Modulation of proteome profile of banana (Musa spp.) under water deficit

Modulation of proteome profile of banana (Musa spp.) under water deficit | 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 Modulation of proteome profile of banana (Musa spp.) under water deficit Adriadna Souza Santos, Ariana Silva Santos, Nayara de Almeida Santos, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6296060/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Water deficit is one of the main abiotic stresses affecting banana cultivation compromising the productivity and sustainability of plantations. The response of plants to water restriction involves complex metabolic regulation and molecular networks that are fundamental for defense and adaptation. In our work, proteomic analysis was conducted on the roots of Musa spp. diploids aiming to identify differentially expressed proteins associated with tolerance to water deficiency and investigate their functional interactions. The contrasting genotypes regarding water deficit tolerance, PMGB043 (susceptible), and PMGB099 (tolerant), were selected based on physiological parameters. Protein analysis by 2D-SDS-PAGE revealed 260 and 188 spots in the PMGB043 genotype, under control and severe stress conditions, respectively, while in the PMGB099 genotype, 373 spots were detected under control and 426 under severe water stress conditions. Water deficit modulated the proteins expression in crucial processes such as transport, oxidation, methylation, energy metabolism, and defense responses suggesting different adaptation mechanisms between the genotypes. This is the first proteomic study to analyze the impact of water restriction on banana roots grown in a hydroponic system providing an in-depth insight into the molecular basis of water deficit tolerance in Musa spp. The results offer new perspectives to direct strategies for gene editing and precision breeding in developing banana cultivars that are more tolerant to water deficit. Drought tolerance Root proteomics Bananas Hydroponic system Mass spectrometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Water deficit is among the most damaging abiotic stress to global agriculture limiting the distribution, diversity, and productivity of numerous plant species, including bananas ( Musa spp. ) (Yang et al. 2020 ). In one of the driest regions of Brazil the Northeast, where one of the main banana production hubs is located within semiarid conditions or a sub-humid dry climate, rainfall variability and climate change have increased climate risks and extended periods of water deficits. These changes have led to an increase in atmospheric water demand, intensifying the impacts of drought and contributing to the expansion of semiarid areas (CEMADEN; INPE, 2025). This scenario points to the urgent need for climate adaptation strategies focusing on the rational use of water in irrigated agriculture, effective soil water management in rainfed systems, and the development of crop varieties capable of utilizing water efficiently not only under normal climate conditions but also during prolonged droughts.Plants' perception of water stress triggers a series of molecular responses starting with the regulation of gene expression and followed by changes in the transcriptome, proteome, and metabolome (Farooq et al. 2009 ). These changes affect physiological and biological processes and can lead to the generation of reactive oxygen species (ROS) and, in extreme cases, cell death (Lau et al., 2023 ). Plants develop complex molecular interaction networks to mitigate these effects, which play a crucial role in detecting and responding to water stress (Mishra et al. 2018 ; Miao et al. 2017 ). Bananas and plantains are among the most widely grown fruits in the world due to their socio-economic and nutritional importance contributing to global food security. Banana growing is an essential economic resource in several countries, especially in South America. In 2023, Brazil was positioned as the world's fifth largest producer, reaching a production of 6.8 million tons. On the global stage, around 135.1 million tons were harvested from a cultivated area of 5.9 million hectares in the same year (FAO, 2024). Banana cultivation is severely restricted by biotic and abiotic factors (Santos et al. 2018 ; Nascimento et al. 2020 ; Santos et al. 2020 ; Soares et al. 2021 ). Among the abiotic factors, water deficit stands out, which, due to climate change, has become more pronounced, threatening the agricultural production of grains, seeds, and fruits worldwide, reducing yields and impacting the growth, physiology, and reproduction of plants, including banana cultivation. In recent years, several studies focused on identifying genes related to the response of banana plants to water deficit, highlighting important gene families (Xu et al. 2014 ; Sreedharan et al. 2013 ; Hu et al. 2017 ; Wei et al. 2016 ; Miao et al. 2017 ). However, little is known about the gene product (proteins and/or enzymes) in bananas under water deficiency. Recent proteomic studies have provided a deeper insight into plant responses to water deficit revealing proteins involved in carbohydrate metabolism, energy, genetic information processing, and biosynthesis of secondary metabolites (Mattos-Moreira et al. 2018 ; Lau et al. 2023 ). The response to water stress is influenced not only by the duration and severity of the stress, but also by genotypic and epigenetic factors (Lau et al. 2023 ). Several studies in crops such as cotton (Xiao et al. 2020 ), licorice (Zhang et al. 2022 ) and banana (Mohd Amnan et al., 2021 ), pitaya (Wang et al. 2021 ) and wheat (Yan et al. 2021 ) have shown that water stress alters the expression of proteins associated with defense and adaptation. In bananas, proteins related to stress, primary metabolism and responses to heat shock have shown differential regulation under drought conditions (Ji et al. 2019 ), which indicate them as strong candidates for exploratory and functional studies. However, there is a significant gap in understanding the proteomic mechanisms involved in the response to water deficit in banana plants. Although some studies have explored the relationship between genomic constitution and drought tolerance especially in leaves and rhizomes of different varieties (Carpentier et al. 2007 ; Vanhove et al. 2012 ; Mattos-Moreira, 2018), few studies have focused on roots, which play a central role in water absorption and the initial response to water stress. The genomic constitution - the banana plant has an "A" genome - Musa acuminata and a "B" genome - Musa balbisiana (Simmonds & Shepherd, 1955 ), also affects banana plants differently regarding water deficit (Carpentier et al. 2007 ; Vanhove et al. 2012 ). The proteomics of meristems from multiple banana shoots with different genetic constitutions (AAAh, AAA, AAB, AABp and ABB) revealed that specific isoforms of phosphoglycerate kinase, UDP-glucose pyrophosphorylase (UGPase), phosphoglycomutase, ASR (Aba, Stress, and Ripening) may contribute to dehydration tolerance in in vitro assays, especially the ABB variety (Carpentier et al., 2007 ). This relationship between gene constitution and tolerance to deficit was also observed in the study of the proteome of leaves of the banana varieties Cachaco (ABB, tolerant) and Mbwazirume (AAA, susceptible) contrasting in terms of water deficit, showing that a new balance occurred in the stressed plants grown in vitro (Vanhove et al., 2012 ). The rhizome protein profile of banana genotypes contrasting in terms of tolerance to water deficit showed that the expression of proteins related to growth, plant cell development and heat shock, play a significant role in the tolerance of the BRS Tropical variety (AAAB) in relation to the susceptible Prata-Anã plant (AAB) (Mattos; Moreira et al., 2018). We hypothesize that the contrasting drought tolerance in diploid genotypes of Musa spp. is associated with distinct patterns of root proteomic regulation that reflect differences in metabolic adjustment and stress responsiveness under water deficit, and that tolerant genotypes present proteomic profiles indicative of adaptive mechanisms that maintain cellular homeostasis, while sensitive genotypes present signs of metabolic imbalance under stress. Therefore, given the strong relationship between genomic constitution and water deficit, this study presents the first proteomic analysis of banana roots subjected to water deficit in a hydroponic system. This work aims to identify differentially expressed proteins associated with water deficit tolerance in improved diploids (with tolerance to yellow Sigatoka, Fusarium wilt race 1 and some to black Sigatoka) of Musa spp. and to investigate the functional interactions of these proteins. This pioneering study has the potential to significantly broaden the understanding of the molecular mechanisms underlying water stress tolerance offering new opportunities for genetic improvement to obtain more tolerant banana cultivars. MATERIAL AND METHODS Plant material The experiment was conducted under controlled greenhouse conditions at Embrapa Cassava and Fruits (12°40′S, 39°06′W). Air temperature and relative humidity were monitored throughout the experimental period using a portable thermo-hygrometer, ensuring continuous recording of microclimatic conditions. The plants were selected according to contrasting responses to water deficit in a hydroponic system composed of 115 L of distilled water and 325 g of Forth Soluble fertilizer (10% (p/v) N; 42% (p/v) de P2O5; 10% (p/v) K2O; 0.03% (p/v) B; 1.4% (p/v) S; 0.2% (p/v) Fe; 1% (p/v) Mg) of the brand Forth Jardim, as described by Santos et al. ( 2020 ) ensuring no nutritional constraints. Thus, this study used the diploids PMGB043 (Susceptible to water deficit) and PMGB099 (Tolerant to water deficit) obtained from the Banana Active Germplasm Bank (BAG) at Embrapa Mandioca e Fruticultura (Cruz das Almas, Bahia, Brazil). Twenty plants measuring 8 to 10 cm in height were transplanted and grown for a 21-day acclimation period in PVC tubes filled with sterile washed coarse sand immersed in a nutrient solution, according to Santos et al. ( 2020 ). The experiment used five plants per treatment, totaling 10 plants in control conditions and 10 plants in severe water deficit. After acclimation, the plants were divided into two groups: (i) control (Ctrl) - plants with a nutrient solution supply maintained close to field capacity (0.3 cm³ cm − 3 ) and (ii) severe water deficit (SWD) - plants with substrate moisture content below 0.05 cm³ cm − 3 , stomatal resistance greater than that of the respective control, and wilted and flaccid leaves for two consecutive days. The control group plants remained in the nutrient solution (field capacity), for another 11 days until the end of the experiment at day 32. On the other hand, the plants in the severe water deficit group were removed from the nutrient solution and kept for 11 days under stress conditions. At 32 days after application of the treatments, the root mass of the plants were collected and frozen in liquid nitrogen and immediately stored at -80°C. To preserve the experimental characteristics, the plants were freeze-dried and stored at -20°C before starting the analysis. Although plants were grown hydroponically, the solution was constantly aerated to prevent hypoxia or waterlogging conditions. Thus, the control plants did not experience oxygen deprivation. Protein extraction The roots of five plants of the PMGB043 and PMGB099 genotypes were collected. This material made up the “pool” (0.04 grams) of roots under control and severe water deficit conditions for protein extraction at the Proteomics Laboratory of the State University of Santa Cruz (UESC). The extraction followed the protocol described by Pirovani et al., ( 2008 ) modified by Bertolde et al. ( 2014 ) where the modifications included more washes to the extraction steps in TCA and acetone (Supplementary Fig. 1). After the initial cleaning of the macerate by washing with TCA in acetone and TCA in water, i) sonication was carried out (3 pulses 5 s, with 10 s intervals and 70% amplitude) to resuspend the precipitate in SDS-dense (30% (p/v) sucrose, 2% (p/v) SDS, Tris 0.1 mol L − 1 pH 8.0 and 2-mercaptoethanol); ii) an equal volume of phenol (buffered with Tris, pH 8.0) was added to the sample resuspended in SDS-dense and, iii) the mixture was homogenized in a vortex. In the final step of washing the protein precipitate with ammonium acetate in methanol, 80% (v/v) acetone was used instead of 80% (v/v) ethanol. Followed by drying the pellet at room temperature. Immediately after, the pellet was resuspended in 800 µL of rehydration buffer (7 M urea, 2 M thiourea, 2% (p/v) CHAPS, 0.002% (v/v) bromophenol blue). Proteins were quantified using the 2-D Quant Kit according to the manufacturer's instructions (GE Healthcare). 1D and 2D electrophoresis After the quantification step, the samples were analyzed by SDS-PAGE in mini electrophoresis cuvettes (Omniphor), with 8 x 10 cm gels, containing 12.5% ​​(p/v) acrylamide. 25 µg of each sample were used and from this gel it was possible to observe the profile of total protein bands (Laemmli 1970 ). On the other hand, the first dimension, consisting of isoelectric focusing (IEF), to produce the two-dimensional gels, a total of 350 µg of proteins from each sample were applied, previously solubilized in rehydration buffer, in which dithiothreitol (DTT) was added, at a concentration of 50 mmol L-1 and 0.5% (v/v) of ampholytes for pH 3–10 non-linear (NL) (Amersham Bioscienses), in 13 cm strips with an immobilized pH gradient (IPG-immobilized pH gradient) between 3–10 NL and then subjected to the EthanIPGphor III isoelectric focusing unit. The second dimension was conducted on a 12.5% ​​(p/v) polyacrylamide gels were prepared in triplicate for each treatment, using 30% (p/v) acrylamide/bisacrylamide solutions (29.2 g of acrylamide and 0.8 g of N-methyl-bisacrylamide), 1X resolution buffer (0.375 mol L − 1 Tris-HCl, pH 8.8, 0.1% (p/v) SDS), 60 µL of 10% (v/v) ammonium persulfate and 6 µL of N,N,N',N'Tetramethylethylenediamine (TEMED). Polyacrylamide gel in the HOEFER SE600 Ruby vertical electrophoresis system (AmershamBioscience). The protein spots were visualized by impregnation with 0.08% Coomassie Brilliant Blue dye (Neuhoff et al. 1988 ). The gels were left for 1 hour in fixation buffer (40% (v/v) ethanol and 10% (v/v) acetic acid) and 5 days in colloidal Coomassie blue dye (8% (v/v) ammonium sulphate, 0.08% (v/v) phosphoric acid, 0.08% (v/v) Coomassie blue G-250 and 20% (v/v) methanol). Afterwards, the gels were kept in distilled water under gentle agitation until the dye was removed. Gels were prepared in triplicates for each pool of samples from the different treatments and genotypes. Analysis of 2D images The gel images were scanned with LabScanner (AmershamBioscience) and analyzed for the identification and relative quantification of the spots using ImageMaster 2D Platinum 7.0 (GE Healthcare) taking into account the area and intensity of the spots. The control samples were compared with the water deficit samples of the respective genotypes, PMGB043 and PMGB099. For each treatment, a reference gel was established for the triplicate. The program detected the spots unique to each treatment and the relative accumulation of the proteins presented in the spots for each treatment. The differential analysis was based on the ANOVA calculation one-way. The calculation was made for both treatments, Control x PMGB043 and Control x PMGB099. The proteins considered differential were those with spots with p-value ≤ 0.05 and Fold change ≥ 1.5. Preparation of spots for mass spectrometry (LC/MS-MS) The differential and exclusive spots were excised from the gel using a scalpel, cut into smaller pieces and placed in microtubes. They were then destained in 200 µL NH 4 HCO 3 containing 50% acetonitrile and the supernatant discarded. The gel fragments were dehydrated in 100 µL of 100% (v/v) acetonitrile for 5 min and vacuum dried in the Concentrator 5301 (Eppendorf) for 10 min. 4 µL of trypsin Gold (Promega) 25 ng µL − 1 were added and kept at 4 ºC for 10 min to absorb the solution into the gel fragments. Subsequently, NH 4 HCO 3 was added until the fragments were covered and left at 37ºC for 16 hours for the trypsin to act. The supernatant was collected and transferred to a new tube. The peptides were recovered from the gel fragments by two elutions with 50 µL of 50% (v/v) acetonitrile containing 0.1% (v/v) formic acid, shaken for 15 min in a vortex with each wash. The samples were then concentrated under vacuum to a volume of between 10 and 15 µL. Identification of spots by mass spectrometry (LC/MS/MS) The spots were analyzed at the Center for Biotechnology and Genetics (CBG) of the State University of Santa Cruz (UESC) and at the National Center for Research in Energy and Materials (CNPEM) in Campinas, São Paulo, Brazil. In the CBG, the peptides were analyzed by online nano flow liquid chromatography tandem mass spectrometry (LC-MS/MS) on a nanoAcquity chromatograph (Waters, Milford, MA) coupled to a Q-Tof micro mass spectrometer (Waters). At CNPEM-SP, the peptides were separated by hydrophobicity gradient on a C18 column (100 µm x 100 mm) (Waters) using a nano Acquity Ultra Performance LC chromatograph (Waters) coupled to a nanospray ESI interface and a Q-Tof Premier mass spectrometer (Waters). The peptides were injected in a volume of 4.5 µL and first passed through a Symmetry C18 trapping column (180 µm x 20 mm) for desalting at a flow rate of 5µLmin − 1 for 2 minutes. The peptides were then loaded onto the analytical column and eluted in a gradient of 2–90% (v/v) acetonitrile containing 0.1% (v/v) formic acid for 10 minutes at a flow rate of 0.6 µL min − 1 . The voltage for the nano electrospray was 3.5 kV for the 30 V cone and the source temperature, 80 ºC. The instrument was operated in DDA (Data Dependent Analysis) mode in order to acquire and fragment (MS/MS) the three most intense peaks of each MS spectrum (top three mode). After fragmentation by MS/MS the ion was placed on an exclusion list for 20 seconds. The spectra were acquired using the MassLynx v.4.1 software and the raw files were converted into a peak list in mgf (mascotgeneric file) format using the MascotDistiller v.2.3.2.0, 2009 program (Matrix Science Ldt.). Identification and functional categorization of proteins The Mascot Server v.2.3.01.0 program (Matrix Science Ltda.) was used to identify the proteins, allowing one missed cleavage by trypsin, fixed carbamidomethylation modification, variable methionine oxidation modification and 0.1 Da mass tolerance for MS and 0.1 Da mass tolerance for MSMS, as parameters. Searches were conducted against the UniProtKB Musa acuminata proteome (taxonomy ID: 4641), accessed in 2019, which contained approximately 35,000 protein sequences at that time. Although database versions are periodically updated, the use of the 2019 dataset ensures consistency with the original LC-MS/MS analysis. The FASTA sequences of the identified proteins were obtained from the access numbers resulting from the MASCOT software search ( http://www.matrixscience.com/ ). These sequences were submitted to functional annotation, where their ontologies and biological functions were performed using the Musa acuminata dataset available in the UniProt knowledge base ( www.uniprot.org ) and BLAST2Go ( www.blast2go.com ). The proteins were categorized by Biological Process (BP), Molecular Function (MF) and Cellular Component (CC). Differential protein analysis The total spots detected using ImageMaster, considering their intensities and normalization (p-value ≤ 0.05) for the PMGB043 and PMGB099 genotypes in the control and water deficit conditions, were plotted graphically in VolcanoPlot using the Rstudio statistical environment. To visualize the identified and significant proteins, proteins with a p-value ≤ 0.05 and Fold Change ≥ 1.5, were considered; this was corrected to the logarithmic scale of log 2 FC > 0.6. The Fold Change corresponds to the number of times protein expression changed from the control condition to the water deficit condition. Therefore, this was obtained from the difference in intensity of the treatment in comparison to the control condition, then corrected to the log 2 FC scale. The identified and differentially accumulated proteins set was submitted to the Venn Diagram via < http://bioinformatics.psb.ugent.be/webtools/Venn/%3E . Western blot Approximately 0.6 g of roots were macerated in mortars with liquid nitrogen in the presence of polyvinylpolypyrrolidone (PVPP) at 0.07 g per g of tissue and the proteins extracted according to the phenolic extraction method (Bertolde et al., 2014 ; Pirovani et al., 2008 ). After quantifying the protein extract using the 2D Quant Kit (GeHealthCare), 20 µg of each sample were separated on a mini SDS-PAGE gel (12.5% ​​(v/v) acrylamide). Analysis of protein accumulation followed the method of Sambrook and Russell (1989). The membranes were probed individually by one hour of incubation with the polyclonal primary antibodies (Agrisera AB) against Catalase (EC 1.11.1.6; 57/55 kDa) and ADH (EC 1.1.1.1; 42 kDa), both in a ratio of 1:2000. The membrane was washed three times with TBS-T buffer, and under agitation the membranes were incubated for 60 min with the secondary antibody Rabbit Anti-IgG Alkaline phosphatase conjugated (AP, ZIMED Laboratories Inc. San Francisco - CA/USA), diluted at a concentration of 1:10.000. 5-Bromo-4-chloro-3-indolyl phosphate (BCIP) and pnitrotetrazolium (NBT; Promega, USA) were used as substrates for the colorimetric reaction of alkaline phosphatase activity, for viewing images on membranes. Quantification of the bands from triplicate assays was carried out using the GelQuantNET V 1.7.8 software and the results normalized based on a gel stained with colloidal comassie blue G 250 0.08% (p/v) (Neuhoff et al., 1988 ). Protein-protein interaction (PPI) network analysis Protein-protein interaction (PPI) networks were conducted consideringdifferentially expressed with a Fold Change value ≥ 1.5 and p-value < 0.005, using the String protein bank (version 11.0). All the software analyses were conducted against the proteins of Musa acuminata (AA genome). PPI information was obtained by using different prediction methods in the software, such as neighborhood, experiments, co-expression, gene fusion, databases and co-occurrence. Interactions were visualized with a medium confidence cut-off (0.400) using Musa acuminata (AA genome) as the standard organism. The networks obtained from the String database were superimposed to obtain a consensus network. This network was used to analyze clusters with the fastgreedy community function in Igraph of the R statistical environment. The biological processes associated with the clusters generated in R were analyzed using the Cytoscape 3.8.2 plugin BiNGO (Biological Network Gene Ontology) version 1.4 with multiple tests associated with the FDR algorithm with a significance level of p < 0.05. The centrality analysis of the proteins in the PMG043 and PMGB099 networks was calculated using the Igraph package's betweenness function. Nodes with values above the mediation average were considered to be betweenness. Nodes above the degree average were considered hubs, and nodes above and below the average of both centralities were considered hub-bottlenecks and common, respectively. RESULTS Protein profile of Musa acuminata roots on 1D and 2D PAGE The protein profile in SDS-PAGE 12.5% shows bands with molecular masses distributed between 14 and ~150 kDa from different extraction processes (Supplementary Figure 2). Figure 1 shows the efficiency and quality of M. acuminata root protein extraction using the Bertolde et al. (2014) method with modifications, where the bands are better distributed, delimited, and with less drag. The protein profile of the diploid genotypes PMGB043 and PMGB099 under control and severe stress conditions is visualized in the 2-DE gels (Figure 2). The proteomic map analysis of the PMGB043 genotype detected 260 and 188 spots under Ctrl and SWD conditions, respectively. For the PMGB099 genotype, 373 and 426 spotswere detected in the Control and SWD treatments, respectively. The spots are well-focused, with no horizontal or vertical drag, even for the most abundant proteins. A total of 1246 spots were detected (ANOVA p < 0.05) in the M. acuminata genotypes (Figure 3). When comparing the intensity of the spotson the PMGB043 genotype between the Ctrl and SWD treatments, 120 spotswere detected in common. Also, for PMGB043, the exclusivity of the spots was observed, with 140 spots detected for Ctrl and 67 spots in the SWD treatment (Figure 3A). The PMGB099 genotype in both conditions stood out with a greater number of spots, with 258 spots in common and 115 and 168 exclusive spots inCtrl and SWD, respectively (Figure 3B). Identified and differential proteins Of the total number of proteins identified, 362 were identified and differentially accumulated (Supplementary Table 1 and 2). The PMGB043 and PMGB099 genotypes showed a dynamic protein accumulation profile in the SWD treatment compared to Ctrl. Most of the proteins identified in the PMGB043 genotype under SWD conditions were down accumulated. As for the PMGB099 genotype, most of the proteins showed higher accumulation (Figure 4). The exclusively accumulated and differentially expressed proteins (ANOVA p < 0.05 and Fold change ≥ 1.5) were categorized, as determined by the Blast2GO software. In the BP category, proteins were related to defense response, energy, metabolism, methylation, signal transduction, translation, transport and hormones (Figure 4). Only the PMGB043 genotype showed expression of enzymes that regulate indole-3-acetic acid (IAA) levels during SWD treatment. In this genotype, proteins related to transduction and oxidation processes were up accumulated by 57% and 63%, respectively, compared to Ctrl (Figure 4 A). When compared with Ctrl, the proteins identified in the PMGB099 genotype under SWD conditions all showed positive accumulation in quantities equal to or greater than 50% (Figure 4 B). The functional categorization shown in Figure 4 highlights that most of the differentially accumulated proteins in both genotypes are related to metabolic and cellular processes, particularly those associated with energy production, response to oxidative stress, and ion transport. These categories mirror the main physiological adjustments observed in plants under water deficit, such as reallocation of energy resources, activation of antioxidant defense, and osmotic regulation. The enrichment of these functions in PMGB099 supports its ability to sustain metabolic activity and mitigate stress effects more efficiently than PMGB043. The cellular location of the identified and differential proteins of the evaluated genotypes, was predicted (Figure 5). Most of the proteins are cytoplasmic for both genotypes. However, the PMGB043 genotype showed proteins located in the Golgi complex, apoplast, nucleus, cytoskeleton, extracellular space, plasma membrane, chloroplast, mitochondria and cytoplasm (Figure 5A). Despite being a small group, the predicted proteins in the apoplast and extracellular region are noteworthy because they are predicted regions for defense proteins. Under SWD conditions, the proteins of the PMGB099 genotype were located in the Golgi complex, peroxisomes, extracellular space, chloroplast, mitochondria, nucleus, plasma membrane and cytoplasm (Figure 5B). Figure 6 shows an integrated view of the expression level of differentially accumulated proteins between two genotypes of M. acuminata . The dots above the horizontal dashed line (Threshold) represent proteins with significantly different abundances (p <0.05). The blue dots represent proteins with down-accumulation and the red dots, up-accumulation of proteins. The Venn diagram shows the proteins identified, 118 showed with down- accumulation in the PMB043 genotype, 58 in the PMGB099 genotype and 1 protein in both (left). The venn diagram shows proteins with up-accumulation, being 84 in the PMGB043 genotype, 73 in the PMBG099 genotype and 12 in both (right). Protein interaction - PPI network PPI networks were formed between induced and repressed differentially expressed proteins for each genotype (Figures 7 and 8). The proteins showed direct or indirect interaction through the number of nodes observed. The PPI network of the PMGB043 genotype resulted in nine clusters with enriched functions associated with metabolism, defense response, oxidative stress, and transduction during water stress (Figure 7). In addition, it is possible to observe proteins characterized as degree (hub), those with means ≥ 63.5, and betweenness (bottleneck) with means≥ 237.36. The interaction network of the PMGB099 genotype is represented by four clusters with the following enriched biological processes related to water stress: metabolic processes, response to water stress, signaling, root elongation, antioxidant enzymes, among others (Figure 8). Degree and betweenness proteins were also identified with averages ≥ 120 and ≥ 175.39, respectively. Immunodetection of proteins CAT accumulation in the PMGB043 genotype was similar in the Ctrl and SWD treatments and in the PMGB099 genotype there was a reduction in accumulation in the SWD treatments compared to Ctrl (Figure 9A and 9B). The immunoblotting of ADH shows a higher accumulation of this enzyme in the genotypes in control condition compared to the water deficit treatments in both genotypes (Figure 9A and 9B). DISCUSSION Optimizing protein extraction as a foundation for root proteome analysis in Musa spp The analysis of protein profiles by electrophoresis in two-dimensional (2D) gels requires high-quality protein extracts, free of contaminants such as polysaccharides, lipids and phenolic compounds, which can interfere with the separation and detection of proteins. In order to guarantee accurate results, it is essential to use an efficient protein extraction process, especially for samples from plant roots, whose chemical characteristics make purification more complex. Although there are protocols for extracting protein from banana roots comparisons between different methods result in low quality gels full of vertical striations and without proof (marking) of the number of spots detected (Vaganan et al. 2015 ). In addition, Musa spp. species can vary in the amounts and types of secondary compounds, polysaccharides and other plant polymers that interfere with the quality of the gels used for proteomic analysis (Surabhi, 2016 ). Additional washes with acetone during the process of extracting proteins from banana roots proved to be highly effective in removing unwanted contaminants that could compromise proteomic analysis (Supplementary Fig. 2). This extra purification step significantly improved the quality of the protein extracts, ensuring greater efficiency in the separation and identification of proteins during electrophoresis in two-dimensional gels (Fig. 1 ). Thus, the optimization of the protein extraction protocol used in this study proved essential for obtaining high-quality and reproducible 2D gels from Musa spp. roots grown under hydroponic conditions. This improvement minimized interference from secondary metabolites and ensured efficient downstream identification of drought-responsive proteins. Extraction tests using the protocols of Bertolde et al. ( 2014 ) and Pirovani et al. ( 2008 ) revealed the need to increase the number of washes during the protein extraction process from banana roots grown in an adapted hydroponic system (Supplementary Fig. 1). The proteomic profiles observed here reflect the physiological differences previously described for both genotypes, with PMGB099 maintaining metabolic activity and PMGB043 showing stronger signs of energy limitation and oxidative stress under water deficit. Water deficit induces the expression of differential proteins in contrasting genotypes of M. acuminata. In our work, eleven isoforms of sucrose synthase (SUS) were positively accumulated in the PMGB043 genotype and two in the PMGB099 genotype (Supplementary Tables 1 and 2). SUS is related to a series of metabolic processes, such as sucrose distribution in tissues, starch and cellulose synthesis, cell wall formation, response to abiotic stresses and nitrogen fixation (Ciereszko 2009 ). Furthermore, in plants grown in vitro , it has been reported that the processes of sugar absorption and metabolization drive respiration, resulting in limiting oxygen levels (Carpentier et al. 2010 ). Heat shock proteins (HSPs) were up-accumulated in the PMGB099 genotype (spot 70 - Supplementary Table 2). Studies indicate that up-regulation of this molecular chaperone triggers water stress response mechanisms in Musa sp (Carpentier et al. 2007 ; Mattos-Moreira et al. 2018 ). The HSP/chaperone system under normal growth conditions or during and after stress determines the fate of denatured or non-native proteins. Thus, they are responsible for the folding, assembly, translocation and degradation of proteins in many normal cellular processes, stabilize proteins and membranes, and can assist in the refolding of proteins under stress conditions (Wang et al. 2004 ). The enzymes pyruvate decarboxylase and pyruvate kinase (spots 451 and 31 Supplementary Tables 1 and 2) were positively regulated in the roots of genotypes PMGB043 and PMGB099. Although the genotypes did not show visual characteristics of hypoxia (Supplementary Fig. 3), the fermentative pathway may have been used to meet metabolic demands in the hydroponic system (Santos et al. 2020 ). The study of the meristematic tissue of the Cachaco variety ( Musa paradisiaca ABB), which is tolerant to environmental stresses, shows that the energetic state of the tissue affects the induction of fermentative enzymes under low oxygen. In addition, mechanisms to adjust oxygen consumption are linked to the availability of pyruvate (Carpentier et al. 2010 ). Thus, even under aerobic conditions, alcoholic fermentation plays an important role in preventing anoxia by controlling the level of pyruvate, as occurred in Arabidopsis knockout mutants for alcohol dehydrogenase grown in a hydroponic system (Zabalza et al. 2009 ). The genotypes studied showed positive regulation of the alcohol dehydrogenase enzyme (spots 17, 126, PMGB043; 300, 456, PMGB099) in plants from the control treatment. In Arabidopsis subjected to environmental stress, positively accumulated alcohol dehydrogenase has been linked to improved acclimatization and is a candidate protein marker for tolerant varieties under adverse conditions (Dolferus, et al. 1994 ). In recent years, several studies have been focused on identifying genes related to the response of banana plants to water deficit highlighting important gene families such as aquaporins, which are responsible for transporting water and maintaining osmotic balance (Xu et al. 2014 ; Sreedharan et al. 2013 ; Hu et al. 2017 ). In addition, genes such as MaSWEETs , involved in sugar transport, play a central role not only in energy production, but also in stress signaling and cellular water balance (Farooq et al. 2009 ; Miao et al. 2017 ). Other genes include those encoding superoxide dismutases ( MaSODs ) which eliminate ROS and participate in hormonal responses, and transcription factors such as MusaNAC68 and MusaDHN-1 , which regulate the expression of defense and resistance genes (Feng et al., 2015 ; Negi et al., 2015; Shekhawat et al., 2011 ). Genes encoding transcription factors have also been characterized such as MusaNAC68 (Negi et al. 2015) and MusaNAC042 (Tak et al., 2017 ). Of the NAC proteins, MusaDHN-1 (Shekhawat et al. 2011 ), which expresses specific hydrophilic proteins known as late embryogenesis abundant (LEA) and the MaHsfs gene (Wei et al. 2016 ) which regulates the expression of heat shock proteins. Transcription factors such as these are proteins that regulate the expression of plant defense and resistance genes, so they are potential targets for gene manipulation (Sreedharan et al. 2012 ; Shekhawat et al. 2013). Plants subjected to water stress exhibited defense responses associated with differential protein expression, dependent on genotype. Genotype PMG043 exhibited greater accumulation of differential proteins, presenting higher amounts of proteins identified in the soil water deficit treatment, when compared with genotype PMG099 under the same conditions. Protein modulation as a defense strategy against water deficit Two-thirds of the proteins related to the defense response in the PMGB043 genotype were repressed, while in the PMGB099 genotype, most of the defense enzymes were up-accumulated. Water stress is one of the main abiotic factors that can impact banana cultivation. In plants deprived of oxygen due to waterlogging (Blokhina et al. 2001 ) or under water deficit (Rukundo et al. 2012 ), oxidative stress is an expected response. Thus, the production of antioxidant enzymes, such a CAT, GPX and SOD, can limit the cellular damage caused by ROS during stress in Musa spp. (Surendar, et al. 2013 ). Cysteine synthase was down-accumulated in the susceptible genotype PMGB043 (spots 224, 225, 220; Supplementary Table 1) and up-accumulated in the tolerant genotype PMGB99 (spots 374, 445; Supplementary Table 2). Cysteine plays a fundamental role in the primary and secondary metabolism of plants. In addition, it seems to behave as a signaling molecule in the cytosol triggering immune responses, and in the mitochondria, acting in the detoxification of cyanide; essential for the development of root hairs and plant responses to pathogens (Romero et al. 2014 ). A study with Musa spp. varieties indicates that this protein is related to ROS detoxification in plants tolerant to water stress (Vanhove et al 2012 ). Four isoforms of H + -ATPase typeV (V-ATPase) were down-accumulated during stress in the PMGB043 genotype and one in the PMGB099 genotype (Supplementary Tables 1 and 2). The V-ATPase carries out ATP-dependent proton transport from the cytosol to the lumen of the endomembrane compartments, contributing to cytosolic pH homeostasis (Seidel, 2022 ). These proteins are related to plant responses to environmental stress and are fundamental for maintaining homeostasis (Magnotta-Gogarten, 2002). Among the known functions of V-ATPase are the separation of membrane transport proteins, exocytosis, endocytic digestion, recycling of neurotransmitters and energization of membrane transport systems and epithelia (Beyenbach-Wieczorek, 2006). By regulating proton gradients and vacuolar ion transport, V-ATPases help maintain cytosolic pH and osmotic balance. The up-regulation of this enzyme in the tolerant genotype indicates an integrated response that stabilizes cellular homeostasis and water retention under drought stress. The identification of the enzyme indolyl-3-acetic acid amidohydrolase (ILR1) exclusively in the PMGB043 genotype suggests that this genotype may be involved in specific mechanisms regulating auxin homeostasis. Auxins, essential hormones for plant growth and development, are controlled by a complex regulatory network in which the ILR1 conjugate may act as a key point. Its function in modulating indolyl-3-acetic acid (IAA) levels, both inactivation and temporary storage, can directly affect processes such as root elongation playing a crucial role in the response to water stress in Musa spp. In Arabidopsis , it has been reported that some indolyl-3-acetic acid (IAA) conjugates can be used to determine the tissue and subcellular localization of attached IAA, inhibiting root elongation (Leclere et al. 2002 ). Protein Interaction Networks (PPI) reveal potential proteins for plant breeding The PPI networks whose confidence score was 0.400, represent the proteins identified and grouped, whether up or down accumulated in the different clusters for the PMGB043 and PMGB099 genotypes and reveals a global view of the main biological processes involved in the plant's response to water deficit. In addition, they make it possible to select groups of highly interacting proteins that participate in the cellular machinery with a view to future studies on banana plant breeding. The interactions of the proteins in the PPI networks (Figs. 7 and 8 ) are related to the functional modules and protein complexes described by Wang et al. ( 2010 ) and Spirin and Mirny ( 2003 ). Functional modules are groups of proteins in which interactions occur at a different place or time, such as signaling proteins and metabolic pathways while protein complexes participate in molecular machineries that occur at the same place. Building PPI networks is still a challenge for non-model plants, especially when it comes to proteomic data (Santos et al. 2020 ). The biological systems most affected in the PMGB043 genotype (susceptible) are primary metabolism and oxidative phosphorylation, which is evident in Cluster A (Fig. 7 ), corroborating the amount of proteins that were identified in table 1, which mostly presented negative regulation. A larger amount of proteins is observed in this Cluster (102 proteins). Primary metabolism is related to a number of essential plant functions, such as photosynthesis, respiration and the transport of solutes. It seems that under water deficit there is a reduction in plant biomass, both in the roots and in the aerial part and an increase in the production of primary metabolites to tolerate and/or protect the tissues from damage, increased production of intracellular oxidants and subsequently lipid peroxidation, under stress (Mundim; Pringle 2018 ). Increased ROS production promotes oxidative stress, resulting from the accumulation of oxidants at the cellular level. As a defense response, plants produce secondary metabolites that help prevent or alleviate these changes. These regulators act to protect and regulate cellular homeostasis (Thukkaram et al. 2025 ). In the PMGB099 genotype (tolerant), proteins related to primary and secondary metabolism were identified, both protein expressions had greater accumulation in the roots of plants, which were subjected to water stress (Fig. 8 ; Cluster D − 452 proteins). According to Lavinsky et al. (2015), plants under water stress are able to allocate biomass to leaves or roots altering the metabolic activity of these organs. Furthermore, plants subjected to abiotic stresses exhibit defense mechanisms, one of which is the production of metabolites responsible for the antioxidant defense system, hormonal signaling, ion transport, among others (Thukkaram et al. 2025 ). Although secondary metabolites were not directly quantified in this study, the up-accumulation of proteins related to phenolic metabolism and antioxidant defense suggests a possible mobilization of these compounds in response to water deficit. This inference is consistent with previous reports showing that secondary metabolites contribute to drought tolerance in plants. In the tolerant genotype, several proteins involved in phenolic compound biosynthesis and reactive oxygen species (ROS) scavenging were more abundant, reinforcing the role of these pathways in maintaining cellular homeostasis under stress. The identification of clusters related to metabolic processes, antioxidant activity, and signal transduction supports the hypothesis that drought tolerance arises from the integration of cellular metabolism and stress signaling. Rather than the action of individual proteins, these networks reveal a systems-level reorganization of root metabolism that maintains cellular homeostasis under water deficit. The Western blot analysis showed a higher accumulation of the proteins that were analyzed in the control treatments of both genotypes (PMGB043 - susceptible and PMGB099 - tolerant), contrasting for tolerance to water deficit. Cat is an enzyme that acts in the process of eliminating ROS; products of oxidative stress promoted by biotic or abiotic stress at the cellular level (Hossain et al. 2012 ). In the present study, the accumulation of the enzyme in the control treatment may suggest a mild adaptive response of banana roots to the hydroponic environment, although not associated with actual hypoxia, since the solution was aerated throughout the experiment. they were subjected to during the experiment. Subsequently, the plants that were subjected to severe water stress also showed a greater accumulation of the enzyme, when compared to the moderate water deficiency treatment, in both genotypes evaluated. It is suggested that the hydroponic control solution may have caused some level of stress in the plants. This is corroborated by the activation of the fermentation pathway and the upregulation of stress-related enzymes. When analyzing the accumulation of alcohol dehydrogenase in the roots of banana plants subjected to the treatments, the control treatments of both genotypes (PMGB043 and PMGB099) showed higher concentrations of the protein compared to the treatments under severe water deficiency demonstrating that the banana plants subjected to the hydroponic treatments showed a response pattern similar to those observed in waterlogging-tolerant plants, although no hypoxic conditions were imposed in this experiment. Alcohol dehydrogenase (ADH) is directly related to the defense response and survival of plants in anaerobic environments (Shen et al. 2021 ). Overall, the proteomic and PPI analyses revealed that the proteins modulated under water deficit in both genotypes were mainly associated with metabolism, defense response, energy production, transport, and signal transduction. In the tolerant genotype (PMGB099), up-regulated clusters were enriched in oxidative stress response, primary and secondary metabolism, and root elongation processes, indicating a coordinated activation of energy balance and defense mechanisms. In contrast, in the susceptible genotype (PMGB043), most proteins involved in primary metabolism and oxidative phosphorylation were down-accumulated, suggesting an impaired energy homeostasis under stress. These integrated responses reflect how drought tolerance in Musa spp. results from the concerted modulation of functional protein groups rather than isolated proteins. Together, the functional groups highlighted in this study reveal that drought tolerance in Musa spp. arises from the coordinated regulation of energy metabolism, redox homeostasis, and vacuolar transport, enabling sustained physiological activity under stress. These processes appear to work together to maintain cellular stability and mitigate oxidative and osmotic damage, which are key determinants of drought resilience in banana roots. CONCLUSION This pioneering proteomic study of Musa spp. roots under water deficit provides the first comprehensive evidence of how contrasting diploid genotypes respond at the protein level to drought stress. The tolerant genotype, PMGB099, exhibited a coordinated up-regulation of proteins associated with energy metabolism, antioxidant defense, ion transport, and signal transduction, whereas the susceptible genotype, PMGB043, showed a reduction in proteins related to primary metabolism and cellular homeostasis. These findings reveal that drought tolerance in Musa spp. roots results from the integration of metabolic, redox, and signaling pathways that together sustain physiological activity under water limitation. The differential accumulation of key proteins involved in energy balance, oxidative stress mitigation, and vacuolar regulation highlights potential molecular markers for identifying and selecting drought-tolerant banana genotypes. These results establish a molecular framework for understanding root adaptation mechanisms in Musa spp., offering valuable insights for breeding programs and biotechnological strategies aimed at developing cultivars resilient to water scarcity and promoting the sustainability of banana production in drought-affected regions. Declarations Author contributions ASS and CPP, conceived and designed the research. ASS, performed the research. ASS, ASS, and NAS analyzed the proteomic data and ASS and MACF analyzed the physiological data. ASS wrote the original manuscript. EPA provided the banana plants. CFF, CPP, EPA revised the manuscript and CPP acquired funding and supervised the work. All authors read and approved the final version of the manuscript. Funding This work was supported by the coordination of Improvement of Higher Education Personnel (CAPES) –PhD scholarship to ASS; FINEP (433/2016) for financing studies and projects, Conselho Nacional de Desenvolvimento Científico e Tecnologia (Process: 303765/2019-4) and Embrapa Mandioca e Fruticultura for the infrastructure and technical support. Acknowledgments Embrapa Mandioca e Fruticultura for the infrastructure for developing the research; the coordination of the Improvement of Higher Education Personnel and the Bahia State Research Support Foundation for granting the scholarships. Conflict of interests The authors declare no conflics of interest. Data availability statement The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author. References Bertolde, F. Z., Almeida, A. A. F., Silva, F. A. C., Oliveira, T. M., & Pirovani, C. P. (2014). 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BMC genomics, 23(1), 524. https://doi.org/10.1186/s12864-022-08733-z Supplementary Files SupplementaryInfo.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Nov, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 25 Oct, 2025 First submitted to journal 22 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":271115,"visible":true,"origin":"","legend":"\u003cp\u003eProtein profile of \u003cem\u003eM. acuminata \u003c/em\u003eroots in 1D-PAGE. Extractions carried out using the protocol of Bertolde et al. (2014) with modifications. MM - corresponds to Molecular Mass Marker; C - corresponds to control; M - corresponds to moderate stress and S - corresponds to severe stress.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/d1218cc8ddb6edccc73f5dff.png"},{"id":94986628,"identity":"961ab7bb-f36c-475c-bc46-8ee757e8b682","added_by":"auto","created_at":"2025-11-03 07:00:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein profile of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMusa acuminata \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eroots in 2D-PAGE. \u003c/strong\u003eProtein samples from the PMGB043 and PMGB099 genotypes, susceptible and tolerant to water deficit, respectively, focused on 13 cm strips with a 3-10 non-linear (NL) pH gradient. The gels were stained with colloidal coomassie blue (Neuhoff et al. 1988). Red arrows correspond to differentially expressed spots; black arrows correspond to exclusively detected spots.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/2a4ffea69db72ad8566253eb.png"},{"id":94859260,"identity":"332403b5-fd84-46c7-b77e-dd1572192284","added_by":"auto","created_at":"2025-10-31 12:52:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54568,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of spots\u003cem\u003e \u003c/em\u003edetected on \u003cem\u003eMusa acuminata \u003c/em\u003eroot gels for the PMGB043 (susceptible) and PMGB099 (tolerant) genotypes, under control and severe water stress conditions.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/9c583bad4cdbd71014577da7.png"},{"id":94985699,"identity":"6bbfbe50-9dce-439d-a2de-251c4f437d7b","added_by":"auto","created_at":"2025-11-03 06:58:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67750,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of Biological Processes for differentially accumulated proteins in response to severe water deficit identified in the genotypes PMGB043 and PMGB099 \u003cem\u003eof M. acuminata\u003c/em\u003e. Comparison between control and severe stress treatments.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/abb0ba02d43d1d47eb8b5d64.png"},{"id":94859264,"identity":"80390a88-209a-4dfd-add5-4eeae60fb282","added_by":"auto","created_at":"2025-10-31 12:52:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60498,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of the cellular location of the differentially expressed proteins in response to water deficit identified in the \u003cem\u003eMusa acuminata \u003c/em\u003egenotypes PMGB043 and PMGB099. Comparison between control and severe stress treatments.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/c076b7b5a67c7a11d008459d.png"},{"id":94985365,"identity":"501b7784-a8e5-4a3c-8173-a277e3806e95","added_by":"auto","created_at":"2025-11-03 06:58:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":112803,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano Plot and Venn Diagram showing the variation in the differential accumulation of the proteins identified in the PMGB043 (A and B) and PMGB099 (C and D) genotypes under water deficit conditions compared to the control condition. Proteins with reduced and increased accumulation are represented by blue and red dots, respectively, considering p-value \u0026gt; 0.05 and FC \u0026gt; 1.5 and \u0026lt; -1.5 (Log2 FC \u0026gt; 0.6 and \u0026lt; -0.06). Leaked dots on the Volcano Plot represent significant but unidentified proteins and gray dots represent detected spots. The Venn diagrams (B and D) represent the number of proteins with a significant difference in expression. Blue diagrams (B) represent down-accumulated proteins and red diagrams (D), up-accumulated proteins.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/a31484c70dcac1f7acb62753.png"},{"id":94859279,"identity":"5174ace7-7f2f-412b-aa5c-4ebefaaf6b9f","added_by":"auto","created_at":"2025-10-31 12:52:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":279231,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of proteins differentially accumulated in the PMGB043 genotype when subjected to water deficit. The nodes represent proteins, and the connectors represent the predicted interaction between the proteins identified in String 11. The larger nodes in red and blue represent proteins identified in the proteome and with differential accumulation, up and down, respectively. In the center is the main network predicted for all proteins with nine surrounding clusters (A - J) with the respective functional processes enriched by the Cytoscape 3.2.1 BiNGO plug-in. The different shapes of the nodes represent their centrality, degree or betweenness in the interaction network.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/2d145986e4477a217eb9f314.png"},{"id":94859281,"identity":"83c45656-ead1-4702-a564-9fab7ea27386","added_by":"auto","created_at":"2025-10-31 12:52:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":172463,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network of proteins differentially accumulated in the PMGB099 genotype when subjected to water deficit. The nodes represent proteins, and the connectors represent the predicted interaction between the proteins identified in String 11. The larger nodes in red and blue represent proteins identified in the proteome and with differential accumulation, up and down, respectively. In the center is the main network predicted for all proteins with four surrounding clusters (A - D) with the respective functional processes enriched by the Cytoscape 3.2.1 BiNGO plug-in. The different shapes of the nodes represent their centrality, degree or betweenness in the interaction network.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/d92d3bf38d63d3557a2bd7d3.png"},{"id":94859300,"identity":"48937bc7-c2cc-49d3-8d4c-1146db11692f","added_by":"auto","created_at":"2025-10-31 12:52:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":118857,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Accumulation of catalase (CAT) and alcoholic dehydrogenase (ADH) in roots of genotype PMGB043 and (B) accumulation of catalase (CAT) and alcoholic dehydrogenase (ADH) in roots of genotype PMGB099 of banana subjected to water deficiency. Relative accumulation determined from nitrocellulose membrane imaging using Gel Quant Net 8.0 Software. The relative quantification of each spot corresponds to the proteins analyzed by Western blotting. 20 μg of protein was loaded into the gels for all treatments.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/fe5e209e8fe166e57080ba29.png"},{"id":95220948,"identity":"468c9658-63c7-47d9-b694-1b548f6c6497","added_by":"auto","created_at":"2025-11-05 16:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2189319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/e20977d6-3940-4f53-84e1-050694231a42.pdf"},{"id":94985937,"identity":"c0dfae62-acf2-4454-9452-72c1271c3985","added_by":"auto","created_at":"2025-11-03 06:59:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1015713,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInfo.docx","url":"https://assets-eu.researchsquare.com/files/rs-6296060/v1/155e0857501a1309a6a65817.docx"}],"financialInterests":"","formattedTitle":"Modulation of proteome profile of banana (Musa spp.) under water deficit","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWater deficit is among the most damaging abiotic stress to global agriculture limiting the distribution, diversity, and productivity of numerous plant species, including bananas (\u003cem\u003eMusa spp.\u003c/em\u003e) (Yang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In one of the driest regions of Brazil the Northeast, where one of the main banana production hubs is located within semiarid conditions or a sub-humid dry climate, rainfall variability and climate change have increased climate risks and extended periods of water deficits. These changes have led to an increase in atmospheric water demand, intensifying the impacts of drought and contributing to the expansion of semiarid areas (CEMADEN; INPE, 2025). This scenario points to the urgent need for climate adaptation strategies focusing on the rational use of water in irrigated agriculture, effective soil water management in rainfed systems, and the development of crop varieties capable of utilizing water efficiently not only under normal climate conditions but also during prolonged droughts.Plants' perception of water stress triggers a series of molecular responses starting with the regulation of gene expression and followed by changes in the transcriptome, proteome, and metabolome (Farooq et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These changes affect physiological and biological processes and can lead to the generation of reactive oxygen species (ROS) and, in extreme cases, cell death (Lau et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plants develop complex molecular interaction networks to mitigate these effects, which play a crucial role in detecting and responding to water stress (Mishra et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Miao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBananas and plantains are among the most widely grown fruits in the world due to their socio-economic and nutritional importance contributing to global food security. Banana growing is an essential economic resource in several countries, especially in South America. In 2023, Brazil was positioned as the world's fifth largest producer, reaching a production of 6.8\u0026nbsp;million tons. On the global stage, around 135.1\u0026nbsp;million tons were harvested from a cultivated area of 5.9\u0026nbsp;million hectares in the same year (FAO, 2024). Banana cultivation is severely restricted by biotic and abiotic factors (Santos et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nascimento et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Soares et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Among the abiotic factors, water deficit stands out, which, due to climate change, has become more pronounced, threatening the agricultural production of grains, seeds, and fruits worldwide, reducing yields and impacting the growth, physiology, and reproduction of plants, including banana cultivation.\u003c/p\u003e\u003cp\u003eIn recent years, several studies focused on identifying genes related to the response of banana plants to water deficit, highlighting important gene families (Xu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sreedharan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Miao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, little is known about the gene product (proteins and/or enzymes) in bananas under water deficiency. Recent proteomic studies have provided a deeper insight into plant responses to water deficit revealing proteins involved in carbohydrate metabolism, energy, genetic information processing, and biosynthesis of secondary metabolites (Mattos-Moreira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lau et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The response to water stress is influenced not only by the duration and severity of the stress, but also by genotypic and epigenetic factors (Lau et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several studies in crops such as cotton (Xiao et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), licorice (Zhang et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and banana (Mohd Amnan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), pitaya (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and wheat (Yan et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have shown that water stress alters the expression of proteins associated with defense and adaptation. In bananas, proteins related to stress, primary metabolism and responses to heat shock have shown differential regulation under drought conditions (Ji et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which indicate them as strong candidates for exploratory and functional studies.\u003c/p\u003e\u003cp\u003eHowever, there is a significant gap in understanding the proteomic mechanisms involved in the response to water deficit in banana plants. Although some studies have explored the relationship between genomic constitution and drought tolerance especially in leaves and rhizomes of different varieties (Carpentier et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Vanhove et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mattos-Moreira, 2018), few studies have focused on roots, which play a central role in water absorption and the initial response to water stress. The genomic constitution - the banana plant has an \"A\" genome - \u003cem\u003eMusa acuminata\u003c/em\u003e and a \"B\" genome - \u003cem\u003eMusa balbisiana\u003c/em\u003e (Simmonds \u0026amp; Shepherd, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1955\u003c/span\u003e), also affects banana plants differently regarding water deficit (Carpentier et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Vanhove et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe proteomics of meristems from multiple banana shoots with different genetic constitutions (AAAh, AAA, AAB, AABp and ABB) revealed that specific isoforms of phosphoglycerate kinase, UDP-glucose pyrophosphorylase (UGPase), phosphoglycomutase, ASR (Aba, Stress, and Ripening) may contribute to dehydration tolerance in \u003cem\u003ein vitro\u003c/em\u003e assays, especially the ABB variety (Carpentier et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This relationship between gene constitution and tolerance to deficit was also observed in the study of the proteome of leaves of the banana varieties Cachaco (ABB, tolerant) and Mbwazirume (AAA, susceptible) contrasting in terms of water deficit, showing that a new balance occurred in the stressed plants grown \u003cem\u003ein vitro\u003c/em\u003e (Vanhove et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The rhizome protein profile of banana genotypes contrasting in terms of tolerance to water deficit showed that the expression of proteins related to growth, plant cell development and heat shock, play a significant role in the tolerance of the BRS Tropical variety (AAAB) in relation to the susceptible Prata-An\u0026atilde; plant (AAB) (Mattos; Moreira et al., 2018).\u003c/p\u003e\u003cp\u003eWe hypothesize that the contrasting drought tolerance in diploid genotypes of Musa spp. is associated with distinct patterns of root proteomic regulation that reflect differences in metabolic adjustment and stress responsiveness under water deficit, and that tolerant genotypes present proteomic profiles indicative of adaptive mechanisms that maintain cellular homeostasis, while sensitive genotypes present signs of metabolic imbalance under stress.\u003c/p\u003e\u003cp\u003eTherefore, given the strong relationship between genomic constitution and water deficit, this study presents the first proteomic analysis of banana roots subjected to water deficit in a hydroponic system. This work aims to identify differentially expressed proteins associated with water deficit tolerance in improved diploids (with tolerance to yellow Sigatoka, \u003cem\u003eFusarium\u003c/em\u003e wilt race 1 and some to black Sigatoka) of \u003cem\u003eMusa spp.\u003c/em\u003e and to investigate the functional interactions of these proteins. This pioneering study has the potential to significantly broaden the understanding of the molecular mechanisms underlying water stress tolerance offering new opportunities for genetic improvement to obtain more tolerant banana cultivars.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003ch2\u003ePlant material\u003c/h2\u003e\u003cp\u003eThe experiment was conducted under controlled greenhouse conditions at Embrapa Cassava and Fruits (12\u0026deg;40\u0026prime;S, 39\u0026deg;06\u0026prime;W). Air temperature and relative humidity were monitored throughout the experimental period using a portable thermo-hygrometer, ensuring continuous recording of microclimatic conditions. The plants were selected according to contrasting responses to water deficit in a hydroponic system composed of 115 L of distilled water and 325 g of Forth Soluble fertilizer (10% (p/v) N; 42% (p/v) de P2O5; 10% (p/v) K2O; 0.03% (p/v) B; 1.4% (p/v) S; 0.2% (p/v) Fe; 1% (p/v) Mg) of the brand Forth Jardim, as described by Santos et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) ensuring no nutritional constraints. Thus, this study used the diploids PMGB043 (Susceptible to water deficit) and PMGB099 (Tolerant to water deficit) obtained from the Banana Active Germplasm Bank (BAG) at Embrapa Mandioca e Fruticultura (Cruz das Almas, Bahia, Brazil). Twenty plants measuring 8 to 10 cm in height were transplanted and grown for a 21-day acclimation period in PVC tubes filled with sterile washed coarse sand immersed in a nutrient solution, according to Santos et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The experiment used five plants per treatment, totaling 10 plants in control conditions and 10 plants in severe water deficit. After acclimation, the plants were divided into two groups: (i) control (Ctrl) - plants with a nutrient solution supply maintained close to field capacity (0.3 cm\u0026sup3; cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and (ii) severe water deficit (SWD) - plants with substrate moisture content below 0.05 cm\u0026sup3; cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, stomatal resistance greater than that of the respective control, and wilted and flaccid leaves for two consecutive days. The control group plants remained in the nutrient solution (field capacity), for another 11 days until the end of the experiment at day 32. On the other hand, the plants in the severe water deficit group were removed from the nutrient solution and kept for 11 days under stress conditions.\u003c/p\u003e\u003cp\u003eAt 32 days after application of the treatments, the root mass of the plants were collected and frozen in liquid nitrogen and immediately stored at -80\u0026deg;C. To preserve the experimental characteristics, the plants were freeze-dried and stored at -20\u0026deg;C before starting the analysis.\u003c/p\u003e\u003cp\u003eAlthough plants were grown hydroponically, the solution was constantly aerated to prevent hypoxia or waterlogging conditions. Thus, the control plants did not experience oxygen deprivation.\u003c/p\u003e\n\u003ch3\u003eProtein extraction\u003c/h3\u003e\n\u003cp\u003eThe roots of five plants of the PMGB043 and PMGB099 genotypes were collected. This material made up the \u0026ldquo;pool\u0026rdquo; (0.04 grams) of roots under control and severe water deficit conditions for protein extraction at the Proteomics Laboratory of the State University of Santa Cruz (UESC). The extraction followed the protocol described by Pirovani et al., (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) modified by Bertolde et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) where the modifications included more washes to the extraction steps in TCA and acetone (Supplementary Fig. 1).\u003c/p\u003e\u003cp\u003eAfter the initial cleaning of the macerate by washing with TCA in acetone and TCA in water, i) sonication was carried out (3 pulses 5 s, with 10 s intervals and 70% amplitude) to resuspend the precipitate in SDS-dense (30% (p/v) sucrose, 2% (p/v) SDS, Tris 0.1 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e pH 8.0 and 2-mercaptoethanol); ii) an equal volume of phenol (buffered with Tris, pH 8.0) was added to the sample resuspended in SDS-dense and, iii) the mixture was homogenized in a vortex. In the final step of washing the protein precipitate with ammonium acetate in methanol, 80% (v/v) acetone was used instead of 80% (v/v) ethanol. Followed by drying the pellet at room temperature. Immediately after, the pellet was resuspended in 800 \u0026micro;L of rehydration buffer (7 M urea, 2 M thiourea, 2% (p/v) CHAPS, 0.002% (v/v) bromophenol blue). Proteins were quantified using the 2-D Quant Kit according to the manufacturer's instructions (GE Healthcare).\u003c/p\u003e\u003cp\u003e\u003cem\u003e1D and 2D electrophoresis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAfter the quantification step, the samples were analyzed by SDS-PAGE in mini electrophoresis cuvettes (Omniphor), with 8 x 10 cm gels, containing 12.5% ​​(p/v) acrylamide. 25 \u0026micro;g of each sample were used and from this gel it was possible to observe the profile of total protein bands (Laemmli \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1970\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, the first dimension, consisting of isoelectric focusing (IEF), to produce the two-dimensional gels, a total of 350 \u0026micro;g of proteins from each sample were applied, previously solubilized in rehydration buffer, in which dithiothreitol (DTT) was added, at a concentration of 50 mmol L-1 and 0.5% (v/v) of ampholytes for pH 3\u0026ndash;10 non-linear (NL) (Amersham Bioscienses), in 13 cm strips with an immobilized pH gradient (IPG-immobilized pH gradient) between 3\u0026ndash;10 NL and then subjected to the EthanIPGphor III isoelectric focusing unit.\u003c/p\u003e\u003cp\u003eThe second dimension was conducted on a 12.5% ​​(p/v) polyacrylamide gels were prepared in triplicate for each treatment, using 30% (p/v) acrylamide/bisacrylamide solutions (29.2 g of acrylamide and 0.8 g of N-methyl-bisacrylamide), 1X resolution buffer (0.375 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Tris-HCl, pH 8.8, 0.1% (p/v) SDS), 60 \u0026micro;L of 10% (v/v) ammonium persulfate and 6 \u0026micro;L of N,N,N',N'Tetramethylethylenediamine (TEMED). Polyacrylamide gel in the HOEFER SE600 Ruby vertical electrophoresis system (AmershamBioscience). The protein \u003cem\u003espots\u003c/em\u003e were visualized by impregnation with 0.08% Coomassie Brilliant Blue dye (Neuhoff et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The gels were left for 1 hour in fixation buffer (40% (v/v) ethanol and 10% (v/v) acetic acid) and 5 days in colloidal Coomassie blue dye (8% (v/v) ammonium sulphate, 0.08% (v/v) phosphoric acid, 0.08% (v/v) Coomassie blue G-250 and 20% (v/v) methanol). Afterwards, the gels were kept in distilled water under gentle agitation until the dye was removed. Gels were prepared in triplicates for each pool of samples from the different treatments and genotypes.\u003c/p\u003e\n\u003ch3\u003eAnalysis of 2D images\u003c/h3\u003e\n\u003cp\u003eThe gel images were scanned with LabScanner (AmershamBioscience) and analyzed for the identification and relative quantification of the spots using ImageMaster 2D Platinum 7.0 (GE Healthcare) taking into account the area and intensity of the spots. The control samples were compared with the water deficit samples of the respective genotypes, PMGB043 and PMGB099.\u003c/p\u003e\u003cp\u003eFor each treatment, a reference gel was established for the triplicate. The program detected the spots unique to each treatment and the relative accumulation of the proteins presented in the spots for each treatment. The differential analysis was based on the ANOVA calculation one-way. The calculation was made for both treatments, Control x PMGB043 and Control x PMGB099. The proteins considered differential were those with spots with p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and Fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5.\u003c/p\u003e\n\u003ch3\u003ePreparation of spots for mass spectrometry (LC/MS-MS)\u003c/h3\u003e\n\u003cp\u003eThe differential and exclusive spots were excised from the gel using a scalpel, cut into smaller pieces and placed in microtubes. They were then destained in 200 \u0026micro;L NH\u003csub\u003e4\u003c/sub\u003eHCO\u003csub\u003e3\u003c/sub\u003e containing 50% acetonitrile and the supernatant discarded. The gel fragments were dehydrated in 100 \u0026micro;L of 100% (v/v) acetonitrile for 5 min and vacuum dried in the Concentrator 5301 (Eppendorf) for 10 min. 4 \u0026micro;L of trypsin Gold (Promega) 25 ng \u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were added and kept at 4 \u0026ordm;C for 10 min to absorb the solution into the gel fragments. Subsequently, NH\u003csub\u003e4\u003c/sub\u003eHCO\u003csub\u003e3\u003c/sub\u003e was added until the fragments were covered and left at 37\u0026ordm;C for 16 hours for the trypsin to act. The supernatant was collected and transferred to a new tube. The peptides were recovered from the gel fragments by two elutions with 50 \u0026micro;L of 50% (v/v) acetonitrile containing 0.1% (v/v) formic acid, shaken for 15 min in a vortex with each wash. The samples were then concentrated under vacuum to a volume of between 10 and 15 \u0026micro;L.\u003c/p\u003e\n\u003ch3\u003eIdentification of spots by mass spectrometry (LC/MS/MS)\u003c/h3\u003e\n\u003cp\u003eThe spots were analyzed at the Center for Biotechnology and Genetics (CBG) of the State University of Santa Cruz (UESC) and at the National Center for Research in Energy and Materials (CNPEM) in Campinas, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\u003cp\u003eIn the CBG, the peptides were analyzed by online nano flow liquid chromatography tandem mass spectrometry (LC-MS/MS) on a nanoAcquity chromatograph (Waters, Milford, MA) coupled to a Q-Tof micro mass spectrometer (Waters).\u003c/p\u003e\u003cp\u003eAt CNPEM-SP, the peptides were separated by hydrophobicity gradient on a C18 column (100 \u0026micro;m x 100 mm) (Waters) using a nano Acquity Ultra Performance LC chromatograph (Waters) coupled to a nanospray ESI interface and a Q-Tof Premier mass spectrometer (Waters). The peptides were injected in a volume of 4.5 \u0026micro;L and first passed through a Symmetry C18 trapping column (180 \u0026micro;m x 20 mm) for desalting at a flow rate of 5\u0026micro;Lmin\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 2 minutes. The peptides were then loaded onto the analytical column and eluted in a gradient of 2\u0026ndash;90% (v/v) acetonitrile containing 0.1% (v/v) formic acid for 10 minutes at a flow rate of 0.6 \u0026micro;L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The voltage for the nano electrospray was 3.5 kV for the 30 V cone and the source temperature, 80 \u0026ordm;C. The instrument was operated in DDA (Data Dependent Analysis) mode in order to acquire and fragment (MS/MS) the three most intense peaks of each MS spectrum (top three mode). After fragmentation by MS/MS the ion was placed on an exclusion list for 20 seconds. The spectra were acquired using the MassLynx v.4.1 software and the raw files were converted into a peak list in mgf (mascotgeneric file) format using the MascotDistiller v.2.3.2.0, 2009 program (Matrix Science Ldt.).\u003c/p\u003e\u003ch2\u003eIdentification and functional categorization of proteins\u003c/h2\u003e\u003cp\u003eThe Mascot Server v.2.3.01.0 program (Matrix Science Ltda.) was used to identify the proteins, allowing one missed cleavage by trypsin, fixed carbamidomethylation modification, variable methionine oxidation modification and 0.1 Da mass tolerance for MS and 0.1 Da mass tolerance for MSMS, as parameters.\u003c/p\u003e\u003cp\u003eSearches were conducted against the UniProtKB Musa acuminata proteome (taxonomy ID: 4641), accessed in 2019, which contained approximately 35,000 protein sequences at that time. Although database versions are periodically updated, the use of the 2019 dataset ensures consistency with the original LC-MS/MS analysis.\u003c/p\u003e\u003cp\u003eThe FASTA sequences of the identified proteins were obtained from the access numbers resulting from the MASCOT software search (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.matrixscience.com/\u003c/span\u003e\u003cspan address=\"http://www.matrixscience.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These sequences were submitted to functional annotation, where their ontologies and biological functions were performed using the \u003cem\u003eMusa acuminata\u003c/em\u003e dataset available in the UniProt knowledge base (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.uniprot.org\u003c/span\u003e\u003cspan address=\"http://www.uniprot.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and BLAST2Go (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.blast2go.com\u003c/span\u003e\u003cspan address=\"http://www.blast2go.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The proteins were categorized by Biological Process (BP), Molecular Function (MF) and Cellular Component (CC).\u003c/p\u003e\n\u003ch3\u003eDifferential protein analysis\u003c/h3\u003e\n\u003cp\u003eThe total spots detected using ImageMaster, considering their intensities and normalization (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05) for the PMGB043 and PMGB099 genotypes in the control and water deficit conditions, were plotted graphically in VolcanoPlot using the Rstudio statistical environment. To visualize the identified and significant proteins, proteins with a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and Fold Change\u0026thinsp;\u0026ge;\u0026thinsp;1.5, were considered; this was corrected to the logarithmic scale of log\u003csub\u003e2\u003c/sub\u003e FC\u0026thinsp;\u0026gt;\u0026thinsp;0.6. The Fold Change corresponds to the number of times protein expression changed from the control condition to the water deficit condition. Therefore, this was obtained from the difference in intensity of the treatment in comparison to the control condition, then corrected to the log\u003csub\u003e2\u003c/sub\u003e FC scale. The identified and differentially accumulated proteins set was submitted to the Venn Diagram via \u0026lt;\u0026thinsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/%3E\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eWestern blot\u003c/h3\u003e\n\u003cp\u003eApproximately 0.6 g of roots were macerated in mortars with liquid nitrogen in the presence of polyvinylpolypyrrolidone (PVPP) at 0.07 g per g of tissue and the proteins extracted according to the phenolic extraction method (Bertolde et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pirovani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). After quantifying the protein extract using the 2D Quant Kit (GeHealthCare), 20 \u0026micro;g of each sample were separated on a mini SDS-PAGE gel (12.5% ​​(v/v) acrylamide). Analysis of protein accumulation followed the method of Sambrook and Russell (1989). The membranes were probed individually by one hour of incubation with the polyclonal primary antibodies (Agrisera AB) against Catalase (EC 1.11.1.6; 57/55 kDa) and ADH (EC 1.1.1.1; 42 kDa), both in a ratio of 1:2000. The membrane was washed three times with TBS-T buffer, and under agitation the membranes were incubated for 60 min with the secondary antibody Rabbit Anti-IgG Alkaline phosphatase conjugated (AP, ZIMED Laboratories Inc. San Francisco - CA/USA), diluted at a concentration of 1:10.000. 5-Bromo-4-chloro-3-indolyl phosphate (BCIP) and pnitrotetrazolium (NBT; Promega, USA) were used as substrates for the colorimetric reaction of alkaline phosphatase activity, for viewing images on membranes. Quantification of the bands from triplicate assays was carried out using the GelQuantNET V 1.7.8 software and the results normalized based on a gel stained with colloidal comassie blue G 250 0.08% (p/v) (Neuhoff et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eProtein-protein interaction (PPI) network analysis\u003c/h2\u003e\u003cp\u003eProtein-protein interaction (PPI) networks were conducted consideringdifferentially expressed with a Fold Change value\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005, using the \u003cem\u003eString\u003c/em\u003e protein bank (version 11.0). All the software analyses were conducted against the proteins of \u003cem\u003eMusa acuminata\u003c/em\u003e (AA genome). PPI information was obtained by using different prediction methods in the software, such as neighborhood, experiments, co-expression, gene fusion, databases and co-occurrence. Interactions were visualized with a medium confidence cut-off (0.400) using \u003cem\u003eMusa acuminata\u003c/em\u003e (AA genome) as the standard organism.\u003c/p\u003e\u003cp\u003eThe networks obtained from the String database were superimposed to obtain a consensus network. This network was used to analyze clusters with the fastgreedy community function in Igraph of the R statistical environment. The biological processes associated with the clusters generated in R were analyzed using the Cytoscape 3.8.2 plugin BiNGO (Biological Network Gene Ontology) version 1.4 with multiple tests associated with the FDR algorithm with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eThe centrality analysis of the proteins in the PMG043 and PMGB099 networks was calculated using the Igraph package's betweenness function. Nodes with values above the mediation average were considered to be betweenness. Nodes above the degree average were considered hubs, and nodes above and below the average of both centralities were considered hub-bottlenecks and common, respectively.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cem\u003eProtein profile of Musa acuminata roots on 1D and 2D PAGE\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe protein profile in SDS-PAGE 12.5% shows bands with molecular masses distributed between 14 and ~150 kDa from different extraction processes (Supplementary Figure 2). Figure 1 shows the efficiency and quality of \u003cem\u003eM. acuminata \u003c/em\u003eroot protein extraction using the Bertolde et al. (2014) method with modifications, where the bands are better distributed, delimited, and with less drag. \u003c/p\u003e\n\u003cp\u003eThe protein profile of the diploid genotypes PMGB043 and PMGB099 under control and severe stress conditions is visualized in the 2-DE gels (Figure 2). The proteomic map analysis of the PMGB043 genotype detected 260 and 188 spots under Ctrl and SWD conditions, respectively. For the PMGB099 genotype, 373 and 426 spotswere detected in the Control and SWD treatments, respectively. The spots are well-focused, with no horizontal or vertical drag, even for the most abundant proteins.\u003c/p\u003e\n\u003cp\u003eA total of 1246 spots were detected (ANOVA p \u0026lt; 0.05) in the \u003cem\u003eM. acuminata \u003c/em\u003egenotypes (Figure 3). When comparing the intensity of the spotson the PMGB043 genotype between the Ctrl and SWD treatments, 120 spotswere detected in common. Also, for PMGB043, the exclusivity of the spots was observed, with 140 spots detected for Ctrl and 67 spots in the SWD treatment (Figure 3A). The PMGB099 genotype in both conditions stood out with a greater number of spots, with 258 spots in common and 115 and 168 exclusive spots inCtrl and SWD, respectively (Figure 3B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e Identified and differential proteins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the total number of proteins identified, 362 were identified and differentially accumulated (Supplementary Table 1 and 2). The PMGB043 and PMGB099 genotypes showed a dynamic protein accumulation profile in the SWD treatment compared to Ctrl. Most of the proteins identified in the PMGB043 genotype under SWD conditions were down accumulated. As for the PMGB099 genotype, most of the proteins showed higher accumulation (Figure 4). \u003c/p\u003e\n\u003cp\u003eThe exclusively accumulated and differentially expressed proteins (ANOVA p \u0026lt; 0.05 and Fold change ≥ 1.5) were categorized, as determined by the Blast2GO software. In the BP category, proteins were related to defense response, energy, metabolism, methylation, signal transduction, translation, transport and hormones (Figure 4).\u003c/p\u003e\n\u003cp\u003eOnly the PMGB043 genotype showed expression of enzymes that regulate indole-3-acetic acid (IAA) levels during SWD treatment. In this genotype, proteins related to transduction and oxidation processes were up accumulated by 57% and 63%, respectively, compared to Ctrl (Figure 4 A). \u003c/p\u003e\n\u003cp\u003eWhen compared with Ctrl, the proteins identified in the PMGB099 genotype under SWD conditions all showed positive accumulation in quantities equal to or greater than 50% (Figure 4 B).\u003c/p\u003e\n\u003cp\u003eThe functional categorization shown in Figure 4 highlights that most of the differentially accumulated proteins in both genotypes are related to metabolic and cellular processes, particularly those associated with energy production, response to oxidative stress, and ion transport. These categories mirror the main physiological adjustments observed in plants under water deficit, such as reallocation of energy resources, activation of antioxidant defense, and osmotic regulation. The enrichment of these functions in PMGB099 supports its ability to sustain metabolic activity and mitigate stress effects more efficiently than PMGB043.\u003c/p\u003e\n\u003cp\u003eThe cellular location of the identified and differential proteins of the evaluated genotypes, was predicted (Figure 5). Most of the proteins are cytoplasmic for both genotypes. However, the PMGB043 genotype showed proteins located in the Golgi complex, apoplast, nucleus, cytoskeleton, extracellular space, plasma membrane, chloroplast, mitochondria and cytoplasm (Figure 5A). Despite being a small group, the predicted proteins in the apoplast and extracellular region are noteworthy because they are predicted regions for defense proteins. Under SWD conditions, the proteins of the PMGB099 genotype were located in the Golgi complex, peroxisomes, extracellular space, chloroplast, mitochondria, nucleus, plasma membrane and cytoplasm (Figure 5B).\u003c/p\u003e\n\u003cp\u003eFigure 6 shows an integrated view of the expression level of differentially accumulated proteins between two genotypes of \u003cem\u003eM. acuminata\u003c/em\u003e. The dots above the horizontal dashed line (Threshold) represent proteins with significantly different abundances (p \u0026lt;0.05). The blue dots represent proteins with down-accumulation and the red dots, up-accumulation of proteins. The Venn diagram shows the proteins identified, 118 showed with down- accumulation in the PMB043 genotype, 58 in the PMGB099 genotype and 1 protein in both (left). The venn diagram shows proteins with up-accumulation, being 84 in the PMGB043 genotype, 73 in the PMBG099 genotype and 12 in both (right).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProtein interaction - PPI network\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePPI networks were formed between induced and repressed differentially expressed proteins for each genotype (Figures 7 and 8). The proteins showed direct or indirect interaction through the number of nodes observed.\u003c/p\u003e\n\u003cp\u003eThe PPI network of the PMGB043 genotype resulted in nine clusters with enriched functions associated with metabolism, defense response, oxidative stress, and transduction during water stress (Figure 7). In addition, it is possible to observe proteins characterized as degree (hub), those with means ≥ 63.5, and betweenness (bottleneck) with means≥ 237.36.\u003c/p\u003e\n\u003cp\u003eThe interaction network of the PMGB099 genotype is represented by four clusters with the following enriched biological processes related to water stress: metabolic processes, response to water stress, signaling, root elongation, antioxidant enzymes, among others (Figure 8). Degree and betweenness proteins were also identified with averages ≥ 120 and ≥ 175.39, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImmunodetection of proteins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCAT accumulation in the PMGB043 genotype was similar in the Ctrl and SWD treatments and in the PMGB099 genotype there was a reduction in accumulation in the SWD treatments compared to Ctrl (Figure 9A and 9B). The immunoblotting of ADH shows a higher accumulation of this enzyme in the genotypes in control condition compared to the water deficit treatments in both genotypes (Figure 9A and 9B).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eOptimizing protein extraction as a foundation for root proteome analysis in Musa spp\u003c/h2\u003e\u003cp\u003eThe analysis of protein profiles by electrophoresis in two-dimensional (2D) gels requires high-quality protein extracts, free of contaminants such as polysaccharides, lipids and phenolic compounds, which can interfere with the separation and detection of proteins. In order to guarantee accurate results, it is essential to use an efficient protein extraction process, especially for samples from plant roots, whose chemical characteristics make purification more complex.\u003c/p\u003e\u003cp\u003eAlthough there are protocols for extracting protein from banana roots comparisons between different methods result in low quality gels full of vertical striations and without proof (marking) of the number of spots detected (Vaganan et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, \u003cem\u003eMusa\u003c/em\u003e spp. species can vary in the amounts and types of secondary compounds, polysaccharides and other plant polymers that interfere with the quality of the gels used for proteomic analysis (Surabhi, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditional washes with acetone during the process of extracting proteins from banana roots proved to be highly effective in removing unwanted contaminants that could compromise proteomic analysis (Supplementary Fig.\u0026nbsp;2). This extra purification step significantly improved the quality of the protein extracts, ensuring greater efficiency in the separation and identification of proteins during electrophoresis in two-dimensional gels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, the optimization of the protein extraction protocol used in this study proved essential for obtaining high-quality and reproducible 2D gels from Musa spp. roots grown under hydroponic conditions. This improvement minimized interference from secondary metabolites and ensured efficient downstream identification of drought-responsive proteins.\u003c/p\u003e\u003cp\u003eExtraction tests using the protocols of Bertolde et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Pirovani et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) revealed the need to increase the number of washes during the protein extraction process from banana roots grown in an adapted hydroponic system (Supplementary Fig.\u0026nbsp;1). The proteomic profiles observed here reflect the physiological differences previously described for both genotypes, with PMGB099 maintaining metabolic activity and PMGB043 showing stronger signs of energy limitation and oxidative stress under water deficit.\u003c/p\u003e\u003cp\u003e\u003cem\u003eWater deficit induces the expression of differential proteins in contrasting genotypes of M. acuminata.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn our work, eleven isoforms of sucrose synthase (SUS) were positively accumulated in the PMGB043 genotype and two in the PMGB099 genotype (Supplementary Tables\u0026nbsp;1 and 2). SUS is related to a series of metabolic processes, such as sucrose distribution in tissues, starch and cellulose synthesis, cell wall formation, response to abiotic stresses and nitrogen fixation (Ciereszko \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, in plants grown \u003cem\u003ein vitro\u003c/em\u003e, it has been reported that the processes of sugar absorption and metabolization drive respiration, resulting in limiting oxygen levels (Carpentier et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHeat shock proteins (HSPs) were up-accumulated in the PMGB099 genotype (spot 70 \u003cem\u003e-\u003c/em\u003e Supplementary Table\u0026nbsp;2). Studies indicate that up-regulation of this molecular chaperone triggers water stress response mechanisms in \u003cem\u003eMusa\u003c/em\u003e sp (Carpentier et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mattos-Moreira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The HSP/chaperone system under normal growth conditions or during and after stress determines the fate of denatured or non-native proteins. Thus, they are responsible for the folding, assembly, translocation and degradation of proteins in many normal cellular processes, stabilize proteins and membranes, and can assist in the refolding of proteins under stress conditions (Wang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe enzymes pyruvate decarboxylase and pyruvate kinase (spots 451 and 31 Supplementary Tables\u0026nbsp;1 and 2) were positively regulated in the roots of genotypes PMGB043 and PMGB099. Although the genotypes did not show visual characteristics of hypoxia (Supplementary Fig.\u0026nbsp;3), the fermentative pathway may have been used to meet metabolic demands in the hydroponic system (Santos et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study of the meristematic tissue of the Cachaco variety (\u003cem\u003eMusa paradisiaca\u003c/em\u003e ABB), which is tolerant to environmental stresses, shows that the energetic state of the tissue affects the induction of fermentative enzymes under low oxygen. In addition, mechanisms to adjust oxygen consumption are linked to the availability of pyruvate (Carpentier et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thus, even under aerobic conditions, alcoholic fermentation plays an important role in preventing anoxia by controlling the level of pyruvate, as occurred in \u003cem\u003eArabidopsis\u003c/em\u003e knockout mutants for alcohol dehydrogenase grown in a hydroponic system (Zabalza et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe genotypes studied showed positive regulation of the alcohol dehydrogenase enzyme (spots 17, 126, PMGB043; 300, 456, PMGB099) in plants from the control treatment. In \u003cem\u003eArabidopsis\u003c/em\u003e subjected to environmental stress, positively accumulated alcohol dehydrogenase has been linked to improved acclimatization and is a candidate protein marker for tolerant varieties under adverse conditions (Dolferus, et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, several studies have been focused on identifying genes related to the response of banana plants to water deficit highlighting important gene families such as aquaporins, which are responsible for transporting water and maintaining osmotic balance (Xu et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sreedharan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, genes such as \u003cem\u003eMaSWEETs\u003c/em\u003e, involved in sugar transport, play a central role not only in energy production, but also in stress signaling and cellular water balance (Farooq et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Miao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Other genes include those encoding superoxide dismutases (\u003cem\u003eMaSODs\u003c/em\u003e) which eliminate ROS and participate in hormonal responses, and transcription factors such as \u003cem\u003eMusaNAC68\u003c/em\u003e and \u003cem\u003eMusaDHN-1\u003c/em\u003e, which regulate the expression of defense and resistance genes (Feng et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Negi et al., 2015; Shekhawat et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Genes encoding transcription factors have also been characterized such as \u003cem\u003eMusaNAC68\u003c/em\u003e (Negi et al. 2015) and \u003cem\u003eMusaNAC042\u003c/em\u003e (Tak et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Of the NAC proteins, \u003cem\u003eMusaDHN-1\u003c/em\u003e (Shekhawat et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which expresses specific hydrophilic proteins known as late embryogenesis abundant (LEA) and the \u003cem\u003eMaHsfs\u003c/em\u003e gene (Wei et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) which regulates the expression of heat shock proteins. Transcription factors such as these are proteins that regulate the expression of plant defense and resistance genes, so they are potential targets for gene manipulation (Sreedharan et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shekhawat et al. 2013).\u003c/p\u003e\u003cp\u003ePlants subjected to water stress exhibited defense responses associated with differential protein expression, dependent on genotype. Genotype PMG043 exhibited greater accumulation of differential proteins, presenting higher amounts of proteins identified in the soil water deficit treatment, when compared with genotype PMG099 under the same conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eProtein modulation as a defense strategy against water deficit\u003c/h2\u003e\u003cp\u003eTwo-thirds of the proteins related to the defense response in the PMGB043 genotype were repressed, while in the PMGB099 genotype, most of the defense enzymes were up-accumulated. Water stress is one of the main abiotic factors that can impact banana cultivation. In plants deprived of oxygen due to waterlogging (Blokhina et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) or under water deficit (Rukundo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), oxidative stress is an expected response. Thus, the production of antioxidant enzymes, such a CAT, GPX and SOD, can limit the cellular damage caused by ROS during stress in \u003cem\u003eMusa\u003c/em\u003e spp. (Surendar, et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCysteine synthase was down-accumulated in the susceptible genotype PMGB043 (spots 224, 225, 220; Supplementary Table\u0026nbsp;1) and up-accumulated in the tolerant genotype PMGB99 (spots 374, 445; Supplementary Table\u0026nbsp;2). Cysteine plays a fundamental role in the primary and secondary metabolism of plants. In addition, it seems to behave as a signaling molecule in the cytosol triggering immune responses, and in the mitochondria, acting in the detoxification of cyanide; essential for the development of root hairs and plant responses to pathogens (Romero et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A study with \u003cem\u003eMusa\u003c/em\u003e spp. varieties indicates that this protein is related to ROS detoxification in plants tolerant to water stress (Vanhove et al \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFour isoforms of H\u003csup\u003e+\u003c/sup\u003e -ATPase typeV (V-ATPase) were down-accumulated during stress in the PMGB043 genotype and one in the PMGB099 genotype (Supplementary Tables\u0026nbsp;1 and 2). The V-ATPase carries out ATP-dependent proton transport from the cytosol to the lumen of the endomembrane compartments, contributing to cytosolic pH homeostasis (Seidel, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These proteins are related to plant responses to environmental stress and are fundamental for maintaining homeostasis (Magnotta-Gogarten, 2002). Among the known functions of V-ATPase are the separation of membrane transport proteins, exocytosis, endocytic digestion, recycling of neurotransmitters and energization of membrane transport systems and epithelia (Beyenbach-Wieczorek, 2006). By regulating proton gradients and vacuolar ion transport, V-ATPases help maintain cytosolic pH and osmotic balance. The up-regulation of this enzyme in the tolerant genotype indicates an integrated response that stabilizes cellular homeostasis and water retention under drought stress.\u003c/p\u003e\u003cp\u003eThe identification of the enzyme indolyl-3-acetic acid amidohydrolase (ILR1) exclusively in the PMGB043 genotype suggests that this genotype may be involved in specific mechanisms regulating auxin homeostasis. Auxins, essential hormones for plant growth and development, are controlled by a complex regulatory network in which the ILR1 conjugate may act as a key point. Its function in modulating indolyl-3-acetic acid (IAA) levels, both inactivation and temporary storage, can directly affect processes such as root elongation playing a crucial role in the response to water stress in \u003cem\u003eMusa\u003c/em\u003e spp. In \u003cem\u003eArabidopsis\u003c/em\u003e, it has been reported that some indolyl-3-acetic acid (IAA) conjugates can be used to determine the tissue and subcellular localization of attached IAA, inhibiting root elongation (Leclere et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eProtein Interaction Networks (PPI) reveal potential proteins for plant breeding\u003c/h2\u003e\u003cp\u003eThe PPI networks whose confidence score was 0.400, represent the proteins identified and grouped, whether up or down accumulated in the different clusters for the PMGB043 and PMGB099 genotypes and reveals a global view of the main biological processes involved in the plant's response to water deficit. In addition, they make it possible to select groups of highly interacting proteins that participate in the cellular machinery with a view to future studies on banana plant breeding.\u003c/p\u003e\u003cp\u003eThe interactions of the proteins in the PPI networks (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) are related to the functional modules and protein complexes described by Wang et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Spirin and Mirny (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Functional modules are groups of proteins in which interactions occur at a different place or time, such as signaling proteins and metabolic pathways while protein complexes participate in molecular machineries that occur at the same place. Building PPI networks is still a challenge for non-model plants, especially when it comes to proteomic data (Santos et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe biological systems most affected in the PMGB043 genotype (susceptible) are primary metabolism and oxidative phosphorylation, which is evident in Cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), corroborating the amount of proteins that were identified in table 1, which mostly presented negative regulation. A larger amount of proteins is observed in this Cluster (102 proteins). Primary metabolism is related to a number of essential plant functions, such as photosynthesis, respiration and the transport of solutes. It seems that under water deficit there is a reduction in plant biomass, both in the roots and in the aerial part and an increase in the production of primary metabolites to tolerate and/or protect the tissues from damage, increased production of intracellular oxidants and subsequently lipid peroxidation, under stress (Mundim; Pringle \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Increased ROS production promotes oxidative stress, resulting from the accumulation of oxidants at the cellular level. As a defense response, plants produce secondary metabolites that help prevent or alleviate these changes. These regulators act to protect and regulate cellular homeostasis (Thukkaram et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the PMGB099 genotype (tolerant), proteins related to primary and secondary metabolism were identified, both protein expressions had greater accumulation in the roots of plants, which were subjected to water stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Cluster D \u0026minus;\u0026thinsp;452 proteins). According to Lavinsky et al. (2015), plants under water stress are able to allocate biomass to leaves or roots altering the metabolic activity of these organs. Furthermore, plants subjected to abiotic stresses exhibit defense mechanisms, one of which is the production of metabolites responsible for the antioxidant defense system, hormonal signaling, ion transport, among others (Thukkaram et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough secondary metabolites were not directly quantified in this study, the up-accumulation of proteins related to phenolic metabolism and antioxidant defense suggests a possible mobilization of these compounds in response to water deficit. This inference is consistent with previous reports showing that secondary metabolites contribute to drought tolerance in plants. In the tolerant genotype, several proteins involved in phenolic compound biosynthesis and reactive oxygen species (ROS) scavenging were more abundant, reinforcing the role of these pathways in maintaining cellular homeostasis under stress.\u003c/p\u003e\u003cp\u003eThe identification of clusters related to metabolic processes, antioxidant activity, and signal transduction supports the hypothesis that drought tolerance arises from the integration of cellular metabolism and stress signaling. Rather than the action of individual proteins, these networks reveal a systems-level reorganization of root metabolism that maintains cellular homeostasis under water deficit.\u003c/p\u003e\u003cp\u003eThe Western blot analysis showed a higher accumulation of the proteins that were analyzed in the control treatments of both genotypes (PMGB043 - susceptible and PMGB099 - tolerant), contrasting for tolerance to water deficit. Cat is an enzyme that acts in the process of eliminating ROS; products of oxidative stress promoted by biotic or abiotic stress at the cellular level (Hossain et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the present study, the accumulation of the enzyme in the control treatment may suggest a mild adaptive response of banana roots to the hydroponic environment, although not associated with actual hypoxia, since the solution was aerated throughout the experiment. they were subjected to during the experiment. Subsequently, the plants that were subjected to severe water stress also showed a greater accumulation of the enzyme, when compared to the moderate water deficiency treatment, in both genotypes evaluated. It is suggested that the hydroponic control solution may have caused some level of stress in the plants. This is corroborated by the activation of the fermentation pathway and the upregulation of stress-related enzymes.\u003c/p\u003e\u003cp\u003eWhen analyzing the accumulation of alcohol dehydrogenase in the roots of banana plants subjected to the treatments, the control treatments of both genotypes (PMGB043 and PMGB099) showed higher concentrations of the protein compared to the treatments under severe water deficiency demonstrating that the banana plants subjected to the hydroponic treatments showed a response pattern similar to those observed in waterlogging-tolerant plants, although no hypoxic conditions were imposed in this experiment. Alcohol dehydrogenase (ADH) is directly related to the defense response and survival of plants in anaerobic environments (Shen et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, the proteomic and PPI analyses revealed that the proteins modulated under water deficit in both genotypes were mainly associated with metabolism, defense response, energy production, transport, and signal transduction.\u003c/p\u003e\u003cp\u003eIn the tolerant genotype (PMGB099), up-regulated clusters were enriched in oxidative stress response, primary and secondary metabolism, and root elongation processes, indicating a coordinated activation of energy balance and defense mechanisms. In contrast, in the susceptible genotype (PMGB043), most proteins involved in primary metabolism and oxidative phosphorylation were down-accumulated, suggesting an impaired energy homeostasis under stress.\u003c/p\u003e\u003cp\u003eThese integrated responses reflect how drought tolerance in Musa spp. results from the concerted modulation of functional protein groups rather than isolated proteins. Together, the functional groups highlighted in this study reveal that drought tolerance in Musa spp. arises from the coordinated regulation of energy metabolism, redox homeostasis, and vacuolar transport, enabling sustained physiological activity under stress. These processes appear to work together to maintain cellular stability and mitigate oxidative and osmotic damage, which are key determinants of drought resilience in banana roots.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis pioneering proteomic study of Musa spp. roots under water deficit provides the first comprehensive evidence of how contrasting diploid genotypes respond at the protein level to drought stress. The tolerant genotype, PMGB099, exhibited a coordinated up-regulation of proteins associated with energy metabolism, antioxidant defense, ion transport, and signal transduction, whereas the susceptible genotype, PMGB043, showed a reduction in proteins related to primary metabolism and cellular homeostasis. These findings reveal that drought tolerance in Musa spp. roots results from the integration of metabolic, redox, and signaling pathways that together sustain physiological activity under water limitation.\u003c/p\u003e\u003cp\u003eThe differential accumulation of key proteins involved in energy balance, oxidative stress mitigation, and vacuolar regulation highlights potential molecular markers for identifying and selecting drought-tolerant banana genotypes. These results establish a molecular framework for understanding root adaptation mechanisms in Musa spp., offering valuable insights for breeding programs and biotechnological strategies aimed at developing cultivars resilient to water scarcity and promoting the sustainability of banana production in drought-affected regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eASS and CPP, conceived and designed the research. ASS, performed the research. ASS, ASS, and NAS analyzed the proteomic data and ASS and MACF analyzed the physiological data. ASS wrote the original manuscript. EPA provided the banana plants. CFF, CPP, EPA revised the manuscript and CPP acquired funding and supervised the work. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the coordination of Improvement of Higher Education Personnel (CAPES) –PhD scholarship to ASS; FINEP (433/2016) for financing studies and projects, Conselho Nacional de Desenvolvimento Científico e Tecnologia (Process: 303765/2019-4) and Embrapa Mandioca e Fruticultura for the infrastructure and technical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmbrapa Mandioca e Fruticultura for the infrastructure for developing the research; the coordination of the Improvement of Higher Education Personnel and the Bahia State Research Support Foundation for granting the scholarships.\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflics of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBertolde, F. Z., Almeida, A. A. F., Silva, F. A. C., Oliveira, T. M., \u0026amp; Pirovani, C. P. (2014). Efficient method of protein extraction from Theobroma cacao L. roots for two-dimensional gel electrophoresis and mass spectrometry analyses. Genet Mol Res, 13(3), 5036-5047. http://dx.doi.org/10.4238/2014.July.4.19\u003c/li\u003e\n \u003cli\u003eBeyenbach, K. W., \u0026amp; Wieczorek, H. (2006). The V-type H+ ATPase: molecular structure and function, physiological roles and regulation. Journal of Experimental Biology, 209(4), 577-589. doi: 10.1242/jeb.02014\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBlokhina, O. B., Chirkova, T. V., \u0026amp; Fagerstedt, K. V. (2001). 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BMC genomics, 23(1), 524. https://doi.org/10.1186/s12864-022-08733-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-physiologiae-plantarum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acpp","sideBox":"Learn more about [Acta Physiologiae Plantarum](http://link.springer.com/journal/11738)","snPcode":"11738","submissionUrl":"https://www.editorialmanager.com/acpp/default2.aspx","title":"Acta Physiologiae Plantarum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Drought tolerance, Root proteomics, Bananas, Hydroponic system, Mass spectrometry","lastPublishedDoi":"10.21203/rs.3.rs-6296060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6296060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater deficit is one of the main abiotic stresses affecting banana cultivation compromising the productivity and sustainability of plantations. The response of plants to water restriction involves complex metabolic regulation and molecular networks that are fundamental for defense and adaptation. In our work, proteomic analysis was conducted on the roots of \u003cem\u003eMusa\u003c/em\u003e spp. diploids aiming to identify differentially expressed proteins associated with tolerance to water deficiency and investigate their functional interactions. The contrasting genotypes regarding water deficit tolerance, PMGB043 (susceptible), and PMGB099 (tolerant), were selected based on physiological parameters. Protein analysis by 2D-SDS-PAGE revealed 260 and 188 spots in the PMGB043 genotype, under control and severe stress conditions, respectively, while in the PMGB099 genotype, 373 spots were detected under control and 426 under severe water stress conditions. Water deficit modulated the proteins expression in crucial processes such as transport, oxidation, methylation, energy metabolism, and defense responses suggesting different adaptation mechanisms between the genotypes. This is the first proteomic study to analyze the impact of water restriction on banana roots grown in a hydroponic system providing an in-depth insight into the molecular basis of water deficit tolerance in \u003cem\u003eMusa\u003c/em\u003e spp. The results offer new perspectives to direct strategies for gene editing and precision breeding in developing banana cultivars that are more tolerant to water deficit.\u003c/p\u003e","manuscriptTitle":"Modulation of proteome profile of banana (Musa spp.) under water deficit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 12:52:00","doi":"10.21203/rs.3.rs-6296060/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-04T12:06:28+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T07:54:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-25T12:42:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Physiologiae Plantarum","date":"2025-10-22T08:21:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-physiologiae-plantarum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acpp","sideBox":"Learn more about [Acta Physiologiae Plantarum](http://link.springer.com/journal/11738)","snPcode":"11738","submissionUrl":"https://www.editorialmanager.com/acpp/default2.aspx","title":"Acta Physiologiae Plantarum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"27794ded-ee8b-49fc-8b11-26fa31df007a","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T17:45:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 12:52:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6296060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6296060","identity":"rs-6296060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}