Identification of candidate genes and proteins for tasseling stage drought tolerance through integrated transcriptomic and proteomic analysis approach in maize

Identification of candidate genes and proteins for tasseling stage drought tolerance through integrated transcriptomic and proteomic analysis approach in maize | 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 Identification of candidate genes and proteins for tasseling stage drought tolerance through integrated transcriptomic and proteomic analysis approach in maize Songtao Liu, Hanbo Shi, Linan Yan, Chao Jiang, Haichao Zhao, Haibo Lu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6492029/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted 10 You are reading this latest preprint version Abstract Drought stress particularly at the tasseling stage is the most devastating abiotic factor and major contributor to yield reduction in maize ( Zea mays L.). Despite recent scientific evolution in deciphering maize drought stress responses, the overall picture of key genes and proteins regulating maize tasseling drought tolerant is not understood. In this study, we conducted comparative physiological, transcriptome and proteomic analyses approach to monitor the changes in the leaf tissues of two contrasting hybrid cultivars exposed to drought stress at the tasseling stage. We identified 1701 differentially expressed genes (DEGs) in RNA-sequence runs and 424 differentially expressed proteins (DEPs) from an iTRAQ-based analysis. Mapman analysis revealed that several regulatory processes were influenced by drought conditions, especially signal transduction, cell-wall remodelling, cellular redox homeostasis, and hormone metabolism were observed in both mRNA- and protein-level. However, transcription factor regulation and secondary metabolism were specifically identified at the transcript level, whereas photosynthesis was uniquely identified to be affected by drought stress at the protein level. Meanwhile, a weak correlation between DEGs and DEPs was observed, indicating the drought response of maize at tasseling stage is largely regulated post-transcriptionally. Furthermore, comparative physiological analysis and qRT-PCR results substantiated the trancriptomic and proteomic findings. Additionally, we screened ZmPOD , ZmRAV1 , ZmTPP and performed phenotypical and physiological characterizations of transgenic Arabidopsis lines and wild-type. Resultantly, the transgenic Arabidopsis lines exhibited stronger tolerance to drought than the WT. This functional verification reinforces the reliability of our omics-based candidate gene selection. Overall, our research provides an elaborate understanding of drought-responsive genes and pathways mediating maize drought tolerance at the tasseling stage. transcriptome proteome physiological responses drought stress Zea may L. tasseling stage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Climate change is having a detrimental impact on agricultural production, particularly in low-latitude arid and semi-arid regions, which are predominantly home to developing countries (Pereira, 2016). Key climate-related challenges, such as erratic rainfall patterns, rising atmospheric temperatures, and increasing carbon dioxide concentrations, contribute to severe droughts in these vulnerable environments (Wheeler & von Braun, 2013, Huang et al., 2023). Among all abiotic stresses, drought exerts the most profound and devastating effects on crop growth and productivity in agricultural systems (Walter et al., 2011; Danilevskaya et al., 2019). With global climate change continuing to escalate, the frequency and intensity of droughts are projected to rise, posing significant threats to future agricultural sustainability (Edmeades, 2013; Wang et al.,2023). Alarmingly, global food demand is expected to nearly double by 2050, necessitating historically unprecedented annual increases in crop production (IPCC, 2014). In response to these challenges, while modifications in crop production and farm management practices are essential, the development of climate-resilient crop varieties has been widely recognized as the most cost-effective and sustainable strategy for mitigating climate-induced environmental stresses, including drought (Acevedo et al., 2020). Consequently, there is an urgent need to unravel the intricate molecular mechanisms governing drought stress responses to inform crop genetic improvement efforts aimed at enhancing drought tolerance. Maize ( Zea mays L.) is a globally significant crop, serving as a primary source of food, biofuel, and livestock feed (Song et al., 2015; Jiang et al., 2024). Beyond its agricultural and economic importance, maize also plays a crucial role in industrial applications and is widely used as a model organism in plant research (Aslam et al., 2015). Among cereal crops, maize ranks as one of the top three most essential staples worldwide, alongside rice ( Oryza sativa L.) and wheat ( Triticum aestivum L.) (Opitz et al., 2016). However, maize production is highly susceptible to drought stress, posing a serious threat in regions prone to water scarcity, particularly in arid and semi-arid zones such as northern China (Min et al., 2016). The crop's vulnerability is most pronounced from the pre-flowering stage to the grain-filling phase, with the tasseling period being the most water-intensive and critical for grain yield formation (Zheng et al., 2010). Consequently, drought stress during this stage can significantly reduce yield, impacting overall productivity. To sustain stable crop production in the face of climate change, understanding plant responses to water deficit conditions is essential (Virlouvet et al., 2018; Hrabayashi et al., 2023). Plants employ a range of physiological, biochemical, and molecular mechanisms to adapt to drought stress (Pandey & Shukla, 2015). Drought-tolerant maize genotypes typically maintain higher relative water content (RWC) under water-limited conditions compared to drought-sensitive varieties (Anupama et al., 2019). Proline, a key osmoprotectant, functions as a biochemical marker for drought tolerance by helping plants retain cellular moisture under water-deficit conditions (Ashraf & Foolad, 2007). Additionally, plants mitigate oxidative stress caused by reactive oxygen species (ROS) through antioxidant defense systems, which involve key enzymes such as catalase (CAT), superoxide dismutase (SOD), and peroxidases (Mahajan et al., 2005; Nuccio et al., 2015). At the molecular level, drought responses in maize are governed by a complex network of genes that regulate stress perception, signal transduction, and gene expression (Sharp et al., 2004; Zhu, 2016). Various transcription factors (TFs), kinases, and phytohormones play critical roles in orchestrating drought tolerance and modulating gene expression under stress conditions (Khan et al., 2019). While extensive research has been conducted to unravel the molecular mechanisms underlying drought resistance (Min et al., 2016; Zheng et al., 2010), the intricate regulatory networks governing drought-responsive genes remain incompletely understood (Khan et al., 2019). Further exploration of these genetic pathways is essential to enhance maize resilience against drought stress and ensure sustainable crop yields under challenging environmental conditions. With the advancement of next-generation sequencing technologies, including genomics, transcriptomics, and proteomics, researchers can now conduct comprehensive and quantitative analyses of gene expression models. RNA sequencing (RNA-Seq), a high-throughput transcriptomic approach, has emerged as a powerful tool for examining dynamic gene expression patterns and elucidating plant-environment interactions (McGettigan, 2012). Similarly, the isobaric tags for relative and absolute quantification (iTRAQ) proteomic technique, known for its high reproducibility and robust quantitative accuracy, has been widely applied to investigate plant protein responses to abiotic stresses (Zhao et al., 2018; Luo et al., 2018; Zenda et al., 2018; Liu et al., 2019). These two approaches have proven instrumental in capturing the dynamic range of gene and protein expression levels under stress conditions. However, despite their potential, research focusing on maize drought response specifically at the tasseling stage remains limited, even though this period is critical for grain yield and highly susceptible to water stress. To gain a better understanding of the complex molecular mechanisms that underline the response of maize to drought stress, in this present study, RNA-seq and iTRAQ were utilized parallel to detect the changes of genes and proteins between water-deficit and well-watered conditions in maize hybrid cultivars at the tasseling stage. The comparative analysis revealed the common and different regulatory mechanisms in response to drought stress between the drought-tolerant genotype Nongdan 476 (ND476) and drought-sensitive genotype Zhongxin 978 (ZX978). Subsequently, the combination analysis of RNA-seq and iTRAQ indicated uniquely and commonly regulatory mechanisms at transcript and post-transcriptional level. Additionally, physiology parameters were measured to perform an overview of comprehensive drought stress acclimation process. Together with the use of quantitative reverse transcription polymerase chain reaction (qRT-PCR) and the function of the ZmPOD , ZmRAV1 , ZmTPP genes in drought response to validate the sequencing results, this data provide resources for future genetic analyses of candidate genes related to drought stress response, in addition to being harnessed for molecular breeding of drought-improved maize varieties. Materials and methods Plant materials and drought stress treatment The two maize hybrid cultivars with contrasting drought tolerance—ND476 (drought-tolerant) and ZX978 (drought-sensitive)—used in this study were provided by the North China Key Laboratory for Crop Germplasm Resources of Education Ministry (Hebei Agricultural University, Baoding, China). ND476 is recognized as a highly drought-resistant hybrid line, whereas ZX978 is classified as a drought-sensitive hybrid, as identified by the Dryland Research Institute of Hebei Academy of Agricultural and Forestry Sciences. The field experiment was conducted in May 2018 at the Qing Yuan Experiment Station of Hebei Agricultural University, Baoding, Hebei Province, China (115.5602790°E, 38.7950930°N, 118 m), under a fully automated rain-proof shelter. A randomized complete block design was employed, with both control and drought stress treatments replicated three times. Each experimental plot covered 25 m² (5 m × 5 m), with a planting arrangement of 60 cm row spacing and 30 cm plant spacing, resulting in 128 plants per plot. Seeds were sown at a depth of 6 cm, two per station, in plots fertilized with 512 kg ha⁻² of compound fertilizer. Following Hsiao (1973), the well-watered control plots were maintained at 70–80% soil water content, whereas the drought-stress plots were maintained at 15–20%. Soil moisture was monitored using a TZS-1 soil moisture measurement instrument (Zhejiang Tuopu Technology Co., Ltd.), with a one-meter-deep waterproof membrane installed between the control and treatment plots to prevent lateral water infiltration. Drought treatment began when the twelfth leaf was fully expanded and continued until tassel emergence. At tasseling, leaf tissues were collected from the flag leaves of three biological replicates under both control and drought stress conditions. All collected samples were immediately frozen in liquid nitrogen and stored at − 80°C for further analysis. Leaf samples for physiological characteristic assays were collected from both control and drought-treated plants at the onset of the treatment, followed by subsequent collections every three days. Leaf RWC was assessed following the method described by Galmés et al. (2007). POD activity was determined using Han’s guaiacol method (Han et al., 2008), while proline content was quantified using the ninhydrin-based assay according to Bates et al. (1973). Lipid peroxidation levels were evaluated by measuring MDA accumulation via the thiobarbituric acid (TBA) reaction, following the protocol established by Dhindsa et al. (1981). Total RNA extraction, cDNA library construction and transcriptome analysis Leaf samples were sent to Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China) for RNA isolation, cDNA library construction, and RNA sequencing. In brief, total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA, USA), and its quality and concentration were assessed using a NanoDrop 1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA) and electrophoresis on 1% agarose gels. cDNA libraries were then constructed following the manufacturer’s protocol using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) and subsequently sequenced on the Illumina HiSeq 4000 platform using paired-end sequencing technology. The raw sequencing data (FASTQ format) generated from the Illumina HiSeq 4000 system underwent initial processing using in-house Perl scripts. Low-quality reads were filtered out, and the remaining clean reads were aligned to the maize reference genome (B73 RefGen_v4) using TopHat 2.0.12, a splice-aware aligner that allows intron-spanning gaps of up to 50 kb (Trapnell et al., 2009). Only reads with a perfect match or a single mismatch were retained for further analysis and genome annotation. Gene expression was quantified by counting mapped read numbers using HTSeq v0.6.1 and normalized as fragments per kilobase of transcript per million mapped reads (FPKM). Differential gene expression analysis was conducted using the DESeq R package (v1.10.1) (Anders & Huber, 2010), with genes exhibiting a fold change (FC) ≥ 2 and a q-value < 0.05 classified as differentially expressed. Expression validation of DEGs with qRT-PCR To generate the cDNA template, 1 µg of total RNA, previously extracted and returned from Shanghai Majorbio Bio-pharm Technology Co., Ltd. was reverse-transcribed in a 20 µL reaction volume using the HiFiscript cDNA Synthesis Kit (CWBIO, Beijing, China), following the manufacturer’s protocol. To validate the sequencing results, 12 genes were selected based on their functional relevance, and specific primers for qRT-PCR analysis were designed using Premier 5 Designer software (Supplementary Table S1 ). Quantitative real-time PCR (qRT-PCR) was performed using 2× Fast Super EvaGreen® qPCR Mastermix (US Everbright Inc., Suzhou, China) on a C1000 Thermal Cycler (CFX96 Real-Time System, Bio-Rad). Each 20 µL qRT-PCR reaction mixture contained 1 µL of cDNA template, 1 µL each of forward and reverse primers (50 pmol), 10 µL qPCR master mix, and 7 µL ddH₂O. The maize GAPDH gene (accession No. X07156), known for its stable and constitutive expression, was used as an internal reference gene. The amplification protocol followed the method described by Zenda et al. (2018). Each sample was analyzed in three biological replicates, and gene expression levels were calculated using the 2 ⁻ΔΔCT method (Livak & Schmittgen, 2011). Protein extraction and isobaric labeling Proteins were extracted from the same leaf samples used for RNA-Seq analysis using the cold acetone precipitation method, as detailed in our previous study (Liu et al., 2019). The extracted proteins were resuspended in 8 M urea, and their concentrations were determined using the Pierce Bicinchoninic Acid (BCA) Protein Assay Kit (23225, Thermo Fisher Scientific, Shanghai, China) following the manufacturer's protocol. Absorbance was measured at 562 nm using a SpectraMax iD3 Multi-Mode Microplate Reader (Molecular Devices, Shanghai, China). Protein integrity was assessed by SDS-PAGE (tricine-sodium dodecyl sulfate polyacrylamide gel electrophoresis) (Swägger, 2006). A total of 100 µg of protein was enzymatically digested with trypsin (Promega, Madison, WI, USA) at a protein-to-trypsin ratio of 50:1 at 37°C overnight (16 h), followed by a second 4-hour digestion at a ratio of 100:1. Post-digestion, peptides were desalted using a Strata X C18 SPE column (Phenomenex) and vacuum-dried. The dried peptides were then reconstituted in 0.5 M TEAB and labeled using the iTRAQ reagent kit, following the manufacturer’s instructions. Each unit of iTRAQ reagent, defined as the amount required to label 100 µg of protein, was dissolved in 70 µL acetonitrile. The labeled peptide mixtures were incubated at room temperature for 2 hours, then pooled, desalted, and vacuum-dried to differentiate isobaric tags. For control samples, drought-sensitive ZX978 was labeled with iTRAQ tag 127, while drought-tolerant ND476 was labeled with tag 129. For drought-treated samples, ZX978 and ND476 were labeled with tags 126 and 128, respectively. Strong cation exchange (SCX) and LC-MS/MS analysis Each labeled peptide sample was fractionated using high-pH reverse-phase high-performance liquid chromatography (HPLC) on an Agilent 300Extend C18 column (5 µm particle size, 4.6 mm inner diameter, 250 mm length). The resulting peptide fractions were then resuspended in 0.1% formic acid (solvent A) and loaded onto a strong cation exchange (SCX) reversed-phase analytical column. Peptide separation was achieved using a gradient elution, starting with an increase from 6–23% solvent B (0.1% formic acid in 98% acetonitrile) over 26 minutes, followed by an increase from 23–35% over 8 minutes, then rising to 80% within 3 minutes, and maintaining 80% for the final 3 minutes, all at a constant flow rate of 400 nL/min on an EASY-nLC 1000 ultra-performance liquid chromatography (UPLC) system. Mass spectrometry (LC-MS) analysis was conducted on a Q-Exactive mass spectrometer (Triple TOF 5600 Plus) equipped with an AB SCIEX analytical column. The m/z scan range for full-scan acquisition was set between 350 and 1800, with a resolving power of 120 K. Intact peptides were detected in the Orbitrap mass analyzer at a resolution of 70,000 at 200 m/z, with automatic gain control (AGC) set at 1 × 10⁶ and the fixed first mass set to 100 m/z. For tandem mass spectrometry (MS/MS), peptides were selected based on a normalized collision energy (NCE) setting of 28, and fragment ions were analyzed in the Orbitrap at a resolution of 17,500. A data-dependent acquisition (DDA) method was employed, alternating between one MS scan followed by 20 MS/MS scans, with a dynamic exclusion time of 15.0 seconds. Protein identification and quantification The original MS/MS data files were converted into mgf format using Proteome Discoverer 1.4 (Thermo Fisher Scientific Inc., Waltham, MA, USA). The converted data were then analyzed using Mascot software version 2.2 (Matrix Science, London, UK) (v.1.5.2.8) for database searching. The MGF files were queried against the Uniprot Zea mays L. database, which comprises 132,339 sequences, using the Mascot search engine. The Mascot search was performed with the following parameters: trypsin as the specified enzyme, allowing a maximum of two missed cleavages; fragment mass tolerance set at ± 0.02 Da; peptide mass tolerance at ± 20 ppm; and monoisotopic mass values. Fixed modifications included carbamidomethylation of cysteine (Cys) and oxidation of methionine (Met), as well as iTRAQ 8-plex labeling (Y), iTRAQ 8-plex (N-terminal), and iTRAQ 8-plex (K). To ensure high data reliability, a confidence threshold of 95% was applied, and the false discovery rate (FDR) was adjusted to < 1%. Peptide identification was considered significant only if the Mascot score exceeded 40. Differentially expressed proteins (DEPs) were identified based on protein abundance levels, with a significance threshold of p-value 1.3 (up-regulated) or < 0.77 (down-regulated). Functional enrichment analysis To interpret the biological and functional properties of DEGs and DEPs, GO analysis was performed to categorize the biological processes involvement in response to drought stress. The GO terms and categories with a p -vaule ≤ 0.05 were considered significant according to Fisher's exact test. Moreover, MapMan was used to classify maize genes into hierarchical categories. A protein interaction network was constructed using the String program (version 10.5) ( http://www.string-db.org/ ). Correlation of proteomics and transcriptomics When all identified proteins are compared against all genes using BLAST reciprocal best hits (RBH) analysis (Altschul et al., 1990), they are considered the same if they qualify as "best BLAST hits". Based on this criterion, the shared DEGs and DEPs from RNA-Seq and iTRAQ quantitative analysis are identified. Function Validation of Drought Candidate Gene in Transgenic Arabidopsis Lines The target gene fragments were cloned into the pGreen-35S-6HA expression vector and introduced into Arabidopsis thaliana (Col-0) via Agrobacterium tumefaciens-mediated floral dip transformation. Mature Arabidopsis plants were dipped following standard procedures. Seeds harvested from the transformed plants were collected and dried, representing the T 0 generation.T 0 seeds were stratified at 4℃for 2–4 days, then sown densely onto a soil mixture of nutrient soil and vermiculite (1:1, v/v). When seedlings developed two well-formed true leaves, they were sprayed with Basta herbicide (25 mg/L glufosinate ammonium, working dilution 1:2000) every other day for a total of three treatments to select for transgenic-positive seedlings. Positive seedlings were then grown under normal conditions, and leaf tissues were collected for DNA and RNA extraction using the CTAB method and standard RNA isolation protocols, respectively. PCR and qRT-PCR were performed to confirm the presence and expression of the transgenes. Homozygous T 3 generation plants were obtained through subsequent propagation and used for further phenotypic and physiological analyses. To evaluate drought tolerance during germination, seeds from both wild-type (WT) and transgenic lines were surface-sterilized with 15% sodium hypochlorite for 5 minutes, rinsed 2–3 times with sterile water, and then sown on 1/2 MS solid medium. After 4 days, uniform seedlings were transferred to 1/2 MS medium supplemented with 0 mmol/L, 200 mmol/L, or 300 mmol/L mannitol and vertically grown for 9 days under controlled conditions. Root length was measured, and root growth status was recorded. A separate set of seedlings was transplanted into plug trays containing soil and grown for approximately 25 days until reaching similar growth stages. WT and transgenic plants were then divided into two groups: a control group with normal watering, and a drought treatment group, in which water was withheld for 7 days. After treatment, phenotypic differences were observed, and samples were collected for physiological measurements. Statistical analysis of physiological data All statistical analyses were conducted using SPSS statistical software (version 22.0; SPSS Institute Ltd., Armonk, NY, USA), with data presented as mean ± standard error of the mean. To evaluate differences in physiological parameters across treatments and genotypes, a two-way analysis of variance (ANOVA) followed by the least significant difference (LSD) test was applied. Meanwhile, qRT-PCR data were analyzed using one-way ANOVA and Duncan's multiple range test. Statistical significance was determined at p < 0.05 . Results Physiological characterization of the two maize hybrid cultivars under drought stress The physiological drought stress responses of the two maize hybrid cultivars, ND476 and ZX978, were evaluated at the tasseling stage of maize plants exposed to gradual natural drought stress under field conditions. Compared to the control groups, the leaf RWC of two hybrid cultivars significantly ( p < 0.01) decreased as the duration of drought stress increased, with ZX978 exhibiting a much steeper decline than ND476 (Fig. 1 A). Moreover, physiological revealed that from the 3 to the 12 day of drought treatment, both Pro content and guaiacol POD activity showed a significant increase ( p < 0.01 ) in both cultivars. However, ND476 consistently maintained higher values at all time points under stress conditions (Fig. 1 B-C). Furthermore, the analysis of MDA content indicated that the drought-sensitive genotype ZX978 exhibited higher MDA levels than the tolerant ND476, with a sharp decline observed in ND476 from the 9 to the 12 day of drought stress (Fig. 1 D). In summary, under drought conditions, ND476 exhibited higher RWC, Pro content, and POD activity, whereas ZX978 showed relatively higher MDA levels, suggesting greater sensitivity to drought stress. Transcriptome analysis of DEGs responsive to drought stress Transcriptome profiling of two contrary tolerance hybrid cultivars under drought stress After removing low-quality reads, a total of 66.8 million clean reads, each 150 bp in length, were obtained from the twelve samples, averaging 5.56 million reads per sample (Table S2). Among these high-quality reads, 84.7–92.9% were successfully mapped to the maize reference genome B73. Quality assessment metrics, including the Q30 base percentage and GC content, surpassed 96.01% and 55.64%, respectively, demonstrating the reliability and reproducibility of the sequencing data (Table S2). Principal component analysis (PCA) was performed to evaluate the relationships among the twelve samples. The results showed that biological replicates within each group clustered closely, while a clear distinction was observed between drought-tolerant and drought-sensitive cultivars (Fig. S1 ). These findings confirm that the experiment was highly reproducible and suitable for further analysis. Differentially expressed genes (DEGs) analysis To detect the transcriptional variations that occur in response to drought stress, genes of two contrary cultivars were tested for differential expression between the well-watered and water-deficit conditions used Cuffdiff software package. Within the tolerant genotype ND476, a total of 658 DEGs displayed differential abundance before and after drought treatment, with 399 DEGs being up-regulated and 259 down-regulated (Fig. 2 A). Meanwhile, in the sensitive genotype ZX978, 1088 DEGs including 923 up-regulated and 175 down-regulated were identified before and after drought treatment (Fig. 2 A). The Venn diagram shows a comparative analysis of the DEGs described above. There were 603 DEGs that were specifically expressed in drought-tolerant genotype ND476, including 365 up-regulated, and 238 down-regulated. A total of 1043 DEGs unique to drought-sensitive genotype ZX978, 878 were up-regulated and 165 were down-regulated (Fig. 2 B). Meanwhile, 55 DEGs were commonly identified in the two cultivars under drought treatment, and are shown in Fig. 2 D. Functional annotation and classification of drought-responsive DEGs To reveal the underlying biological functions of the identified DEGs, gene ontology (GO) enrichment analysis ( p -value < 0.05) was conducted by agriGO web-based program ( http://systemsbiology.cau.edu.cn/agriGOv2/# ). In drought-tolerant genotype ND476, oxidation-reduction process (GO:0055114), oxylipin metabolic process (GO:0031407) and single-organism metabolic process (GO:0044710) were the most significantly enriched biological process (BP) terms (Fig. 3 A). With regards to drought stress response, GO terms response to abiotic stimulus (GO:0009628), response to water (GO:0009415), response to water deprivation (GO:0009414), and response to stress (GO:0006950) were apparent under BP category (Table 1 ). On the other hand, in drought-sensitive genotype ZX978, external encapsulating structure organization (GO:0045229), cell wall organization or biogenesis (GO:0071554) and negative regulation of catalytic activity (GO:0043086) were prominent in BP category (Fig. 3 A). Meanwhile, GO terms defense response (GO:0006952), response to stimulus (GO:0050896) and response to stress (GO:0006950) were annotated under BP category in responding drought treatment (Table 1 ). Table 1 The DEGs/DEPs enriched in the GO terms related to drought responses Comparison GO term Ontology Description DEGs/DEPs number p-value ND_DEGs GO:0009415 P response to water 7 2.50E-03 GO:0009414 P response to water deprivation 7 2.50E-03 GO:0050896 P response to stimulus 21 1.10E-02 GO:0006950 P response to stress 46 2.00E-02 ZX_DEGs GO:0006952 P defense response 30 4.00E-08 GO:0050896 P response to stimulus 119 3.30E-03 GO:0006950 P response to stress 70 4.50E-03 ND_DEPs GO:0006950 P response to stress 5 1.20E-02 GO:0050896 P response to stimulus 6 3.10E-02 ZX_DEPs GO:0006950 P response to stress 7 5.00E-02 GO:0050896 P response to stimulus 10 4.30E-02 Then, MapMan software was used to map the DEGs with over-represented ‘regulation’ and ‘metabolism’ terms. Resultantly, we observed that the cell cycle, RNA regulation of transcription, ribosomal protein synthesis, cell organisation, redox of ascorbate and glutathione, hormone metabolism of jasmonate (JA), and secondary metabolism of flavonoids were the most significantly enriched in tolerant hybrid cultivar ND476 (Fig. 3 B, Table 2 ). However, the enriched categories of these DEGs expressed in ZX978 included cell wall, hormone metabolism, peroxidases, secondary metabolism of phenylpropanoids and and RNA regulation of transcription (Fig. 3 C, Table 2 ). Together, these results offer a general overview of the functional metabolic pathways altered by drought stress in maize tolerant and sensitive hybrid cultivars at the transcriptional level. Table 2 Classification of drought-response regulated DEGs into different categories according to annotate by MapMan Category Nongdan476 DEGs Zhongxin 978 DEGs Nongdan476 DEPs Zhongxin 978 DEPs Function annotion Up- Down- Up- Down- Up- Down- Up- Down- Photosynthesis 2 3 5 0 0 8 11 8 Photosystem I/II, electron carrier (ox/red), photorespiration Transport 24 14 40 4 3 0 1 5 Ammonium, ABC, metabolite, amino acids, sugars, Signalling 27 10 55 8 2 1 4 2 Receptor-like kinase, calmodulin, MAPK Plant hormones 28 10 35 7 1 2 3 6 ABA, BR, JA, SA, GA, auxin, ethylene Transcription factors 43 25 43 14 10 4 5 4 C2H2 zinc finger family, HB, HSF, MYB,WRKY,bZIP DEGs related to detoxification 11 7 17 0 2 14 15 0 Thioredoxin, GST, peroxidase, ascorbate and glutathione DEGs involoved in defense 10 7 22 4 2 1 8 2 HSPs, PRs DEGs response to abiotic 11 13 30 3 5 7 15 0 Response to heat, drought/salt, cold Secondary metabolism 23 5 23 8 4 2 0 2 Isoprenoids, flavonoids, sulfur-containing, phenylpropanoids Validation of DEGs by qRT-PCR To validate the DEGs results from RNA-seq data, we performed a supporting experiment by using qRT-PCR analysis on 12 randomly selected genes related to drought stress response. These genes were selected based on following criterion: their expression patterns changed remarkably according to the DEG data, or their functions have been identified according to GO and MapMan enrichment analyses. Our results showed that all the tested genes presented similar expression patterns as the differential analysis results from RNA-seq (Fig. S2A). Interestingly, a high consistence (correlation coefficient, R 2 , of 92.11) between the qRT-PCR and RNA-seq was observed (Fig. S2B). In a nutshell, the qRT-PCR analysis results confirmed our transcriptomics analysis-based findings. Proteomic analysis of DEPs responsible for drought stress Analysis of drought-responsive differentially expressed proteins (DEPs) With the help of Mascot software (version 2.2), 17 5356 spectra were matched with known spectra (Uniprot Zea mays L. database: 132339 − 2018.01.12), 68 234 peptides, 56 163 unique peptides, and 5451 proteins were identified from twelve samples (Fig. S3). The PCA results showed a clear separation between the drought-sensitive cultivar ZX978 and the drought-tolerant cultivar ND476 (Fig. S4). Interestingly, the replicates of each treatment clustered together were similar with the PCA result of RNA-Seq. Proteins with p -vaule 1.3 or 1 were used for a subsequent analysis as differentially expressed proteins (DEPs). Resultantly, in the tolerant cultivar ND476, we observed 187 DEPs (comprising 90 up-regulated and 97 down-regulated) before and after drought treatment. In the sensitive cultivar ZX978, 271 DEPs including 178 up-regulated and 93 down-regulated were identified before and after drought treatment (Fig. 2 A). The Venn diagram displays a comparative analysis of the DEPs described above. Among the 153 DEPs specific to ND476 under drought stress conditions, 75 were up-regulated and 78 were down-regulated. Of the 237 DEPs unique to ZX978, 154 were up-regulated and 83 were down-regulated (Fig. 2 C). Meanwhile, 34 DEPs were observed commonly expressed in two hybrid cultivars under drought stress conditions, and are shown by the hierarchical clustering analysis (Fig. 2 E). Functional annotation and classification of drought-responsive DEPs To gain a comprehensive understanding of proteomic changes, GO enrichment analysis of biological processes was performed using agriGO. In the drought-tolerant genotype ND476, key biological processes such as hydrogen peroxide metabolism (GO:0042743), reactive oxygen species metabolism (GO:0072593), and oxidative stress response (GO:0006979) were significantly enriched (Fig. 3 A). Additionally, the GO terms response to stress (GO:0006950) and response to stimulus (GO:0050896) were also enriched, highlighting ND476’s adaptive mechanisms under drought stress (Table 1 ). In contrast, the drought-sensitive genotype ZX978 exhibited significant enrichment in organonitrogen compound metabolism (GO:1901564), small molecule metabolism (GO:0044281), and photosynthesis (GO:0015979) within the biological process category (Fig. 3 A). Similarly, response to stress (GO:0006950) and response to stimulus (GO:0050896) were also enriched, indicating shared but distinct stress adaptation pathways in ZX978 (Table 1 ). Further functional enrichment analysis using MapMan identified key DEPs in ND476 associated with transport, photosynthesis, the TCA cycle/organic transformation, peroxidases, cell wall, RNA processing, and ribosomal protein synthesis (Fig. 3 C, Table 2 ). In contrast, ZX978 showed significant enrichment in amino acid metabolism, glycolysis, transport, redox, major CHO metabolism, abiotic stress response, light reaction in photosynthesis, and hormone metabolism of abscisic acid (Fig. 3 D, Table 2 ). Protein-Protein interaction (PPI) analysis of DEPs To predict how drought stress signals are transmitted within maize leaf cells to regulate specific cellular functions, we conducted a protein-protein interaction (PPI) network analysis using the web-based tool STRING 10.5 ( http://www.string-db.org/ ; accessed 1 May 2020). By selecting DEPs with confidence scores above 0.7, we identified two major hub networks and four interacting protein pairs in the drought-tolerant hybrid cultivar ND476 (Fig. 4 A).The primary network consists of eleven proteins, primarily associated with ribosomal translation and RNA processing (Supplementary Table S3). The second cluster comprises eight proteins involved in photosynthesis (Table S3). Additionally, four protein pairs were predicted to participate in the drought stress response (Fig. 4 A). In the drought-sensitive cultivar ZX978, PPI analysis revealed one large and one small cluster, along with seven interacting protein pairs (Fig. 4 B). Correlation of transcriptome and proteome data To evaluate the congruence between RNA-Seq and iTRAQ, we performed a global correlation analysis based on the mRNA and protein data. Of the quantified proteins from iTRAQ, 85.31% and 85.67% were detected in the transcriptomic profiles in tolerant genotype ND476 and sensitive genotype ZX978, respectively (Fig. 5 A). A scatter plot analysis based on the log 2 -transformed mRNA and protein ratios was used to show the distribution of the corresponding gene expression and protein accumulation ratio. The result revealed a poor correlation coefficients in ND476 (r Pearson correlation = 0.0441) (Fig. 5 B), and ZX978 (r Pearson correlation = 0.1679) (Fig. 5 C), respectively, between the expression levels of all quantified proteins and their corresponding mRNAs indicating transcription and expression of space and time inconsistency. Among the 658 DEGs and 187 DEPs identified in ND476, only 6 were commonly regulated both transcriptionally and translationally in response to drought (Fig. 5 D), of which 4 gene had the same trend and 2 genes had the opposite tread at the mRNA and protein levels (Table 3 ). Extracellular ribonuclease LE ( Zm00001d022630 ) involved in secondary metabolites of phenylpropanoid and ribonuclease-3 ( Zm00001d032186 ) related to RNA phosphodiester bond hydrolysis showed increased abundance at transcription level, but decreased abundance at translation level. Natterin-4 ( Zm00001d004344 ) was up-regulated between the two levels; whilst guaiacol peroxidase-1 ( Zm00001d040702 ), putative uncharacterized protein ( Zm00001d011461 ) and CP12-1 ( Zm00001d044925 ) were down-regulated between the two levels. In the sensitive line ZX978, 7 DEPs could match to DEGs which showed up-regulated trend between the two levels (Fig. 5 E). Protein P21 ( Zm00001d033455 ), stress-induced protein-1( Zm00001d021901 ) related to defense response, pathogenesis-related protein-1 ( Zm00001d018734 ) involved in plant hormone signal transduction, SKU5 similar-13 ( Zm00001d012524 ), cysteine proteinase inhibitor-5 ( Zm00001d049111 ) associated with negative regulation of endopeptidase activity, basic endochitinase A ( Zm00001d009936 ) related to carbohydrate metabolic process, and 23.6 kDa heat shock protein mitochondrial ( Zm00001d052194 ) involved in response to heat showed increased abundance across two levels in response to drought (Table 3 ). Table 3 Overlap of DEGs and DEPs in two maize hybrid lines in response to drought Comparision 1 ID 2 Description 3 mRNA Protein Function annotation 7 Log2FC 4 Pvaule 5 Expr. 6 Log2FC 4 Pvaule 5 Expr. 6 NDD_NDC Zm00001d004344/B4FHK4 Natterin-4 1.22 2.04E-02 up 0.45 2.92E-03 up Zm00001d044925/B6U3H3 CP12-1 -1.07 2.89E-02 down -0.57 6.06E-03 down Calvin cycle Zm00001d040702/A5H8G4 Guaiacol peroxidase 1 -1.17 4.38E-02 down -0.63 1.35E-02 down Response to abiotic/ Response to oxidative stress Zm00001d022630/B6SSH9 Extracellular ribonuclease LE 1.17 1.19E-03 up -0.64 3.66E-02 down Phenylpropanoid biosynthesis/ Biosynthesis of secondary metabolites Zm00001d011461/B6SMQ8 Putative uncharacterized protein -1.16 1.06E-02 down -1.02 2.81E-02 down Zm00001d032186/B4FBD6 Ribonuclease 1 1.13 2.98E-02 up -1.18 1.58E-02 down Endoribonuclease activity ZXD_ZXC Zm00001d033455/A0A1D6KZ34 Protein P21 1.23 6.86E-07 up 1.26 1.27E-02 up Response to abiotic Zm00001d021901/P33679 Stress-induced protein 1 1.50 9.56E-08 up 0.98 1.41E-02 up Response to abiotic Zm00001d018734/A0A1D6HRU2 Pathogenesis-related protein 1 1.08 2.97E-07 up 0.87 2.22E-03 up Response to abiotic/MAPK signaling pathway - plant/Plant hormone signal transduction Zm00001d012524/C0PFW1 SKU5 similar 13 3.03 4.45E-02 up 0.70 1.60E-02 up Oxidation-reduction process Zm00001d049111/Q4FZ48 Cysteine proteinase inhibitor 5 4.25 3.52E-04 up 0.55 3.10E-03 up Negative regulation of endopeptidase activity Zm00001d009936/B6TR38 Basic endochitinase A 1.10 1.10E-02 up 0.53 4.81E-02 up Carbohydrate metabolic process Zm00001d052194/O64960 23.6 kDa heat shock protein mitochondrial 1.07 6.10E-03 up 0.48 4.16E-03 up Response to abiotic 1 Comparison, comparison groups, NDD_NDC, the tolerant line ND476 before and after drought treatment, ZXD_ZXC, the sensitive line ZX978 before and after drought treatment; 2 ID, gene ID/protein identifying number in the UniProt database; 3 Description, Protein functional characteristics derived from Gene Ontology classification; 4 Log2(FC), quantitative measurement of differential expression calculated as log2-transformed ratio between treatment and control groupsis ; 5 Pvalue, statistical significant level (using a paired t-test) < 0.05; 6 Expr, gene expression level. Up-, up-regulated; Down-, down-regulated; 7 Function annotation, GO or MapMan annotation in which the identified gene was found to be significantly enriched. Drought candidate genes function validation in transgenics Arabidopsis thaliana According to bioinformatics analysis, we observed ZmPOD DEG commonly regulated both transcriptionally and translationally, we also observed ZmRAV1 and ZmTPP specifically expressed in the drought-tolerant line ND476 under drought condition. Through GO annotation and KEGG enrichment analyses,we found out that the peroxidases, starch and sucrose metabolism, and RNA regulation of transcription was significantly enriched after drought treatments both of transcript levels and the protein levels. Therefore, combining our analysis results and information from the published literature, we hypothesized ZmPOD, ZmRAV1 and ZmTPP as potential contributor to drought stress tolerance, and we selected them for function verification in model plant Arabidopsis thaliana. During the germination stage, under normal conditions (0 mmol/L mannitol), the root lengths of transgenic lines were comparable to those of the wild type (WT). However, under osmotic stress induced by 200 mmol/L and 300 mmol/L mannitol on 1/2 MS medium, transgenic plants showed significantly enhanced root growth (Fig. 6 A). Specifically, at 300 mmol/L mannitol, transgenic lines exhibited approximately 30–50% longer root lengths compared to WT, indicating improved osmotic stress tolerance during early development (Fig. 6 B). At the seedling stage (25-day-old plants), after a 7-day drought treatment, transgenic plants maintained more robust growth, with less wilting and dehydration symptoms compared to WT controls. Biochemical assays further supported these observations: transgenic lines displayed significantly higher POD and SOD activities, which are key antioxidant enzymes, suggesting reduced oxidative damage (Fig. 7 ). Overall, these results demonstrate that the maize candidate genes contribute to enhanced drought tolerance in Arabidopsis by promoting root growth under osmotic stress and improving antioxidant defense mechanisms under drought conditions. Discussion Improvement of drought tolerance in maize is one of the most challenging tasks owing to high complicacy of the traits and poor comprehension of plant response against drought stress. To this end, a full understanding of physiological, biochemical, and molecular regulatory networks relating to drought tolerance in plants becomes imperative in an attempt to improve that trait. Therefore, in the current paper, we have performed comparative transcriptome and proteomic analysis of two contrasting maize (drought-tolerant ND476 and drought-sensitive ZX978) hybrid cultivars, and we report key DEGs, DEPs and regulatory mechanisms involved in maize drought stress tolerance. Additionally, comparative physiological analyses of the two maize hybrid cultivars buttress the bioinformatics analysis results. Our findings enhance our further understanding of the mechanisms modulating drought tolerance in maize at tasseling stage, as well as providing foundational base to our future targeted cloning studies. Maize hybrid cultivars` contrasting physiological responses to drought stress In maize, as in other crop species, different genotypes exhibit varying responses to drought and other environmental stresses. These responses manifest at multiple levels, including physiological and molecular, and can vary across different growth stages. In this study, our findings demonstrated a significant decline in leaf RWC in both hybrid cultivars under drought conditions. However, the drought-tolerant cultivar ND476 consistently maintained a higher leaf RWC than the sensitive genotype ZX978 throughout most of the stress exposure period (Fig. 1 A). We propose that this higher RWC helped minimize cell turgor loss and structural damage, thereby reducing cellular stress. Similarly, our previous research (Zenda et al., 2018) reported that the drought-tolerant maize inbred line YE8112 exhibited significantly higher RWC than the sensitive inbred line MO17 at the seedling stage under both control and drought conditions. Under environmental stress, plants employ various strategies to maintain cellular integrity, including sustaining membrane and protein stability through osmotic adjustment and turgor maintenance, which help mitigate ROS damage (Oliver et al., 2007). Peroxidases serve as a primary defense mechanism by scavenging hydrogen peroxide (H₂O₂). In this study, ND476 exhibited higher peroxidase (POD) activity than ZX978 under drought conditions (Fig. 1 B), suggesting a stronger ROS detoxification capacity in ND476. Additionally, elevated proline levels reduce cell water potential, allowing plants to sustain turgor pressure and temporarily buffer against drought stress (Kumar et al., 2003). The greater accumulation of proline in ND476 under water deficit conditions may have contributed to its enhanced drought tolerance compared to ZX978 (Fig. 1 C). MDA is a byproduct of lipid peroxidation and is widely recognized as an indicator of cell membrane damage (Zhao et al., 2016). The increase in MDA levels observed in both maize hybrid cultivars under drought stress suggests that water deficiency can trigger membrane lipid peroxidation and cellular injury through ROS accumulation. Notably, the drought-tolerant genotype ND476 exhibited lower MDA content compared to the sensitive genotype ZX978, indicating a more effective ROS detoxification system in ND476, which likely contributed to improved cell membrane stability (Fig. 1 D). Overall, our findings demonstrate that the two maize hybrids exhibited distinct physiological responses to drought stress, with ND476 showing greater tolerance than ZX978. This enhanced drought resilience in ND476 may be attributed to its superior ROS scavenging capacity, better osmotic adjustment leading to higher water retention, and improved membrane stability. Cell wall and signaling related genes are vital for maize survival under drought In this study, numerous genes associated with cell wall metabolism were up-regulated in the drought-tolerant genotype ND476 in response to drought stress (Fig. 8 ). Notably, beta-expansins, O-glycosyl hydrolase, and leucine-rich repeat (LRR) proteins were expressed at both the transcript and protein levels. Beta-expansins, along with alpha-expansins, are well known as key regulators of cell wall modifications, particularly during tissue elongation (Reidy et al., 2001). O-glycosyl hydrolases have been linked to abiotic stress adaptation, phytohormone activation, cell wall remodeling, and lignification in rice (Opassiri et al., 2006). LRR proteins, which are believed to be cell wall-associated, contribute to growth rate regulation and modifications in cell wall composition and extensibility. Previous research has shown that glycine-rich and protein-rich proteins play roles in cell wall remodeling during drought stress at the flowering stage in maize (Li et al., 2007). Similarly, studies in soybean ( Glycine max L.) have reported differential expression of genes encoding cell wall proteins under drought stress (Creelman and Mullet, 1991). Furthermore, transcriptomic analysis of two maize inbred lines subjected to drought stress revealed significant changes in the expression of cell wall-related genes (Zenda et al., 2018). Sensing of the stress begins at cell wall and a signal is transmitted to the cell through the plasma membrane by changing the expression of a series of metabolites and proteins (Nouri et al., 2011). Furthermore, signaling related genes (such as calcium, G-proteins and protein kinases) also showed altered expression in the tolerant genotype ND476 at both transcript and protein levels in response to drought stress exposure (Fig. 8 ). The signals from the receptors on the cell membranes are transduced downstream and this lead to the generation of secondary messengers including Ca 2+ , sugars and ROS (Wu et al., 2017). Calcium binding proteins sense the perturbation in the cytosolic Ca 2+ level, further modulating the intracellular calcium level (Oliver et al., 2007). Recently, GTP-binding proteins (small G-proteins) have been an intensively studied group of regulatory GTP hydrolyses related to cell signaling (Li et al., 2007). Umeda et al. (1994) revealed that GTP-binding proteins were salt induced and possibly associated with salt-stress signaling. Protein kinases, as the central signal transduction machinery, initiate phosporylation cascades and play vital roles in drought responses (Singh and Laxmi 2015). At the end of the signal transduction cascade, protein kinases modulate transcription factors (TFs), consequently influencing corresponding response to the downstream drought responsive genes (Wang et al., 2016). In summary, at the initial stage of stress signal transduction, cell wall perceived stress, secondary messengers and protein kinases bridging the gap between perception and transmission of the signals to the target genes and contributing to maize response to drought. Transcription factor (TF) related genes are essential in regulating drought stress response at the transcript level Transcription factors (TFs) act as critical controllers of multiple downstream stress-responsive genes. TF target genes constitute a regulon that is related to the repression/activation of genes involved in abiotic stress responses (Wang et al., 2016). A bunch of TF families such as bmZIP, bHLH, WRKY, NAC, and MYB has played a critical role in drought stress response research (Li et al., 2007; Singh et al., 2015; Zhao et al., 2016). Herein, more than 26 drought-responsive TF families were differentially expressed in the tolerant genotype ND476 under drought conditions at transcription level, including five MYB (2 up- and 3 down-), five bHLH (3 up- and 2 down-), four AP2/EREBP (all down-), four NAC (2 up- and 2 down-), three C3H zinc finger family (1 up- and 2 down-), three HB (all up-), two WRKY (1 up- and 1 down-) and two C 2 H 2 zinc finger family (all down-) that were regulated in response to drought stress (Fig. 8 ). The role of TFs in regulating stress tolerance across various crops, including maize, has been extensively studied (Wang et al., 2016). In a transcriptome analysis investigating drought-responsive genes in two contrasting maize inbred lines, Zhang et al. (2017) identified five bZIP, three MYB, and one AP2/EREBP TFs associated with drought stress adaptation. Similarly, Song et al. (2017) reported that several TFs, including C 2 H 2 , NAC, bHLH, and MYB, exhibited expression patterns closely linked to plant water potential, highlighting their involvement in maize drought response during flowering. In maize, multiple NAC, MYB, and WRKY genes have been identified, cloned, and characterized for their roles in abiotic stress regulatory pathways. Over-expression of ZmSNAC1 in Arabidopsis significantly improved drought tolerance at the germination stage, indicating that ZmSNAC1 plays a positive role in drought resistance (Lu et al., 2013). More recently, Wu et al. (2019) demonstrated that ZmMYB3R over-expression enhanced maize resilience to drought and salt stress. Conversely, constitutive expression of ZmWRKY17 in Arabidopsis led to a marked decline in salt stress tolerance, as evidenced by increased relative electrolyte leakage, higher MDA content, reduced cotyledon greening rate, and impaired root growth (Cai et al., 2017). These findings suggest that ZmWRKY17 may act as a negative regulator in maize abiotic stress responses. Overall, the differential expression of TF genes likely plays a crucial role in the drought tolerance of the maize hybrid cultivar ND476 by orchestrating complex regulatory networks that enhance stress adaptation. Photosynthesis related proteins contribute to drought tolerance Under drought stress, plant growth is inhibited due to reduced biomass accumulation per unit area and decreased photosynthetic efficiency. Previous studies have shown that abiotic stress suppresses photosynthetic activity (Sharma et al., 2012; Wu et al., 2019), with net photosynthesis declining in maize during the V9-V10 stages under drought conditions, ultimately leading to abnormal ear primordium development (Song et al., 2017). In this study, we observed that drought stress not only down-regulated genes associated with PSII but also those related to PSI and photosynthetic electron carriers in the drought-tolerant maize genotype ND476 (Fig. 8 ). Additionally, MapMan enrichment analysis revealed that photosynthesis-related pathways were significantly affected in ND476 under water deficit conditions (Fig. 3 D). The decline in photosynthetic activity can be attributed to the accumulation of ROS, which cause damage within the PSII reaction center of the thylakoid membranes when excess light energy is not efficiently dissipated. In response, plants regulate PSII-associated proteins to maintain a balance between light absorption, energy utilization, and non-photochemical quenching mechanisms (Zenda et al., 2018). A study by Thirunavukkarasu et al. (2017) on maize similarly reported that genes involved in photosynthesis were down-regulated under drought conditions. Consequently, the observed reduction in photosynthesis-related protein abundance in the hybrid cultivar ND476 suggests that drought stress negatively impacts the photosynthetic process. Plant hormones play vital roles in drought stress response regulation Phytohormones play crucial roles in regulating plant growth, development, nutrient allocation and source/sink transitions for adapting to stressful environments (Peleg and Blumwald, 2011). In our study, several ABA, JA, BRs, Gas and auxin genes were altered in their expressions at transcript and protein levels in response to drought stress (Fig. 8 ). ABA is a crucial signaling molecule in plant responses to abiotic stress. Under drought conditions, plants accumulate ABA, which subsequently activates downstream stress responses (Zhu, 2016). At the molecular level, ABA serves as a key regulator of gene expression, protein synthesis, signaling cascades, and the production of essential protective compounds that mitigate water loss. While ABA is the most extensively studied stress-related hormone, the role of other phytohormones in environmental stress adaptation is becoming increasingly evident. BRs contribute to stress tolerance by inducing the expression of stress-responsive genes, enhancing antioxidant enzyme activity to counter oxidative damage, maintaining photosynthetic efficiency, and promoting osmoprotectant accumulation under drought conditions (Peleg and Blumwald, 2011). In a transcriptome study investigating gene expression changes under 20% polyethylene glycol (PEG) 6000-induced drought stress in common buckwheat (Fagopyrum esculentum), Wu et al. (2019) reported that BR-related genes were highly associated with drought response. Similarly, transcriptomic analyses in maize have shown a reduction in GA levels under abiotic stress, suggesting that GA may negatively influence stress adaptation (Li et al., 2017). Auxin regulates the expression of numerous genes collectively termed primary auxin response genes, which fall into three major families: Aux/IAA, GH3, and SAUR. Recent studies suggest that auxin is also involved in stress and defense responses. Expression profiling and mutant analysis indicate that auxin pathway suppression plays a crucial role in plant defense mechanisms (Wang et al., 2007). Consistent with our findings, genes involved in SA and JA biosynthesis were significantly up-regulated in common buckwheat seedlings under drought conditions (Wu et al., 2019). Overall, interactions between different plant hormones create a complex network of synergistic and antagonistic relationships, which collectively regulate maize responses to drought stress. Response to stress- and response to stimuli- related genes under drought conditions Our GO enrichment analysis of genes expressed in the drought-tolerant genotype ND476 identified 46 DEGs and 5 DEPs significantly associated with the GO term “response to stress (GO:0006950).” Further examination revealed that these genes were also enriched in the GO category “response to stimuli (GO:0050896).” This group included several heat shock proteins (HSPs), dehydrins (DHNs), peroxidases, glutathione S-transferases (GSTs), and mitogen-activated protein kinases (MAPKs), among others (Supplementary Table S4). In this study, small heat shock proteins (sHSPs) and HSP70 were up-regulated under drought conditions. HSPs play a crucial role in plant stress protection by ensuring proper protein folding, preventing aggregation, and maintaining cellular homeostasis under adverse conditions (Wang et al., 2004). Additionally, DHNs, a specialized subgroup of late embryogenesis abundant (LEA) proteins, showed increased abundance during drought stress. These proteins are widely present in plants and are responsive to abscisic acid (ABA), with their expression being induced by this phytohormone. DHNs have been recognized as key players in plant stress adaptation (Hanin et al., 2010). Interestingly, eight peroxidases were down-regulated while one was up-regulated in response to drought stress. Peroxidases serve as a primary defense mechanism, mitigating oxidative damage by neutralizing toxic peroxides and ROS (Sharma et al., 2012). Similar protective roles of peroxidases against ROS-induced damage have been observed in wheat under drought conditions (Sheoran et al., 2015). Likewise, Khan and Komatsu (2016) highlighted their critical function in ROS scavenging and maintaining redox homeostasis in soybean roots. Moreover, two key detoxification enzymes, GSTs, exhibited down-regulation under drought stress. GSTs facilitate the detoxification of harmful compounds, including ROS and xenobiotics, through conjugation reactions. Additionally, one MAPK was up-regulated, while another was down-regulated, suggesting a complex regulatory network involved in redox homeostasis, signaling pathways, and abiotic stress interactions. Jonak et al. (1996) demonstrated that MAPK cascades are essential for enzyme activation and deactivation via phosphorylation and dephosphorylation, enabling rapid and specific signal transduction in response to external stimuli. Overall, these findings suggest that under drought conditions, maize hybrid ND476 enhances cellular redox balance and activates drought-responsive gene expression to regulate ROS levels, thereby improving its resistance to drought stress. Secondary metabolism related pathways play vital roles in response to drought As previously stated (Zenda et al., 2018), secondary metabolism plays a crucial role in the plant response to drought stress. In this study, we observed that numerous enzymes associated with secondary metabolite biosynthesis were activated in the drought-tolerant hybrid cultivar ND476 at both transcript and protein levels under drought conditions (Fig. 8 ). Wink (2013) highlighted that while primary metabolites are essential for plant growth and development, secondary metabolites primarily contribute to ecological functions, particularly in plant defense mechanisms.Supporting this, Król et al. (2017) demonstrated the significant role of secondary metabolism in grapevine under prolonged drought stress. Consistent with our findings, multiple metabolic pathways, including flavone and flavonol biosynthesis, as well as isoflavonoid and flavonoid biosynthesis, have been shown to undergo alterations in response to abiotic stress in Rehmannia glutinosa L. (Tian et al., 2017). Collectively, the observed changes in gene expression related to secondary metabolism following water deprivation indicate a broad metabolic shift in maize as an adaptive response to drought stress. Function Analysis of ZmPOD, ZmRAV1, and ZmTPP In this study, we functionally validated three maize drought-responsive candidate genes, ZmPOD , ZmRAV1 , and ZmTPP , in Arabidopsis thaliana . Our findings revealed that transgenic Arabidopsis lines over-expressing these genes exhibited enhanced drought tolerance at both germination and seedling stages, which is consistent with their transcriptional and translational regulation patterns observed in the drought-tolerant maize line ND476. ZmPOD encodes a peroxidase enzyme, which is pivotal in scavenging ROS generated under drought-induced oxidative stress. The significantly higher POD and SOD activities observed in transgenic lines suggest that ZmPOD contributes to the detoxification of ROS, maintaining cellular redox homeostasis, and protecting membrane integrity. Previous studies have highlighted the role of PODs in various abiotic stresses, including drought and salinity, where enhanced peroxidase activity correlates with increased stress tolerance in multiple plant species, such as rice and wheat (Zhang et al., 2016, Wang et al., 2018). ZmRAV1 , a member of the RAV transcription factor family, was specifically expressed in the drought-tolerant ND476 line under drought stress, implying a regulatory role. RAV family transcription factors have been reported to integrate stress signals and modulate down-stream gene expression related to growth inhibition and stress adaptation (Fu et al., 2014). Furthermore, the GO enrichment results suggested that the "regulation of transcription" pathway was activated at both the transcriptome and proteome levels, supporting the hypothesis that ZmRAV1 may act as a central regulator coordinating drought-responsive gene expression networks. ZmTPP encodes a trehalose-6-phosphate phosphatase, an enzyme involved in the trehalose biosynthesis pathway, which has been associated with stress tolerance through its role in carbohydrate metabolism and osmoprotection (Paul et al., 2001). The KEGG enrichment analysis revealed that "starch and sucrose metabolism" was significantly enriched, indicating that ZmTPP might modulate sugar signaling and energy homeostasis under drought stress. Accumulation of trehalose and related metabolites has been shown to enhance drought tolerance by stabilizing proteins and membranes, as well as improving water retention capacity in plants (Lyu et al., 2012). Taken together, our data provide strong evidence that ZmPOD , ZmRAV1 , and ZmTPP act synergistically to enhance drought tolerance via distinct but complementary mechanisms, including ROS scavenging, transcriptional regulation, and osmotic adjustment. Importantly, the consistent phenotypical and biochemical improvements observed in the transgenic Arabidopsis lines validate the accuracy and biological relevance of our integrated transcriptomic and proteomic analyses. This functional verification reinforces the reliability of our omics-based candidate gene selection and highlights the effectiveness of combining multi-omics with reverse genetics approaches for uncovering key drought-responsive genes. Conclusion To unravel the molecular mechanisms underpinning maize drought tolerance, in the present study, we have applied a comprehensive physiological, transcriptomic and proteomic analysis approach to decipher the differential responses of tolerant ND476 and sensitive ZX978 maize hybrid cultivars to field drought stress at the tasseling stage. Our physiological analysis revealed that the drought-tolerant genotype ND476 demonstrated superior resilience to drought stress, primarily due to its enhanced ROS scavenging capacity and effective osmotic regulation, which contributed to improved cell water retention and greater membrane stability. RNA-seq and iTRAQ analysis identified 1701 DEGs and 424 DEPs, respectively to respond to drought stress treatment. Chief among those DEGs and DEPs were those related to signal transduction, cell-wall remodelling, cellular redox homeostasis, and hormone metabolism. Further, MapMan analysis also revealed that the transcription factor regulation and secondary metabolism play critical roles in maize response to drought stress at mRNA-level, as well as photosynthesis play important roles at the protein-level. However, a weak correlation between DEGs and DEPs was observed, indicating the drought response of maize in tasseling stage is largely regulated post-transcriptionally. Additionally, the expression changes of ten representative DEGs that were detected by qRT-PCR analysis were highly correlated (R 2 =92.11) to the corresponding RNA-seq expression changes. In addition, we characterized and analyzed the function of drought response genes ZmPOD , ZmRAV1 , and ZmTPP through phenotypic and physiological analyses of the WT plant and transgenic Arabidopsis lines. Under drought stress, the transgenic Arabidopsis lines had stronger resistance to drought stress than the WT plant. Overall, this study provide an elaborate understanding of the molecular networks mediating maize drought tolerance and offer fundamental basis for further targeted researches such as cloning and downstream analysis of the identified specific individual genes. Abbreviations DEGs:Differentially Expressed Genes; DEPs:Differentially Expressed Proteins; RNA-Seq:RNA Sequencing; iTRAQ: Isobaric Tags for Relative and Absolute Quantitation; qRT-PCR:Quantitative Real-Time Polymerase Chain Reaction; ROS:Reactive Oxygen Species; WT:Wild-Type; BCA:Bicinchoninic Acid; SDS-PAGE: Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis; SCX:Strong Cation Exchange; LC-MS/MS:Liquid Chromatography–Tandem Mass Spectrometry; PCA:Principal Component Analysis; GO:Gene Ontology; FC: Fold Change FDR:False Discovery Rate; RWC:Relative Water Content; Pro: Proline; POD:Peroxidase; MDA:Malondialdehyde; ND476:Drought-tolerant maize genotype (Nongdan 476); ZX978:Drought-sensitive maize genotype (Zhongxin 978); TCA:Tricarboxylic Acid (Cycle); MS:Murashige and Skoog; HSPs: Heat Shock Proteins; DHNs: Dehydrins; GSTs: Glutathione S-transferases; MAPKs: Mitogen-Activated Protein Kinases Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: All authors have reviewed the final version of the manuscript and have given their consent for publication. Availability of data and materials: Data will be made available on request. Conflicts of Interest : The authors declare that they have no conflict of interest. Furthermore, the founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Authors' contributions: SL, ZH, HZ and SW conceived and designed the experiment; SL, HS, LY, CJ and HL performed the investigations and collected data; SL, HS, LY, CJ and HL analyzed the data; SL wrote the original manuscript. Acknowledgments: The authors sincerely thank Prof. Huijun Duan (Hebei Agricultural University, China) for helping with valuable suggestions. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6492029","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449294428,"identity":"86107084-cac9-488f-84d5-31bb750035af","order_by":0,"name":"Songtao Liu","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Songtao","middleName":"","lastName":"Liu","suffix":""},{"id":449294429,"identity":"4ca3b94f-11f3-4fa6-aaab-b212bf79026e","order_by":1,"name":"Hanbo Shi","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Hanbo","middleName":"","lastName":"Shi","suffix":""},{"id":449294430,"identity":"920f9ef4-02bb-4ed9-8fe7-8310208b94b9","order_by":2,"name":"Linan Yan","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Linan","middleName":"","lastName":"Yan","suffix":""},{"id":449294431,"identity":"71656c3a-20c9-4cfa-aa52-8a7dabbbbc38","order_by":3,"name":"Chao Jiang","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Jiang","suffix":""},{"id":449294432,"identity":"60305981-b709-478c-8c5c-2836f7b3d2fd","order_by":4,"name":"Haichao Zhao","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Haichao","middleName":"","lastName":"Zhao","suffix":""},{"id":449294433,"identity":"c8369946-1bc2-4cf8-9d16-89e552b774c1","order_by":5,"name":"Haibo Lu","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Lu","suffix":""},{"id":449294434,"identity":"6882b997-854b-4a92-abd3-aec037c38a43","order_by":6,"name":"Haoyang Li","email":"","orcid":"","institution":"Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"Haoyang","middleName":"","lastName":"Li","suffix":""},{"id":449294435,"identity":"0ab0fd0f-e643-47d4-840e-df4a78ebb0b8","order_by":7,"name":"Shuo Wang","email":"","orcid":"","institution":"Nankai University","correspondingAuthor":false,"prefix":"","firstName":"Shuo","middleName":"","lastName":"Wang","suffix":""},{"id":449294436,"identity":"1340e163-59a9-43fc-af00-864b07f337d4","order_by":8,"name":"Zhihong Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoUlEQVRIiWNgGAWjYBACAwkGBmYGBhsefv4G0rSkyUjOOECalsM2Bg0JRGoxl+49Jl3w5zyPAcMBxg8fc4jQYjnnXJr0DJ7bPObMDcySM7cR47AbOWbSPBK3eSwbDrAx8xKvxeAcj8GBBJK0JBwgRcudM8bWPAeSeSRnHGwm0i+3ewxv8/yxs+fnbz744SMxWpAAYwNp6kfBKBgFo2AU4AYATIwxiODZ+CgAAAAASUVORK5CYII=","orcid":"","institution":"Hebei North University","correspondingAuthor":true,"prefix":"","firstName":"Zhihong","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-04-21 03:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6492029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6492029/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07264-5","type":"published","date":"2025-10-08T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81703025,"identity":"312f7f35-0b75-4536-a02e-982c24f8b492","added_by":"auto","created_at":"2025-04-30 13:10:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1987334,"visible":true,"origin":"","legend":"\u003cp\u003ePhysiological responses of maize hybrid cultivars ND476 and ZX978 to drought stress following 12 days of exposure under water-sufficient and water-limited conditions. (A) Leaf RWC; (B) POD activity; (C) Proline (Pro) content; and (D) MDA content. Data are expressed as mean ± SE (n = 3). Different letters above the line graphs indicate significant differences (p \u0026lt; 0.01) among treatments at specific time points.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/b1b5fef915ffb6464d311f75.png"},{"id":81702914,"identity":"cddd7f8c-cb23-4a85-a23c-c5f150be8bec","added_by":"auto","created_at":"2025-04-30 13:10:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2461441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eNumber of differentially\u003cstrong\u003e \u003c/strong\u003eexpression genes (DEGs) and proteins (DEPs) expressed in ND476 and ZX978. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram analysis of DEGs. \u003cstrong\u003e(C) \u003c/strong\u003eVenn diagram analysis of DEPs. \u0026nbsp;\u003cstrong\u003e(D)\u003c/strong\u003e Clustering analysis of DEGs shared by ND476 and ZX978. (\u003cstrong\u003eE\u003c/strong\u003e) Clustering analysis of DEPs shared by ND476 and ZX978. Each row indicates a gene differentially expressed (up-regulated- red, and down-regulated - green).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/b2d495de2c500a152737c598.png"},{"id":81702810,"identity":"7cb04530-c9e2-4e3e-86d9-a59fe9bfde3c","added_by":"auto","created_at":"2025-04-30 13:10:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2100803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eGene ontology (GO) enrichment analysis of the DEGs and DEPs. \u003cstrong\u003e(B-E)\u003c/strong\u003e MapMan pathway enrichment analysis of the DEGs and DEPs. Sub-figures show the most significantly enriched pathways in (B) DEGs of ND476; (C) DEGs of ZX978; (D) DEPs of ND476; (E) DEPs of ZX978.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/77fbfd9a07fd6a4c2cd8cb71.png"},{"id":81703381,"identity":"62f369bd-dda6-47d1-a48f-e834a47166f2","added_by":"auto","created_at":"2025-04-30 13:11:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3958872,"visible":true,"origin":"","legend":"\u003cp\u003eProtein interaction network consisting of DEPs identified in drought stressed maize leaves of (\u003cstrong\u003eA\u003c/strong\u003e) ND476 (\u003cstrong\u003eB\u003c/strong\u003e) ZX978. The network was established using the String program with a confidence score higher than 0.7.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/ba82bfdbb925cc9979c85e5c.png"},{"id":81703029,"identity":"1a8edcc3-7cba-4cf8-b3c8-9d32e2728075","added_by":"auto","created_at":"2025-04-30 13:10:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3289276,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between protein and gene expression. (\u003cstrong\u003eA\u003c/strong\u003e) Venn diagrams showing correlations between transcriptome and proteome data sets from ND476 and ZX978 after drought treatment. \u003cstrong\u003e(B-C)\u003c/strong\u003e Spearman correlation coefficients for ND476 (\u003cstrong\u003eB\u003c/strong\u003e), and ZX978 (\u003cstrong\u003eC\u003c/strong\u003e). \u003cstrong\u003e(D-E)\u003c/strong\u003e Venn diagrams showing correlations between DEGs and DEPs data sets from ND476 (\u003cstrong\u003eD\u003c/strong\u003e), and ZX978 (\u003cstrong\u003eE\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/84a0de8d5e06790361e20134.png"},{"id":81702797,"identity":"ae4869ae-355c-471b-b883-8b0d72429c04","added_by":"auto","created_at":"2025-04-30 13:10:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9205283,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of \u003cem\u003eZmPOD\u003c/em\u003e, \u003cem\u003eZmRAV1\u003c/em\u003e,\u003cem\u003e ZmTPP\u003c/em\u003eover-expressing seedlings to mannitol stress. (A) Growth of wild-type and over-expressing seedlings at different concentrations of mannitol; (B)Statistical graph of root length of wild-type and over-expressing seedlings (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/1da82d233bea2d5823edac56.png"},{"id":81703182,"identity":"2c9bb5e9-7909-45a0-9a17-ebfd37c75201","added_by":"auto","created_at":"2025-04-30 13:10:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6680105,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic changes and SOD, POD activity variations in \u003cem\u003eArabidopsis \u003c/em\u003eover-expressing genes before and after drought treatment (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). (A)ZmPOD genes; (B) \u003cem\u003eZmRAV1\u003c/em\u003e gene; (C) \u003cem\u003eZmTPP\u003c/em\u003egene.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/10e756ff116a47ade210e3ff.png"},{"id":81703147,"identity":"4650bbcb-be0d-4b1b-9fab-f4bcb88baa33","added_by":"auto","created_at":"2025-04-30 13:10:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":7800138,"visible":true,"origin":"","legend":"\u003cp\u003eDrought stress-related genes and proteins altered in response to 12-day drought treatment. Genes (up-red, down-green) and proteins (up-yellow, down-blue) that were differentially expressed between well-watered and drought conditions at the gene and protein levels in drought tolerant hybrid cultivar ND476.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/eb0783481a57aaf4123979ca.png"},{"id":93419408,"identity":"197e314b-f94f-4545-8413-e94e07e2369b","added_by":"auto","created_at":"2025-10-13 15:59:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36024860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/c1ec2427-6d2a-41bc-97e3-2036cd29f406.pdf"},{"id":81702965,"identity":"9139ba4d-f1ff-45c6-b709-f9596938dcf9","added_by":"auto","created_at":"2025-04-30 13:10:37","extension":"rar","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":773711,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.rar","url":"https://assets-eu.researchsquare.com/files/rs-6492029/v1/e90e635a8936d4cd59b50622.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of candidate genes and proteins for tasseling stage drought tolerance through integrated transcriptomic and proteomic analysis approach in maize","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change is having a detrimental impact on agricultural production, particularly in low-latitude arid and semi-arid regions, which are predominantly home to developing countries (Pereira, 2016). Key climate-related challenges, such as erratic rainfall patterns, rising atmospheric temperatures, and increasing carbon dioxide concentrations, contribute to severe droughts in these vulnerable environments (Wheeler \u0026amp; von Braun, 2013, Huang et al., 2023). Among all abiotic stresses, drought exerts the most profound and devastating effects on crop growth and productivity in agricultural systems (Walter et al., 2011; Danilevskaya et al., 2019). With global climate change continuing to escalate, the frequency and intensity of droughts are projected to rise, posing significant threats to future agricultural sustainability (Edmeades, 2013; Wang et al.,2023). Alarmingly, global food demand is expected to nearly double by 2050, necessitating historically unprecedented annual increases in crop production (IPCC, 2014). In response to these challenges, while modifications in crop production and farm management practices are essential, the development of climate-resilient crop varieties has been widely recognized as the most cost-effective and sustainable strategy for mitigating climate-induced environmental stresses, including drought (Acevedo et al., 2020). Consequently, there is an urgent need to unravel the intricate molecular mechanisms governing drought stress responses to inform crop genetic improvement efforts aimed at enhancing drought tolerance.\u003c/p\u003e \u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is a globally significant crop, serving as a primary source of food, biofuel, and livestock feed (Song et al., 2015; Jiang et al., 2024). Beyond its agricultural and economic importance, maize also plays a crucial role in industrial applications and is widely used as a model organism in plant research (Aslam et al., 2015). Among cereal crops, maize ranks as one of the top three most essential staples worldwide, alongside rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) and wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) (Opitz et al., 2016). However, maize production is highly susceptible to drought stress, posing a serious threat in regions prone to water scarcity, particularly in arid and semi-arid zones such as northern China (Min et al., 2016). The crop's vulnerability is most pronounced from the pre-flowering stage to the grain-filling phase, with the tasseling period being the most water-intensive and critical for grain yield formation (Zheng et al., 2010). Consequently, drought stress during this stage can significantly reduce yield, impacting overall productivity.\u003c/p\u003e \u003cp\u003eTo sustain stable crop production in the face of climate change, understanding plant responses to water deficit conditions is essential (Virlouvet et al., 2018; Hrabayashi et al., 2023). Plants employ a range of physiological, biochemical, and molecular mechanisms to adapt to drought stress (Pandey \u0026amp; Shukla, 2015). Drought-tolerant maize genotypes typically maintain higher relative water content (RWC) under water-limited conditions compared to drought-sensitive varieties (Anupama et al., 2019). Proline, a key osmoprotectant, functions as a biochemical marker for drought tolerance by helping plants retain cellular moisture under water-deficit conditions (Ashraf \u0026amp; Foolad, 2007). Additionally, plants mitigate oxidative stress caused by reactive oxygen species (ROS) through antioxidant defense systems, which involve key enzymes such as catalase (CAT), superoxide dismutase (SOD), and peroxidases (Mahajan et al., 2005; Nuccio et al., 2015).\u003c/p\u003e \u003cp\u003eAt the molecular level, drought responses in maize are governed by a complex network of genes that regulate stress perception, signal transduction, and gene expression (Sharp et al., 2004; Zhu, 2016). Various transcription factors (TFs), kinases, and phytohormones play critical roles in orchestrating drought tolerance and modulating gene expression under stress conditions (Khan et al., 2019). While extensive research has been conducted to unravel the molecular mechanisms underlying drought resistance (Min et al., 2016; Zheng et al., 2010), the intricate regulatory networks governing drought-responsive genes remain incompletely understood (Khan et al., 2019). Further exploration of these genetic pathways is essential to enhance maize resilience against drought stress and ensure sustainable crop yields under challenging environmental conditions.\u003c/p\u003e \u003cp\u003eWith the advancement of next-generation sequencing technologies, including genomics, transcriptomics, and proteomics, researchers can now conduct comprehensive and quantitative analyses of gene expression models. RNA sequencing (RNA-Seq), a high-throughput transcriptomic approach, has emerged as a powerful tool for examining dynamic gene expression patterns and elucidating plant-environment interactions (McGettigan, 2012). Similarly, the isobaric tags for relative and absolute quantification (iTRAQ) proteomic technique, known for its high reproducibility and robust quantitative accuracy, has been widely applied to investigate plant protein responses to abiotic stresses (Zhao et al., 2018; Luo et al., 2018; Zenda et al., 2018; Liu et al., 2019). These two approaches have proven instrumental in capturing the dynamic range of gene and protein expression levels under stress conditions. However, despite their potential, research focusing on maize drought response specifically at the tasseling stage remains limited, even though this period is critical for grain yield and highly susceptible to water stress.\u003c/p\u003e \u003cp\u003eTo gain a better understanding of the complex molecular mechanisms that underline the response of maize to drought stress, in this present study, RNA-seq and iTRAQ were utilized parallel to detect the changes of genes and proteins between water-deficit and well-watered conditions in maize hybrid cultivars at the tasseling stage. The comparative analysis revealed the common and different regulatory mechanisms in response to drought stress between the drought-tolerant genotype Nongdan 476 (ND476) and drought-sensitive genotype Zhongxin 978 (ZX978). Subsequently, the combination analysis of RNA-seq and iTRAQ indicated uniquely and commonly regulatory mechanisms at transcript and post-transcriptional level. Additionally, physiology parameters were measured to perform an overview of comprehensive drought stress acclimation process. Together with the use of quantitative reverse transcription polymerase chain reaction (qRT-PCR) and the function of the \u003cem\u003eZmPOD\u003c/em\u003e, \u003cem\u003eZmRAV1\u003c/em\u003e, \u003cem\u003eZmTPP\u003c/em\u003e genes in drought response to validate the sequencing results, this data provide resources for future genetic analyses of candidate genes related to drought stress response, in addition to being harnessed for molecular breeding of drought-improved maize varieties.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and drought stress treatment\u003c/h2\u003e \u003cp\u003eThe two maize hybrid cultivars with contrasting drought tolerance\u0026mdash;ND476 (drought-tolerant) and ZX978 (drought-sensitive)\u0026mdash;used in this study were provided by the North China Key Laboratory for Crop Germplasm Resources of Education Ministry (Hebei Agricultural University, Baoding, China). ND476 is recognized as a highly drought-resistant hybrid line, whereas ZX978 is classified as a drought-sensitive hybrid, as identified by the Dryland Research Institute of Hebei Academy of Agricultural and Forestry Sciences. The field experiment was conducted in May 2018 at the Qing Yuan Experiment Station of Hebei Agricultural University, Baoding, Hebei Province, China (115.5602790\u0026deg;E, 38.7950930\u0026deg;N, 118 m), under a fully automated rain-proof shelter. A randomized complete block design was employed, with both control and drought stress treatments replicated three times. Each experimental plot covered 25 m\u0026sup2; (5 m \u0026times; 5 m), with a planting arrangement of 60 cm row spacing and 30 cm plant spacing, resulting in 128 plants per plot. Seeds were sown at a depth of 6 cm, two per station, in plots fertilized with 512 kg ha⁻\u0026sup2; of compound fertilizer. Following Hsiao (1973), the well-watered control plots were maintained at 70\u0026ndash;80% soil water content, whereas the drought-stress plots were maintained at 15\u0026ndash;20%. Soil moisture was monitored using a TZS-1 soil moisture measurement instrument (Zhejiang Tuopu Technology Co., Ltd.), with a one-meter-deep waterproof membrane installed between the control and treatment plots to prevent lateral water infiltration. Drought treatment began when the twelfth leaf was fully expanded and continued until tassel emergence. At tasseling, leaf tissues were collected from the flag leaves of three biological replicates under both control and drought stress conditions. All collected samples were immediately frozen in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for further analysis.\u003c/p\u003e \u003cp\u003eLeaf samples for physiological characteristic assays were collected from both control and drought-treated plants at the onset of the treatment, followed by subsequent collections every three days. Leaf RWC was assessed following the method described by Galm\u0026eacute;s et al. (2007). POD activity was determined using Han\u0026rsquo;s guaiacol method (Han et al., 2008), while proline content was quantified using the ninhydrin-based assay according to Bates et al. (1973). Lipid peroxidation levels were evaluated by measuring MDA accumulation via the thiobarbituric acid (TBA) reaction, following the protocol established by Dhindsa et al. (1981).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTotal RNA extraction, cDNA library construction and transcriptome analysis\u003c/h3\u003e\n\u003cp\u003eLeaf samples were sent to Shanghai Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China) for RNA isolation, cDNA library construction, and RNA sequencing. In brief, total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA, USA), and its quality and concentration were assessed using a NanoDrop 1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA) and electrophoresis on 1% agarose gels. cDNA libraries were then constructed following the manufacturer\u0026rsquo;s protocol using the NEBNext\u0026reg; Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg; (NEB, USA) and subsequently sequenced on the Illumina HiSeq 4000 platform using paired-end sequencing technology.\u003c/p\u003e \u003cp\u003eThe raw sequencing data (FASTQ format) generated from the Illumina HiSeq 4000 system underwent initial processing using in-house Perl scripts. Low-quality reads were filtered out, and the remaining clean reads were aligned to the maize reference genome (B73 RefGen_v4) using TopHat 2.0.12, a splice-aware aligner that allows intron-spanning gaps of up to 50 kb (Trapnell et al., 2009). Only reads with a perfect match or a single mismatch were retained for further analysis and genome annotation. Gene expression was quantified by counting mapped read numbers using HTSeq v0.6.1 and normalized as fragments per kilobase of transcript per million mapped reads (FPKM). Differential gene expression analysis was conducted using the DESeq R package (v1.10.1) (Anders \u0026amp; Huber, 2010), with genes exhibiting a fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;2 and a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 classified as differentially expressed.\u003c/p\u003e\n\u003ch3\u003eExpression validation of DEGs with qRT-PCR\u003c/h3\u003e\n\u003cp\u003eTo generate the cDNA template, 1 \u0026micro;g of total RNA, previously extracted and returned from Shanghai Majorbio Bio-pharm Technology Co., Ltd. was reverse-transcribed in a 20 \u0026micro;L reaction volume using the HiFiscript cDNA Synthesis Kit (CWBIO, Beijing, China), following the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003cp\u003eTo validate the sequencing results, 12 genes were selected based on their functional relevance, and specific primers for qRT-PCR analysis were designed using Premier 5 Designer software (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Quantitative real-time PCR (qRT-PCR) was performed using 2\u0026times; Fast Super EvaGreen\u0026reg; qPCR Mastermix (US Everbright Inc., Suzhou, China) on a C1000 Thermal Cycler (CFX96 Real-Time System, Bio-Rad). Each 20 \u0026micro;L qRT-PCR reaction mixture contained 1 \u0026micro;L of cDNA template, 1 \u0026micro;L each of forward and reverse primers (50 pmol), 10 \u0026micro;L qPCR master mix, and 7 \u0026micro;L ddH₂O. The maize GAPDH gene (accession No. X07156), known for its stable and constitutive expression, was used as an internal reference gene. The amplification protocol followed the method described by Zenda et al. (2018). Each sample was analyzed in three biological replicates, and gene expression levels were calculated using the 2\u003csup\u003e⁻ΔΔCT\u003c/sup\u003e method (Livak \u0026amp; Schmittgen, 2011).\u003c/p\u003e\n\u003ch3\u003eProtein extraction and isobaric labeling\u003c/h3\u003e\n\u003cp\u003eProteins were extracted from the same leaf samples used for RNA-Seq analysis using the cold acetone precipitation method, as detailed in our previous study (Liu et al., 2019). The extracted proteins were resuspended in 8 M urea, and their concentrations were determined using the Pierce Bicinchoninic Acid (BCA) Protein Assay Kit (23225, Thermo Fisher Scientific, Shanghai, China) following the manufacturer's protocol. Absorbance was measured at 562 nm using a SpectraMax iD3 Multi-Mode Microplate Reader (Molecular Devices, Shanghai, China). Protein integrity was assessed by SDS-PAGE (tricine-sodium dodecyl sulfate polyacrylamide gel electrophoresis) (Sw\u0026auml;gger, 2006). A total of 100 \u0026micro;g of protein was enzymatically digested with trypsin (Promega, Madison, WI, USA) at a protein-to-trypsin ratio of 50:1 at 37\u0026deg;C overnight (16 h), followed by a second 4-hour digestion at a ratio of 100:1. Post-digestion, peptides were desalted using a Strata X C18 SPE column (Phenomenex) and vacuum-dried. The dried peptides were then reconstituted in 0.5 M TEAB and labeled using the iTRAQ reagent kit, following the manufacturer\u0026rsquo;s instructions. Each unit of iTRAQ reagent, defined as the amount required to label 100 \u0026micro;g of protein, was dissolved in 70 \u0026micro;L acetonitrile. The labeled peptide mixtures were incubated at room temperature for 2 hours, then pooled, desalted, and vacuum-dried to differentiate isobaric tags. For control samples, drought-sensitive ZX978 was labeled with iTRAQ tag 127, while drought-tolerant ND476 was labeled with tag 129. For drought-treated samples, ZX978 and ND476 were labeled with tags 126 and 128, respectively.\u003c/p\u003e\n\u003ch3\u003eStrong cation exchange (SCX) and LC-MS/MS analysis\u003c/h3\u003e\n\u003cp\u003eEach labeled peptide sample was fractionated using high-pH reverse-phase high-performance liquid chromatography (HPLC) on an Agilent 300Extend C18 column (5 \u0026micro;m particle size, 4.6 mm inner diameter, 250 mm length). The resulting peptide fractions were then resuspended in 0.1% formic acid (solvent A) and loaded onto a strong cation exchange (SCX) reversed-phase analytical column. Peptide separation was achieved using a gradient elution, starting with an increase from 6\u0026ndash;23% solvent B (0.1% formic acid in 98% acetonitrile) over 26 minutes, followed by an increase from 23\u0026ndash;35% over 8 minutes, then rising to 80% within 3 minutes, and maintaining 80% for the final 3 minutes, all at a constant flow rate of 400 nL/min on an EASY-nLC 1000 ultra-performance liquid chromatography (UPLC) system.\u003c/p\u003e \u003cp\u003eMass spectrometry (LC-MS) analysis was conducted on a Q-Exactive mass spectrometer (Triple TOF 5600 Plus) equipped with an AB SCIEX analytical column. The m/z scan range for full-scan acquisition was set between 350 and 1800, with a resolving power of 120 K. Intact peptides were detected in the Orbitrap mass analyzer at a resolution of 70,000 at 200 m/z, with automatic gain control (AGC) set at 1 \u0026times; 10⁶ and the fixed first mass set to 100 m/z. For tandem mass spectrometry (MS/MS), peptides were selected based on a normalized collision energy (NCE) setting of 28, and fragment ions were analyzed in the Orbitrap at a resolution of 17,500. A data-dependent acquisition (DDA) method was employed, alternating between one MS scan followed by 20 MS/MS scans, with a dynamic exclusion time of 15.0 seconds.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtein identification and quantification\u003c/h2\u003e \u003cp\u003eThe original MS/MS data files were converted into mgf format using Proteome Discoverer 1.4 (Thermo Fisher Scientific Inc., Waltham, MA, USA). The converted data were then analyzed using Mascot software version 2.2 (Matrix Science, London, UK) (v.1.5.2.8) for database searching. The MGF files were queried against the Uniprot \u003cem\u003eZea mays\u003c/em\u003e L. database, which comprises 132,339 sequences, using the Mascot search engine. The Mascot search was performed with the following parameters: trypsin as the specified enzyme, allowing a maximum of two missed cleavages; fragment mass tolerance set at \u0026plusmn;\u0026thinsp;0.02 Da; peptide mass tolerance at \u0026plusmn;\u0026thinsp;20 ppm; and monoisotopic mass values. Fixed modifications included carbamidomethylation of cysteine (Cys) and oxidation of methionine (Met), as well as iTRAQ 8-plex labeling (Y), iTRAQ 8-plex (N-terminal), and iTRAQ 8-plex (K). To ensure high data reliability, a confidence threshold of 95% was applied, and the false discovery rate (FDR) was adjusted to \u0026lt;\u0026thinsp;1%. Peptide identification was considered significant only if the Mascot score exceeded 40. Differentially expressed proteins (DEPs) were identified based on protein abundance levels, with a significance threshold of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.3 (up-regulated) or \u0026lt;\u0026thinsp;0.77 (down-regulated).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eTo interpret the biological and functional properties of DEGs and DEPs, GO analysis was performed to categorize the biological processes involvement in response to drought stress. The GO terms and categories with a \u003cem\u003ep\u003c/em\u003e-vaule\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered significant according to Fisher's exact test. Moreover, MapMan was used to classify maize genes into hierarchical categories. A protein interaction network was constructed using the String program (version 10.5) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCorrelation of proteomics and transcriptomics\u003c/h3\u003e\n\u003cp\u003eWhen all identified proteins are compared against all genes using BLAST reciprocal best hits (RBH) analysis (Altschul et al., 1990), they are considered the same if they qualify as \"best BLAST hits\". Based on this criterion, the shared DEGs and DEPs from RNA-Seq and iTRAQ quantitative analysis are identified.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunction Validation of Drought Candidate Gene in Transgenic Arabidopsis Lines\u003c/h2\u003e \u003cp\u003eThe target gene fragments were cloned into the pGreen-35S-6HA expression vector and introduced into \u003cem\u003eArabidopsis thaliana\u003c/em\u003e (Col-0) via Agrobacterium tumefaciens-mediated floral dip transformation. Mature \u003cem\u003eArabidopsis\u003c/em\u003e plants were dipped following standard procedures. Seeds harvested from the transformed plants were collected and dried, representing the T\u003csub\u003e0\u003c/sub\u003e generation.T\u003csub\u003e0\u003c/sub\u003e seeds were stratified at 4℃for 2\u0026ndash;4 days, then sown densely onto a soil mixture of nutrient soil and vermiculite (1:1, v/v). When seedlings developed two well-formed true leaves, they were sprayed with Basta herbicide (25 mg/L glufosinate ammonium, working dilution 1:2000) every other day for a total of three treatments to select for transgenic-positive seedlings. Positive seedlings were then grown under normal conditions, and leaf tissues were collected for DNA and RNA extraction using the CTAB method and standard RNA isolation protocols, respectively. PCR and qRT-PCR were performed to confirm the presence and expression of the transgenes. Homozygous T\u003csub\u003e3\u003c/sub\u003e generation plants were obtained through subsequent propagation and used for further phenotypic and physiological analyses.\u003c/p\u003e \u003cp\u003eTo evaluate drought tolerance during germination, seeds from both wild-type (WT) and transgenic lines were surface-sterilized with 15% sodium hypochlorite for 5 minutes, rinsed 2\u0026ndash;3 times with sterile water, and then sown on 1/2 MS solid medium. After 4 days, uniform seedlings were transferred to 1/2 MS medium supplemented with 0 mmol/L, 200 mmol/L, or 300 mmol/L mannitol and vertically grown for 9 days under controlled conditions. Root length was measured, and root growth status was recorded.\u003c/p\u003e \u003cp\u003eA separate set of seedlings was transplanted into plug trays containing soil and grown for approximately 25 days until reaching similar growth stages. WT and transgenic plants were then divided into two groups: a control group with normal watering, and a drought treatment group, in which water was withheld for 7 days. After treatment, phenotypic differences were observed, and samples were collected for physiological measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis of physiological data\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using SPSS statistical software (version 22.0; SPSS Institute Ltd., Armonk, NY, USA), with data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean. To evaluate differences in physiological parameters across treatments and genotypes, a two-way analysis of variance (ANOVA) followed by the least significant difference (LSD) test was applied. Meanwhile, qRT-PCR data were analyzed using one-way ANOVA and Duncan's multiple range test. Statistical significance was determined at \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological characterization of the two maize hybrid cultivars under drought stress\u003c/h2\u003e \u003cp\u003eThe physiological drought stress responses of the two maize hybrid cultivars, ND476 and ZX978, were evaluated at the tasseling stage of maize plants exposed to gradual natural drought stress under field conditions. Compared to the control groups, the leaf RWC of two hybrid cultivars significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) decreased as the duration of drought stress increased, with ZX978 exhibiting a much steeper decline than ND476 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Moreover, physiological revealed that from the 3 to the 12 day of drought treatment, both Pro content and guaiacol POD activity showed a significant increase (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) in both cultivars. However, ND476 consistently maintained higher values at all time points under stress conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). Furthermore, the analysis of MDA content indicated that the drought-sensitive genotype ZX978 exhibited higher MDA levels than the tolerant ND476, with a sharp decline observed in ND476 from the 9 to the 12 day of drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In summary, under drought conditions, ND476 exhibited higher RWC, Pro content, and POD activity, whereas ZX978 showed relatively higher MDA levels, suggesting greater sensitivity to drought stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analysis of DEGs responsive to drought stress\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eTranscriptome profiling of two contrary tolerance hybrid cultivars under drought stress\u003c/h2\u003e \u003cp\u003eAfter removing low-quality reads, a total of 66.8\u0026nbsp;million clean reads, each 150 bp in length, were obtained from the twelve samples, averaging 5.56\u0026nbsp;million reads per sample (Table S2). Among these high-quality reads, 84.7\u0026ndash;92.9% were successfully mapped to the maize reference genome B73. Quality assessment metrics, including the Q30 base percentage and GC content, surpassed 96.01% and 55.64%, respectively, demonstrating the reliability and reproducibility of the sequencing data (Table S2). Principal component analysis (PCA) was performed to evaluate the relationships among the twelve samples. The results showed that biological replicates within each group clustered closely, while a clear distinction was observed between drought-tolerant and drought-sensitive cultivars (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings confirm that the experiment was highly reproducible and suitable for further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes (DEGs) analysis\u003c/h2\u003e \u003cp\u003eTo detect the transcriptional variations that occur in response to drought stress, genes of two contrary cultivars were tested for differential expression between the well-watered and water-deficit conditions used Cuffdiff software package. Within the tolerant genotype ND476, a total of 658 DEGs displayed differential abundance before and after drought treatment, with 399 DEGs being up-regulated and 259 down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Meanwhile, in the sensitive genotype ZX978, 1088 DEGs including 923 up-regulated and 175 down-regulated were identified before and after drought treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The Venn diagram shows a comparative analysis of the DEGs described above. There were 603 DEGs that were specifically expressed in drought-tolerant genotype ND476, including 365 up-regulated, and 238 down-regulated. A total of 1043 DEGs unique to drought-sensitive genotype ZX978, 878 were up-regulated and 165 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Meanwhile, 55 DEGs were commonly identified in the two cultivars under drought treatment, and are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and classification of drought-responsive DEGs\u003c/h2\u003e \u003cp\u003eTo reveal the underlying biological functions of the identified DEGs, gene ontology (GO) enrichment analysis (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was conducted by agriGO web-based program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://systemsbiology.cau.edu.cn/agriGOv2/#\u003c/span\u003e\u003cspan address=\"http://systemsbiology.cau.edu.cn/agriGOv2/#\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In drought-tolerant genotype ND476, oxidation-reduction process (GO:0055114), oxylipin metabolic process (GO:0031407) and single-organism metabolic process (GO:0044710) were the most significantly enriched biological process (BP) terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). With regards to drought stress response, GO terms response to abiotic stimulus (GO:0009628), response to water (GO:0009415), response to water deprivation (GO:0009414), and response to stress (GO:0006950) were apparent under BP category (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other hand, in drought-sensitive genotype ZX978, external encapsulating structure organization (GO:0045229), cell wall organization or biogenesis (GO:0071554) and negative regulation of catalytic activity (GO:0043086) were prominent in BP category (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Meanwhile, GO terms defense response (GO:0006952), response to stimulus (GO:0050896) and response to stress (GO:0006950) were annotated under BP category in responding drought treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe DEGs/DEPs enriched in the GO terms related to drought responses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOntology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDEGs/DEPs number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eND_DEGs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0009415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.50E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0009414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to water deprivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.50E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0050896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZX_DEGs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003edefense response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0050896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.30E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.50E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eND_DEPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0050896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.10E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZX_DEPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0006950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO:0050896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eresponse to stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.30E-02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThen, MapMan software was used to map the DEGs with over-represented \u0026lsquo;regulation\u0026rsquo; and \u0026lsquo;metabolism\u0026rsquo; terms. Resultantly, we observed that the cell cycle, RNA regulation of transcription, ribosomal protein synthesis, cell organisation, redox of ascorbate and glutathione, hormone metabolism of jasmonate (JA), and secondary metabolism of flavonoids were the most significantly enriched in tolerant hybrid cultivar ND476 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, the enriched categories of these DEGs expressed in ZX978 included cell wall, hormone metabolism, peroxidases, secondary metabolism of phenylpropanoids and and RNA regulation of transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Together, these results offer a general overview of the functional metabolic pathways altered by drought stress in maize tolerant and sensitive hybrid cultivars at the transcriptional level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of drought-response regulated DEGs into different categories according to annotate by MapMan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNongdan476 DEGs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eZhongxin 978 DEGs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNongdan476 DEPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eZhongxin 978 DEPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFunction annotion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUp-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDown-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDown-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUp-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDown-\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhotosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePhotosystem I/II, electron carrier (ox/red), photorespiration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAmmonium, ABC, metabolite, amino acids, sugars,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignalling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eReceptor-like kinase, calmodulin, MAPK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant hormones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eABA, BR, JA, SA, GA, auxin, ethylene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranscription factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC2H2 zinc finger family, HB, HSF, MYB,WRKY,bZIP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEGs related to detoxification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eThioredoxin, GST, peroxidase, ascorbate and glutathione\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEGs involoved in defense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHSPs, PRs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEGs response to abiotic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResponse to heat, drought/salt, cold\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIsoprenoids, flavonoids, sulfur-containing, phenylpropanoids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eValidation of DEGs by qRT-PCR\u003c/h2\u003e \u003cp\u003eTo validate the DEGs results from RNA-seq data, we performed a supporting experiment by using qRT-PCR analysis on 12 randomly selected genes related to drought stress response. These genes were selected based on following criterion: their expression patterns changed remarkably according to the DEG data, or their functions have been identified according to GO and MapMan enrichment analyses. Our results showed that all the tested genes presented similar expression patterns as the differential analysis results from RNA-seq (Fig. S2A). Interestingly, a high consistence (correlation coefficient, R\u003csup\u003e2\u003c/sup\u003e, of 92.11) between the qRT-PCR and RNA-seq was observed (Fig. S2B). In a nutshell, the qRT-PCR analysis results confirmed our transcriptomics analysis-based findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProteomic analysis of DEPs responsible for drought stress\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003eAnalysis of drought-responsive differentially expressed proteins (DEPs)\u003c/h2\u003e \u003cp\u003eWith the help of Mascot software (version 2.2), 17 5356 spectra were matched with known spectra (Uniprot \u003cem\u003eZea mays\u003c/em\u003e L. database: 132339\u0026thinsp;\u0026minus;\u0026thinsp;2018.01.12), 68 234 peptides, 56 163 unique peptides, and 5451 proteins were identified from twelve samples (Fig. S3). The PCA results showed a clear separation between the drought-sensitive cultivar ZX978 and the drought-tolerant cultivar ND476 (Fig. S4). Interestingly, the replicates of each treatment clustered together were similar with the PCA result of RNA-Seq.\u0026nbsp;Proteins with \u003cem\u003ep\u003c/em\u003e-vaule\u0026thinsp;\u0026lt;\u0026thinsp;0.05, difference ratio reaches\u0026thinsp;\u0026gt;\u0026thinsp;1.3 or \u0026lt;\u0026thinsp;0.77 and peptides\u0026thinsp;\u0026gt;\u0026thinsp;1 were used for a subsequent analysis as differentially expressed proteins (DEPs). Resultantly, in the tolerant cultivar ND476, we observed 187 DEPs (comprising 90 up-regulated and 97 down-regulated) before and after drought treatment. In the sensitive cultivar ZX978, 271 DEPs including 178 up-regulated and 93 down-regulated were identified before and after drought treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The Venn diagram displays a comparative analysis of the DEPs described above. Among the 153 DEPs specific to ND476 under drought stress conditions, 75 were up-regulated and 78 were down-regulated. Of the 237 DEPs unique to ZX978, 154 were up-regulated and 83 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Meanwhile, 34 DEPs were observed commonly expressed in two hybrid cultivars under drought stress conditions, and are shown by the hierarchical clustering analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and classification of drought-responsive DEPs\u003c/h2\u003e \u003cp\u003eTo gain a comprehensive understanding of proteomic changes, GO enrichment analysis of biological processes was performed using agriGO. In the drought-tolerant genotype ND476, key biological processes such as hydrogen peroxide metabolism (GO:0042743), reactive oxygen species metabolism (GO:0072593), and oxidative stress response (GO:0006979) were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, the GO terms response to stress (GO:0006950) and response to stimulus (GO:0050896) were also enriched, highlighting ND476\u0026rsquo;s adaptive mechanisms under drought stress (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the drought-sensitive genotype ZX978 exhibited significant enrichment in organonitrogen compound metabolism (GO:1901564), small molecule metabolism (GO:0044281), and photosynthesis (GO:0015979) within the biological process category (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, response to stress (GO:0006950) and response to stimulus (GO:0050896) were also enriched, indicating shared but distinct stress adaptation pathways in ZX978 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther functional enrichment analysis using MapMan identified key DEPs in ND476 associated with transport, photosynthesis, the TCA cycle/organic transformation, peroxidases, cell wall, RNA processing, and ribosomal protein synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, ZX978 showed significant enrichment in amino acid metabolism, glycolysis, transport, redox, major CHO metabolism, abiotic stress response, light reaction in photosynthesis, and hormone metabolism of abscisic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eProtein-Protein interaction (PPI) analysis of DEPs\u003c/h2\u003e \u003cp\u003eTo predict how drought stress signals are transmitted within maize leaf cells to regulate specific cellular functions, we conducted a protein-protein interaction (PPI) network analysis using the web-based tool STRING 10.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed 1 May 2020). By selecting DEPs with confidence scores above 0.7, we identified two major hub networks and four interacting protein pairs in the drought-tolerant hybrid cultivar ND476 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).The primary network consists of eleven proteins, primarily associated with ribosomal translation and RNA processing (Supplementary Table S3). The second cluster comprises eight proteins involved in photosynthesis (Table S3). Additionally, four protein pairs were predicted to participate in the drought stress response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the drought-sensitive cultivar ZX978, PPI analysis revealed one large and one small cluster, along with seven interacting protein pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of transcriptome and proteome data\u003c/h2\u003e \u003cp\u003eTo evaluate the congruence between RNA-Seq and iTRAQ, we performed a global correlation analysis based on the mRNA and protein data. Of the quantified proteins from iTRAQ, 85.31% and 85.67% were detected in the transcriptomic profiles in tolerant genotype ND476 and sensitive genotype ZX978, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A scatter plot analysis based on the log\u003csub\u003e2\u003c/sub\u003e-transformed mRNA and protein ratios was used to show the distribution of the corresponding gene expression and protein accumulation ratio. The result revealed a poor correlation coefficients in ND476 (r\u003csub\u003ePearson correlation\u003c/sub\u003e = 0.0441) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and ZX978 (r\u003csub\u003ePearson correlation\u003c/sub\u003e = 0.1679) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), respectively, between the expression levels of all quantified proteins and their corresponding mRNAs indicating transcription and expression of space and time inconsistency.\u003c/p\u003e \u003cp\u003eAmong the 658 DEGs and 187 DEPs identified in ND476, only 6 were commonly regulated both transcriptionally and translationally in response to drought (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), of which 4 gene had the same trend and 2 genes had the opposite tread at the mRNA and protein levels (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Extracellular ribonuclease LE (\u003cem\u003eZm00001d022630\u003c/em\u003e) involved in secondary metabolites of phenylpropanoid and ribonuclease-3 (\u003cem\u003eZm00001d032186\u003c/em\u003e) related to RNA phosphodiester bond hydrolysis showed increased abundance at transcription level, but decreased abundance at translation level. Natterin-4 (\u003cem\u003eZm00001d004344\u003c/em\u003e) was up-regulated between the two levels; whilst guaiacol peroxidase-1 (\u003cem\u003eZm00001d040702\u003c/em\u003e), putative uncharacterized protein (\u003cem\u003eZm00001d011461\u003c/em\u003e) and CP12-1 (\u003cem\u003eZm00001d044925\u003c/em\u003e) were down-regulated between the two levels. In the sensitive line ZX978, 7 DEPs could match to DEGs which showed up-regulated trend between the two levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Protein P21 (\u003cem\u003eZm00001d033455\u003c/em\u003e), stress-induced protein-1(\u003cem\u003eZm00001d021901\u003c/em\u003e) related to defense response, pathogenesis-related protein-1 (\u003cem\u003eZm00001d018734\u003c/em\u003e) involved in plant hormone signal transduction, SKU5 similar-13 (\u003cem\u003eZm00001d012524\u003c/em\u003e), cysteine proteinase inhibitor-5 (\u003cem\u003eZm00001d049111\u003c/em\u003e) associated with negative regulation of endopeptidase activity, basic endochitinase A (\u003cem\u003eZm00001d009936\u003c/em\u003e) related to carbohydrate metabolic process, and 23.6 kDa heat shock protein mitochondrial (\u003cem\u003eZm00001d052194\u003c/em\u003e) involved in response to heat showed increased abundance across two levels in response to drought (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverlap of DEGs and DEPs in two maize hybrid lines in response to drought\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eComparision\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eID\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDescription\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003emRNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFunction annotation\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog2FC\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvaule\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpr.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLog2FC\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePvaule\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eExpr.\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eNDD_NDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d004344/B4FHK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatterin-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.04E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.92E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d044925/B6U3H3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCP12-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.89E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.06E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCalvin cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d040702/A5H8G4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuaiacol peroxidase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.38E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.35E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResponse to abiotic/ Response to oxidative stress\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d022630/B6SSH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtracellular ribonuclease LE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.66E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePhenylpropanoid biosynthesis/\u003c/p\u003e \u003cp\u003eBiosynthesis of secondary metabolites\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d011461/B6SMQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePutative uncharacterized protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.81E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d032186/B4FBD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRibonuclease 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.98E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEndoribonuclease activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eZXD_ZXC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d033455/A0A1D6KZ34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein P21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.86E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.27E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResponse to abiotic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d021901/P33679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStress-induced protein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.56E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.41E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResponse to abiotic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d018734/A0A1D6HRU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathogenesis-related protein 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.97E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResponse to abiotic/MAPK signaling pathway - plant/Plant hormone signal transduction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d012524/C0PFW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSKU5 similar 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.45E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.60E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOxidation-reduction process\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d049111/Q4FZ48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCysteine proteinase inhibitor 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNegative regulation of endopeptidase activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d009936/B6TR38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasic endochitinase A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.81E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCarbohydrate metabolic process\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZm00001d052194/O64960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.6 kDa heat shock protein mitochondrial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.10E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.16E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eResponse to abiotic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eComparison, comparison groups, NDD_NDC, the tolerant line ND476 before and after drought treatment, ZXD_ZXC, the sensitive line ZX978 before and after drought treatment; \u003csup\u003e2\u003c/sup\u003eID, gene ID/protein identifying number in the UniProt database; \u003csup\u003e3\u003c/sup\u003eDescription, Protein functional characteristics derived from Gene Ontology classification; \u003csup\u003e4\u003c/sup\u003eLog2(FC), quantitative measurement of differential expression calculated as log2-transformed ratio between treatment and control groupsis ; \u003csup\u003e5\u003c/sup\u003ePvalue, statistical significant level (using a paired t-test)\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e6\u003c/sup\u003eExpr, gene expression level. Up-, up-regulated; Down-, down-regulated; \u003csup\u003e7\u003c/sup\u003eFunction annotation, GO or MapMan annotation in which the identified gene was found to be significantly enriched.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eDrought candidate genes function validation in transgenics Arabidopsis thaliana\u003c/h2\u003e \u003cp\u003eAccording to bioinformatics analysis, we observed \u003cem\u003eZmPOD\u003c/em\u003e DEG commonly regulated both transcriptionally and translationally, we also observed \u003cem\u003eZmRAV1\u003c/em\u003e and \u003cem\u003eZmTPP\u003c/em\u003e specifically expressed in the drought-tolerant line ND476 under drought condition. Through GO annotation and KEGG enrichment analyses,we found out that the peroxidases, starch and sucrose metabolism, and RNA regulation of transcription was significantly enriched after drought treatments both of transcript levels and the protein levels. Therefore, combining our analysis results and information from the published literature, we hypothesized \u003cem\u003eZmPOD, ZmRAV1\u003c/em\u003e and \u003cem\u003eZmTPP\u003c/em\u003e as potential contributor to drought stress tolerance, and we selected them for function verification in model plant \u003cem\u003eArabidopsis thaliana.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eDuring the germination stage, under normal conditions (0 mmol/L mannitol), the root lengths of transgenic lines were comparable to those of the wild type (WT). However, under osmotic stress induced by 200 mmol/L and 300 mmol/L mannitol on 1/2 MS medium, transgenic plants showed significantly enhanced root growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Specifically, at 300 mmol/L mannitol, transgenic lines exhibited approximately 30\u0026ndash;50% longer root lengths compared to WT, indicating improved osmotic stress tolerance during early development (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eAt the seedling stage (25-day-old plants), after a 7-day drought treatment, transgenic plants maintained more robust growth, with less wilting and dehydration symptoms compared to WT controls. Biochemical assays further supported these observations: transgenic lines displayed significantly higher POD and SOD activities, which are key antioxidant enzymes, suggesting reduced oxidative damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, these results demonstrate that the maize candidate genes contribute to enhanced drought tolerance in \u003cem\u003eArabidopsis\u003c/em\u003e by promoting root growth under osmotic stress and improving antioxidant defense mechanisms under drought conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eImprovement of drought tolerance in maize is one of the most challenging tasks owing to high complicacy of the traits and poor comprehension of plant response against drought stress. To this end, a full understanding of physiological, biochemical, and molecular regulatory networks relating to drought tolerance in plants becomes imperative in an attempt to improve that trait. Therefore, in the current paper, we have performed comparative transcriptome and proteomic analysis of two contrasting maize (drought-tolerant ND476 and drought-sensitive ZX978) hybrid cultivars, and we report key DEGs, DEPs and regulatory mechanisms involved in maize drought stress tolerance. Additionally, comparative physiological analyses of the two maize hybrid cultivars buttress the bioinformatics analysis results. Our findings enhance our further understanding of the mechanisms modulating drought tolerance in maize at tasseling stage, as well as providing foundational base to our future targeted cloning studies.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eMaize hybrid cultivars` contrasting physiological responses to drought stress\u003c/h2\u003e \u003cp\u003eIn maize, as in other crop species, different genotypes exhibit varying responses to drought and other environmental stresses. These responses manifest at multiple levels, including physiological and molecular, and can vary across different growth stages. In this study, our findings demonstrated a significant decline in leaf RWC in both hybrid cultivars under drought conditions. However, the drought-tolerant cultivar ND476 consistently maintained a higher leaf RWC than the sensitive genotype ZX978 throughout most of the stress exposure period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We propose that this higher RWC helped minimize cell turgor loss and structural damage, thereby reducing cellular stress. Similarly, our previous research (Zenda et al., 2018) reported that the drought-tolerant maize inbred line YE8112 exhibited significantly higher RWC than the sensitive inbred line MO17 at the seedling stage under both control and drought conditions.\u003c/p\u003e \u003cp\u003eUnder environmental stress, plants employ various strategies to maintain cellular integrity, including sustaining membrane and protein stability through osmotic adjustment and turgor maintenance, which help mitigate ROS damage (Oliver et al., 2007). Peroxidases serve as a primary defense mechanism by scavenging hydrogen peroxide (H₂O₂). In this study, ND476 exhibited higher peroxidase (POD) activity than ZX978 under drought conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), suggesting a stronger ROS detoxification capacity in ND476. Additionally, elevated proline levels reduce cell water potential, allowing plants to sustain turgor pressure and temporarily buffer against drought stress (Kumar et al., 2003). The greater accumulation of proline in ND476 under water deficit conditions may have contributed to its enhanced drought tolerance compared to ZX978 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eMDA is a byproduct of lipid peroxidation and is widely recognized as an indicator of cell membrane damage (Zhao et al., 2016). The increase in MDA levels observed in both maize hybrid cultivars under drought stress suggests that water deficiency can trigger membrane lipid peroxidation and cellular injury through ROS accumulation. Notably, the drought-tolerant genotype ND476 exhibited lower MDA content compared to the sensitive genotype ZX978, indicating a more effective ROS detoxification system in ND476, which likely contributed to improved cell membrane stability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eOverall, our findings demonstrate that the two maize hybrids exhibited distinct physiological responses to drought stress, with ND476 showing greater tolerance than ZX978. This enhanced drought resilience in ND476 may be attributed to its superior ROS scavenging capacity, better osmotic adjustment leading to higher water retention, and improved membrane stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eCell wall and signaling related genes are vital for maize survival under drought\u003c/h2\u003e \u003cp\u003eIn this study, numerous genes associated with cell wall metabolism were up-regulated in the drought-tolerant genotype ND476 in response to drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Notably, beta-expansins, O-glycosyl hydrolase, and leucine-rich repeat (LRR) proteins were expressed at both the transcript and protein levels. Beta-expansins, along with alpha-expansins, are well known as key regulators of cell wall modifications, particularly during tissue elongation (Reidy et al., 2001). O-glycosyl hydrolases have been linked to abiotic stress adaptation, phytohormone activation, cell wall remodeling, and lignification in rice (Opassiri et al., 2006). LRR proteins, which are believed to be cell wall-associated, contribute to growth rate regulation and modifications in cell wall composition and extensibility. Previous research has shown that glycine-rich and protein-rich proteins play roles in cell wall remodeling during drought stress at the flowering stage in maize (Li et al., 2007). Similarly, studies in soybean (\u003cem\u003eGlycine max\u003c/em\u003e L.) have reported differential expression of genes encoding cell wall proteins under drought stress (Creelman and Mullet, 1991). Furthermore, transcriptomic analysis of two maize inbred lines subjected to drought stress revealed significant changes in the expression of cell wall-related genes (Zenda et al., 2018).\u003c/p\u003e \u003cp\u003eSensing of the stress begins at cell wall and a signal is transmitted to the cell through the plasma membrane by changing the expression of a series of metabolites and proteins (Nouri et al., 2011). Furthermore, signaling related genes (such as calcium, G-proteins and protein kinases) also showed altered expression in the tolerant genotype ND476 at both transcript and protein levels in response to drought stress exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The signals from the receptors on the cell membranes are transduced downstream and this lead to the generation of secondary messengers including Ca\u003csup\u003e2+\u003c/sup\u003e, sugars and ROS (Wu et al., 2017). Calcium binding proteins sense the perturbation in the cytosolic Ca\u003csup\u003e2+\u003c/sup\u003e level, further modulating the intracellular calcium level (Oliver et al., 2007). Recently, GTP-binding proteins (small G-proteins) have been an intensively studied group of regulatory GTP hydrolyses related to cell signaling (Li et al., 2007). Umeda et al. (1994) revealed that GTP-binding proteins were salt induced and possibly associated with salt-stress signaling. Protein kinases, as the central signal transduction machinery, initiate phosporylation cascades and play vital roles in drought responses (Singh and Laxmi 2015). At the end of the signal transduction cascade, protein kinases modulate transcription factors (TFs), consequently influencing corresponding response to the downstream drought responsive genes (Wang et al., 2016). In summary, at the initial stage of stress signal transduction, cell wall perceived stress, secondary messengers and protein kinases bridging the gap between perception and transmission of the signals to the target genes and contributing to maize response to drought.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eTranscription factor (TF) related genes are essential in regulating drought stress response at the transcript level\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTranscription factors (TFs) act as critical controllers of multiple downstream stress-responsive genes. TF target genes constitute a regulon that is related to the repression/activation of genes involved in abiotic stress responses (Wang et al., 2016). A bunch of TF families such as bmZIP, bHLH, WRKY, NAC, and MYB has played a critical role in drought stress response research (Li et al., 2007; Singh et al., 2015; Zhao et al., 2016). Herein, more than 26 drought-responsive TF families were differentially expressed in the tolerant genotype ND476 under drought conditions at transcription level, including five MYB (2 up- and 3 down-), five bHLH (3 up- and 2 down-), four AP2/EREBP (all down-), four NAC (2 up- and 2 down-), three C3H zinc finger family (1 up- and 2 down-), three HB (all up-), two WRKY (1 up- and 1 down-) and two C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e zinc finger family (all down-) that were regulated in response to drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe role of TFs in regulating stress tolerance across various crops, including maize, has been extensively studied (Wang et al., 2016). In a transcriptome analysis investigating drought-responsive genes in two contrasting maize inbred lines, Zhang et al. (2017) identified five bZIP, three MYB, and one AP2/EREBP TFs associated with drought stress adaptation. Similarly, Song et al. (2017) reported that several TFs, including C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e, NAC, bHLH, and MYB, exhibited expression patterns closely linked to plant water potential, highlighting their involvement in maize drought response during flowering. In maize, multiple NAC, MYB, and WRKY genes have been identified, cloned, and characterized for their roles in abiotic stress regulatory pathways. Over-expression of \u003cem\u003eZmSNAC1\u003c/em\u003e in \u003cem\u003eArabidopsis\u003c/em\u003e significantly improved drought tolerance at the germination stage, indicating that \u003cem\u003eZmSNAC1\u003c/em\u003e plays a positive role in drought resistance (Lu et al., 2013). More recently, Wu et al. (2019) demonstrated that \u003cem\u003eZmMYB3R\u003c/em\u003e over-expression enhanced maize resilience to drought and salt stress. Conversely, constitutive expression of \u003cem\u003eZmWRKY17\u003c/em\u003e in \u003cem\u003eArabidopsis\u003c/em\u003e led to a marked decline in salt stress tolerance, as evidenced by increased relative electrolyte leakage, higher MDA content, reduced cotyledon greening rate, and impaired root growth (Cai et al., 2017). These findings suggest that \u003cem\u003eZmWRKY17\u003c/em\u003e may act as a negative regulator in maize abiotic stress responses.\u003c/p\u003e \u003cp\u003eOverall, the differential expression of TF genes likely plays a crucial role in the drought tolerance of the maize hybrid cultivar ND476 by orchestrating complex regulatory networks that enhance stress adaptation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003ePhotosynthesis related proteins contribute to drought tolerance\u003c/h2\u003e \u003cp\u003eUnder drought stress, plant growth is inhibited due to reduced biomass accumulation per unit area and decreased photosynthetic efficiency. Previous studies have shown that abiotic stress suppresses photosynthetic activity (Sharma et al., 2012; Wu et al., 2019), with net photosynthesis declining in maize during the V9-V10 stages under drought conditions, ultimately leading to abnormal ear primordium development (Song et al., 2017). In this study, we observed that drought stress not only down-regulated genes associated with PSII but also those related to PSI and photosynthetic electron carriers in the drought-tolerant maize genotype ND476 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Additionally, MapMan enrichment analysis revealed that photosynthesis-related pathways were significantly affected in ND476 under water deficit conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The decline in photosynthetic activity can be attributed to the accumulation of ROS, which cause damage within the PSII reaction center of the thylakoid membranes when excess light energy is not efficiently dissipated. In response, plants regulate PSII-associated proteins to maintain a balance between light absorption, energy utilization, and non-photochemical quenching mechanisms (Zenda et al., 2018). A study by Thirunavukkarasu et al. (2017) on maize similarly reported that genes involved in photosynthesis were down-regulated under drought conditions. Consequently, the observed reduction in photosynthesis-related protein abundance in the hybrid cultivar ND476 suggests that drought stress negatively impacts the photosynthetic process.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePlant hormones play vital roles in drought stress response regulation\u003c/h3\u003e\n\u003cp\u003ePhytohormones play crucial roles in regulating plant growth, development, nutrient allocation and source/sink transitions for adapting to stressful environments (Peleg and Blumwald, 2011). In our study, several ABA, JA, BRs, Gas and auxin genes were altered in their expressions at transcript and protein levels in response to drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). ABA is a crucial signaling molecule in plant responses to abiotic stress. Under drought conditions, plants accumulate ABA, which subsequently activates downstream stress responses (Zhu, 2016). At the molecular level, ABA serves as a key regulator of gene expression, protein synthesis, signaling cascades, and the production of essential protective compounds that mitigate water loss. While ABA is the most extensively studied stress-related hormone, the role of other phytohormones in environmental stress adaptation is becoming increasingly evident. BRs contribute to stress tolerance by inducing the expression of stress-responsive genes, enhancing antioxidant enzyme activity to counter oxidative damage, maintaining photosynthetic efficiency, and promoting osmoprotectant accumulation under drought conditions (Peleg and Blumwald, 2011). In a transcriptome study investigating gene expression changes under 20% polyethylene glycol (PEG) 6000-induced drought stress in common buckwheat (Fagopyrum esculentum), Wu et al. (2019) reported that BR-related genes were highly associated with drought response. Similarly, transcriptomic analyses in maize have shown a reduction in GA levels under abiotic stress, suggesting that GA may negatively influence stress adaptation (Li et al., 2017).\u003c/p\u003e \u003cp\u003eAuxin regulates the expression of numerous genes collectively termed primary auxin response genes, which fall into three major families: Aux/IAA, GH3, and SAUR. Recent studies suggest that auxin is also involved in stress and defense responses. Expression profiling and mutant analysis indicate that auxin pathway suppression plays a crucial role in plant defense mechanisms (Wang et al., 2007). Consistent with our findings, genes involved in SA and JA biosynthesis were significantly up-regulated in common buckwheat seedlings under drought conditions (Wu et al., 2019).\u003c/p\u003e \u003cp\u003eOverall, interactions between different plant hormones create a complex network of synergistic and antagonistic relationships, which collectively regulate maize responses to drought stress.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eResponse to stress- and response to stimuli- related genes under drought conditions\u003c/h2\u003e \u003cp\u003eOur GO enrichment analysis of genes expressed in the drought-tolerant genotype ND476 identified 46 DEGs and 5 DEPs significantly associated with the GO term \u0026ldquo;response to stress (GO:0006950).\u0026rdquo; Further examination revealed that these genes were also enriched in the GO category \u0026ldquo;response to stimuli (GO:0050896).\u0026rdquo; This group included several heat shock proteins (HSPs), dehydrins (DHNs), peroxidases, glutathione S-transferases (GSTs), and mitogen-activated protein kinases (MAPKs), among others (Supplementary Table S4). In this study, small heat shock proteins (sHSPs) and HSP70 were up-regulated under drought conditions. HSPs play a crucial role in plant stress protection by ensuring proper protein folding, preventing aggregation, and maintaining cellular homeostasis under adverse conditions (Wang et al., 2004). Additionally, DHNs, a specialized subgroup of late embryogenesis abundant (LEA) proteins, showed increased abundance during drought stress. These proteins are widely present in plants and are responsive to abscisic acid (ABA), with their expression being induced by this phytohormone. DHNs have been recognized as key players in plant stress adaptation (Hanin et al., 2010).\u003c/p\u003e \u003cp\u003eInterestingly, eight peroxidases were down-regulated while one was up-regulated in response to drought stress. Peroxidases serve as a primary defense mechanism, mitigating oxidative damage by neutralizing toxic peroxides and ROS (Sharma et al., 2012). Similar protective roles of peroxidases against ROS-induced damage have been observed in wheat under drought conditions (Sheoran et al., 2015). Likewise, Khan and Komatsu (2016) highlighted their critical function in ROS scavenging and maintaining redox homeostasis in soybean roots. Moreover, two key detoxification enzymes, GSTs, exhibited down-regulation under drought stress. GSTs facilitate the detoxification of harmful compounds, including ROS and xenobiotics, through conjugation reactions. Additionally, one MAPK was up-regulated, while another was down-regulated, suggesting a complex regulatory network involved in redox homeostasis, signaling pathways, and abiotic stress interactions. Jonak et al. (1996) demonstrated that MAPK cascades are essential for enzyme activation and deactivation via phosphorylation and dephosphorylation, enabling rapid and specific signal transduction in response to external stimuli.\u003c/p\u003e \u003cp\u003eOverall, these findings suggest that under drought conditions, maize hybrid ND476 enhances cellular redox balance and activates drought-responsive gene expression to regulate ROS levels, thereby improving its resistance to drought stress.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eSecondary metabolism related pathways play vital roles in response to drought\u003c/h2\u003e \u003cp\u003eAs previously stated (Zenda et al., 2018), secondary metabolism plays a crucial role in the plant response to drought stress. In this study, we observed that numerous enzymes associated with secondary metabolite biosynthesis were activated in the drought-tolerant hybrid cultivar ND476 at both transcript and protein levels under drought conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Wink (2013) highlighted that while primary metabolites are essential for plant growth and development, secondary metabolites primarily contribute to ecological functions, particularly in plant defense mechanisms.Supporting this, Kr\u0026oacute;l et al. (2017) demonstrated the significant role of secondary metabolism in grapevine under prolonged drought stress. Consistent with our findings, multiple metabolic pathways, including flavone and flavonol biosynthesis, as well as isoflavonoid and flavonoid biosynthesis, have been shown to undergo alterations in response to abiotic stress in \u003cem\u003eRehmannia glutinosa\u003c/em\u003e L. (Tian et al., 2017). Collectively, the observed changes in gene expression related to secondary metabolism following water deprivation indicate a broad metabolic shift in maize as an adaptive response to drought stress.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eFunction Analysis of ZmPOD, ZmRAV1, and ZmTPP\u003c/h2\u003e \u003cp\u003eIn this study, we functionally validated three maize drought-responsive candidate genes, \u003cem\u003eZmPOD\u003c/em\u003e, \u003cem\u003eZmRAV1\u003c/em\u003e, and \u003cem\u003eZmTPP\u003c/em\u003e, in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. Our findings revealed that transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines over-expressing these genes exhibited enhanced drought tolerance at both germination and seedling stages, which is consistent with their transcriptional and translational regulation patterns observed in the drought-tolerant maize line ND476.\u003c/p\u003e \u003cp\u003e \u003cem\u003eZmPOD\u003c/em\u003e encodes a peroxidase enzyme, which is pivotal in scavenging ROS generated under drought-induced oxidative stress. The significantly higher POD and SOD activities observed in transgenic lines suggest that \u003cem\u003eZmPOD\u003c/em\u003e contributes to the detoxification of ROS, maintaining cellular redox homeostasis, and protecting membrane integrity. Previous studies have highlighted the role of PODs in various abiotic stresses, including drought and salinity, where enhanced peroxidase activity correlates with increased stress tolerance in multiple plant species, such as rice and wheat (Zhang et al., 2016, Wang et al., 2018).\u003c/p\u003e \u003cp\u003e \u003cem\u003eZmRAV1\u003c/em\u003e, a member of the RAV transcription factor family, was specifically expressed in the drought-tolerant ND476 line under drought stress, implying a regulatory role. RAV family transcription factors have been reported to integrate stress signals and modulate down-stream gene expression related to growth inhibition and stress adaptation (Fu et al., 2014). Furthermore, the GO enrichment results suggested that the \"regulation of transcription\" pathway was activated at both the transcriptome and proteome levels, supporting the hypothesis that \u003cem\u003eZmRAV1\u003c/em\u003e may act as a central regulator coordinating drought-responsive gene expression networks.\u003c/p\u003e \u003cp\u003e \u003cem\u003eZmTPP\u003c/em\u003e encodes a trehalose-6-phosphate phosphatase, an enzyme involved in the trehalose biosynthesis pathway, which has been associated with stress tolerance through its role in carbohydrate metabolism and osmoprotection (Paul et al., 2001). The KEGG enrichment analysis revealed that \"starch and sucrose metabolism\" was significantly enriched, indicating that \u003cem\u003eZmTPP\u003c/em\u003e might modulate sugar signaling and energy homeostasis under drought stress. Accumulation of trehalose and related metabolites has been shown to enhance drought tolerance by stabilizing proteins and membranes, as well as improving water retention capacity in plants (Lyu et al., 2012).\u003c/p\u003e \u003cp\u003eTaken together, our data provide strong evidence that \u003cem\u003eZmPOD\u003c/em\u003e, \u003cem\u003eZmRAV1\u003c/em\u003e, and \u003cem\u003eZmTPP\u003c/em\u003e act synergistically to enhance drought tolerance via distinct but complementary mechanisms, including ROS scavenging, transcriptional regulation, and osmotic adjustment. Importantly, the consistent phenotypical and biochemical improvements observed in the transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines validate the accuracy and biological relevance of our integrated transcriptomic and proteomic analyses. This functional verification reinforces the reliability of our omics-based candidate gene selection and highlights the effectiveness of combining multi-omics with reverse genetics approaches for uncovering key drought-responsive genes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo unravel the molecular mechanisms underpinning maize drought tolerance, in the present study, we have applied a comprehensive physiological, transcriptomic and proteomic analysis approach to decipher the differential responses of tolerant ND476 and sensitive ZX978 maize hybrid cultivars to field drought stress at the tasseling stage. Our physiological analysis revealed that the drought-tolerant genotype ND476 demonstrated superior resilience to drought stress, primarily due to its enhanced ROS scavenging capacity and effective osmotic regulation, which contributed to improved cell water retention and greater membrane stability. RNA-seq and iTRAQ analysis identified 1701 DEGs and 424 DEPs, respectively to respond to drought stress treatment. Chief among those DEGs and DEPs were those related to signal transduction, cell-wall remodelling, cellular redox homeostasis, and hormone metabolism. Further, MapMan analysis also revealed that the transcription factor regulation and secondary metabolism play critical roles in maize response to drought stress at mRNA-level, as well as photosynthesis play important roles at the protein-level. However, a weak correlation between DEGs and DEPs was observed, indicating the drought response of maize in tasseling stage is largely regulated post-transcriptionally. Additionally, the expression changes of ten representative DEGs that were detected by qRT-PCR analysis were highly correlated (R\u003csup\u003e2\u003c/sup\u003e=92.11) to the corresponding RNA-seq expression changes. In addition, we characterized and analyzed the function of drought response genes \u003cem\u003eZmPOD\u003c/em\u003e, \u003cem\u003eZmRAV1\u003c/em\u003e, and \u003cem\u003eZmTPP\u003c/em\u003e through phenotypic and physiological analyses of the WT plant and transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines. Under drought stress, the transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines had stronger resistance to drought stress than the WT plant. Overall, this study provide an elaborate understanding of the molecular networks mediating maize drought tolerance and offer fundamental basis for further targeted researches such as cloning and downstream analysis of the identified specific individual genes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDEGs:Differentially Expressed Genes; DEPs:Differentially Expressed Proteins; RNA-Seq:RNA Sequencing; iTRAQ: Isobaric Tags for Relative and Absolute Quantitation; qRT-PCR:Quantitative Real-Time Polymerase Chain Reaction; ROS:Reactive Oxygen Species; WT:Wild-Type; BCA:Bicinchoninic Acid; SDS-PAGE: Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis; SCX:Strong Cation Exchange; LC-MS/MS:Liquid Chromatography–Tandem Mass Spectrometry; PCA:Principal Component Analysis; GO:Gene Ontology; FC: Fold Change\u003c/p\u003e\n\u003cp\u003eFDR:False Discovery Rate; RWC:Relative Water Content; Pro: Proline; POD:Peroxidase; MDA:Malondialdehyde; ND476:Drought-tolerant maize genotype (Nongdan 476); ZX978:Drought-sensitive maize genotype (Zhongxin 978); TCA:Tricarboxylic Acid (Cycle); MS:Murashige and Skoog; HSPs: Heat Shock Proteins; DHNs: Dehydrins; GSTs: Glutathione S-transferases; MAPKs: Mitogen-Activated Protein Kinases \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors have reviewed the final version of the manuscript and have given their consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e: The authors declare that they have no conflict of interest. Furthermore, the founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003eSL, ZH, HZ and SW conceived and designed the experiment; SL, HS, LY, CJ and HL performed the investigations and collected data; SL, HS, LY, CJ and HL analyzed the data; SL wrote the original manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors sincerely thank Prof. Huijun Duan (Hebei Agricultural University, China) for helping with valuable suggestions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundin\u003c/strong\u003eg: Modern Agricultural Industrial Technology System in Hebei Province (HBCT2023020202), Zhangjiakou Science and Technology Bureau Project (2311025C),Doctoral Start-up Fund of Hebei North University (BSJJ202224).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e: The raw sequencing data were deposited at the NCBI Sequence Read Archive (SRA, Accession SPR212360)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAltschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., 1990. 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Cell 167, 313\u0026ndash;324.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"transcriptome, proteome, physiological responses, drought stress, Zea may L., tasseling stage","lastPublishedDoi":"10.21203/rs.3.rs-6492029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6492029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought stress particularly at the tasseling stage is the most devastating abiotic factor and major contributor to yield reduction in maize (\u003cem\u003eZea mays\u003c/em\u003e L.). Despite recent scientific evolution in deciphering maize drought stress responses, the overall picture of key genes and proteins regulating maize tasseling drought tolerant is not understood. In this study, we conducted comparative physiological, transcriptome and proteomic analyses approach to monitor the changes in the leaf tissues of two contrasting hybrid cultivars exposed to drought stress at the tasseling stage. We identified 1701 differentially expressed genes (DEGs) in RNA-sequence runs and 424 differentially expressed proteins (DEPs) from an iTRAQ-based analysis. Mapman analysis revealed that several regulatory processes were influenced by drought conditions, especially signal transduction, cell-wall remodelling, cellular redox homeostasis, and hormone metabolism were observed in both mRNA- and protein-level. However, transcription factor regulation and secondary metabolism were specifically identified at the transcript level, whereas photosynthesis was uniquely identified to be affected by drought stress at the protein level. Meanwhile, a weak correlation between DEGs and DEPs was observed, indicating the drought response of maize at tasseling stage is largely regulated post-transcriptionally. Furthermore, comparative physiological analysis and qRT-PCR results substantiated the trancriptomic and proteomic findings. Additionally, we screened \u003cem\u003eZmPOD\u003c/em\u003e, \u003cem\u003eZmRAV1\u003c/em\u003e, \u003cem\u003eZmTPP\u003c/em\u003e and performed phenotypical and physiological characterizations of transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines and wild-type. Resultantly, the transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines exhibited stronger tolerance to drought than the WT. This functional verification reinforces the reliability of our omics-based candidate gene selection. Overall, our research provides an elaborate understanding of drought-responsive genes and pathways mediating maize drought tolerance at the tasseling stage.\u003c/p\u003e","manuscriptTitle":"Identification of candidate genes and proteins for tasseling stage drought tolerance through integrated transcriptomic and proteomic analysis approach in maize","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 13:04:34","doi":"10.21203/rs.3.rs-6492029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T09:32:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T03:17:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-06T10:35:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337306812634668971446323998428339148555","date":"2025-05-02T03:15:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326410978084107962750501677853553918895","date":"2025-04-28T19:07:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T15:26:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T15:15:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T05:34:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T09:30:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-04-25T09:28:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c8d2012-66c2-4029-9269-740be0df7872","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T15:58:36+00:00","versionOfRecord":{"articleIdentity":"rs-6492029","link":"https://doi.org/10.1186/s12870-025-07264-5","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-10-08 15:56:51","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-04-30 13:04:34","video":"","vorDoi":"10.1186/s12870-025-07264-5","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07264-5","workflowStages":[]},"version":"v1","identity":"rs-6492029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6492029","identity":"rs-6492029","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}