-22 02:42:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1446089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7401744/v1/b9d9497c-c651-4cf8-ab1d-0e7733e6482c.pdf"}],"financialInterests":"","formattedTitle":"Plant microRNA networks in abiotic stress pathways: Meta-analyses of sRNA data associated with drought, heat and salt stress responses in Arabidopsis thaliana","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMicroRNAs (miRNAs) are small, non-coding RNA molecules that play a crucial role in post-transcriptional gene regulation by binding to target messenger RNAs (mRNAs), leading to their degradation or translational repression. These molecules, approximately 22 nucleotides in length, have been found to be highly conserved across plant species, underscoring their evolutionary significance in regulating gene expression in response to various physiological and environmental stimuli (Jones-Rhoades et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Recent breakthroughs have demonstrated that miRNAs are fundamental in modulating stress responses in plants, highlighting their potential as key regulatory elements in plant adaptation to abiotic stresses such as drought, salinity, and temperature fluctuations (Sunkar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn plants, miRNAs were first identified in the model organism \u003cem\u003eArabidopsis thaliana\u003c/em\u003e in 2002, paving the way for extensive research into their roles in plant development and stress adaptation (Reinhart et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Since then, plant miRNAs have been extensively studied, revealing their critical functions in various biological processes, including leaf morphogenesis, root architecture, reproductive development, and defense mechanisms against pathogens (Jones-Rhoades and Bartel \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mallory and Vaucheret \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn response to different developmental cues, environmental signals, and stress conditions, plant cells tightly regulate protein production through miRNA-mediated gene silencing(Chen \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The process begins with the transcription of primary miRNA (pri-miRNA) molecules by RNA polymerase II from specific \u003cem\u003eMICRORNA (MIR)\u003c/em\u003e genes. These precursors adopt a characteristic stem-loop structure and are processed into mature miRNAs, which are subsequently incorporated into the miRNA-induced silencing complex (miRISC) to direct target gene regulation via mRNA cleavage or translational repression(Voinnet, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This dynamic regulatory mechanism allows plants to tweak gene expression in response to external stimuli, thereby enhancing their resilience to adverse environmental conditions.\u003c/p\u003e\u003cp\u003eAmong various model organisms, \u003cem\u003eArabidopsis thaliana\u003c/em\u003e has emerged as a powerful system for studying miRNA function due to its small genome size, short life cycle, and ease of genetic manipulation(Meyers et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Over the past two decades, research on \u003cem\u003eArabidopsis\u003c/em\u003e miRNAs has revealed their roles in abiotic stress adaptation, including responses to drought, salinity, and temperature extremes. Several miRNA families, such as miR393, miR398, and miR169, have been shown to modulate stress-responsive genes and signaling pathways, enhancing plant tolerance to unfavorable conditions (Sunkar and Zhu \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aims to identify novel miRNAs and their target pathways involved in \u003cem\u003eArabidopsis thaliana\u0026rsquo;s\u003c/em\u003e response to drought, salinity, and heat stress. To achieve this, a small RNA (sRNA) sequencing dataset was utilized to analyze differentially expressed mature miRNAs in 15-day-old \u003cem\u003eArabidopsis\u003c/em\u003e seedlings subjected to these stress conditions(Pegler et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By integrating bioinformatics analyses such as miRNA target prediction and functional annotation, the study seeks to uncover key miRNA-mediated regulatory networks that contribute to plant adaptation to environmental stresses. The findings from this study will not only enhance our understanding of plant stress biology but may also offer valuable insights for developing stress-resilient crops through genetic engineering or miRNA-based biotechnological approaches.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and data acquisition\u003c/h2\u003e\u003cp\u003eThis study sought to identify significant changes in the biological environment of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e by analysing the expression levels of microRNAs (miRNAs) and their target genes in response to abiotic stress conditions, specifically heat, drought \u0026amp; salinity. For this purpose, the required dataset was extracted from Pegler et. al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which dealt with sRNA sequencing of 15-day old stressed \u003cem\u003eArabidopsis thaliana\u003c/em\u003e seedlings. The extracted Log\u003csub\u003e2\u003c/sub\u003e Fold Changes (FC) value for miRNA expression under each stress condition were sorted from smallest to largest to identify miRNAs with significant up- and down- regulation.\u003c/p\u003e\u003cp\u003eMicroRNA selection was based on differential expression levels determined using Log\u003csub\u003e2\u003c/sub\u003e fold change (FC) values. MicroRNAs with a Log\u003csub\u003e2\u003c/sub\u003e FC greater than 1.0 were classified as up-regulated, while those with a Log\u003csub\u003e2\u003c/sub\u003e FC less than \u0026minus;\u0026thinsp;1.0 were considered down-regulated. These thresholds were selected due to their biological relevance in identifying miRNAs with significant differential expression, ensuring a robust distinction between up- and down-regulated candidates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Obtaining targets of miRNA\u003c/h2\u003e\u003cp\u003eThe pMIREN tool was used for target identification. This comprehensive plant miRNA database contains over 20,000 annotated miRNAs from various species, including 20,388 miRNA loci, 5,757 families, and 141,327 predicted miRNA-target pairs. It provides a valuable resource for data mining and miRNA functional studies(Guo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Target Gene Identification\u003c/h2\u003e\u003cp\u003eTarget gene IDs for each microRNA were retrieved from the PmiREN database using the corresponding microRNA identifier or relevant keywords for each stress condition. Predicted target gene names were then cross-referred using TAIR database (Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e: Swarbreck et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Transcription factor binding site analyses\u003c/h2\u003e\u003cp\u003eAfter this, a statistical analysis was done using TFBS-TDT Hub for transcription factor binding sites. Plant species (\u003cem\u003eArabidopsis thaliana\u003c/em\u003e) was selected for which TFBS analysis was required and all target gene IDs extracted using PmiREN were entered one target gene ID per line. Quick heat maps were generated, using the FIMS algorithm (Find Individual Motif Sites) to visualize miRNA expression pattern. Key parameters such as p-value (-log10), were used to assist statistical significance which was prevalent from the strength of the blue color (Grau and Franco-Zorrilla, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e: Yu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Protein-Protein Interaction Analysis and Statistical Analysis\u003c/h2\u003e\u003cp\u003eThe STRING database was employed to study about the protein-protein interaction for miRNA target genes. The protein coded by each target IDs were extracted using TAIR database and were recorded in the excel sheet. After, this the protein names were entered in STRING database together one protein per line for multiple protein dataset and a protein-protein interaction map was generated. This included network analysis to identify interaction hubs and clusters, which may represent critical regulatory components under abiotic stress condition (Szklarczyk et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Gene ontology analyses\u003c/h2\u003e\u003cp\u003eAll the target IDs obtained for each miRNA for each of the abiotic stress, were analysed for GO (gene ontology) enrichment terms. This was achieved through the shinyGO tool, which estimated the GO enrichment through fold enrichment and FDR value (Ge et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn this study, we conducted a comprehensive meta-analysis of differentially expressed microRNAs (miRNAs) in Arabidopsis thaliana under various abiotic stresses, including heat, drought, and salinity. Categorization of miRNA targets enabled us to investigate regulatory mechanisms and transcriptional pathways associated with each stress type.\u003c/p\u003e\u003cp\u003eUnder heat stress, we observed a significant upregulation of miRNAs, including miR156, miR159, and miR398, while miR164 and miR169 were downregulated. These miRNAs have been previously associated with stress adaptation, including the modulation of ROS scavenging (miR398) (Chowdhury et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and the regulation of flowering time (Wang, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) (miR156). Transcription factor (TF) family enrichment analysis using the TDT hub revealed an overrepresentation of AP2 and BBR/BPC TFs, implying that these families may act as key regulators of heat-responsive miRNA targets (Fig.\u0026nbsp;1a). STRING-based protein-protein interaction analysis of the miRNA target genes under heat stress identified several core proteins, including BCAT3 (BRANCHED-CHAIN-AMINO-TRANSFERASE-3) and BCAT5 (BRANCHED-CHAIN-AMINO-TRANSFERASE-5), which are involved in branched-chain amino acid metabolism and potentially aid in osmo-protection and energy balance during heat exposure (Diebold et al. 2002). Additionally, SERK (SOMATIC EMBRYOGENESIS RECEPTOR KINASE) and NAC054, both known for their roles in developmental reprogramming and stress signal transduction (Santos et al. 2009, Xu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), were found as core components emphasizing their functional relevance in heat-stress adaptation (Fig.\u0026nbsp;1b).\u003c/p\u003e\u003cp\u003emiRNAs such as miR851, miR771, and miR157b were upregulated under drought stress, potentially contributing to protective gene regulation (Camargo-Ramirez et al. 2018, Shikata et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while miR4228, miR172, and miR5020 were downregulated, possibly removing repression on stress-adaptive targets. TF family enrichment of the targets was distinctively lower compared to other stress conditions. However, BBR/BPC remained the most enriched, suggesting a selective regulatory response (Fig.\u0026nbsp;2a). The STRING PPI network for drought-responsive miRNA targets highlighted key proteins, including LBD37 (LOB-DOMAIN-CONTAINING-37) (Chai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a negative regulator of nitrogen-responsive genes; GPAT9 (GLYCEROL-3-PHOSPHATE-ACYL-TRANSFERASE-9), involved in membrane lipid remodelling (Gong et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and PGD1 (PLASTID-GALACTOLIPID-DEGRADATION-1), which contributes to redox balance through the oxidative pentose phosphate pathway (Du et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These targets perhaps indicate metabolic adjustments and signalling events required during drought tolerance.\u003c/p\u003e\u003cp\u003eUnder salt stress, miR156a (Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and miR169e showed marked upregulation, aligning with their known roles in developmental plasticity (Sorin et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), whereas miR857 and miR778 were downregulated, suggesting a fine-tuned modulation of lignin biosynthesis and transcriptional repression pathways under salinity (Zhao et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Bennet et al. 2022). The transcriptional landscape of the targets was visibly wider, with extensive enrichment across multiple TF families (Fig.\u0026nbsp;3a). Families such as WRKY, YABBY, and zf-HD displayed the most substantial enrichment (with the highest \u0026ndash;log10[FDR] values), indicating widespread transcriptional rewiring under salinity conditions. The PPI network highlighted several well-characterized stress-responsive proteins (Fig.\u0026nbsp;3b), including NADP-ME2 (NADP-MALIC-ENZYME-2), which regulates malate metabolism and cellular pH homeostasis (Badia et al. 2015); ACO1 (1-AMINOCYCLOPROPANE-1-CARBOXYLATE-OXIDASE), a key enzyme in ethylene biosynthesis (Houben et al. 2019); and MYB101 and MYB33 (Wang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), both transcription factors involved in ABA signalling and the osmotic stress response. Additionally, LOX2 (LIPOXYGENASE-2) (Yang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and TIFY family proteins, which are repressors of jasmonate signalling (Chung et al. 2009), were observed as network hubs, suggesting that both ethylene and jasmonate pathways are highly active during salt stress responses. Our study also indicates that these pathways also interact with miRNA regulation to shape a plant\u0026rsquo;s response towards the stress.\u003c/p\u003e\u003cp\u003eGene ontology enrichment analyses of the miRNA targets revealed a striking and consistent overrepresentation of developmental processes across all three abiotic stresses: heat (Fig.\u0026nbsp;4), drought (Fig.\u0026nbsp;5) and salt (Fig.\u0026nbsp;6). In the Biological Process category (sub part a ), terms such as embryonic meristem development, floral organ development, were prominently enriched, indicating that stress-responsive miRNAs may intersect with developmental reprogramming pathways (Yang et al. 2023) The Cellular Component (subpart b) showed a concentration of targets in compartments mostly associated with transcriptional activity and intercellular communication, such as the secretory vesicles, and plasmodesmata. In the Molecular function category (subpart c), key terms like DNA-binding transcription factor activity, mRNA binding, and ligase activity suggest regulatory flexibility at both transcriptional and post-transcriptional levels. KEGG (Kyoto Encyclopedia of genes and genomes ) analyses (subpart d) of the targets specifically from heat stress highlighted enriched terms including biosynthesis of secondary metabolites and amino acid metabolism, pointing toward active metabolic adjustments in response to stress. Taken together, these results suggest that stress-induced miRNAs not only modulate canonical stress pathways but may also regulate quintessential developmental and metabolic functions, likely to optimize growth and survival under adverse stressors.\u003c/p\u003e"},{"header":"4. Discussion and Conclusion","content":"\u003cp\u003eThe meta-analyses of differentially expressed miRNAs under abiotic stress conditions in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e reveal the intricate and stress-specific regulatory landscapes regulated by miRNA\u0026ndash;target interactions. The identification of unique miRNA expression profiles under heat, drought, and salt stress, along with distinct enrichments in transcription factor families and PPI network configurations, highlights the efficiency with which plants modulate gene expression in response to environmental perturbations. The enrichment of developmental GO terms across stresses points to the fact that stress acclimation juxtaposes with developmental reprogramming.\u003c/p\u003e\u003cp\u003eThe involvement of TF families such as AP2, WRKY, and BBR/BPC, and PPI network hubs like SERK, LBD37, and MYB101 further emphasizes the integration of hormonal, metabolic, and transcriptional signalling cascades in miRNA-mediated responses. Interestingly, the relatively less TF enrichment of the targets of drought induced miRNAs, contrasted with the extensive TF and pathway from the targets enriched from miRNAs differentially expressed under salt stress. This highlights differential levels of regulatory plasticity among different stress conditions. Collectively, our analysis reveals how stress-responsive miRNAs do not act alone but form dynamic, condition-specific regulatory hubs that bridge environmental signals with developmental and metabolic processes. These insights not only deepen our understanding of plant stress biology but also point toward potential genetic nodes for engineering enhanced stress resilient plants.\u003c/p\u003e\u003cp\u003eOur analysis of the already published Pegler et al. data set showed that miRNA-mediated regulation of abiotic stress responses in Arabidopsis is highly stressor-specific, with each stress condition leading to a unique set of TF enrichments and PPI network configurations. The present findings therefore offer meaningful insights into the intricacies of miRNA\u0026ndash;target networks and their modulation of transcriptional and post-transcriptional pathways during the adaptation to abiotic stress in plants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTJ contributed to investigation, analysis, writing and reviewing and editing. HT, SS and GG contributed to data mining and analysis. RV contributed to conceptualization, supervision and analysis. SB contributed to investigation, analysis and writing, reviewing and editing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBennett M, Piya S, Baum TJ and Hewezi T (2022) miR778 mediates gene expression, histone modification, and DNA methylation during cyst nematode parasitism. Plant\u003cem\u003e \u003c/em\u003ePhysiology 189(4): 2432\u0026ndash;2453. https://doi.org/10.1093/plphys/kiac228\u003c/li\u003e\n\u003cli\u003eCamargo-Ram\u0026iacute;rez R, Val-Torregrosa B and San Segundo B (2018) MiR858-Mediated Regulation of Flavonoid-Specific MYB Transcription Factor Genes Controls Resistance to Pathogen Infection in Arabidopsis. 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Plant Physiol. 169(4):2539-52. doi: 10.1104/pp.15.01011\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"miRNA, abiotic stress, Arabidopsis thaliana, psRNAtarget, STRING, transcription factors, protein-protein interaction (PPI), gene ontology, stress response","lastPublishedDoi":"10.21203/rs.3.rs-7401744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7401744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicroRNAs (miRNAs) are a diverse group of small regulatory RNAs in plants that modulate gene expression at the post-transcriptional or translational level. This study focuses on identifying novel miRNAs and their target pathways involved in responses to drought, salt, and heat stress. A small RNA (sRNA) sequencing dataset was analysed to identify mature miRNAs expressed in 15-day-old \u003cem\u003eArabidopsis thaliana\u003c/em\u003e seedlings subjected to abiotic stress conditions. Differential expression analysis was performed using log\u003csub\u003e2\u003c/sub\u003eFC-based filtering (stress vs. control), which led to the identification of significantly upregulated and downregulated miRNAs, including novel families such as miR3932b, miR5630b, and miR169j. Target genes of these miRNAs were predicted using the psRNA target tool (pmiREN), and protein-protein interaction (PPI) analysis was conducted using STRING. Additionally, analyses with the Find Individual Motif Sites (FIM) algorithm showed significant enrichment of crucial transcription factor families, including AP2 (APETALA2), WRKY, BBR (BARLEY B RECOMBINANT), etc. The identified miRNA targets were also found to be \u0026nbsp;involved in signal transduction pathways other than stress, including most significant developmental and growth processes. Our findings therefore offer meaningful insights into the intricacies of miRNA–target networks and their modulation of transcriptional pathways during plant acclimation to abiotic stresses.\u003c/p\u003e","manuscriptTitle":"Plant microRNA networks in abiotic stress pathways: Meta-analyses of sRNA data associated with drought, heat and salt stress responses in Arabidopsis thaliana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 10:37:25","doi":"10.21203/rs.3.rs-7401744/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab5e1233-b300-40a5-91e5-5177eb222806","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T13:00:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 10:37:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7401744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7401744","identity":"rs-7401744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}