Genotype-specific transcriptomic response to drought stress in potato cultivars modulated by microbial biostimulants

Genotype-specific transcriptomic response to drought stress in potato cultivars modulated by microbial biostimulants | 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 Genotype-specific transcriptomic response to drought stress in potato cultivars modulated by microbial biostimulants Rachele Tamburino, Lorenza Sannino, Francesca Palomba, Emanuela Russo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7656066/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Background Drought is a major abiotic stress that significantly limits potato productivity and tuber quality. Microbial biostimulants have emerged as promising tools to improve crop resilience and promote sustainable agriculture. This study aimed to investigate the transcriptomic response to severe drought stress in three potato cultivars (Camelia, Cicero, and Agata) characterized by differing polyphenol content. In addition, the potential mitigating effects of a microorganism-based biostimulant mixture composed by two Trichoderma species, were evaluated. Results Severe drought stress led to a significant reduction in tuber yield in Camelia and Agata, whereas Cicero exhibited greater tolerance. Application of the microbial biostimulant slightly alleviated yield loss in Camelia but was associated with an increased proportion of non-commercial tubers. Polyphenol content varied according to genotype and treatment, Cicero displayed elevated polyphenol levels in the skin under drought, while Camelia showed increased levels in both skin and pulp when drought was combined with biostimulant application. Transcriptomic analysis revealed genotype-specific drought response strategies. Camelia exhibited enhanced proteostasis and osmoprotection via anthocyanin accumulation, while Cicero maintained photosynthetic activity, redox homeostasis, and genome stability. Trichoderma -based biostimulant treatment modulated these responses by enhancing endoplasmic reticulum protein quality control in Camelia, and promoting photosynthesis and sugar metabolism in Cicero. Conclusions Potato responses to drought are highly genotype-dependent and involve complex trade-offs among growth, stress defense, and metabolic reprogramming. Microbial biostimulants offer a promising approach to enhance drought resilience and tuber nutraceutical quality. However, their efficacy is genotype-specific and requires targeted application strategies for optimal results. Solanum tuberosum drought stress Trichoderma spp. NGS differential gene expression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Climate change is exerting an increasing impact on global food production systems. Rising temperatures, coupled with the growing frequency and severity of drought events, are significantly impairing crop development and yield, thereby reducing food availability and accessibility. These adverse conditions could continue to escalate, posing a critical challenge to the sustainability of agriculture. In this context, enhancing crop resilience has become a strategic priority for global food security, especially for nutritionally and economically important crops. To develop climate-resilient crops, it is essential a comprehensive understanding of plant responses to environmental stress and the identification of genes conferring tolerance (Demirel et al., 2020 ; Joshi et al., 2016 ; Obidiegwu et al., 2015 ; Ponce et al., 2022 ). Potato ( Solanum tuberosum L.) is a staple food crop ranked fourth in global consumption. Its tubers are rich in carbohydrates produced via leaf photosynthesis, and contain significant levels of vitamins, minerals, and phenolic compounds (Docimo et al., 2023 ). The accumulation of these health-promoting compounds is genotype-dependent and influenced by environmental conditions, particularly temperature. Generally, low temperatures promote phenolic biosynthesis (Da Ros et al., 2020 ). Despite its global importance, potato is highly susceptible to drought stress due to its shallow root system and physiological sensitivity (Lv et al., 2024 ). Drought is the second major cause of potato yield loss after pathogen infection and adversely affects multiple physiological and biochemical processes, including photosynthesis, nutrient assimilation, and tuber development (Dahal et al., 2019 ; Alvarez-Morezuelas et al., 2022a ). The extent of yield reduction depends on both environmental factors (e.g., severity and duration of drought) and plant characteristics (e.g., genotype and developmental stage). Among these processes, photosynthesis is particularly critical. Drought impairs carbon partitioning by triggering a cascade of molecular and physiological responses that determine plant survival (Aliche et al., 2020 ; Gervais et al., 2021 ; Obidiegwu et al., 2015 ). Furthermore, drought stress negatively regulates genes involved in stomatal function, hormonal signaling, particularly abscisic acid, and antioxidant defense, ultimately compromising plant growth and productivity (Nour et al., 2024 ). Facing climate-induced stress, plant scientists are adopting integrated strategies that combine traditional breeding approaches with modern genomic tools and the use of biostimulants. Among biostimulants, soil microorganisms have shown significant promise in improving plant growth and stress resilience while maintaining ecological sustainability. In particular, Trichoderma spp. are widely recognized for their ability to stimulate root development, improve water and nutrient uptake, enhance photosynthetic efficiency, and mitigate oxidative damage by modulating reactive oxygen species (ROS) levels (Rawal et al., 2022 ; Akbari et al., 2024 ; Cañada-Coyote et al., 2021 ; Guler et al., 2016 ; Li et al., 2022a , b ). These beneficial effects have been documented in a variety of crops under drought conditions, including rice, tomato, and durum wheat (Gusain et al., 2014 ; Pehlivan et al., 2018 ; Silletti et al., 2021 ). Although several studies have characterized the molecular response of potato to drought (Alvarez-Morezuelas et al., 2022a , b ; Chen et al., 2019 ; Da Ros et al., 2020 ; Demirel et al., 2020 ; Gervais et al., 2021 ; Ponce et al., 2022 ), the molecular mechanisms underlying the interaction between Trichoderma -based biostimulants and drought tolerance remains largely unexplored. Hence, in-depth analyses of these interactions are crucial to optimize the use of these beneficial microorganisms in sustainable crop management. This study aimed to select potato genotypes with high polyphenol content, compounds that act as antioxidants contributing to abiotic stress tolerance. Subsequently, we evaluated the performance of the three most promising potato genotypes under drought, with and without a Trichoderma -based biostimulant application. The two genotypes showing the best performance in terms of yield-related parameters (i.e., tuber number, weight, and size) and polyphenol content, were selected for transcriptomic analysis using RNA sequencing (RNA-seq) to investigate the molecular basis of their drought response. Our results provide novel insights into the gene networks modulated by drought and microbial treatment, laying the groundwork for breeding and biostimulant-based strategies to enhance potato resilience under climate change scenario. Methods Plant, materials and treatment conditions A collection of nine potato cultivars (kindly provided by Coppola Patate srl and Dr. M. Mazzei), were included for preliminary screening from those available on the market (Table 1 ). Table 1 Potato genotypes used in this work Genotype Tuber number/plant Tuber weight/plant (g) Tuber shape Skin colour Pulp colour Agata 9–11 350–400 Oval Yellow Yellow Blue Star 12–14 400–450 Long oval Purple Purple Camelia 9–11 400–420 Oval Dark yellow Dark yellow Cayman 12–14 420–450 Round/oval Yellow Light yellow Cicero 11–12 400–420 Oval Yellow Yellow Double Fun 12–14 400–450 Oval Purple Yellow- Purple Emanuelle 12–14 420–460 Oval/long oval Dark yellow Dark yellow Sunita 9–11 380–400 Round/oval Yellow Yellow Violet Queen 15–17 300–360 Long oval Purple Purple Tubers were washed with distilled water, sliced separating skin and pulp and lyophilized using a freeze-dryer alpha 1–2 LD plus (Martin Christ Gefriertrocknungsanlagen GmbH, Germany). To ensure homogeneity of the plant material for drought experiments, apical buds of plants obtained from the tubers were first sterilized by immersion in 70% ethanol for 30 sec, then in 1.5% NaClO containing 0.1% (v/v) Tween20 for 15 min with gentle stirring. The buds were then washed five times, each for 5 min, in Milli-Q water. Subsequently, the sterilized buds were sown in vitro under controlled conditions (16 h light 40 µmol photons m − 2 s − 1 and 8 h dark at 24°C) on Murashige & Skoog (MS) medium with B5 vitamins (Duchefa, The Netherlands), solidified with 0.8% (w/v) agar, with 30 g/l sucrose and 250 mg/l of cefotaxime. The in vitro rooted seedlings of selected cultivars (i.e., Agata, Camelia and Cicero) were transplanted into 18cm diameter pots and irrigated with tap water at field capacity daily for 2 weeks. Forty plants per genotype were cultivated and divided into two subsets, differing in the treatment with a biostimulant mixture applied twice via irrigation (i.e., at transplant and two days before water withholding) (Fig. 1). Figure 1. Schematic representation of drought stress experiment. Plants were divided into two groups based on the presence or absence of biostimulant application and subsequently subjected to cycles of water deficit (D1, D2, corresponding to the first and second drought periods) followed by rewatering phases (R1, R2, corresponding to the first and second recovery periods). A new bioformulation containing two Trichoderma stains from the collection of CNR-IPSP was used for the treatment. The Trichoderma strains were characterised at the morphological and molecular levels as reported by Napolitano et al. ( 2024 ), then inoculated into flasks containing Potato Dextrose Broth (PDB) and left to grow separately in a rotating incubator at 25°C for 7 days at 150 rpm. Once the minimum concentration of the culture was 10 5 CFU the two strains were mixed and let it grow for 7 more days in a bioreactor. The resulting fermented product, diluted at 10 6 CFU, was applied to the potato plants. Drought stress was induced by withholding water until the soil relative water content (SRWC) reached at 10 ± 2%. The water deficit was generally maintained for 13–16 days by daily monitoring soil moisture contents in the pots and compensating the water loss; this condition was termed as D1. Meanwhile, the control pots were kept well-watered at 80 ± 5% SRWC. Subsequently, all pots were re-watered to an SRWC of 80% (R1) for 2 days, after which a second dehydration treatment (D2) was performed for 10 days. Finally, plants were re-watered to well-watered level (R2) until crop maturity. Soil water status was estimated gravimetrically before water application to pots (Batool et al., 2020 ). Four replicates per conditions were performed. Leaves were collected at the end of each step and stored at -80°C. Following tuberization, tubers were recovered, weighted and their size measured to assess tuber yield in all applied conditions. Polyphenols content determination Total polyphenol content (TPC) was assessed both in pulp and skin of tubers by Folin-Ciocalteu assay as previously described (Celano et al., 2017 ). Briefly, 100 mg of lyophilized pulp and skin of tubers were extracted in 1 ml 80% (v/v) methanol and sonicated for 30 min at 10°C and then centrifuged at 12000 xg for 20 min at 4°C. Supernatant was recovered and kept on ice at dark till the usage. In a 96-well plate, 20 µL of each sample extract was added to 150 µL of Folin–Ciocalteu reagent diluted with distilled water (1:30, v/v) and 30 µL of 20% (w/v) Na 2 CO 3 , incubated at room temperature in the dark for 45 min and then absorbance at 765 nm was measured in a Multiskan™ Sky microplate spectrophotometer (Thermo Scientific, Waltham, MA, USA). Gallic acid (GA) was used as reference standard and TPC was estimated from the GA calibration curve (range 31.25–750 µg/mL, 6 levels; r 2 = 0.998). The results were expressed as GA equivalents per g of dry leaf (mg GAE/g DW, means ± SD of three biological replicates). Statistical differences between treatments and controls samples were assessed using Student’s t test (*P ≤ 0.05, **P ≤ 0.01); whilst a threeway ANOVA was performed to evaluate the effects of genotype, treatment, biostimulant and their interactions. Total RNA isolation and sequencing Total RNA was extracted from leaves of three biological replicates of drought stressed Camelia and Cicero plants (D1 step, hereafter termed as D and D + mix conditions) and their respective controls using the RNeasy Plant Mini Kit (Qiagen, Germania) following manufacturer’s instructions. The integrity and quality of RNA was evaluated by denaturing agarose gel electrophoresis and nanodrop measurements (Additional file 1). High-throughput sequencing service and preliminary data processing of 24 RNA-seq libraries were carried out by Sequentia Biotech SL ( https://www.sequentiabiotech.com/ ). The raw reads were quality-checked using Trimmomatic v0.39, applying a minimum read length of 35 bp and a minimum sequencing quality of 20. Filtered reads were mapped to the potato reference genome v3 (Ensembl Genomes 57) using STAR aligner v2.7.9a with default parameters. Count matrix was build using featureCounts v2.0.3, considering only reads with a minimum alignment quality score of 40. The resulting count matrix was normalized using the TMM method, and low expressed genes were filtered out using HTSfilter v1.28.0. Subsequent analyses, including principal component analysis (PCA) were performed using FactoMineR and pheatmap R packages (R software 4.3.0), respectively. After both analyses, libraries that did not group with their respective biological replicates were excluded from downstream analyses. Differential expression and Gene Ontology (GO) analyses Differential expression analysis was carried out with edgeR R package (R software 4.3.0), only considering differential expressed genes (DEGs) with a log fold-change (LogFC) higher than 1 or lower than − 1 and False Discovery Rate (FDR) lower than 0.05. Four different contrasts were analysed per genotype: Drought vs Control (D vs C) Drought + mix vs Control + mix (D + mix vs C + mix) Control + mix vs Control (C + mix vs C) Drought + mix vs Drought (D + mix vs D) Taking these comparisons, a GO enrichment analysis of up-regulated and downregulated genes was performed using clusterProfiler R package (R software 4.3.0). Quantitative real-time PCR validation To validate the RNA-seq data, five genes were selected among either the most positively or negatively affected categories for qRT-PCR analysis. An aliquot of each sample was retrotranscribed to cDNA by digesting trace amount of DNA using DNase kit (Invitrogen) and using RevertAid 1st strand cDNA synthesis kit (ThermoFisher Scientific, USA). cDNA quality was further evaluated by RT-PCR amplification of 18S rRNA gene and amplification products were visualized by agarose gel electrophoresis (Additional file 1). Diluted cDNA (1:20) was used as template for semi-quantitative real time using the Platinum™ SYBR™ Green qPCR SuperMix-UDG (Applied Biosystems, USA). All reactions were set using 6.25 µl of 2x SYBR green dye, 0.6 µM of forward and reverse primer mix, 4.5 µl template cDNA and run on the 7900HT Fast Real Time PCR (Applied Biosystems, USA). Primers used and program cycle parameters are listed in Additional file 2. The relative expression levels were calculated using the 2 −∆∆CT method (Livak & Schmittgen, 2001 ). EF-1α was used as internal control. Data representing three biological replicates and three technical replicates are expressed as mean ± standard deviation. Statistical analysis The statistical analysis was performed in Microsoft Excel (Windows 11 Enterprise) and R software. One- and three-way ANOVA and RNA-seq data analysis were conducted in R software 4.3.0. Significancy of polyphenol contents and gene expression levels was analysed using Student’s t-test. The differences between different treatments were considered significant when the P -value was lower than 0.05. Results Screening of potato genotypes for drought stress tolerance In order to select the cultivar for drought stress experiment, the total polyphenol content (TPC) was measured in both tubers’ skin and pulp of collected cultivars (Fig. 2 ). The Folin-Ciocalteau assay revealed that polyphenol content was higher in the skin than in the pulp for all the tested genotypes, among them, Camelia exhibited the highest TPC in both tissues (i.e., 132.9 ± 5.5 and 36.7 ± 4.9 mg GAE/g DW in skin and pulp, respectively), followed by Cicero, whose pulp showed a polyphenol content comparable to that of Camelia’s pulp (i.e., 33.3 ± 5.2 mg GAE/g DW). Based on their high TPC and suitability for multiple purposes (i.e, processed, table consumption and industrial use), Camelia, Cicero and Agata were selected for experiment under severe water deficit conditions, with or without treatment using the microorganism-based biostimulant mixture according to the scheme reported in Fig. 1. Plant performance was assessed by measuring tuber yield (Table 2 ) and polyphenol content in both skin and pulp following water deficit treatments (Fig. 3 ). The three cultivars exhibited distinct responses to drought, which were also influenced by biostimulant treatment. Tuber weight decreased in Camelia and Agata tubers under all applied conditions (up to 37.7% and 44.3%, respectively) compared to their control, although this reduction was mitigated (up to 10.7% and 28.3%, respectively) when biostimulant mix was applied (Table 2 ). Interestingly, Camelia tubers from D1 + mix and Agata from D2 + mix even showed a slight increase in weight (3 and 1%, respectively) compared to the control. Conversely, Cicero tubers significantly increased in weight under drought (up to 29.1% in R1) compared to the control, but this gain was reversed upon biostimulant application, with a weight reduction of up to 27.9%. Regarding size, biostimulant-treated Camelia tubers under all applied conditions were smaller than controls, whereas Cicero and Agata showed no significant size changes. Table 2 Yield of tubers expressed as mean weight and number of tubers in all experimental conditions. Camelia Cicero Agata Treatment Weight ± sd (g) N. tubers (average) Size Weight ± sd (g) N. tubers (average) Size Weight ± sd (g) N. tubers (average) Size C 85.7 ± 11.9 4.75 M/L 64.9 ± 6.3 2.8 S/M 129.4 ± 6.1 11.5 M/L D1 53.4 ± 19.8* 3.75 M/L 71.2 ± 5.4 2 M 85.6 ± 20.5** 4 M/L R1 65.3 ± 17.2 4.5 M/L 83.8 ± 4.6** 3.5 S/M 72.1 ± 19.4** 5.5 M/L D2 73.9 ± 10.6 3.5 M/L 78.1 ± 6.9 2.5 S/M 77.3 ± 7.0** 3.8 M/L R2 57.3 ± 19.8* 2.75 M/L 78.4 ± 3.4* 2 S/M 99.0 ± 11.3** 4.3 M/L C + mix 74.3 ± 21.5 5 M/L 96.9 ± 17.8 2.3 M 99.5 ± 28.1 4.8 M/L D1 + mix 76.5 ± 9.2 4.5 S/M 83.4 ± 3.9* 1.5 S/M 71.4 ± 12.0 4.3 M/L R1 + mix 71.9 ± 6.9 3.25 S/M 78.3 ± 3.5* 3 S/M 77.6 ± 21.2 5.5 M/L D2 + mix 66.3 ± 14.9 3.5 S/M 69.8 ± 6.2** 2.2 S/M 100.4 ± 9.7 6 M/L R2 + mix 74.0 ± 13.1 5 S/M 77.4 ± 6.9 2 S/M 93.1 ± 7.0 4 M/L Three way ANOVA_weight P value Three way ANOVA_number P value genotype *** genotype *** treatment *** treatment ** biostimulant ns biostimulant ns genotype:treatment ns genotype:treatment ns genotype:biostimulant ns genotype:biostimulant ns treatment:biostimulant ns treatment:biostimulant * genotype:treatment:biostimulant ns genotype:treatment:biostimulant * S: small, size ≤ 3 cm; M: medium, 3 cm < size < 5 cm; L: large, size ≥ 5 cm; ‘***’, ‘**’, ‘*’ and ‘ns’ indicate significant at P < 0.001, P < 0.01, P 0.05, respectively. Tuber nutritional quality was evaluated by measuring the polyphenol content in both pulp and skin (Fig. 3 ). In Camelia, pulp polyphenols significantly increased (up to 1.7-fold) under all experimental conditions with biostimulants, while no significant changes were observed in the absence of biostimulants. TPC from skin also increased significantly up to 1.2-fold in both R1 and R1 + mix conditions. In Cicero, skin polyphenols significantly increased up to 1.6-fold under drought stress but slightly decreased with biostimulant application (i.e., D2 + mix). Pulp TPC in Cicero remained largely unchanged, except for a 59% reduction in R2. In Agata, TPC increased significantly in D1 pulp (1.4-fold) and R1 and R2 skin (up to 2.4-fold) without biostimulants, while no significant variations were observed with biostimulant mix. Threeway ANOVA indicated that both genotypes and treatment significantly affected all measured parameters (P < 0.01). However, genotype-treatment interactions were only significant for polyphenols in tuber pulp. The biostimulant application also had a significant effect on polyphenol content in both tissues, and its interaction with genotype was statistically significant. Principal Component Analysis (PCA) of tuber weight, tuber number and polyphenol content in skin and pulp revealed that the first two principal components together explained the entire variance (PC1: 77.9%. PC2: 22.1%; Additional file 3). Tuber weight and TPC in skin and pulp were the main contributors to PC1. Notably, polyphenol content in skin and pulp was positively correlated, with particularly high values in Camelia genotype, while TPC negatively correlated with tuber weight. Transcriptional changes induced by drought and drought + biostimulant treatment in leaves Based on yield and TPC results, Camelia and Cicero cultivars were selected for RNA-seq analysis considering four conditions: control (C) drought (D), control with biostimulant (C + mix), and drought with biostimulant (D + mix), with D and D + mix corresponding to the first imposed water stress (Fig. 1). Indeed, due to poor RNA quality obtained from leaves of some conditions (i.e., R1, D2 and R2), RNA-seq analysis was restricted to D1. PCA of all the libraries revealed a clear genotype-specific clustering. Notably, the Camelia samples grouped differently depending on the biostimulant treatment (Additional file 4). PC1 explaining 40.6% of the total variance, primarily separated the genotypes, suggesting a genotype-specific response to drought. Biostimulant treatment induced substantial transcriptional reprogramming in Camelia. A summary of differentially expressed genes (DEGs) is showed in Fig. 4 , in general, Camelia showed a higher number of differentially expressed genes. Specifically, in the D vs C comparison, Camelia had 4,305 DEGs (1,749 up-regulated, 2,556 down-regulated), while Cicero had only 426 DEGs (229 up-regulated and 197 down-regulated) (Fig. 4 ). The two genotypes shared just 4.3% of total DEGs (Additional file 5A). In D + mix vs C + mix, Camelia had 5,215 DEGs (2,150 up-regulated and 3,065 down-regulated) and Cicero 934 (360 up-regulated and 574 down-regulated) (Fig. 4 ). The 7.7% of total DEGs were shared (Additional file 5A). For D + mix vs D comparison, Camelia had 855 DEGs (413 up-regulated and 442 down-regulated), and Cicero 757 (160 up-regulated and 597 down-regulated), with 3.4% overlap (Fig. 4 , Additional file 5A). In C + mix vs C, Camelia had 164 DEGs (68 up-regulated and 94 down-regulated), whereas Cicero had just one, with no overlap (Fig. 4 , Additional file 5A). In all comparisons the number of down-regulated genes was higher than up-regulated which means that plants stop regular active metabolism as main mechanism of response to the drought stress. These results indicate that Camelia and Cicero exhibited genotype-specific response to water deficit and biostimulant application. Further analysis of shared DEGs (Additional file 5B) showed that in the D vs C, 156 genes (59 up-regulated and 97 down-regulated) had the same trend in both cultivars, while 39 genes showed opposite trend. In D + mix vs C + mix, 267 genes (60 up-regulated and 207 down-regulated) shared the same trend, whilst 170 genes were oppositely regulated. In D + mix vs D, 24 shared genes (2 up-regulated and 22 down-regulated) were similarly regulated, while 28 genes displayed opposite trend. Camelia also had more DEGs consistently present across all conditions (Additional file 6A), particularly in the D vs C and D + mix vs C + mix comparisons (2994 overlapping DEGs). In Cicero, the largest overlap (313 DEGs) was between the D + mix vs C + mix and D + mix vs D (Additional file 6B). qRT-PCR validation of five randomly selected DEGs confirmed the RNA-seq findings (Additional file 7). Gene Ontology (GO) enrichment analysis In Camelia, the higher number of DEGs identified compared to Cicero resulted in a greater overlap across the analysed comparisons, reflecting a broader range of affected Gene Ontology terms (Additional file 8C and 7F). The most impacted biological processes (BPs) were consistently enriched across the D vs C, D + mix vs C + mix and D + mix vs D comparisons. These included up-regulated categories associated to transmembrane transport, protein folding and xyloglucan metabolic processes, as well as down-regulated categories related to translation and protein modification (Fig. 5 A and Additional file 9A). Interestingly, the photosynthesis-related categories exhibited divergent expression patterns depending on the condition, being predominantly up-regulated in the D vs C comparison. Secondary metabolism and senescence-related processes were exclusively up-regulated in drought-stressed plants while GO categories linked to calcium-mediated signalling were strongly induced by biostimulant application (Additional file 9A). At the molecular functions (MF) level, in D vs C and D + mix vs C + mix comparisons, the most affected categories included those related to energy and signal transduction, such as ATP and nucleotide binding, along with transferase and protein kinase activities (Additional file 8A). These molecular functions were primarily associated with membrane-localized compartments Additional file 8B and 9C). In Cicero, the most significantly enriched BPs in both the D vs C and D + mix vs C + mix comparisons were primarily related to photosynthesis (Fig. 5 B and Additional file 10A). Furthermore, exposure to drought (D vs C) enhanced the expression of genes involved in chromatin remodelling consistent with a possible inhibition of cell division to conserve energy. Conversely, pathways associated with abscisic acid (ABA) and ethylene signalling, as well as defense responses, protein folding and glutathione metabolism, were down-regulated under drought stress (Additional file 10A). Notably, biostimulant application under drought conditions (i.e., D + mix vs C + mix), triggered the activation of primary metabolic pathways including gluconeogenesis, fructose metabolism and carbon fixation (Fig. 5 B and Additional file 10A). These pathways are known to contribute to stress adaptation via the accumulation of soluble sugars that act as osmoprotectors (Samraoui et al., 2025 ). However, other related processes such as carbohydrate metabolism were suppressed. Additionally, genes related to circadian rhythm were up-regulated, potentially enhancing photosynthetic and metabolic efficiency under stress (Bargunam et al., 2025 ; Vajjiravel et al., 2024 ). Consistently, increased expression of chlorophyll binding activity at the MF level suggested a possible role for the biostimulant in preserving photosynthetic capacity (Additional file 8D and 10B). However, in the D + mix vs D comparison, photosynthesis-related genes were down-regulated. This may seem contradictory with the D + mix vs C + mix result, but it can be explained by a mild induction of these genes mediated by Trichoderma treatment. When compared with the C + mix condition, this induction appears as up-regulation, whereas when compared with the D conditions, it appears as down-regulation due to the stronger effect of drought on photosynthesis. Such an effect suggests a possible adaptive modulation mediated by the biostimulant. Similarly, transcriptomic profile in tomato treated with Ascophyllum nodosum seaweed showed mild up-regulation of photosynthesis-related genes, exceeding the baseline in irrigated controls (Kanojia et al., 2024 ). Overall, these findings suggest that Cicero adopts a distinct, biostimulant-supported strategy, to cope to water deficit, largely involving metabolic reprogramming. The altered biological processes were predominantly associated with subcellular compartments such as the chloroplast, thylakoid and other plastid structures (Additional file 8E and 10C). A clear genotype-dependent transcriptional response emerged between Camelia and Cicero under stress condition. To further explore their intrinsic transcriptional differences, their transcriptomic profiles under non-stress conditions, specifically in the control (C) and control with biostimulant (C + mix) were compared. Under control conditions (i.e., C Camelia vs C Cicero) a total of 5,929 DEGs were identified with 2,325 up-regulated in Camelia and 3,604 up-regulated in Cicero. Gene Ontology (GO) enrichment analysis revealed that Camelia exhibited a significant up-regulation of BPs related to translation, ribosome biogenesis, and photosynthesis including key functions such as light harvesting and photosystem I activity (Additional file 11A). Additional enrichment was observed in genes associated with protein folding, phosphorylation, response to hydrogen peroxide, and phosphate starvation response, suggesting a transcriptional state primed for stress anticipation or mitigation. In contrast, BPs down-regulated in Camelia, and thus up-regulated in Cicero in control conditions, included primary metabolic and structural processes, such as carbohydrate metabolic process, cell wall organization, cell wall biogenesis, glucan metabolic process, and proteolysis (Additional file 11A). Further reductions in expression were seen in genes involved in lipid catabolism, phosphate ion transport, and defense response to other organisms, indicating a lower investment in baseline metabolic and defense functions in Camelia compared to Cicero (Additional file 11A). Consistently, one of the most enriched MFs in Camelia was the structural constituent of the ribosome (Additional file 11B), aligned with the enrichment of ribosome-associated cellular compartments (Additional file 11C). Under biostimulant-treated control conditions (i.e., C + mix Camelia vs C + mix Cicero), the transcriptomic comparison between Camelia and Cicero revealed 5,260 DEGs, with 2,106 genes up-regulated in Camelia, and 3,154 in Cicero. GO analysis showed that Camelia maintained a transcriptional profile strongly oriented toward protein biosynthesis, with significant up-regulation of BPs such as translation, ribosome biogenesis, and rRNA processing. This pattern was accompanied by enhanced activation of photosynthesis-related pathways, including light harvesting (Additional file 12A). These trends were further supported by enriched MFs related to ribosomal activity and protein folding, such as structural ribosome constituents, ATP-dependent chaperone activity, unfolded protein binding, protein self-association and heat shock protein binding (Additional file 12B and 12C). Moreover, Camelia showed increased expression of genes involved in stress responses, including those induced by heat, hydrogen peroxide, and salt stress, suggesting a transcriptional state primed for environmental resilience. In contrast, Cicero exhibited a transcriptional profile more oriented toward defense-related GO categories, including responses to biotic stimuli (e.g., fungi and other organisms), as well as pathways associated with lipid and carbohydrate metabolism, proteolysis, and transmembrane transport (Additional file 12A). Discussion Drought stress is one of the most significant factors that limit plant growth and yield. Potato is particularly susceptible to water withholding thus in the last years there has been an increasing attention to research in this field (Kaur et al., 2025 ; Sutula et al., 2025 ; Yang et al., 2025 ; Laila et al., 2025 ; Beauclaire et al., 2024 ; Lv et al., 2023). Beneficial soil microorganisms support crops by promoting growth and enhancing tolerance to both abiotic and biotic stresses (Muhammad et al., 2024 ; Akbari et al., 2024 ). In this study, we investigated the performances of three potato genotypes (i.e., Camelia, Cicero, and Agata), selected for their polyphenol content and agronomic value, under severe drought stress also evaluating the effect of a Trichoderma -based biostimulant mixture. Previous studies demonstrated that short periods of water deficit imposed during the vegetative or tuberization phases markedly reduce both tuber number and yield, while increasing the proportion of small tubers (< 35 mm in diameter) (Wagg et al., 2021 ). Consistent with these findings, in the present study, drought stress significantly reduced tuber yield in the cultivars Camelia and Agata, confirming their sensitivity to water limitation (Table 2 ). Notably, in Camelia, biostimulant treatment partially mitigated yield loss, suggesting enhanced drought resilience. However, biostimulant treatment also led to a higher proportion of small-sized tubers, with a consequent reduction of the commercial quality. This aligns with the results of Batool et al ( 2020 ), who observed that PGPR-based biostimulants could mitigate yield losses under drought. Fungal biostimulants may prioritize stress tolerance over sink development by modulating specific hormonal and metabolic pathways. These results underscore the importance of considering both “genotype” and “environmental context” when evaluating biostimulant efficacy. Drought triggers alterations, both stimulatory and inhibitory effects, in non-enzymatic antioxidants such as phenolic compounds that clearly reflect the complex metabolic response to environmental stressors (Król et al., 2014 ; Shin et al., 2021 ; Nahuelcura et al., 2023 , 2024 ). The increase of phenolic compounds plays an essential role in neutralizing ROS and maintaining redox balance (Leopoldini et al., 2004 ; Qiao et al., 2024 ). The impact of drought on phenolic content can be genotype- and treatment-specific. In our study, the treatment with Trichoderma induced in Camelia a consistent increase in polyphenols in both skin and pulp (Fig. 3 ), suggesting enhanced secondary metabolism and nutritional quality of tubers. Similar effects were observed in tomato, where a simulated drought stress, by PEG treatment, either with or without Trichoderma application, induced an increase in total phenolic content (De Palma et al., 2021 ). In contrast, Cicero and Agata displayed more variable and less pronounced changes. For example, Cicero showed increased skin polyphenols under drought alone, which was reversed by biostimulant treatment, while Agata exhibited increased pulp polyphenols only in the absence of biostimulant. These results highlight the role of genotype in adapting secondary metabolite responses to environmental stresses. To investigate the molecular responses of the most drought-performing potato cultivars (i.e., Camelia and Cicero), high-throughput RNA sequencing was employed to profile transcriptome changes in the leaves of plants exposed to water deficit either alone (D) or in combination with a Trichoderma -based biostimulant mixture (D + mix). Each genotype exhibited distinct molecular responses. In Camelia, drought (D) triggered the up-regulation of genes related to protein folding, protein oligomerization, and various amino acid transport and carbohydrate metabolic processes, suggesting an attempt to preserve metabolic homeostasis under stress. At the same time, GO terms related to protein synthesis, including translation, ribosome biogenesis, and rRNA processing, were significantly suppressed. Interestingly, photosynthesis-related genes were up-regulated, which may reflect an adaptative strategy to maintain energy production despite reduced stomatal conductance and CO 2 availability (Chauhan et al., 2023 ). Additionally, activation of oxidative and osmotic stress-related genes, alongside suppression of biotic defense genes, suggested a trade-off between biotic and abiotic stress pathways (Berens et al., 2019 ). Genes involved in the anthocyanin biosynthesis were also up-regulated, supporting their role in both antioxidant defense and osmotic adjustment (Dabravolski and Isayenkov, 2023 ). Carbohydrate metabolism was also elicited, consistent with a drought tolerance strategy, as these molecules are essential for energy production, osmoregulation and osmoprotection, thus linking growth, development, and carbon status as observed by Diniz et al. ( 2020 ). Suppression of DNA replication and repair and hormonal signalling pathways further indicated cell division and growth inhibition (Serrano-Mislata et al., 2025 ). Down-regulation of water transport and glutathione-related genes reflected impaired oxidative stress mitigation, although subcellular redox dynamics are known to affect glutathione metabolism (Dorion et al., 2021 ; Hipsch et al., 2021 ). Enhanced expression of leaf senescence genes may support nutrient remobilization from aging tissues to other vital areas of the plant (Griffiths et al., 2014 ). Altogether, these responses reflect a shift toward cellular adaptation and metabolic conservation under drought. When Camelia was treated with the biostimulant under drought (D + mix), the transcriptomic response in both D + mix vs C + mix and D + mix vs D comparisons was broader and more integrative. Similarly to what observed in the D vs C, growth-related processes (e.g., translation, ribosome biogenesis, and DNA replication) were suppressed. However, in contrast to the untreated drought conditions, photosynthesis-related genes, including light harvesting and photosystem I, were also repressed suggesting a shift from maintaining photosynthetic activity toward energy conservation. This contrast with previous reports in rice where Trichoderma treatment enhanced photosynthetic gene expression (i.e., ribulose-bisphosphate carboxylase small chain A, ferredoxin-NADP reductase, photosystem I subunit O, photosystem II core complex proteins psbY) under drought (Bashyal et al., 2021 ). Water transport genes remained down-regulated, consistent with a conserved drought avoidance strategy. Notably, biostimulant treatment strongly induced genes involved in protein quality control (e.g., protein folding, chaperones, endoplasmic reticulum (ER) unfolded protein response), suggesting an enhanced capacity for proteostasis and ER stress response (Manghwar & Li, 2022 ). Ion transport and signalling pathways, especially calcium signalling, were activated, in agreement with their role in abiotic stress perception (Lohani et al., 2022 ; Xu et al., 2022 ). Up-regulation of genes involved in membrane remodelling and cell wall modification further supports structural adaptation under stress (Molina et al., 2024 ). These data suggest that the biostimulant modulates Camelia’s stress response from a defense-maintenance to a damage-mitigation mode, promoting cellular integrity and survival. In Cicero, drought (D) induced strong transcriptional down-regulation of defense-related genes (e.g., glutathione metabolism-related genes), hormonal signalling (i.e., ethylene and ABA) and protein processing pathways. Conversely, photosynthesis-related genes (e.g., light-harvesting complex and chlorophyll biosynthesis) were up-regulated, possibly to maintain redox balance and prevent ROS accumulation as previously observed in Arabidopsis and Malus domestica (Cruz de Carvalho 2008 ; Zhao et al., 2020 ). Additional up-regulated genes included those involved in transmembrane transport, nitric oxide biosynthesis (a redox signaling molecule), and chromatin remodelling, suggesting mechanisms for genome stability and stress adaptation (Rezayian et al., 2023 ; Han and Wagner, 2014 ; Liu et al., 2018 ; Kim et al., 2015 a,b). Moderate induction of amino acid biosynthesis pathways (e.g., lysine and threonine) supports limited but targeted metabolic reprogramming for osmotic protection as previously observed in sandbur and wheat (Yang et al., 2021 ; Bowne et al 2012 ). Overall, Cicero’s response reflected a minimalist survival strategy, characterized by selective activation of key stress-related processes and suppression of non-essential metabolic activity. Under combined drought and biostimulant treatment (D + mix), Cicero’s transcriptomic response shifted toward a more adaptive reprogramming. When transcriptomic response was analysed in comparison with Trichoderma -treated plant in control conditions (i.e., D + mix vs C + mix), photosynthesis, photorespiration, and sugar metabolism pathways (e.g., sucrose and fructose biosynthesis) were activated, indicating improved energy production and osmoprotection. This is consistent with previous studies showing sugar signaling’s role in stress responses (Ramel et al., 2009 ; Yang et al., 2021 ). Similar results were reported in Pinus massoniana seedlings inoculated with Trichoderma longibrachiatum , which induced the accumulation of osmotic substances (e.g., sugars, proline) to regulate osmotic pressure under drought stress (Yu et al., 2023 ). Notably, photosynthesis was up-regulated in D + mix vs C + mix, but down-regulated in D + mix vs D, suggesting that the biostimulant’s effect on photosynthesis is context-dependent. Circadian rhythm-related genes were also activated, potentially restoring physiological homeostasis under stress, as previously observed in the model plant Arabidopsis (Blair et al., 2019 , Grundy et al., 2015 ). Furthermore, hormonal signalling (e.g., cytokinin) and phosphate starvation responses were enhanced. Although cytokinins can negatively interact with phosphate signalling (Argueso et al., 2009 ), they are known to improve drought tolerance in several crops (Zhang et al., 2010 ; Peleg et al., 2011 ; Rivero et al., 2010 ; Ghanem et al., 2011 ). Meanwhile, key genes related to primary metabolism (carbohydrates, lipids, amines), cell wall biogenesis, and general transmembrane transport were strongly down-regulated, indicating a strategic reduction in energy-consuming processes in favour of sustaining core metabolic and photosynthetic functions. The analysis of transcriptomic profile in the D + mix vs D comparison only the GO category related to the assembly of transcription preinitiation complex resulted up-regulated. Among the down-regulated BPs, in addition to those described in the D + mix vs C + mix comparison, were detected water and sulfate transport, proteolysis, DNA replication and cell wall biogenesis suggesting a stronger inhibition of cell growth-related processes in this condition. Conclusions Overall, the results of the present work reveal significant genotype-dependent variability in potato responses to drought particularly at the transcriptomic level. Camelia and Cicero, the two cultivars exhibiting the strongest drought performance, employed distinct molecular strategies to cope with water deficit. Camelia demonstrated a pronounced suppression of growth alongside strong activation of proteostasis mechanisms and anthocyanin accumulation, suggesting a stress tolerance mechanism centered on cellular protection (Additional file 13A). In contrast, Cicero maintained photosynthetic efficiency and redox homeostasis, while down-regulated growth and defense pathways (Additional file 14A). The application of Trichoderma -based biostimulant mixture modulated these responses in a genotype-specific manner. In Camelia, it enhanced protein folding, stability, and ion transport regulation (Additional file 13B), while in Cicero, it stimulated photosynthesis and soluble sugar accumulation (Additional file 14B). From an agronomic perspective, our results demonstrated that the biostimulant mix partially mitigated drought-induced yield loss in Camelia but also increased the proportion of small, non-commercial tubers. While this represents a drawback from a market perspective, it can be considered a beneficial stress-adaptative strategy by increasing reproductive output. Moreover, polyphenol accumulation exhibited distinct genotype-specific patterns, with Camelia emerging as the most responsive genotype in terms of improving nutritional value under combined drought and biostimulant treatment. These findings highlight the importance of a genotype-tailored approach when assessing the effectiveness and agronomic relevance of microbial biostimulants, taking into account the complex interplay between plant genetics, environmental stress, and microbial composition. Declarations Data availability All data generated or analysed during this study are included in this published article and its supplementary information files. The datasets generated or analysed during this study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17084816. Acknowledgments We thank Mr R. Nocerino and Dr. Marco Porcelli (CNR-IBBR, Portici, Italy) for assistance in plant growth. Funding This research was partially funded by grants Agritech National Center (European Union Next-GenerationEU, PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022) and “Ministero dello Sviluppo Economico” FCS-Horizon 2020 PON I&C 2014-2020, Project No. F/200088/01-03/X45 “Sviluppo di nuove tecnologie nell’agricoltura di precisione per la produzione sostenibile di genotipi di patata con elevate qualità nutrizionali (SOS-TATA)”. Author information Authors affiliations Istituto di Bioscienze e BioRisorse (IBBR), Consiglio Nazionale delle Ricerche (CNR), Via Università 133, 80055 Portici, Italy Rachele Tamburino, Lorenza Sannino, Emanuela Russo, Maria Consiglia Esposito, Nunzia Scotti Istituto per la BioEconomia (IBE), Consiglio Nazionale delle Ricerche (CNR), Via P. Gobetti 101, 40129 Bologna, Italy Rachele Tamburino (present address) Istituto per la Protezione Sostenibile delle Piante (IPSP), Consiglio Nazionale delle Ricerche (CNR), P. le E. Fermi 1, 80055 Portici, Italy Francesca Palomba, Adriana Sacco, Michelina Ruocco Corresponding author Correspondence to Nunzia Scotti ( [email protected] ) Authors contributions N.S. and M.R. conceived and designed the study. R.T. and L.S. performed drought experiments and biochemical analysis. R.T., L.S., E.R., F.P., M.C.E. performed RNA isolation and qRT-PCR analysis experiments. A.S. carried out the microbial biostimulant growth and mixture preparation. R.T., A.S., M.R., N.S. analysed the data. R.T. and N.S. wrote the manuscript. R.T., A.S., M.R., N.S. revised the manuscript. All authors have read and agreed to the final version of the manuscript. Ethics declarations Ethics approval and consent to participate Not applicable. Clinical trial number Not applicable. Consent for publication Not applicable. 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Supplementary Files Additionalfile1.pdf Additionalfile2.pdf Additionalfile3.pdf Additionalfile4.pdf Additionalfile5.pdf Additionalfile6.pdf Additionalfile7.pdf Additionalfile8.pdf Additionalfile9.pdf Additionalfile10.pdf Additionalfile11.pdf Additionalfile12.pdf Additionalfile13.pdf Additionalfile14.pdf Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 22 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor assigned by journal 09 Oct, 2025 Editor invited by journal 01 Oct, 2025 Submission checks completed at journal 01 Oct, 2025 First submitted to journal 01 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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21:41:29","extension":"html","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227723,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/83a3501ce5fb4480befef259.html"},{"id":94229849,"identity":"da7abee0-757f-4f3c-8c75-ba407a256a4b","added_by":"auto","created_at":"2025-10-23 21:33:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3275189,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of drought stress experiment. Plants were divided into two groups based on the presence or absence of biostimulant application and subsequently subjected to cycles of water deficit (D1, D2, corresponding to the first and second drought periods) followed by rewatering phases (R1, R2, corresponding to the first and second recovery periods).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/f39c7734ee93f5a479e5e95a.png"},{"id":94229852,"identity":"1d7d4bcc-d5e8-4689-a0a1-2c268a1bf8ac","added_by":"auto","created_at":"2025-10-23 21:33:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":730721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTotal polyphenol content (TPC) in skin and pulp tubers of a potato cultivars’ collection.\u003c/strong\u003e Data represent the mean (±SD) of three biological replicates. Different letters indicate significant differences between TPC within the same tissue using the Tukey test (P \u0026lt; 0.05; n = 3).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/948329f9bf4ff186afe49e69.png"},{"id":94230154,"identity":"07ca9f73-1ff4-4661-83e1-18563e991fad","added_by":"auto","created_at":"2025-10-23 21:41:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2925801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTotal polyphenol content (TPC) in tubers from diverse drought-stressed potato cultivars without and with biostimulant. \u003c/strong\u003eTPC in skin and pulp tubers of Camelia (A, D), Cicero (B, E) and Agata (C, F). Data represent the mean (±SD) of three biological replicates. ‘**’ and ‘*’ indicate significant at P \u0026lt; 0.01 and P \u0026lt; 0.05, respectively.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/c0b420089b93c36d2e42aba7.png"},{"id":94229858,"identity":"08e5a25c-90e3-4e49-87b8-fa2363b4ab13","added_by":"auto","created_at":"2025-10-23 21:33:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":720505,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of identified Differentially Expressed Genes (DEGs) in Camelia and Cicero genotypes. Number of DEGs identified in the comparisons D vs C, D+mix vs C+mix, D+mix vs D, and C+mix vs C. DEGs were selected using a cut-off of P-adjust \u0026lt; 0.05 and |log2FC| ≥ 1.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/59f01014611c69902fc78efa.png"},{"id":94230221,"identity":"b420502d-ba19-4376-b200-7bdc2fb6eb13","added_by":"auto","created_at":"2025-10-23 21:49:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31405694,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of potato genotypes Camelia and Cicero. Gene Ontology (GO) enrichment analysis of the four pairwise comparisons in potato genotypes Camelia (A) and Cicero (B), categorized into Biological Processes (BP). Each circle represents a GO term; circle size and color indicate the number of counts and the p-value, respectively.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/7d2d3e15ec2f2a68b2b2c421.png"},{"id":97178290,"identity":"efd84ec6-c424-4e4c-af92-38e2f63ec80b","added_by":"auto","created_at":"2025-12-01 16:07:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":41320938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/0cc28f02-b226-47a0-b5ca-2c37eb1d217e.pdf"},{"id":94230151,"identity":"6bc657b2-ef86-4518-9b1b-56219d9502ee","added_by":"auto","created_at":"2025-10-23 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21:33:29","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":3147465,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/79ea51707f5202cc530f6094.pdf"},{"id":94229876,"identity":"7d151109-7cce-40b1-899b-22b7aeda5943","added_by":"auto","created_at":"2025-10-23 21:33:28","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":3013779,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile12.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/8382d4b4924f903e4450fd85.pdf"},{"id":94230167,"identity":"35f7fd2b-e198-44f5-8fb3-442ca73b3c33","added_by":"auto","created_at":"2025-10-23 21:41:29","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":962874,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile13.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/64a74857987ac07fe52deb96.pdf"},{"id":94230164,"identity":"87871c17-c333-41f5-9aed-14e1a603b411","added_by":"auto","created_at":"2025-10-23 21:41:29","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1023903,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile14.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656066/v1/9e48d8f3e29305483b8fbf48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genotype-specific transcriptomic response to drought stress in potato cultivars modulated by microbial biostimulants","fulltext":[{"header":"Background","content":"\u003cp\u003eClimate change is exerting an increasing impact on global food production systems. Rising temperatures, coupled with the growing frequency and severity of drought events, are significantly impairing crop development and yield, thereby reducing food availability and accessibility. These adverse conditions could continue to escalate, posing a critical challenge to the sustainability of agriculture.\u003c/p\u003e\u003cp\u003eIn this context, enhancing crop resilience has become a strategic priority for global food security, especially for nutritionally and economically important crops. To develop climate-resilient crops, it is essential a comprehensive understanding of plant responses to environmental stress and the identification of genes conferring tolerance (Demirel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Joshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Obidiegwu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ponce et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePotato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is a staple food crop ranked fourth in global consumption. Its tubers are rich in carbohydrates produced via leaf photosynthesis, and contain significant levels of vitamins, minerals, and phenolic compounds (Docimo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The accumulation of these health-promoting compounds is genotype-dependent and influenced by environmental conditions, particularly temperature. Generally, low temperatures promote phenolic biosynthesis (Da Ros et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite its global importance, potato is highly susceptible to drought stress due to its shallow root system and physiological sensitivity (Lv et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Drought is the second major cause of potato yield loss after pathogen infection and adversely affects multiple physiological and biochemical processes, including photosynthesis, nutrient assimilation, and tuber development (Dahal et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alvarez-Morezuelas et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The extent of yield reduction depends on both environmental factors (e.g., severity and duration of drought) and plant characteristics (e.g., genotype and developmental stage). Among these processes, photosynthesis is particularly critical. Drought impairs carbon partitioning by triggering a cascade of molecular and physiological responses that determine plant survival (Aliche et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gervais et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Obidiegwu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, drought stress negatively regulates genes involved in stomatal function, hormonal signaling, particularly abscisic acid, and antioxidant defense, ultimately compromising plant growth and productivity (Nour et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFacing climate-induced stress, plant scientists are adopting integrated strategies that combine traditional breeding approaches with modern genomic tools and the use of biostimulants. Among biostimulants, soil microorganisms have shown significant promise in improving plant growth and stress resilience while maintaining ecological sustainability. In particular, \u003cem\u003eTrichoderma\u003c/em\u003e spp. are widely recognized for their ability to stimulate root development, improve water and nutrient uptake, enhance photosynthetic efficiency, and mitigate oxidative damage by modulating reactive oxygen species (ROS) levels (Rawal et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Akbari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ca\u0026ntilde;ada-Coyote et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guler et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003eb\u003c/span\u003e). These beneficial effects have been documented in a variety of crops under drought conditions, including rice, tomato, and durum wheat (Gusain et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pehlivan et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Silletti et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough several studies have characterized the molecular response of potato to drought (Alvarez-Morezuelas et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Da Ros et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Demirel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gervais et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ponce et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the molecular mechanisms underlying the interaction between \u003cem\u003eTrichoderma\u003c/em\u003e-based biostimulants and drought tolerance remains largely unexplored. Hence, in-depth analyses of these interactions are crucial to optimize the use of these beneficial microorganisms in sustainable crop management.\u003c/p\u003e\u003cp\u003eThis study aimed to select potato genotypes with high polyphenol content, compounds that act as antioxidants contributing to abiotic stress tolerance. Subsequently, we evaluated the performance of the three most promising potato genotypes under drought, with and without a \u003cem\u003eTrichoderma\u003c/em\u003e-based biostimulant application. The two genotypes showing the best performance in terms of yield-related parameters (i.e., tuber number, weight, and size) and polyphenol content, were selected for transcriptomic analysis using RNA sequencing (RNA-seq) to investigate the molecular basis of their drought response.\u003c/p\u003e\u003cp\u003eOur results provide novel insights into the gene networks modulated by drought and microbial treatment, laying the groundwork for breeding and biostimulant-based strategies to enhance potato resilience under climate change scenario.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePlant, materials and treatment conditions\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eA collection of nine potato cultivars (kindly provided by Coppola Patate srl and Dr. M. Mazzei), were included for preliminary screening from those available on the market (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePotato genotypes used in this work\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTuber number/plant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTuber weight/plant (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTuber shape\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSkin colour\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePulp colour\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgata\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e350\u0026ndash;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlue Star\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u0026ndash;450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLong oval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePurple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePurple\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamelia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u0026ndash;420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDark yellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDark yellow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCayman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420\u0026ndash;450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRound/oval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLight yellow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCicero\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u0026ndash;420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDouble Fun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400\u0026ndash;450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePurple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYellow- Purple\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmanuelle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420\u0026ndash;460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOval/long oval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDark yellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDark yellow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSunita\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e380\u0026ndash;400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRound/oval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYellow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eViolet Queen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300\u0026ndash;360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLong oval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePurple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePurple\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTubers were washed with distilled water, sliced separating skin and pulp and lyophilized using a freeze-dryer alpha 1\u0026ndash;2 LD plus (Martin Christ Gefriertrocknungsanlagen GmbH, Germany).\u003c/p\u003e\u003cp\u003eTo ensure homogeneity of the plant material for drought experiments, apical buds of plants obtained from the tubers were first sterilized by immersion in 70% ethanol for 30 sec, then in 1.5% NaClO containing 0.1% (v/v) Tween20 for 15 min with gentle stirring. The buds were then washed five times, each for 5 min, in Milli-Q water. Subsequently, the sterilized buds were sown \u003cem\u003ein vitro\u003c/em\u003e under controlled conditions (16 h light 40 \u0026micro;mol photons m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 8 h dark at 24\u0026deg;C) on Murashige \u0026amp; Skoog (MS) medium with B5 vitamins (Duchefa, The Netherlands), solidified with 0.8% (w/v) agar, with 30 g/l sucrose and 250 mg/l of cefotaxime.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003ein vitro\u003c/em\u003e rooted seedlings of selected cultivars (i.e., Agata, Camelia and Cicero) were transplanted into 18cm diameter pots and irrigated with tap water at field capacity daily for 2 weeks. Forty plants per genotype were cultivated and divided into two subsets, differing in the treatment with a biostimulant mixture applied twice \u003cem\u003evia\u003c/em\u003e irrigation (i.e., at transplant and two days before water withholding) (Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1. Schematic representation of drought stress experiment.\u003c/b\u003e Plants were divided into two groups based on the presence or absence of biostimulant application and subsequently subjected to cycles of water deficit (D1, D2, corresponding to the first and second drought periods) followed by rewatering phases (R1, R2, corresponding to the first and second recovery periods).\u003c/p\u003e\u003cp\u003eA new bioformulation containing two \u003cem\u003eTrichoderma\u003c/em\u003e stains from the collection of CNR-IPSP was used for the treatment. The \u003cem\u003eTrichoderma\u003c/em\u003e strains were characterised at the morphological and molecular levels as reported by Napolitano et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), then inoculated into flasks containing Potato Dextrose Broth (PDB) and left to grow separately in a rotating incubator at 25\u0026deg;C for 7 days at 150 rpm. Once the minimum concentration of the culture was 10\u003csup\u003e5\u003c/sup\u003e CFU the two strains were mixed and let it grow for 7 more days in a bioreactor. The resulting fermented product, diluted at 10\u003csup\u003e6\u003c/sup\u003e CFU, was applied to the potato plants. Drought stress was induced by withholding water until the soil relative water content (SRWC) reached at 10\u0026thinsp;\u0026plusmn;\u0026thinsp;2%. The water deficit was generally maintained for 13\u0026ndash;16 days by daily monitoring soil moisture contents in the pots and compensating the water loss; this condition was termed as D1. Meanwhile, the control pots were kept well-watered at 80\u0026thinsp;\u0026plusmn;\u0026thinsp;5% SRWC. Subsequently, all pots were re-watered to an SRWC of 80% (R1) for 2 days, after which a second dehydration treatment (D2) was performed for 10 days. Finally, plants were re-watered to well-watered level (R2) until crop maturity. Soil water status was estimated gravimetrically before water application to pots (Batool et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Four replicates per conditions were performed. Leaves were collected at the end of each step and stored at -80\u0026deg;C.\u003c/p\u003e\u003cp\u003eFollowing tuberization, tubers were recovered, weighted and their size measured to assess tuber yield in all applied conditions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePolyphenols content determination\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTotal polyphenol content (TPC) was assessed both in pulp and skin of tubers by Folin-Ciocalteu assay as previously described (Celano et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Briefly, 100 mg of lyophilized pulp and skin of tubers were extracted in 1 ml 80% (v/v) methanol and sonicated for 30 min at 10\u0026deg;C and then centrifuged at 12000 xg for 20 min at 4\u0026deg;C. Supernatant was recovered and kept on ice at dark till the usage. In a 96-well plate, 20 \u0026micro;L of each sample extract was added to 150 \u0026micro;L of Folin\u0026ndash;Ciocalteu reagent diluted with distilled water (1:30, v/v) and 30 \u0026micro;L of 20% (w/v) Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e, incubated at room temperature in the dark for 45 min and then absorbance at 765 nm was measured in a Multiskan\u0026trade; Sky microplate spectrophotometer (Thermo Scientific, Waltham, MA, USA). Gallic acid (GA) was used as reference standard and TPC was estimated from the GA calibration curve (range 31.25\u0026ndash;750 \u0026micro;g/mL, 6 levels; r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.998). The results were expressed as GA equivalents per g of dry leaf (mg GAE/g DW, means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of three biological replicates). Statistical differences between treatments and controls samples were assessed using Student\u0026rsquo;s t test (*P\u0026thinsp;\u0026le;\u0026thinsp;0.05, **P\u0026thinsp;\u0026le;\u0026thinsp;0.01); whilst a threeway ANOVA was performed to evaluate the effects of genotype, treatment, biostimulant and their interactions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTotal RNA isolation and sequencing\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was extracted from leaves of three biological replicates of drought stressed Camelia and Cicero plants (D1 step, hereafter termed as D and D\u0026thinsp;+\u0026thinsp;mix conditions) and their respective controls using the RNeasy Plant Mini Kit (Qiagen, Germania) following manufacturer\u0026rsquo;s instructions. The integrity and quality of RNA was evaluated by denaturing agarose gel electrophoresis and nanodrop measurements (Additional file 1). High-throughput sequencing service and preliminary data processing of 24 RNA-seq libraries were carried out by Sequentia Biotech SL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sequentiabiotech.com/\u003c/span\u003e\u003cspan address=\"https://www.sequentiabiotech.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe raw reads were quality-checked using Trimmomatic v0.39, applying a minimum read length of 35 bp and a minimum sequencing quality of 20. Filtered reads were mapped to the potato reference genome v3 (Ensembl Genomes 57) using STAR aligner v2.7.9a with default parameters. Count matrix was build using featureCounts v2.0.3, considering only reads with a minimum alignment quality score of 40. The resulting count matrix was normalized using the TMM method, and low expressed genes were filtered out using HTSfilter v1.28.0. Subsequent analyses, including principal component analysis (PCA) were performed using FactoMineR and pheatmap R packages (R software 4.3.0), respectively. After both analyses, libraries that did not group with their respective biological replicates were excluded from downstream analyses.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDifferential expression and Gene Ontology (GO) analyses\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDifferential expression analysis was carried out with edgeR R package (R software 4.3.0), only considering differential expressed genes (DEGs) with a log fold-change (LogFC) higher than 1 or lower than \u0026minus;\u0026thinsp;1 and False Discovery Rate (FDR) lower than 0.05. Four different contrasts were analysed per genotype:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDrought vs Control (D vs C)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDrought\u0026thinsp;+\u0026thinsp;mix vs Control\u0026thinsp;+\u0026thinsp;mix (D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eControl\u0026thinsp;+\u0026thinsp;mix vs Control (C\u0026thinsp;+\u0026thinsp;mix vs C)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDrought\u0026thinsp;+\u0026thinsp;mix vs Drought (D\u0026thinsp;+\u0026thinsp;mix vs D)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTaking these comparisons, a GO enrichment analysis of up-regulated and downregulated genes was performed using clusterProfiler R package (R software 4.3.0).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eQuantitative real-time PCR validation\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo validate the RNA-seq data, five genes were selected among either the most positively or negatively affected categories for qRT-PCR analysis. An aliquot of each sample was retrotranscribed to cDNA by digesting trace amount of DNA using DNase kit (Invitrogen) and using RevertAid 1st strand cDNA synthesis kit (ThermoFisher Scientific, USA). cDNA quality was further evaluated by RT-PCR amplification of 18S rRNA gene and amplification products were visualized by agarose gel electrophoresis (Additional file 1). Diluted cDNA (1:20) was used as template for semi-quantitative real time using the Platinum\u0026trade; SYBR\u0026trade; Green qPCR SuperMix-UDG (Applied Biosystems, USA). All reactions were set using 6.25 \u0026micro;l of 2x SYBR green dye, 0.6 \u0026micro;M of forward and reverse primer mix, 4.5 \u0026micro;l template cDNA and run on the 7900HT Fast Real Time PCR (Applied Biosystems, USA). Primers used and program cycle parameters are listed in Additional file 2. The relative expression levels were calculated using the 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e method (Livak \u0026amp; Schmittgen, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). EF-1α was used as internal control. Data representing three biological replicates and three technical replicates are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis was performed in Microsoft Excel (Windows 11 Enterprise) and R software. One- and three-way ANOVA and RNA-seq data analysis were conducted in R software 4.3.0. Significancy of polyphenol contents and gene expression levels was analysed using Student\u0026rsquo;s t-test. The differences between different treatments were considered significant when the \u003cem\u003eP\u003c/em\u003e-value was lower than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eScreening of potato genotypes for drought stress tolerance\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn order to select the cultivar for drought stress experiment, the total polyphenol content (TPC) was measured in both tubers\u0026rsquo; skin and pulp of collected cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Folin-Ciocalteau assay revealed that polyphenol content was higher in the skin than in the pulp for all the tested genotypes, among them, Camelia exhibited the highest TPC in both tissues (i.e., 132.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 and 36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 mg GAE/g DW in skin and pulp, respectively), followed by Cicero, whose pulp showed a polyphenol content comparable to that of Camelia\u0026rsquo;s pulp (i.e., 33.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 mg GAE/g DW).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on their high TPC and suitability for multiple purposes (i.e, processed, table consumption and industrial use), Camelia, Cicero and Agata were selected for experiment under severe water deficit conditions, with or without treatment using the microorganism-based biostimulant mixture according to the scheme reported in Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003ePlant performance was assessed by measuring tuber yield (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and polyphenol content in both skin and pulp following water deficit treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The three cultivars exhibited distinct responses to drought, which were also influenced by biostimulant treatment.\u003c/p\u003e\u003cp\u003eTuber weight decreased in Camelia and Agata tubers under all applied conditions (up to 37.7% and 44.3%, respectively) compared to their control, although this reduction was mitigated (up to 10.7% and 28.3%, respectively) when biostimulant mix was applied (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, Camelia tubers from D1\u0026thinsp;+\u0026thinsp;mix and Agata from D2\u0026thinsp;+\u0026thinsp;mix even showed a slight increase in weight (3 and 1%, respectively) compared to the control. Conversely, Cicero tubers significantly increased in weight under drought (up to 29.1% in R1) compared to the control, but this gain was reversed upon biostimulant application, with a weight reduction of up to 27.9%. Regarding size, biostimulant-treated Camelia tubers under all applied conditions were smaller than controls, whereas Cicero and Agata showed no significant size changes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eYield of tubers expressed as mean weight and number of tubers in all experimental conditions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCamelia\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCicero\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eAgata\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTreatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight\u0026thinsp;\u0026plusmn;\u0026thinsp;sd (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN. tubers (average)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeight\u0026thinsp;\u0026plusmn;\u0026thinsp;sd (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN. tubers (average)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eWeight\u0026thinsp;\u0026plusmn;\u0026thinsp;sd (g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eN. tubers (average)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e129.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eD1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e72.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eD2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eC\u0026thinsp;+\u0026thinsp;mix\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.3\u0026thinsp;\u0026plusmn;\u0026thinsp;21.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eD1\u0026thinsp;+\u0026thinsp;mix\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR1\u0026thinsp;+\u0026thinsp;mix\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eD2\u0026thinsp;+\u0026thinsp;mix\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR2\u0026thinsp;+\u0026thinsp;mix\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eS/M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e93.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eM/L\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThree way ANOVA_weight\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003eThree way ANOVA_number\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003egenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003egenotype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003etreatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003etreatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ebiostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003ebiostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003egenotype:treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003egenotype:treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003egenotype:biostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003egenotype:biostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003etreatment:biostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003etreatment:biostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003egenotype:treatment:biostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003egenotype:treatment:biostimulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eS: small, size\u0026thinsp;\u0026le;\u0026thinsp;3 cm; M: medium, 3 cm\u0026thinsp;\u0026lt;\u0026thinsp;size\u0026thinsp;\u0026lt;\u0026thinsp;5 cm; L: large, size\u0026thinsp;\u0026ge;\u0026thinsp;5 cm; \u0026lsquo;***\u0026rsquo;, \u0026lsquo;**\u0026rsquo;, \u0026lsquo;*\u0026rsquo; and \u0026lsquo;ns\u0026rsquo; indicate significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, respectively.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTuber nutritional quality was evaluated by measuring the polyphenol content in both pulp and skin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Camelia, pulp polyphenols significantly increased (up to 1.7-fold) under all experimental conditions with biostimulants, while no significant changes were observed in the absence of biostimulants. TPC from skin also increased significantly up to 1.2-fold in both R1 and R1\u0026thinsp;+\u0026thinsp;mix conditions. In Cicero, skin polyphenols significantly increased up to 1.6-fold under drought stress but slightly decreased with biostimulant application (i.e., D2\u0026thinsp;+\u0026thinsp;mix). Pulp TPC in Cicero remained largely unchanged, except for a 59% reduction in R2. In Agata, TPC increased significantly in D1 pulp (1.4-fold) and R1 and R2 skin (up to 2.4-fold) without biostimulants, while no significant variations were observed with biostimulant mix.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThreeway ANOVA indicated that both genotypes and treatment significantly affected all measured parameters (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, genotype-treatment interactions were only significant for polyphenols in tuber pulp.\u003c/p\u003e\u003cp\u003eThe biostimulant application also had a significant effect on polyphenol content in both tissues, and its interaction with genotype was statistically significant.\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA) of tuber weight, tuber number and polyphenol content in skin and pulp revealed that the first two principal components together explained the entire variance (PC1: 77.9%. PC2: 22.1%; Additional file 3). Tuber weight and TPC in skin and pulp were the main contributors to PC1. Notably, polyphenol content in skin and pulp was positively correlated, with particularly high values in Camelia genotype, while TPC negatively correlated with tuber weight.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTranscriptional changes induced by drought and drought\u0026thinsp;+\u0026thinsp;biostimulant treatment in leaves\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eBased on yield and TPC results, Camelia and Cicero cultivars were selected for RNA-seq analysis considering four conditions: control (C) drought (D), control with biostimulant (C\u0026thinsp;+\u0026thinsp;mix), and drought with biostimulant (D\u0026thinsp;+\u0026thinsp;mix), with D and D\u0026thinsp;+\u0026thinsp;mix corresponding to the first imposed water stress (Fig.\u0026nbsp;1). Indeed, due to poor RNA quality obtained from leaves of some conditions (i.e., R1, D2 and R2), RNA-seq analysis was restricted to D1.\u003c/p\u003e\u003cp\u003ePCA of all the libraries revealed a clear genotype-specific clustering. Notably, the Camelia samples grouped differently depending on the biostimulant treatment (Additional file 4). PC1 explaining 40.6% of the total variance, primarily separated the genotypes, suggesting a genotype-specific response to drought. Biostimulant treatment induced substantial transcriptional reprogramming in Camelia. A summary of differentially expressed genes (DEGs) is showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, in general, Camelia showed a higher number of differentially expressed genes. Specifically, in the D vs C comparison, Camelia had 4,305 DEGs (1,749 up-regulated, 2,556 down-regulated), while Cicero had only 426 DEGs (229 up-regulated and 197 down-regulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The two genotypes shared just 4.3% of total DEGs (Additional file 5A). In D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix, Camelia had 5,215 DEGs (2,150 up-regulated and 3,065 down-regulated) and Cicero 934 (360 up-regulated and 574 down-regulated) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe 7.7% of total DEGs were shared (Additional file 5A). For D\u0026thinsp;+\u0026thinsp;mix vs D comparison, Camelia had 855 DEGs (413 up-regulated and 442 down-regulated), and Cicero 757 (160 up-regulated and 597 down-regulated), with 3.4% overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Additional file 5A). In C\u0026thinsp;+\u0026thinsp;mix vs C, Camelia had 164 DEGs (68 up-regulated and 94 down-regulated), whereas Cicero had just one, with no overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Additional file 5A). In all comparisons the number of down-regulated genes was higher than up-regulated which means that plants stop regular active metabolism as main mechanism of response to the drought stress. These results indicate that Camelia and Cicero exhibited genotype-specific response to water deficit and biostimulant application.\u003c/p\u003e\u003cp\u003eFurther analysis of shared DEGs (Additional file 5B) showed that in the D vs C, 156 genes (59 up-regulated and 97 down-regulated) had the same trend in both cultivars, while 39 genes showed opposite trend. In D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix, 267 genes (60 up-regulated and 207 down-regulated) shared the same trend, whilst 170 genes were oppositely regulated. In D\u0026thinsp;+\u0026thinsp;mix vs D, 24 shared genes (2 up-regulated and 22 down-regulated) were similarly regulated, while 28 genes displayed opposite trend.\u003c/p\u003e\u003cp\u003eCamelia also had more DEGs consistently present across all conditions (Additional file 6A), particularly in the D vs C and D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix comparisons (2994 overlapping DEGs). In Cicero, the largest overlap (313 DEGs) was between the D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix and D\u0026thinsp;+\u0026thinsp;mix vs D (Additional file 6B). qRT-PCR validation of five randomly selected DEGs confirmed the RNA-seq findings (Additional file 7).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn Camelia, the higher number of DEGs identified compared to Cicero resulted in a greater overlap across the analysed comparisons, reflecting a broader range of affected Gene Ontology terms (Additional file 8C and 7F). The most impacted biological processes (BPs) were consistently enriched across the D vs C, D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix and D\u0026thinsp;+\u0026thinsp;mix vs D comparisons. These included up-regulated categories associated to transmembrane transport, protein folding and xyloglucan metabolic processes, as well as down-regulated categories related to translation and protein modification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and Additional file 9A). Interestingly, the photosynthesis-related categories exhibited divergent expression patterns depending on the condition, being predominantly up-regulated in the D vs C comparison. Secondary metabolism and senescence-related processes were exclusively up-regulated in drought-stressed plants while GO categories linked to calcium-mediated signalling were strongly induced by biostimulant application (Additional file 9A).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the molecular functions (MF) level, in D vs C and D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix comparisons, the most affected categories included those related to energy and signal transduction, such as ATP and nucleotide binding, along with transferase and protein kinase activities (Additional file 8A). These molecular functions were primarily associated with membrane-localized compartments Additional file 8B and 9C).\u003c/p\u003e\u003cp\u003eIn Cicero, the most significantly enriched BPs in both the D vs C and D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix comparisons were primarily related to photosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Additional file 10A). Furthermore, exposure to drought (D vs C) enhanced the expression of genes involved in chromatin remodelling consistent with a possible inhibition of cell division to conserve energy. Conversely, pathways associated with abscisic acid (ABA) and ethylene signalling, as well as defense responses, protein folding and glutathione metabolism, were down-regulated under drought stress (Additional file 10A). Notably, biostimulant application under drought conditions (i.e., D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix), triggered the activation of primary metabolic pathways including gluconeogenesis, fructose metabolism and carbon fixation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Additional file 10A). These pathways are known to contribute to stress adaptation via the accumulation of soluble sugars that act as osmoprotectors (Samraoui et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, other related processes such as carbohydrate metabolism were suppressed. Additionally, genes related to circadian rhythm were up-regulated, potentially enhancing photosynthetic and metabolic efficiency under stress (Bargunam et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Vajjiravel et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consistently, increased expression of chlorophyll binding activity at the MF level suggested a possible role for the biostimulant in preserving photosynthetic capacity (Additional file 8D and 10B). However, in the D\u0026thinsp;+\u0026thinsp;mix vs D comparison, photosynthesis-related genes were down-regulated. This may seem contradictory with the D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix result, but it can be explained by a mild induction of these genes mediated by \u003cem\u003eTrichoderma\u003c/em\u003e treatment. When compared with the C\u0026thinsp;+\u0026thinsp;mix condition, this induction appears as up-regulation, whereas when compared with the D conditions, it appears as down-regulation due to the stronger effect of drought on photosynthesis. Such an effect suggests a possible adaptive modulation mediated by the biostimulant. Similarly, transcriptomic profile in tomato treated with \u003cem\u003eAscophyllum nodosum\u003c/em\u003e seaweed showed mild up-regulation of photosynthesis-related genes, exceeding the baseline in irrigated controls (Kanojia et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, these findings suggest that Cicero adopts a distinct, biostimulant-supported strategy, to cope to water deficit, largely involving metabolic reprogramming. The altered biological processes were predominantly associated with subcellular compartments such as the chloroplast, thylakoid and other plastid structures (Additional file 8E and 10C).\u003c/p\u003e\u003cp\u003eA clear genotype-dependent transcriptional response emerged between Camelia and Cicero under stress condition. To further explore their intrinsic transcriptional differences, their transcriptomic profiles under non-stress conditions, specifically in the control (C) and control with biostimulant (C\u0026thinsp;+\u0026thinsp;mix) were compared. Under control conditions (i.e., C Camelia vs C Cicero) a total of 5,929 DEGs were identified with 2,325 up-regulated in Camelia and 3,604 up-regulated in Cicero.\u003c/p\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis revealed that Camelia exhibited a significant up-regulation of BPs related to translation, ribosome biogenesis, and photosynthesis including key functions such as light harvesting and photosystem I activity (Additional file 11A). Additional enrichment was observed in genes associated with protein folding, phosphorylation, response to hydrogen peroxide, and phosphate starvation response, suggesting a transcriptional state primed for stress anticipation or mitigation. In contrast, BPs down-regulated in Camelia, and thus up-regulated in Cicero in control conditions, included primary metabolic and structural processes, such as carbohydrate metabolic process, cell wall organization, cell wall biogenesis, glucan metabolic process, and proteolysis (Additional file 11A). Further reductions in expression were seen in genes involved in lipid catabolism, phosphate ion transport, and defense response to other organisms, indicating a lower investment in baseline metabolic and defense functions in Camelia compared to Cicero (Additional file 11A). Consistently, one of the most enriched MFs in Camelia was the structural constituent of the ribosome (Additional file 11B), aligned with the enrichment of ribosome-associated cellular compartments (Additional file 11C).\u003c/p\u003e\u003cp\u003eUnder biostimulant-treated control conditions (i.e., C\u0026thinsp;+\u0026thinsp;mix Camelia vs C\u0026thinsp;+\u0026thinsp;mix Cicero), the transcriptomic comparison between Camelia and Cicero revealed 5,260 DEGs, with 2,106 genes up-regulated in Camelia, and 3,154 in Cicero.\u003c/p\u003e\u003cp\u003eGO analysis showed that Camelia maintained a transcriptional profile strongly oriented toward protein biosynthesis, with significant up-regulation of BPs such as translation, ribosome biogenesis, and rRNA processing. This pattern was accompanied by enhanced activation of photosynthesis-related pathways, including light harvesting (Additional file 12A). These trends were further supported by enriched MFs related to ribosomal activity and protein folding, such as structural ribosome constituents, ATP-dependent chaperone activity, unfolded protein binding, protein self-association and heat shock protein binding (Additional file 12B and 12C). Moreover, Camelia showed increased expression of genes involved in stress responses, including those induced by heat, hydrogen peroxide, and salt stress, suggesting a transcriptional state primed for environmental resilience. In contrast, Cicero exhibited a transcriptional profile more oriented toward defense-related GO categories, including responses to biotic stimuli (e.g., fungi and other organisms), as well as pathways associated with lipid and carbohydrate metabolism, proteolysis, and transmembrane transport (Additional file 12A).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDrought stress is one of the most significant factors that limit plant growth and yield. Potato is particularly susceptible to water withholding thus in the last years there has been an increasing attention to research in this field (Kaur et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sutula et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Laila et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Beauclaire et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lv et al., 2023). Beneficial soil microorganisms support crops by promoting growth and enhancing tolerance to both abiotic and biotic stresses (Muhammad et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Akbari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we investigated the performances of three potato genotypes (i.e., Camelia, Cicero, and Agata), selected for their polyphenol content and agronomic value, under severe drought stress also evaluating the effect of a \u003cem\u003eTrichoderma\u003c/em\u003e-based biostimulant mixture.\u003c/p\u003e\u003cp\u003ePrevious studies demonstrated that short periods of water deficit imposed during the vegetative or tuberization phases markedly reduce both tuber number and yield, while increasing the proportion of small tubers (\u0026lt;\u0026thinsp;35 mm in diameter) (Wagg et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consistent with these findings, in the present study, drought stress significantly reduced tuber yield in the cultivars Camelia and Agata, confirming their sensitivity to water limitation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, in Camelia, biostimulant treatment partially mitigated yield loss, suggesting enhanced drought resilience. However, biostimulant treatment also led to a higher proportion of small-sized tubers, with a consequent reduction of the commercial quality. This aligns with the results of Batool et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who observed that PGPR-based biostimulants could mitigate yield losses under drought. Fungal biostimulants may prioritize stress tolerance over sink development by modulating specific hormonal and metabolic pathways. These results underscore the importance of considering both \u0026ldquo;genotype\u0026rdquo; and \u0026ldquo;environmental context\u0026rdquo; when evaluating biostimulant efficacy.\u003c/p\u003e\u003cp\u003eDrought triggers alterations, both stimulatory and inhibitory effects, in non-enzymatic antioxidants such as phenolic compounds that clearly reflect the complex metabolic response to environmental stressors (Kr\u0026oacute;l et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nahuelcura et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The increase of phenolic compounds plays an essential role in neutralizing ROS and maintaining redox balance (Leopoldini et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Qiao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The impact of drought on phenolic content can be genotype- and treatment-specific. In our study, the treatment with \u003cem\u003eTrichoderma\u003c/em\u003e induced in Camelia a consistent increase in polyphenols in both skin and pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting enhanced secondary metabolism and nutritional quality of tubers. Similar effects were observed in tomato, where a simulated drought stress, by PEG treatment, either with or without \u003cem\u003eTrichoderma\u003c/em\u003e application, induced an increase in total phenolic content (De Palma et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, Cicero and Agata displayed more variable and less pronounced changes. For example, Cicero showed increased skin polyphenols under drought alone, which was reversed by biostimulant treatment, while Agata exhibited increased pulp polyphenols only in the absence of biostimulant. These results highlight the role of genotype in adapting secondary metabolite responses to environmental stresses.\u003c/p\u003e\u003cp\u003eTo investigate the molecular responses of the most drought-performing potato cultivars (i.e., Camelia and Cicero), high-throughput RNA sequencing was employed to profile transcriptome changes in the leaves of plants exposed to water deficit either alone (D) or in combination with a \u003cem\u003eTrichoderma\u003c/em\u003e-based biostimulant mixture (D\u0026thinsp;+\u0026thinsp;mix). Each genotype exhibited distinct molecular responses. In Camelia, drought (D) triggered the up-regulation of genes related to protein folding, protein oligomerization, and various amino acid transport and carbohydrate metabolic processes, suggesting an attempt to preserve metabolic homeostasis under stress. At the same time, GO terms related to protein synthesis, including translation, ribosome biogenesis, and rRNA processing, were significantly suppressed. Interestingly, photosynthesis-related genes were up-regulated, which may reflect an adaptative strategy to maintain energy production despite reduced stomatal conductance and CO\u003csub\u003e2\u003c/sub\u003e availability (Chauhan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, activation of oxidative and osmotic stress-related genes, alongside suppression of biotic defense genes, suggested a trade-off between biotic and abiotic stress pathways (Berens et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genes involved in the anthocyanin biosynthesis were also up-regulated, supporting their role in both antioxidant defense and osmotic adjustment (Dabravolski and Isayenkov, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Carbohydrate metabolism was also elicited, consistent with a drought tolerance strategy, as these molecules are essential for energy production, osmoregulation and osmoprotection, thus linking growth, development, and carbon status as observed by Diniz et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Suppression of DNA replication and repair and hormonal signalling pathways further indicated cell division and growth inhibition (Serrano-Mislata et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Down-regulation of water transport and glutathione-related genes reflected impaired oxidative stress mitigation, although subcellular redox dynamics are known to affect glutathione metabolism (Dorion et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hipsch et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Enhanced expression of leaf senescence genes may support nutrient remobilization from aging tissues to other vital areas of the plant (Griffiths et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Altogether, these responses reflect a shift toward cellular adaptation and metabolic conservation under drought.\u003c/p\u003e\u003cp\u003eWhen Camelia was treated with the biostimulant under drought (D\u0026thinsp;+\u0026thinsp;mix), the transcriptomic response in both D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix and D\u0026thinsp;+\u0026thinsp;mix vs D comparisons was broader and more integrative. Similarly to what observed in the D vs C, growth-related processes (e.g., translation, ribosome biogenesis, and DNA replication) were suppressed. However, in contrast to the untreated drought conditions, photosynthesis-related genes, including light harvesting and photosystem I, were also repressed suggesting a shift from maintaining photosynthetic activity toward energy conservation. This contrast with previous reports in rice where \u003cem\u003eTrichoderma\u003c/em\u003e treatment enhanced photosynthetic gene expression (i.e., ribulose-bisphosphate carboxylase small chain A, ferredoxin-NADP reductase, photosystem I subunit O, photosystem II core complex proteins psbY) under drought (Bashyal et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Water transport genes remained down-regulated, consistent with a conserved drought avoidance strategy. Notably, biostimulant treatment strongly induced genes involved in protein quality control (e.g., protein folding, chaperones, endoplasmic reticulum (ER) unfolded protein response), suggesting an enhanced capacity for proteostasis and ER stress response (Manghwar \u0026amp; Li, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ion transport and signalling pathways, especially calcium signalling, were activated, in agreement with their role in abiotic stress perception (Lohani et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Up-regulation of genes involved in membrane remodelling and cell wall modification further supports structural adaptation under stress (Molina et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These data suggest that the biostimulant modulates Camelia\u0026rsquo;s stress response from a defense-maintenance to a damage-mitigation mode, promoting cellular integrity and survival.\u003c/p\u003e\u003cp\u003eIn Cicero, drought (D) induced strong transcriptional down-regulation of defense-related genes (e.g., glutathione metabolism-related genes), hormonal signalling (i.e., ethylene and ABA) and protein processing pathways. Conversely, photosynthesis-related genes (e.g., light-harvesting complex and chlorophyll biosynthesis) were up-regulated, possibly to maintain redox balance and prevent ROS accumulation as previously observed in Arabidopsis and \u003cem\u003eMalus domestica\u003c/em\u003e (Cruz de Carvalho \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additional up-regulated genes included those involved in transmembrane transport, nitric oxide biosynthesis (a redox signaling molecule), and chromatin remodelling, suggesting mechanisms for genome stability and stress adaptation (Rezayian et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Han and Wagner, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim et al., 2015 a,b). Moderate induction of amino acid biosynthesis pathways (e.g., lysine and threonine) supports limited but targeted metabolic reprogramming for osmotic protection as previously observed in sandbur and wheat (Yang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bowne et al \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Overall, Cicero\u0026rsquo;s response reflected a minimalist survival strategy, characterized by selective activation of key stress-related processes and suppression of non-essential metabolic activity.\u003c/p\u003e\u003cp\u003eUnder combined drought and biostimulant treatment (D\u0026thinsp;+\u0026thinsp;mix), Cicero\u0026rsquo;s transcriptomic response shifted toward a more adaptive reprogramming. When transcriptomic response was analysed in comparison with \u003cem\u003eTrichoderma\u003c/em\u003e-treated plant in control conditions (i.e., D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix), photosynthesis, photorespiration, and sugar metabolism pathways (e.g., sucrose and fructose biosynthesis) were activated, indicating improved energy production and osmoprotection. This is consistent with previous studies showing sugar signaling\u0026rsquo;s role in stress responses (Ramel et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar results were reported in \u003cem\u003ePinus massoniana\u003c/em\u003e seedlings inoculated with \u003cem\u003eTrichoderma longibrachiatum\u003c/em\u003e, which induced the accumulation of osmotic substances (e.g., sugars, proline) to regulate osmotic pressure under drought stress (Yu et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, photosynthesis was up-regulated in D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix, but down-regulated in D\u0026thinsp;+\u0026thinsp;mix vs D, suggesting that the biostimulant\u0026rsquo;s effect on photosynthesis is context-dependent. Circadian rhythm-related genes were also activated, potentially restoring physiological homeostasis under stress, as previously observed in the model plant Arabidopsis (Blair et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Grundy et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, hormonal signalling (e.g., cytokinin) and phosphate starvation responses were enhanced. Although cytokinins can negatively interact with phosphate signalling (Argueso et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), they are known to improve drought tolerance in several crops (Zhang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Peleg et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rivero et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ghanem et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Meanwhile, key genes related to primary metabolism (carbohydrates, lipids, amines), cell wall biogenesis, and general transmembrane transport were strongly down-regulated, indicating a strategic reduction in energy-consuming processes in favour of sustaining core metabolic and photosynthetic functions. The analysis of transcriptomic profile in the D\u0026thinsp;+\u0026thinsp;mix vs D comparison only the GO category related to the assembly of transcription preinitiation complex resulted up-regulated. Among the down-regulated BPs, in addition to those described in the D\u0026thinsp;+\u0026thinsp;mix vs C\u0026thinsp;+\u0026thinsp;mix comparison, were detected water and sulfate transport, proteolysis, DNA replication and cell wall biogenesis suggesting a stronger inhibition of cell growth-related processes in this condition.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, the results of the present work reveal significant genotype-dependent variability in potato responses to drought particularly at the transcriptomic level. Camelia and Cicero, the two cultivars exhibiting the strongest drought performance, employed distinct molecular strategies to cope with water deficit. Camelia demonstrated a pronounced suppression of growth alongside strong activation of proteostasis mechanisms and anthocyanin accumulation, suggesting a stress tolerance mechanism centered on cellular protection (Additional file 13A). In contrast, Cicero maintained photosynthetic efficiency and redox homeostasis, while down-regulated growth and defense pathways (Additional file 14A). The application of \u003cem\u003eTrichoderma\u003c/em\u003e-based biostimulant mixture modulated these responses in a genotype-specific manner. In Camelia, it enhanced protein folding, stability, and ion transport regulation (Additional file 13B), while in Cicero, it stimulated photosynthesis and soluble sugar accumulation (Additional file 14B). From an agronomic perspective, our results demonstrated that the biostimulant mix partially mitigated drought-induced yield loss in Camelia but also increased the proportion of small, non-commercial tubers. While this represents a drawback from a market perspective, it can be considered a beneficial stress-adaptative strategy by increasing reproductive output. Moreover, polyphenol accumulation exhibited distinct genotype-specific patterns, with Camelia emerging as the most responsive genotype in terms of improving nutritional value under combined drought and biostimulant treatment. These findings highlight the importance of a genotype-tailored approach when assessing the effectiveness and agronomic relevance of microbial biostimulants, taking into account the complex interplay between plant genetics, environmental stress, and microbial composition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files. The datasets generated or analysed during this study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17084816.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Mr R. Nocerino and Dr. Marco Porcelli (CNR-IBBR, Portici, Italy) for assistance in plant growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partially funded by grants Agritech National Center (European Union Next-GenerationEU, PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) \u0026ndash; MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 \u0026ndash; D.D. 1032 17/06/2022, CN00000022) and \u0026ldquo;Ministero dello Sviluppo Economico\u0026rdquo; FCS-Horizon 2020 PON I\u0026amp;C 2014-2020, Project No. F/200088/01-03/X45 \u0026ldquo;Sviluppo di nuove tecnologie nell\u0026rsquo;agricoltura di precisione per la produzione sostenibile di genotipi di patata con elevate qualit\u0026agrave; nutrizionali (SOS-TATA)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors affiliations\u003c/p\u003e\n\u003cp\u003eIstituto di Bioscienze e BioRisorse (IBBR), Consiglio Nazionale delle Ricerche (CNR), Via Universit\u0026agrave; 133, 80055 Portici, Italy\u003c/p\u003e\n\u003cp\u003eRachele Tamburino, Lorenza Sannino, Emanuela Russo, Maria Consiglia Esposito, Nunzia Scotti\u003c/p\u003e\n\u003cp\u003eIstituto per la BioEconomia (IBE), Consiglio Nazionale delle Ricerche (CNR), Via P. Gobetti 101, 40129 Bologna, Italy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRachele Tamburino (present address)\u003c/p\u003e\n\u003cp\u003eIstituto per la Protezione Sostenibile delle Piante (IPSP), Consiglio Nazionale delle Ricerche (CNR), P. le E. Fermi 1, 80055 Portici, Italy\u003c/p\u003e\n\u003cp\u003eFrancesca Palomba, Adriana Sacco, Michelina Ruocco\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Nunzia Scotti ([email protected])\u003c/p\u003e\n\u003cp\u003eAuthors contributions\u003c/p\u003e\n\u003cp\u003eN.S. and M.R. conceived and designed the study. R.T. and L.S. performed drought experiments and biochemical analysis. R.T., L.S., E.R., F.P., M.C.E. performed RNA isolation and qRT-PCR analysis experiments. A.S. carried out the microbial biostimulant growth and mixture preparation. R.T., A.S., M.R., N.S. analysed the data. R.T. and N.S. wrote the manuscript. R.T., A.S., M.R., N.S. revised the manuscript. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkbari SI, Prismantoro D, Permadi N, Rossiana N, Miranti M, Mispan MS, et al. 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Plant Physiol Biochem. 2020;154:517\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.plaphy.2020.06.022\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2020.06.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Solanum tuberosum, drought stress, Trichoderma spp., NGS, differential gene expression","lastPublishedDoi":"10.21203/rs.3.rs-7656066/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7656066/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDrought is a major abiotic stress that significantly limits potato productivity and tuber quality. Microbial biostimulants have emerged as promising tools to improve crop resilience and promote sustainable agriculture. This study aimed to investigate the transcriptomic response to severe drought stress in three potato cultivars (Camelia, Cicero, and Agata) characterized by differing polyphenol content. In addition, the potential mitigating effects of a microorganism-based biostimulant mixture composed by two \u003cem\u003eTrichoderma\u003c/em\u003e species, were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSevere drought stress led to a significant reduction in tuber yield in Camelia and Agata, whereas Cicero exhibited greater tolerance. Application of the microbial biostimulant slightly alleviated yield loss in Camelia but was associated with an increased proportion of non-commercial tubers. Polyphenol content varied according to genotype and treatment, Cicero displayed elevated polyphenol levels in the skin under drought, while Camelia showed increased levels in both skin and pulp when drought was combined with biostimulant application. Transcriptomic analysis revealed genotype-specific drought response strategies. Camelia exhibited enhanced proteostasis and osmoprotection via anthocyanin accumulation, while Cicero maintained photosynthetic activity, redox homeostasis, and genome stability. \u003cem\u003eTrichoderma\u003c/em\u003e-based biostimulant treatment modulated these responses by enhancing endoplasmic reticulum protein quality control in Camelia, and promoting photosynthesis and sugar metabolism in Cicero.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePotato responses to drought are highly genotype-dependent and involve complex trade-offs among growth, stress defense, and metabolic reprogramming. Microbial biostimulants offer a promising approach to enhance drought resilience and tuber nutraceutical quality. However, their efficacy is genotype-specific and requires targeted application strategies for optimal results.\u003c/p\u003e","manuscriptTitle":"Genotype-specific transcriptomic response to drought stress in potato cultivars modulated by microbial biostimulants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 21:33:22","doi":"10.21203/rs.3.rs-7656066/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-22T14:23:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T15:21:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87739288221410201594245178403933943848","date":"2025-10-19T14:47:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T15:27:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169892708173623209577995722334271695899","date":"2025-10-10T10:35:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T15:17:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-09T06:38:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-01T13:07:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-01T12:00:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-10-01T11:32:01+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":"d01f4890-4631-4123-9010-c26e80074c53","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:00:10+00:00","versionOfRecord":{"articleIdentity":"rs-7656066","link":"https://doi.org/10.1186/s12870-025-07810-1","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-11-28 15:57:11","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-10-23 21:33:22","video":"","vorDoi":"10.1186/s12870-025-07810-1","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07810-1","workflowStages":[]},"version":"v1","identity":"rs-7656066","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7656066","identity":"rs-7656066","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}