Integrative WGCNA Analysis Uncovers the Molecular Framework of Melatonin-Mediated Drought Stress Mitigation in Potato

Integrative WGCNA Analysis Uncovers the Molecular Framework of Melatonin-Mediated Drought Stress Mitigation in Potato | 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 Integrative WGCNA Analysis Uncovers the Molecular Framework of Melatonin-Mediated Drought Stress Mitigation in Potato Songtao Liu, Yan Wang, Xinwei Wang, Xiaonuo Zhang, Yanmin Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7382006/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in BMC Plant Biology → Version 1 posted 14 You are reading this latest preprint version Abstract Drought is one of the major abiotic stress factors limiting the growth, development, and yield of potato ( Solanum tuberosum L.). Melatonin, a novel plant hormone, has recently shown significant potential in enhancing plant stress resistance. However, its regulatory mechanisms in response to drought stress in potato remain unclear. In this study, potato seedlings were treated with different concentrations of exogenous melatonin (50, 100, and 150 µmol/L) under controlled drought conditions to systematically evaluate their physiological and molecular responses. The results demonstrated that appropriate melatonin application—especially at 100 µmol/L—effectively alleviated drought-induced growth inhibition, oxidative stress, and photosynthetic impairment. This was evidenced by increased plant height, enhanced photosynthetic efficiency, reduced reactive oxygen species (ROS) accumulation, decreased cell death and lipid peroxidation, as well as elevated antioxidant enzyme activities (SOD, CAT, POD) and levels of osmoprotectants (proline and soluble sugars). Transcriptome analysis revealed that melatonin modulates numerous drought-responsive differentially expressed genes (DEGs), including multiple transcription factor families (e.g., MYB, NAC, ERF), and pathways related to photosynthesis, antioxidative metabolism, hormone signaling, and carbon metabolism. Furthermore, weighted gene co-expression network analysis (WGCNA) and Mfuzz clustering identified key gene modules and central hub genes strongly associated with photosynthetic performance and antioxidant indicators. This study provides a theoretical foundation for applying melatonin in potato drought stress mitigation and lays a molecular basis for developing hormone-based drought-resistant agricultural strategies. Potato Melatonin Drought stress Antioxidant system Photosynthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Water is essential for plant growth, but scarcity can hinder their development. Plants have evolved adaptive mechanisms to cope with drought, which disrupts their physiology and biochemistry, affecting growth and yield [ 1 ]. Drought disrupts plant growth and yield by affecting physiology and biochemistry, leading to reduced water uptake, stomatal closure, and hormonal changes[ 2 , 3 ]. It decreases photosynthesis by lowering stomatal conductance and altering leaf water use efficiency, impairing Photosystem II's efficiency [ 4 , 5 ]. Drought also increases reactive oxygen species, boosting antioxidant enzymes like superoxide dismutase, peroxidase, and catalase to mitigate oxidative stress [ 6 , 7 ]. Furthermore, drought stress also leads to an increase in the content of proline and soluble sugars in plants. As important osmotic regulators, the increase in proline and sugar content facilitates the maintenance cellular osmotic balance and protects cellular structures from damage [ 8 , 9 ]. The phytohormone abscisic acid (ABA) plays a pivotal role in mediating drought response and tolerance by regulating stress-responsive genes. Central to this mechanism are the ABA-responsive element (ABRE) binding proteins/factors (AREB/ABFs), which transcriptionally activate downstream targets, thereby constituting the AREB/ABF regulon. Simultaneously, ABA-independent pathways engage dehydration-responsive element binding protein (DREB) regulons and NAC (NAM, ATAF, and CUC) regulons to facilitate drought adaptation. Furthermore, transcription factors such as MYB/MYC, WRKY, and nuclear factor-Y (NF-Y) enhance drought resilience through complementary regulatory networks [ 10 ]. The potato ( Solanum tuberosum L.) is among the most widely consumed plants worldwide, ranking third in terms of crop consumption, with an annual global production of 370 million tons [ 11 , 12 ]. It is regarded as a nutritious source of carbohydrates, dietary fiber, protein, vitamins, antioxidants, and minerals [ 13 ]. The frequency of drought occurrences is increasing as global temperatures rise [ 14 ]. Contemporary potato varieties are often regarded as vulnerable to drought, primarily due to their shallow root structures [ 15 ]. Nonetheless, this crop exhibits significant sensitivity to drought conditions, posing a substantial threat to its growth, development, and yield—especially in regions impacted by climate change that leads to hotter and drier environments [ 16 ]. Melatonin (N-acetyl-5-methoxytryptamine, MT), a small indole molecule, is extensively distributed across various plant species and functions as a growth regulator with notable physiological roles [ 17 ]. The exogenous application of melatonin has been shown to exert significant regulatory effects on plant photosynthesis and stress resistance [ 18 – 23 ]. Firstly, melatonin enhances photosynthetic efficiency, particularly under stress conditions. For example, in wheat, exogenous melatonin application has been observed to increase thylakoid protein levels through reversible phosphorylation, thereby enhancing the photochemical efficiency of photosystem II (PSII) and augmenting energy dissipation capacity [ 24 ]. In tea plants, melatonin has been reported to improve photosynthetic capacity in a dose-dependent manner, leading to increased chlorophyll content and photosynthetic rates [ 25 ]. Secondly, melatonin plays a critical role in modulating the antioxidant system in plants. Empirical studies suggest that the exogenous application of melatonin can decrease the accumulation of reactive oxygen species (ROS) by enhancing the activity of antioxidant enzymes, thereby mitigating oxidative damage. For instance, in mung beans, melatonin has been documented to enhance the activities of catalase (CAT), glutathione reductase (GR), and ascorbate peroxidase (APX), thereby augmenting the plants' antioxidant capacity under conditions of salt stress [ 26 ]. Similarly, in sweet corn, melatonin has been shown to mitigate the detrimental effects of herbicide toxicity by increasing the activity of antioxidant enzymes and upregulating the expression of associated genes [ 27 ]. In citrus, the application of exogenous melatonin mitigates water loss and sustains redox homeostasis, highlighting the pivotal function of the PtABF4-PtbHLH28-PtCOMT5 molecular module in modulating melatonin accumulation and root development under conditions of drought stress [ 28 ]. Moreover, melatonin plays a crucial role in bolstering plant stress resistance by modulating the metabolism of proline and soluble sugars. In apple plants, the application of exogenous melatonin has been demonstrated to reduce the uptake and accumulation of cadmium, thereby alleviating cadmium toxicity through an increase in proline and soluble sugar content [ 29 ]. In grapevines, melatonin facilitates seedling growth and adaptability to environmental variations by enhancing the activity of enzymes involved in sucrose metabolism [ 30 ]. In summary, exogenous melatonin impacts plant photosynthesis, the antioxidant system, and the metabolism of proline and soluble sugars through multiple pathways, thereby enhancing both plant stress resistance and growth potential. These findings offer a theoretical basis for the application of melatonin in agricultural practices. Although melatonin has shown promise in improving plant drought tolerance, the precise regulatory mechanisms by which it operates in Solanum tuberosum L. are not well understood. This research examines the effects of exogenous melatonin application on enhancing drought adaptability in potato seedlings. It focuses on the modulation of osmotic homeostasis, chlorophyll fluorescence dynamics, antioxidant enzyme activities, and transcriptomic approaches with the aim of elucidating the molecular basis underlying the physiological adaptations induced by melatonin. 2. Materials and Methods 2.1 Plant Materials and Drought Treatment The experiment was conducted in September 2023 within the controlled environment of the artificial climate chamber located at Hebei North University. A pot cultivation method was employed, utilizing pots with dimensions of 14.7 cm in diameter and 12.5 cm in height. The growth medium consisted of a 1:1 volumetric ratio of vermiculite to nutrient soil. Uniformly sized, non-toxic pre-basic potato seeds (Holland No. 15) were selected for sowing, with each pot containing one seed, oriented with the bud facing upward. Upon uniform seedling emergence, standard management practices were implemented, maintaining the field capacity at approximately 75%.A single-factor experimental design was adopted, featuring five distinct treatments: CK (control, normal conditions), D (drought with the application of purified water), M1 (drought with the application of 50 µmol/L melatonin, MT), M2(drought with the application of 100 µmol/L MT), and M3 (drought with the application of 150 µmol/L MT). Each treatment consisted of three biological replicates, with each replicate containing five seedlings. The drought treatment commenced when the potato plants had developed seven leaves, approximately 50 days post-planting, at which point the field capacity was approximately 30%. Various concentrations of MT were administered through foliar spraying. This spraying procedure was conducted four times, with a two-day interval between each application. Spraying was performed at 16:00, ensuring uniformity and consistency in the spraying volume, with the criterion being leaf wetness to the point of slight dripping. Leaf samples were collected two days following the final melatonin treatment for subsequent investigations, including biochemical index determination and physiological analysis. 2.2 Phenotypic and Physiological Characterizations 2.2.1Antioxidant Enzyme Activity and MDA Content in Leaves Upper leaves, positioned similarly, were harvested from potato plants belonging to the control (CK) group and various treatment groups. These leaves were subsequently utilized for the assessment of physiological parameters, including malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, peroxidase (POD) activity, and catalase (CAT) activity. The measurements were conducted using appropriate assay kits provided by Solarbio (Beijing). 2.2.2. Determination of Proline and Soluble Sugars Content in Leaves To quantify the proline content, 0.3 g of fresh tissue sample was homogenized with 3 mL of 3% sulfosalicylic acid solution. The homogenate was subsequently heated in a water bath at 100℃ for 10 minutes. Following cooling, the homogenate was filtered, and the resulting supernatant was combined with glacial acetic acid and 2.5% acidic ninhydrin reagent. This mixture was then subjected to heating in a boiling water bath for 30 minutes. Upon cooling, 4 mL of toluene was added, and the solution was vigorously shaken to extract the red chromophore. The mixture was then centrifuged at 3000 rpm for 5 minutes, and the upper phase was utilized for proline content analysis by measuring absorbance at 520 nm. Additionally, the leaves were employed for the determination of Soluble Sugars (SS) content, which was assessed using specific test kits (Solarbio, Beijing). 2.2.3 Assays for Cell Death in Leaves To assess cell death, dead cells were stained using the trypan blue method as described by Lin et al [ 31 ]. Detached leaves were infiltrated with a lactophenol-trypan blue solution at100°C for 1 min, followed by immersion in boiling water for 5 minutes, and subsequently stained for 12 hours. To eliminate background staining, the samples were immersed in a 2.5 g/mL chloral hydrate solution for 3 days. Finally, the tissues were equilibrated in 70% (v/v) glycerol for photographing. 2.2.4 DAB and NBT Staining of Leaves The detection of reactive oxygen species (ROS) through histochemical methods was conducted in accordance with the protocol established by Chen et al [ 32 ]. In summary, hydrogen peroxide (H₂O₂) and superoxide anion radicals (O₂⁻) were visualized using 3,3'-diaminobenzidine (DAB) at a concentration of 1 mg/mL and nitro blue tetrazolium (NBT) at a concentration of 1 mg/mL, respectively. Following staining, the tissues were subjected to destaining in 85% (v/v) boiling ethanol for a duration of 2 hours before proceeding with image documentation. 2.2.5 Determination of Chlorophyll Content and Chlorophyll Fluorescence Parameters The relative chlorophyll content, quantified as SPAD values, was measured using a SPAD-502 chlorophyll meter (Konica Minolta, Japan). Concurrently, photosynthetic parameters were evaluated on potato leaves employing a portable fluorometer (PAM 2500 Portable Chlorophyll Fluorometer, WALZ). For these assessments, the fourth leaf from the apical bud on lateral branches was selected, dark-adapted for 30 minutes, and subsequently analyzed to determine the maximum quantum yield of PSII photochemistry (Fv/Fm) and effective quantum yield of PSII photochemistry (Φ PSII ). A minimum of five biological replicates were analyzed per treatment. 2.3. Total RNA Extraction, cDNA Library Construction, and Transcriptome Analysis Total RNA was extracted from the tissue using TRIzol® Reagent according the manufacturer’s instructions. Then RNA quality was determined by 5300 Bioanalyser (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). Only high-quality RNA sample was used to construct sequencing library. cDNA libraries were then constructed following the manufacturer’s protocol using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) and subsequently sequenced on the NovaSeq Reagent Kit platform using paired-end sequencing technology. 2.4.Quality control and Read Mapping The raw paired end reads were trimmed and quality controlled by fastp with default parameters [ 33 ]. Then clean reads were separately aligned to reference genome (Stuberosum_686_v6.1) with orientation mode using HISAT2 software [ 34 ]. The mapped reads of each sample were assembled by StringTie in a reference-based approach. Quality control metrics, including the Q30 base percentage (an indicator of the overall reproducibility and quality of the assay) and GC content were assessed. Only reads with a perfect match or a single mismatch were retained for further analysis and genome annotation. 2.5. Differential Expression Genes (DEGs) Analysis To identify DEGs between two different samples, the expression level of each transcript was calculated according to the transcripts per million reads (TPM) method. Gene differential expression analysis was conducted using DESeq R package (v1.10.1) [ 35 ], whereby the ratios of TPM values between the control and treatments were analyzed, and the resulting P-values corrected for multiplicity using the Benjamini and Hochberg method [ 36 ]. Genes with a fold change (FC) ≥ 2 and FDR < 0.05 were classified as differentially expressed. 2.6. Functional Enrichment Analysis, and Identification of Key Drought-Responsive DEGs To identify key genes involved in melatonin-mediated drought stress responses in potato, four different treatments were compared in a pairwise manner, resulting in six comparison groups. DEGs that met the selection criteria (|FC|≥2 and FDR < 0.05) across these comparisons were subsequently analyzed using a Venn diagram, enabling the identification of potential key candidate drought-responsive DEGs. Then, DEGs exhibiting similar expression trends were grouped via Mfuzz clustering, based on log-transformed fold-change values, using the R statistical environment. Subsequently, co-expression network construction was carried out using the WGCNA package (version 1.47) in R. In addition, to further explore the biological significance of DEGs, gene ontology (GO) enrichment analysis was conducted using agriGO web-based program ( http://systemsbiology.cau.edu.cn/agriGOv2/# ). KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis was performed by KOBAS. 2.7. Quantitative Real Time-PCR (qRT-PCR) Analysis To validate the RNA-seq data, qRT-PCR was conducted on 9 genes randomly selected for expression analysis. Gene-specific primers were designed using Primer Premier 5 software (Premier Biosoft International, Palo Alto, CA, USA). Total RNA of high integrity was isolated from Chinese cabbage samples and subsequently reverse-transcribed into cDNA using the HiFiscript cDNA Synthesis Kit (CWBIO, Beijing, China) following the manufacturer’s instructions. qRT-PCR program was run in a Bio-Rad iQ5 Thermo Cycler (Bio-Rad, Hercules, CA, USA) using 2 × Fast Super Evagreen qPCR mastermix (US Everbright Inc., Daly City, CA, USA), where fluorescence intensity directly corresponded to the DNA quantity present. A stable reference gene ACTIN was used as an internal control for data normalization. The relative mRNA abundance for each gene was determined by the 2 − ΔΔCT method. 2.8. Statistical Analysis of Physiological Data Statistical analyses were conducted using SPSS software (version 22.0; IBM Corp., Armonk, NY, USA), and results are expressed as mean ± standard error (SE). For physiological measurements, two-way analysis of variance (ANOVA), followed by least significant difference (LSD) post hoc tests, was applied to assess differences among treatments and genotypes. In contrast, the qRT-PCR expression data were analyzed using one-way ANOVA, with Duncan’s multiple range test employed for pairwise comparisons. A significance threshold of p < 0.05 was adopted throughout. 3. Results 3.1 Phenotypic and Physiological Responses of Potato to Drought Stress at Different Treatments Drought stress significantly inhibited the early growth and development of potato seedlings, as evidenced by reduced plant height and stem diameter, increased membrane lipid peroxidation (indicated by elevated MDA content), enhanced accumulation of reactive oxygen species (ROS), and a rise in dead cell numbers in leaves (Fig. 1 ). Additionally, drought induced increases in proline and soluble sugar contents, elevated antioxidant enzyme activities (SOD, CAT, POD, Pro), and a decline in chlorophyll fluorescence parameters (SPAD, Fv/Fm, Φ PⅡ ). Exogenous melatonin application showed a dose-dependent effect on enhancing drought tolerance in potato. Appropriate concentrations of melatonin—particularly 50 µmol/L and 100 µmol/L—effectively alleviated drought-induced damage by promoting plant height, enhancing photosynthetic performance, activating antioxidant defense systems, and reducing membrane peroxidation and ROS accumulation, thereby mitigating cellular damage and improving stress resistance. However, at 150 µmol/L, some physiological indicators (e.g., SOD and POD activity, ROS levels) showed adverse changes, suggesting a possible stress or toxic effect at excessive melatonin concentrations. Staining results further confirmed these findings. Drought stress significantly increased cell death and ROS accumulation in potato leaves. Exogenous melatonin at 50 µmol/L and 100 µmol/L markedly reduced the staining areas of Trypan blue, DAB, and NBT, indicating reduced oxidative damage and cell death, with 100 µmol/L showing the most effective mitigation. In contrast, 150 µmol/L melatonin led to increased staining, suggesting potential toxicity at higher concentrations. Therefore, moderate melatonin application can alleviate drought-induced oxidative stress, while excessive application may have detrimental effects. 3.2. Summary and Quality Assessment of RNA-Seq Results To clarify different concentrations of exogenous melatonin on potato seedling growth under drought stress, RNA-seq analysis was performed on 12 leaf samples of cultivar Helan15. The principal component analysis (PCA) results showed that there was low consistence amongst the three replications of the D, probably due to a technical failure (Figure S1 ). Thus, sequencing results were analyzed by retrieving sample D1. Pearson correlation coefficients between samples were also calculated and displayed in form of a heatmap. The results showed each R 2 (between the two samples) to be higher than 90% (Figure S2). Overall, these results showed that our experiment is reproducible and reliable, meeting the demands for further analyses. Subsequently, eleven samples were used for RNA-seq transcriptome analysis. The raw sequencing data were deposited into the NCBI Sequence Read Archive (SRA, Accession PRJNA1301854). After filtering, a total of 54.42 million clean reads were obtained from these 12 samples, among which 83.35–85.85% clean reads were mapped onto unique positions on the reference genome (Stuberosum_686_v6.1). Meanwhile, the Q30 base percentage and GC percentage exceeded 93.17% and 42.02%, respectively (Table 1 ). Table 1 Summary details of the RNA-seq results for the eleven leaf samples Sample Raw reads Clean reads Multiple mapped Uniquely mapped Q30(%) GC content(%) CK1_1 44548564 44344302 1181735(2.66%) 37794805(85.23%) 93.51 42.41 CK1_2 45896472 45721372 1192451(2.61%) 38959735(85.21%) 93.90 42.29 CK1_3 43660996 43482050 1200649(2.76%) 37284979(85.75%) 93.75 42.59 D_2 53956554 53721234 1458040(2.71%) 46121625(85.85%) 93.43 42.63 D_3 42303314 42095688 1231256(2.92%) 35723610(84.86%) 93.50 42.18 M1_1 41592044 41431674 1127568(2.72%) 35306040(85.22%) 93.95 42.19 M1_2 43061012 42856784 1146936(2.68%) 36568176(85.33%) 93.47 42.33 M1_3 45054296 44813712 1236598(2.76%) 37774917(84.29%) 93.20 42.02 M2_1 44325590 44123800 1190791(2.7%) 37414996(84.8%) 93.60 42.30 M2_2 43912252 43704100 1238057(2.83%) 37285385(85.31%) 93.48 42.54 M2_3 50323642 50100184 1386919(2.77%) 42482747(84.8%) 93.80 42.33 3.3. Gene Differential Expression Analysis To investigate the effects of different concentrations of exogenous melatonin on the growth of potato seedlings under drought stress, four treatments were established: CK (normal watering), D (drought + spraying purified water), M1 (drought + spraying 50 µmol/L melatonin), and M2 (drought + spraying 100 µmol/L melatonin). Six pairwise comparison groups were generated for transcriptome analysis, which revealed significant differences in gene expression among the different treatments. Compared with the normal watering treatment (CK), melatonin application under drought stress significantly altered gene expression patterns in potato seedlings (Fig. 2 A). The comparison group M2_vs_CK exhibited the highest number of DEGs, with a total of 1,098 DEGs, including 298 up-regulated and 800 down-regulated DEGs, indicating that high-concentration melatonin exerted a strong regulatory effect on gene expression. The M2_vs_D comparison group (i.e., high-concentration melatonin vs. drought alone) also yielded a large number of DEGs (1,042 in total), further suggesting that 100 µmol/L melatonin might play a significant role in alleviating drought stress. In contrast, only 179 DEGs were detected between the D and CK groups, with 111 up-regulated and 68 down-regulated, indicating that drought alone had a relatively limited effect on transcriptional changes. The M1_vs_CK comparison group revealed 417 DEGs (142 up-regulated, 275 down-regulated), while the M2_vs_M1 comparison identified 266 DEGs (161 up-regulated, 105 down-regulated), suggesting that high and moderate concentrations of melatonin exerted distinct regulatory effects. Additionally, 135 DEGs (42 up-regulated, 93 down-regulated) were identified in the M1_vs_D group, indicating that even a lower concentration of melatonin (50 µmol/L) could influence transcriptional responses under drought stress to some extent. In summary, melatonin application—particularly at 100 µmol/L—significantly enhanced the gene expression response of potato seedlings under drought stress, likely by regulating a large number of up- and down-regulated DEGs to improve drought tolerance. A total of 2,340 differentially expressed DEGs (DEGs) were identified across the six comparison groups. Venn diagram analysis revealed that the majority of DEGs exhibited group-specific expression patterns (Fig. 2 B). For example, 710, 666, 123, 108, 63, and 31 DEGs were uniquely expressed in the M2_vs_D, M2_vs_CK, M1_vs_CK, M2_vs_M1, D_vs_CK, and M1_vs_D groups, respectively. In addition, 153 DEGs were shared between the M2_vs_CK and M1_vs_CK groups, suggesting these DEGs may be closely related to the transcriptional responses induced by melatonin treatment under drought conditions. Furthermore, 28 DEGs were commonly expressed in the M1_vs_D and M2_vs_D comparisons, implying that these DEGs may participate in shared regulatory pathways triggered by different melatonin concentrations under drought stress, and thus have potential functional significance in drought mitigation. Notably, 149 DEGs were shared between M2_vs_CK and M2_vs_D, indicating that these DEGs likely represent core response factors under 100 µmol/L melatonin treatment, distinguishing it from both normal water conditions (CK) and drought stress alone (D). This highlights the specific transcriptional regulation induced by high-concentration melatonin. 3.4. DEGs Encoding Transcription Factors To further explore the transcriptional regulatory mechanisms underlying the observed gene expression changes, transcription factors (TFs) were identified from the DEGs. In total, 64 TF families were represented among the DEGs, with a wide range of gene counts across families (Table 2 ). Among them, the MYB and MYB_related families were the most abundant, comprising 178 and 147 DEGs, respectively. Other TF families with a high number of members included bHLH (139 DEGs), ERF (145 DEGs), B3 (122 DEGs), HB-other (105 DEGs), NAC (113 DEGs), and M_type (146 DEGs). Additionally, WRKY (90 DEGs), bZIP (76 DEGs), GRAS (56 DEGs), and LBD (AS2/LOB) (51 DEGs) were also prominently represented. Several TF families, while smaller in gene number, may still play important regulatory roles. These include Whirly (3 DEGs), RAV (2 DEGs), S1Fa-like (3 DEGs), and NF-X1 (3 DEGs). The diverse representation and differential expression of transcription factors suggest a complex and multi-layered transcriptional regulation network, potentially involved in stress response, hormone signaling, and developmental processes in the studied samples. Table 2 Number of transcription factors identified in six comparisons TF family Gene number TF family Gene number AP2 27 HD-ZIP 28 ARF 25 HSF 30 B3 122 LBD (AS2/LOB) 51 BBR-BPC 6 bZIP 76 BES1 9 LSD 4 C2H2 26 MIKC 38 C3H 44 MYB 178 CAMTA 10 MYB_related 147 CO-like 15 M_type 146 CPP 4 NAC 113 DBB 20 NF-X1 3 Dof 36 NF-YA 12 E2F/DP 8 Nin-like 29 EIL 10 RAV 2 ERF 145 S1Fa-like 3 FAR1 25 SBP 16 GATA 38 SRS 8 GRAS 56 TALE 5 GRF 12 TCP 36 GeBP 19 WRKY 90 HB-PHD 2 Whirly 3 HB-other 105 YABBY 7 bHLH 139 ZF-HD 21 3.5. Mfuzz Analysis of DEGs Mfuzz clustering analysis was conducted to identify gene clusters exhibiting distinct expression profiles in potato. Subsequently, GO and KEGG enrichment analyses were conducted to explore the biological functions of these special gene clusters. The DEGs identified from the four treatments were grouped into 10 clusters with different expression patterns. Among these, the DEGs of gene clusters 3,4,5,8 showed higher level of participation (Fig. 3 ). Gene cluster 3 was specifically highly expressed under drought stress with 50 µmol/L melatonin treatment (M1). GO enrichment analysis indicated that this cluster was significantly enriched in response to external stimulus, defense response, and response to stimulus, all of which are associated with stress responses (Fig. 4 A). KEGG pathway analysis further showed significant enrichment in starch and sucrose metabolism, steroid biosynthesis, and biosynthesis of various plant secondary metabolites (Fig. 4 B). Gene cluster 4 showed specific high expression under drought treatment (D). GO enrichment analysis revealed significant enrichment in glutathione metabolic process, chitin catabolic process, and amino sugar catabolic process, suggesting its involvement in antioxidant defense (Fig. 4 A). KEGG analysis indicated that this cluster was primarily enriched in phenylalanine metabolism, phenylpropanoid biosynthesis, and glutathione metabolism, all pathways known to be associated with stress defense (Fig. 3 B). Gene cluster 5 was specifically highly expressed under drought stress with 100 µmol/L melatonin treatment (M2). GO enrichment analysis showed strong associations with photosynthetic activity, being significantly enriched in photosynthesis, light harvesting, photosynthesis, and response to abiotic stimulus (Fig. 4 A). Corresponding KEGG pathways included photosynthesis - antenna proteins, photosynthesis, and carotenoid biosynthesis (Fig. 4 B). In contrast, Gene cluster 8 showed reduced expression under both drought and melatonin treatments. GO enrichment analysis revealed significant enrichment in ncRNA metabolic process, rRNA processing, and rRNA metabolic process, all related to ribosome biogenesis (Fig. 4 A). KEGG pathway analysis indicated enrichment in ribosome biogenesis in eukaryotes, protein processing in endoplasmic reticulum, and aminoacyl-tRNA biosynthesis, suggesting suppression of basic cellular processes under stress conditions (Fig. 4 B). 3.6. WGCNA Analysis of Differentially Expressed Genes In this study, a consensus network was constructed using WGCNA, and several modules significantly associated with physiological traits were identified. DEGs in each module were subjected to GO and KEGG enrichment analyses. After filtering, a total of 1,963 DEGs were divided into 8 modules, consisting of between 16 to 636 co-expressed DEGs (Fig. 5 ). The MEgreen module contains 114 DEGs and shows a significant positive correlation with POD and CAT activities ( P < 0.05 ). GO enrichment analysis revealed that this module is significantly enriched in biological processes such as response to salicylic acid, regulation of response to stimulus, and response to symbiont. KEGG pathway analysis indicated significant enrichment in circadian rhythm - plant and taurine and hypotaurine metabolism (Table 3 ). The MEbrown module comprises 471 DEGs, which are significantly negatively correlated with SOD activity and positively correlated with the photosynthetic parameter FV/F₀. GO terms significantly enriched in this module include photosynthesis, light harvesting in photosystem I, photosynthesis, light harvesting, and carbohydrate metabolic process. KEGG pathways mainly enriched include photosynthesis - antenna proteins, starch and sucrose metabolism, and biosynthesis of various plant secondary metabolites. The MEred and MEyellow modules contain 77 and 137 DEGs, respectively, both showing a significant positive correlation with FV/F₀. The MEred module is significantly enriched in GO terms such as malate transmembrane transporter activity, chloroplast inner membrane, and C4-dicarboxylate transmembrane transporter activity, and in KEGG pathways such as plant hormone signal transduction, and MAPK signaling pathway - plant. The MEyellow module is enriched in GO terms including serine-type endopeptidase inhibitor activity, and response to extracellular stimulus, with significant enrichment in the plant hormone signal transduction pathway in KEGG. The MEblue module includes 477 DEGs, GO enrichment analysis shows this module is significantly associated with oxidoreductase activity, glutathione transferase activity, and heme binding. KEGG pathway analysis indicates enrichment in glutathione metabolism, MAPK signaling pathway - plant, and phenylpropanoid biosynthesis. The MEblack module consists of 35 DEGs, which are significantly negatively correlated with POD, CAT, and SOD activities, however, show positively correlated with SD. GO enrichment reveals significant involvement in water transmembrane transporter activity, and water channel activity. The MEturquoise module contains the largest number of DEGs (636), which are significantly negatively correlated with SS and Pro content. GO analysis indicates significant enrichment in rRNA processing, rRNA metabolic process, and ncRNA processing. KEGG enrichment analysis highlights involvement in ribosome biogenesis in eukaryotes, protein processing in endoplasmic reticulum, and spliceosome pathways. Table 3 GO and KEGG Enrichment Analysis of DEGs in Special Modules. Module GO Enrichment Terms KEGG Enrichment Pathways Terms FDR Pathways FDR MEgreen response to salicylic acid 2.79E-02 circadian rhythm - plant 1.87E-03 response to symbiont 2.79E-02 taurine and hypotaurine metabolism 2.00E-02 MEbrown regulation of response to stimulus 4.67E-02 photosynthesis - antenna proteins 5.55E-09 photosynthesis, light harvesting in photosystem I 1.49E-07 starch and sucrose metabolism 2.00E-06 photosynthesis, light harvesting 1.49E-07 biosynthesis of various plant secondary metabolites 7.47E-03 carbohydrate metabolic process 6.82E-07 pentose and glucuronate interconversions 1.30E-02 photosystem I 2.02E-06 steroid biosynthesis 1.60E-02 photosystem II 3.46E-05 MEred malate transmembrane transporter activity 7.06E-03 plant hormone signal transduction 1.13E-02 C4-dicarboxylate transmembrane transporter activity 7.06E-03 MAPK signaling pathway - plant 4.53E-02 chloroplast inner membrane 4.06E-02 plastid inner membrane 4.06E-02 MEyellow serine-type endopeptidase inhibitor activity 2.16E-02 plant hormone signal transduction 3.55E-02 response to extracellular stimulus 4.52E-02 MEblue oxidoreductase activity 7.91E-08 glutathione metabolism 2.38E-03 heme binding 2.29E-04 MAPK signaling pathway - plant 3.80E-02 tetrapyrrole binding 7.93E-04 phenylpropanoid biosynthesis 4.46E-02 glutathione transferase activity 9.38E-04 MEblack water transmembrane transporter activity 4.09E-02 water channel activity 4.09E-02 MEturquoise rRNA processing 9.97E-30 ribosome biogenesis in eukaryotes 7.72E-18 rRNA metabolic process 2.28E-29 protein processing in endoplasmic reticulum 4.76E-06 ncRNA processing 4.26E-26 spliceosome 1.96E-03 ncRNA metabolic process 4.88E-22 ribonucleoprotein complex biogenesis 2.16E-19 3.7. Identification of Hub Genes Associated with Drought Most metabolic processes are complex, due to not only the actions of single genes, but also interactions among combinations of genes. A sizeable portion of the genes in each network module exhibited extremely high connectivity with other genes belonging to other modules, and were designated as hub genes. Owing to their central position within the network clusters, the hub genes were considered to be vital components of the networks. Therefore, we conducted a Network Analyzer-based analysis and found that 10% of the genes in the modules as hub genes and chosen for further study (Table S1 ) According to the gene classification results, 63 TFs from the 196 hub genes were identified, and they belonged to distinct families, such as MYB ( Soltu.DM.06G004440.v6.1 ), MYB-related ( Soltu.DM.02G004510.v6.1 ), bHLH ( Soltu.DM.01G041140.v6.1 , Soltu.DM.08G022630.v6.1 ), ERF ( Soltu.DM.10G005000.v6.1, Soltu.DM.12G025240.v6.1 ), and AP2 ( Soltu.DM.10G023790.v6.1 ), among others. The core genes with high connection were also identified, including peroxygenase, probable protein phosphatase 2C, protein kinase, photosystem-related, among others (Table S1 ). 3.8. Quantitative Real-Time PCR (qRT-PCR) Validation To assess the reliability of the RNA-seq data, 9 DEGs were randomly selected for validation via quantitative real-time PCR (qRT-PCR). Gene-specific primers were designed using Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, CA, USA) (Table S2). The qRT-PCR results closely mirrored the RNA-seq expression profiles, with consistent expression trends observed across all tested genes (Figure S3). A high correlation coefficient (R 2 = 0.94) between the two datasets further confirmed the robustness and accuracy of the transcriptome data. 4. Discussion Drought stress significantly influences the growth, development, and yield of potato, one of the world’s most important food crops. Prolonged water deficit conditions disrupt physiological and biochemical processes, leading to reduced photosynthetic capacity, oxidative damage, and impaired nutrient uptake. These adverse effects ultimately compromise tuber development and quality. Understanding the molecular and physiological mechanisms by which potato responds to drought stress is essential for developing strategies to improve drought tolerance, especially in regions experiencing increased climate variability and water scarcity. In recent years, melatonin has emerged as a promising plant growth regulator with multifunctional roles in stress mitigation, including antioxidant defense, photosynthesis stabilization, and gene regulation. This study aims to systematically evaluate the regulatory effects of different concentrations of melatonin (MT) on the physiological and molecular responses of potato seedlings under drought stress. Four treatments were established: normal watering (CK), drought stress with purified water spray (D), and drought stress with foliar application of 50 µmol/L MT (T1) and 100 µmol/L MT (T2). Based on these treatments, physiological parameter measurements and RNA-seq transcriptome analysis were conducted. Furthermore, Mfuzz clustering and WGCNA were integrated to identify key regulatory pathways and hub genes involved in the melatonin-mediated drought response. The findings of this study will provide theoretical support and potential applications for understanding the molecular mechanisms by which melatonin alleviates drought stress and for improving drought tolerance in potato. 4.1. Potato Differential Responses to Drought Gradient at the Phenotypic and Physiological Levels The present study demonstrates that drought stress imposes significant physiological constraints on potato seedlings, primarily through two interconnected pathways: growth inhibition and oxidative damage. The observed reduction in plant height and stem diameter reflects a fundamental resource allocation shift, where limited water availability likely restricts cell expansion and photosynthetic capacity. This is further corroborated by the decline in chlorophyll fluorescence parameters (Fv/Fo), indicative of impaired photosystem II efficiency and reduced photosynthetic electron transport under water deficit. Concomitantly, drought triggered a pronounced oxidative burst, evidenced by elevated MDA content (marker of membrane lipid peroxidation), accumulation of ROS, and increased cell death. While the plant initiated compensatory responses, including the upregulation of proline and soluble sugars (likely acting as osmolytes and ROS scavengers) and enhanced activities of key antioxidant enzymes (SOD, CAT, POD), these endogenous defenses proved insufficient to fully mitigate the stress-induced damage under severe drought conditions. A key finding of this study is the potent, yet concentration-dependent, role of exogenous melatonin in enhancing potato drought tolerance. Melatonin application at 50 µmol/L and 100 µmol/L effectively counteracted multiple detrimental effects of drought. The promotion of plant growth parameters and photosynthetic performance suggests melatonin may improve water use efficiency or carbon assimilation. More critically, melatonin at these optimal doses robustly activated the antioxidant defense system, leading to significantly reduced ROS accumulation (as confirmed by DAB and NBT staining), decreased membrane peroxidation (lower MDA), and diminished cell death (reduced Trypan blue staining). This aligns with the well-established role of melatonin as a potent antioxidant and master regulator of redox homeostasis, capable of both directly scavenging ROS and up-regulating endogenous antioxidant enzyme activities. However, the hormetic nature of melatonin’s action is clearly illustrated by the results obtained with 150 µmol/L application. At this higher concentration, melatonin itself appeared to induce stress, as reflected in the paradoxical increase in ROS levels and the decline in some antioxidant enzyme activities (SOD, POD), ultimately leading to greater cell death compared to lower melatonin doses. This suggests that excessive melatonin may disrupt cellular redox balance, potentially through pro-oxidant effects or interference with normal metabolic/defense signaling pathways, outweighing its beneficial effects. In conclusion, our findings confirm that moderate exogenous melatonin supplementation (particularly 100 µmol/L) is a highly effective strategy for alleviating drought stress in potato seedlings. It achieves this primarily by bolstering the antioxidant defense machinery and preserving membrane integrity, thereby mitigating oxidative damage and promoting growth recovery. Crucially, this study highlights the strict dose-dependency of melatonin’s efficacy, establishing an optimal concentration range and providing a critical warning against the potential phytotoxicity of supra-optimal applications. This knowledge is essential for developing melatonin-based biostimulant strategies for sustainable potato cultivation under water-limited conditions. 4.2. Transcription Factors (TFs) Related Genes Are a Vital Component of Drought Response Machinery The identification of TFs among the DEGs provides crucial insights into the regulatory architecture underlying the biological responses observed in the studied samples. In total, 64 TF families were represented, highlighting the diverse and multilayered transcriptional regulation potentially involved in stress adaptation, hormone signaling, and development (Table 2 ). Notably, the MYB and MYB_related families were the most abundant, with 178 and 147 DEGs, respectively. These TFs are widely recognized for their roles in secondary metabolism, cell fate determination, and responses to abiotic stress [ 37 ]. Their high representation implies they may function as central regulators in orchestrating complex stress-response pathways. Similarly, the significant presence of bHLH (139 DEGs), ERF (145 DEGs), NAC (113 DEGs), and M-type (146 DEGs) TFs further supports the hypothesis of a stress-responsive transcriptional reprogramming. Members of the bHLH and NAC families are frequently implicated in abiotic stress tolerance, such as drought and salinity, as well as developmental processes like lateral root formation [ 38 ]. ERFs, which are part of the AP2/ERF superfamily, are closely linked to ethylene signaling and defense responses, suggesting a potential role in biotic stress or pathogen interactions [ 39 ]. For instance, in a study on Populus trichocarpa under drought stress, also reported MYB, NAC, and ERF as the top three enriched TF families, supporting their conserved roles in abiotic stress responses across diverse plant species [ 40 ]. Similarly, Zhang et al. investigated salt stress responses in Glycine max and identified a significant number of DEGs encoding bZIP, WRKY, and bHLH transcription factors, which are believed to function in ABA-dependent signaling pathways and transcriptional reprogramming of antioxidant enzymes [ 41 ]. The presence of B3 (122 DEGs), HB-other (105 DEGs), and bZIP (76 DEGs) TFs also suggests involvement in hormone-mediated signal transduction, particularly abscisic acid (ABA) and auxin pathways, which are central to plant growth regulation under stress. Interestingly, while families such as Whirly, RAV, S1Fa-like, and NF-X1 exhibited fewer DEGs (2–3 genes each), their involvement should not be overlooked. Despite their smaller gene counts, members of these families have been associated with chloroplast function, DNA repair, or developmental gene silencing, indicating that even low-abundance TFs might have specific and critical regulatory functions [ 42 ]. Altogether, the dynamic and varied distribution of transcription factor families among DEGs underscores a highly coordinated regulatory network likely modulating gene expression in response to both intrinsic developmental cues and extrinsic environmental signals. 4.3. Photosynthesis Related Genes Play a Critical Role in Drought Stress Response Regulation Photosynthesis-related genes emerged as pivotal components in the transcriptional response to drought stress and melatonin treatments in potato. Both Mfuzz clustering and WGCNA analysis independently highlighted photosynthetic pathways as being tightly regulated under varying stress conditions, suggesting their central role in stress adaptation and recovery processes. In the Mfuzz analysis, cluster 5 was particularly noteworthy, displaying high expression specifically under drought stress combined with 100 µmol/L melatonin treatment (M2). This cluster showed significant GO enrichment in terms such as photosynthesis, light harvesting, and response to abiotic stimulus, indicating that photosynthetic machinery may be actively maintained or restored when melatonin is applied under stress. Correspondingly, KEGG pathways such as photosynthesis -antenna proteins, photosynthesis, and carotenoid biosynthesis were enriched, reflecting enhanced light energy utilization and potential photoprotection mechanisms (Fig. 4 ). These results suggest that melatonin at higher concentrations may promote or stabilize photosynthetic gene expression, possibly contributing to improved drought resilience. Complementarily, WGCNA results reinforced this finding. The MEbrown module, which contains 471 DEGs, showed a significant positive correlation with FV/F0, a key photosynthetic efficiency parameter, and was enriched in GO terms like photosynthesis, light harvesting in photosystem I, and carbohydrate metabolic process. These functions are essential not only for energy production but also for the regulation of downstream stress signaling and metabolic adjustment. The KEGG enrichment in this module further supports its central role, with significant terms including photosynthesis-antenna proteins, starch and sucrose metabolism, and biosynthesis of various plant secondary metabolites, pathways that contribute to both energy storage and stress tolerance mechanisms (Table 3 ). Recent research has provided growing evidence that melatonin plays a key regulatory role in maintaining photosynthetic activity under drought stress by modulating the expression of photosynthesis-related genes and protecting chloroplast structure and function. For instance, Zhang et al. investigated the effect of melatonin on drought-stressed Malus hupehensis and found that melatonin application significantly enhanced net photosynthetic rate (Pn) and chlorophyll fluorescence parameters (Fv/Fm) [ 43 ]. Transcriptomic analysis revealed up-regulation of genes encoding photosystem II reaction center proteins (PsbA, PsbD) and light-harvesting chlorophyll-binding proteins, which are essential for maintaining the efficiency of the light-dependent reactions of photosynthesis under stress conditions. Similarly, exogenous melatonin treatment in drought-stressed maize seedlings not only increased chlorophyll content and stomatal conductance, but also elevated the expression of genes involved in the Calvin cycle, such as rbcL and FBA, suggesting a role of melatonin in sustaining carbon fixation under stress [ 44 ]. Melatonin-treated tomato under drought stress exhibited significantly higher photosynthetic electron transport rates and CO 2 assimilation. RNA-seq analysis identified that genes associated with photosynthesis-antenna proteins, chloroplast development, and redox homeostasis were differentially expressed, supporting the idea that melatonin modulates both energy production and oxidative protection in chloroplasts [ 45 ]. Altogether, these results strongly indicate that photosynthetic function is not merely suppressed under drought, but is dynamically modulated, particularly in response to melatonin treatments. The transcriptional up-regulation of photosynthesis-related genes along with enhanced expression of light-harvesting and carbon metabolism pathways suggests that maintaining photosynthetic capacity is a key strategy for drought resistance in potato. Moreover, the co-expression patterns with antioxidant and hormone-responsive genes further highlight the multifaceted regulatory role of photosynthesis in orchestrating the broader stress response network. 4.4. Significantly Enriched Metabolic Pathways of DEGs Under Drought Stress Transcriptomic reprogramming under drought stress often involves the coordinated regulation of key metabolic pathways that mediate energy homeostasis, cellular protection, and signaling integration. In this study, both Mfuzz clustering and WGCNA analysis revealed a set of consistently enriched metabolic pathways, which appear to play central roles in potato's drought response, especially under exogenous melatonin treatment. Starch and sucrose metabolism was prominently enriched in both cluster 3 (Mfuzz) and the MEbrown module (WGCNA). Genes involved in starch degradation (e.g.,β-amylase, glucan phosphorylase) and sucrose biosynthesis were up-regulated under drought combined with melatonin treatment, suggesting enhanced carbon remobilization to support osmotic adjustment and energy supply. This finding aligns with Yin et al., who observed that melatonin application in Oryza sativa under drought promoted sucrose accumulation and up-regulated sucrose synthase genes, facilitating improved root growth and water uptake [ 46 ]. Phenylpropanoid biosynthesis also enriched in cluster 4 and MEblue, is associated with the synthesis of lignin, flavonoids, and other secondary metabolites with antioxidative and structural functions. Under drought, these compounds help in reinforcing cell walls and scavenging ROS. A previous study by Ma et al. revealed that melatonin treatment in Brassica napus significantly up-regulated genes involved in phenylpropanoid biosynthesis, particularly PAL, C4H, and 4CL, resulting in increased lignin deposition and drought tolerance [ 47 ]. Our results suggest that melatonin may similarly enhance structural defenses and redox buffering through this pathway. Genes related to glutathione metabolism, such as glutathione S-transferases (GSTs) and glutathione reductase, were significantly enriched in cluster 4 and the MEblue module. This suggests the activation of antioxidant defense mechanisms to counteract drought-induced oxidative stress. Importantly, this pathway showed a negative correlation with photosynthetic efficiency (FV/F₀), indicating a possible resource allocation trade-off between growth and defense. In Arabidopsis , melatonin-enhanced glutathione biosynthesis led to a marked reduction in H₂O₂ levels and increased drought tolerance [ 48 ]. The convergence of glutathione metabolism in multiple expression clusters and modules highlights its core protective role in redox regulation during abiotic stress. In cluster 3, significant enrichment was observed in biosynthesis of various plant secondary metabolites and steroid biosynthesis. These metabolic processes are known to mediate hormone-like signaling, membrane stabilization, and interaction with ABA and brassinosteroid pathways under stress. Steroids such as campesterol and stigmasterol have been implicated in improving membrane fluidity and signaling efficiency, facilitating stress perception and transduction. Recent work by Wang et al. in Medicago truncatula found that melatonin induced expression of sterol biosynthesis genes, promoting drought tolerance by modulating membrane stability and ABA sensitivity [ 49 ]. Thus, our results support a role for melatonin in metabolic rewiring toward signaling lipids under stress. Conversely, cluster 8 and the MEturquoise module involved in ribosome biogenesis, aminoacyl-tRNA biosynthesis, and protein processing in the ER. These pathways, though essential under normal conditions, were likely suppressed to conserve energy and redirect resources toward stress-specific processes. Similar suppression of ribosome biogenesis was reported in maize under drought [ 50 ], which helps reduce translation burden and prevent misfolded protein accumulation. Our results affirm that basic biosynthetic pathways are transcriptionally repressed under drought, especially when protective pathways are activated by melatonin. 5. Conclusion This study systematically elucidates the multi-level mechanisms by which exogenous melatonin enhances drought tolerance in potato seedlings. Appropriate concentrations of melatonin (50–100 µmol/L) significantly alleviated drought-induced growth inhibition and oxidative damage. The underlying mechanisms include activation of the antioxidant defense system, leading to increased activities of key enzymes such as SOD, CAT, and POD. Accumulation of osmolytes (proline and soluble sugars) to maintain cellular water potential and osmotic balance. Stabilization of the photosynthetic apparatus, with improved PSII efficiency and chlorophyll content. Molecular reprogramming of drought responses through the regulation of transcription factors (e.g., MYB, bHLH, NAC) and key metabolic pathways, including photosynthesis, phenylpropanoid biosynthesis, and glutathione metabolism. WGCNA and clustering further identified several modules and hub genes closely associated with drought-related traits, highlighting the central regulatory role of melatonin in plant drought stress response. Notably, the effects of melatonin are dose-dependent—excessive application (150 µmol/L) may trigger secondary stress responses and exacerbate cellular damage, underscoring the importance of precise concentration control in agricultural practice. Overall, this research not only deepens our theoretical understanding of melatonin-mediated stress regulation in plants but also provides valuable insights for improving drought resistance in potato through melatonin-based agronomic strategies. Declarations Author Contributions :X.Zhai conceived the study. S.Liu, Y.Wang, X.Wang, X.Zhang, Y.Li, Y.Chen, J.Yang, L.Liu, and X.Zhai performed the experiments and carried out the analysis. S.Liu and X.Zhai wrote the manuscript. All authors have read and agreed to the published version of the manuscript. Funding : This study was financially supported by the Zhangjiakou Science and Technology Program Project (No. 2311003A), the Doctoral Research Initiation Fund of Hebei North University (No. BSJJ202225), the Basic Scientific Research Operating Expenses Program for Provincial Universities (No. JYT2025009), Science Research Project of Hebei Education Department (No. BJK2022028), and the College Student Innovation and Entrepreneurship Training Program of Hebei North University (No. S202510092056). Ethics approval and consent to participate: Not applicable. Institutional Review Board Statement : Not applicable. Consent for publication: All authors have read and agreed to the published version of the manuscript. 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Supplementary Files Supplementarymaterial.rar Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 26 Sep, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Editor invited by journal 27 Aug, 2025 Submission checks completed at journal 26 Aug, 2025 First submitted to journal 26 Aug, 2025 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. 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11:46:11","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52480,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/cf6ffe9465ac143abea826a6.png"},{"id":92080538,"identity":"b222816c-3717-4e91-afda-00f49c3174f3","added_by":"auto","created_at":"2025-09-24 11:46:12","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41348,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/886911d4fadabda03df6c735.png"},{"id":92080532,"identity":"e87e06a3-f085-4f63-8c8c-f3e9a82bb9ab","added_by":"auto","created_at":"2025-09-24 11:46:11","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155268,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/d3958a69aff74e4225761fa8.png"},{"id":92080548,"identity":"34fc7aa4-8f6c-4a2d-8e6f-973664fb6be0","added_by":"auto","created_at":"2025-09-24 11:46:13","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179958,"visible":true,"origin":"","legend":"","description":"","filename":"258193bd663a4dc3b1697dc31c4347f11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/4725e9119d7919800b190cfb.xml"},{"id":92080527,"identity":"24c105d7-7b94-4557-a097-ad90a3c1d919","added_by":"auto","created_at":"2025-09-24 11:46:11","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190772,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/d26355d85fa66b7a8759727b.html"},{"id":92080544,"identity":"865ff3af-d5a1-4f82-878c-abb09e04054d","added_by":"auto","created_at":"2025-09-24 11:46:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":596788,"visible":true,"origin":"","legend":"\u003cp\u003ePhysiological and phenotypic responses of potato plants under different treatments. (A) Phenotypes of potato plants in CK (control), D (drought), M1, M2, and M3 groups. Histograms showing various indices of potato plants in each group: (B) Plant height; (C) Stem diameter; (D) Soluble sugar content; (E) SOD (superoxide dismutase) activity; (F) CAT (catalase) activity; (G) POD (peroxidase) activity; (H) Proline content; (I) Chlorophyll content; (J) Maximum photochemical efficiency of PSII; (K) Actual photochemical efficiency of PSII; (L) Staining results of potato leaves with NBT, DAB, and Trypan Blue in different groups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/f909f75202086c077f75998a.png"},{"id":92080549,"identity":"482864e7-2a50-4786-9aea-12d4b8879fae","added_by":"auto","created_at":"2025-09-24 11:46:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":780564,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed DEGs (DEGs) for drought tolerance in potato. (A) Number of DEGs identified in each comparison group, where Up represents up-regulated DEGs and Down represents down-regulated DEGs. (B) Venn plot analysis of DEGs identified in six groups.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/84045e69a227abd5189c2a51.png"},{"id":92080534,"identity":"3cef7e4a-0c74-4cc1-a9dc-e83011730c5f","added_by":"auto","created_at":"2025-09-24 11:46:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3062596,"visible":true,"origin":"","legend":"\u003cp\u003eMfuzz clustering analysis of differentially expressed genes (DEGs). The y-axis represents changes in gene expression levels, while the x-axis indicates different treatment conditions. The color gradient reflects the membership value of each gene within a specific cluster—higher values indicate stronger association and greater significance within the cluster.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/a3876ca22bfe1c0336230a87.png"},{"id":92080524,"identity":"52cc1f2a-569c-47de-a76e-2636a2e17d82","added_by":"auto","created_at":"2025-09-24 11:46:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":306109,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of DEGs with high membership in Mfuzz clusters.(A) GO enrichment analysis of clusters 3, 4, 5, and 8.(B) KEGG pathway enrichment analysis of clusters 3, 4, 5, and 8.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/b265d2c0a95c4890415ed526.png"},{"id":92080537,"identity":"4a95ba06-cc55-4a2c-8b2d-84046907da8a","added_by":"auto","created_at":"2025-09-24 11:46:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1075625,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression network analysis of differentially expressed genes (DEGs). (A) Co-expression network analysis identifying gene modules associated with potato drought stress responses under four different treatment conditions. (B) Correlation analysis between gene modules and phenotypic or physiological traits.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/5076cf3c71b6e6ec54c135e1.png"},{"id":102785326,"identity":"ac0445d9-2742-45ca-aef4-76d9ff948259","added_by":"auto","created_at":"2026-02-16 16:05:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3848623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/dd0721aa-ebf9-4180-9105-d513b53c5905.pdf"},{"id":92080546,"identity":"6d3f27c1-5755-4828-9c12-1bc790da0c2d","added_by":"auto","created_at":"2025-09-24 11:46:13","extension":"rar","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":629102,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.rar","url":"https://assets-eu.researchsquare.com/files/rs-7382006/v1/f8cc50a0cbf73a179b4462a9.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative WGCNA Analysis Uncovers the Molecular Framework of Melatonin-Mediated Drought Stress Mitigation in Potato","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWater is essential for plant growth, but scarcity can hinder their development. Plants have evolved adaptive mechanisms to cope with drought, which disrupts their physiology and biochemistry, affecting growth and yield [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Drought disrupts plant growth and yield by affecting physiology and biochemistry, leading to reduced water uptake, stomatal closure, and hormonal changes[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It decreases photosynthesis by lowering stomatal conductance and altering leaf water use efficiency, impairing Photosystem II's efficiency [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Drought also increases reactive oxygen species, boosting antioxidant enzymes like superoxide dismutase, peroxidase, and catalase to mitigate oxidative stress [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, drought stress also leads to an increase in the content of proline and soluble sugars in plants. As important osmotic regulators, the increase in proline and sugar content facilitates the maintenance cellular osmotic balance and protects cellular structures from damage [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The phytohormone abscisic acid (ABA) plays a pivotal role in mediating drought response and tolerance by regulating stress-responsive genes. Central to this mechanism are the ABA-responsive element (ABRE) binding proteins/factors (AREB/ABFs), which transcriptionally activate downstream targets, thereby constituting the AREB/ABF regulon. Simultaneously, ABA-independent pathways engage dehydration-responsive element binding protein (DREB) regulons and NAC (NAM, ATAF, and CUC) regulons to facilitate drought adaptation. Furthermore, transcription factors such as MYB/MYC, WRKY, and nuclear factor-Y (NF-Y) enhance drought resilience through complementary regulatory networks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is among the most widely consumed plants worldwide, ranking third in terms of crop consumption, with an annual global production of 370\u0026nbsp;million tons [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It is regarded as a nutritious source of carbohydrates, dietary fiber, protein, vitamins, antioxidants, and minerals [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The frequency of drought occurrences is increasing as global temperatures rise [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Contemporary potato varieties are often regarded as vulnerable to drought, primarily due to their shallow root structures [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nonetheless, this crop exhibits significant sensitivity to drought conditions, posing a substantial threat to its growth, development, and yield\u0026mdash;especially in regions impacted by climate change that leads to hotter and drier environments [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMelatonin (N-acetyl-5-methoxytryptamine, MT), a small indole molecule, is extensively distributed across various plant species and functions as a growth regulator with notable physiological roles [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The exogenous application of melatonin has been shown to exert significant regulatory effects on plant photosynthesis and stress resistance [\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Firstly, melatonin enhances photosynthetic efficiency, particularly under stress conditions. For example, in wheat, exogenous melatonin application has been observed to increase thylakoid protein levels through reversible phosphorylation, thereby enhancing the photochemical efficiency of photosystem II (PSII) and augmenting energy dissipation capacity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In tea plants, melatonin has been reported to improve photosynthetic capacity in a dose-dependent manner, leading to increased chlorophyll content and photosynthetic rates [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Secondly, melatonin plays a critical role in modulating the antioxidant system in plants. Empirical studies suggest that the exogenous application of melatonin can decrease the accumulation of reactive oxygen species (ROS) by enhancing the activity of antioxidant enzymes, thereby mitigating oxidative damage. For instance, in mung beans, melatonin has been documented to enhance the activities of catalase (CAT), glutathione reductase (GR), and ascorbate peroxidase (APX), thereby augmenting the plants' antioxidant capacity under conditions of salt stress [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Similarly, in sweet corn, melatonin has been shown to mitigate the detrimental effects of herbicide toxicity by increasing the activity of antioxidant enzymes and upregulating the expression of associated genes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In citrus, the application of exogenous melatonin mitigates water loss and sustains redox homeostasis, highlighting the pivotal function of the PtABF4-PtbHLH28-PtCOMT5 molecular module in modulating melatonin accumulation and root development under conditions of drought stress [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, melatonin plays a crucial role in bolstering plant stress resistance by modulating the metabolism of proline and soluble sugars. In apple plants, the application of exogenous melatonin has been demonstrated to reduce the uptake and accumulation of cadmium, thereby alleviating cadmium toxicity through an increase in proline and soluble sugar content [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In grapevines, melatonin facilitates seedling growth and adaptability to environmental variations by enhancing the activity of enzymes involved in sucrose metabolism [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In summary, exogenous melatonin impacts plant photosynthesis, the antioxidant system, and the metabolism of proline and soluble sugars through multiple pathways, thereby enhancing both plant stress resistance and growth potential. These findings offer a theoretical basis for the application of melatonin in agricultural practices.\u003c/p\u003e\u003cp\u003eAlthough melatonin has shown promise in improving plant drought tolerance, the precise regulatory mechanisms by which it operates in Solanum tuberosum L. are not well understood. This research examines the effects of exogenous melatonin application on enhancing drought adaptability in potato seedlings. It focuses on the modulation of osmotic homeostasis, chlorophyll fluorescence dynamics, antioxidant enzyme activities, and transcriptomic approaches with the aim of elucidating the molecular basis underlying the physiological adaptations induced by melatonin.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Plant Materials and Drought Treatment\u003c/h2\u003e\u003cp\u003eThe experiment was conducted in September 2023 within the controlled environment of the artificial climate chamber located at Hebei North University. A pot cultivation method was employed, utilizing pots with dimensions of 14.7 cm in diameter and 12.5 cm in height. The growth medium consisted of a 1:1 volumetric ratio of vermiculite to nutrient soil. Uniformly sized, non-toxic pre-basic potato seeds (Holland No. 15) were selected for sowing, with each pot containing one seed, oriented with the bud facing upward. Upon uniform seedling emergence, standard management practices were implemented, maintaining the field capacity at approximately 75%.A single-factor experimental design was adopted, featuring five distinct treatments: CK (control, normal conditions), D (drought with the application of purified water), M1 (drought with the application of 50 \u0026micro;mol/L melatonin, MT), M2(drought with the application of 100 \u0026micro;mol/L MT), and M3 (drought with the application of 150 \u0026micro;mol/L MT). Each treatment consisted of three biological replicates, with each replicate containing five seedlings. The drought treatment commenced when the potato plants had developed seven leaves, approximately 50 days post-planting, at which point the field capacity was approximately 30%. Various concentrations of MT were administered through foliar spraying. This spraying procedure was conducted four times, with a two-day interval between each application. Spraying was performed at 16:00, ensuring uniformity and consistency in the spraying volume, with the criterion being leaf wetness to the point of slight dripping. Leaf samples were collected two days following the final melatonin treatment for subsequent investigations, including biochemical index determination and physiological analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Phenotypic and Physiological Characterizations\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1Antioxidant Enzyme Activity and MDA Content in Leaves\u003c/h2\u003e\u003cp\u003eUpper leaves, positioned similarly, were harvested from potato plants belonging to the control (CK) group and various treatment groups. These leaves were subsequently utilized for the assessment of physiological parameters, including malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, peroxidase (POD) activity, and catalase (CAT) activity. The measurements were conducted using appropriate assay kits provided by Solarbio (Beijing).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Determination of Proline and Soluble Sugars Content in Leaves\u003c/h2\u003e\u003cp\u003eTo quantify the proline content, 0.3 g of fresh tissue sample was homogenized with 3 mL of 3% sulfosalicylic acid solution. The homogenate was subsequently heated in a water bath at 100℃ for 10 minutes. Following cooling, the homogenate was filtered, and the resulting supernatant was combined with glacial acetic acid and 2.5% acidic ninhydrin reagent. This mixture was then subjected to heating in a boiling water bath for 30 minutes. Upon cooling, 4 mL of toluene was added, and the solution was vigorously shaken to extract the red chromophore. The mixture was then centrifuged at 3000 rpm for 5 minutes, and the upper phase was utilized for proline content analysis by measuring absorbance at 520 nm. Additionally, the leaves were employed for the determination of Soluble Sugars (SS) content, which was assessed using specific test kits (Solarbio, Beijing).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Assays for Cell Death in Leaves\u003c/h2\u003e\u003cp\u003eTo assess cell death, dead cells were stained using the trypan blue method as described by Lin et al [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Detached leaves were infiltrated with a lactophenol-trypan blue solution at100\u0026deg;C for 1 min, followed by immersion in boiling water for 5 minutes, and subsequently stained for 12 hours. To eliminate background staining, the samples were immersed in a 2.5 g/mL chloral hydrate solution for 3 days. Finally, the tissues were equilibrated in 70% (v/v) glycerol for photographing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 DAB and NBT Staining of Leaves\u003c/h2\u003e\u003cp\u003eThe detection of reactive oxygen species (ROS) through histochemical methods was conducted in accordance with the protocol established by Chen et al [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In summary, hydrogen peroxide (H₂O₂) and superoxide anion radicals (O₂⁻) were visualized using 3,3'-diaminobenzidine (DAB) at a concentration of 1 mg/mL and nitro blue tetrazolium (NBT) at a concentration of 1 mg/mL, respectively. Following staining, the tissues were subjected to destaining in 85% (v/v) boiling ethanol for a duration of 2 hours before proceeding with image documentation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5 Determination of Chlorophyll Content and Chlorophyll Fluorescence Parameters\u003c/h2\u003e\u003cp\u003eThe relative chlorophyll content, quantified as SPAD values, was measured using a SPAD-502 chlorophyll meter (Konica Minolta, Japan). Concurrently, photosynthetic parameters were evaluated on potato leaves employing a portable fluorometer (PAM 2500 Portable Chlorophyll Fluorometer, WALZ). For these assessments, the fourth leaf from the apical bud on lateral branches was selected, dark-adapted for 30 minutes, and subsequently analyzed to determine the maximum quantum yield of PSII photochemistry (Fv/Fm) and effective quantum yield of PSII photochemistry (Φ\u003csub\u003ePSII\u003c/sub\u003e). A minimum of five biological replicates were analyzed per treatment.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Total RNA Extraction, cDNA Library Construction, and Transcriptome Analysis\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from the tissue using TRIzol\u0026reg; Reagent according the manufacturer\u0026rsquo;s instructions. Then RNA quality was determined by 5300 Bioanalyser (Agilent) and quantified using the ND-2000 (NanoDrop Technologies). Only high-quality RNA sample was used to construct sequencing library. cDNA libraries were then constructed following the manufacturer\u0026rsquo;s protocol using the NEBNext\u0026reg; Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg; (NEB, USA) and subsequently sequenced on the NovaSeq Reagent Kit platform using paired-end sequencing technology.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4.Quality control and Read Mapping\u003c/h2\u003e\u003cp\u003eThe raw paired end reads were trimmed and quality controlled by fastp with default parameters [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Then clean reads were separately aligned to reference genome (Stuberosum_686_v6.1) with orientation mode using HISAT2 software [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The mapped reads of each sample were assembled by StringTie in a reference-based approach. Quality control metrics, including the Q30 base percentage (an indicator of the overall reproducibility and quality of the assay) and GC content were assessed. Only reads with a perfect match or a single mismatch were retained for further analysis and genome annotation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Differential Expression Genes (DEGs) Analysis\u003c/h2\u003e\u003cp\u003eTo identify DEGs between two different samples, the expression level of each transcript was calculated according to the transcripts per million reads (TPM) method. Gene differential expression analysis was conducted using DESeq R package (v1.10.1) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], whereby the ratios of TPM values between the control and treatments were analyzed, and the resulting P-values corrected for multiplicity using the Benjamini and Hochberg method [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Genes with a fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were classified as differentially expressed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Functional Enrichment Analysis, and Identification of Key Drought-Responsive DEGs\u003c/h2\u003e\u003cp\u003eTo identify key genes involved in melatonin-mediated drought stress responses in potato, four different treatments were compared in a pairwise manner, resulting in six comparison groups. DEGs that met the selection criteria (|FC|\u0026ge;2 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across these comparisons were subsequently analyzed using a Venn diagram, enabling the identification of potential key candidate drought-responsive DEGs. Then, DEGs exhibiting similar expression trends were grouped via Mfuzz clustering, based on log-transformed fold-change values, using the R statistical environment. Subsequently, co-expression network construction was carried out using the WGCNA package (version 1.47) in R. In addition, to further explore the biological significance of DEGs, gene ontology (GO) enrichment analysis was conducted using agriGO web-based program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://systemsbiology.cau.edu.cn/agriGOv2/#\u003c/span\u003e\u003cspan address=\"http://systemsbiology.cau.edu.cn/agriGOv2/#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis was performed by KOBAS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Quantitative Real Time-PCR (qRT-PCR) Analysis\u003c/h2\u003e\u003cp\u003eTo validate the RNA-seq data, qRT-PCR was conducted on 9 genes randomly selected for expression analysis. Gene-specific primers were designed using Primer Premier 5 software (Premier Biosoft International, Palo Alto, CA, USA). Total RNA of high integrity was isolated from Chinese cabbage samples and subsequently reverse-transcribed into cDNA using the HiFiscript cDNA Synthesis Kit (CWBIO, Beijing, China) following the manufacturer\u0026rsquo;s instructions. qRT-PCR program was run in a Bio-Rad iQ5 Thermo Cycler (Bio-Rad, Hercules, CA, USA) using 2 \u0026times; Fast Super Evagreen qPCR mastermix (US Everbright Inc., Daly City, CA, USA), where fluorescence intensity directly corresponded to the DNA quantity present. A stable reference gene ACTIN was used as an internal control for data normalization. The relative mRNA abundance for each gene was determined by the 2\u0026thinsp;\u0026minus;\u0026thinsp;\u003csup\u003eΔΔCT\u003c/sup\u003e method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Statistical Analysis of Physiological Data\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using SPSS software (version 22.0; IBM Corp., Armonk, NY, USA), and results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE). For physiological measurements, two-way analysis of variance (ANOVA), followed by least significant difference (LSD) post hoc tests, was applied to assess differences among treatments and genotypes. In contrast, the qRT-PCR expression data were analyzed using one-way ANOVA, with Duncan\u0026rsquo;s multiple range test employed for pairwise comparisons. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted throughout.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Phenotypic and Physiological Responses of Potato to Drought Stress at Different Treatments\u003c/h2\u003e\u003cp\u003eDrought stress significantly inhibited the early growth and development of potato seedlings, as evidenced by reduced plant height and stem diameter, increased membrane lipid peroxidation (indicated by elevated MDA content), enhanced accumulation of reactive oxygen species (ROS), and a rise in dead cell numbers in leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, drought induced increases in proline and soluble sugar contents, elevated antioxidant enzyme activities (SOD, CAT, POD, Pro), and a decline in chlorophyll fluorescence parameters (SPAD, Fv/Fm, Φ\u003csub\u003ePⅡ\u003c/sub\u003e). Exogenous melatonin application showed a dose-dependent effect on enhancing drought tolerance in potato. Appropriate concentrations of melatonin\u0026mdash;particularly 50 \u0026micro;mol/L and 100 \u0026micro;mol/L\u0026mdash;effectively alleviated drought-induced damage by promoting plant height, enhancing photosynthetic performance, activating antioxidant defense systems, and reducing membrane peroxidation and ROS accumulation, thereby mitigating cellular damage and improving stress resistance. However, at 150 \u0026micro;mol/L, some physiological indicators (e.g., SOD and POD activity, ROS levels) showed adverse changes, suggesting a possible stress or toxic effect at excessive melatonin concentrations.\u003c/p\u003e\u003cp\u003eStaining results further confirmed these findings. Drought stress significantly increased cell death and ROS accumulation in potato leaves. Exogenous melatonin at 50 \u0026micro;mol/L and 100 \u0026micro;mol/L markedly reduced the staining areas of Trypan blue, DAB, and NBT, indicating reduced oxidative damage and cell death, with 100 \u0026micro;mol/L showing the most effective mitigation. In contrast, 150 \u0026micro;mol/L melatonin led to increased staining, suggesting potential toxicity at higher concentrations. Therefore, moderate melatonin application can alleviate drought-induced oxidative stress, while excessive application may have detrimental effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Summary and Quality Assessment of RNA-Seq Results\u003c/h2\u003e\u003cp\u003eTo clarify different concentrations of exogenous melatonin on potato seedling growth under drought stress, RNA-seq analysis was performed on 12 leaf samples of cultivar Helan15. The principal component analysis (PCA) results showed that there was low consistence amongst the three replications of the D, probably due to a technical failure (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Thus, sequencing results were analyzed by retrieving sample D1. Pearson correlation coefficients between samples were also calculated and displayed in form of a heatmap. The results showed each R\u003csup\u003e2\u003c/sup\u003e (between the two samples) to be higher than 90% (Figure S2). Overall, these results showed that our experiment is reproducible and reliable, meeting the demands for further analyses.\u003c/p\u003e\u003cp\u003eSubsequently, eleven samples were used for RNA-seq transcriptome analysis. The raw sequencing data were deposited into the NCBI Sequence Read Archive (SRA, Accession PRJNA1301854). After filtering, a total of 54.42\u0026nbsp;million clean reads were obtained from these 12 samples, among which 83.35\u0026ndash;85.85% clean reads were mapped onto unique positions on the reference genome (Stuberosum_686_v6.1). Meanwhile, the Q30 base percentage and GC percentage exceeded 93.17% and 42.02%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary details of the RNA-seq results for the eleven leaf samples\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRaw reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClean reads\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultiple mapped\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUniquely mapped\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ30(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGC content(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK1_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44548564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44344302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1181735(2.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37794805(85.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK1_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45896472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45721372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1192451(2.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38959735(85.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK1_3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43660996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43482050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1200649(2.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37284979(85.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53956554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53721234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1458040(2.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46121625(85.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42303314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42095688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1231256(2.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35723610(84.86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41592044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41431674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1127568(2.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35306040(85.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43061012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42856784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1146936(2.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36568176(85.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1_3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45054296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44813712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1236598(2.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37774917(84.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2_1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44325590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44123800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1190791(2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37414996(84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2_2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43912252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43704100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1238057(2.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37285385(85.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2_3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50323642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50100184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1386919(2.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42482747(84.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Gene Differential Expression Analysis\u003c/h2\u003e\u003cp\u003eTo investigate the effects of different concentrations of exogenous melatonin on the growth of potato seedlings under drought stress, four treatments were established: CK (normal watering), D (drought\u0026thinsp;+\u0026thinsp;spraying purified water), M1 (drought\u0026thinsp;+\u0026thinsp;spraying 50 \u0026micro;mol/L melatonin), and M2 (drought\u0026thinsp;+\u0026thinsp;spraying 100 \u0026micro;mol/L melatonin). Six pairwise comparison groups were generated for transcriptome analysis, which revealed significant differences in gene expression among the different treatments. Compared with the normal watering treatment (CK), melatonin application under drought stress significantly altered gene expression patterns in potato seedlings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The comparison group M2_vs_CK exhibited the highest number of DEGs, with a total of 1,098 DEGs, including 298 up-regulated and 800 down-regulated DEGs, indicating that high-concentration melatonin exerted a strong regulatory effect on gene expression. The M2_vs_D comparison group (i.e., high-concentration melatonin vs. drought alone) also yielded a large number of DEGs (1,042 in total), further suggesting that 100 \u0026micro;mol/L melatonin might play a\u003c/p\u003e\u003cp\u003esignificant role in alleviating drought stress. In contrast, only 179 DEGs were detected between the D and CK groups, with 111 up-regulated and 68 down-regulated, indicating that drought alone had a relatively limited effect on transcriptional changes. The M1_vs_CK comparison group revealed 417 DEGs (142 up-regulated, 275 down-regulated), while the M2_vs_M1 comparison identified 266 DEGs (161 up-regulated, 105 down-regulated), suggesting that high and moderate concentrations of melatonin exerted distinct regulatory effects. Additionally, 135 DEGs (42 up-regulated, 93 down-regulated) were identified in the M1_vs_D group, indicating that even a lower concentration of melatonin (50 \u0026micro;mol/L) could influence transcriptional responses under drought stress to some extent. In summary, melatonin application\u0026mdash;particularly at 100 \u0026micro;mol/L\u0026mdash;significantly enhanced the gene expression response of potato seedlings under drought stress, likely by regulating a large number of up- and down-regulated DEGs to improve drought tolerance.\u003c/p\u003e\u003cp\u003eA total of 2,340 differentially expressed DEGs (DEGs) were identified across the six comparison groups. Venn diagram analysis revealed that the majority of DEGs exhibited group-specific expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). For example, 710, 666, 123, 108, 63, and 31 DEGs were uniquely expressed in the M2_vs_D, M2_vs_CK, M1_vs_CK, M2_vs_M1, D_vs_CK, and M1_vs_D groups, respectively. In addition, 153 DEGs were shared between the M2_vs_CK and M1_vs_CK groups, suggesting these DEGs may be closely related to the transcriptional responses induced by melatonin treatment under drought conditions. Furthermore, 28 DEGs were commonly expressed in the M1_vs_D and M2_vs_D comparisons, implying that these DEGs may participate in shared regulatory pathways triggered by different melatonin concentrations under drought stress, and thus have potential functional significance in drought mitigation. Notably, 149 DEGs were shared between M2_vs_CK and M2_vs_D, indicating that these DEGs likely represent core response factors under 100 \u0026micro;mol/L melatonin treatment, distinguishing it from both normal water conditions (CK) and drought stress alone (D). This highlights the specific transcriptional regulation induced by high-concentration melatonin.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.4. DEGs Encoding Transcription Factors\u003c/h2\u003e\u003cp\u003eTo further explore the transcriptional regulatory mechanisms underlying the observed gene expression changes, transcription factors (TFs) were identified from the DEGs. In total, 64 TF families were represented among the DEGs, with a wide range of gene counts across families (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, the MYB and MYB_related families were the most abundant, comprising 178 and 147 DEGs, respectively. Other TF families with a high number of members included bHLH (139 DEGs), ERF (145 DEGs), B3 (122 DEGs), HB-other (105 DEGs), NAC (113 DEGs), and M_type (146 DEGs). Additionally, WRKY (90 DEGs), bZIP (76 DEGs), GRAS (56 DEGs), and LBD (AS2/LOB) (51 DEGs) were also prominently represented. Several TF families, while smaller in gene number, may still play important regulatory roles. These include Whirly (3 DEGs), RAV (2 DEGs), S1Fa-like (3 DEGs), and NF-X1 (3 DEGs). The diverse representation and differential expression of transcription factors suggest a complex and multi-layered transcriptional regulation network, potentially involved in stress response, hormone signaling, and developmental processes in the studied samples.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of transcription factors identified in six comparisons\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTF family\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTF family\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGene number\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHD-ZIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHSF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLBD (AS2/LOB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBBR-BPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ebZIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBES1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC2H2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMIKC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC3H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMYB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCAMTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMYB_related\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM_type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNF-X1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDof\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNF-YA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2F/DP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNin-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRAV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eERF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS1Fa-like\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFAR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGATA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTALE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWRKY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB-PHD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhirly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB-other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYABBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebHLH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZF-HD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Mfuzz Analysis of DEGs\u003c/h2\u003e\u003cp\u003eMfuzz clustering analysis was conducted to identify gene clusters exhibiting distinct expression profiles in potato. Subsequently, GO and KEGG enrichment analyses were conducted to explore the biological functions of these special gene clusters. The DEGs identified from the four treatments were grouped into 10 clusters with different expression patterns. Among these, the DEGs of gene clusters 3,4,5,8 showed higher level of participation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Gene cluster 3 was specifically highly expressed under drought stress with 50 \u0026micro;mol/L melatonin treatment (M1). GO enrichment analysis indicated that this cluster was significantly enriched in response to external stimulus, defense response, and response to stimulus, all of which are associated with stress responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). KEGG pathway analysis further showed significant enrichment in starch and sucrose metabolism, steroid biosynthesis, and biosynthesis of various plant secondary metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eGene cluster 4 showed specific high expression under drought treatment (D). GO enrichment analysis revealed significant enrichment in glutathione metabolic process, chitin catabolic process, and amino sugar catabolic process, suggesting its involvement in antioxidant defense (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). KEGG analysis indicated that this cluster was primarily enriched in phenylalanine metabolism, phenylpropanoid biosynthesis, and glutathione metabolism, all pathways known to be associated with stress defense (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eGene cluster 5 was specifically highly expressed under drought stress with 100 \u0026micro;mol/L melatonin treatment (M2). GO enrichment analysis showed strong associations with photosynthetic activity, being significantly enriched in photosynthesis, light harvesting, photosynthesis, and response to abiotic stimulus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Corresponding KEGG pathways included photosynthesis - antenna proteins, photosynthesis, and carotenoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eIn contrast, Gene cluster 8 showed reduced expression under both drought and melatonin treatments. GO enrichment analysis revealed significant enrichment in ncRNA metabolic process, rRNA processing, and rRNA metabolic process, all related to ribosome biogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). KEGG pathway analysis indicated enrichment in ribosome biogenesis in eukaryotes, protein processing in endoplasmic reticulum, and aminoacyl-tRNA biosynthesis, suggesting suppression of basic cellular processes under stress conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.6. WGCNA Analysis of Differentially Expressed Genes\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study, a consensus network was constructed using WGCNA, and several modules significantly associated with physiological traits were identified. DEGs in each module were subjected to GO and KEGG enrichment analyses. After filtering, a total of 1,963 DEGs were divided into 8 modules, consisting of between 16 to 636 co-expressed DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The MEgreen module contains 114 DEGs and shows a significant positive correlation with POD and CAT activities (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). GO enrichment analysis revealed that this module is significantly enriched in biological processes such as response to salicylic acid, regulation of response to stimulus, and response to symbiont. KEGG pathway analysis indicated significant enrichment in circadian rhythm - plant and taurine and hypotaurine metabolism (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The MEbrown module comprises 471 DEGs, which are significantly negatively correlated with SOD activity and positively correlated with the photosynthetic parameter FV/F₀. GO terms significantly enriched in this module include photosynthesis, light harvesting in photosystem I, photosynthesis, light harvesting, and carbohydrate metabolic process. KEGG pathways mainly enriched include photosynthesis - antenna proteins, starch and sucrose metabolism, and biosynthesis of various plant secondary metabolites. The MEred and MEyellow modules contain 77 and 137 DEGs, respectively, both showing a significant positive correlation with FV/F₀. The MEred module is significantly enriched in GO terms such as malate transmembrane transporter activity, chloroplast inner membrane, and C4-dicarboxylate transmembrane transporter activity, and in KEGG pathways such as plant hormone signal transduction, and MAPK signaling pathway - plant. The MEyellow module is enriched in GO terms including serine-type endopeptidase inhibitor activity, and response to extracellular stimulus, with significant enrichment in the plant hormone signal transduction pathway in KEGG. The MEblue module includes 477 DEGs, GO enrichment analysis shows this module is significantly associated with oxidoreductase activity, glutathione transferase activity, and heme binding. KEGG pathway analysis indicates enrichment in glutathione metabolism, MAPK signaling pathway - plant, and phenylpropanoid biosynthesis. The MEblack module consists of 35 DEGs, which are significantly negatively correlated with POD, CAT, and SOD activities, however, show positively correlated with SD. GO enrichment reveals significant involvement in water transmembrane transporter activity, and water channel activity. The MEturquoise module contains the largest number of DEGs (636), which are significantly negatively correlated with SS and Pro content. GO analysis indicates significant enrichment in rRNA processing, rRNA metabolic process, and ncRNA processing. KEGG enrichment analysis highlights involvement in ribosome biogenesis in eukaryotes, protein processing in endoplasmic reticulum, and spliceosome pathways.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGO and KEGG Enrichment Analysis of DEGs in Special Modules.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModule\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eGO Enrichment Terms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eKEGG Enrichment Pathways\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTerms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFDR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMEgreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresponse to salicylic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.79E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecircadian rhythm - plant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.87E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresponse to symbiont\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.79E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003etaurine and hypotaurine metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eMEbrown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eregulation of response to stimulus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.67E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ephotosynthesis - antenna proteins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.55E-09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ephotosynthesis, light harvesting in photosystem I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003estarch and sucrose metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00E-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ephotosynthesis, light harvesting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ebiosynthesis of various plant secondary metabolites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.47E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecarbohydrate metabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.82E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epentose and glucuronate interconversions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.30E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ephotosystem I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.02E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003esteroid biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.60E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ephotosystem II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.46E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMEred\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emalate transmembrane transporter activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.06E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eplant hormone signal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC4-dicarboxylate transmembrane transporter activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.06E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMAPK signaling pathway - plant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.53E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echloroplast inner membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.06E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eplastid inner membrane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.06E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMEyellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eserine-type endopeptidase inhibitor activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.16E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eplant hormone signal transduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.55E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eresponse to extracellular stimulus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.52E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMEblue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eoxidoreductase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.91E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eglutathione metabolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.38E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eheme binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.29E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMAPK signaling pathway - plant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.80E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003etetrapyrrole binding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.93E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ephenylpropanoid biosynthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.46E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eglutathione transferase activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.38E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMEblack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewater transmembrane transporter activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.09E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewater channel activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.09E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eMEturquoise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erRNA processing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.97E-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eribosome biogenesis in eukaryotes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.72E-18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erRNA metabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.28E-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eprotein processing in endoplasmic reticulum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.76E-06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003encRNA processing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.26E-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003espliceosome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.96E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003encRNA metabolic process\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.88E-22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eribonucleoprotein complex biogenesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.16E-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Identification of Hub Genes Associated with Drought\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMost metabolic processes are complex, due to not only the actions of single genes, but also interactions among combinations of genes. A sizeable portion of the genes in each network module exhibited extremely high connectivity with other genes belonging to other modules, and were designated as hub genes. Owing to their central position within the network clusters, the hub genes were considered to be vital components of the networks. Therefore, we conducted a Network Analyzer-based analysis and found that 10% of the genes in the modules as hub genes and chosen for further study (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAccording to the gene classification results, 63 TFs from the 196 hub genes were identified, and they belonged to distinct families, such as MYB (\u003cem\u003eSoltu.DM.06G004440.v6.1\u003c/em\u003e), MYB-related (\u003cem\u003eSoltu.DM.02G004510.v6.1\u003c/em\u003e), bHLH (\u003cem\u003eSoltu.DM.01G041140.v6.1\u003c/em\u003e, \u003cem\u003eSoltu.DM.08G022630.v6.1\u003c/em\u003e), ERF (\u003cem\u003eSoltu.DM.10G005000.v6.1, Soltu.DM.12G025240.v6.1\u003c/em\u003e), and AP2 (\u003cem\u003eSoltu.DM.10G023790.v6.1\u003c/em\u003e), among others. The core genes with high connection were also identified, including peroxygenase, probable protein phosphatase 2C, protein kinase, photosystem-related, among others (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Quantitative Real-Time PCR (qRT-PCR) Validation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo assess the reliability of the RNA-seq data, 9 DEGs were randomly selected for validation via quantitative real-time PCR (qRT-PCR). Gene-specific primers were designed using Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, CA, USA) (Table S2). The qRT-PCR results closely mirrored the RNA-seq expression profiles, with consistent expression trends observed across all tested genes (Figure S3). A high correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.94) between the two datasets further confirmed the robustness and accuracy of the transcriptome data.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDrought stress significantly influences the growth, development, and yield of potato, one of the world\u0026rsquo;s most important food crops. Prolonged water deficit conditions disrupt physiological and biochemical processes, leading to reduced photosynthetic capacity, oxidative damage, and impaired nutrient uptake. These adverse effects ultimately compromise tuber development and quality. Understanding the molecular and physiological mechanisms by which potato responds to drought stress is essential for developing strategies to improve drought tolerance, especially in regions experiencing increased climate variability and water scarcity. In recent years, melatonin has emerged as a promising plant growth regulator with multifunctional roles in stress mitigation, including antioxidant defense, photosynthesis stabilization, and gene regulation. This study aims to systematically evaluate the regulatory effects of different concentrations of melatonin (MT) on the physiological and molecular responses of potato seedlings under drought stress. Four treatments were established: normal watering (CK), drought stress with purified water spray (D),\u003c/p\u003e\u003cp\u003eand drought stress with foliar application of 50 \u0026micro;mol/L MT (T1) and 100 \u0026micro;mol/L MT (T2). Based on these treatments, physiological parameter measurements and RNA-seq transcriptome analysis\u003c/p\u003e\u003cp\u003ewere conducted. Furthermore, Mfuzz clustering and WGCNA were integrated to identify key regulatory pathways and hub genes involved in the melatonin-mediated drought response. The findings of this study will provide theoretical support and potential applications for understanding\u003c/p\u003e\u003cp\u003ethe molecular mechanisms by which melatonin alleviates drought stress and for improving drought tolerance in potato.\u003c/p\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Potato Differential Responses to Drought Gradient at the Phenotypic and Physiological Levels\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe present study demonstrates that drought stress imposes significant physiological constraints on potato seedlings, primarily through two interconnected pathways: growth inhibition and oxidative damage. The observed reduction in plant height and stem diameter reflects a fundamental resource allocation shift, where limited water availability likely restricts cell expansion and photosynthetic capacity. This is further corroborated by the decline in chlorophyll fluorescence parameters (Fv/Fo), indicative of impaired photosystem II efficiency and reduced photosynthetic electron transport under water deficit. Concomitantly, drought triggered a pronounced oxidative burst, evidenced by elevated MDA content (marker of membrane lipid peroxidation), accumulation of ROS, and increased cell death. While the plant initiated compensatory responses, including the upregulation of proline and soluble sugars (likely acting as osmolytes and ROS scavengers) and enhanced activities of key antioxidant enzymes (SOD, CAT, POD), these endogenous defenses proved insufficient to fully mitigate the stress-induced damage under severe drought conditions.\u003c/p\u003e\u003cp\u003eA key finding of this study is the potent, yet concentration-dependent, role of exogenous melatonin in enhancing potato drought tolerance. Melatonin application at 50 \u0026micro;mol/L and 100 \u0026micro;mol/L effectively counteracted multiple detrimental effects of drought. The promotion of plant growth parameters and photosynthetic performance suggests melatonin may improve water use efficiency or carbon assimilation. More critically, melatonin at these optimal doses robustly activated the antioxidant defense system, leading to significantly reduced ROS accumulation (as confirmed by DAB and NBT staining), decreased membrane peroxidation (lower MDA), and diminished cell death (reduced Trypan blue staining). This aligns with the well-established role of melatonin as a potent antioxidant and master regulator of redox homeostasis, capable of both directly scavenging ROS and up-regulating endogenous antioxidant enzyme activities.\u003c/p\u003e\u003cp\u003eHowever, the hormetic nature of melatonin\u0026rsquo;s action is clearly illustrated by the results obtained with 150 \u0026micro;mol/L application. At this higher concentration, melatonin itself appeared to induce stress, as reflected in the paradoxical increase in ROS levels and the decline in some antioxidant enzyme activities (SOD, POD), ultimately leading to greater cell death compared to lower melatonin doses. This suggests that excessive melatonin may disrupt cellular redox balance, potentially through pro-oxidant effects or interference with normal metabolic/defense signaling pathways, outweighing its beneficial effects.\u003c/p\u003e\u003cp\u003eIn conclusion, our findings confirm that moderate exogenous melatonin supplementation (particularly 100 \u0026micro;mol/L) is a highly effective strategy for alleviating drought stress in potato seedlings. It achieves this primarily by bolstering the antioxidant defense machinery and preserving membrane integrity, thereby mitigating oxidative damage and promoting growth recovery. Crucially, this study highlights the strict dose-dependency of melatonin\u0026rsquo;s efficacy, establishing an optimal concentration range and providing a critical warning against the potential phytotoxicity of supra-optimal applications. This knowledge is essential for developing melatonin-based biostimulant strategies for sustainable potato cultivation under water-limited conditions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Transcription Factors (TFs) Related Genes Are a Vital Component of Drought Response Machinery\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe identification of TFs among the DEGs provides crucial insights into the regulatory architecture underlying the biological responses observed in the studied samples. In total, 64 TF families were represented, highlighting the diverse and multilayered transcriptional regulation potentially involved in stress adaptation, hormone signaling, and development (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the MYB and MYB_related families were the most abundant, with 178 and 147 DEGs, respectively. These TFs are widely recognized for their roles in secondary metabolism, cell fate determination, and responses to abiotic stress [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Their high representation implies they may function as central regulators in orchestrating complex stress-response pathways.\u003c/p\u003e\u003cp\u003eSimilarly, the significant presence of bHLH (139 DEGs), ERF (145 DEGs), NAC (113 DEGs), and M-type (146 DEGs) TFs further supports the hypothesis of a stress-responsive transcriptional reprogramming. Members of the bHLH and NAC families are frequently implicated in abiotic stress tolerance, such as drought and salinity, as well as developmental processes like lateral root formation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. ERFs, which are part of the AP2/ERF superfamily, are closely linked to ethylene signaling and defense responses, suggesting a potential role in biotic stress or pathogen interactions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For instance, in a study on \u003cem\u003ePopulus trichocarpa\u003c/em\u003e under drought stress, also reported MYB, NAC, and ERF as the top three enriched TF families, supporting their conserved roles in abiotic stress responses across diverse plant species [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, Zhang et al. investigated salt stress responses in \u003cem\u003eGlycine max\u003c/em\u003e and identified a significant number of DEGs encoding bZIP, WRKY, and bHLH transcription factors, which are believed to function in ABA-dependent signaling pathways and transcriptional reprogramming of antioxidant enzymes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe presence of B3 (122 DEGs), HB-other (105 DEGs), and bZIP (76 DEGs) TFs also suggests involvement in hormone-mediated signal transduction, particularly abscisic acid (ABA) and auxin pathways, which are central to plant growth regulation under stress. Interestingly, while families such as Whirly, RAV, S1Fa-like, and NF-X1 exhibited fewer DEGs (2\u0026ndash;3 genes each), their involvement should not be overlooked. Despite their smaller gene counts, members of these families have been associated with chloroplast function, DNA repair, or developmental gene silencing, indicating that even low-abundance TFs might have specific and critical regulatory functions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Altogether, the dynamic and varied distribution of transcription factor families among DEGs underscores a highly coordinated regulatory network likely modulating gene expression in response to both intrinsic developmental cues and extrinsic environmental signals.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Photosynthesis Related Genes Play a Critical Role in Drought Stress Response Regulation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePhotosynthesis-related genes emerged as pivotal components in the transcriptional response to drought stress and melatonin treatments in potato. Both Mfuzz clustering and WGCNA analysis independently highlighted photosynthetic pathways as being tightly regulated under varying stress conditions, suggesting their central role in stress adaptation and recovery processes. In the Mfuzz analysis, cluster 5 was particularly noteworthy, displaying high expression specifically under drought stress combined with 100 \u0026micro;mol/L melatonin treatment (M2). This cluster showed significant GO enrichment in terms such as photosynthesis, light harvesting, and response to abiotic stimulus, indicating that photosynthetic machinery may be actively maintained or restored when melatonin is applied under stress. Correspondingly, KEGG pathways such as photosynthesis -antenna proteins, photosynthesis, and carotenoid biosynthesis were enriched, reflecting enhanced light energy utilization and potential photoprotection mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results suggest that melatonin at higher concentrations may promote or stabilize photosynthetic gene expression, possibly contributing to improved drought resilience.\u003c/p\u003e\u003cp\u003eComplementarily, WGCNA results reinforced this finding. The MEbrown module, which contains 471 DEGs, showed a significant positive correlation with FV/F0, a key photosynthetic efficiency parameter, and was enriched in GO terms like photosynthesis, light harvesting in photosystem I, and carbohydrate metabolic process. These functions are essential not only for energy production but also for the regulation of downstream stress signaling and metabolic adjustment. The KEGG enrichment in this module further supports its central role, with significant terms including photosynthesis-antenna proteins, starch and sucrose metabolism, and biosynthesis of various plant secondary metabolites, pathways that contribute to both energy storage and stress tolerance mechanisms (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent research has provided growing evidence that melatonin plays a key regulatory role in maintaining photosynthetic activity under drought stress by modulating the expression of photosynthesis-related genes and protecting chloroplast structure and function. For instance, Zhang et al. investigated the effect of melatonin on drought-stressed \u003cem\u003eMalus hupehensis\u003c/em\u003e and found that melatonin application significantly enhanced net photosynthetic rate (Pn) and chlorophyll fluorescence parameters (Fv/Fm) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Transcriptomic analysis revealed up-regulation of genes encoding photosystem II reaction center proteins (PsbA, PsbD) and light-harvesting chlorophyll-binding proteins, which are essential for maintaining the efficiency of the light-dependent reactions of photosynthesis under stress conditions. Similarly, exogenous melatonin treatment in drought-stressed maize seedlings not only increased chlorophyll content and stomatal conductance, but also elevated the expression of genes involved in the Calvin cycle, such as rbcL and FBA, suggesting a role of melatonin in sustaining carbon fixation under stress [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Melatonin-treated tomato under drought stress exhibited significantly higher photosynthetic electron transport rates and CO\u003csub\u003e2\u003c/sub\u003e assimilation. RNA-seq analysis identified that genes associated with photosynthesis-antenna proteins, chloroplast development, and redox homeostasis were differentially expressed, supporting the idea that melatonin modulates both energy production and oxidative protection in chloroplasts [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAltogether, these results strongly indicate that photosynthetic function is not merely suppressed under drought, but is dynamically modulated, particularly in response to melatonin treatments. The transcriptional up-regulation of photosynthesis-related genes along with enhanced expression of light-harvesting and carbon metabolism pathways suggests that maintaining photosynthetic capacity is a key strategy for drought resistance in potato. Moreover, the co-expression patterns with antioxidant and hormone-responsive genes further highlight the multifaceted regulatory role of photosynthesis in orchestrating the broader stress response network.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Significantly Enriched Metabolic Pathways of DEGs Under Drought Stress\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTranscriptomic reprogramming under drought stress often involves the coordinated regulation of key metabolic pathways that mediate energy homeostasis, cellular protection, and signaling integration. In this study, both Mfuzz clustering and WGCNA analysis revealed a set of consistently enriched metabolic pathways, which appear to play central roles in potato's drought response, especially under exogenous melatonin treatment.\u003c/p\u003e\u003cp\u003eStarch and sucrose metabolism was prominently enriched in both cluster 3 (Mfuzz) and the MEbrown module (WGCNA). Genes involved in starch degradation (e.g.,β-amylase, glucan phosphorylase) and sucrose biosynthesis were up-regulated under drought combined with melatonin treatment, suggesting enhanced carbon remobilization to support osmotic adjustment and energy supply. This finding aligns with Yin et al., who observed that melatonin application in \u003cem\u003eOryza sativa\u003c/em\u003e under drought promoted sucrose accumulation and up-regulated sucrose synthase genes, facilitating improved root growth and water uptake [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePhenylpropanoid biosynthesis also enriched in cluster 4 and MEblue, is associated with the synthesis of lignin, flavonoids, and other secondary metabolites with antioxidative and structural functions. Under drought, these compounds help in reinforcing cell walls and scavenging ROS. A previous study by Ma et al. revealed that melatonin treatment in \u003cem\u003eBrassica napus\u003c/em\u003e significantly up-regulated genes involved in phenylpropanoid biosynthesis, particularly PAL, C4H, and 4CL, resulting in increased lignin deposition and drought tolerance [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Our results suggest that melatonin may similarly enhance structural defenses and redox buffering through this pathway.\u003c/p\u003e\u003cp\u003eGenes related to glutathione metabolism, such as glutathione S-transferases (GSTs) and glutathione reductase, were significantly enriched in cluster 4 and the MEblue module. This suggests the activation of antioxidant defense mechanisms to counteract drought-induced oxidative stress. Importantly, this pathway showed a negative correlation with photosynthetic efficiency (FV/F₀), indicating a possible resource allocation trade-off between growth and defense.\u003c/p\u003e\u003cp\u003eIn \u003cem\u003eArabidopsis\u003c/em\u003e, melatonin-enhanced glutathione biosynthesis led to a marked reduction in H₂O₂ levels and increased drought tolerance [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The convergence of glutathione metabolism in multiple expression clusters and modules highlights its core protective role in redox regulation during abiotic stress.\u003c/p\u003e\u003cp\u003eIn cluster 3, significant enrichment was observed in biosynthesis of various plant secondary metabolites and steroid biosynthesis. These metabolic processes are known to mediate hormone-like signaling, membrane stabilization, and interaction with ABA and brassinosteroid pathways under stress. Steroids such as campesterol and stigmasterol have been implicated in improving membrane fluidity and signaling efficiency, facilitating stress perception and transduction. Recent work by Wang et al. in \u003cem\u003eMedicago truncatula\u003c/em\u003e found that melatonin induced expression of sterol biosynthesis genes, promoting drought tolerance by modulating membrane stability and ABA sensitivity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Thus, our results support a role for melatonin in metabolic rewiring toward signaling lipids under stress.\u003c/p\u003e\u003cp\u003eConversely, cluster 8 and the MEturquoise module involved in ribosome biogenesis, aminoacyl-tRNA biosynthesis, and protein processing in the ER. These pathways, though essential under normal conditions, were likely suppressed to conserve energy and redirect resources toward stress-specific processes. Similar suppression of ribosome biogenesis was reported in maize under drought [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], which helps reduce translation burden and prevent misfolded protein accumulation. Our results affirm that basic biosynthetic pathways are transcriptionally repressed under drought, especially when protective pathways are activated by melatonin.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study systematically elucidates the multi-level mechanisms by which exogenous melatonin enhances drought tolerance in potato seedlings. Appropriate concentrations of melatonin (50\u0026ndash;100 \u0026micro;mol/L) significantly alleviated drought-induced growth inhibition and oxidative damage. The underlying mechanisms include activation of the antioxidant defense system, leading to increased activities of key enzymes such as SOD, CAT, and POD. Accumulation of osmolytes (proline and soluble sugars) to maintain cellular water potential and osmotic balance. Stabilization of the photosynthetic apparatus, with improved PSII efficiency and chlorophyll content. Molecular reprogramming of drought responses through the regulation of transcription factors (e.g., MYB, bHLH, NAC) and key metabolic pathways, including photosynthesis, phenylpropanoid biosynthesis, and glutathione metabolism. WGCNA and clustering further identified several modules and hub genes closely associated with drought-related traits, highlighting the central regulatory role of melatonin in plant drought stress response. Notably, the effects of melatonin are dose-dependent\u0026mdash;excessive application (150 \u0026micro;mol/L) may trigger secondary stress responses and exacerbate cellular damage, underscoring the importance of precise concentration control in agricultural practice. Overall, this research not only deepens our theoretical understanding of melatonin-mediated stress regulation in plants but also provides valuable insights for improving drought resistance in potato through melatonin-based agronomic strategies.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:X.Zhai conceived the study. S.Liu, Y.Wang, X.Wang, X.Zhang, Y.Li, Y.Chen, J.Yang, L.Liu, and X.Zhai performed the experiments and carried out the analysis. S.Liu and X.Zhai wrote the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was financially supported by the Zhangjiakou Science and Technology Program Project (No. 2311003A), the Doctoral Research Initiation Fund of Hebei North University (No. BSJJ202225), the Basic Scientific Research Operating Expenses Program for Provincial Universities (No. JYT2025009), Science Research Project of Hebei Education Department (No. BJK2022028), and the College Student Innovation and Entrepreneurship Training Program of Hebei North University (No. S202510092056).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e: Not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe raw sequencing data were deposited into the NCBI Sequence Read Archive (SRA, Accession PRJNA1301854)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest. Further, the authors declare that the funder did not play any role in the design of the study; collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRaza A, Mubarik MS, Sharif R, Habib M, Jabeen W, Zhang C, Chen H, Chen Z-H, Siddique KHM, Zhuang W, et al. Developing drought-smart, ready-to-grow future crops. Plant Genome. 2023:16(1):e20279. https://doi.org/10.1002/tpg2.20279\u003c/li\u003e\n\u003cli\u003eBuragohain K, Tamuly D, Sonowal S, and Nath R. Impact of drought stress on plant growth and its management using plant growth promoting rhizobacteria. Indian J Microbiol. 2024:64(2):287\u0026ndash;303. https://doi.org/10.1007/s12088-024-01201-0\u003c/li\u003e\n\u003cli\u003eWang R, Qin X, Pan H, Li D, Xiao X, Jin Y, Wang Y, and Liang H. 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Plant J. 2021, 108, 1454\u0026ndash;1469. https://doi.org/10.1111/tpj.15530\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":"Potato, Melatonin, Drought stress, Antioxidant system, Photosynthesis","lastPublishedDoi":"10.21203/rs.3.rs-7382006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7382006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought is one of the major abiotic stress factors limiting the growth, development, and yield of potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.). Melatonin, a novel plant hormone, has recently shown significant potential in enhancing plant stress resistance. However, its regulatory mechanisms in response to drought stress in potato remain unclear. In this study, potato seedlings were treated with different concentrations of exogenous melatonin (50, 100, and 150 \u0026micro;mol/L) under controlled drought conditions to systematically evaluate their physiological and molecular responses. The results demonstrated that appropriate melatonin application\u0026mdash;especially at 100 \u0026micro;mol/L\u0026mdash;effectively alleviated drought-induced growth inhibition, oxidative stress, and photosynthetic impairment. This was evidenced by increased plant height, enhanced photosynthetic efficiency, reduced reactive oxygen species (ROS) accumulation, decreased cell death and lipid peroxidation, as well as elevated antioxidant enzyme activities (SOD, CAT, POD) and levels of osmoprotectants (proline and soluble sugars). Transcriptome analysis revealed that melatonin modulates numerous drought-responsive differentially expressed genes (DEGs), including multiple transcription factor families (e.g., MYB, NAC, ERF), and pathways related to photosynthesis, antioxidative metabolism, hormone signaling, and carbon metabolism. Furthermore, weighted gene co-expression network analysis (WGCNA) and Mfuzz clustering identified key gene modules and central hub genes strongly associated with photosynthetic performance and antioxidant indicators. This study provides a theoretical foundation for applying melatonin in potato drought stress mitigation and lays a molecular basis for developing hormone-based drought-resistant agricultural strategies.\u003c/p\u003e","manuscriptTitle":"Integrative WGCNA Analysis Uncovers the Molecular Framework of Melatonin-Mediated Drought Stress Mitigation in Potato","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 11:45:53","doi":"10.21203/rs.3.rs-7382006/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-26T16:23:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T18:04:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T23:05:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T17:38:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T14:32:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163500740023650985584218206546906975604","date":"2025-09-16T07:27:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1510336698538868656362800609369280966","date":"2025-09-16T04:00:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275667955302763775637254697178598973214","date":"2025-09-15T19:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259870195879985336397489903253281534398","date":"2025-09-15T19:22:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T19:20:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T13:26:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-28T03:34:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-27T02:49:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-08-27T02:44:48+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":"51787499-4575-4840-8bf2-e8af37831432","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:01:52+00:00","versionOfRecord":{"articleIdentity":"rs-7382006","link":"https://doi.org/10.1186/s12870-026-08315-1","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2026-02-09 15:59:02","publishedOnDateReadable":"February 9th, 2026"},"versionCreatedAt":"2025-09-24 11:45:53","video":"","vorDoi":"10.1186/s12870-026-08315-1","vorDoiUrl":"https://doi.org/10.1186/s12870-026-08315-1","workflowStages":[]},"version":"v1","identity":"rs-7382006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7382006","identity":"rs-7382006","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}