Integrative Physiological, Metabolomic, and Transcriptomic Analyses Provide New Insights into Potato Drought Stress Responses

Integrative Physiological, Metabolomic, and Transcriptomic Analyses Provide New Insights into Potato Drought Stress Responses | 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 Physiological, Metabolomic, and Transcriptomic Analyses Provide New Insights into Potato Drought Stress Responses Shunjuan Gao, Rundong Su, Weidong Wang, Wenping Qiu, Yang Cui, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7885634/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in BMC Plant Biology → Version 1 posted 12 You are reading this latest preprint version Abstract Background Potato ( Solanum tuberosum L.) is an essential crop for food production and industrial use, yet its growth and development are substantially constrained by drought stress. Drought not only causes marked reductions in tuber yield but also compromises overall plant growth and health. Flavonoids, due to their antioxidant capacity, play a critical role in drought tolerance, and hormone signaling pathways also modulate drought responses. TFs further coordinate these processes by regulating genes in flavonoid biosynthesis and across hormone-signaling pathways. To elucidate the molecular basis of potato adaptation to drought, we conducted integrated metabolomic and transcriptomic analyses of two cultivars subjected to drought treatment. Results In this study, we identified 3,001 metabolites, including 88 classified as flavonoids. Under drought, both HL15 and J8 exhibited pronounced metabolite accumulation alongside significant up-regulation of genes in the flavonoid biosynthetic pathway, indicating a central role for flavonoid metabolism in the drought response of potato. Transcriptome profiling further showed that drought-responsive genes were predominantly enriched in pathways related to flavonoid biosynthesis and plant hormone signal transduction. Correlation analysis, combined with WGCNA, identified three transcription factors that may regulate flavonoid metabolism and hormone signaling under drought conditions. The expression of flavonoid biosynthetic genes, along with the accumulation of most flavonoid metabolites, contributed to enhanced drought tolerance. In addition, plant hormone signaling—particularly the abscisic acid (ABA) pathway—also shaped the drought response. We identified seven key candidate genes involved in regulating flavonoid biosynthesis under drought. Further investigation into flavonoid metabolism and ABA signaling identified three transcription factors as potential regulators of drought tolerance. Conclusions Collectively, these findings demonstrate a significant enrichment of flavonoid pathways and hormone signaling in drought-stressed potato seedlings, providing actionable insights and datasets to inform future studies on drought resistance. Potato Metabolome Transcriptome Drought stress Flavonoids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Given its central place in the architecture of global food security, the potato is a multifaceted strategic crop that addresses food security challenges posed by accelerating climate change and the continued growth of the human population [ 1 ]. As the fourth most widely cultivated crop worldwide in terms of planting area and total output, after rice, wheat, and corn [ 2 ], potato exhibits outstanding nutritional value, high yield potential, and adaptability to diverse agricultural ecosystems [ 3 ]. However, due to its shallow root system, the potato is particularly susceptible to drought stress [ 4 ]. Drought stress causes a substantial decline in tuber yield by inhibiting photosynthetic carbon assimilation and disrupting water balance [ 5 , 6 ]. It also interferes with the distribution of assimilates during tuber expansion, alters tuber quality-related traits, and induces a cascade of physiological and metabolic disorders, including oxidative damage and destruction of photosynthetic structures [ 7 , 8 ]. It threatens overall plant growth, development and health ultimately., Plants have developed a range of physiological and molecular adaptive mechanisms to tolerate drought stress and mitigate its adverse effects on physiological processes over the course of long-term evolution. For example, drought stress promotes the accumulation of antioxidant compounds in the leaves of sweet potatoes (Ipomoea batatas), such as flavonoids, polyphenols, and glutathione. These metabolites help scavenge reactive oxygen species (ROS) and reduce oxidative damage, thereby enhancing the drought adaptability of sweet potato [ 9 ]. Plant hormones serve as central signal integrators under drought stress, coordinating and reprogramming the complex signaling cascades that govern plant development and stress adaptation. For instance, abscisic acid (ABA) levels increase markedly under drought stress, whereas indole-3-acetic acid (IAA) and gibberellin (GA) levels decline, indicating the suppression of growth hormone-responsive genes. This hormonal adjustment restricts shoot elongation, thereby reducing water consumption in above-ground tissues [ 10 , 11 ]. Plant defense responses are closely associated with flavonoid accumulation. Flavonoids constitute a significant class of plant polyphenols comprising over 6,900 secondary metabolites that play diverse roles in plant growth and development [ 12 , 13 ]. Flavonoids are widely distributed in plants and are characterized by a C6-C3-C6 backbone, in which a three-carbon chain (C3) links two aromatic rings [ 14 ]. Based on structural differences in the heterocyclic C ring, flavonoids can be classified into chalcone, aurone, flavone, isoflavone, flavanone, dihydroflavonol, anthocyanin, leucoanthocyanidin, flavanol, and flavan-3-ol [ 15 ]. Flavonoid biosynthesis begins with phenylalanine and proceeds through successive catalysis by key enzymes of the phenylalanine metabolic pathway, including phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate: CoA ligase (4CL), chalcone synthase (CHS), chalcone isomerase (CHI), flavonoid synthase (FNS), and flavone 3-hydroxylase (F3H) [ 26 ]. Due to their antioxidant properties, flavonoids play a crucial role in enhancing plant resistance to abiotic stresses, particularly drought [ 16 , 17 ]. For example, in sweet potato under drought stress, the developmental stage strongly influences the regulation of core flavonoid biosynthetic genes such as CYP75B1 and IF7MAT , affecting processes including branching, tuber germination, and root storage expansion. Stage-resolved transcriptional regulation is accompanied by differential accumulation of flavonol derivatives [ 19 ]. In apple ( Malus domestica ), although flavonoids are crucial for adaptation to environmental stress and typically accumulate under stress conditions, the molecular mechanisms regulating flavonoids under stress remain unclear in potato. Transcription factors (TFs) coordinate flavonoid biosynthesis with growth, development, and stress-response networks [ 20 , 21 ]. The MYB in plants, one of the largest TF groups, is extensively involved in growth and developmental regulation, cell differentiation, primary and secondary metabolism, and responses to biotic and abiotic stresses [ 22 , 23 ]. MYB tightly regulate the expression of structural genes in the flavonoid pathway. Through cis-regulatory elements in the promoter, ThMYB14 (R2R3-MYB) in Tetrastigma hemsleyanum binds to the AAC motif within MBS/MBSI elements upstream of flavonoid biosynthetic genes, thereby enhancing pathway output [ 24 ]. FeMYBF1 in buckwheat (Fagopyrum esculentum Moench) also promotes flavonol synthesis [ 25 ]. Additionally, WRKY are involved in various physiological processes throughout plant growth and development, including leaf senescence, seed development, dormancy, and germination, as well as responses to both biotic and abiotic stresses. Plant hormones also play a crucial role in plant stress resistance. Abscisic acid (ABA) is a plant hormone that mediates stress responses and plays a significant role in both stress resistance and flavonoid biosynthesis [ 26 ]. Drought stress induces the production and accumulation of ABA in plant organs, thereby activating downstream signaling pathways [ 27 ]. In addition, multiple other plant hormones contribute to regulating drought adaptability through changes in biosynthesis and signaling pathways, including gibberellin (GA), auxin (AUX), salicylic acid (SA), brassinosteroid (BR), and ethylene (ET) [ 28 ]. TFs respond to diverse adverse conditions by regulating genes involved in various hormone signaling pathways. Previous studies have demonstrated that transcription factors such as MYB and NAC regulate drought-responsive genes, thereby contributing to enhanced drought tolerance in plants [ 29 , 30 ]. In this study, two potato cultivars with differing drought sensitivities were selected for metabolomic and transcriptomic analyses to elucidate metabolite accumulation and changes in the expression of flavonoid biosynthetic genes in potato under drought stress. The hormone signal transduction pathways of potato were also examined. Furthermore, a pathway gene–transcription factor network associated with the flavonoid biosynthesis pathway under drought stress was constructed, enabling the identification of potential drought-responsive transcription factors. Results Physiological Responses to Drought Stress Drought-induced oxidative status was evaluated by profiling superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities, along with malondialdehyde (MDA) content, in potato seedlings. The results (Figure 1A) showed that under drought stress, POD and CAT activities increased significantly in both HL15 and J8, whereas SOD activity remained unchanged in HL15. Compared with HL15, J8 exhibited higher SOD, POD, and CAT activities. Moreover, as the duration of drought treatment increased, SOD, POD, and CAT activities progressively rose, indicating that J8 possesses a stronger antioxidant capacity. Analysis of MDA content revealed a significant reduction in both cultivars under drought stress, suggesting that drought mitigated lipid peroxidation in potato cell membranes. Drought-Responsive Metabolite Profiling in Potato Seedlings To further investigate metabolite dynamics in Potato Seedlings under drought stress, we performed non-targeted liquid chromatography–tandem mass spectrometry (LC–MS) to characterize metabolite composition. The metabolome comprised 3,001 annotated features, including 191 terpenoids (35.90%), 95 steroids (17.86%), 88 flavonoids (16.54%), 50 phenolic acids (9.40%), 35 organic acids (6.58%), 21 alkaloids (3.95%), 17 lignans (3.20%), 14 indoles (2.63%), 10 coumarins (1.88%), four quinones (0.75%), four tannins (0.75%), and three astragalosides (0.56%) (Figure 1B). Principal component analysis (PCA) was performed to assess differences in metabolite accumulation between the two cultivars under drought stress across four developmental stages (Figure 1C). The results showed that the variance explained by the first principal component (PC1) was 51.50%, and that of the second principal component (PC2) was 25.50%. Along the PC1 axis, the two cultivars were separated, indicating significant differences in metabolite accumulation between the drought-sensitive cultivar HL15 and the drought-resistant cultivar J8, which persisted under drought stress. Interestingly, the two cultivars were completely separated along PC2 at 0 h and 12 h, and partially separated between 12 h and 24 h; however, they showed slight separation between 24 h and 36 h. This indicated that under drought stress, metabolite accumulation differed most markedly at 12 h. As time progressed, these differences diminished, and by 24 h, there was little distinction from 36 h. These findings suggest that the seedlings of two potato cultivars responded most strongly to drought stress during the first 24 hours. Differentially accumulated metabolites (DAMs) were defined using the combined thresholds |log2FC| ≥ 1 and VIP > 1 to capture drought-responsive shifts in metabolite accumulation. The results (Figure 1D) showed that comparisons H12 vs. H0, H24 vs. H0, H36 vs. H0, J12 vs. J0, J24 vs. J0, and J36 vs. J0 yielded 702 (495 upregulated, 207 downregulated), 775 (471 up, 304 down), 655 (453 up, 202 down), 620 (351 up, 269 down), 733 (459 up, 274 down), and 678 (420 up, 258 down) DAMs, respectively. Ranking metabolites by fold change (Supplementary Fig. S1) showed that at 12 h, 24 h, and 36 h vs. 0 h, most of the top 20 features with the most considerable inter-cultivar differences were upregulated DAMs. Moreover, a higher proportion of upregulated metabolites was observed in the drought-resistant cultivar J8 than in the drought-sensitive cultivar HL15. Over time, this proportion gradually declined. Notably, flavonoid metabolites were relatively abundant in both cultivars, and their fold-change values continued to increase over time. To examine pathway-level changes under drought stress, drought-induced DAMs were analyzed using KEGG enrichment to identify significantly perturbed pathways in potato seedlings. As shown in Figure 2, KEGG pathways consistently enriched during J8 development included α-linolenic acid metabolism, linoleic acid metabolism, flavone/flavonol biosynthesis, and phenylpropanoid biosynthesis. Enrichment of α-linolenic acid metabolism, together with flavanone and flavonol synthesis, was observed across all five comparison groups, whereas the flavonoid biosynthesis pathway showed significant enrichment in three groups. In J8, phenylpropanoid biosynthesis increased over time under drought stress, and phenylalanine metabolism was also significantly enriched in H24 vs. H0. Phenylalanine metabolism provides the precursor for the phenylpropanoid biosynthetic pathway. These four pathways play central roles in orchestrating drought responses in potato seedlings. Notably, enrichment of the flavonoid biosynthesis and flavone/flavonol synthesis pathways in HL15 was higher than in J8 at 12 h but decreased thereafter. This may reflect the shorter response duration of drought-sensitive cultivars compared with drought-resistant cultivars [31]. Transcriptome Analysis of the responses of the Two Potato cultivars to Drought Stress We sequenced 24 samples to investigate the molecular regulatory mechanisms in tissue-cultured seedlings of two potato cultivars subjected to drought at multiple time points. After quality filtering, a total of 163.72 Gb of clean data were retained. For each sample, clean data exceeded 5.99 Gb, with more than 96.36% of bases achieving a quality score above Q30. Clean reads from each sample were mapped to the reference genome, with alignment rates ranging from 91.04% to 93.54%. PCA analysis was performed on the transcriptome data of potato seedlings exposed to drought stress (Figure 3A). The results indicated significant differences between cultivars at different treatment stages. PC1 explained 58.60% of the variance, and PC2 explained 19.13%. Samples at 0 h were clearly separated from those at 12 h, 24 h, and 36 h, whereas no clear separation was observed among the latter three time points. This indicated that gene expression in potato underwent substantial changes under drought stress. Differentially expressed genes (DEGs) were identified using the criteria |log2FC| ≥ 1 and FDR ≤ 0.05 (Supplementary Fig. S2). The numbers of DEGs were as follows: H12 vs. H0, 18,543 (8,772 upregulated, 9,771 downregulated); H24 vs. H0, 20,559 (9,131 up, 11,428 down); H36 vs. H0, 17,104 (8,015 up, 9,089 down); J12 vs. J0, 16,331 (7,621 up, 8,710 down); J24 vs. J0, 17,983 (8,286 up, 9,697 down); J36 vs. J0, 16,600 (7,587 up, 9,013 down). Among these comparisons, both HL15 and J8 exhibited the highest numbers of DEGs at 24 h of drought treatment. Across all three time points, the drought-sensitive cultivar HL15 exhibited more total DEGs, as well as more upregulated and downregulated genes, than the drought-resistant cultivar J8. A total of 7,122 DEGs were identified across six groups (Figure 3B). Cluster analysis based on H0 gene expression classified these DEGs into two distinct groups (Figure 3C). Group I transcripts were abundantly expressed at 0 h in both H0 and J0 but decreased significantly after drought stress was applied. In contrast, Group II showed the opposite trend. Expression levels in H0 and J0 remained relatively low but increased to some extent after drought stress. These results indicated that H0 and J0 exhibited highly similar expression patterns, and that H12, H24, and H36 also showed expression profiles similar to J12, J24, and J36, suggesting that the overall gene expression trends of tissue-culture seedlings from different potato cultivars were broadly similar under drought stress. KEGG enrichment analysis (Supplementary Fig. S3) showed that ten metabolic pathways, including phenylalanine metabolism, plant hormone signal transduction, and α-linolenic acid metabolism, were significantly enriched across all stages of drought treatment. Similarly, phenylpropanoid biosynthesis was significantly enriched across all five comparison groups. Interestingly, enrichment of plant hormone signal transduction, phenylalanine metabolism, and phenylpropanoid biosynthesis first increased and then decreased in HL15, whereas it continued to rise in J8. Consistent enrichment of plant hormone signaling was observed across all stages, underscoring the pivotal contribution of hormones to drought adaptation in potato seedlings. We also employed GO enrichment analysis (Figure 4) to demonstrate that hexosyltransferase activity was highly enriched in HL15, while glycosyltransferase activity was enriched in four groups and showed an increasing trend in HL15. Additionally, xyloglucosyltransferase activity was significantly enriched in three groups, with enrichment increasing over time under drought stress. Activities of various glycosyltransferases were enhanced under drought stress. Their responses to stimuli were significantly enriched during the first 24 h in HL15 and maintained an upward trend. This regulation may trigger activation of flavonoid biosynthesis genes and further promote expression of genes mediating interactions between flavonoids and plant hormones. These findings suggest that flavonoid metabolism may represent an essential mechanism by which potato seedlings respond to drought stress. Metabolite and transcript changes in flavonoid biosynthesis pathways under drought stress To investigate in greater detail the transcriptional and metabolic changes of the flavonoid biosynthetic pathway in response to drought, 75 DEGs were identified and mapped to the flavonoid biosynthesis pathway to elucidate changes in metabolites and gene expression (Figure 5). The results indicated that phenylalanine accumulated to higher levels in J8 than in HL15 under drought stress, with peak accumulation in both cultivars within 24 h, although the overall increase was modest. PAL expression levels in J8 were higher than those in HL15, with six genes showing significant upregulation. In HL15, expression of Soltu.Atl_v3.09_0G005420 and Soltu.Atl_v3.09_2G005240 at 24 h of drought treatment was markedly higher than at other stages, likely contributing to a substantial accumulation of cinnamic acid in the subsequent reaction. Between cultivars, C4H exhibited significant expression differences, with overall upregulation, reaching its highest level at 12 h of drought treatment. J8 exhibited higher expression levels than HL15. Approximately half of the 4CL genes exhibited an upward trend compared to 0 h, peaking at 12 h or 24 h, and then decreasing by 36 h, indicating that 4CL expression was primarily concentrated during the initial 24 h. Transcript levels of CHS genes Soltu.Atl_v3.09_2G019460 and Soltu.Atl_v3.09_3G023610 were upregulated in J8 but downregulated in HL15. In J8, both genes were transiently induced before subsequently decreasing, reaching their lowest levels at 36 h. This may reflect that potato seedlings responded to drought stress by upregulating CHS genes within the first 24 h. Sustained stress likely caused severe plant injury, perturbing CHS transcriptional regulation and resulting in reduced expression. Expression levels of PAL , C4H , and CHS were significantly upregulated at 12 h or 24 h of drought treatment, then slightly decreased at 36 h, indicating that phenylalanine accumulation mainly peaked at 24 h. Using apigenin as a substrate, luteolin was synthesized through F3'H. We identified an F3'H gene ( Soltu.Atl_v3.03_3G031010 ), which showed peak expression at 24 h, with HL15 exhibiting higher expression than J8. Luteolin accumulation was also mainly concentrated in HL15, consistent with the phenylalanine-to-cinnamic acid accumulation pattern mediated by PAL. F3H also catalyzed the Conversion of Naringenin to form dihydrokaempferol, which showed an upregulated trend in HL15, and was subsequently converted to kaempferol through FLS. We identified 13 genes encoding FLS. Among them, only five were consistently upregulated in HL15, whereas 10 were upregulated in J8. FLS is a key enzyme in flavone biosynthesis, and its high expression directly influences flavone production [32]. Dihydrokaempferol can also be converted into dihydroquercetin via F3'H, and subsequently into quercetin through FLS catalysis. Quercetin accumulation increased in HL15 but decreased in J8, which was opposite to the gene expression trend of FLS . Dihydroflavonols (dihydrokaempferol and dihydroquercetin) were converted into leucoanthocyanidins (leucopelargonidin and leucocyanidin) through DFR. Only one gene, Soltu.Atl_v3.02_2G030350 was upregulated in both cultivars across all time periods. Leucoanthocyanidins are important precursors of anthocyanins, which are synthesized into anthocyanins (cyanidin and pelargonidin) through LDOX/ANS. Four genes encoding LDOX/ANS were significantly upregulated in both cultivars across all time periods, with the highest expression during the later stages of drought stress (24 h and 36 h). Among them, expression of Soltu.Atl_v3.10_0G009010 increased approximately 70-fold in HL15 and 20-fold in J8 compared with 0 h. Anthocyanins were further modified through C3G (cyanidin 3-O-glucoside) and Pg3G (pelargonidin 3-O-glucoside) through 3GT-mediated glycosylation. Approximately half of the genes encoding 3GT were upregulated, with higher expression levels observed primarily at 12 h and 24 h. Overall, drought treatment broadly induced transcription of flavonoid biosynthetic genes. In J8, the drought-resistant cultivar, metabolite accumulation and higher gene expression were mainly associated with the flavonol branch. In contrast, in the drought-sensitive cultivar, these changes were primarily linked to the anthocyanin branch in HL15. Metabolite and Transcript Changes in Plant Hormone Signal Transduction Pathways under Drought Stress Plant hormone signaling also responds actively to drought stress. Within the GA signaling pathway, GID1 expression was markedly upregulated at 12 h under drought in both cultivars and remained elevated at all time points in J8. We identified two genes encoding DELLA proteins, among which Soltu.Atl_v3.04_4G018600 was significantly upregulated in both cultivars, with expression in J8 increasing approximately six-fold compared with 0 h. In the SA signaling pathway, two NPR1 genes were identified. Of these, Soltu.Atl_v3.02_3G012800 was significantly upregulated in both cultivars, with stronger induction in J8. All five TGA genes were significantly upregulated, reaching their maximal expression at 12 h, accompanied by a sharp induction of Soltu.Atl_v3.02_3G012800 indicates that the first 12 hours represented the strongest SA signaling under drought stress. PR-1 was significantly induced at all time points in both cultivars, with higher expression observed during the 0 h–24 h period. BR signaling genes were also responsive to drought stress. Five BR-related genes encoding BAK1 were generally downregulated, a pattern associated with significant downregulation of most BSK-encoding genes. About half of the BIN2-encoding genes showed upregulation; moreover, downstream genes encoding BZR1/2 and TCH4 also showed considerable downregulation. Overall, the BR pathway was generally inhibited under drought stress, potentially through synergistic or antagonistic interactions with other hormone signaling pathways [33]. In the ET signaling pathway, ETR expression was upregulated under drought stress, with a more substantial increase in HL15. This coincided with a significant increase in CTR1 expression. Overall increases in CTR1 expression activated MPK6, whose expression generally showed an upward trend. Approximately half of the EIN3-encoding genes were upregulated through MPK6 phosphorylation, which could activate ERF1/2, thereby increasing their expression and enhancing drought stress tolerance. We identified 27 DEGs within the auxin signaling pathway, spanning four gene families. Under drought stress, AUX1 family members were downregulated, resulting in a reduced intracellular auxin concentration and decreased transcriptional activation of AUX/IAA. Consequently, among the 16 AUX/IAA genes identified, 10 were significantly downregulated, whereas three showed an upward trend. This may be because AUX/IAA inhibits downstream ARF activity, leading to reduced ARF expression [34]. Accordingly, all ARF genes we identified were downregulated except Soltu.Atl_v3.05_2G016300 . The AUX/IAA–ARF complex inhibited ARF binding to DNA, thereby blocking expression of downstream GH3 . Of the four GH3 genes identified, only Soltu.Atl_v3.05_4G018110 was consistently upregulated across all four time points in both cultivars. In the ABA signaling pathway, 22 DEGs were identified across four gene families. PYR/PYL family genes exhibited a mixed pattern, with approximately half upregulated and the other half downregulated. The downregulated genes Soltu.Atl_v3.03_3G022470 and Soltu.Atl_v3.03_4G012050 showed a decrease in expression of up to 40-fold compared with 0 h. Drought treatment significantly induced all six PP2C genes, along with six SnRK2 genes and three ABF genes. Regulatory Network of Transcription Factors and Flavonoid Biosynthesis and Plant Hormone Signal Transduction Genes The flavonoid pathway and plant hormone signal transduction are tightly regulated at the transcriptional level, with transcription factors (TFs) serving as principal drivers. A total of 1,858 transcriptional fragments were identified in this study. Among them, the MYB family was the most abundant (436 genes), followed by the GRAS family (220 genes) and the B3 family (153 genes) (Figure 7A). A correlation network (r > 0.7) was constructed to elucidate further interactions between these TFs and genes associated with flavonoid biosynthesis and plant hormone signaling pathways (Figure 7B). We observed significant positive correlations between eight MYB family genes and several flavonoid pathway genes in the network, including LDOX , PAL , 4CL , and 3GT . Two MYB transcription factor genes ( Soltu.Atl_v3.03_1G026050 , Soltu.Atl_v3.01_3G036240 ) were significantly positively correlated with the PAL gene ( Soltu.Atl_v3.06_2G003550 ), suggesting that these two genes may regulate PAL expression. Moreover, these MYB genes were also significantly positively correlated with GH3 , ABF , and PP2C in the plant hormone pathways. Notably, Soltu.Atl_v3.01_3G036240 was also significantly positively correlated with two LDOX family genes ( Soltu.Atl_v3.10_0G009010 , Soltu.Atl_v3.06_2G028800 ). Expression of these two LDOX genes was significantly upregulated under drought stress, which might be partially regulated by Soltu.Atl_v3.01_3G036240 . We also found strong positive correlations between GRAS transcription factor family members and CHS and 3GT , as well as positive correlations with IAA , GH3 , and SnRK2 genes in the ABA and AUX signaling pathways. Previous studies have shown that GRAS proteins can activate IAA-related genes and promote GH3 expression, whereas relatively few studies have reported on SnRK2. Its role under drought stress remains to be clarified. In addition, we identified an NAC transcription factor ( Soltu.Atl_v3.02_0G004120 ), which was positively correlated with PAL and 3GT genes in the flavonoid biosynthesis pathway and with the PP2C gene ( Soltu.Atl_v3.03_1G031950 ) in the ABA signaling pathway. This suggests that Soltu.Atl_v3.02_0G004120 may act as a key regulator of genes governing flavonoid metabolism and ABA signaling. Weighted Gene Co-Expression Network Analysis Weighted gene co-expression network analysis (WGCNA)was employed to explore the gene regulatory architecture underlying drought responses in potato. To ensure that the co-expression network conformed to a scale-free topology, the pick Soft Threshold function in the WGCNA package was applied to calculate weight values, and a soft-threshold power of β = 16 was selected for module detection (Figure 8A). The co-expression network partitioned 35,682 genes into nine modules (Figures 8B, S4). Correlation analysis between each module and physiological/biochemical indicators at corresponding time points confirmed the presence of nine distinct modules (Figure 8D). The brown and blue modules were positively correlated with SOD activity, indicating that their expression was closely associated with enhanced antioxidant capacity mediated by SOD in potato. The blue module showed the strongest correlation with SOD activity and may directly enhance SOD activity by regulating SOD-associated genes or participating in its post-translational modification. The blue, brown, and black modules were positively correlated with POD, suggesting a synergistic effect of these modules in H₂O₂ clearance and related metabolic pathways. The blue and brown modules were simultaneously positively correlated with SOD and POD, implying that they may play central roles in antioxidant defense. On the one hand, they promoted clearance of superoxide anion radicals by SOD; on the other, they enhanced POD-mediated decomposition of H₂O₂. The blue, brown, and black modules were also positively correlated with CAT. The correlation patterns of these three modules further enriched the functional network of antioxidant responses. The pink, blue, yellow, and gray modules were positively correlated with MAD, highlighting their specific roles in oxidative damage. The brown module was particularly noteworthy: at 0 h, it was negatively correlated with both cultivars, but it became positively correlated under drought stress (Figure 8C). To comprehensively characterize the brown module, genes with correlation ≥ 0.8 and P ≤ 0.05 were filtered, and the top 50 genes with the highest intramodular connectivity were used to construct the co-expression network (Figure 8E). The brown module network included genes related to flavonoid biosynthesis and plant hormone signal transduction (ABA, ET, AUX, and SA). Key structural genes involved in flavonoid biosynthesis were identified, including C4H , PAL , DFR , and 3GT , with C4H being the most represented. Among hormone signal transduction pathways, ABA-related genes predominated, suggesting key roles in the drought response of potato. Notably, co-expressed genes such as UGT , USP , ABF , and PP2C are linked to flavonoid biosynthesis and ABA signal transduction, indicating that these genes may simultaneously regulate flavonoid synthesis and ABA-mediated drought responses. Discussion As a staple crop for billions of people worldwide, potato provides essential nutrients such as carbohydrates and vitamins. However, its growing season is highly vulnerable to drought stress [ 1 ], which also represents a major abiotic stressor that drives the accumulation of metabolites [ 35 ]. Therefore, investigating the metabolic and molecular mechanisms of potato under drought stress is essential. In this study, two cultivars with contrasting drought sensitivities were selected. By integrating metabolomic and transcriptomic analyses, we investigated the dynamic changes in potato metabolites in response to drought stress. Our data provide reliable resources for studying potato metabolism in response to drought stress. Drought stress can disrupt plant metabolism and alter the accumulation of secondary metabolites [ 36 , 37 ]. In Morus alba leaves, drought stress significantly altered the levels of lipids and lipid-like molecules, phenylpropanoids, polyketide compounds, and organic oxides [ 38 ]. The accumulation of flavonoids in plant tissues is regarded as a marker of plant stress and can effectively protect plants against abiotic stress [ 39 , 40 ]. During plant responses to drought stress, flavonoids efficiently scavenge reactive oxygen species (ROS) through their abundant phenolic hydroxyl groups, thereby alleviating oxidative damage and enhancing drought adaptability [ 26 ]. Under drought stress, sweet potatoes accumulated significant amounts of flavonoids, thereby increasing their drought tolerance [ 41 ]. A similar trend was observed in blueberries [ 42 ]. In this study, flavonoids accumulated markedly under drought stress (Fig. 5 ). This was further supported by the enrichment of phenylalanine metabolism (Figure S3 ) and of glycosyltransferases involved in flavonoid modification (Fig. 4 ). Six PAL genes and five C4H genes were consistently upregulated in response to drought stress (Fig. 5 ), and these genes also exhibited co-expression in the WGCNA networks (Fig. 8 E). In susceptible genotypes, PAL was significantly downregulated in stems, roots, and spikes, contrasting with the upregulation observed in tolerant genotypes [ 43 ]. Similar to millet, PAL showed significantly higher expression in the drought-resistant cultivar J8 than in the drought-sensitive HL15, suggesting that PAL may play a key role in potato responses to drought stress [ 44 ]. The activity of C4H directly influences subsequent flavonoid synthesis [ 45 ], and plants usually exhibit selective upregulation of C4H expression under drought stress [ 46 ]. All C4H genes identified in this study were found to be upregulated. Notably, Soltu.Atl_v3.06_4G022990 exhibited higher transcript levels than other coding genes, suggesting that it may be the key gene regulating C4H activity. In sweet potato, drought stress activates components of the abscisic acid (ABA) signaling cascade, which subsequently induces the transcription of downstream stress-responsive genes [ 47 ]. In this study, the expression of two PYR/PYL genes, six PP2C genes, six SnRK2 genes, and three ABF genes in potato seedlings under drought stress was all up-regulated, and key genes identified in the WGCNA network also included PP2C , SnRK2 , and ABF (Fig. 7 B). Numerous studies have shown that overexpression of PYR/PYL enhances ABA signaling to promote water uptake while simultaneously inhibiting PP2C, a negative regulator of ABA signaling [ 48 , 49 ]. Members of the PP2C phosphatase family are strongly induced by drought stress and ABA treatment, thereby enhancing plant drought tolerance [ 50 , 51 ]. We observed significant induction of six PP2C genes, accompanied by parallel up-regulation of downstream SnRK2 and ABF transcripts. A similar pattern was observed in Arabidopsis thaliana and maize leaves, where most PYR/PYL s were down-regulated under drought stress, whereas PP2C , SnRK2 , and ABF transcripts were up-regulated in response [ 52 , 53 ]. Our analysis revealed that the MYB transcription factor (TF) family contained the most significant number of differentially expressed TFs. Among plant TF families, MYBs constitute one of the largest groups and are extensively involved in mediating responses to abiotic stresses such as drought [ 54 – 56 ]. VcMYBPA1 has been shown to induce anthocyanin accumulation and participate in drought-responsive flavonoid biosynthesis by regulating the expression of flavonoid biosynthetic genes [ 57 ]. The correlation network (Fig. 7 B) revealed that most MYBs were positively correlated with flavonoid biosynthetic genes, such as LDOX, PAL, 4CL, and 3GT, thereby promoting flavonoid synthesis and enhancing drought tolerance, similar to those found in honeysuckle [ 58 ]. Moreover, two MYBs ( Soltu.Atl_v3.03_1G026050 and Soltu.Atl_v3.01_3G036240 ) were significantly positively correlated with ABF and PP2C in the ABA signaling pathway. Previous studies have shown that many MYBs are induced by ABA and are involved in drought stress responses [ 59 , 60 ]. Therefore, these MYBs may participate in drought stress responses by regulating flavonoid biosynthetic genes and ABA signaling pathway components. Significant positive correlations were also observed between four GRAS TFs and CHS and 3GT in the flavonoid biosynthesis pathway, as well as with hormone signaling genes such as IAA , GH3 , and SnRK2 . Previous studies have demonstrated that GRAS proteins promote the transcriptional activation of CHS and 3GT , thereby regulating the accumulation of flavonoid metabolites. Moreover, GRAS proteins are core regulators in hormone signaling pathways and play positive roles in drought stress responses [ 61 , 62 ]. We also identified a NAC transcription factor ( Soltu.Atl_v3.02_0G004120 ), whose expression was significantly upregulated across all three time points after drought stress, increasing by approximately sevenfold. After identifying key drought-responsive genes through the WGCNA regulatory network, we divided them into nine modules and conducted correlation analyses between each module and the four physiological indicators (Fig. 8 D). The study revealed that genes in the brown module were positively correlated with SOD, CAT, and POD, but negatively correlated with MDA. This trend was consistent with findings in safflower ( Carthamus tinctorius L.) [ 63 ]. Moreover, genes in the brown module mainly included those related to flavonoid biosynthesis and hormone signal transduction, such as C4H , PAL , PP2C , and SnRK2 (Fig. 8 E). Interestingly, flavonoid biosynthesis pathways were co-expressed with hormone signaling genes, including UGT , USP , ABF , and PP2C . Among these, ABF and PP2C belong to the ABA signaling pathway, suggesting that ABA signaling may influence the expression of flavonoid biosynthetic genes. Thus, they may act synergistically to enhance potato drought tolerance [ 64 ]. Previous studies have demonstrated that UGT enables plants to resist abiotic stress [ 65 – 67 ]. In chrysanthemum, higher accumulation of UGT-modified flavonoids improved drought and salt tolerance, thereby facilitating adaptation to salinity and drought [ 68 ]. UGT is also involved in ABA homeostasis and responds to various abiotic stresses such as drought [ 69 ]. The UGT ( Soltu.Atl_v3.10_2G009190 ) was connected to all genes in the network, suggesting that it may regulate flavonoid biosynthesis and ABA signaling gene expression. Similar to UGT, USP is involved in responses to heat and drought stress[ 70 ], and the USP identified here was also closely associated with flavonoid synthesis and ABA signaling, although its specific functions require further investigation. Conclusions This study investigated the potential molecular mechanisms underlying flavonoid biosynthesis in potato seedlings under drought stress using an integrated metabolomic and transcriptomic approach. We found that the expression of flavonoid biosynthetic genes, together with the accumulation of most flavonoid metabolites, contributed to enhanced drought tolerance in potato. Moreover, plant hormone signaling, particularly abscisic acid (ABA), also influenced the drought stress response in potato. We identified seven key candidate genes involved in regulating flavonoid biosynthesis in potato seedlings under drought stress. Furthermore, a more detailed analysis of potato flavonoid metabolism and ABA signaling revealed three transcription factors as potential regulators of drought tolerance. In summary, this study clarified the crucial role of potato flavonoid metabolism and plant hormone signaling in the response to drought stress, providing an important reference for understanding potato metabolism under drought conditions and offering insights for breeding drought-tolerant potato varieties. Materials and Methods Plant Materials and Growth Conditions Seedlings of the drought-resistant potato cultivar Ji Zhangshu 8 (J8) and the drought-sensitive cultivar Holland 15 (HL15), obtained from Hebei Agricultural University, were maintained by tissue culture subculture in the State Key Laboratory of Hebei Agricultural University. After 21 days of subculture, potato seedlings with uniform growth status were transferred to liquid MS medium containing 20% polyethylene glycol 6000 (PEG6000). They were grown in a controlled chamber at 22 ± 1°C with ~ 70% relative humidity, under a 16-h light (6:00–22:00)/8-h dark (22:00–6:00) photoperiod, at a light intensity of 2000–3000 lx. Whole seedlings were collected after 0, 12, 24, and 36 h of treatment, with three biological replicates per time point. At each sampling point, seedlings with uniform growth status were selected, rinsed with distilled water, and 2 g of tissue from each was placed in sampling tubes. A total of nine tubes were prepared per stage, resulting in 36 tubes across the four stages. The collected seedlings were rapidly frozen in liquid nitrogen and stored at − 80°C for subsequent RNA extraction, as well as physiological and biochemical analyses, and metabolomic and transcriptomic sequencing. Determination of Physiological Indicators The physiological indicators of tissue-cultured seedlings treated for 0, 12, 24, and 36 h were determined by Wuhan Domino Biotechnology Co., Ltd. Measurements were performed using commercial assay kits, with three replicates per treatment. The superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and malondialdehyde (MDA) assay kits were all obtained from Suzhou Ke Ming Biotechnology Co., Ltd. Metabolite Profiling Frozen potato tissue-cultured seedlings (100 ± 5 mg per sample) were transferred to microcentrifuge tubes and mixed with 400 µL of extraction solvent (methanol: water = 4:1, v/v) spiked with four internal standards, including L-2-chlorophenylalanine (0.02 mg/mL). Samples were processed by sequential cryogenic homogenization (− 10°C, 50 Hz, 6 min), ultrasonic extraction (5°C, 40 kHz, 30 min), incubation at − 20°C for 30 min, and centrifugation at 13,000 g for 15 min at 4°C. The resulting supernatants were transferred to autosampler vials with inner inserts for subsequent instrumental analysis. A pooled quality control (QC) sample was generated by mixing 20 µL of supernatant from each sample. Ultra-high-performance liquid chromatography (UHPLC) coupled with a TripleTOF 6600 mass spectrometer (AB SCIEX) was employed for LC–MS analysis. Analytes were separated at 40°C using an ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm i.d., 1.7 µm; Waters, Milford, USA). The mobile phase consisted of solvent A (2% acetonitrile in water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid). The flow rate was 0.40 mL/min, with an injection volume of 5 µL. The elution gradient was programmed as follows: 0–7.4 min, 2% B; 7.5–12.9 min, 35% B; 13–14.4 min, 95% B; and 14.5–16 min, 2% B. Mass spectrometry conditions were as follows: electrospray ionization (ESI) temperature, 500°C; ion source voltage, 5500 V; and both nebulizer and auxiliary gas pressures, 50 psi. Transcript Assay For each sample, total RNA was extracted using the MJZol RNA extraction kit (Shanghai Meiji Biomedical Technology Co., Ltd., China) according to the manufacturer’s instructions. RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). RNA integrity was evaluated by agarose gel electrophoresis using Biowest Agarose (Biowest, Spain), and the RNA quality number (RQN) was determined using an Agilent 5300 system (Agilent, USA). Purified mRNA was randomly fragmented into ~ 300 bp fragments at an appropriate temperature using the fragmentation buffer. First-strand cDNA was synthesized from mRNA using random primers and reverse transcriptase, followed by second-strand cDNA synthesis. End-repair mix was added to convert the double-stranded cDNA into blunt ends. Adapter-ligated fragments were purified, size-selected, and amplified by PCR; the resulting products were purified to generate the final sequencing library. Sequencing was performed on the NovaSeq X Plus platform (Illumina, USA). Clean reads were aligned to the potato reference genome ATL_v3 ( http://spuddb.uga.edu/ATL_v3_download.shtml ). Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) were identified using the thresholds |Log2FC| ≥ 1 and FDR ≤ 0.05. WGCNA Weighted gene co-expression network analysis (WGCNA) was performed in R using the WGCNA package (v1.71) [ 71 ]. For stringent feature selection, the varFilter function from the genefilter package was applied with the interquartile range (IQR) method, retaining 35,682 genes for analysis. The mergeCutHeight parameter was set to 0.25. Based on module annotations, the 50 genes with the highest intramodular connectivity were selected, and the corresponding networks were visualized using Cytoscape (v3.10.1). Statistical Analysis Unsupervised principal component analysis (PCA) was performed using the ropls package in R. Prior to PCA, differentially accumulated metabolites (DAMs) were identified using the criteria Log2FC ≥ 1 and VIP > 1. All heatmaps were generated using TBtools II (v1.12) [ 72 ]. Venn diagrams were generated using the VennDiagram package in R. KEGG compound classification and functional analyses were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/ , accessed on April 22, 2025). Gene annotation was performed using the Pfam database ( http://pfam.xfam.org/ , accessed on June 14, 2025) and the Gene Ontology (GO) database ( http://www.geneontology.org/ , accessed on June 24, 2025). Omics correlation analysis was conducted using the online platform Majorbio ( https://v.majorbio.com , accessed on March 13, 2025). Abbreviations ROS Reactive Oxygen Species C3 Three-carbon chain TF Transcription factor ABA Abscisic acid IAA Indole-3-acetic acid GA Gibberellin AUX Auxin SA Salicylic acid BR Brassinosteroid ET Ethylene SOD Superoxide dismutase POD Peroxidase CAT Catalase MDA Malondialdehyde LC–MS Liquid Chromatography–tandem Mass Spectrometry PCA Principal Component Analysis DAM Differentially Accumulated Metabolite DEG Differentially Expressed Gene KEGG Kyoto Encyclopedia of Genes and Genomes GO Gene Ontology WGCNA Weighted Gene Co-expression Network Declarations Acknowledgements Not applicable. Authors’ contributions S. G. wrote the paper, R. S. and W. W. conducted the data analysis, W. Q. and Y. C. managed the material, J. S., M. Y. and J.Z. provided the material, J. C. designed the experiment. Funding This study was supported by the National Science and Technology Resource Sharing Service Platform Program (NCGRC-2025-44), Ministry of Science and Technology and Ministry of Finance — Operation Service of the Potato Sub-Repository. Availability of date and materials All data sheets and codes to process data are available upon request to the corresponding author, Jianghui Cui ( [email protected] ). The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2025)[73] in National Genomics Data Center (Nucleic Acids Res 2025)[74], China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA031995) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. Ethics approval and consent to participate No animals or humans were involved in this study, and there are no ethical issues involved in this paper. 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Supplementary Files FigureS1.jpg FigureS2.jpg FigureS3.pdf FigureS4.jpg Additionalfile5.xlsx Additionalfile6.xlsx SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 13 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 05 Nov, 2025 Editor assigned by journal 02 Nov, 2025 Editor invited by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 29 Oct, 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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07:29:50","extension":"xml","order_by":49,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172838,"visible":true,"origin":"","legend":"","description":"","filename":"82734f541fab4405b21b38d755eeba631structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/d6d1703ad1b8b0b7171e2292.xml"},{"id":96092379,"identity":"78e42c50-f7c9-483e-ab36-52de41b00cf7","added_by":"auto","created_at":"2025-11-17 13:41:24","extension":"html","order_by":50,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190320,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/c5b3b883772a27b4e9e17ce1.html"},{"id":96092322,"identity":"e29826e3-7262-4ab2-b807-2b63c44bbeac","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1308508,"visible":true,"origin":"","legend":"\u003cp\u003ePhysiological indicators and metabolite profiles of HL15 and J8. (A) Physiological index differences of HL15 and J8: SOD, POD, CAT, and MDA. All data in (A) are the means±SD (n = 3), *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ns, not significant, Student's t-test.; different lowercase letters denote significant Different letters indicate significant differences between samples using p value ≤ 0.05. (B) The classification of metabolites detected in the two potato cultivars. (C) Principal component analysis of metabolites of HL15 and J8. PC1 represents the first principal component, and PC2 represents the second principal component. (D) The number of DAMS in the six comparison groups (H12 vs. H0, H24 vs. H0, H36 vs. H0, J12 vs. J0, J24 vs. J0, J36 vs. J0).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/c01ba19306c1700a611d4298.jpg"},{"id":96248703,"identity":"d6520d8f-fc8b-4906-bb6d-87cc02dd2baa","added_by":"auto","created_at":"2025-11-19 07:28:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1156028,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of DAMs in six comparison groups. The red font represents the focused pathways.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/c2b27e894d7514fc1f8f3a48.jpg"},{"id":96092326,"identity":"0e883d9a-61fb-4722-96c5-ddc71535f3c1","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":937109,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome analysis of HL15 and J8. (A) Principal component analysis. (B) Venn diagram showing differentially expressed genes (DEGs) in the six comparison groups. (C) Expression heatmap of overlapped DEGs in the six comparison groups.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/5fd1277da6c38678a8074377.jpg"},{"id":96092328,"identity":"0d5f473c-6708-42dc-93c3-9190f8810529","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1512092,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of DEGs in six comparison groups. The red font represents the module of focus.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/32159aca3fe5042b73cecb36.jpg"},{"id":96092334,"identity":"82bfff48-b6f1-44ca-89ed-0e3917d06675","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":443894,"visible":true,"origin":"","legend":"\u003cp\u003eSimplified flavonoid biosynthesis pathway model. PAL, phenylalanine ammonia-lyase; C4H, cinnamate-4-hydroxylase; 4CL, 4-coumarate: CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3H, flavanone 3-hydroxylase; F3 'H, flavonoid 3'-hydroxylase; FLS, flavonol synthase; DRF, dihydroflavonol 4-reductase; LDOX/ANS, leucoanthocyanidin dioxygenase/anthocyanidin synthase; 3GT, UDP-Glucose: flavonoid 3-O-glycosyltransferase; FNS, flavone synthase; UGT, UDP-Glycosyltransferase.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/56bb600d34dfdcf5ebf244b0.jpg"},{"id":96250037,"identity":"046b1153-9642-469a-a85b-4b867014dcfa","added_by":"auto","created_at":"2025-11-19 07:37:12","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":690753,"visible":true,"origin":"","legend":"\u003cp\u003eSimplified model of plant hormone signal transduction pathways.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/2544981b22adb99acbbf5850.jpg"},{"id":96247165,"identity":"9210238a-9477-4f11-8586-8f9f55556c95","added_by":"auto","created_at":"2025-11-19 07:27:13","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1476027,"visible":true,"origin":"","legend":"\u003cp\u003eThe screen of candidate transcription factors related to the drought response in HL15 and J8. (A) The top ten TF families with the highest number of differentially expressed genes. (B) Correlation network of TFs related to the flavonoid biosynthesis pathway and plant hormone signal transduction pathways. The red line indicates a positive correlation, and the blue line indicates a negative correlation.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/891813d0f3477f678e448b42.jpg"},{"id":96092345,"identity":"16ec9ac3-465c-4db8-b80c-647c24adbb2b","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4099244,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) and physiological indicators analysis. (A) Soft threshold for gene plate division. (B) Display the hierarchical clustering tree of 9 modules. (C) Module-sample relationships. Each cell contains the corresponding correlation and p-value. Red and blue represent positive and negative correlations respectively. (D) Module - physiological indicators relationships. (E) Co-expression networks of the flavonoid biosynthesis pathway and plant hormone signal transduction pathways in brown.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/3cc324dc02ba105063e3c548.jpg"},{"id":106415090,"identity":"c190735b-6e6d-45b2-8203-2a165d73eb10","added_by":"auto","created_at":"2026-04-08 10:32:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12601797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/530ce241-b98a-43df-bd0c-420526284e63.pdf"},{"id":96250049,"identity":"25c2e576-3220-4e70-bf2b-c0665df982ae","added_by":"auto","created_at":"2025-11-19 07:37:13","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1209360,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/f938e61b0284d88d3ce04627.jpg"},{"id":96246874,"identity":"99a6d466-fb2f-47a4-85fd-404d12817c98","added_by":"auto","created_at":"2025-11-19 07:26:48","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2409770,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/f941dd2f271156b979583c59.jpg"},{"id":96250138,"identity":"8851fc3f-cc3e-43d3-880d-256f4124c50c","added_by":"auto","created_at":"2025-11-19 07:37:36","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":602426,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/32bd8ea58e2dbca1ea616cac.pdf"},{"id":96092331,"identity":"3d0d5517-8903-4cc5-b013-6fdac9c9b46c","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":893582,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/4a36e7d87a0b94d0a3f1e866.jpg"},{"id":96250087,"identity":"ef463dbe-4a94-41ee-9751-f916b7982757","added_by":"auto","created_at":"2025-11-19 07:37:25","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":9251650,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/bab52374d18de4bf026d1724.xlsx"},{"id":96248807,"identity":"706cec8c-72fe-4674-a0f4-8a7f90dad493","added_by":"auto","created_at":"2025-11-19 07:29:18","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":247313,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/51237e25fc4fc1d960020b3c.xlsx"},{"id":96092336,"identity":"c2d71709-534b-4183-ba3b-b002d4026fb6","added_by":"auto","created_at":"2025-11-17 13:41:23","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13825,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7885634/v1/4705bfff2ad7bf2377be9c62.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Physiological, Metabolomic, and Transcriptomic Analyses Provide New Insights into Potato Drought Stress Responses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGiven its central place in the architecture of global food security, the potato is a multifaceted strategic crop that addresses food security challenges posed by accelerating climate change and the continued growth of the human population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the fourth most widely cultivated crop worldwide in terms of planting area and total output, after rice, wheat, and corn [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], potato exhibits outstanding nutritional value, high yield potential, and adaptability to diverse agricultural ecosystems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, due to its shallow root system, the potato is particularly susceptible to drought stress [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Drought stress causes a substantial decline in tuber yield by inhibiting photosynthetic carbon assimilation and disrupting water balance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It also interferes with the distribution of assimilates during tuber expansion, alters tuber quality-related traits, and induces a cascade of physiological and metabolic disorders, including oxidative damage and destruction of photosynthetic structures [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It threatens overall plant growth, development and health ultimately., Plants have developed a range of physiological and molecular adaptive mechanisms to tolerate drought stress and mitigate its adverse effects on physiological processes over the course of long-term evolution. For example, drought stress promotes the accumulation of antioxidant compounds in the leaves of sweet potatoes (Ipomoea batatas), such as flavonoids, polyphenols, and glutathione. These metabolites help scavenge reactive oxygen species (ROS) and reduce oxidative damage, thereby enhancing the drought adaptability of sweet potato [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Plant hormones serve as central signal integrators under drought stress, coordinating and reprogramming the complex signaling cascades that govern plant development and stress adaptation. For instance, abscisic acid (ABA) levels increase markedly under drought stress, whereas indole-3-acetic acid (IAA) and gibberellin (GA) levels decline, indicating the suppression of growth hormone-responsive genes. This hormonal adjustment restricts shoot elongation, thereby reducing water consumption in above-ground tissues [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePlant defense responses are closely associated with flavonoid accumulation. Flavonoids constitute a significant class of plant polyphenols comprising over 6,900 secondary metabolites that play diverse roles in plant growth and development [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Flavonoids are widely distributed in plants and are characterized by a C6-C3-C6 backbone, in which a three-carbon chain (C3) links two aromatic rings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on structural differences in the heterocyclic C ring, flavonoids can be classified into chalcone, aurone, flavone, isoflavone, flavanone, dihydroflavonol, anthocyanin, leucoanthocyanidin, flavanol, and flavan-3-ol [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Flavonoid biosynthesis begins with phenylalanine and proceeds through successive catalysis by key enzymes of the phenylalanine metabolic pathway, including phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate: CoA ligase (4CL), chalcone synthase (CHS), chalcone isomerase (CHI), flavonoid synthase (FNS), and flavone 3-hydroxylase (F3H) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Due to their antioxidant properties, flavonoids play a crucial role in enhancing plant resistance to abiotic stresses, particularly drought [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For example, in sweet potato under drought stress, the developmental stage strongly influences the regulation of core flavonoid biosynthetic genes such as \u003cem\u003eCYP75B1\u003c/em\u003e and \u003cem\u003eIF7MAT\u003c/em\u003e, affecting processes including branching, tuber germination, and root storage expansion. Stage-resolved transcriptional regulation is accompanied by differential accumulation of flavonol derivatives [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In apple (\u003cem\u003eMalus domestica\u003c/em\u003e), although flavonoids are crucial for adaptation to environmental stress and typically accumulate under stress conditions, the molecular mechanisms regulating flavonoids under stress remain unclear in potato.\u003c/p\u003e\u003cp\u003eTranscription factors (TFs) coordinate flavonoid biosynthesis with growth, development, and stress-response networks [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The MYB in plants, one of the largest TF groups, is extensively involved in growth and developmental regulation, cell differentiation, primary and secondary metabolism, and responses to biotic and abiotic stresses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. MYB tightly regulate the expression of structural genes in the flavonoid pathway. Through cis-regulatory elements in the promoter, ThMYB14 (R2R3-MYB) in Tetrastigma hemsleyanum binds to the AAC motif within \u003cem\u003eMBS/MBSI\u003c/em\u003e elements upstream of flavonoid biosynthetic genes, thereby enhancing pathway output [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. FeMYBF1 in buckwheat (Fagopyrum esculentum Moench) also promotes flavonol synthesis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, WRKY are involved in various physiological processes throughout plant growth and development, including leaf senescence, seed development, dormancy, and germination, as well as responses to both biotic and abiotic stresses.\u003c/p\u003e\u003cp\u003ePlant hormones also play a crucial role in plant stress resistance. Abscisic acid (ABA) is a plant hormone that mediates stress responses and plays a significant role in both stress resistance and flavonoid biosynthesis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Drought stress induces the production and accumulation of ABA in plant organs, thereby activating downstream signaling pathways [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, multiple other plant hormones contribute to regulating drought adaptability through changes in biosynthesis and signaling pathways, including gibberellin (GA), auxin (AUX), salicylic acid (SA), brassinosteroid (BR), and ethylene (ET) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. TFs respond to diverse adverse conditions by regulating genes involved in various hormone signaling pathways. Previous studies have demonstrated that transcription factors such as MYB and NAC regulate drought-responsive genes, thereby contributing to enhanced drought tolerance in plants [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, two potato cultivars with differing drought sensitivities were selected for metabolomic and transcriptomic analyses to elucidate metabolite accumulation and changes in the expression of flavonoid biosynthetic genes in potato under drought stress. The hormone signal transduction pathways of potato were also examined. Furthermore, a pathway gene\u0026ndash;transcription factor network associated with the flavonoid biosynthesis pathway under drought stress was constructed, enabling the identification of potential drought-responsive transcription factors.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhysiological Responses to Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrought-induced oxidative status was evaluated by profiling superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities, along with malondialdehyde (MDA) content, in potato seedlings. The results (Figure 1A) showed that under drought stress, POD and CAT activities increased significantly in both HL15 and J8, whereas SOD activity remained unchanged in HL15. Compared with HL15, J8 exhibited higher SOD, POD, and CAT activities. Moreover, as the duration of drought treatment increased, SOD, POD, and CAT activities progressively rose, indicating that J8 possesses a stronger antioxidant capacity. Analysis of MDA content revealed a significant reduction in both cultivars under drought stress, suggesting that drought mitigated lipid peroxidation in potato cell membranes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrought-Responsive Metabolite Profiling in Potato Seedlings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate metabolite dynamics in Potato Seedlings under drought stress, we performed non-targeted liquid chromatography\u0026ndash;tandem mass spectrometry (LC\u0026ndash;MS) to characterize metabolite composition. The metabolome comprised 3,001 annotated features, including 191 terpenoids (35.90%), 95 steroids (17.86%), 88 flavonoids (16.54%), 50 phenolic acids (9.40%), 35 organic acids (6.58%), 21 alkaloids (3.95%), 17 lignans (3.20%), 14 indoles (2.63%), 10 coumarins (1.88%), four quinones (0.75%), four tannins (0.75%), and three astragalosides (0.56%) (Figure 1B). Principal component analysis (PCA) was performed to assess differences in metabolite accumulation between the two cultivars under drought stress across four developmental stages (Figure 1C). The results showed that the variance explained by the first principal component (PC1) was 51.50%, and that of the second principal component (PC2) was 25.50%. Along the PC1 axis, the two cultivars were separated, indicating significant differences in metabolite accumulation between the drought-sensitive cultivar HL15 and the drought-resistant cultivar J8, which persisted under drought stress. Interestingly, the two cultivars were completely separated along PC2 at 0 h and 12 h, and partially separated between 12 h and 24 h; however, they showed slight separation between 24 h and 36 h. This indicated that under drought stress, metabolite accumulation differed most markedly at 12 h. As time progressed, these differences diminished, and by 24 h, there was little distinction from 36 h. These findings suggest that the seedlings of two potato cultivars responded most strongly to drought stress during the first 24 hours.\u003c/p\u003e\n\u003cp\u003eDifferentially accumulated metabolites (DAMs) were defined using the combined thresholds |log2FC| \u0026ge; 1 and VIP \u0026gt; 1 to capture drought-responsive shifts in metabolite accumulation. The results (Figure 1D) showed that comparisons H12 vs. H0, H24 vs. H0, H36 vs. H0, J12 vs. J0, J24 vs. J0, and J36 vs. J0 yielded 702 (495 upregulated, 207 downregulated), 775 (471 up, 304 down), 655 (453 up, 202 down), 620 (351 up, 269 down), 733 (459 up, 274 down), and 678 (420 up, 258 down) DAMs, respectively. Ranking metabolites by fold change (Supplementary Fig. S1) showed that at 12 h, 24 h, and 36 h vs. 0 h, most of the top 20 features with the most considerable inter-cultivar differences were upregulated DAMs. Moreover, a higher proportion of upregulated metabolites was observed in the drought-resistant cultivar J8 than in the drought-sensitive cultivar HL15. Over time, this proportion gradually declined. Notably, flavonoid metabolites were relatively abundant in both cultivars, and their fold-change values continued to increase over time.\u003c/p\u003e\n\u003cp\u003eTo examine pathway-level changes under drought stress, drought-induced DAMs were analyzed using KEGG enrichment to identify significantly perturbed pathways in potato seedlings. As shown in Figure 2, KEGG pathways consistently enriched during J8 development included \u0026alpha;-linolenic acid metabolism, linoleic acid metabolism, flavone/flavonol biosynthesis, and phenylpropanoid biosynthesis. Enrichment of \u0026alpha;-linolenic acid metabolism, together with flavanone and flavonol synthesis, was observed across all five comparison groups, whereas the flavonoid biosynthesis pathway showed significant enrichment in three groups. In J8, phenylpropanoid biosynthesis increased over time under drought stress, and phenylalanine metabolism was also significantly enriched in H24 vs. H0. Phenylalanine metabolism provides the precursor for the phenylpropanoid biosynthetic pathway. These four pathways play central roles in orchestrating drought responses in potato seedlings. Notably, enrichment of the flavonoid biosynthesis and flavone/flavonol synthesis pathways in HL15 was higher than in J8 at 12 h but decreased thereafter. This may reflect the shorter response duration of drought-sensitive cultivars compared with drought-resistant cultivars [31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome Analysis of the responses of the Two Potato cultivars to Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sequenced 24 samples to investigate the molecular regulatory mechanisms in tissue-cultured seedlings of two potato cultivars subjected to drought at multiple time points. After quality filtering, a total of 163.72 Gb of clean data were retained. For each sample, clean data exceeded 5.99 Gb, with more than 96.36% of bases achieving a quality score above Q30. Clean reads from each sample were mapped to the reference genome, with alignment rates ranging from 91.04% to 93.54%.\u003c/p\u003e\n\u003cp\u003ePCA analysis was performed on the transcriptome data of potato seedlings exposed to drought stress (Figure 3A). The results indicated significant differences between cultivars at different treatment stages. PC1 explained 58.60% of the variance, and PC2 explained 19.13%. Samples at 0 h were clearly separated from those at 12 h, 24 h, and 36 h, whereas no clear separation was observed among the latter three time points. This indicated that gene expression in potato underwent substantial changes under drought stress. Differentially expressed genes (DEGs) were identified using the criteria |log2FC| \u0026ge; 1 and FDR \u0026le; 0.05 (Supplementary Fig. S2). The numbers of DEGs were as follows: H12 vs. H0, 18,543 (8,772 upregulated, 9,771 downregulated); H24 vs. H0, 20,559 (9,131 up, 11,428 down); H36 vs. H0, 17,104 (8,015 up, 9,089 down); J12 vs. J0, 16,331 (7,621 up, 8,710 down); J24 vs. J0, 17,983 (8,286 up, 9,697 down); J36 vs. J0, 16,600 (7,587 up, 9,013 down). Among these comparisons, both HL15 and J8 exhibited the highest numbers of DEGs at 24 h of drought treatment. Across all three time points, the drought-sensitive cultivar HL15 exhibited more total DEGs, as well as more upregulated and downregulated genes, than the drought-resistant cultivar J8. A total of 7,122 DEGs were identified across six groups (Figure 3B). Cluster analysis based on H0 gene expression classified these DEGs into two distinct groups (Figure 3C). Group I transcripts were abundantly expressed at 0 h in both H0 and J0 but decreased significantly after drought stress was applied. In contrast, Group II showed the opposite trend. Expression levels in H0 and J0 remained relatively low but increased to some extent after drought stress. These results indicated that H0 and J0 exhibited highly similar expression patterns, and that H12, H24, and H36 also showed expression profiles similar to J12, J24, and J36, suggesting that the overall gene expression trends of tissue-culture seedlings from different potato cultivars were broadly similar under drought stress.\u003c/p\u003e\n\u003cp\u003eKEGG enrichment analysis (Supplementary Fig. S3) showed that ten metabolic pathways, including phenylalanine metabolism, plant hormone signal transduction, and \u0026alpha;-linolenic acid metabolism, were significantly enriched across all stages of drought treatment. Similarly, phenylpropanoid biosynthesis was significantly enriched across all five comparison groups. Interestingly, enrichment of plant hormone signal transduction, phenylalanine metabolism, and phenylpropanoid biosynthesis first increased and then decreased in HL15, whereas it continued to rise in J8. Consistent enrichment of plant hormone signaling was observed across all stages, underscoring the pivotal contribution of hormones to drought adaptation in potato seedlings.\u003c/p\u003e\n\u003cp\u003eWe also employed GO enrichment analysis (Figure 4) to demonstrate that hexosyltransferase activity was highly enriched in HL15, while glycosyltransferase activity was enriched in four groups and showed an increasing trend in HL15. Additionally, xyloglucosyltransferase activity was significantly enriched in three groups, with enrichment increasing over time under drought stress. Activities of various glycosyltransferases were enhanced under drought stress. Their responses to stimuli were significantly enriched during the first 24 h in HL15 and maintained an upward trend. This regulation may trigger activation of flavonoid biosynthesis genes and further promote expression of genes mediating interactions between flavonoids and plant hormones. These findings suggest that flavonoid metabolism may represent an essential mechanism by which potato seedlings respond to drought stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite and transcript changes in flavonoid biosynthesis pathways under drought stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate in greater detail the transcriptional and metabolic changes of the flavonoid biosynthetic pathway in response to drought, 75 DEGs were identified and mapped to the flavonoid biosynthesis pathway to elucidate changes in metabolites and gene expression (Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results indicated that phenylalanine accumulated to higher levels in J8 than in HL15 under drought stress, with peak accumulation in both cultivars within 24 h, although the overall increase was modest. PAL expression levels in J8 were higher than those in HL15, with six genes showing significant upregulation. In HL15, expression of \u003cem\u003eSoltu.Atl_v3.09_0G005420\u003c/em\u003e and \u003cem\u003eSoltu.Atl_v3.09_2G005240\u0026nbsp;\u003c/em\u003eat 24 h of drought treatment was markedly higher than at other stages, likely contributing to a substantial accumulation of cinnamic acid in the subsequent reaction. Between cultivars, \u003cem\u003eC4H\u003c/em\u003e exhibited significant expression differences, with overall upregulation, reaching its highest level at 12 h of drought treatment. J8 exhibited higher expression levels than HL15. Approximately half of the \u003cem\u003e4CL\u003c/em\u003e genes exhibited an upward trend compared to 0 h, peaking at 12 h or 24 h, and then decreasing by 36 h, indicating that \u003cem\u003e4CL\u003c/em\u003e expression was primarily concentrated during the initial 24 h. Transcript levels of \u003cem\u003eCHS\u003c/em\u003e genes \u003cem\u003eSoltu.Atl_v3.09_2G019460\u003c/em\u003e and \u003cem\u003eSoltu.Atl_v3.09_3G023610\u003c/em\u003e were upregulated in J8 but downregulated in HL15. In J8, both genes were transiently induced before subsequently decreasing, reaching their lowest levels at 36 h. This may reflect that potato seedlings responded to drought stress by upregulating \u003cem\u003eCHS\u003c/em\u003e genes within the first 24 h. Sustained stress likely caused severe plant injury, perturbing \u003cem\u003eCHS\u003c/em\u003e transcriptional regulation and resulting in reduced expression. Expression levels of \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, and \u003cem\u003eCHS\u003c/em\u003e were significantly upregulated at 12 h or 24 h of drought treatment, then slightly decreased at 36 h, indicating that phenylalanine accumulation mainly peaked at 24 h.\u003c/p\u003e\n\u003cp\u003eUsing apigenin as a substrate, luteolin was synthesized through F3\u0026apos;H. We identified an \u003cem\u003eF3\u0026apos;H\u0026nbsp;\u003c/em\u003egene (\u003cem\u003eSoltu.Atl_v3.03_3G031010\u003c/em\u003e), which showed peak expression at 24 h, with HL15 exhibiting higher expression than J8. Luteolin accumulation was also mainly concentrated in HL15, consistent with the phenylalanine-to-cinnamic acid accumulation pattern mediated by PAL. F3H also catalyzed the Conversion of Naringenin to form dihydrokaempferol, which showed an upregulated trend in HL15, and was subsequently converted to kaempferol through FLS. We identified 13 genes encoding FLS. Among them, only five were consistently upregulated in HL15, whereas 10 were upregulated in J8. FLS is a key enzyme in flavone biosynthesis, and its high expression directly influences flavone production [32]. Dihydrokaempferol can also be converted into dihydroquercetin via F3\u0026apos;H, and subsequently into quercetin through FLS catalysis. Quercetin accumulation increased in HL15 but decreased in J8, which was opposite to the gene expression trend of \u003cem\u003eFLS\u003c/em\u003e. Dihydroflavonols (dihydrokaempferol and dihydroquercetin) were converted into leucoanthocyanidins (leucopelargonidin and leucocyanidin) through DFR. Only one gene, \u003cem\u003eSoltu.Atl_v3.02_2G030350\u003c/em\u003e was upregulated in both cultivars across all time periods. Leucoanthocyanidins are important precursors of anthocyanins, which are synthesized into anthocyanins (cyanidin and pelargonidin) through LDOX/ANS. Four genes encoding LDOX/ANS were significantly upregulated in both cultivars across all time periods, with the highest expression during the later stages of drought stress (24 h and 36 h). Among them, expression of \u003cem\u003eSoltu.Atl_v3.10_0G009010\u003c/em\u003e increased approximately 70-fold in HL15 and 20-fold in J8 compared with 0 h. Anthocyanins were further modified through C3G (cyanidin 3-O-glucoside) and Pg3G (pelargonidin 3-O-glucoside) through 3GT-mediated glycosylation. Approximately half of the genes encoding 3GT were upregulated, with higher expression levels observed primarily at 12 h and 24 h. Overall, drought treatment broadly induced transcription of flavonoid biosynthetic genes. In J8, the drought-resistant cultivar, metabolite accumulation and higher gene expression were mainly associated with the flavonol branch. In contrast, in the drought-sensitive cultivar, these changes were primarily linked to the anthocyanin branch in HL15.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite and Transcript Changes in Plant Hormone Signal Transduction Pathways under Drought Stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlant hormone signaling also responds actively to drought stress. Within the GA signaling pathway, \u003cem\u003eGID1\u003c/em\u003e expression was markedly upregulated at 12 h under drought in both cultivars and remained elevated at all time points in J8. We identified two genes encoding DELLA proteins, among which \u003cem\u003eSoltu.Atl_v3.04_4G018600\u003c/em\u003e was significantly upregulated in both cultivars, with expression in J8 increasing approximately six-fold compared with 0 h. In the SA signaling pathway, two \u003cem\u003eNPR1\u003c/em\u003e genes were identified. Of these, \u003cem\u003eSoltu.Atl_v3.02_3G012800\u003c/em\u003e was significantly upregulated in both cultivars, with stronger induction in J8. All five \u003cem\u003eTGA\u003c/em\u003e genes were significantly upregulated, reaching their maximal expression at 12 h, accompanied by a sharp induction of \u003cem\u003eSoltu.Atl_v3.02_3G012800\u003c/em\u003e indicates that the first 12 hours represented the strongest SA signaling under drought stress. PR-1 was significantly induced at all time points in both cultivars, with higher expression observed during the 0 h\u0026ndash;24 h period. BR signaling genes were also responsive to drought stress. Five BR-related genes encoding BAK1 were generally downregulated, a pattern associated with significant downregulation of most BSK-encoding genes. About half of the BIN2-encoding genes showed upregulation; moreover, downstream genes encoding BZR1/2 and TCH4 also showed considerable downregulation. Overall, the BR pathway was generally inhibited under drought stress, potentially through synergistic or antagonistic interactions with other hormone signaling pathways [33]. In the ET signaling pathway, \u003cem\u003eETR\u003c/em\u003e expression was upregulated under drought stress, with a more substantial increase in HL15. This coincided with a significant increase in \u003cem\u003eCTR1\u003c/em\u003e expression. Overall increases in \u003cem\u003eCTR1\u003c/em\u003e expression activated MPK6, whose expression generally showed an upward trend. Approximately half of the EIN3-encoding genes were upregulated through MPK6 phosphorylation, which could activate ERF1/2, thereby increasing their expression and enhancing drought stress tolerance.\u003c/p\u003e\n\u003cp\u003eWe identified 27 DEGs within the auxin signaling pathway, spanning four gene families. Under drought stress, AUX1 family members were downregulated, resulting in a reduced intracellular auxin concentration and decreased transcriptional activation of AUX/IAA. Consequently, among the 16 \u003cem\u003eAUX/IAA\u003c/em\u003e genes identified, 10 were significantly downregulated, whereas three showed an upward trend. This may be because AUX/IAA inhibits downstream ARF activity, leading to reduced \u003cem\u003eARF\u003c/em\u003e expression [34]. Accordingly, all \u003cem\u003eARF\u003c/em\u003e genes we identified were downregulated except \u003cem\u003eSoltu.Atl_v3.05_2G016300\u003c/em\u003e. The AUX/IAA\u0026ndash;ARF complex inhibited ARF binding to DNA, thereby blocking expression of downstream \u003cem\u003eGH3\u003c/em\u003e. Of the four \u003cem\u003eGH3\u003c/em\u003e genes identified, only \u003cem\u003eSoltu.Atl_v3.05_4G018110\u003c/em\u003e was consistently upregulated across all four time points in both cultivars. In the ABA signaling pathway, 22 DEGs were identified across four gene families. PYR/PYL family genes exhibited a mixed pattern, with approximately half upregulated and the other half downregulated. The downregulated genes \u003cem\u003eSoltu.Atl_v3.03_3G022470\u003c/em\u003e and \u003cem\u003eSoltu.Atl_v3.03_4G012050\u003c/em\u003e showed a decrease in expression of up to 40-fold compared with 0 h. Drought treatment significantly induced all six \u003cem\u003ePP2C\u003c/em\u003e genes, along with six \u003cem\u003eSnRK2\u003c/em\u003e genes and three \u003cem\u003eABF\u003c/em\u003e genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegulatory Network of Transcription Factors and Flavonoid Biosynthesis and Plant Hormone Signal Transduction Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flavonoid pathway and plant hormone signal transduction are tightly regulated at the transcriptional level, with transcription factors (TFs) serving as principal drivers. A total of 1,858 transcriptional fragments were identified in this study. Among them, the MYB family was the most abundant (436 genes), followed by the GRAS family (220 genes) and the B3 family (153 genes) (Figure 7A). A correlation network (r \u0026gt; 0.7) was constructed to elucidate further interactions between these TFs and genes associated with flavonoid biosynthesis and plant hormone signaling pathways (Figure 7B). We observed significant positive correlations between eight MYB family genes and several flavonoid pathway genes in the network, including \u003cem\u003eLDOX\u003c/em\u003e, \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e, and \u003cem\u003e3GT\u003c/em\u003e. Two MYB transcription factor genes (\u003cem\u003eSoltu.Atl_v3.03_1G026050\u003c/em\u003e, \u003cem\u003eSoltu.Atl_v3.01_3G036240\u003c/em\u003e) were significantly positively correlated with the \u003cem\u003ePAL\u003c/em\u003e gene (\u003cem\u003eSoltu.Atl_v3.06_2G003550\u003c/em\u003e), suggesting that these two genes may regulate \u003cem\u003ePAL\u003c/em\u003e expression. Moreover, these MYB genes were also significantly positively correlated with \u003cem\u003eGH3\u003c/em\u003e, \u003cem\u003eABF\u003c/em\u003e, and \u003cem\u003ePP2C\u003c/em\u003e in the plant hormone pathways. Notably, \u003cem\u003eSoltu.Atl_v3.01_3G036240\u003c/em\u003e was also significantly positively correlated with two \u003cem\u003eLDOX\u003c/em\u003e family genes (\u003cem\u003eSoltu.Atl_v3.10_0G009010\u003c/em\u003e, \u003cem\u003eSoltu.Atl_v3.06_2G028800\u003c/em\u003e). Expression of these two \u003cem\u003eLDOX\u003c/em\u003e genes was significantly upregulated under drought stress, which might be partially regulated by \u003cem\u003eSoltu.Atl_v3.01_3G036240\u003c/em\u003e. We also found strong positive correlations between GRAS transcription factor family members and \u003cem\u003eCHS\u003c/em\u003e and \u003cem\u003e3GT\u003c/em\u003e, as well as positive correlations with \u003cem\u003eIAA\u003c/em\u003e, \u003cem\u003eGH3\u003c/em\u003e, and \u003cem\u003eSnRK2\u003c/em\u003e genes in the ABA and AUX signaling pathways. Previous studies have shown that GRAS proteins can activate IAA-related genes and promote \u003cem\u003eGH3\u003c/em\u003e expression, whereas relatively few studies have reported on SnRK2. Its role under drought stress remains to be clarified.\u003c/p\u003e\n\u003cp\u003eIn addition, we identified an NAC transcription factor (\u003cem\u003eSoltu.Atl_v3.02_0G004120\u003c/em\u003e), which was positively correlated with \u003cem\u003ePAL\u003c/em\u003e and \u003cem\u003e3GT\u003c/em\u003e genes in the flavonoid biosynthesis pathway and with the \u003cem\u003ePP2C\u003c/em\u003e gene (\u003cem\u003eSoltu.Atl_v3.03_1G031950\u003c/em\u003e) in the ABA signaling pathway. This suggests that \u003cem\u003eSoltu.Atl_v3.02_0G004120\u003c/em\u003e may act as a key regulator of genes governing flavonoid metabolism and ABA signaling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-Expression Network Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeighted gene co-expression network analysis (WGCNA)was employed to explore the gene regulatory architecture underlying drought responses in potato. To ensure that the co-expression network conformed to a scale-free topology, the \u003cem\u003epick Soft Threshold\u003c/em\u003e function in the WGCNA package was applied to calculate weight values, and a soft-threshold power of \u0026beta; = 16 was selected for module detection (Figure 8A). The co-expression network partitioned 35,682 genes into nine modules (Figures 8B, S4). Correlation analysis between each module and physiological/biochemical indicators at corresponding time points confirmed the presence of nine distinct modules (Figure 8D). The brown and blue modules were positively correlated with SOD activity, indicating that their expression was closely associated with enhanced antioxidant capacity mediated by SOD in potato. The blue module showed the strongest correlation with SOD activity and may directly enhance SOD activity by regulating SOD-associated genes or participating in its post-translational modification. The blue, brown, and black modules were positively correlated with POD, suggesting a synergistic effect of these modules in H₂O₂ clearance and related metabolic pathways. The blue and brown modules were simultaneously positively correlated with SOD and POD, implying that they may play central roles in antioxidant defense. On the one hand, they promoted clearance of superoxide anion radicals by SOD; on the other, they enhanced POD-mediated decomposition of H₂O₂. The blue, brown, and black modules were also positively correlated with CAT. The correlation patterns of these three modules further enriched the functional network of antioxidant responses. The pink, blue, yellow, and gray modules were positively correlated with MAD, highlighting their specific roles in oxidative damage. The brown module was particularly noteworthy: at 0 h, it was negatively correlated with both cultivars, but it became positively correlated under drought stress (Figure 8C). To comprehensively characterize the brown module, genes with correlation \u0026ge; 0.8 and \u003cem\u003eP\u003c/em\u003e \u0026le; 0.05 were filtered, and the top 50 genes with the highest intramodular connectivity were used to construct the co-expression network (Figure 8E). The brown module network included genes related to flavonoid biosynthesis and plant hormone signal transduction (ABA, ET, AUX, and SA). Key structural genes involved in flavonoid biosynthesis were identified, including \u003cem\u003eC4H\u003c/em\u003e, \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eDFR\u003c/em\u003e, and \u003cem\u003e3GT\u003c/em\u003e, with \u003cem\u003eC4H\u003c/em\u003e being the most represented. Among hormone signal transduction pathways, ABA-related genes predominated, suggesting key roles in the drought response of potato. Notably, co-expressed genes such as \u003cem\u003eUGT\u003c/em\u003e, \u003cem\u003eUSP\u003c/em\u003e, \u003cem\u003eABF\u003c/em\u003e, and \u003cem\u003ePP2C\u003c/em\u003e are linked to flavonoid biosynthesis and ABA signal transduction, indicating that these genes may simultaneously regulate flavonoid synthesis and ABA-mediated drought responses.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs a staple crop for billions of people worldwide, potato provides essential nutrients such as carbohydrates and vitamins. However, its growing season is highly vulnerable to drought stress [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], which also represents a major abiotic stressor that drives the accumulation of metabolites [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, investigating the metabolic and molecular mechanisms of potato under drought stress is essential. In this study, two cultivars with contrasting drought sensitivities were selected. By integrating metabolomic and transcriptomic analyses, we investigated the dynamic changes in potato metabolites in response to drought stress. Our data provide reliable resources for studying potato metabolism in response to drought stress.\u003c/p\u003e\u003cp\u003eDrought stress can disrupt plant metabolism and alter the accumulation of secondary metabolites [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In Morus alba leaves, drought stress significantly altered the levels of lipids and lipid-like molecules, phenylpropanoids, polyketide compounds, and organic oxides [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The accumulation of flavonoids in plant tissues is regarded as a marker of plant stress and can effectively protect plants against abiotic stress [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. During plant responses to drought stress, flavonoids efficiently scavenge reactive oxygen species (ROS) through their abundant phenolic hydroxyl groups, thereby alleviating oxidative damage and enhancing drought adaptability [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Under drought stress, sweet potatoes accumulated significant amounts of flavonoids, thereby increasing their drought tolerance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A similar trend was observed in blueberries [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, flavonoids accumulated markedly under drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This was further supported by the enrichment of phenylalanine metabolism (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e) and of glycosyltransferases involved in flavonoid modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Six \u003cem\u003ePAL\u003c/em\u003e genes and five C4H genes were consistently upregulated in response to drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and these genes also exhibited co-expression in the WGCNA networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). In susceptible genotypes, \u003cem\u003ePAL\u003c/em\u003e was significantly downregulated in stems, roots, and spikes, contrasting with the upregulation observed in tolerant genotypes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Similar to millet, \u003cem\u003ePAL\u003c/em\u003e showed significantly higher expression in the drought-resistant cultivar J8 than in the drought-sensitive HL15, suggesting that \u003cem\u003ePAL\u003c/em\u003e may play a key role in potato responses to drought stress [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The activity of C4H directly influences subsequent flavonoid synthesis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and plants usually exhibit selective upregulation of \u003cem\u003eC4H\u003c/em\u003e expression under drought stress [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. All \u003cem\u003eC4H\u003c/em\u003e genes identified in this study were found to be upregulated. Notably, \u003cem\u003eSoltu.Atl_v3.06_4G022990\u003c/em\u003e exhibited higher transcript levels than other coding genes, suggesting that it may be the key gene regulating C4H activity.\u003c/p\u003e\u003cp\u003eIn sweet potato, drought stress activates components of the abscisic acid (ABA) signaling cascade, which subsequently induces the transcription of downstream stress-responsive genes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In this study, the expression of two \u003cem\u003ePYR/PYL\u003c/em\u003e genes, six \u003cem\u003ePP2C\u003c/em\u003e genes, six \u003cem\u003eSnRK2\u003c/em\u003e genes, and three \u003cem\u003eABF\u003c/em\u003e genes in potato seedlings under drought stress was all up-regulated, and key genes identified in the WGCNA network also included \u003cem\u003ePP2C\u003c/em\u003e, \u003cem\u003eSnRK2\u003c/em\u003e, and \u003cem\u003eABF\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Numerous studies have shown that overexpression of \u003cem\u003ePYR/PYL\u003c/em\u003e enhances ABA signaling to promote water uptake while simultaneously inhibiting PP2C, a negative regulator of ABA signaling [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Members of the PP2C phosphatase family are strongly induced by drought stress and ABA treatment, thereby enhancing plant drought tolerance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We observed significant induction of six \u003cem\u003ePP2C\u003c/em\u003e genes, accompanied by parallel up-regulation of downstream \u003cem\u003eSnRK2\u003c/em\u003e and \u003cem\u003eABF\u003c/em\u003e transcripts. A similar pattern was observed in Arabidopsis thaliana and maize leaves, where most \u003cem\u003ePYR/PYL\u003c/em\u003es were down-regulated under drought stress, whereas \u003cem\u003ePP2C\u003c/em\u003e, \u003cem\u003eSnRK2\u003c/em\u003e, and \u003cem\u003eABF\u003c/em\u003e transcripts were up-regulated in response [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur analysis revealed that the MYB transcription factor (TF) family contained the most significant number of differentially expressed TFs. Among plant TF families, MYBs constitute one of the largest groups and are extensively involved in mediating responses to abiotic stresses such as drought [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. \u003cem\u003eVcMYBPA1\u003c/em\u003e has been shown to induce anthocyanin accumulation and participate in drought-responsive flavonoid biosynthesis by regulating the expression of flavonoid biosynthetic genes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The correlation network (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) revealed that most MYBs were positively correlated with flavonoid biosynthetic genes, such as LDOX, PAL, 4CL, and 3GT, thereby promoting flavonoid synthesis and enhancing drought tolerance, similar to those found in honeysuckle [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Moreover, two MYBs (\u003cem\u003eSoltu.Atl_v3.03_1G026050\u003c/em\u003e and \u003cem\u003eSoltu.Atl_v3.01_3G036240\u003c/em\u003e) were significantly positively correlated with \u003cem\u003eABF\u003c/em\u003e and \u003cem\u003ePP2C\u003c/em\u003e in the ABA signaling pathway. Previous studies have shown that many MYBs are induced by ABA and are involved in drought stress responses [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Therefore, these MYBs may participate in drought stress responses by regulating flavonoid biosynthetic genes and ABA signaling pathway components. Significant positive correlations were also observed between four GRAS TFs and \u003cem\u003eCHS\u003c/em\u003e and \u003cem\u003e3GT\u003c/em\u003e in the flavonoid biosynthesis pathway, as well as with hormone signaling genes such as \u003cem\u003eIAA\u003c/em\u003e, \u003cem\u003eGH3\u003c/em\u003e, and \u003cem\u003eSnRK2\u003c/em\u003e. Previous studies have demonstrated that GRAS proteins promote the transcriptional activation of \u003cem\u003eCHS\u003c/em\u003e and \u003cem\u003e3GT\u003c/em\u003e, thereby regulating the accumulation of flavonoid metabolites. Moreover, GRAS proteins are core regulators in hormone signaling pathways and play positive roles in drought stress responses [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. We also identified a NAC transcription factor (\u003cem\u003eSoltu.Atl_v3.02_0G004120\u003c/em\u003e), whose expression was significantly upregulated across all three time points after drought stress, increasing by approximately sevenfold.\u003c/p\u003e\u003cp\u003eAfter identifying key drought-responsive genes through the WGCNA regulatory network, we divided them into nine modules and conducted correlation analyses between each module and the four physiological indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). The study revealed that genes in the brown module were positively correlated with SOD, CAT, and POD, but negatively correlated with MDA. This trend was consistent with findings in safflower (\u003cem\u003eCarthamus tinctorius\u003c/em\u003e L.) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Moreover, genes in the brown module mainly included those related to flavonoid biosynthesis and hormone signal transduction, such as \u003cem\u003eC4H\u003c/em\u003e, \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003ePP2C\u003c/em\u003e, and \u003cem\u003eSnRK2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Interestingly, flavonoid biosynthesis pathways were co-expressed with hormone signaling genes, including \u003cem\u003eUGT\u003c/em\u003e, \u003cem\u003eUSP\u003c/em\u003e, \u003cem\u003eABF\u003c/em\u003e, and \u003cem\u003ePP2C\u003c/em\u003e. Among these, \u003cem\u003eABF\u003c/em\u003e and \u003cem\u003ePP2C\u003c/em\u003e belong to the ABA signaling pathway, suggesting that ABA signaling may influence the expression of flavonoid biosynthetic genes. Thus, they may act synergistically to enhance potato drought tolerance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Previous studies have demonstrated that UGT enables plants to resist abiotic stress [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In chrysanthemum, higher accumulation of UGT-modified flavonoids improved drought and salt tolerance, thereby facilitating adaptation to salinity and drought [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. UGT is also involved in ABA homeostasis and responds to various abiotic stresses such as drought [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The \u003cem\u003eUGT\u003c/em\u003e (\u003cem\u003eSoltu.Atl_v3.10_2G009190\u003c/em\u003e) was connected to all genes in the network, suggesting that it may regulate flavonoid biosynthesis and ABA signaling gene expression. Similar to UGT, USP is involved in responses to heat and drought stress[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], and the \u003cem\u003eUSP\u003c/em\u003e identified here was also closely associated with flavonoid synthesis and ABA signaling, although its specific functions require further investigation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study investigated the potential molecular mechanisms underlying flavonoid biosynthesis in potato seedlings under drought stress using an integrated metabolomic and transcriptomic approach. We found that the expression of flavonoid biosynthetic genes, together with the accumulation of most flavonoid metabolites, contributed to enhanced drought tolerance in potato. Moreover, plant hormone signaling, particularly abscisic acid (ABA), also influenced the drought stress response in potato. We identified seven key candidate genes involved in regulating flavonoid biosynthesis in potato seedlings under drought stress. Furthermore, a more detailed analysis of potato flavonoid metabolism and ABA signaling revealed three transcription factors as potential regulators of drought tolerance. In summary, this study clarified the crucial role of potato flavonoid metabolism and plant hormone signaling in the response to drought stress, providing an important reference for understanding potato metabolism under drought conditions and offering insights for breeding drought-tolerant potato varieties.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePlant Materials and Growth Conditions\u003c/h2\u003e\u003cp\u003eSeedlings of the drought-resistant potato cultivar Ji Zhangshu 8 (J8) and the drought-sensitive cultivar Holland 15 (HL15), obtained from Hebei Agricultural University, were maintained by tissue culture subculture in the State Key Laboratory of Hebei Agricultural University. After 21 days of subculture, potato seedlings with uniform growth status were transferred to liquid MS medium containing 20% polyethylene glycol 6000 (PEG6000). They were grown in a controlled chamber at 22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C with ~\u0026thinsp;70% relative humidity, under a 16-h light (6:00\u0026ndash;22:00)/8-h dark (22:00\u0026ndash;6:00) photoperiod, at a light intensity of 2000\u0026ndash;3000 lx. Whole seedlings were collected after 0, 12, 24, and 36 h of treatment, with three biological replicates per time point. At each sampling point, seedlings with uniform growth status were selected, rinsed with distilled water, and 2 g of tissue from each was placed in sampling tubes. A total of nine tubes were prepared per stage, resulting in 36 tubes across the four stages. The collected seedlings were rapidly frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent RNA extraction, as well as physiological and biochemical analyses, and metabolomic and transcriptomic sequencing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDetermination of Physiological Indicators\u003c/h2\u003e\u003cp\u003eThe physiological indicators of tissue-cultured seedlings treated for 0, 12, 24, and 36 h were determined by Wuhan Domino Biotechnology Co., Ltd. Measurements were performed using commercial assay kits, with three replicates per treatment. The superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and malondialdehyde (MDA) assay kits were all obtained from Suzhou Ke Ming Biotechnology Co., Ltd.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMetabolite Profiling\u003c/h2\u003e\u003cp\u003eFrozen potato tissue-cultured seedlings (100\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mg per sample) were transferred to microcentrifuge tubes and mixed with 400 \u0026micro;L of extraction solvent (methanol: water\u0026thinsp;=\u0026thinsp;4:1, v/v) spiked with four internal standards, including L-2-chlorophenylalanine (0.02 mg/mL). Samples were processed by sequential cryogenic homogenization (\u0026minus;\u0026thinsp;10\u0026deg;C, 50 Hz, 6 min), ultrasonic extraction (5\u0026deg;C, 40 kHz, 30 min), incubation at \u0026minus;\u0026thinsp;20\u0026deg;C for 30 min, and centrifugation at 13,000 g for 15 min at 4\u0026deg;C. The resulting supernatants were transferred to autosampler vials with inner inserts for subsequent instrumental analysis. A pooled quality control (QC) sample was generated by mixing 20 \u0026micro;L of supernatant from each sample. Ultra-high-performance liquid chromatography (UHPLC) coupled with a TripleTOF 6600 mass spectrometer (AB SCIEX) was employed for LC\u0026ndash;MS analysis. Analytes were separated at 40\u0026deg;C using an ACQUITY UPLC BEH C18 column (100 mm \u0026times; 2.1 mm i.d., 1.7 \u0026micro;m; Waters, Milford, USA). The mobile phase consisted of solvent A (2% acetonitrile in water with 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid). The flow rate was 0.40 mL/min, with an injection volume of 5 \u0026micro;L. The elution gradient was programmed as follows: 0\u0026ndash;7.4 min, 2% B; 7.5\u0026ndash;12.9 min, 35% B; 13\u0026ndash;14.4 min, 95% B; and 14.5\u0026ndash;16 min, 2% B. Mass spectrometry conditions were as follows: electrospray ionization (ESI) temperature, 500\u0026deg;C; ion source voltage, 5500 V; and both nebulizer and auxiliary gas pressures, 50 psi.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eTranscript Assay\u003c/h2\u003e\u003cp\u003eFor each sample, total RNA was extracted using the MJZol RNA extraction kit (Shanghai Meiji Biomedical Technology Co., Ltd., China) according to the manufacturer\u0026rsquo;s instructions. RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). RNA integrity was evaluated by agarose gel electrophoresis using Biowest Agarose (Biowest, Spain), and the RNA quality number (RQN) was determined using an Agilent 5300 system (Agilent, USA). Purified mRNA was randomly fragmented into ~\u0026thinsp;300 bp fragments at an appropriate temperature using the fragmentation buffer. First-strand cDNA was synthesized from mRNA using random primers and reverse transcriptase, followed by second-strand cDNA synthesis. End-repair mix was added to convert the double-stranded cDNA into blunt ends. Adapter-ligated fragments were purified, size-selected, and amplified by PCR; the resulting products were purified to generate the final sequencing library. Sequencing was performed on the NovaSeq X Plus platform (Illumina, USA). Clean reads were aligned to the potato reference genome ATL_v3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://spuddb.uga.edu/ATL_v3_download.shtml\u003c/span\u003e\u003cspan address=\"http://spuddb.uga.edu/ATL_v3_download.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes (DEGs) were identified using the thresholds |Log2FC| \u0026ge; 1 and FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eWGCNA\u003c/h2\u003e\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) was performed in R using the WGCNA package (v1.71) [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. For stringent feature selection, the varFilter function from the genefilter package was applied with the interquartile range (IQR) method, retaining 35,682 genes for analysis. The mergeCutHeight parameter was set to 0.25. Based on module annotations, the 50 genes with the highest intramodular connectivity were selected, and the corresponding networks were visualized using Cytoscape (v3.10.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eUnsupervised principal component analysis (PCA) was performed using the ropls package in R. Prior to PCA, differentially accumulated metabolites (DAMs) were identified using the criteria Log2FC\u0026thinsp;\u0026ge;\u0026thinsp;1 and VIP\u0026thinsp;\u0026gt;\u0026thinsp;1. All heatmaps were generated using TBtools II (v1.12) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Venn diagrams were generated using the VennDiagram package in R. KEGG compound classification and functional analyses were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on April 22, 2025). Gene annotation was performed using the Pfam database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pfam.xfam.org/\u003c/span\u003e\u003cspan address=\"http://pfam.xfam.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on June 14, 2025) and the Gene Ontology (GO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on June 24, 2025). Omics correlation analysis was conducted using the online platform Majorbio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://v.majorbio.com\u003c/span\u003e\u003cspan address=\"https://v.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on March 13, 2025).\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eROS \u0026nbsp; \u0026nbsp; \u0026nbsp; Reactive Oxygen Species\u003c/p\u003e\n\u003cp\u003eC3 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Three-carbon chain\u003c/p\u003e\n\u003cp\u003eTF \u0026nbsp; \u0026nbsp; \u0026nbsp;Transcription factor\u003c/p\u003e\n\u003cp\u003eABA \u0026nbsp; \u0026nbsp; Abscisic acid\u003c/p\u003e\n\u003cp\u003eIAA \u0026nbsp; \u0026nbsp; \u0026nbsp;Indole-3-acetic acid\u003c/p\u003e\n\u003cp\u003eGA \u0026nbsp; \u0026nbsp; \u0026nbsp; Gibberellin\u003c/p\u003e\n\u003cp\u003eAUX \u0026nbsp; \u0026nbsp; Auxin\u003c/p\u003e\n\u003cp\u003eSA \u0026nbsp; \u0026nbsp; \u0026nbsp; Salicylic acid\u003c/p\u003e\n\u003cp\u003eBR \u0026nbsp; \u0026nbsp; \u0026nbsp; Brassinosteroid\u003c/p\u003e\n\u003cp\u003eET \u0026nbsp; \u0026nbsp; \u0026nbsp; Ethylene\u003c/p\u003e\n\u003cp\u003eSOD \u0026nbsp; \u0026nbsp; Superoxide dismutase\u003c/p\u003e\n\u003cp\u003ePOD \u0026nbsp; \u0026nbsp; Peroxidase\u003c/p\u003e\n\u003cp\u003eCAT \u0026nbsp; \u0026nbsp; Catalase\u003c/p\u003e\n\u003cp\u003eMDA \u0026nbsp; \u0026nbsp;Malondialdehyde\u003c/p\u003e\n\u003cp\u003eLC\u0026ndash;MS \u0026nbsp;Liquid Chromatography\u0026ndash;tandem Mass Spectrometry\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eDAM \u0026nbsp; \u0026nbsp;Differentially Accumulated Metabolite\u003c/p\u003e\n\u003cp\u003eDEG \u0026nbsp; \u0026nbsp; Differentially Expressed Gene\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene Ontology\u003c/p\u003e\n\u003cp\u003eWGCNA \u0026nbsp;Weighted Gene Co-expression Network\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. G. wrote the paper, R. S. and W. W. conducted the data analysis, W. Q. and Y. C. managed the material, J. S., M. Y. and J.Z. provided the material, J. C. designed the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Science and Technology Resource Sharing Service Platform Program (NCGRC-2025-44), Ministry of Science and Technology and Ministry of Finance \u0026mdash; Operation Service of the Potato Sub-Repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of date and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data sheets and codes to process data are available upon request to the corresponding author, Jianghui Cui ([email protected]). The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics \u0026amp; Bioinformatics 2025)[73] in National Genomics Data Center (Nucleic Acids Res 2025)[74], China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA031995) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo animals or humans were involved in this study, and there are no ethical issues involved in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicting interests, and all authors have approved the manuscript and agree with its submission to BMC Plant Biology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDevaux A, Goffart JP, Kromann P, et al. 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Contribution of ABA UDP-glucosyltransferases in coordination of ABA biosynthesis and catabolism for ABA homeostasis. Plant Signal Behav. 2014;9(7):e28888. doi: 10.4161/psb.28888.\u003c/li\u003e\n\u003cli\u003eAkram A, Arshad K, Hafeez M N. Cloning and expression of universal stress protein 2 (USP2) gene in Escherichia coli[J]. BIOLOGICAL \u0026amp; CLINICAL SCIENCES RESEARCH JOURNAL, 2021, 2021(1).\u003c/li\u003e\n\u003cli\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008; 9:559. doi: 10.1186/1471-2105-9-559.\u003c/li\u003e\n\u003cli\u003eChen C, Wu Y, Li J, et al. TBtools-II: A \u0026quot;one for all, all for one\u0026quot; bioinformatics platform for biological big-data mining. Mol Plant. 2023; 16(11):1733-1742. doi: 10.1016/j.molp.2023.09.010.\u003c/li\u003e\n\u003cli\u003eThe GSA Family in 2025: A Broadened Sharing Platform for Multi-Omics and Multimodal Data. Genomics, Proteomics \u0026amp; Bioinformatics 2025 Sep 22;23(4): qzaf072. https://doi.org/10.1093/gpbjnl/qzaf072 [PMID=40857552]\u003c/li\u003e\n\u003cli\u003eDatabase Resources of the National Genomics Data Center, China National Center for Bioinformation in 2025. Nucleic Acids Res 2025 Jan 6;53(D1): D30-D44. https://doi.org/10.1093/nar/gkae978 [PMID=39530327]\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, Metabolome, Transcriptome, Drought stress, Flavonoids","lastPublishedDoi":"10.21203/rs.3.rs-7885634/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7885634/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePotato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is an essential crop for food production and industrial use, yet its growth and development are substantially constrained by drought stress. Drought not only causes marked reductions in tuber yield but also compromises overall plant growth and health. Flavonoids, due to their antioxidant capacity, play a critical role in drought tolerance, and hormone signaling pathways also modulate drought responses. TFs further coordinate these processes by regulating genes in flavonoid biosynthesis and across hormone-signaling pathways. To elucidate the molecular basis of potato adaptation to drought, we conducted integrated metabolomic and transcriptomic analyses of two cultivars subjected to drought treatment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this study, we identified 3,001 metabolites, including 88 classified as flavonoids. Under drought, both HL15 and J8 exhibited pronounced metabolite accumulation alongside significant up-regulation of genes in the flavonoid biosynthetic pathway, indicating a central role for flavonoid metabolism in the drought response of potato. Transcriptome profiling further showed that drought-responsive genes were predominantly enriched in pathways related to flavonoid biosynthesis and plant hormone signal transduction. Correlation analysis, combined with WGCNA, identified three transcription factors that may regulate flavonoid metabolism and hormone signaling under drought conditions. The expression of flavonoid biosynthetic genes, along with the accumulation of most flavonoid metabolites, contributed to enhanced drought tolerance. In addition, plant hormone signaling\u0026mdash;particularly the abscisic acid (ABA) pathway\u0026mdash;also shaped the drought response. We identified seven key candidate genes involved in regulating flavonoid biosynthesis under drought. Further investigation into flavonoid metabolism and ABA signaling identified three transcription factors as potential regulators of drought tolerance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eCollectively, these findings demonstrate a significant enrichment of flavonoid pathways and hormone signaling in drought-stressed potato seedlings, providing actionable insights and datasets to inform future studies on drought resistance.\u003c/p\u003e","manuscriptTitle":"Integrative Physiological, Metabolomic, and Transcriptomic Analyses Provide New Insights into Potato Drought Stress Responses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 13:41:18","doi":"10.21203/rs.3.rs-7885634/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-05T06:40:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-13T14:44:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T16:37:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T09:24:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140606011668876898100595932927993436951","date":"2025-11-28T04:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153483086410348673884930867095480528693","date":"2025-11-26T10:06:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67984574449825989522146450854546739696","date":"2025-11-26T03:16:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-05T17:16:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-02T11:19:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T16:49:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-29T13:52:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-10-29T13:48:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2c0051a-d85c-4c94-91c2-765a37fc202c","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T11:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-7885634","link":"https://doi.org/10.1186/s12870-026-08575-x","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2026-03-17 15:58:41","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2025-11-17 13:41:18","video":"","vorDoi":"10.1186/s12870-026-08575-x","vorDoiUrl":"https://doi.org/10.1186/s12870-026-08575-x","workflowStages":[]},"version":"v1","identity":"rs-7885634","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7885634","identity":"rs-7885634","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}