Unveiling the Molecular Mechanisms of γ-polyglutamic acid-Mediated Drought Tolerance in Cotton through Transcriptomic and Physiological Analyses

Unveiling the Molecular Mechanisms of γ-polyglutamic acid-Mediated Drought Tolerance in Cotton through Transcriptomic and Physiological Analyses | 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 Unveiling the Molecular Mechanisms of γ-polyglutamic acid-Mediated Drought Tolerance in Cotton through Transcriptomic and Physiological Analyses Ziyu Wang, Xin Zhang, Yunhao Liusui, Wanwan Fu, AiXia Han, Dongmei Zhao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5413622/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2025 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Background Drought severely impacts the growth of cotton, and the application of plant biostimulants offers an effective approach to enhancing crop drought tolerance. γ-Poly-glutamic acid (γ-PGA) is a novel and environmentally friendly biostimulant, but its functions and mechanisms in responding to drought stress in cotton are still unclear. Results This study aims to elucidate the role of the plant biostimulant γ-PGA in the response of cotton to drought stress. Cotton seedlings treated with exogenous γ-PGA and a control group were subjected to drought conditions, and phenotypic observations along with measurements of relevant physiological indicators were conducted. The results showed that the activities of superoxide dismutase (SOD) and peroxidase (POD), as well as the proline (PRO) content in the leaves of γ-PGA-treated cotton, were higher than those in the control group, while malondialdehyde (MDA) content was lower. This indicates that exogenous application of γ-PGA can effectively enhance cotton's tolerance to drought stress. Subsequently, transcriptome sequencing was performed on the leaves of cotton plants under drought stress in both the γ-PGA-treated and control groups. The results revealed that γ-PGA treatment led to the upregulation of 1,658 genes and the downregulation of 589 genes. Among these differentially expressed genes (DEGs), 233 were transcription factors, suggesting that γ-PGA participates in the cotton drought stress response by regulating the expression of numerous transcription factors. Most of the DEGs were associated with the plant hormone signal transduction pathways and the MAPK signaling pathway, indicating that γ-PGA enhances cotton's drought tolerance primarily by regulating these two signaling pathways. Conclusions This study elucidates the effects of exogenous γ-PGA on drought resistance in cotton, as well as the molecular mechanisms underlying this resistance. The findings provide a theoretical foundation for the future application of γ-PGA to enhance drought tolerance in cotton. γ-PGA cotton drought tolerance transcriptome transcription factors plant hormones MAPK signaling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cotton is one of the most labor-intensive crops globally and is a vital strategic resource that has significant implications for national economies and livelihoods. According to a report by the Intergovernmental Panel on Climate Change (IPCC) released in September 2013, average global temperatures are projected to rise by 1.8 to 4.0 degrees Celsius by the year 2100. This temperature increase is expected to lead to widespread droughts in many major regions, severely impacting crop yield and quality. Drought stress is currently one of the most detrimental abiotic stresses, leading to insufficient water availability for plants and severely disrupting essential physiological processes such as photosynthesis, transpiration, and nutrient uptake in crops[ 1 – 3 ].These effects ultimately hinder the normal growth and development of cotton, significantly reducing both its yield and quality. Therefore, it is crucial to further enhance cotton's tolerance to drought stress. In the context of the nation's vigorous endorsement of the advancement and utilization of green fertilizer products, environmentally sustainable and efficient microbial inoculants, along with their metabolites, have gained significant traction in agricultural practices. γ-PGA, a biopolymer that is environmentally benign, was initially identified in the capsule of Bacillus anthracis and represents a key constituent of the Bacillus capsule [ 4 ]. Studies have revealed that γ-PGA exhibits a plethora of beneficial attributes, indicating extensive potential applications in sectors including medicine, environmental science, and agriculture. γ-PGA is an anionic polymer formed from D-glutamic acid and L-glutamic acid monomers, characterized by glutamic acid units interconnected via peptide bonds between the α-amino group and the γ-carboxyl group. Its molecular weight spans from 100 kDa to 10,000 kDa. The high density of free hydrophilic carboxyl groups and hydrogen bonds endows γ-PGA with remarkable water retention capabilities and potent ion adsorption characteristics [ 5 ]. Furthermore, γ-PGA demonstrates biocompatibility and is amenable to complete biodegradation by biological systems. It also has the capacity to enhance nutrient availability, improve fertilizer utilization efficiency, stimulate root development in crops, and promote protein biosynthesis[ 6 , 7 ]. γ-PGA serves a crucial regulatory function in plant growth and development. Research indicates that the use of γ-PGA-based compound fertilizers can significantly enhance nutrient absorption efficiency during the growth phases of crops such as maize, rice, and soybeans [ 8 ]. Furthermore, it promotes leaf expansion and root proliferation, thereby improving the photosynthetic capacity of these crops [ 8 ]. Notable enhancements in seedling length, germination index, and vigor index were observed when tobacco seeds were treated with γ-PGA solutions at varying concentrations [ 9 ]. The γ-PGA produced through the cultivation of Bacillus species on rapeseed meal as a substrate significantly increased the height and shoot dry biomass of watermelon seedlings [ 10 ]. Existing literature confirms that γ-PGA plays a pivotal role in mediating plant responses to abiotic stress. Specifically, γ-PGA can significantly elevate the levels of osmotic regulators and the activity of antioxidant enzymes within crops, thus enhancing stress resilience. The application of γ-PGA has been shown to effectively augment root activity in Chinese flowering cabbage, increase the activities of superoxide dismutase (SOD) and catalase (CAT) in foliage, and reduce malondialdehyde (MDA) levels, thereby improving stress tolerance. Additionally, exogenous γ-PGA application promotes the absorption of essential nutrients such as nitrogen, phosphorus, potassium, calcium, magnesium, copper, zinc, iron, and manganese in Chinese flowering cabbage [ 11 ]. In an investigation where 12.5% polyethylene glycol-6000 (PEG-6000) was used to simulate drought stress in rice seedlings at the two-leaf stage, findings revealed that the incorporation of γ-PGA mitigated the adverse effects induced by PEG treatment. Specifically, γ-PGA preserved leaf fresh weight, maintained higher leaf vitality, and minimized leaf wilting and desiccation. Rice seedlings subjected to γ-PGA treatment exhibited reduced reactive oxygen species (ROS) accumulation in leaves, alongside elevated levels of soluble sugars and proline, thereby enhancing drought stress tolerance. γ-PGA also improves heat stress tolerance in Chinese cabbage (Brassica rapa) by facilitating carotenoid biosynthesis, promoting photosynthesis, and modulating ROS signaling [ 12 ]. In peach seedlings, γ-PGA stimulates root development and significantly enhances root vitality while promoting the accumulation of osmotic adjustment compounds under drought stress conditions, thereby reducing transpiration rates and improving drought resistance. Furthermore, γ-PGA treatment promotes the expression of genes associated with abscisic acid (ABA) biosynthesis in Brassica napus , leading to increased ABA accumulation under drought stress, thus enhancing drought tolerance [ 13 ]. γ-PGA enhances the cold stress resistance of rapeseed seedlings by promoting proline accumulation and increasing the total antioxidant capacity of the seedlings [ 14 , 15 ]. Further analyses have shown that γ-PGA enhances stress tolerance in canola by modulating the signaling pathways of Ca²⁺, H 2 O 2 , brassinosteroids, and jasmonic acid [ 15 , 16 ]. The aforementioned research indicates that the utilization of γ-PGA can improve plant resilience to abiotic stressors. Nonetheless, there is a scarcity of literature addressing the influence of γ-PGA on cotton's response to drought stress. Prior investigations have predominantly concentrated on the physiological mechanisms through which γ-PGA augments plant tolerance to abiotic challenges, with relatively limited studies exploring the underlying molecular pathways. This study intends to elucidate the effects of exogenous γ-PGA on cotton's drought tolerance by assessing the phenotypic responses of cotton subjected to drought stress in both γ-PGA-treated and control conditions. We will further analyze the levels of proline and MDA, alongside the enzymatic activities of POD and SOD, across these groups. Additionally, RNA sequencing (RNA-seq) will be used to investigate how exogenous γ-PGA affects gene expression in cotton under drought stress, aiming to clarify the molecular mechanisms that enhance drought tolerance Results Exogenous application of γ-PGA enhances drought tolerance in cotton To investigate the effect of exogenous γ-PGA application on cotton's drought tolerance, cotton seedlings treated with γ-PGA and control groups were subjected to drought treatment. The results showed that under normal irrigation conditions, there was no significant phenotypic difference between the control and γ-PGA-treated cotton plants. However, after 14 days of natural drought, the control group exhibited severe leaf wilting, while the γ-PGA-treated group maintained a better phenotype. After two days of rewatering, the γ-PGA-treated cotton plants largely recovered to normal growth conditions, whereas the control group still displayed poor growth status. This indicates that exogenous application of γ-PGA significantly enhanced cotton's tolerance to drought stress (Fig. 1A). Subsequently, physiological parameters of leaves from both control and γ-PGA-treated cotton plants under drought stress were analyzed. The results revealed that under drought conditions, the PRO content, SOD and POD enzyme activities were lower in the control group compared to the γ-PGA-treated group, while the MDA content was significantly higher in the control group (Fig. 1B). These findings suggest that γ-PGA treatment enhanced the antioxidant capacity of cotton plants and mitigated cellular damage caused by drought stress. Transcriptome Analysis of Cotton Plants in the Control and PGA Treatment Groups under Drought Stress Quality Control and Alignment Results of the Transcriptome Transcriptome analysis was performed on samples drawn from three control cohorts and three γ-PGA-treated cohorts. The quality assessment of the transcriptomic data revealed that the proportion of Q30 bases was at least 94.91%, with GC content varying between 43.13% and 43.53%, signifying high-quality sequencing that is appropriate for subsequent investigations. The reads from each sample were mapped to the reference genome, yielding alignment rates between 91.64% and 95.29%. These outcomes fulfill the necessary criteria for further analysis (Table S1). Analysis of Sample Replication Correlation The biological replication correlation of the six transcriptomic samples was assessed by calculating the Pearson correlation coefficient (r). The closer the r² value is to 1, the stronger the correlation between replicate samples, indicating higher reliability for downstream analysis. After analysis, it was found that CK1 and CK2 in the three biological replicates of the control group exhibited a strong correlation, while CK3 showed a lower correlation with CK1 and CK2. Similarly, in the γ-PGA treatment group, T2 and T3 in the three biological replicates demonstrated a strong correlation（Fig. 2A). We then performed principal component analysis (PCA) on the six transcriptome samples. The results showed that CK1 and CK2 in the control group were relatively consistent, while CK3 exhibited a higher degree of dispersion compared to CK1 and CK2. In the γ-PGA treatment group, T1 showed a higher degree of dispersion from T2 and T3, while T2 and T3 were more similar in composition (Fig. 2B). Therefore, we proceeded to use CK1 and CK2 from the control group and T2 and T3 from the γ-PGA treatment group for further analysis. Differential Gene Expression Analysis Between Control and Experimental Groups Under Drought Stress To examine the alterations in gene expression induced by γ-PGA treatment, we conducted a comparative analysis of the gene expression profiles between the control and γ-PGA-treated groups. The parameters for identifying DEGs were established as a fold change≥1.5 and a p-value < 0.05. Under conditions of drought stress, we identified a total of 2,245 DEGs between the control and γ-PGA-treated groups. Of these, 1,658 genes exhibited upregulation, while 589 genes demonstrated downregulation in the γ-PGA-treated cotton plants relative to the control group (Fig. 3). GO Enrichment Analysis An analysis of the DEGs was conducted through Gene Ontology (GO) terms, categorizing their annotations into three principal domains: cellular component (CC), molecular function (MF), and biological process (BP). Under the CC category, DEGs exhibited significant enrichment in terms such as nucleus, extracellular region, chloroplast thylakoid membrane, photosystem II, photosystem, and cell wall (Fig. 4A). The MF enrichment analysis indicated notable significance for several specific GO terms, including DNA-binding transcription factor activity, sequence-specific DNA binding, transcription corepressor activity, and chlorophyll binding (Fig. 4B). In the BP domain, the highest gene representation was found in regulation of defense response, followed by processes such as photosynthesis, regulation of response to stress, regulation of the jasmonic acid-mediated signaling pathway, protein-chromophore linkage, and lipid metabolic process (Fig. 5A). Our focus was narrowed to two biological processes pertinent to stress response: regulation of defense response (GO:0031347) and regulation of response to stress (GO:0080134), which collectively encompass 30 genes (Fig. 5B). Further investigation of the DEGs associated with these two biological processes revealed that 20 out of the total 30 DEGs belong to the TIFY gene family, suggesting a positive response of TIFY genes to γ-PGA treatment (Fig. 5B, Table S2). γ-PGA Treatment Alters the Expression of Numerous Transcription Factors Transcription factors play a crucial regulatory role in plant responses to drought stress by modulating the expression of downstream genes [15]. GO annotation revealed that a substantial number of DEGs were enriched in the “nucleus” component of the CC category and in the “DNA-binding transcription factor activity” term of the MF category, indicating the presence of multiple transcription factors among the DEGs. Further classification and annotation of these DEGs showed that the expression of 233 transcription factors was significantly altered under γ-PGA treatment, accounting for 10.14% of the total DEGs. These 233 transcription factors belong to 25 distinct families, including ARF, BES1, bHLH, bZIP, C2H2, C3H, CO-like, Dof, ERF, G2-like, GATA, GRAS, HD-ZIP, HSF, LBD, MADS, NAC, NF-YA, RAV, TCP, Trihelix, WOX, WRKY, MYB, and YABBY. Among these, ERF, WRKY, NAC, and MYB were the most abundant, with 47, 39, 29, and 22 transcription factors, respectively (Table S3). Expression analysis revealed that the majority of ERF transcription factors were upregulated following γ-PGA treatment. Among these upregulated ERF transcription factors were several known key regulatory factors involved in plant drought responses, such as the homologs of AtDERB2D , Ghi_A03G01081.gene , and GhA05G00761.gene , whose expression was induced by γ-PGA treatment (Fig. 6A). WRKY transcription factors have been demonstrated to be closely associated with drought responses in various plant species. All 39 differentially expressed WRKY transcription factors were upregulated, indicating that γ-PGA treatment enhances the transcription of WRKY transcription factors under drought stress conditions (Fig. 6B). Furthermore, the expression levels of 28 NAC transcription factors were higher in γ-PGA-treated cotton plants compared to the control, suggesting that γ-PGA regulates the expression of NAC transcription factors during drought stress (Fig. 6C). ONAC023 is a key regulatory factor and hub gene in rice’s drought stress response [16]. In this study, the expression of Ghi_A05G22571.gene , an ortholog of ONAC023, was significantly upregulated by γ-PGA treatment. MYB transcription factors are the most abundant class of transcription factors in plants and play important roles in various biological processes. A total of 22 MYB transcription factors were identified among the DEGs (Fig. 6D). Some of these differentially expressed MYB transcription factors include known important regulatory factors in plant drought response, such as Ghi_D12G04101.gene , which is a direct homolog of the key drought response regulator AtMYB20 , and its transcription is induced by γ-PGA treatment. The expression of the homolog of the key drought response gene AtMYB88 , Ghi_D13G03891.gene , was also upregulated under γ-PGA treatment conditions. KEGG Pathway Enrichment Analysis KEGG pathway enrichment analysis was conducted on the DEGs, revealing that a total of 851 DEGs were enriched across 118 distinct metabolic pathways, with some genes participating in multiple pathways (Table S3). The most significantly enriched pathways include Plant Hormone Signal Transduction (103 DEGs), MAPK Signaling Pathway (90 DEGs), alpha-Linolenic acid metabolism (32 DEGs), Photosynthesis (28 DEGs), Glycerophospholipid metabolism (27 DEGs), ABC Transporters (23 DEGs), Glycerolipid metabolism (22 DEGs), Photosynthesis - Antenna Proteins (17 DEGs), Cutin, suberine and wax biosynthesis (15 DEGs), and Diterpenoid Biosynthesis (14 DEGs) (Fig. 7 and Table S4). DEGs Related to Plant Hormone Signal Transduction KEGG annotation analysis revealed that 103 DEGs were significantly enriched in the plant hormone signal transduction pathway. These DEGs are primarily involved in the signaling pathways of auxin (IAA), gibberellin (GA), abscisic acid (ABA), ethylene (ETH), jasmonic acid (JA), and salicylic acid (SA) (Figure 7). In the auxin signal transduction pathway, the transcription level of the auxin carrier AUX1 was significantly upregulated following treatment with γ-PGA. Additionally, multiple Auxin/INDOLE-3-ACETIC ACID (Aux/IAA) repressor proteins were induced by γ-PGA. The expression of AUXIN RESPONSE FACTOR (ARF) transcription factors was also upregulated under this treatment, indicating that γ-PGA can regulate the auxin signal transduction pathway (Fig. 8). In the ABA signal transduction pathway, the expression of ABA receptors PYR (PYRACTIN)/PYL (PYR1-LIKE), serine/threonine-protein kinase SnRK2, and MAP3K17/18 was upregulated following γ-PGA treatment, while the expression of the ABA negative regulator PP2C was downregulated. This suggests that treatment with poly-γ-glutamic acid enhances ABA hormone signal transduction (Fig. 8). Moreover, γ-PGA treatment increased the accumulation of transcripts for the JA receptor COI1, and several MYC2 transcription factors were found to be upregulated (Fig. 8). Furthermore, treatment with γ-PGA enhanced the expression of mitogen-activated protein kinase kinases MPK3/6, thereby promoting ethylene biosynthesis. The transcription level of the key transcription factor in the ethylene signal transduction pathway, ERF1, was also upregulated following this treatment. This indicates that poly-γ-glutamic acid can regulate ethylene synthesis and promote the transduction of ethylene signals (Fig. 8). Discussion Exogenous Application of γ-PGA Enhances Cotton Tolerance to Drought Stress Throughout its growth and development, cotton is frequently subjected to various abiotic stresses that significantly affect its yield and quality, posing substantial threats to the cotton industry. [17]. Enhancing cotton’s tolerance to drought and mitigating its negative impacts on yield and quality present critical challenges in contemporary cotton research. Biostimulants are recognized for their ability to promote crop growth, improve nutrient uptake and utilization, and induce the biosynthesis of defensive biomolecules in plants [18]. The application of biostimulants has emerged as an effective strategy to enhance crop drought resistance [19]. As an environmentally friendly biostimulant, γ-PGA has been used to increase stress resistance in various crops. For example, exogenous application of γ-PGA has been shown to enhance drought tolerance in Brassica napus by promoting abscisic acid accumulation [20]. Similarly, maize seedlings treated with γ-PGA exhibited improved drought resistance, attributed to enhanced photosynthesis and changes in the rhizosphere microbial community [15]. In this study, we found that γ-PGA treatment improved the drought tolerance of cotton seedlings, further supporting the role of γ-PGA in enhancing crop resistance to abiotic stress. These findings offer new theoretical insights for the future application of γ-PGA in improving crop stress resilience. γ-PGA Modulates the Physiological Response of Cotton Seedlings to Drought Stress When plants encounter drought stress, they modify their physiological state to enhance their ability to withstand adverse conditions [21]. Drought stress often results in the excessive accumulation of ROS within plant cells. SOD and POD are key enzymes in the plant ROS scavenging system, functioning to eliminate excess ROS and prevent damage to plant cells [22]. Our data indicate that cotton seedlings treated with γ-PGA exhibit higher activities of SOD and POD enzymes under drought stress conditions compared to the control group. This suggests that γ-PGA can enhance the activities of SOD and POD, thereby reducing ROS accumulation in cotton cells. PRO, an important osmolyte in plants, accumulates in response to drought stress to help mitigate excessive water loss [23,24]. In this study, cotton plants treated with γ-PGA accumulated higher levels of proline compared to the control group, indicating that γ-PGA promotes proline accumulation, thus reducing water loss in cotton seedlings under drought stress conditions. Our research findings suggest that γ-PGA can enhance cotton’s tolerance to drought stress by modulating its physiological state. γ-PGA Enhances Cotton Drought Stress Tolerance by Modulating the Expression of Multiple Transcription Factors Transcription factors play a crucial regulatory role in the plant response to drought stress by controlling the expression of numerous downstream genes[18]. AP2/ERF (APETALA2/ethylene responsive factor) is a plant-specific transcription factor family characterized by the presence of the AP2 domain. Numerous ERFs have been reported to be involved in abiotic stress responses. For instance, the overexpression of PtoERF15 has been shown to contribute to the maintenance of stem water potential, thereby enhancing drought tolerance in Populus [19]. MdDREB2A promotes the expression of the MdNIR1 gene by binding to the DRE cis-elments in its promoter, thereby enhancing nitrogen absorption and drought stress tolerance in apple seedlings.[20]. MdERF38 enhances drought tolerance by positively regulating anthocyanin biosynthesis[21]. Transcriptome data analysis revealed that the transcription levels of 45 ERF transcription factors were significantly altered under γ-PGA treatment, indicating that γ-PGA can influence the expression of ERF transcription factors, thereby enhancing cotton's tolerance to drought stress. The WRKY transcription factor gene family plays a crucial role in regulating transcriptional reprogramming associated with plant stress responses [22]. The overexpression of SlWRKY6 enhances drought tolerance in tomato ( Solanum lycopersicum L. ) by strengthening antioxidant defenses and promoting stomatal closure through the ABA signaling pathway[15].The expression of 40 WRKY transcription factors was altered under γ-PGA treatment, indicating that γ-PGA can regulate the expression of WRKY transcription factors. MYB transcription factors, the largest family of transcription factors in plants, play a pivotal role in controlling numerous biological processes[23]. GhMYB36 is a positive regulator of drought response in cotton, and its overexpression enhances cotton's tolerance to drought stress[24]. γ-PGA treatment led to altered accumulation of transcripts from 40 MYB transcription factors under drought stress, suggesting that γ-PGA can influence the transcription of MYB transcription factors. NAC is another important family of transcription factors in plants, playing a critical role in the plant's defense against drought stress. GhNAC4 enhances cotton's tolerance to drought stress by promoting the synthesis of the secondary cell wall [25]. γ-PGA treatment led to changes in the expression of 40 NAC transcription factors, indicating that γ-PGA exerts a regulatory effect on NAC transcription factors. In total, γ-PGA treatment resulted in significant changes in the expression of 233 transcription factors under drought stress, accounting for one-seventh of the DEGs. This suggests that γ-PGA enhances cotton's ability to withstand drought stress by regulating the expression of numerous transcription factors. γ-PGA can influence the signaling pathways of various plant hormones Plant hormones not only play a crucial role in regulating various processes of plant growth and development, but they also play a significant role in the plant's response to abiotic stresses[26]. The plant hormone ABA plays a crucial regulatory role in the plant's response to abiotic stress. Upon exposure to drought stress, the levels of ABA rapidly accumulate in plants. ABA binds to its receptors, PYR/PYL, leading to a conformational change in the PYR/PYL proteins. This conformational change promotes the interaction between PYR/PYL and the negative regulatory protein phosphatases PP2C, thereby inhibiting the activity of PP2C. The inhibition of PP2C releases its suppression on the key positive regulator in the ABA signaling pathway, the SnRK2 kinase. Activated SnRK2 can then phosphorylate and activate a series of downstream ABA-responsive factors, enabling the plant to better withstand adverse conditions [27]. Our study revealed that γ-PGA treatment significantly upregulated the expression of PYR/PYL and SnRK2, positive regulators of the ABA signaling pathway, while downregulating the expression of PP2C, a negative regulator. This suggests that γ-PGA promotes ABA signaling transduction, thereby enhancing drought tolerance in cotton. Auxin primarily functions in regulating plant growth and development. Recent studies have demonstrated its role in plant responses to abiotic stress [28]. Auxin signaling is primarily regulated by two classes of transcription factors: Aux/IAA proteins and ARFs [29,30]. Overexpression of the rice Aux/IAA protein OsIAA6 enhanced drought tolerance in rice[31]. Arabidopsis IAA5 and IAA19 are associated with osmotic stress responses, and mutations in IAA5 and IAA19 reduced tolerance to PEG treatment in Arabidopsis [32]. The expression of multiple cotton Aux/IAA proteins was upregulated under γ-PGA treatment, suggesting that γ-PGA promotes the transcription of Aux/IAA genes. Small auxin-up RNA genes (SAURs) are a crucial class of auxin-responsive genes. Overexpression of the poplar SAUR gene, PtSAUR8 , in Arabidopsis reduced plant sensitivity to drought stress[31]. Compared to the control group, the transcription levels of several SAURs genes in the γ-PGA-treated cotton plants were increased, indicating that the expression of these genes is regulated by γ-PGA. The Gretchen Hagen3 (GH3) genes are a primary family of early auxin-responsive genes, which facilitate auxin homeostasis by conjugating excess auxin with amino acids[33]. The MdGH3 RNAi apple plants exhibit greater drought stress tolerance compared to wild-type plants[34]. Transcriptome data indicate that the expression of a cotton GH3 gene is suppressed by γ-PGA treatment. Our study shows that the accumulation of transcripts related to multiple auxins signaling pathway genes is altered under γ-PGA treatment, suggesting that γ-PGA can enhance cotton's tolerance to drought stress by modulating auxin signaling pathways. JA plays a crucial role in regulating plant responses and defenses against biotic and abiotic stresses and has garnered extensive research attention[35]. The expression of the JA receptor COI1 gene is downregulated under γ-PGA treatment. The transcription levels of several JAZ family members, which are inhibitors of the jasmonic acid signaling pathway, are enhanced by γ-PGA treatment. Some studies have found that the core transcription factor MYC2 in the JA signaling pathway plays an important role in plant responses to drought. PpnMYC2 positively regulates poplar's drought response by modulating the expression of genes related to stomatal density[36]. Compared to the control group, the transcription levels of several MYC2 genes are elevated in plants treated with γ-PGA. Our data suggest that γ-PGA may participate in cotton's response to drought stress by modulating jasmonic acid signaling transduction. Ethylene regulates various biological processes in plants. When a plant perceives stress signals, the ethylene content within the plant changes. The ethylene signal is transmitted through its corresponding signaling pathway, regulating downstream genes and causing physiological changes in plant cells to adapt to the changing environment[37]. MPK3/6 can phosphorylate and stabilize EIN3, a key transcription factor in the ethylene signaling pathway[38]. γ-PGA treatment promotes the expression of several MPK3/6 genes, and the transcription of multiple ERF genes is also upregulated. This indicates that γ-PGA can enhance the transduction of the ethylene signaling pathway, thereby improving cotton's drought stress tolerance. Therefore, we can conclude from the above results that γ-PGA exerts a positive regulatory effect in helping plants cope with drought stress by modulating the expression of transcription factors and the transduction of plant hormone signaling. Materials and Methods Plant Material The experimental material utilized in this study was the Upland cotton variety Xinluzao 42, provided by the Institute of Industrial Crops at the Xinjiang Academy of Agricultural Sciences. Cotton seeds were placed on moist filter paper for three days to facilitate germination. Following germination, when the seedlings reached approximately 8 mm in length, they were transplanted into pots containing a soil mixture of nutrient soil and vermiculite in a 3:1 volume ratio. The soil in each pot was pre-weighed to ensure uniformity in soil content across all pots, with two seedlings planted in each pot. After the cotyledons fully expanded, weaker seedlings were removed to minimize experimental variability. Once the cotton seedlings developed their first true leaf, different experimental treatments were applied. The control group (CK) and the treatment group were placed in separate trays, with each tray containing 15 pots of experimental material. The control group was irrigated with distilled water according to standard procedures, while the treatment group received irrigation with a γ-PGA solution at a concentration of 50 mg/L. Drought Treatment Experiment Eighteen pots containing three-week-old cotton plants, exhibiting similar growth statuses from both the control group and the γ-PGA treatment group, were selected for drought treatment. Watering was subsequently halted to induce natural drought conditions. Phenotypic differences between the control and γ-PGA treatment groups were photographed and recorded once they became apparent. Measurement of Physiological Indicators Absolute Soil Water Content (ASWC) is utilized to evaluate the severity of drought stress experienced by cotton plants. The formula for calculating ASWC (%) is as follows: ((weight of wet mixed nutrient soil + weight of pot) - (weight of dried mixed nutrient soil + weight of pot)) / weight of dried mixed nutrient soil × 100%. Moderate drought is defined as an ASWC of approximately 10%. When the cotton plants in the control group reach moderate drought conditions, leaf samples from both the control and treatment groups are collected for analysis of physiological indicators. The physiological parameters measured include MDA and Pro content, as well as the activities of POD and SOD enzymes. For all physiological measurements, 0.1g of ground tissue is utilized, and each measurement is conducted in triplicate. Detection assays are performed using test kits provided by Suzhou Grace Biotechnology Co., Ltd., and experimental procedures are carried out in accordance with the manufacturer’s instructions. RNA Sequencing and Analysis When the cotton plants in the control group experienced moderate drought conditions, RNA was extracted from the leaves of both the control and γ-PGA treatment groups for transcriptome sequencing. The extraction procedure adhered to the TRIzol Reagent guidelines provided by Invitrogen. mRNA was enriched using Oligo dT, followed by fragmentation, reverse transcription to synthesize cDNA, and ligation of adapters. The Illumina sequencing platform was employed to construct an Illumina PE library for 2×150 bp sequencing, with three biological replicates for each sample. The raw image data obtained from Illumina sequencing were converted into sequence data through base calling, resulting in the generation of original sequencing data files. After filtering out adapters and low-quality sequencing data, clean reads were acquired. Utilizing Hisat2 software, the clean reads were aligned to the cotton reference genome (https://yanglab.hzau.edu.cn/static/cott/download/genome/TM-1_WHU.genome.fa.gz), after which StringTie software was employed to assemble the reads mapped to the reference genome. The assembled transcripts were subsequently used for further analysis. The expression levels of the assembled transcripts were assessed using StringTie software. Initially, the number of clean reads mapped to each transcript (Count) was calculated, followed by the estimation of the FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) value for each gene based on gene length and Count, reflecting the gene's expression level. PCA Analysis Principal Component Analysis (PCA) reduces a set of variables to a smaller number of linearly uncorrelated variables, known as principal components. In transcriptome data analysis, gene expression levels serve as variables for dimensionality reduction, resulting in a lower-dimensional dataset that facilitates the assessment of sample dispersion and reveals the underlying patterns of sample distribution. In this study, PCA was conducted on the transcriptome data from both the control and treatment groups using the R package FactoMineR. The results of the analysis were visualized with the R package ggplot2. DEGs Analysis DEGs between the control group and the γ-PGA treatment group was conducted using DESeq2 software. The analysis employed a filter with criteria of Fold Change≥1.5 and false discovery rate (FDR) < 0.05 to identify DEGs. GO Classification and Enrichment Analysis The protein sequences of Gossypium hirsutum were downloaded from the CottonMD database (https://yanglab.hzau.edu.cn/static/cott/download/pep/TM-1_WHU.pep.gz) and submitted to the eggNOG mapper database (http://eggnog-mapper.embl.de/) for GO term annotation. Enrichment analysis of the GO terms associated with the DEGs was performed using the R package topGO, with GO terms exhibiting a p-value less than 0.05 considered significantly enriched. The results of the enrichment analysis were visualized using the barplot package in R [39]. KEGG Pathway Enrichment Analysis The KEGG annotation data for cotton genes was obtained from the eggNOG mapper database (http://eggnog-mapper.embl.de/). Subsequently, DEGs were mapped to their corresponding KEGG pathways using the R package clusterProfiler, with pathways exhibiting a p-value of less than 0.05 considered significantly enriched [40]. Finally, the R package ggplot2 was employed to create bubble charts that visualize the significantly enriched KEGG pathways. Conclusions In this study, we investigated the effects of the biostimulant γ-PGA on the drought resistance of cotton and its underlying molecular mechanisms. Our results indicate that the exogenous application of polyglutamic acid significantly enhances cotton’s tolerance to drought stress by promoting proline accumulation, reducing MDA levels, and increasing the activities of SOD and POD enzymes. Transcriptome analysis revealed that, under drought stress conditions, polyglutamic acid treatment led to significant changes in the expression of 2,247 genes, including 233 transcription factors across 25 categories among the DEGs. The majority of the DEGs were enriched in plant hormone and MAPK signaling pathways. These findings suggest that γ-PGA enhances cotton’s drought resistance by modulating the expression of multiple transcription factors and genes associated with plant hormones and MAPK signaling pathways. Our research elucidates the impact of γ-PGA on cotton’s drought tolerance and its molecular mechanisms, providing valuable data and a theoretical foundation for the future application of γ-PGA in improving cotton’s drought resistance. Declarations Acknowledgements We would like to express our sincere gratitude to all individuals who contributed to the completion of this research. This study was made possible through the generous support of the Xinjiang Uygur Autonomous Region (grant number: 2022D01B39) and the “Tianchi Talents” introduction plan of the Xinjiang Uygur Autonomous Region. Author Contributions J.B.Z. and Y.J.G. proposed this idea and designed the research project; Z.Y.W., W.W.F., X.Z., Y.H.L.S., A.X.H., J.F.Y., and Y.F.T. conducted the experiments; J.B.Z. and Y.J.G. analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript. Compliance with Ethical Standards Cotton is a common crop extensively cultivated in the world. This study does not contain any research requiring ethical consent or approval. Consent for publication Not applicable. Funding This research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (grant number: 2022D01B39) and the “Tianchi Talents” introduction plan of Xinjiang Uygur Autonomous Region. 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J Exp Bot. 2023 ;74(22):7000-7014. Verma S, Negi NP, Pareek S, Mudgal G, Kumar D. Auxin response factors in plant adaptation to drought and salinity stress. Physiol Plant. 2022 ;174(3):e13714. Luo J, Zhou JJ, Zhang JZ. Aux/IAA Gene Family in Plants: Molecular Structure, Regulation, and Function. Int J Mol Sci. 2018 ;19(1):259. Jung H, Lee DK, Choi YD, Kim JK. OsIAA6, a member of the rice Aux/IAA gene family, is involved in drought tolerance and tiller outgrowth. Plant Sci. 2015 ;236:304-312. Shani E, Salehin M, Zhang Y, et al. Plant Stress Tolerance Requires Auxin-Sensitive Aux/IAA Transcriptional Repressors. Curr Biol. 2017 ;27(3):437-444. Li J, Min X, Luo K, et al. Molecular characterization of the GH3 family in alfalfa under abiotic stress. Gene. 2023 ;851:146982. Jiang L, Shen W, Liu C, et al. Engineering drought-tolerant apple by knocking down six GH3 genes and potential application of transgenic apple as a rootstock. Hortic Res. 2022 ;9:uhac122. Wang Y, Mostafa S, Zeng W, Jin B. Function and Mechanism of Jasmonic Acid in Plant Responses to Abiotic and Biotic Stresses. Int J Mol Sci. 2021 ;22(16):8568. Xia Y, Jiang S, Wu W, Du K, Kang X. MYC2 regulates stomatal density and water use efficiency via targeting EPF2/EPFL4/EPFL9 in poplar. New Phytol. 2024 ;241(6):2506-2522. Binder BM. Ethylene signaling in plants. J Biol Chem. 2020 ;295(22):7710-7725. Yoo SD, Cho YH, Tena G, Xiong Y, Sheen J. Dual control of nuclear EIN3 by bifurcate MAPK cascades in C2H4 signalling. Nature. 2008 ;451(7180):789-795. Alexa, A.; Rahnenführer, J. Gene Set Enrichment Analysis with topGO. Bioconductor Improv . 2009 ;27, 1–26. Xu S, Hu E, Cai Y, et al. Using clusterProfiler to characterize multiomics data. Nat Protoc. 2024 ;19(11):3292-3320. Additional Declarations No competing interests reported. Supplementary Files Suplementaryfiles.docx Supplementary Information Table S1: Analysis of transcriptome sequencing quality and napping rate.. Table S2: Gene annotation information for BPrelated to regulation of defense response and regulation of response to stress. Table S3: Transcription factors among DEGs. Table S4: KEGG enrichment analysis of DEGs. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 22 Jan, 2025 Reviews received at journal 19 Jan, 2025 Reviewers agreed at journal 11 Jan, 2025 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 05 Dec, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers invited by journal 14 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 07 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5413622","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":386910585,"identity":"b88fad11-33d4-49a3-a502-ff6d216a6dc3","order_by":0,"name":"Ziyu Wang","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ziyu","middleName":"","lastName":"Wang","suffix":""},{"id":386910586,"identity":"8cb29aa5-f9a9-4e02-ae3f-ff42e6472fdc","order_by":1,"name":"Xin Zhang","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":386910587,"identity":"6a224d29-1e22-4cd0-8580-a94dcaa873df","order_by":2,"name":"Yunhao Liusui","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yunhao","middleName":"","lastName":"Liusui","suffix":""},{"id":386910588,"identity":"52ea8e85-038a-4dda-805a-5e40f43fa7d7","order_by":3,"name":"Wanwan Fu","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wanwan","middleName":"","lastName":"Fu","suffix":""},{"id":386910589,"identity":"081c058a-7fba-428c-b04c-54f5ec615a88","order_by":4,"name":"AiXia Han","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"AiXia","middleName":"","lastName":"Han","suffix":""},{"id":386910590,"identity":"61208c23-1de3-4c51-9e3c-9ebe50dc1d51","order_by":5,"name":"Dongmei Zhao","email":"","orcid":"","institution":"Xinjiang Huir Agricultural Group Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Zhao","suffix":""},{"id":386910591,"identity":"ba331fe9-2ef0-4c69-b959-ee0ece403def","order_by":6,"name":"Jisheng Yue","email":"","orcid":"","institution":"Xinjiang Huir Agricultural Group Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jisheng","middleName":"","lastName":"Yue","suffix":""},{"id":386910592,"identity":"1d22a58b-f778-420a-9f0b-29de9d8595bc","order_by":7,"name":"Yongfeng Tu","email":"","orcid":"","institution":"Xinjiang Huir Agricultural Group Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Tu","suffix":""},{"id":386910593,"identity":"99520aa5-7df2-46cc-a098-ac9edb649c63","order_by":8,"name":"Jingbo Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3RvQrCMBDA8ZRCXE67RpD6CoGCk+CrXFCcVASXDoJCpR2sex/D0TESyBT3jvoGdXPyY1dM3Rzym+9PchwhjvOHaCOREoGuAj+4njFe2pMWaCGrTgvb2TriZ6PtScgm0bHoh8iN7LUvG7/Gx8BwBZPejJQ4jsWakiDbomWXdK7AjBdegboUhw5h5rS3vKL2qplrL2EiLYWhhLOpJWHIVfP+8FI2pHOR+nWSET8WQEUOmpJ6CWiUFdCINXKfodFg3aWbJap6nTIcKPCut3gZBtnue/IGfht3HMdxPnoCwLNM1tZCOWwAAAAASUVORK5CYII=","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Jingbo","middleName":"","lastName":"Zhang","suffix":""},{"id":386910594,"identity":"209dd47c-d408-4fa4-b035-33a01bed8766","order_by":9,"name":"Yanjun Guo","email":"","orcid":"","institution":"Xinjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-11-08 04:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5413622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5413622/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-06406-z","type":"published","date":"2025-03-27T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71044632,"identity":"5612b9a7-dca1-4f39-8bad-2361d823eedc","added_by":"auto","created_at":"2024-12-10 14:31:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10415013,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic analysis and measurement of related physiological indices of cotton seedlings under drought stress.\u003cstrong\u003e (A). \u003c/strong\u003ePhenotype of cotton seedlings treated with 50mg/L γ-PGA under drought stress. \u003cstrong\u003e(B). \u003c/strong\u003eMeasurement of related physiological indices (proline, malondialdehyde, superoxide dismutase, peroxidase) after 14 days of drought stress. Data are presented as mean ± SD (n ≥ 3 replicates). Significant differences are indicated by asterisks (**\u003cem\u003ep\u003c/em\u003e ≤ 0.01).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/0946cb7d3459480dbedb6901.png"},{"id":71043700,"identity":"4b160eb7-80bf-4002-8878-90d664c42371","added_by":"auto","created_at":"2024-12-10 14:23:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1891899,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Sample Replication Correlation. \u003cstrong\u003e(A).\u003c/strong\u003eCorrelation analysis of all samples. The color key indicates the degree of similarity between samples.\u003cstrong\u003e (B).\u003c/strong\u003ePrincipal Component Analysis (PCA) of all samples. The red squares represent the three samples from the control group, while the blue squares represent the three samples from the γ-PGA treatment group.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/116511c51ccd4df4554143d5.png"},{"id":71043702,"identity":"fc3e4100-4882-40a5-a82f-cd3454896c6c","added_by":"auto","created_at":"2024-12-10 14:23:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2347544,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of DEGs between the control group and the γ-PGA-treated group.\u003cstrong\u003e \u003c/strong\u003eRed indicates upregulated genes, blue indicates downregulated genes, and gray represents genes with no significant difference in expression.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/39e9d67b365c3e2301e520b2.png"},{"id":71045676,"identity":"7dbb79a0-1774-4916-bf84-0001fc0da882","added_by":"auto","created_at":"2024-12-10 14:39:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4068108,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs GO enrichment analysis. \u003cstrong\u003e\u0026nbsp;(A). \u003c/strong\u003eGO enrichment analysis based on CC.\u003cstrong\u003e (B).\u003c/strong\u003e GO enrichment analysis based on MF. (All enrichment results were selected using a significance threshold of p-value \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/ec45bc2ecb2b5e47e9c4d481.png"},{"id":71043705,"identity":"e8e722b9-80ab-4d3e-80f5-d8863b689668","added_by":"auto","created_at":"2024-12-10 14:23:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4572294,"visible":true,"origin":"","legend":"\u003cp\u003eGO functional enrichment based on BP and analysis of related gene expression patterns. \u003cstrong\u003e(A). \u003c/strong\u003eGO functional enrichment based on BP. (Enrichment results were selected using a significance threshold of \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05). \u003cstrong\u003e(B). \u003c/strong\u003eThe expression patterns of genes belonging to the BP of regulation of defense response (GO:0031347) and regulation of response to stress (GO:0080134).\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/10211ed637042a9096ce0cdb.png"},{"id":71044636,"identity":"de4dee6b-6bea-4578-a920-ee7d1ad44ef9","added_by":"auto","created_at":"2024-12-10 14:31:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6900111,"visible":true,"origin":"","legend":"\u003cp\u003eExpression profiles of transcription factors among DEGs. \u003cstrong\u003e(A). \u003c/strong\u003eERF gene family. \u003cstrong\u003e(B)\u003c/strong\u003e WRKY gene famly. \u003cstrong\u003e(C). \u003c/strong\u003eNAC gene family.\u003cstrong\u003e(D). \u003c/strong\u003eMYB gene family.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/772fd001ea0572c7460fe939.png"},{"id":71044635,"identity":"92864184-af3e-4d66-9928-29e88ea81f22","added_by":"auto","created_at":"2024-12-10 14:31:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4452973,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of DEGs. The rich factor represents the ratio of the proportion of DEGs to the total number of \u003cem\u003eGossypium hirsutum\u003c/em\u003e genes annotated as involved in a given pathway. The size of the circles represents the number of genes. The color of the circles represents numerical value of -log10(P_value).\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/fe57e80af8ab0e1cefb33c6c.png"},{"id":71043707,"identity":"2df1fe8b-ae7c-4535-8c18-439a48bfaa96","added_by":"auto","created_at":"2024-12-10 14:23:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4749036,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of gene expression related to hormone signaling pathways. The red boxes in the hormone signaling pathway indicate that the expression of the corresponding genes is upregulated underγ-PGA treatment, while the green boxes indicate that the expression of the corresponding genes is downregulated under γ-PGA treatment. The heatmap shows the expression patterns of the cotton genes corresponding to the red and blue boxes in the control group and the γ-PGA treatment group.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/0702a8535d5ed05e14a1a739.png"},{"id":79605106,"identity":"4f7b897e-c06b-4d57-b3ef-c8ab1cbc2fb3","added_by":"auto","created_at":"2025-03-31 16:10:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36345017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/8f92cc54-084c-4d20-95ac-8b3c317461c7.pdf"},{"id":71044633,"identity":"7bcd1f17-094a-4703-bd6d-304d5413ce39","added_by":"auto","created_at":"2024-12-10 14:31:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":52783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1:\u003c/strong\u003e Analysis of transcriptome sequencing quality and napping rate..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2: \u003c/strong\u003eGene annotation information for BPrelated to regulation of defense response and regulation of response to stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3: \u003c/strong\u003eTranscription factors among DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4: \u003c/strong\u003eKEGG enrichment analysis of DEGs.\u003c/p\u003e","description":"","filename":"Suplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-5413622/v1/0e5356a45cddee14118c086a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Molecular Mechanisms of γ-polyglutamic acid-Mediated Drought Tolerance in Cotton through Transcriptomic and Physiological Analyses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCotton is one of the most labor-intensive crops globally and is a vital strategic resource that has significant implications for national economies and livelihoods. According to a report by the Intergovernmental Panel on Climate Change (IPCC) released in September 2013, average global temperatures are projected to rise by 1.8 to 4.0 degrees Celsius by the year 2100. This temperature increase is expected to lead to widespread droughts in many major regions, severely impacting crop yield and quality. Drought stress is currently one of the most detrimental abiotic stresses, leading to insufficient water availability for plants and severely disrupting essential physiological processes such as photosynthesis, transpiration, and nutrient uptake in crops[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].These effects ultimately hinder the normal growth and development of cotton, significantly reducing both its yield and quality. Therefore, it is crucial to further enhance cotton's tolerance to drought stress.\u003c/p\u003e \u003cp\u003eIn the context of the nation's vigorous endorsement of the advancement and utilization of green fertilizer products, environmentally sustainable and efficient microbial inoculants, along with their metabolites, have gained significant traction in agricultural practices. γ-PGA, a biopolymer that is environmentally benign, was initially identified in the capsule of \u003cem\u003eBacillus anthracis\u003c/em\u003e and represents a key constituent of the \u003cem\u003eBacillus capsule\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have revealed that γ-PGA exhibits a plethora of beneficial attributes, indicating extensive potential applications in sectors including medicine, environmental science, and agriculture. γ-PGA is an anionic polymer formed from D-glutamic acid and L-glutamic acid monomers, characterized by glutamic acid units interconnected via peptide bonds between the α-amino group and the γ-carboxyl group. Its molecular weight spans from 100 kDa to 10,000 kDa. The high density of free hydrophilic carboxyl groups and hydrogen bonds endows γ-PGA with remarkable water retention capabilities and potent ion adsorption characteristics [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, γ-PGA demonstrates biocompatibility and is amenable to complete biodegradation by biological systems. It also has the capacity to enhance nutrient availability, improve fertilizer utilization efficiency, stimulate root development in crops, and promote protein biosynthesis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eγ-PGA serves a crucial regulatory function in plant growth and development. Research indicates that the use of γ-PGA-based compound fertilizers can significantly enhance nutrient absorption efficiency during the growth phases of crops such as maize, rice, and soybeans [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, it promotes leaf expansion and root proliferation, thereby improving the photosynthetic capacity of these crops [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Notable enhancements in seedling length, germination index, and vigor index were observed when tobacco seeds were treated with γ-PGA solutions at varying concentrations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The γ-PGA produced through the cultivation of \u003cem\u003eBacillus species\u003c/em\u003e on rapeseed meal as a substrate significantly increased the height and shoot dry biomass of watermelon seedlings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Existing literature confirms that γ-PGA plays a pivotal role in mediating plant responses to abiotic stress. Specifically, γ-PGA can significantly elevate the levels of osmotic regulators and the activity of antioxidant enzymes within crops, thus enhancing stress resilience. The application of γ-PGA has been shown to effectively augment root activity in Chinese flowering cabbage, increase the activities of superoxide dismutase (SOD) and catalase (CAT) in foliage, and reduce malondialdehyde (MDA) levels, thereby improving stress tolerance. Additionally, exogenous γ-PGA application promotes the absorption of essential nutrients such as nitrogen, phosphorus, potassium, calcium, magnesium, copper, zinc, iron, and manganese in Chinese flowering cabbage [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In an investigation where 12.5% polyethylene glycol-6000 (PEG-6000) was used to simulate drought stress in rice seedlings at the two-leaf stage, findings revealed that the incorporation of γ-PGA mitigated the adverse effects induced by PEG treatment. Specifically, γ-PGA preserved leaf fresh weight, maintained higher leaf vitality, and minimized leaf wilting and desiccation. Rice seedlings subjected to γ-PGA treatment exhibited reduced reactive oxygen species (ROS) accumulation in leaves, alongside elevated levels of soluble sugars and proline, thereby enhancing drought stress tolerance. γ-PGA also improves heat stress tolerance in Chinese cabbage (Brassica rapa) by facilitating carotenoid biosynthesis, promoting photosynthesis, and modulating ROS signaling [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In peach seedlings, γ-PGA stimulates root development and significantly enhances root vitality while promoting the accumulation of osmotic adjustment compounds under drought stress conditions, thereby reducing transpiration rates and improving drought resistance. Furthermore, γ-PGA treatment promotes the expression of genes associated with abscisic acid (ABA) biosynthesis in \u003cem\u003eBrassica napus\u003c/em\u003e, leading to increased ABA accumulation under drought stress, thus enhancing drought tolerance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. γ-PGA enhances the cold stress resistance of rapeseed seedlings by promoting proline accumulation and increasing the total antioxidant capacity of the seedlings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Further analyses have shown that γ-PGA enhances stress tolerance in canola by modulating the signaling pathways of Ca\u0026sup2;⁺, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, brassinosteroids, and jasmonic acid [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aforementioned research indicates that the utilization of γ-PGA can improve plant resilience to abiotic stressors. Nonetheless, there is a scarcity of literature addressing the influence of γ-PGA on cotton's response to drought stress. Prior investigations have predominantly concentrated on the physiological mechanisms through which γ-PGA augments plant tolerance to abiotic challenges, with relatively limited studies exploring the underlying molecular pathways. This study intends to elucidate the effects of exogenous γ-PGA on cotton's drought tolerance by assessing the phenotypic responses of cotton subjected to drought stress in both γ-PGA-treated and control conditions. We will further analyze the levels of proline and MDA, alongside the enzymatic activities of POD and SOD, across these groups. Additionally, RNA sequencing (RNA-seq) will be used to investigate how exogenous γ-PGA affects gene expression in cotton under drought stress, aiming to clarify the molecular mechanisms that enhance drought tolerance\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eExogenous application of \u0026gamma;-PGA enhances drought tolerance in cotton\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the effect of exogenous \u0026gamma;-PGA application on cotton\u0026apos;s drought tolerance, cotton seedlings treated with \u0026gamma;-PGA and control groups were subjected to drought treatment. The results showed that under normal irrigation conditions, there was no significant phenotypic difference between the control and \u0026gamma;-PGA-treated cotton plants. However, after 14 days of natural drought, the control group exhibited severe leaf wilting, while the \u0026gamma;-PGA-treated group maintained a better phenotype. After two days of rewatering, the \u0026gamma;-PGA-treated cotton plants largely recovered to normal growth conditions, whereas the control group still displayed poor growth status. This indicates that exogenous application of \u0026gamma;-PGA significantly enhanced cotton\u0026apos;s tolerance to drought stress (Fig. 1A). Subsequently, physiological parameters of leaves from both control and \u0026gamma;-PGA-treated cotton plants under drought stress were analyzed. The results revealed that under drought conditions, the PRO content, SOD and POD enzyme activities were lower in the control group compared to the \u0026gamma;-PGA-treated group, while the MDA content was significantly higher in the control group (Fig. 1B). These findings suggest that \u0026gamma;-PGA treatment enhanced the antioxidant capacity of cotton plants and mitigated cellular damage caused by drought stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome Analysis of Cotton Plants in the Control and PGA Treatment Groups under Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Control and Alignment Results of the Transcriptome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptome analysis was performed on samples drawn from three control cohorts and three \u0026gamma;-PGA-treated cohorts. The quality assessment of the transcriptomic data revealed that the proportion of Q30 bases was at least 94.91%, with GC content varying between 43.13% and 43.53%, signifying high-quality sequencing that is appropriate for subsequent investigations. The reads from each sample were mapped to the reference genome, yielding alignment rates between 91.64% and 95.29%. These outcomes fulfill the necessary criteria for further analysis (Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Sample Replication Correlation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe biological replication correlation of the six transcriptomic samples was assessed by calculating the Pearson correlation coefficient (r). The closer the r\u0026sup2; value is to 1, the stronger the correlation between replicate samples, indicating higher reliability for downstream analysis. After analysis, it was found that CK1 and CK2 in the three biological replicates of the control group exhibited a strong correlation, while CK3 showed a lower correlation with CK1 and CK2. Similarly, in the \u0026gamma;-PGA treatment group, T2 and T3 in the three biological replicates demonstrated a strong correlation（Fig. 2A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then performed principal component analysis (PCA) on the six transcriptome samples. The results showed that CK1 and CK2 in the control group were relatively consistent, while CK3 exhibited a higher degree of dispersion compared to CK1 and CK2. In the \u0026gamma;-PGA treatment group, T1 showed a higher degree of dispersion from T2 and T3, while T2 and T3 were more similar in composition (Fig. 2B).\u003c/p\u003e\n\u003cp\u003eTherefore, we proceeded to use CK1 and CK2 from the control group and T2 and T3 from the \u0026gamma;-PGA treatment group for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Gene Expression Analysis Between Control and Experimental Groups Under Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the alterations in gene expression induced by \u0026gamma;-PGA treatment, we conducted a comparative analysis of the gene expression profiles between the control and \u0026gamma;-PGA-treated groups. The parameters for identifying DEGs\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere established as a fold change\u0026ge;1.5 and a p-value \u0026lt; 0.05. Under conditions of drought stress, we identified a total of 2,245 DEGs between the control and \u0026gamma;-PGA-treated groups. Of these, 1,658 genes exhibited upregulation, while 589 genes demonstrated downregulation in the \u0026gamma;-PGA-treated cotton plants relative to the control group (Fig. 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis of the DEGs\u003cem\u003e\u0026nbsp;\u003c/em\u003ewas conducted through Gene Ontology (GO) terms, categorizing their annotations into three principal domains: cellular component (CC), molecular function (MF), and biological process (BP). Under the CC category, DEGs exhibited significant enrichment in terms such as nucleus, extracellular region, chloroplast thylakoid membrane, photosystem II, photosystem, and cell wall (Fig. 4A). The MF enrichment analysis indicated notable significance for several specific GO terms, including DNA-binding transcription factor activity, sequence-specific DNA binding, transcription corepressor activity, and chlorophyll binding (Fig. 4B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the BP domain, the highest gene representation was found in regulation of defense response, followed by processes such as photosynthesis, regulation of response to stress, regulation of the jasmonic acid-mediated signaling pathway, protein-chromophore linkage, and lipid metabolic process (Fig. 5A). Our focus was narrowed to two biological processes pertinent to stress response: regulation of defense response (GO:0031347) and regulation of response to stress (GO:0080134), which collectively encompass 30 genes (Fig. 5B). Further investigation of the DEGs associated with these two biological processes revealed that 20 out of the total 30 DEGs belong to the TIFY gene family, suggesting a positive response of TIFY genes to \u0026gamma;-PGA treatment (Fig. 5B, Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gamma;-PGA Treatment Alters the Expression of Numerous Transcription Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription factors play a crucial regulatory role in plant responses to drought stress by modulating the expression of downstream genes [15]. GO annotation revealed that a substantial number of DEGs were enriched in the \u0026ldquo;nucleus\u0026rdquo; component of the CC category and in the \u0026ldquo;DNA-binding transcription factor activity\u0026rdquo; term of the MF category, indicating the presence of multiple transcription factors among the DEGs. Further classification and annotation of these DEGs showed that the expression of 233 transcription factors was significantly altered under \u0026gamma;-PGA treatment, accounting for 10.14% of the total DEGs. These 233 transcription factors belong to 25 distinct families, including ARF, BES1, bHLH, bZIP, C2H2, C3H, CO-like, Dof, ERF, G2-like, GATA, GRAS, HD-ZIP, HSF, LBD, MADS, NAC, NF-YA, RAV, TCP, Trihelix, WOX, WRKY, MYB, and YABBY. Among these, ERF, WRKY, NAC, and MYB were the most abundant, with 47, 39, 29, and 22 transcription factors, respectively (Table S3).\u003c/p\u003e\n\u003cp\u003eExpression analysis revealed that the majority of ERF transcription factors were upregulated following \u0026gamma;-PGA treatment. Among these upregulated ERF transcription factors were several known key regulatory factors involved in plant drought responses, such as the homologs of \u003cem\u003eAtDERB2D\u003c/em\u003e, \u003cem\u003eGhi_A03G01081.gene\u003c/em\u003e, and \u003cem\u003eGhA05G00761.gene\u003c/em\u003e, whose expression was induced by \u0026gamma;-PGA treatment (Fig. 6A). WRKY transcription factors have been demonstrated to be closely associated with drought responses in various plant species. All 39 differentially expressed WRKY transcription factors were upregulated, indicating that \u0026gamma;-PGA treatment enhances the transcription of WRKY transcription factors under drought stress conditions (Fig. 6B). Furthermore, the expression levels of 28 NAC transcription factors were higher in \u0026gamma;-PGA-treated cotton plants compared to the control, suggesting that \u0026gamma;-PGA regulates the expression of NAC transcription factors during drought stress (Fig. 6C). \u003cem\u003eONAC023\u003c/em\u003e is a key regulatory factor and hub gene in rice\u0026rsquo;s drought stress response [16]. In this study, the expression of \u003cem\u003eGhi_A05G22571.gene\u003c/em\u003e, an ortholog of ONAC023, was significantly upregulated by \u0026gamma;-PGA treatment. MYB transcription factors are the most abundant class of transcription factors in plants and play important roles in various biological processes. A total of 22 MYB transcription factors were identified among the DEGs (Fig. 6D). Some of these differentially expressed MYB transcription factors include known important regulatory factors in plant drought response, such as \u003cem\u003eGhi_D12G04101.gene\u003c/em\u003e, which is a direct homolog of the key drought response regulator \u003cem\u003eAtMYB20\u003c/em\u003e, and its transcription is induced by \u0026gamma;-PGA treatment. The expression of the homolog of the key drought response gene \u003cem\u003eAtMYB88\u003c/em\u003e, \u003cem\u003eGhi_D13G03891.gene\u003c/em\u003e, was also upregulated under \u0026gamma;-PGA treatment conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG Pathway Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment analysis was conducted on the DEGs, revealing that a total of 851 DEGs were enriched across 118 distinct metabolic pathways, with some genes participating in multiple pathways (Table S3). The most significantly enriched pathways include Plant Hormone Signal Transduction (103 DEGs), MAPK Signaling Pathway (90 DEGs), alpha-Linolenic acid metabolism (32 DEGs), Photosynthesis (28 DEGs), Glycerophospholipid metabolism (27 DEGs), ABC Transporters (23 DEGs), Glycerolipid metabolism (22 DEGs), Photosynthesis - Antenna Proteins (17 DEGs), Cutin, suberine and wax biosynthesis (15 DEGs), and Diterpenoid Biosynthesis (14 DEGs) (Fig. 7 and Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs Related to Plant Hormone Signal Transduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG annotation analysis revealed that 103 DEGs were significantly enriched in the plant hormone signal transduction pathway. These DEGs are primarily involved in the signaling pathways of auxin (IAA), gibberellin (GA), abscisic acid (ABA), ethylene (ETH), jasmonic acid (JA), and salicylic acid (SA) (Figure 7).\u003c/p\u003e\n\u003cp\u003eIn the auxin signal transduction pathway, the transcription level of the auxin carrier AUX1 was significantly upregulated following treatment with \u0026gamma;-PGA. Additionally, multiple Auxin/INDOLE-3-ACETIC ACID (Aux/IAA) repressor proteins were induced by \u0026gamma;-PGA. The expression of AUXIN RESPONSE FACTOR (ARF) transcription factors was also upregulated under this treatment, indicating that \u0026gamma;-PGA can regulate the auxin signal transduction pathway (Fig. 8).\u003c/p\u003e\n\u003cp\u003eIn the ABA signal transduction pathway, the expression of ABA receptors PYR (PYRACTIN)/PYL (PYR1-LIKE), serine/threonine-protein kinase SnRK2, and MAP3K17/18 was upregulated following \u0026gamma;-PGA treatment, while the expression\u0026nbsp;of the\u0026nbsp;ABA negative regulator PP2C was downregulated. This suggests that treatment with poly-\u0026gamma;-glutamic acid enhances ABA hormone signal transduction (Fig. 8).\u003c/p\u003e\n\u003cp\u003eMoreover, \u0026gamma;-PGA treatment increased the accumulation of transcripts for the JA receptor COI1, and several MYC2 transcription factors were found to be upregulated (Fig. 8).\u003c/p\u003e\n\u003cp\u003eFurthermore, treatment with \u0026gamma;-PGA enhanced the expression of mitogen-activated protein kinase kinases MPK3/6, thereby promoting ethylene biosynthesis. The transcription level of the key transcription factor in the ethylene signal transduction pathway, ERF1, was also upregulated following this treatment. This indicates that poly-\u0026gamma;-glutamic acid can regulate ethylene synthesis and promote the transduction of ethylene signals (Fig. 8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eExogenous Application of \u0026gamma;-PGA Enhances Cotton Tolerance to Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroughout its growth and development, cotton is frequently subjected to various abiotic stresses that significantly affect its yield and quality, posing substantial threats to the cotton industry. [17]. Enhancing cotton\u0026rsquo;s tolerance to drought and mitigating its negative impacts on yield and quality present critical challenges in contemporary cotton research. Biostimulants are recognized for their ability to promote crop growth, improve nutrient uptake and utilization, and induce the biosynthesis of defensive biomolecules in plants [18]. The application of biostimulants has emerged as an effective strategy to enhance crop drought resistance [19]. As an environmentally friendly biostimulant, \u0026gamma;-PGA has been used to increase stress resistance in various crops. For example, exogenous application of \u0026gamma;-PGA has been shown to enhance drought tolerance in \u003cem\u003eBrassica napus\u003c/em\u003e by promoting abscisic acid accumulation [20]. Similarly, maize seedlings treated with \u0026gamma;-PGA exhibited improved drought resistance, attributed to enhanced photosynthesis and changes in the rhizosphere microbial community [15]. In this study, we found that \u0026gamma;-PGA treatment improved the drought tolerance of cotton seedlings, further supporting the role of \u0026gamma;-PGA in enhancing crop resistance to abiotic stress. These findings offer new theoretical insights for the future application of\u0026nbsp;\u0026gamma;-PGA in improving crop stress resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gamma;-PGA Modulates the Physiological Response of Cotton Seedlings to Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen plants encounter drought stress, they modify their physiological state to enhance their ability to withstand adverse conditions [21]. Drought stress often results in the excessive accumulation of ROS within plant cells. SOD and POD are key enzymes in the plant ROS scavenging system, functioning to eliminate excess ROS and prevent damage to plant cells [22]. Our data indicate that cotton seedlings treated with \u0026gamma;-PGA exhibit higher activities of SOD and POD enzymes under drought stress conditions compared to the control group. This suggests that \u0026gamma;-PGA can enhance the activities of SOD and POD, thereby reducing ROS accumulation in cotton cells. PRO, an important osmolyte in plants, accumulates in response to drought stress to help mitigate excessive water loss [23,24]. In this study, cotton plants treated with \u0026gamma;-PGA accumulated higher levels of proline compared to the control group, indicating that \u0026gamma;-PGA promotes proline accumulation, thus reducing water loss in\u0026nbsp;cotton seedlings under drought stress conditions. Our research findings suggest that \u0026gamma;-PGA can enhance cotton\u0026rsquo;s tolerance to drought stress by modulating its physiological state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gamma;-PGA Enhances Cotton Drought Stress Tolerance by Modulating the Expression of Multiple Transcription Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription factors play a crucial regulatory role in the plant response to drought stress by controlling the expression of numerous downstream genes[18]. AP2/ERF (APETALA2/ethylene responsive factor) is a plant-specific transcription factor family characterized by the presence of the AP2 domain. Numerous ERFs have been reported to be involved in abiotic stress responses. For instance, the overexpression of \u003cem\u003ePtoERF15\u003c/em\u003e has been shown to contribute to the maintenance of stem water potential, thereby enhancing drought tolerance in\u0026nbsp;\u003cem\u003ePopulus\u003c/em\u003e [19]. \u003cem\u003eMdDREB2A\u003c/em\u003e promotes the expression of the \u003cem\u003eMdNIR1\u003c/em\u003e gene by binding to the DRE cis-elments in its promoter, thereby enhancing nitrogen absorption and drought stress tolerance in apple seedlings.[20]. \u003cem\u003eMdERF38\u003c/em\u003e enhances drought tolerance by positively regulating anthocyanin biosynthesis[21]. Transcriptome data analysis revealed that the transcription levels of 45 ERF transcription factors were significantly altered under \u0026gamma;-PGA treatment, indicating that \u0026gamma;-PGA can influence the expression of ERF transcription factors, thereby enhancing cotton\u0026apos;s tolerance to drought stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe WRKY transcription factor gene family plays a crucial role in regulating transcriptional reprogramming associated with plant stress responses [22]. The overexpression of \u003cem\u003eSlWRKY6\u003c/em\u003e enhances drought tolerance in tomato (\u003cem\u003eSolanum lycopersicum L.\u003c/em\u003e) by strengthening antioxidant defenses and promoting stomatal closure through the ABA signaling pathway[15].The expression of 40 WRKY transcription factors was altered under \u0026gamma;-PGA treatment, indicating that \u0026gamma;-PGA can regulate the expression of WRKY transcription factors. MYB transcription factors, the largest family of transcription factors in plants, play a pivotal role in controlling numerous biological processes[23]. \u003cem\u003eGhMYB36\u003c/em\u003e is a positive regulator of drought response in cotton, and its overexpression enhances cotton\u0026apos;s tolerance to drought stress[24]. \u0026gamma;-PGA treatment led to altered accumulation of transcripts from 40 MYB transcription factors under drought stress, suggesting that \u0026gamma;-PGA can influence the transcription of MYB transcription factors. NAC is another important family of transcription factors in plants, playing a critical role in the plant\u0026apos;s defense against drought stress. \u003cem\u003eGhNAC4\u003c/em\u003e enhances cotton\u0026apos;s tolerance to drought stress by promoting the synthesis of the secondary cell wall [25]. \u0026gamma;-PGA treatment led to changes in the expression of 40 NAC transcription factors, indicating that \u0026gamma;-PGA exerts a regulatory effect on NAC transcription factors. In total, \u0026gamma;-PGA treatment resulted in significant changes in the expression of 233 transcription factors under drought stress, accounting for one-seventh of the DEGs. This suggests that \u0026gamma;-PGA enhances cotton\u0026apos;s ability to withstand drought stress by regulating the expression of numerous transcription factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026gamma;-PGA can influence the signaling pathways of various plant hormones\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlant hormones not only play a crucial role in regulating various processes of plant growth and development, but they also play a significant role in the plant\u0026apos;s response to abiotic stresses[26]. The plant hormone ABA plays a crucial regulatory role in the plant\u0026apos;s response to abiotic stress. Upon exposure to drought stress, the levels of ABA rapidly accumulate in plants. ABA binds to its receptors, PYR/PYL, leading to a conformational change in the PYR/PYL proteins. This conformational change promotes the interaction between PYR/PYL and the negative regulatory protein phosphatases PP2C, thereby inhibiting the activity of PP2C. The inhibition of PP2C releases its suppression on the key positive regulator in the ABA signaling pathway, the SnRK2 kinase. Activated SnRK2 can then phosphorylate and activate a series of downstream ABA-responsive factors, enabling the plant to better withstand adverse conditions [27]. Our study revealed that \u0026gamma;-PGA treatment significantly upregulated the expression of PYR/PYL and SnRK2, positive regulators of the ABA signaling pathway, while downregulating the expression of PP2C, a negative regulator. This suggests that \u0026gamma;-PGA promotes ABA signaling transduction, thereby enhancing drought tolerance in cotton.\u003c/p\u003e\n\u003cp\u003eAuxin primarily functions in regulating plant growth and development. Recent studies have demonstrated its role in plant responses to abiotic stress [28]. Auxin signaling is primarily regulated by two classes of transcription factors: Aux/IAA proteins and ARFs [29,30]. Overexpression of the rice Aux/IAA protein \u003cem\u003eOsIAA6\u003c/em\u003e enhanced drought tolerance in rice[31]. Arabidopsis\u003cem\u003e\u0026nbsp;IAA5\u003c/em\u003e and \u003cem\u003eIAA19\u003c/em\u003e are associated with osmotic stress responses, and mutations in \u003cem\u003eIAA5\u003c/em\u003e and \u003cem\u003eIAA19\u0026nbsp;\u003c/em\u003ereduced tolerance to PEG treatment in \u003cem\u003eArabidopsis\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e[32].\u0026nbsp;The expression of multiple cotton Aux/IAA proteins was upregulated under\u0026nbsp;\u0026gamma;-PGA\u0026nbsp;treatment, suggesting that\u0026nbsp;\u0026gamma;-PGA\u0026nbsp;promotes the transcription of Aux/IAA genes. Small auxin-up RNA genes (SAURs) are a crucial class of auxin-responsive genes. Overexpression of the poplar SAUR gene, \u003cem\u003ePtSAUR8\u003c/em\u003e, in \u003cem\u003eArabidopsis\u003c/em\u003e reduced plant sensitivity to drought stress[31]. Compared to\u0026nbsp;the control group, the transcription levels of several \u003cem\u003eSAURs\u003c/em\u003e genes in the\u0026nbsp;\u0026gamma;-PGA-treated cotton plants were increased, indicating that the expression of these genes is regulated by\u0026nbsp;\u0026gamma;-PGA. The Gretchen Hagen3 (GH3) genes are a primary family of early auxin-responsive genes, which facilitate auxin homeostasis by conjugating excess auxin with amino acids[33]. The \u003cem\u003eMdGH3\u003c/em\u003e RNAi apple plants exhibit greater drought stress tolerance compared to wild-type plants[34]. Transcriptome data indicate that the expression of a cotton GH3 gene is suppressed by\u0026nbsp;\u0026gamma;-PGA\u0026nbsp;treatment. Our study shows that the accumulation of transcripts related to multiple auxins signaling pathway genes is altered under\u0026nbsp;\u0026gamma;-PGA\u0026nbsp;treatment, suggesting that\u0026nbsp;\u0026gamma;-PGA\u0026nbsp;can enhance cotton\u0026apos;s tolerance to drought stress by modulating auxin signaling pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJA plays a crucial role in regulating plant responses and defenses against biotic and abiotic stresses and has garnered extensive research attention[35]. The expression of the JA receptor \u003cem\u003eCOI1\u003c/em\u003e gene is downregulated under \u0026gamma;-PGA treatment. The transcription levels of several JAZ family members, which are inhibitors of the jasmonic acid signaling pathway, are enhanced by \u0026gamma;-PGA treatment. Some studies have found that the core transcription factor MYC2 in the JA signaling pathway plays an important role in plant responses to drought. \u003cem\u003ePpnMYC2\u003c/em\u003e positively regulates poplar\u0026apos;s drought response by modulating the expression of genes related to stomatal density[36]. Compared to the control group, the transcription levels of several \u003cem\u003eMYC2\u003c/em\u003e genes are elevated in plants treated with \u0026gamma;-PGA. Our data suggest that \u0026gamma;-PGA may participate in cotton\u0026apos;s response to drought stress by modulating jasmonic acid signaling transduction.\u003c/p\u003e\n\u003cp\u003eEthylene regulates various biological processes in plants. When a plant perceives stress signals, the ethylene content within the plant changes. The ethylene signal is transmitted through its corresponding signaling pathway, regulating downstream genes and causing physiological changes in plant cells to adapt to the changing environment[37]. MPK3/6 can phosphorylate and stabilize EIN3, a key transcription factor in the ethylene signaling pathway[38]. \u0026gamma;-PGA treatment promotes the expression of several MPK3/6 genes, and the transcription of multiple ERF genes is also upregulated. This indicates that \u0026gamma;-PGA can enhance the transduction of the ethylene signaling pathway, thereby improving cotton\u0026apos;s drought stress tolerance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, we can conclude from the above results that \u0026gamma;-PGA exerts a positive regulatory effect in helping plants cope with drought stress by modulating the expression of transcription factors and the transduction of plant hormone signaling.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003ePlant Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental material utilized in this study was the Upland cotton variety Xinluzao 42, provided by the Institute of Industrial Crops at the Xinjiang Academy of Agricultural Sciences. Cotton seeds were placed on moist filter paper for three days to facilitate germination. Following germination, when the seedlings reached approximately 8 mm in length, they were transplanted into pots containing a soil mixture of nutrient soil and vermiculite in a 3:1 volume ratio. The soil in each pot was pre-weighed to ensure uniformity in soil content across all pots, with two seedlings planted in each pot. After the cotyledons fully expanded, weaker seedlings were removed to minimize experimental variability. Once the cotton seedlings developed their first true leaf, different experimental treatments were applied. The control group (CK) and the treatment group were placed in separate trays, with each tray containing 15 pots of experimental material. The control group was irrigated with distilled water according to standard procedures, while the treatment group received irrigation with a \u0026gamma;-PGA solution at a concentration of 50 mg/L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrought Treatment Experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEighteen pots containing three-week-old cotton plants, exhibiting similar growth statuses from both the control group and the \u0026gamma;-PGA treatment group, were selected for drought treatment. Watering was subsequently halted to induce natural drought conditions. Phenotypic differences between the control and \u0026gamma;-PGA treatment groups were photographed and recorded once they became apparent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Physiological Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbsolute Soil Water Content (ASWC) is utilized to evaluate the severity of drought stress experienced by cotton plants. The formula for calculating ASWC (%) is as follows: ((weight of wet mixed nutrient soil + weight of pot) - (weight of dried mixed nutrient soil + weight of pot)) / weight of dried mixed nutrient soil\u0026nbsp;\u0026times;\u0026nbsp;100%. Moderate drought is defined as an ASWC of approximately 10%. When the cotton plants in the control group reach moderate drought conditions, leaf samples from both the control and treatment\u0026nbsp;groups are collected for analysis of physiological indicators. The physiological parameters measured include MDA and Pro content, as well as the activities of POD and SOD enzymes. For all physiological measurements, 0.1g of ground tissue is utilized, and each measurement is conducted in triplicate. Detection assays are performed using test kits provided by Suzhou Grace Biotechnology Co., Ltd., and experimental procedures are carried out in accordance with the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Sequencing and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen the cotton plants in the control group experienced moderate drought conditions, RNA was extracted from the leaves of both the control and \u0026gamma;-PGA treatment groups for transcriptome sequencing. The extraction procedure adhered to the TRIzol Reagent guidelines provided by Invitrogen. mRNA was enriched using Oligo dT, followed by fragmentation, reverse transcription to synthesize cDNA, and ligation of adapters. The Illumina sequencing platform was employed to construct an Illumina PE library for 2\u0026times;150 bp sequencing, with three biological replicates for each sample. The raw image data obtained from Illumina sequencing were converted into sequence data through base calling, resulting in the generation of original sequencing data files. After filtering out adapters and low-quality sequencing data, clean reads were acquired. Utilizing Hisat2 software, the clean reads were aligned to the cotton reference genome (https://yanglab.hzau.edu.cn/static/cott/download/genome/TM-1_WHU.genome.fa.gz), after which StringTie software was employed to assemble the reads mapped to the reference genome. The assembled transcripts were subsequently used for further analysis. The expression levels of the assembled transcripts were assessed using StringTie software. Initially, the number of clean reads mapped to each transcript (Count) was calculated, followed by the estimation of the FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) value for each gene based on gene length and Count, reflecting the gene\u0026apos;s expression level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) reduces a set of variables to a smaller number of linearly uncorrelated variables, known as principal components. In transcriptome data analysis, gene expression levels serve as variables for dimensionality reduction, resulting in a lower-dimensional dataset that facilitates the assessment of sample dispersion and reveals the underlying patterns of sample distribution. In this study, PCA was conducted on the transcriptome data from both the control and treatment groups using the R package FactoMineR. The results of the analysis were visualized with the R package ggplot2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDEGs between the control group and the \u0026gamma;-PGA treatment group was conducted using DESeq2 software. The analysis employed a filter with criteria of Fold Change\u0026ge;1.5 and false discovery rate (FDR) \u0026lt; 0.05 to identify DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO Classification and Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protein sequences of Gossypium hirsutum were downloaded from the CottonMD database (https://yanglab.hzau.edu.cn/static/cott/download/pep/TM-1_WHU.pep.gz) and submitted to the eggNOG mapper database (http://eggnog-mapper.embl.de/) for GO term annotation. Enrichment analysis of the GO terms associated with the DEGs was performed using the R package topGO, with GO terms exhibiting a p-value less than 0.05 considered significantly enriched. The results of the enrichment analysis were visualized using the barplot package in R [39].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG Pathway Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe KEGG annotation data for cotton genes was obtained from the eggNOG mapper database (http://eggnog-mapper.embl.de/). Subsequently, DEGs were mapped to their corresponding KEGG pathways using the R package clusterProfiler, with pathways exhibiting a p-value of less than 0.05 considered significantly enriched [40]. Finally, the R package ggplot2 was employed to create bubble charts that visualize the significantly enriched KEGG pathways.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we investigated the effects of the biostimulant γ-PGA on the drought resistance of cotton and its underlying molecular mechanisms. Our results indicate that the exogenous application of polyglutamic acid significantly enhances cotton\u0026rsquo;s tolerance to drought stress by promoting proline accumulation, reducing MDA levels, and increasing the activities of SOD and POD enzymes. Transcriptome analysis revealed that, under drought stress conditions, polyglutamic acid treatment led to significant changes in the expression of 2,247 genes, including 233 transcription factors across 25 categories among the DEGs. The majority of the DEGs were enriched in plant hormone and MAPK signaling pathways. These findings suggest that γ-PGA enhances cotton\u0026rsquo;s drought resistance by modulating the expression of multiple transcription factors and genes associated with plant hormones and MAPK signaling pathways. Our research elucidates the impact of γ-PGA on cotton\u0026rsquo;s drought tolerance and its molecular mechanisms, providing valuable data and a theoretical foundation for the future application of γ-PGA in improving cotton\u0026rsquo;s drought resistance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals who contributed to the completion of this research. This study was made possible through the generous support of the Xinjiang Uygur Autonomous Region (grant number: 2022D01B39) and the\u0026nbsp;\u0026ldquo;Tianchi Talents\u0026rdquo;\u0026nbsp;introduction plan of the Xinjiang Uygur Autonomous Region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.B.Z. and Y.J.G. proposed this idea and designed the research project; Z.Y.W., W.W.F., X.Z., Y.H.L.S., A.X.H., J.F.Y., and Y.F.T. conducted the experiments; J.B.Z. and Y.J.G. analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCotton is a common crop extensively cultivated in the world.\u0026nbsp;\u003c/em\u003eThis study does not contain any research requiring ethical consent or approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;This research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (grant number: 2022D01B39) and the \u0026ldquo;Tianchi Talents\u0026rdquo; introduction plan of Xinjiang Uygur Autonomous Region.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;Transcriptome data involved in this study can be obtained from the corresponding author ([email protected]) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that none of the authors have any competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFahad S, Bajwa A.A, Nazir U, Anjum SA, Farooq A, Zohaib A,Sadia S, Nasim W, Adkins S, Saud S, et al. 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Nat Protoc. \u003cstrong\u003e2024\u003c/strong\u003e;19(11):3292-3320. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"γ-PGA, cotton, drought tolerance, transcriptome, transcription factors, plant hormones, MAPK signaling","lastPublishedDoi":"10.21203/rs.3.rs-5413622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5413622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDrought severely impacts the growth of cotton, and the application of plant biostimulants offers an effective approach to enhancing crop drought tolerance. γ-Poly-glutamic acid (γ-PGA) is a novel and environmentally friendly biostimulant, but its functions and mechanisms in responding to drought stress in cotton are still unclear.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study aims to elucidate the role of the plant biostimulant γ-PGA in the response of cotton to drought stress. Cotton seedlings treated with exogenous γ-PGA and a control group were subjected to drought conditions, and phenotypic observations along with measurements of relevant physiological indicators were conducted. The results showed that the activities of superoxide dismutase (SOD) and peroxidase (POD), as well as the proline (PRO) content in the leaves of γ-PGA-treated cotton, were higher than those in the control group, while malondialdehyde (MDA) content was lower. This indicates that exogenous application of γ-PGA can effectively enhance cotton's tolerance to drought stress. Subsequently, transcriptome sequencing was performed on the leaves of cotton plants under drought stress in both the γ-PGA-treated and control groups. The results revealed that γ-PGA treatment led to the upregulation of 1,658 genes and the downregulation of 589 genes. Among these differentially expressed genes (DEGs), 233 were transcription factors, suggesting that γ-PGA participates in the cotton drought stress response by regulating the expression of numerous transcription factors. Most of the DEGs were associated with the plant hormone signal transduction pathways and the MAPK signaling pathway, indicating that γ-PGA enhances cotton's drought tolerance primarily by regulating these two signaling pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study elucidates the effects of exogenous γ-PGA on drought resistance in cotton, as well as the molecular mechanisms underlying this resistance. The findings provide a theoretical foundation for the future application of γ-PGA to enhance drought tolerance in cotton.\u003c/p\u003e","manuscriptTitle":"Unveiling the Molecular Mechanisms of γ-polyglutamic acid-Mediated Drought Tolerance in Cotton through Transcriptomic and Physiological Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 14:23:02","doi":"10.21203/rs.3.rs-5413622/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-22T07:18:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-19T15:16:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196703346009975015404956772678320868561","date":"2025-01-12T04:20:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-06T02:42:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87433542417618548987724166865378940817","date":"2024-12-05T09:41:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334369068975971429181273579495225303748","date":"2024-11-26T08:29:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-14T15:43:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T07:57:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T07:54:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-11-08T04:45:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3fb19b2-e70e-47b5-9980-2cffc7514203","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T16:05:23+00:00","versionOfRecord":{"articleIdentity":"rs-5413622","link":"https://doi.org/10.1186/s12870-025-06406-z","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-03-27 15:57:26","publishedOnDateReadable":"March 27th, 2025"},"versionCreatedAt":"2024-12-10 14:23:02","video":"","vorDoi":"10.1186/s12870-025-06406-z","vorDoiUrl":"https://doi.org/10.1186/s12870-025-06406-z","workflowStages":[]},"version":"v1","identity":"rs-5413622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5413622","identity":"rs-5413622","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}