Enhancing Stress Resilience in a Drought-Tolerant Zea mays Cultivar by Integrating Morpho-Physiological and Proteomic Characterization

Abstract

Maize is not only a staple across the sub-Saharan Africa (SSA) region but also a substantially economically valuable cereal crop. As a seasonal crop, its successful cultivation depends on favorable rainfall patterns and climatic conditions. However, environmental stresses such as drought limit its productivity. Enhancing stress resilience requires understanding the morphological, physiological, and proteomic response mechanisms that contribute to drought tolerance. Hence, it is critical to understand its adaptive capacity at the protein level to achieve improved stress-tolerant cultivars and increased yields in the future. Our study investigated drought stress responses in a drought-tolerant maize cultivar subjected to polyethylene glycol (PEG)-induced water deficit, combining one-dimensional gel electrophoresis (1DE) with LC-MS/MS to profile the leaf proteome. From the analysis, 50 of the 439 identified maize leaf proteins were further studied due to their significant differential expression and functional relevance, revealing the interconnection between the proteomic patterns as well as the morphological and physiological responses that enable drought resilience. These insights provide a foundation for improving stress-tolerant maize cultivars through integrative characterization approaches.

1. Introduction

Recently, climate change has intensified food insecurity across sub-Saharan Africa (SSA) with long-term negative macroeconomic effects, particularly on economic growth and poverty [1]. The agricultural sector supports over 70% of South African regions; however, feeding approximately 9.9 billion people has become a challenging task [2,3,4]. Drought has been a prevalent problem inhibiting crop production for years, affecting agricultural yields globally and particularly in semi-arid areas [5]. Drought is a climatic phenomenon defined as a sufficiently long period of imbalance between precipitation and evaporation that can increase soil water scarcity and cause water stress in plants, limiting their development and/or production [5,6]. However, drought can also occur as a result of high light intensity, high temperature, and dry winds [7]. Drought affects morphology, physiology, and molecular processes, such as delayed flowering, decreased dry matter accumulation and distribution, decreased photosynthetic rate, metabolic limitations, and oxidative chloroplast damage [5]. Nonetheless, plants have developed coping mechanisms such as osmotic adjustment, changes in photosynthetic pigments, leaf reduction, hormone stimulation, and accumulation of antioxidant scavengers [8]. Hence, being mindful of plants such as halophytes, gypsophytes, and xerophytes that are naturally adapted to grow under stressful conditions, research indicates that natural plant species have a greater tolerance and faster mechanisms to adapt or endure biotic and abiotic stresses than cultivated plant species [9].

On the contrary, Lippmann [10] states that, unlike natural plant species that rely on natural selection, species selection by scientists and breeders greatly improves crop resilience and resistance to extreme conditions; thus, the generation of artificially selected climate change-resilient crops by plant breeding is a realistic goal. To meet food demands, plant scientists and breeders are under pressure to improve and develop high-yielding crops that are resilient and resistant to extreme conditions.

The application of “omics” technologies such as genomics, transcriptomics, proteomics, and metabolomics to agriculture ensures consistency and predictability in plant breeding processes and produces high-quality food crops of nutritional value that are resistant to both biotic and abiotic stresses [11]. Proteomics is a cutting-edge technology that focuses on the qualitative and quantitative study of proteins, specifically their composition, structures, functions, and interactions with other proteins that control cellular activities [12,13]. Proteomics analysis is significant for various applications such as (i) better understanding of biology at the whole plant level; (ii) food analysis and safety issues; (iii) promoting molecular markers development for plant improvement support using modern technologies and breeding programs; and (iv) biomarker development for food security and human health [14].

While there are economically viable methods to increase crop production under harsh conditions, the production of drought-tolerant maize plants is a potential strategy to meet the growing food demand in both developed and developing countries. However, an understanding of the physiological and genetic mechanisms in the developmental stages is essential [5]. Hence, this study investigated the morphological, physiological, and molecular mechanisms regulating drought resistance in a Z. mays (CAP 311) cultivar exposed to drought stress.

2. Materials and Methods

2.1. Plant Material, Sterilization, Growth, and Treatment Conditions

The Z. mays cultivar CAP 311 was carefully chosen for this study. The seeds were obtained from Capstone Seeds Potchefstroom, RSA. The seeds were selected for uniformity based on size and physical appearance for each pot. The seeds were surface-sterilized in a 50 mL Falcon tube with 70% (v/v) ethanol and vortexed for a minute, followed by further decontamination with 1.25% (v/v) sodium hypochlorite solution (bleach) for 10 min. Subsequently, sterilized seeds were washed three times with 3 mL of sterile distilled water. The washed seeds were allowed to imbibe in sterile distilled water at room temperature for 20 min to break seed dormancy and promote germination. Five sterilized seeds were spread over sterile wet vermiculite in each of 12 Petri dishes of 90 mm × 15 mm size. The whole set was placed in a growth chamber system (Lab Companion, Model GC-300TL, Jeio Tech, Daejeon, Republic of Korea) with bright diffused light, 70–80% relative humidity, and 23 °C temperature for 7 days. On the 8th day, the sprouts were transplanted into 12 plastic plant pots (20 cm diameter). Each pot was filled with a 3:2 (v/v) mixture of sterile potting soil (Culterra professional potting mix, Builders, Park Rynie, South Africa) and vermiculite (Sanscape vermiculite, Builders, Park Rynie, South Africa). All seedlings were watered with 200 mL of sterilized tap water at an interval of 2 days for 20 days of long days (16 h) and short nights (8 h) under greenhouse conditions. After 20 days of seedling growth, maize plants were split into three independent groups (4 plants per treatment) in a random selection to eliminate any variation due to environmental conditions. Treatment of plants was carried out, where the control plants (untreated) were irrigated with sterilized tap water only, and experimental plants (2 treatment groups) were irrigated with either 10% (w/v) or 25% (w/v) polyethylene glycol (PEG) (6000) solutions at an interval of 2 days for 20 days under greenhouse conditions. The plants were closely monitored until the treatment was stopped. The plant tissues (leaves, roots, and shoots) were collected considering four biological replicates in each treatment group for morphological, physiological, and molecular analysis. The harvested samples were frozen in liquid nitrogen to stop any enzymatic activity. The tissue samples were ground in liquid nitrogen using a mortar and pestle and stored at −80 °C until further processing.

2.2. Morphological Measurements

Growth Functional Traits

Height for all plant treatments was measured in centimeters (cm). The number of leaves per pot plant was counted manually. The length (cm) for randomly selected shoots and roots from each treatment group was measured to assess the plants’ compatibility with the varying treatments. The leaf area measurements were obtained according to Vázquez-Arellano [15] with the formula: LA = 0.75 × L × W, where L and W are the length and width of the maize leaf, respectively, not considering leaves touching the ground. The leaf and root fresh weights (FWs) were immediately weighed after harvesting using an electronic weighing balance (Radwag, model # PS 750/C/2, Lasec, Midrand, RSA) in grams. The leaf turgid weight (TW) was recorded after soaking in water overnight. The leaf dry weight (DW) was recorded after drying in an oven (NWU Instrument Makery, model#AA838MB-AT, Potchefstroom, RSA) at 80 °C for 24 h. The relative water content (RWC) was then determined as leaf RWC (%) = [(FW − DW)/(TW − DW)] × 100, where FW is fresh weight, TW is turgid weight, and DW is dry weight [16]. All plant growth parameters were measured in three independent biological replicates.

2.3. Physiological Measurements

2.3.1. Gas Exchange Parameters

The effect of drought stress was assayed by measuring the photosynthetic rate (A) (µmol. m⁻².s⁻¹), transpiration rate (E) (mol. m⁻².s⁻¹), and stomatal water conductance (gs) (mol. m⁻².s⁻¹) using a portable gas analyzer LCpro-SD (ADC BioScientific, Hertfordshire, UK) on fully developed plant attached leaves. The respiration rate was recorded as the negative values of transpiration against time. The measurements were taken for 3 min at 10 s intervals under ambient temperature (25–27 °C).

2.3.2. Leaf Stomatal Density

To assess the variation in stomatal number, the abaxial and adaxial surfaces of selected leaves were carefully smeared with a thin layer of clear nail polish and allowed to dry for 3–5 min. Using clear tape (16 mm × 20 mm), thin imprints were peeled off from both leaf surfaces, mounted on a microscope slide, and smoothed to avoid air bubbles according to Wu and Zhao [17]. Field-of-view (FOV) micrographs per slide were captured at 400x magnification using a Primo Star light microscope (Carl Zeiss Microscopy, Jena, Germany) connected to a digital camera (Axiocam 208 color, Zeiss, Jena, Germany). Image analysis was performed using ImageJ 1.x software (https://imagej.net/ij/) to measure the length and width of the stomatal opening in µm. To estimate the stomatal density, the number of stomata per FOV was divided by the area of each field, i.e., stomatal density = stomata/area of FOV, where FOV = field number/magnification number and area of FOV =$\pi r^2$ [18].

2.3.3. Protein Extraction and Quantification

Protein was extracted from the leaves of five biological replicates of the control (non-treated) and experimental groups (10 and 25% PEG treated). The protein crude extraction was prepared using a NucleoSpin^® TriPrep kit (Catalog# 740966, Macherey-Nagel, Düren, Germany) by adding 350 µL lysis buffer (RP1) and 3.5 µL reducing agent (β-mercaptoethanol) to the ground leaf tissues, followed by centrifugation of the homogenate for 1 min at 11,000× g in a NucleoSpin^® Filter. About 350 µL of 70% ethanol was added to the flow-through in a sterile Eppendorf tube and vortexed twice for 5 s. A total of 700 µL of the recovered flow-through was transferred into a fresh sterile Eppendorf tube and one volume of protein precipitator (PP) was added to it, followed by vigorous mixing. The cocktail mixture was incubated at room temperature for 10 min to increase protein yield, followed by centrifugation for 5 min at 11,000× g. Subsequently, the obtained supernatant was discarded, while the precipitate was washed with 500 µL of 50% ethanol and centrifuged for 1 min at 11,000× g. The pellet was air-dried at room temperature for 10 min. Two sets of the dried pellets were stored in separate 2 mL Eppendorf tubes (one set for 1 SDS-PAGE and the other set for liquid chromatography mass spectrometry analysis) at −20 °C.

The total protein concentrations were measured from all sets of pellets for each treatment condition using a 2000 Nanodrop spectrophotometer (Thermo Scientific Inc., Santa Clara, CA, USA). A set of known protein concentration per sample (about 40 μL) was kept aside for sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis to determine its purity and quality. The protein concentration was calculated from an absorbance of 280 nm through a NanoDrop following a modified Beer’s Law equation with an E1% of 6.7 [19].

2.4. SDS-PAGE-Based Protein Profiling and Mass Spectrometric Identification (LC-MS/MS)

SDS-PAGE was prepared according to the Laemmli [20] method, consisting of 12% (v/v) running and 5% (v/v) stacking gels. One set of the stored pellets was thawed on ice to evaluate the quality of varying protein extracts. About 20 µL of protein-solubilizing buffer containing TCEP, PSB-TCEP (Macherey-Nagel, Düren, Germany) was added to the dried pellet and incubated in a D1100 AccuBlock digital dry bath (model # 1660562; Bio-Rad Laboratories, Berkeley, CA, USA) for 5 min at 95 °C for protein denaturation. The samples were allowed to cool to room temperature and centrifuged at 11,000× g for 5 min. About 3 µL of unstained protein marker (Catalog# P7704S New England Biolabs Inc., Ipswich, MA, USA) was run alongside 10 µg of the extracted leaf protein samples from the control and experiments (10 and 25% PEG treated), then electrophoresed at 200 volts until the dye front had reached the end of the gel. Protein gel was stained for 30 min with Coomassie brilliant blue R-250 (Sigma), followed by de-staining (100% acetic acid, ethanol, methanol, and sterile distilled water) for 45 min on an ultra-rocker (Bio-Rad Laboratories Inc., Berkeley, CA, USA). The gel was rinsed with sterile distilled water and visually inspected for osmotic stress-responsive proteins. The developed gel was photographed with a Chemi DOC^TM Imaging system (Bio-Rad Laboratories Inc., Berkeley, CA, USA) using the Bio-Image Lab^TM software, V 3.0.1.

The label-free shotgun proteomics approach was used to measure the peptides present in a sample and determine the abundance of proteins from different samples [21]. A total of 15 pelleted protein samples (5 biological replicates) were sent for LC-MS/MS analysis at the Centre for Proteomic and Genomic Research (CPGR, Woodstock, Cape Town, South Africa).

The 15 protein pellets were resuspended and denatured by adding 2% sodium dodecyl sulphate (SDS, Sigma) and 50 mM triethylammonium bicarbonate (TEAB, Sigma, Montréal, Canada) at 95 °C for 10 min. Subsequently, all the denatured proteins were centrifuged at 10,000× g for 5 min. Protein concentration was determined using the QuantiPro BCA assay kit (Sigma) following the manufacturer’s instructions. HILIC beads (ReSyn Biosciences, Gauteng, RSA) were aliquoted into a new tube and the shipping solution was removed in preparation for the HILIC magnetic bead workflow. The beads were washed for one minute with 250 µL wash buffer (15% acetonitrile (ACN), 100 mM ammonium acetate (Sigma), pH 4.5), followed by resuspension to a concentration of 2.5 mg/mL in loading buffer (30% ACN, 200 mM ammonium acetate, pH 4.5). A total of 20 µg was transferred from each sample to a protein LoBind plate (Merck, Darmstadt, Germany). The lysate was reduced with 20 mM dithiothreitol (DTT; Sigma) and alkylated with 30 mM iodoacetamide (IAA; Sigma) at 95 °C for 10 min. HILIC magnetic beads were added at an equal volume to the sample and at a 5:1 total protein ratio. The plate was then shaken at 900 rpm for 30 min at room temperature to allow the protein to bind to the beads. After binding, the beads were washed four times in 500 µL of 95% ACN for one minute. Trypsin (Promega, Madison, NY, USA) made up in 50 mM triethylammonium bicarbonate (TEAB) was added at a ratio of 1:20 total protein, and LysC (Pierce Thermo Scientific Inc., Rockford, IL, USA) was added at a ratio of 1:250 total protein for digestion. At 45 °C, the plate was incubated for two hours, followed by the removal of the peptide-containing supernatant and dried. Samples were then resuspended in LC loading buffer: 0.1% formic acid (FA), 2.5% ACN.

LCMS analysis was conducted on a Q-Exactive quadrupole-Orbitrap mass spectrometer (Thermo Fischer Scientific, Waltham, MA, USA) coupled with a Dionex Ultimate 3000 nano-UPLC system. Peptides were solubilized in 0.1% FA (Sigma), supplemented with 2% ACN (Burdick and Jackson), followed by loading onto a C18 trap column (300 μm × 5 mm × 5 μm). The trapped samples were washed for 3 min before the peptides were eluted onto the analytical column. Chromatographic separation was performed with a Waters nanoEase (Zenfit) M/Z Peptide CSH C18 column (75 μm × 25 cm × 1.7 μm). Peptides were eluted for analysis from solution A: LC water (Burdick and Jackson, BJLC365), 0.1% FA and solvent B: ACN, 0.1% FA. The multi-step gradient for peptide separation was generated at 300 nL/min. The gradient was then held at 80% solvent B for 10 min before returning it to 2% solvent B for 15 min. The mass spectrometer was operated in positive ion mode with a capillary temperature of 320 °C at 1.95 kV electrospray voltage.

Relative quantification was conducted using Progenesis QI for Proteomics v2.0.5556.29015 (Nonlinear Dynamics, Newcastle upon Tyne, UK). Data processing included peak picking, run alignment, and normalization (singly charged spectra were removed from the processing pipeline). Protein quantitation was performed using the “relative quantitation method with non-conflicting peptides”. PCA plots were generated showing the grouping for various treatments compared with one another.

2.5. Bioinformatics Analysis for Functional Annotation, Gene Mapping, and Protein–Protein Interaction (PPI) Network Construction

To assign functional descriptions to Z. mays sequences, a database search was performed with Byonic Software v3.8.13 (Protein Metrics, Cupertino, CA, USA) using the maize reference proteome sourced from UniProt (www.uniprot.org) dated 12 June 2021. All valid and regulated proteins were received in Excel spreadsheet files. The identified proteins were assigned names, subcellular localization, biological process, cellular component, and molecular functions using in silico bioinformatics tools, i.e., Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html, accessed on 30 June 2021). Gene Ontology, Panther, EXPASY, and literature sources were used for protein comparison within treatments.

UniProt accession numbers corresponding to the 50 key DEPs were cross-referenced to maize gene loci using UniProtKB, MaizeGDB, and NCBI. In case of accessions that lacked a direct UniProt locus cross-reference, the protein sequences were retrieved from UniProt (https://www.uniprot.org/uniprotkb, accessed on 18 October 2025) and BLASTP (https://www.uniprot.org/blast, accessed on 18 October 2025) searched against the Zea mays B73 RefGen_v5 protein set (EnsemblPlants/MaizeGDB). The matching B73 gene model with the highest identity and coverage was recorded as the corresponding maize gene locus (LOCxxxxx/Zm00001ebXXXXX). In addition, all the locus mappings are indicated in

Table 1. Differentially expressed proteins (DEPs) identified in maize seedlings under control and 10% PEG-.

Control								10% PEG
Accession Number	Protein Name	Gene/ Locus ID	Subcellular Location	Biological Processes	Molecular Function	PI	Mr	Accession Number	Protein Name	Gene/ Locus ID	Subcellular Location	Biological Processes	Molecular Function	PI	Mr
tr|B4F833	Diaminopimelate epimerase	LOC100191234	Cytosol	Lysine biosynthetic process via diaminopimelate	Diaminopimelate epimerase activity	5.96	37,799.11	tr|B4F833	Diaminopimelate epimerase	LOC100191234	Cytosol	Lysine biosynthetic process via diaminopimelate	Diaminopimelate epimerase activity	5.96	37,799.11
tr|C4IZ94	Ornithine carbamoyltransferase	LOC100284885	Intracellular membrane	Arginine biosynthetic process via ornithine	Amino acid binding	6.76	40,286.51	tr|C4IZ94	Ornithine carbamoyltransferase	LOC100284885	Intracellular membrane	Arginine biosynthetic process via ornithine	Amino acid binding	6.76	40,286.51
tr|A0A1D6HM49	Subtilisin-like protease SBT1.4	LOC100279566	None predicted	Proteolysis	Serine-type endopeptidase activity	5.35	78,345.99	sp|P21569	Peptidyl-prolyl cis–trans isomerase	CYP	Cytoplasm	Protein folding; protein peptidyl-prolyl isomerization	Cyclosporin A binding	8.91	18,348.99
tr| B4F8V5	NADH dehydrogenase [ubiquinone] iron–sulfur protein 1 mitochondrial	LOC100191453	Membrane	ATP synthesis coupled electron transport	4 iron, 4 sulfur cluster binding	6.10	80,679.62	tr|C0P3R8	Glutathione peroxidase	LOC100272844	None predicted	Response to oxidative stress	Glutathione peroxide activity	9.55	24,993.61
tr|B4FYD3	TOM1-like protein 2	LOC100282255	Membrane	Protein transport	Phosphatidylinositol binding; ubiquitin binding	4.58	43,475.92	tr|A0A804PHY0	Peptidyl-prolyl cis–trans isomerase	LOC103627222	None predicted	Protein folding	Peptidyl-prolyl cis–trans isomerase activity	9.03	16,744.15
tr| K7WFV1	ATP synthase subunit gamma	ZEAMMB73_ Zm00001d048091	Mitochondrial proton-transporting ATP synthase complex, catalytic sector F1F0	Proton motive force-driven ATP synthesis	Proton-transporting ATP synthase activity, rotational mechanism	9.12	35,714.94	tr| B6TQ06	Aminomethyltransferase	LOC100279332	Mitochondrion	Methylation	Aminomethyltransferase activity	8.36	44,034.54
tr| B5AMJ8	Alpha-1,4 glucan phosphorylase	LOC100285259	None predicted	Carbohydrate metabolic process	Glycogen phosphorylase activity	6.86	94,452.82	tr|A0A804NZC5	Glutamyl-tRNA(Gln) amidotransferase subunit B, chloroplastic/mitochondrial	GATB	Chloroplast	Protein biosynthesis; mitochondrial translation	ATP binding	6.17	59,841.51
tr|B4FBF6	Mitochondrial dicarboxylate/tricarboxylate transporter DTC	LOC100274318	Membrane	Transport	Transmembrane transporter activity	9.72	33,147.50	tr|B7ZXQ3	Cysteine proteinases superfamily protein	LOC100283689	Lysosome	Proteolysis involved in protein catabolic process	Cysteine-type endopeptidase activity
tr|C4J410	Heat shock 70 kDa protein	LOC100501536	Cytoplasm	Stress response	ATP binding; ATP hydrolysis activity; ATP-dependent protein folding chaperone; heat shock protein binding; misfolded protein binding; protein folding chaperone; unfolded protein binding	5.07	70,882.41	tr|A0A804NLS4	Adenosylhomocysteinase	LOC100282150	None predicted	One-carbon metabolism	Hydrolase activity	5.63	48,377.59
tr| C4JA45	60S ribosomal protein L5-1 homolog b	ZEAMMB73_ Zm00001d012161	Cytosolic large ribosomal subunit; nucleus	Translation; ribosomal large subunit assembly	5S rRNA binding; structural constituent of ribosome	9.34	34,3377.83	tr|C0P5A9	Optic atrophy 3 protein (OPA3)	LOC100276616	Mitochondrion	Regulation of lipid metabolic process		10.04	19,103.23
tr| K7VA99	60S ribosomal protein L5-1 homolog b	ZEAMMB73_ Zm00001d012161	Cytoplasm; nucleus	Translation	Structural constituent of ribosome	5.86	66,539.76	tr|B4F8L7	Glyceraldehyde-3-phosphate dehydrogenase	LOC542290	Stromule	Response to sucrose; response to light stimulus; response to cold	mRNA, NAD and NADP binding	5.95	47,179.90
sp|P93805	Phosphoglucomutase, cytoplasmic 2 (PGM 2) (Glucose phosphomutase 2)	None retrieved	Cytoplasm	Carbohydrate metabolism; Glucose metabolism	Magnesium ion binding; phosphoglucomutase activity	5.47	63,041.31	tr|A0A1D6LSP5	Elongation factor gamma1	ZEAMMB73_ Zm00001d036959	None predicted	Protein biosynthesis	Elongation factor	5.91	43,457.95
tr|A0A804UKU7	CN hydrolase domain-containing protein	LOC100279870	None predicted	Hydrolase activity	Hydrolase activity, acting on carbon–nitrogen (but not peptide) bonds, in linear amides	8.62	40,920.58	tr|K7V2Z8	Carbamoyl-phosphate synthase (glutamine-hydrolyzing)	LOC103636016	Cytoplasm	Pyrimidine biosynthesis	ATP binding; metal ion binding	5.49	12,7286.83
tr|A0A1D6KEP0	Serine hydroxymethyltransferase	ZEAMMB73_ Zm00001d030859	None predicted	One-carbon metabolism	Glycine hydroxymethyltransferase activity; methyltransferase activity; pyridoxal phosphate binding	7.62	58,546.94	tr|A0A804PU27	Ribosomal_S4e domain-containing protein	None retrieved	Ribosome	Translation	rRNA binding; ribosomal protein	5.79	28,447.42
tr|A0A804U9Z6	Glutamate--cysteine ligase	LOC542026	Plastid, chloroplast	Glutathione biosynthesis	ATP binding; glutamate-cysteine ligase activity	6.46	56,928.23	tr|A0A804NG48	GTP-binding nuclear protein	LOC100282843	Nucleus	Protein transport; Nucleocytoplasmic transport	GTP binding	9.34	19,867.92
tr|A0A804QKD5	Glutathione hydrolase	LOC103634830	Membrane	Glutathione catabolic process	Acyltransferase activity; glutathione hydrolase activity	5.95	66,539.76	sp|P09315	Glyceraldehyde-3-phosphate dehydrogenase A, chloroplastic	GAPA	Chloroplast	Glucose metabolic process; reductive pentose-phosphate cycle; response to light stimulus	NAD and NADP binding	6.25	36,096.26
tr|K7VN08	ATP synthase B chain	LOC100282566	Membrane	Hydrogen ion transport	Proton transmembrane transporter activity	5.48	22,715.27	tr|A0A804MAM1	Methionine aminopeptidase	LOC100192977	None predicted	Protein initiator methionine removal; proteolysis	Metal ion binding; metalloaminopeptidase activity	6.86	37,094.53
tr|B4FBC9	Patellin-1	LOC100192106	Cytoplasm	Cell cycle; cell division	Lipid binding	4.58	68,035.27	tr|B4FRH1	Thioredoxin M1 chloroplastic	LOC100272895	Cytoplasm	None predicted	Oxidoreductase activity; protein-disulfide reductase activity	8.63	19,560.59
tr|C0PAU7	Glucose-6-phosphate isomerase	LOC100382805	Cytosol	Glycolysis	Carbohydrate derivative binding; glucose-6-phosphate isomerase activity; monosaccharide binding	5.56	67,431.16	tr|B4FTT2	Regulator of chromosome condensation2	LOC100273233	Chromatin; cytosol; nucleus	Response to UV-B; entrainment of circadian clock	Chromatin binding; photoreceptor activity; protein homodimerization activity	5.38	47,158.43
tr|B6UBZ9	Cytochrome b-c1 complex subunit 7	LOC100383274	Mitochondrion inner membrane	Electron transport; respiratory chain		9.64	14,801.20	tr|B4FU53	50S ribosomal protein L9, chloroplastic (CL9)	LOC100285012	Ribosome	Translation	Structural constituent of ribosome	9.81	21,660.30
tr|Q2MJJ9	Putative RH2 protein	RH2	None predicted	None predicted	ATP binding; hydrolase activity; RNA binding; RNA helicase activity	5.98	45,945.84	sp|P06586	30S ribosomal protein S3, chloroplastic	rps3	Chloroplast	Translation	rRNA binding	9.76	25,916.37
tr|B4FPJ2	U6 snRNA-associated Sm-like protein LSm3	LOC100194361	Nucleus	mRNA processing; mRNA splicing	RNA binding	4.78	11,235.91	tr|B4F8M8	Exoglucanase1	LOC100191390	Extracellular region	Carbohydrate metabolic process	Hydrolase activity, hydrolyzing O-glycosyl compounds	6.54	64,583.72
tr|C0P7G8	Photosystem II repair protein PSB27-H1 chloroplastic	LOC100282043	Chloroplast thylakoid lumen	Photosystem II assembly and repair	None predicted	9.86	17,757.36	tr|K7VDJ2	40S ribosomal protein S17-4	LOC100282953	Ribonucleoprotein complex; ribosome	Translation	Structural constituent of ribosome	10.14	16,443.05
tr|Q9M640	Coatomer subunit delta	LOC541810	Cytoplasm; Golgi membrane	ER–Golgi transport; protein transport	Retrograde Golgi-to-ER transport of dilysine-tagged proteins	5.54	57,498.47	tr| B8A2X5	Pectinesterase	LOC100280285	None predicted	Cell wall modification; pectin catabolic process	Aspartyl esterase activity; pectinesterase activity	8.76	31,742.89
tr|K7TXI5	Chlorophyll a-b binding protein, chloroplastic	LOC542321	Plastid, chloroplast thylakoid membrane	Photosynthesis	Chlorophyll binding	7.90	26,586.76	tr|A0A1D6HT50	Polyribonucleotide nucleotidyltransferase	LOC103631988	None predicted	mRNA catabolic process; RNA processing	Polyribonucleotide nucleotidyltransferase activity	5.69	95,115.02
tr|B6TCK3	NADH-cytochrome b5 reductase	LOC103649684	None predicted	None predicted	Cytochrome-b5 reductase activity, acting on NAD(P)H	9.03	33,845.03	tr|B4FRD6	Peroxidase	LOC100281280	Plant-type cell wall; extracellular region	Hydrogen peroxide	Heme binding; peroxidase activity	7.12	33,116.72
tr|B4FT19	Oxygen evolving enhancer protein 3 containing protein	LOC100273120	thylakoid	Photosynthesis	Calcium ion binding	7.65	25,342.86	tr|B4FW06	40S ribosomal protein S10-1	LOC100273505	Cytosolic small ribosomal subunit	None predicted	RNA binding; structural constituent of ribosome	9.78	19,938.47
tr|A0A1D6MUE8	Heat shock 70 kDa protein 6 chloroplastic	LOC103650436	Chloroplast	Stress response	ATP binding; ATP-dependent protein folding chaperone; unfolded protein binding	5.08	74,493.16	tr| Q947B9	Glucose-1-phosphate adenylyltransferase	GLG1	Chloroplast	Starch biosynthesis	ATP binding; glucose-1-phosphate adenylyltransferase activity	6.48	56,482.41
sp|P69388	Cytochrome b559 subunit alpha (PSII reaction center subunit V)	psbE	Plastid, chloroplast thylakoid membrane	Photosynthetic electron transport chain	Electron transfer activity; heme binding; iron ion binding	4.64	9444.60	sp|P06589	50S ribosomal protein L22, chloroplastic	rpl22	Chloroplast	Translation; ribosome assembly	rRNA binding; structural constituent of ribosome	10.82	17,655.21
tr|C0P3S5	Fumarylacetoacetate (FAA) hydrolase family	ZEAMMB73_ Zm00001d034739	Mitochondrion	None predicted	Acetylpyruvate hydrolase activity	5.41	24,731.55	tr|B6TGG7	3-oxoacyl-[acyl-carrier-protein] synthase	LOC100279917	None predicted	Fatty acid biosynthetic process	3-oxoacyl-[acyl-carrier-protein] synthase activity	7.57	48,864.83
tr|A0A1D6H5M2	Chalcone-flavonone isomerase family protein	LOC100285485	None predicted	None predicted	Intramolecular lyase activity	7.72	29,540.85	tr|B4FCX3	Proteasome subunit beta	LOC100192865	Cytoplasm; nucleus	Proteasomal protein catabolic process	Threonine-type endopeptidase activity	7.77	28,912.76
tr|A0A804R1C2	Starch synthase, chloroplastic/ amyloplastic	None retrieved	Plastid, chloroplast	Starch biosynthesis	Glycogen (starch) synthase activity	5.61	68,900.48	tr|B6TNT5	Peptide-methionine (S)-S-oxide reductase	LOC100283982	Cytoplasm	Cellular response to oxidative stress	L-methionine-(S)-S-oxide reductase activity; peptide-methionine (S)-S-oxide reductase activity	5.85	20,504.80
tr|B4FQ59	Phosphoribulokinase	LOC100282845	Cytoplasm	Phosphorylation	ATP binding; phosphoribulokinase activity	5.84	44,749.89	tr| P55240	Glucose-1-phosphate adenylyltransferase small subunit	GLG1	Chloroplast; amyloplast	Starch biosynthesis	ATP binding	N/A	N/A
tr|A0A804NUX2	Phosphoribulokinase	None retrieved	None predicted	Reductive pentose-phosphate cycle	ATP binding; phosphoribulokinase activity	9.51	27,481.52	tr|A0A804P7H7	Ribose-5-phosphate isomerase	None retrieved	None predicted	Pentose-phosphate shunt, non-oxidative branch	Ribose-5-phosphate isomerase activity	5.44	31,500.71
sp|P33488	Auxin-binding protein 4 (ABP)	ABP4	Endoplasmic reticulum lumen	Auxin signaling pathway	Receptor	6.14	18,583.08	tr|B4FRC4	Serine-tRNA ligase	LOC541989	Cytosol	Seryl-tRNA aminoacylation	Aminoacyl-tRNA synthetase	6.27	51,663.79
tr|A0A1D6LE55	Peroxidase	LOC103628960	Extracellular region	Hydrogen peroxide catabolic process	Lactoperoxidase activity; heme binding	5.25	34,847.89	tr|A0A804MJA1	Fn3_like domain-containing protein	None retrieved	Extracellular region	Xylan catabolic process	Xylan 1,4-beta-xylosidase activity	8.52	82,057.02
tr|A0A1X7YHG9	ATP synthase subunit alpha, chloroplastic	LOC118474820	Plastid, chloroplast thylakoid membrane	ATP synthesis; Hydrogen ion transport	ATP binding; proton-transporting ATP synthase activity, rotational mechanism; proton-transporting ATPase activity, rotational mechanism	5.87	55,690.85	tr|A0A1D6L554	60S ribosomal protein L13a-1	LOC100191912	Large ribosomal subunit	Translation	Structural constituent of ribosome	10.35	21,187.48
tr|A0A804LI62	PsbP domain-containing protein	LOC103634474	Extrinsic component of membrane	Photosynthesis	Calcium ion binding	7.59	26,587.16	tr| B4FAM6	60S ribosomal protein L13a-1	LOC100191912	Large ribosomal subunit	Translation	Lyase activity; magnesium ion binding; thiamine pyrophosphate binding	10.20	23,600.17
sp|P48186	ATP synthase subunit b, chloroplastic (ATP synthase F(0) sector subunit b) (ATP se subunit I)	atpF	Plastid, chloroplast thylakoid membrane	ATP synthesis	ATP binding; proton-transporting ATP synthase activity, rotational mechanism	9.27	20,981.12	tr|K7UMB3	Signal recognition particle 54 kDa protein chloroplastic	LOC103654352	Signal recognition particle, endoplasmic reticulum targeting	SRP-dependent cotranslational protein targeting to membrane	7S RNA binding; GTP binding; GTPase activity	9.56	59,737.25
tr|A0A804NI62	Legumain	LOC542609	Cellular anatomical entity	Proteolysis involved in protein catabolic process	Cysteine-type endopeptidase activity	5.07	44,767.98	tr| B4FYT5	Protein Kinase C630.09c	LOC100274052	None predicted	Phosphorylation	ATP binding; kinase activity	5.62	47,498.13
tr|A0A1D6E958	Protease Do-like 8 chloroplastic	LOC100272554	Chloroplast thylakoid lumen	Proteolysis; photosystem II repair	Serine-type endopeptidase activity	9.07	51,190.49	sp|P27723	30S ribosomal protein S16, chloroplastic	rps16	Chloroplast	Translation	Structural constituent of ribosome	10.33	10,089.90
tr|A0A3L6EGC3	Germin-like protein	Os08g0459700_1	Secreted, extracellular space, apoplast	None predicted	Manganese ion binding	5.89	20,101.08	tr|A0A804UA42	PRK domain-containing protein	LOC100274052	Integral component of membrane	ATP binding; kinase activity	DNA binding	6.61	58,115.71
tr|A0A096S078	4-coumarate--CoA ligase-like 7	LOC100191850	Membrane	Phenylpropanoid metabolic process	4-coumarate-CoA ligase activity	7.00	53,052.88	tr|B8A1H0	Glucose-6-phosphate 1-epimerase	LOC100273735	Cytoplasm	Carbohydrate metabolic process	Carbohydrate binding	5.68	34,240.88
tr|A0A804N9X8	Pectinesterase	None retrieved	None predicted	Cell wall modification; Pectin catabolic process	Aspartyl esterase activity; pectinesterase activity	6.19	54,997.66	tr|B4G1J8	50S ribosomal protein L3-1 chloroplastic	LOC100193892	Ribonucleoprotein complex; ribosome	Translation	mRNA binding	10.59	27,867.52
tr|Q94F78	Nucleosome/chromatin assembly factor A	nfa104	Cytoplasm; chromatin; nucleus	Nucleus assembly; double-stand break repair	Chromatin binding; histone binding	4.08	29,331.84	tr|B4FGN4	50S ribosomal protein L3-1 chloroplastic	LOC100193892	Ribosome	Translation	Structural constituent of ribosome	7.63	24,415.30
tr|A0A1D6L6U6	Glutathione transferase	LOC542734	cytoplasm	Glutathione metabolic process	Glutathione transferase activity	6.20	25,258.20	tr|C0HFM4	60S ribosomal protein L13a-1	LOC100283655	Cytosolic large ribosomal subunit; ribosome	Negative regulation of translation; translation	mRNA binding; structural constituent of ribosome	10.22	23,629.10
tr|A0A1D6N672	Photosystem II subunit PsbS1	LOC542126	Membrane; plastid	Nonphotochemical quenching; response to high light intensity	None predicted	9.07	28,452.15	tr|B4F9N8	RNA-binding (RRM/RBD/RNP motifs) family protein	LOC100191663	Ribonucleoprotein complex	None predicted	RNA binding	4.99	51,279.59
tr|A0A804LJD2	Guanylate kinase	LOC100283207	None predicted	Phosphorylation	ATP binding; guanylate kinase activity	9.18	32,388.47	tr|A0A804RFJ4	Gp_dh_N domain-containing protein	None retrieved	None predicted	None predicted	NAD binding	6.51	28,369.38
tr|Q6R9G1	NAD(P)H dehydrogenase subunit H	nad7	Mitochondrion; thylakoid	Mitochondrial electron transport	NAD binding; NADH dehydrogenase (ubiquinone) activity; quinone binding	6.74	44,280.13	tr|A0A804PBS5	Inorganic diphosphatase	LOC103626225	Cytoplasm	Phosphate-containing compound metabolic process	Inorganic diphosphate phosphatase activity; magnesium ion binding	5.99	20,994.02
tr|A0A804UKI0	CN hydrolase domain-containing protein	LOC100279870	None predicted	Hydrolase activity	Hydrolase activity, acting on carbon–nitrogen (but not peptide) bonds, in linear amides	8.89	29,227.53	tr|A0A804NZ53	SRP54 domain-containing protein	LOC103654352	Cytoplasm	SRP-dependent cotranslational protein targeting to membrane	RNA-binding; GTPase activity	9.36	56,091.99

Note: “None retrieved” indicates that no corresponding gene or locus ID could be retrieved from NCBI, UniProt, or EnsemblPlants databases for the listed protein accession. “None predicted” denotes that subcellular localization or annotation information was unavailable in the referenced databases at the time of analysis.

and

Table 2. Differentially expressed proteins (DEPs) identified in maize seedlings under control and 25% PEG.

Control									25% PEG
Accession Number	Protein Name	Gene/ Locus ID	Subcellular Location	Biological Processes	Molecular Function	PI	Mr	Accession Number	Protein Name	Gene/ Locus ID	Subcellular Location	Biological Processes	Molecular Function	PI	Mr
TR|B4F833	Diaminopimelate epimerase	LOC100191234	Cytosol	Lysine biosynthetic process via diaminopimelate	Diaminopimelate epimerase activity	5.96	37,799.11	sp|P21569	Peptidyl-prolyl cis–trans isomerase	CYP	Cytoplasm	Protein folding; protein peptidyl-prolyl isomerization	Cyclosporin A binding	8.91	18,348.99
TR|C4IZ94	Ornithine carbamoyltransferase	LOC100284885	Intracellular membrane	Arginine biosynthetic process via ornithine	Amino acid binding	6.76	40,286.51	tr|C0P3R8	Glutathione peroxidase	LOC100272844	None predicted	Response to oxidative stress	Glutathione peroxide activity	9.55	24,993.61
TR|A0A1D6HM49	Subtilisin-like protease SBT1.4	LOC100279566	None predicted	Proteolysis	Serine-type endopeptidase activity	5.35	78,345.99	tr|A0A804PHY0	Peptidyl-prolyl cis–trans isomerase	LOC103627222	None predicted	Protein folding	Peptidyl-prolyl cis–trans isomerase activity	9.03	16,744.15
TR| B4F8V5	NADH dehydrogenase [ubiquinone] iron–sulfur protein 1 mitochondrial	LOC100191453	Membrane	ATP synthesis coupled electron transport	4 iron, 4 sulfur cluster binding	6.10	80,679.62	tr|C0P4P5	Non-reducing end alpha-L-arabinofuranosidase	LOC100193890	None predicted	Cellular carbohydrate catabolic process; L-arabinose metabolic process	Alpha-L-arabinofuranosidase activity	5.05	69,535.87
TR|B4FYD3	TOM1-like protein 2	LOC100282255	Membrane	Protein transport	Phosphatidylinositol binding; ubiquitin binding	4.58	43,475.92	tr|K7VNQ7	Mitochondrial import receptor subunit TOM40-1	LOC110120361	Integral component of membrane	Protein import into mitochondrial matrix	Protein transmembrane transporter activity	6.01	37,345.36
TR| K7WFV1	ATP synthase subunit gamma	LOC100274172	Mitochondrial proton-transporting ATP synthase complex, catalytic sector F1F0	Proton motive force-driven ATP synthesis	Proton-transporting ATP synthase activity, rotational mechanism	9.12	35,714.94	tr|B4FMF7	Aldose 1-epimerase	LOC100217298	None predicted	Hexose metabolic process	Aldose 1-epimerase activity	8.89	36,388.96
TR| B5AMJ8	Alpha-1,4 glucan phosphorylase	LOC100285259	None predicted	Carbohydrate metabolic process	Glycogen phosphorylase activity	6.86	94,452.82	tr|C0P5Y3	5-methyltetrahydropteroyltriglutamate--homocysteine S-methyltransferase	LOC541942	None predicted	Methionine biosynthetic process; methylation	5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase activity; zinc ion binding	5.54	84,492.46
TR|B4FBF6	Mitochondrial dicarboxylate/tricarboxylate transporter DTC	LOC100274318	Membrane	Transport	Transmembrane transporter activity	9.72	33,147.50	tr|Q6RW09	Allene-oxide cyclase	aoc	Chloroplast	Jasmonic acid biosynthetic process	Allene-oxide cyclase activity	9.05	25,777.29
TR|C4J410	Heat shock 70 kDa protein	LOC100501536	Cytoplasm	Stress response	ATP binding; ATP hydrolysis activity; ATP-dependent protein folding chaperone; heat shock protein binding; misfolded protein binding; protein folding chaperone; unfolded protein binding	5.07	70,882.41	tr|A0A804MQI0	Valine-tRNA ligase	LOC100383209	None predicted	Post-embryonic development]; reproductive structure development; valyl-tRNA aminoacylation	Aminoacyl-tRNA editing activity; ATP binding; valine-tRNA ligase activity	6.60	11,6740.55
TR| C4JA45	60S ribosomal protein L5-1 homolog b	LOC10028491	Cytosolic large ribosomal subunit; nucleus	Translation; ribosomal large subunit assembly	5S rRNA binding; structural constituent of ribosome	9.34	34,3377.83	tr|B4FYH2	Pyruvate kinase	LOC100273990	Chloroplast stroma; cytoplasm	Glycolytic process; photosynthesis	ATP binding; kinase activity; magnesium ion binding; potassium ion binding; pyruvate kinase activity	6.26	61,479.03
TR| K7VA99	60S ribosomal protein L5-1 homolog b	LOC100284917	Cytoplasm; nucleus	Translation	Structural constituent of ribosome	5.86	66,539.76	tr|B4FEA2	Mitochondrial carnitine/acylcarnitine carrier-like protein	LOC118476033	Membrane	Nitrogen compound transport; organic anion transport; organophosphate ester transport; transmembrane transport	None predicted	9.54	30,155.95
SP|P93805	Phosphoglucomutase, cytoplasmic 2 (PGM 2) (Glucose phosphomutase 2)	LOC542358	Cytoplasm	Carbohydrate metabolism; Glucose metabolism	Magnesium ion binding; phosphoglucomutase activity	5.47	63,041.31	tr|A0A804R5D8	Ribosomal_S4e domain-containing protein	None retrieved	Ribosome	Translation	Structural constituent of ribosome	6.88	45,232.88
TR|A0A804UKU7	CN hydrolase domain-containing protein	LOC100279870	None predicted	Hydrolase activity	Hydrolase activity, acting on carbon–nitrogen (but not peptide) bonds, in linear amides	8.62	40,920.58	tr|O22453	40S ribosomal protein S4	rps4	Ribosome	Translation	RNA binding	10.20	30,171.37
TR|A0A1D6KEP0	Serine hydroxymethyltransferase	LOC100381786	None predicted	One-carbon metabolism	Glycine hydroxymethyltransferase activity; methyltransferase activity; pyridoxal phosphate binding	7.62	58,546.94	tr|C0P455	60S ribosomal protein L4-1	LOC100382075	Cytosolic large ribosomal subunit	Translation	RNA binding	10.65	44,292.54
TR|A0A804U9Z6	Glutamate--cysteine ligase	LOC542026	Plastid, chloroplast	Glutathione biosynthesis	ATP binding; glutamate-cysteine ligase activity	6.46	56,928.23	tr|B4FWJ8	Binding protein homolog2	LOC732809	Cytoplasm	Protein refolding	Heat shock protein binding	5.10	70,546.74
TR|A0A804QKD5	Glutathione hydrolase	LOC103634830	Membrane	Glutathione catabolic process	Acyltransferase activity; glutathione hydrolase activity	5.95	66,539.76	tr|A0A1D6L210	NADH dehydrogenase [ubiquinone] iron–sulfur protein 1 mitochondrial	LOC100280404	Membrane	ATP synthesis coupled electron transport	4 iron, 4 sulfur cluster binding; NADH dehydrogenase	6.01	80,717.71
TR|K7VN08	ATP synthase B chain	LOC100282566	Membrane	Hydrogen ion transport	Proton transmembrane transporter activity	5.48	22,715.27	tr|A0A804MU73	T-complex protein 1 subunit zeta	LOC100193477	Cytoplasm		ATP binding	6.82	53,590.56
TR|B4FBC9	Patellin-1	LOC100192106	Cytoplasm	Cell cycle; cell division	Lipid binding	4.58	68,035.27	tr|B6THZ8	Threonine synthase	LOC100283397	Cytoplasm	Cysteine biosynthetic process	Pyridoxal phosphate binding	6.44	57,614.35
TR|C0PAU7	Glucose-6-phosphate isomerase	LOC100382805	Cytosol	Glycolysis	Carbohydrate derivative binding; glucose-6-phosphate isomerase activity; monosaccharide binding	5.56	67,431.16	tr|B4FAL9	Fructose-bisphosphate aldolase	LOC542261	Cytosol	Fructose 1,6-bisphosphate metabolic process; glycolytic process	Fructose-bisphosphate aldolase activity	7.52	38,590.16
TR|B6UBZ9	Cytochrome b-c1 complex subunit 7	LOC100383274	Mitochondrion inner membrane	Electron transport; respiratory chain	None predicted	9.64	14,801.20	tr|K7UDG5	Proline--tRNA ligase	LOC100272402	Cytoplasm	Prolyl-tRNA aminoacylation	ATP binding	6.71	58,133.54
TR|Q2MJJ9	Putative RH2 protein	LOC732749	None predicted	None predicted	ATP binding; hydrolase activity; RNA binding; RNA helicase activity	5.98	45,945.84	tr|C0PFN4	Glucose-6-phosphate 1-epimerase	LOC100282752	None predicted	Carbohydrate metabolic process	Carbohydrate binding; glucose-6-phosphate 1-epimerase activity	6.26	36,652.84
TR|B4FPJ2	U6 snRNA-associated Sm-like protein LSm3	LOC100194361	Nucleus	mRNA processing; mRNA splicing	RNA binding	4.78	11,235.91	tr|B4F938	Coproporphyrinogen oxidase	LOC100500945	Cytoplasm	Protoporphyrinogen IX biosynthetic process	Coproporphyrinogen oxidase activity	8.19	44,112.77
TR|C0P7G8	Photosystem II repair protein PSB27-H1 chloroplastic	LOC100282043	Chloroplast thylakoid lumen	Photosystem II assembly and repair	None predicted	9.86	17,757.36	tr|B4F871	Protein DJ-1 homolog D (YLS5)	LOC100280536	None predicted	None predicted	Glyoxalase III activity	5.37	41,209.28
TR|Q9M640	Coatomer subunit delta	LOC541810	Cytoplasm; Golgi membrane	ER-Golgi transport; Protein transport	Retrograde Golgi-to-ER transport of dilysine-tagged proteins	5.54	57,498.47	tr|B4FIH9	Xylose isomerase	LOC100194385	None predicted	D-xylose metabolic process	Metal ion binding; xylose isomerase activity	5.40	51,045.66
TR|K7TXI5	Chlorophyll a-b binding protein, chloroplastic	LOC542321	Plastid, chloroplast thylakoid membrane	photosynthesis	Chlorophyll binding	7.90	26,586.76	tr|B4G1K3	Calcyclin-binding protein Prolyl aminopeptidase) SGT1	LOC100282523	None predicted	None predicted	S100 protein binding; tubulin binding; ubiquitin protein ligase binding	8.69	24,644.44
TR|B6TCK3	NADH-cytochrome b5 reductase	LOC103649684	None predicted	None predicted	Cytochrome-b5 reductase activity, acting on NAD(P)H	9.03	33,845.03	tr|B4FR32	Glyceraldehyde-3-phosphate deHaseN1 (NADP-dependent glyceraldehyde-3-phosphate dehydrogenase)	LOC542583	None predicted	None predicted	Oxidoreductase activity, acting on the aldehyde or oxo group of donors, NAD or NADP as acceptor	6.80	53,284.58
TR|B4FT19	Oxygen evolving enhancer protein 3 containing protein	LOC100273120	Thylakoid	Photosynthesis	Calcium ion binding	7.65	25,342.86	tr|B4FFZ2	Ketol-acid reductoisomerase (EC 1.1.1.86) (Acetohydroxy-acid reductoisomerase) (Alpha-keto-beta-hydroxylacyl reductoisomerase)	LOC100193695	None predicted	Isoleucine biosynthetic process; valine biosynthetic process.	Isomerase activity; ketol-acid reductoisomerase activity; metal ion binding	6.31	63,002.80
TR|A0A1D6MUE8	Heat shock 70 kDa protein 6 chloroplastic	LOC103650436	Chloroplast	Stress response	ATP binding; ATP-dependent protein folding chaperone; unfolded protein binding	5.08	74,493.16	tr|B4FAD4	Isocitrate dehydrogenase [NAD] catalytic subunit 5 mitochondrial	LOC100191841	Mitochondrion	Isocitrate metabolic process; tricarboxylic acid cycle	Isocitrate dehydrogenase (NAD+) activity; magnesium ion binding; NAD binding.	6.52	39,724.58
SP|P69388	Cytochrome b559 subunit alpha (PSII reaction center subunit V)	psbE	Plastid, chloroplast thylakoid membrane	Photosynthetic electron transport chain	Electron transfer activity; heme binding; iron ion binding	4.64	9444.60	tr|B4F9K0	Late embryogenesis abundant protein group 2	LOC100191638	None predicted	Response to desiccation	None predicted	4.92	35,274.07
TR|C0P3S5	Fumarylacetoacetate (FAA) hydrolase family		Mitochondrion	None predicted	Acetylpyruvate hydrolase activity	5.41	24,731.55	tr|A0A1D6JYM4	Glutathione S-transferase L2 chloroplastic	LOC100282747	None predicted	Response to chemical	Glutathione transferase activity	6.15	33,860.53
TR|A0A1D6H5M2	Chalcone-flavonone isomerase family protein	LOC100285485	None predicted	None predicted	Intramolecular lyase activity	7.72	29,540.85	tr|Q6R9L0	NADH dehydrogenase subunit 9	nad9	Mitochondrial respiratory chain complex I	None predicted	NADH dehydrogenase (ubiquinone) activity	8.51	22,594.41
TR|A0A804R1C2	Starch synthase, chloroplastic/amyloplastic	LOC100283765	Plastid, chloroplast	Starch biosynthesis	Glycogen (starch) synthase activity	5.61	68,900.48	tr|B4FT31	Dehydroascorbate reductase	DHAR3	None predicted	Ascorbate glutathione cycle	Glutathione transferase activity	5.54	23,355.73
TR|B4FQ59	Phosphoribulokinase	LOC100282845	Cytoplasm	Phosphorylation	ATP binding; phosphoribulokinase activity	5.84	44,749.89	tr|B4F9K4	17.5 kDa class II heat shock protein	LOC542723	None predicted	Stress response	Protein self-association	5.95	17,868.64
TR|A0A804NUX2	Phosphoribulokinase	None retrieved	None predicted	Reductive pentose-phosphate cycle	ATP binding; phosphoribulokinase activity	9.51	27,481.52	tr|B4F9E8	17.4 kDa class III heat shock protein	LOC100191598	None predicted	Protein complex Oligomerization; protein folding; response to heat; response to hydrogen peroxide; response to salt stress	Protein self-association; unfolded protein binding	6.60	18,347.76
SP|P33488	Auxin-binding protein 4 (ABP)	ABP4	Endoplasmic reticulum lumen	Auxin signaling pathway	Receptor	6.14	18,583.08	tr|B4FQC9	Proline iminopeptidase (PIP)	LOC100272740	Cytoplasm	Aminopeptidase activity	Proteolysis	5.24	36,602.16
TR|A0A1D6LE55	Peroxidase	LOC103628960	Extracellular region	Hydrogen peroxide catabolic process	Lactoperoxidase activity; heme binding	5.25	34,847.89	tr|A0A804NGN9	SGT1	LOC100282745	Plastid; Thylakoid	None predicted	Chaperone binding	5.02	40,229.33
TR|A0A1X7YHG9	ATP synthase subunit alpha, chloroplastic	LOC118474820	Plastid, chloroplast thylakoid membrane	ATP synthesis; Hydrogen ion transport	ATP binding; proton-transporting ATP synthase activity, rotational mechanism; proton-transporting ATPase activity, rotational mechanism	5.87	55,690.85	tr|B6SN61	Grx_C2.1-glutaredoxin subgroup I	LOC100303864	Cytoplasm	Cellular response to oxidative stress	Glutathione disulfide oxidoreductase activity; glutathione oxidoreductase activity; protein-disulfide reductase (glutathione) activity	7.71	13,932.16
TR|A0A804LI62	PsbP domain-containing protein	LOC103634474	Extrinsic component of membrane	Photosynthesis	Calcium ion binding	7.59	26,587.16	tr|A0A1D6MNJ0	1-deoxy-D-xylulose-5-phosphate reductoisomerase	LOC542023	None predicted	Isopentenyl diphosphate biosynthetic process, methylerythritol 4-phosphate pathway	1-deoxy-D-xylulose-5-phosphate reductoisomerase activity; isomerase activity; metal ion binding; NADPH binding	6.44	51,257.01
SP|P48186	ATP synthase subunit b, chloroplastic (ATP synthase F(0) sector subunit b) (ATP se subunit I)	atpF	Plastid, chloroplast thylakoid membrane	ATP synthesis	ATP binding; proton-transporting ATP synthase activity, rotational mechanism	9.27	20,981.12	tr|A0A804PDW7	Luminal-binding protein 5	LOC732808	Endoplasmic reticulum	None predicted	ATP binding; ATP-dependent protein folding chaperone	5.05	69,401.51
TR|A0A804NI62	Legumain	LOC542609	Cellular anatomical entity	proteolysis involved in protein catabolic process	Cysteine-type endopeptidase activity	5.07	44,767.98	tr|A0A804LN04	Branched-chain amino acid aminotransferase	LOC100191754	None predicted	Branched-chain amino acid biosynthetic process; cellular amino acid biosynthetic process	Branched-chain amino acid transaminase activity	5.80	43,223.76
TR|A0A1D6E958	Protease Do-like 8 chloroplastic	LOC100272554	Chloroplast thylakoid lumen	Proteolysis; photosystem II repair	Serine-type endopeptidase activity	9.07	51,190.49	tr|A0A804MU73	T-complex protein 1 subunit zeta	LOC100193477	Cytoplasm	None predicted	ATP binding; ATP hydrolysis activity; ATP-dependent protein folding chaperone; unfolded protein binding	6.82	53,590.56
TR|A0A3L6EGC3	Germin-like protein	LOC100191976	Secreted, extracellular space, apoplast	None predicted	Manganese ion binding	5.89	20,101.08	tr|A0A804RJL5	Aconitate hydratase	LOC100281040	None predicted	4 iron, 4 sulfur cluster binding; hydro-lyase activity; metal ion binding	Oxoacid metabolic process	6.01	966,932.89
TR|A0A096S078	4-coumarate--CoA ligase-like 7	LOC100191850	Membrane	Phenylpropanoid metabolic process	4-coumarate-CoA ligase activity	7.00	53,052.88	tr|A0A1D6JW41	40S ribosomal protein S20-1	LOC103633583	Cytosolic small ribosomal subunit; small ribosomal subunit	Translation	Structural constituent of ribosome	9.51	13,824.16
TR|A0A804N9X8	Pectinesterase	LOC103650921	None predicted	Cell wall modification; Pectin catabolic process	Aspartyl esterase activity; pectinesterase activity	6.19	54,997.66	tr|B6U237	Heat shock 70 kDa protein 14 (Heat shock 70 kDa protein 4)	LOC100285213	None predicted	ATP binding; ATP-dependent protein folding chaperone	None predicted	5.28	93,569.13
TR|Q94F78	Nucleosome/chromatin assembly factor A	nfa104	Cytoplasm; chromatin; nucleus	Nucleus assembly; double-stand break repair	Chromatin binding; histone binding	4.08	29,331.84	tr|A0A1D6JW41	40S ribosomal protein S20-1	LOC103633583	Component cytosolic small ribosomal subunit	Translation	Ribosomal protein	9.51	13,824.16
TR|A0A1D6L6U6	Glutathione transferase	LOC542734	Cytoplasm	Glutathione metabolic process	Glutathione transferase activity	6.20	25,258.20	tr|B4F9K4	17.5 kDa class II heat shock protein	LOC542723	None predicted	Protein self-association; unfolded protein binding	Protein complex oligomerization; protein folding; response to heat; response to hydrogen peroxide; response to salt stress	5.95	17,868.64
TR|A0A1D6N672	Photosystem II subunit PsbS1	LOC542126	Membrane; plastid	Nonphotochemical quenching; response to high light intensity	None predicted	9.07	28,452.15	tr|A0A804RJL5	Aconitate hydratase	LOC100281040	None predicted	Oxoacid metabolic process	Metal ion binding	6.01	96,932.89
TR|A0A804LJD2	Guanylate kinase	LOC100283207	None predicted	Phosphorylation	ATP binding; guanylate kinase activity	9.18	32,388.47	tr|B6T8Q5	T-complex protein 1 subunit zeta	LOC100193477	Cytoplasm	None predicted	ATP binding	6.23	59,087.02
TR|Q6R9G1	NAD(P)H dehydrogenase subunit H	nad7	Mitochondrion; thylakoid	Mitochondrial electron transport	NAD binding; NADH dehydrogenase (ubiquinone) activity; quinone binding	6.74	44,280.13	tr|B4FT31	Dehydroascorbate reductase	DHAR3	None predicted	Glutathione dehydrogenase (ascorbate) activity; glutathione transferase activity	Ascorbate glutathione cycle
TR|A0A804UKI0	CN hydrolase domain-containing protein	LOC100279870	None predicted	Hydrolase activity	Hydrolase activity, acting on carbon–nitrogen (but not peptide) bonds, in linear amides	8.89	29,227.53	tr|K7VJF3	Heat shock 70 kDa protein 5	LOC100272911	Cytoplasm	ATP binding; ATP hydrolysis activity; ATP-dependent protein folding chaperone; heat shock protein binding; misfolded protein binding; protein folding chaperone; unfolded protein binding	Cellular response to unfolded protein; chaperone cofactor-dependent protein refolding; protein refolding	5.22	71,502.04

Note: “None retrieved” indicates that no corresponding gene or locus ID could be retrieved from UniProt, EnsemblPlants, and NCBI databases for the listed protein accession. “None predicted” denotes that subcellular localization or annotation information was unavailable in the referenced databases at the time of analysis.

.

To explore potential protein–protein associations and identify functional clusters, a protein–protein interaction (PPI) analysis was performed using STRING version 12.0 (https://string-db.org/, accessed on 25 October 2025) restricted to the Zea mays taxonomic database and a confidence score threshold of ≥0.4. The resulting interaction datasets were imported into Cytoscape (v3.9.1) for visualization and network topology analysis, enabling identification of major hubs and biological pathways associated with osmotic stress adaptation.

2.6. Statistical Analysis and Data Integration

All experiments were performed in five independent biological replicates. A one-way analysis of variance (ANOVA) test was performed to compare the differences among treatments for the morphological, physiological, and proteomic data. In cases where ANOVA indicated significant effects, mean comparisons were carried out using the Tukey–Kramer post hoc test at a 5% significance level (p < 0.05).

3. Results

3.1. Morphological and Growth Responses of Zea mays

Maize plants were morphologically evaluated for the effects of polyethylene glycol (PEG)-induced osmotic stress. Different growth parameter changes, including plant height, leaf length, leaf number, leaf area, leaf fresh weight, leaf turgid weight, leaf dry weight, root length, and root weight, were measured and recorded for the three groups. Among the morphological traits, leaf color did not show any significant changes between the control (green) and 10% PEG-treated plants (green with brown apex); however, 25% PEG-treated plants showed thinner and dead (brown) leaves. There was no significant difference in plant heights for the 10% PEG-treated seedlings (p = 0.329) in comparison with the control (untreated) plants (Figure 1A). The 10% PEG treatment did not impact the height of maize plants. However, a significant decrease from 42.5 to 34.75 cm in plant height of the 25% PEG-treated Z. mays seedlings (p < 0.05) was evident (Figure 1A). The leaf lengths of different treatments showed a significant decrease (p = 0.013) as compared to the non-treated seedlings from 29.25 to 18.5 cm for the 10% PEG treatment and 19 cm for the 25% PEG treatment (Figure 1A). The PEG treatment demonstrated the negative impact of osmotic stress on the leaf lengths of Z. mays seedlings. However, no significant differences (p > 0.05) in leaf length between the two treatments (10 and 25% PEG) were observed. A significant decrease (p = 0.274) in the number of leaves was observed for the 25% PEG treatment (Figure 1C). In comparison, the 10% PEG treatment showed a decrease in leaf count from 17 to 13 leaves. Interestingly, an increase was noted in the leaf number for the 10% PEG-treated seedlings compared to the control (Figure 1C).

The results further indicated that PEG treatment had no significant effect on leaf fresh weight with an increase in PEG concentration (p = 0.894). Similarly, the leaf fresh weight for the 25% PEG treatment showed no apparent effect as compared to the 10% PEG treatment (Figure 1D). The leaf dry weights across all treatments displayed no apparent statistical differences (p = 0.61) (Figure 1D). However, the leaf turgid weights for the PEG treatments displayed a decrease compared to the control (p = 0.02) from approximately 0.135 to 0.079 and 0.081 g, respectively (Figure 1D). The effect of PEG on the leaf area showed a significant statistical difference (p = 0.004) between the varying treatments (Figure 1E). The leaf areas of the 10 and 25% PEG were significantly reduced compared to the control (non-treated) seedlings from 33.72 to 15.5 and 17.08 cm^2, respectively. Nonetheless, no noticeable significance between the leaf area of 10 and 25% PEG-treated seedlings (p > 0.05) was indicated.

A highly significant difference (p < 0.05) was observed between the control (non-treated) and 25% PEG-treated plants with respect to the relative water content (RWC). The RWC of the control was ~42.5%, while osmotic stress decreased the RWC of the treated plants (10 and 25% PEG). Though the RWC for the 10% PEG-treated plants was ~28.03% and declined for the 25% PEG-treated plants to ~20.73%, no significant differences (p = 0.30) were observed between the two treatments (Figure 1F). Further morphological evaluations were conducted between the control (A) 10% (B) and (C) 25% PEG-treated roots. Maize seedlings that were treated with 10% PEG developed slightly longer roots compared to the 25% PEG treatment. However, seedlings grown under 25% PEG treatment (23.25 cm) showed a significant decrease (p < 0.05) in root length compared to the control (28.5 cm) and 10% PEG-treated seedlings (32 cm) (Figure 1G). As the stress concentration increased, less dense roots developed. Osmotically stressed plants demonstrated a partial higher average root length (Figure 1G) compared to the control plants. In general, there was no significant effect on the root weights within any of the treatments (p = 0.967) (Figure 1H).

3.2. Effects of Osmotic Stress on the Physiological Traits of Z. mays

3.2.1. Evaluation of Osmotic Stress on the Stomatal Morphology, Number, and Density

To evaluate the physiological changes induced by osmotic stress, stomatal analysis was carried out. Stomatal micrographs for all the treatments were taken for morphological observation and comparison. Morphological observations displayed turgid guard cells and open stomata for the control, while 10% and 25% PEG presented flaccid guard cells and closed stomata. Furthermore, the stomatal pore, complex length, and width were measured in micrometers (µm), wherein the 10% PEG-treated plants showed a large pore size (69.18 µm) and a longer width (28.16 µm) and length (66.19 µm) compared to the control pore (20 µm), width (28.19 µm), and length (22.80 µm). The 25% PEG-treated plants had a pore size of 29.55 µm, width of 19.31 µm, and length of 36.40 µm. Additional assessments were carried out to evaluate the stomatal number and density from both leaf surfaces (abaxial and adaxial) for all plant groups (control 10% and 25% PEG). The stomatal number for the upper surface treated with 25% PEG (~6) noticeably decreased in comparison to the control (~7.75) and 10% PEG treatment (~8.25). However, the 10% PEG treatment showed no difference from the control (p > 0.05), yet there was a slight increase in comparison to the 25% PEG treatment (p < 0.05). The lower surface stomatal numbers for the 10 and 25% PEG treatments (~13.75 and 13.25) were not statistically different from the control (~14.75), nor were they different from each other (p > 0.05). Similarly, the stomatal density for the upper surface of the 25% PEG treatment (3157.9 stomata/mm²) noticeably decreased in comparison to the control (4078.95 stomata/mm²) and 10% PEG treatment (4342.13 stomata/mm²). However, the 10% PEG treatment displayed no difference from the control (p > 0.05), yet there was a slight increase in comparison to the 25% PEG treatment (p < 0.05). The lower surface stomatal densities for the 10 and 25% PEG treatments were not statistically different from the control, nor were they different from each other (p > 0.05).

3.2.2. Effects of Osmotic Stress on the Leaf Gaseous Exchange Parameters

The photosynthetic rates of the three treatments showed an unstable trend. The 10% PEG treatment fluctuated repeatedly, whereby at the first 60 s the photosynthetic rate was strongly elevated, followed by a sharp decline at 80 s. It then reached a maximum photosynthetic rate at 120 s, followed by an ultimate decrease at 140 s. The control and 25% PEG treatments maintained moderate photosynthetic rates from 0 to 100 s, followed by a total inhibition from 110 s to the endpoint (Figure 2).

The stomatal conductance for the control significantly increased over time, particularly after 100 s. The 10% PEG treatment constantly remained moderate and stable to the end after the initial pick up at 60 s (Figure 3). Stomatal conductance for the 25% PEG treatment remained flat throughout the measurement period. Generally, PEG treatment strongly inhibited stomatal conductance (Figure 3).

3.2.3. Respiration and Transpiration Rates Under Osmotic Stress

The control reached the highest rate of respiration in comparison with the PEG treatments at 170 s. The 10% PEG treatment maintained a moderate trend, though it was lower than the control; however, the 25% PEG treatment maintained the lowest respiration rate and only peaked at 170 s (Figure 4).

Transpiration rates showed a similar trend across the three treatments, in which there was a gradual increase over time (Figure 5). An increase in PEG treatment concentration showed inhibition in transpiration rate. The control and 10% PEG groups reached their maximum transpiration rates at 180 s (Figure 5).

3.3. Assessment of Maize Protein Expressional Profiles in Response to PEG-Induced Osmotic Stress via One-Dimensional Gel Electrophoresis (1DE)

To evaluate the quality and loading quantities of protein extracts, one-dimensional gel electrophoresis (1DE) was carried out prior to label-free mass spectrometry analysis. Coomassie brilliant blue-stained maize leaf protein bands (in three replicates) were visible (Figure 6A). Numerous osmotic stress-induced proteins were expressed with molecular weights (MW) ranging from 10 to 250 kDa. The protein samples for each treatment showed similarity in protein expression, banding patterns, and abundance, suggesting that protein loading was relatively uniform. The results further showed that the protein extracts from all treatments covered the MW range between 10 and 200 kDa, wherein some bands were more highly expressed than others. The control samples 1 and 4 (lanes 2 and 5) demonstrated partial expression of protein bands, whilst the 10% PEG-treated samples 2 and 5 (lanes 3 and 6) displayed a higher expression of the protein bands indicated by yellow arrows (70 and 20 kDa). The 10% PEG treatment group also uniquely showed an expression of large mass protein between 250 and 200 kDa whilst the 25% PEG-treated samples 3 and 6 (lanes 4 and 7) displayed a greater mass of protein between 70 and 50 kDa (~68 kDa) as indicated by the red arrows, at the same time, showing a uniquely expressed protein between 15 and 10 kDa (~14 kDa) (Figure 6A).

3.4. Bioinformatic Analysis and Functional Annotation of Osmotic Stress Responsive Proteins in Maize Leaves

Approximately 439 differentially expressed valid proteins were identified using LC-MS/MS. Of the 439 proteins, 50 for each treatment were further investigated based on their consistent differential expression (≥2-fold change with statistical significance, p < 0.05) under stress conditions compared to controls. Figure 6B, based on the 50 selected DEPs, reveals that no single protein, 0 (0%), was commonly and significantly differentially expressed across all the three treatments; however, 2 (1.4%) common proteins were expressed between the control and 10% PEG treatment. On the other hand, 10% and 25% PEG treatments had 3 (2.1%) common proteins. The differentially expressed proteins decreased to 45 (31%) at a 10% PEG concentration and 47 (32.4%) at a 25% PEG concentration when compared to 48 (33.1%) in the control (Figure 6B).

3.5. Functional Classification and Expression Pattern Analysis of the Osmotic Stress Responsive Proteins

The proteins were further classified, and the results are tabulated in Table 1 and Table 2, showing protein names, subcellular locations, biological processes, molecular functions, and theoretical Mr/pI. The results revealed that the largest number of proteins were localized in the cytoplasm (28 proteins), while varying proteins were located in the chloroplast (19 proteins), extracellular region (15 proteins), membrane (12 proteins), and ribosome (9 proteins). The remaining proteins were localized to the plastid (8), thylakoid (8), cytosol (6), etc. (Table 1 and Table 2). The functional classifications of these proteins in Figure 6C revealed that at all functional classification levels, large proportions of these proteins were involved in cellular anatomical entities and catalytic activities across all treatments. Followed by cellular and metabolic processes, whilst other functional categories included proteins that are involved in protein-containing complexes, response to stimulus, localization, biological regulation, signaling, binding, transport, structural molecule activity, ATP-dependent activity, and molecular function regulation (Figure 6C). At the cellular component level, proteins were overrepresented in cellular anatomical entities, with the control showing the expression of 13 (81.3%) proteins found at the cell junction, cell periphery, cytosol, chromatin, cytoplasm, endomembrane system, external encapsulating structure, intracellular anatomical structure, membrane and organelle (Figure 6C). The 10% PEG treatment had 16 (69.6%) and the 25% PEG had 12 (70.6%) proteins found at the same location as in the control (Table 1 and Table 2). For the protein-containing complex, the control expressed 3 (18.8%) proteins, i.e., K7VN08, Q9M640, and C4JA45. The 10% PEG treatment group had 6 (30.4%) proteins, i.e., B4FCX3, P06589, C0HFM4, P06586, B4FW06, and P27723. The 25% PEG treatment group had 5 (29.4%) proteins, i.e., C0P455, B4FWJ8, A0A1D6JW41, K7VNQ7, and K7UDG5 (Table 1 and Table 2).

The biological process showed a higher representation of metabolic process proteins for the 10% PEG (10; 43.5%) [i.e., B7ZXQ3, P09315, B4FRC4, C0HFM4, C4IZ94, B4F833, C0P5A9, B4F8L7, K7V2Z8, and P27723] (Table 1) compared to the control (7; 30.4%) [i.e., A0A1D6L6U6, K7WFV1, C4IZ94, B4F833, K7TXI5, C0PAU7, and P93805], whilst in the 25% PEG these proteins were less represented (8; 27.6%) [i.e., B4F938, B4FWJ8, B4FFZ2, B4FAL9, B4FAD4, B4FYH2, K7UDG5, and B6THZ8] in comparison to the control. The cellular process for the control showed 10 (43.5%) expressed proteins, i.e., Q9M640, A0A1D6L6U6, A0A1D6MUE8, C4JA45, K7WFV1, C4IZ94, B4F833, K7TXI5, C0PAU7, and C4J410 (Table 2). In addition, the 10% PEG treatment group had 9 (39.1%) proteins [i.e., P06589, B7ZXQ3, B4FRC4, B6TNT5, C0HFM4, C4IZ94, B4F833, K7V2Z8, and P27723]. The 25% PEG treatment group had 13 (44.8%) proteins involved in cellular processes, i.e., B4F938, B4FWJ8, B4FFZ2, K7VJF3, B4FAL9, B4FAD4, B4FYH2, B6SN61, K7VNQ7, K7UDG5, B4F9E8, B6THZ8, and B4F9K4 (Figure 6C; Table 1 and Table 2).

Proteins responsible for response to stimulus demonstrated a decrease from the control (4; 17.4%) [i.e., A0A1D6MUE8, K7TXI5, A0A1D6LE55, and C4J410] to 10% PEG (2; 8.7%) [i.e., B4FRD6 and B6TNT5] (Figure 6C; Table 1); however, not much difference was observed in 25% PEG (5; 17.2%) as compared to the control [B4FWJ8, K7VJF3, B6SN61, B4F9E8, and B4F9K4] (Table 2). The control had no biological regulation proteins, while the 10% PEG treatment group had 2 (8.7%), i.e., C0HFM4 and C0P5A9, and the 25% PEG treatment group expressed 1 (3.4%), i.e., B4FWJ8. On the contrary, the control had 2 (8.7%) localization proteins, i.e., Q9M640 and K7WFV1, whilst the 10% PEG treatment group had none. The 25% PEG treatment group had 1 (3.4%) localization protein [i.e., K7VNQ7], whilst uniquely showing 1 (3.4%) signaling protein, i.e., B4FWJ8 (Figure 6C; Table 1).

At the molecular function level, the majority of proteins were represented in the catalytic activity category (Figure 6C). The control had 12 proteins (52.2%) responsible for hydrolase, isomerase, ligase, oxidoreductase, and transferase activities. The 10% PEG treatment group had 11 proteins (44.0%), i.e., B4FRD6, B4FRH1, B7ZXQ3, P09315, B8A2X5, B4FRC4, B6TNT5, C0P3R8, C4IZ94, B4F833, and B4F8L7. While 25% PEG had 11 (50.0%), i.e., B4F938, B4FWJ8, B4FFZ2, K7VJF3, C0P3R8, B4FAL9, B4FAD4, B4FYH2, B6SN61, K7UDG5, and B6THZ8 (Table 2). For the binding process, the control had 6 (26.1%) proteins, i.e., B4F8V5, A0A1D6MUE8, C4JA45, C0PAU7, Q94F78, and C4J410 (Table 1). The 10% PEG treatment group had 7 (28.0%), i.e., B4F9N8, P09315, B8A2X5, B4FRC4, C0HFM4, B4FW06, and B4F8L7, while the 25% PEG treatment group had 6 (27.3%) proteins, i.e., C0P455, B4FWJ8, K7VJF3, A0A1D6L210, B4F9E8, and B4F9K4. For transport activity proteins, the control had 2 (8.7%) proteins, i.e., B4FBF6 and Q6R9G1, while the 25% PEG treatment group had 1 (4.5%), i.e., K7VNQ7 (Table 2). The control had 2 (8.7%) ATP-dependent activity proteins, i.e., A0A1D6MUE8 and C4J410, whilst the 25% PEG treatment group had 2 (9.1%) proteins, i.e., K7VJF3 and B4FWJ8. The control had 1 (4.3%) protein for structural molecule activity (C4JA45); for the 10% PEG treatment group, there were 6 (24.0%), i.e., P06589, B4G1J8, C0HFM4, P06586, B4FW06, and P27723; and for the 25% PEG treatment group, there were 2 (9.1%) proteins, i.e., C0P455 and A0A1D6JW41. The 10% PEG treatment group had 1 (4.0%) unique molecular function regulator protein, i.e., B8A2X5 (Figure 6C and Table 1).

3.6. Protein–Protein Interaction Network of Osmotic Stress-Responsive Proteins

To further investigate the functional interrelationships among the identified DEPs, a protein–protein interaction (PPI) analysis was performed using the STRING 12.0 database. The resulting interaction networks were visualized and analyzed in Cytoscape (v3.9.1) to identify clusters, hub proteins, and biological pathways potentially contributing to osmotic stress regulation in Zea mays.

Under the 10% PEG treatment (Figure 7A), the PPI network displayed a dense and highly interconnected cluster, reflecting strong coordination among proteins involved in translation, protein folding, and energy metabolism. Notably, ribosomal subunits (40S S10-1, 40S S17-1, 60S L13a-1, 50S, and 30S proteins), elongation factor γ1, and methionine aminopeptidase formed the core of this network, indicating enhanced regulation of protein synthesis. Peptidyl-prolyl cis–trans isomerase (PPIase) and other folding-related proteins supported proteostasis maintenance. At the same time, ornithine carbamoyl transferase and pectin esterase were associated with amino acid metabolism and cell wall remodeling, respectively. Proteins linked to organellar translation, such as polyribonucleotide nucleotidyl transferase 2 (mitochondrial), and signal recognition particle 54 kDa protein (chloroplastic), highlighted coordinated protection of translational and metabolic functions. Overall, these interactions demonstrate an adaptive proteomic response involving translational reprogramming and metabolic adjustment to sustain cellular homeostasis under mild osmotic stress.

In contrast, the 25% PEG network (Figure 7B) exhibited reduced connectivity and fragmented clusters, indicating weakened interaction networks under severe osmotic stress. Enzymes such as pyruvate kinase and fructose-bisphosphate aldolase were associated with energy metabolism, whereas proline- and valine-tRNA ligases suggested restricted translational efficiency. Although ribosomal proteins (40S S4-3, S20-1, 60S L4-1, and ribosomal S4e domain-containing protein) were still detected, their limited connections implied impaired coordination of protein biosynthesis. The presence of heat shock 70 kDa protein 5, binding protein homolog 2, and T-complex protein 1 subunit zeta pointed to activation of chaperone systems in a less organized manner. Mitochondrial proteins, including aconitate hydratase 3 and NADH dehydrogenase subunits, indicated sustained but stressed respiratory activity. Overall, the 25% PEG treatment reflects a shift from coordinated metabolic regulation toward fragmented and energy-depleting stress responses, revealing the metabolic strain imposed by severe osmotic stress.

4. Discussion

Rapid population growth has led to an increased interest in maize cultivation for food and non-food purposes [22]. However, drought-induced osmotic stress restricts plant growth and development by impairing physiological and biochemical processes. Osmotic stress is perceived by the cell as turgor loss and triggers various signaling cascades for osmotic adjustment [23,24,25]. Proteomic approaches provide insight into plant stress-responsive proteins that underpin adaptive mechanisms. Our study investigated the effects of osmotic stress on the morphology, physiology, and proteomic profiles of maize (Zea mays) seedlings. Polyethylene glycol (PEG)-induced osmotic stress has been widely used to simulate drought stress under controlled conditions [26,27]. In this study, maize seedlings exposed to 10% (w/v) and 25% (w/v) PEG displayed distinct morphological, physiological, and proteomic responses that together illustrated adaptive and maladaptive mechanisms of drought response.

Plants undergo morphological changes when subjected to drought stress, including reduced plant height, leaf area, and biomass [28,29]. After 20 days, PEG treatments significantly affected leaf morphology, particularly leaf number, length, and area.

At the morphological level, mild osmotic stress (10% PEG) induced a slight reduction in leaf size but promoted additional leaf formation and longer roots, suggesting a compensatory growth mechanism. In contrast, severe stress at 25% PEG caused chlorosis and drastically decreased leaf area, senescence, and root proliferation, indicating growth inhibition resulting from dehydration-induced energy limitation [30,31]. These morphological changes demonstrate adaptive strategies, where leaf death under severe stress reduces water loss. Proteomic shifts corresponded with these morphological responses.

Various proteins including those involved in energy metabolism and stress-association were differentially expressed, supporting the observed morphological patterns in response to osmotic stress. Proteomic analysis revealed an overrepresentation of peptidyl-prolyl cis–trans isomerases (PPIases), which are involved in protein folding and stabilization under cellular stress. Even though these enzymes are widely recognized as general stress proteins, their abundance alongside reduced relative water content (RWC) suggests their relation to impaired ion homeostasis [32]. This concurs with reports that PPIase accumulation reflects disturbances in protein folding and ion flux under abiotic stress [33].

Additionally, various proteins related to sugar metabolism and the Krebs cycle were upregulated, highlighting an activation of core carbon metabolism pathways. Upregulation of glycolysis and TCA cycle enzymes under mild stress supports adaptive growth, whereas their suppression under severe stress reflects growth arrest [34,35]. Thus, in our study the enrichment of these pathways under 10% PEG corresponds with maintained root elongation and partial photosynthetic function, whereas their suppression at 25% PEG coincided with growth arrest confirming a dose-dependent metabolic adaptation.

We further focused on evaluating key leaf traits including length, number, weight, area, and plant height to associate morphological changes with overall growth performance (Figure 1).

During mild stress (10% PEG), plant height was maintained, while severe stress (25% PEG) reduced it due to early leaf senescence along with a decline in cell division [30]. Severe drought led to significant damage to maize growth. The leaf lengths for both the 10% and 25% PEG treatments decreased compared to the control, from 29.25 cm to 18.5 cm and 19 cm, respectively. Likewise, mild stress (10% PEG) increased the leaf number and root length, which could be due to hormonal regulation, while severe stress (25% PEG) reduced leaf size and number, reflecting a maladaptive response. Such responses have been observed in plants where low levels of abiotic stress enhance growth parameters [36,37]. Water deficit has been noted to have a negative influence not only on leaf size but also on the number of leaves [38].

The fresh and dry leaf weights showed no significant changes for either treatment compared to the control. Nonetheless, the leaf turgid weights for the 10% and 25% PEG treatments displayed a significant decrease compared to the control (Figure 1D). Weight decrease is a common unfavorable influence of drought stress in plants [39]. Ultimately, the leaf areas for both treatments significantly decreased. To adapt to drought, plant leaves generally adopt smaller leaf areas due to the decrease in leaf turgor pressure, canopy temperature, and the availability of photoassimilates [29]. Allakhverdiev [40] indicated that insufficient leaf area reduces sunlight use efficiency. Equally, the decrease in photosynthesis and canopy structure under drought stress may result in a drop in the total fresh weight of a plant. To a large extent, plant size is determined by the strong interrelation between the number of leaves, their sizes, photosynthesis capacity, and functional traits [26,32]. Generally, the increase in PEG concentration in this study resulted in a significant decrease in all of the morphological traits of leaves.

Under 10% PEG treatment, root proliferation decreased while root length increased (Figure 1G). Zulfqar [41] emphasized that it is common to observe increased root lengths across stressed plant species. This notion was supported by Luo [42]’s observation of longer roots during the initial stages of drought stress in cotton plants. Deeper roots are a desirable trait for water extraction from deeper layers as an adaptation mechanism against drought stress [31]. On the contrary, in 25% PEG-treated maize seedlings, the roots were thinner and had less proliferation and decreased length (Figure 1G). Robin [27] demonstrated similar findings in castor bean, where PEG-induced osmotic stress reduced the length, density, and diameter of root hairs. Yang [29] viewed such root system configurations as enablers for water deficiency in a plant, whereby the reduction in root length could be linked to the drop of relative turgidity and dehydration of the protoplasm, which is associated with turgor loss and reduced cell division as an osmotic stress coping mechanism. When the availability of water is limited, there is a reduction in osmotic pressure resulting in a plant water deficit [27,42,43]. Additionally, the root weights demonstrated no significant change across all of the varying treatment conditions (Figure 1H). Chen [44] found a strong correlation between root weight and plant height and no significant correlation between root depth and plant height in wheat under drought stress. However, in our study, the root weight and plant height for both treatments showed no correlation, whilst there was a correlation between the root length and plant height for the 25% PEG treatment. Genetic and environmental factors such as experimental conditions and stress duration and intensity affect root growth, as well as response to drought [45]. Root morphological changes may be a result of stress resistance or the stimulation of root growth under low-moisture conditions [46]. Overall, our study has demonstrated that all drought-induced treatments influenced the root growth and morphology, though there were differences in the response to root growth.

The observed increase in root length under mild stress (10% PEG) coincided with a higher normalized abundance of energy metabolism enzymes such as pyruvate kinase and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Table 2). While statistical correlation coefficients were not calculated, this pattern suggests a potential association between enhanced energy metabolism and adaptive root elongation under mild stress conditions. Similar associations have been reported in previous studies, where elevated glycolytic enzyme activity supported root growth under osmotic or drought stress in maize and other crops [34,41,47]. In contrast, reduced proliferation under severe stress (25% PEG) reflects metabolic downregulation and energy depletion. These findings, therefore, provide a qualitative association between proteomic shifts in central carbon metabolism and root morphological responses to PEG-induced osmotic stress. Similarly, the abundance of antioxidant-related proteins, including glutathione peroxidase, late embryogenesis abundant (LEA) proteins, and heat shock proteins (HSP70), underscores an integrated defense system that mitigates oxidative stress and prevents protein misfolding.

Apart from the external form, drought stress has different degrees of influence on physiological and biochemical parameters [26,28,29]. Hence, we further assessed the physiological parameters of the Z. mays seedlings. When plants experience drought stress, their stomatal response is probably the first line of defense against dehydration as it is quicker than other changes such as pigmentation, proteins, leaf area, chloroplast ultrastructure, and root growth [48]. In addition to metabolic pathway alterations, complex signaling networks involving abscisic acid (ABA) and numerous systems co-participate in stomatal regulation to restrict excess water loss [7,49].

We further explored the water status of the plant tissues and their ability to survive stressful conditions via relative water content (RWC) [24,50]. RWC is a critical parameter for plant growth and development, as it is directly related to the ability of plants to absorb nutrients, photosynthesize, and maintain their structural integrity [30,51]. It further reflects the incorporated effects of morphological and physiological changes that plants experience to prevent water loss and maintain water balance [52]. As PEG concentration increased in our study, a significant decrease in RWC was noted in all of the treatments, with a decline from ~42.5% (control) to ~28.03% and ~20.73% (10 and 25% PEG), respectively. Our results correlate with the previous studies that have reported similar results, including maize [30,39] and wheat [50]. Several factors contribute to the decrease in RWC in addition to water reduction in the soil. These include enhanced transpiration and reduced osmotic potential in stressed leaves, as well as alterations in cellular structure [53].

When further evaluating the influence of PEG-induced osmotic stress on the physiology of Z. mays seedlings, it was considered that the net photosynthetic rate and leaf stomatal conductance could be affected by the induced stress; thus, these factors were thoroughly studied. Our results demonstrated that the photosynthetic rate for the 10% PEG-treated plants increased to a moderate rate at 60 s, reached its maximum potential at 120 s, and then dropped. Our observation supports that these high rates and drops could possibly be a rapid osmotic stress response [54], whilst the 25% PEG-treated plants unrestrainedly photosynthesized at lower rates than the control. Relatedly, a study on ferns, gymnosperms, and angiosperms found that the maximum photosynthetic rate is positively correlated with stomatal density; however, it had no relation with stomatal size [55]. While Zulfqar [41] put forward that leaf area and photosynthetic rate are directly proportional, we could deduce based on our results that low adaxial stomatal density, as well as low leaf area, are indeed correlated with low photosynthetic rates.

The data presented in Figure 3 demonstrate that PEG-induced osmotic stress strongly inhibited stomatal aperture to reduce water loss through transpiration. The control suggests a healthy physiological activity [56]. The 10% PEG treatment indicates partial stomatal closure, reflecting a mild osmotic stress response with the plant conserving water while still allowing limited gas exchange. Such mild stress triggers adaptive mechanisms without fully inhibiting stomatal function [36]. The 25% PEG treatment remained flat, reflecting severe stomatal closure due to high osmotic stress caused by PEG, reducing water availability and prompting the plant to minimize transpiration. The complete suppression of stomatal conductance is consistent with the findings of [5] that high osmotic stress leads to stomatal closure, reduced CO₂ uptake, and impaired photosynthesis. According to Wu [55], stomatal conductance usually increases under mild drought stress. In our study, the gs of the drought-stressed seedlings showed little to no stomatal activity. Low gs reduces water loss through transpiration and can hinder the improvement of photosynthetic efficiency for a good crop yield [57,58]. Changes in stomatal conductance depend on RWC and can occur rapidly; however, plants do not show large decreases in stomatal conductance when experiencing drought stress gradually [59,60]. Therefore, in our study, we inferred that the PEG-induced stress was rapid and that drought stress decreased photosynthesis by lowering stomatal conductance to carbon dioxide.

In addition to photosynthesis and stomatal conductance, respiration and transpiration rates were also evaluated. The control reached the highest rate of respiration in comparison with the PEG treatments at 170 s. The 10% PEG treatment maintained a moderate trend, though it was lower than the control. This trend could be an indication of the adjustment in the metabolic activity of the plant to cope with the stress [61]. However, the 25% PEG treatment maintained the lowest respiration rate and only peaked at 170 s. The gaseous exchange, as well as transpiration rates, are controlled by the stomata [62]. Zulfqar [41] demonstrated that in the sugarcane crop, when the water content decreased, the CO₂ uptake also decreased as the stomata closed.

Transpiration rates showed a similar trend across the three treatments, in which there was a gradual increase over time. Similar to stomatal conductance, transpiration rates increased under mild water stress, whilst severe stress caused a decrease in rates as a reflex to stimuli [30,63]. An increase in PEG treatment concentration showed inhibition in transpiration rates, i.e., the 25% PEG treatment showed lower transpiration rates than the other treatments. Sikuku [64] identified that the decrease in transpiration, as well as photosynthesis, in rice was due to reduced stomatal conductance. Likewise, similar results were noted in maize [39]. Since our results mirrored a gradual increase, Marchin [65] suggests that these patterns occur either due to water competition driving plants to maintain high rates of transpiration until a level of water depletion or by stomatal closure.

The physiological impairments observed in photosynthesis and stomatal conductance under PEG-induced osmotic stress correspond with the proteomic expression changes detected in this study. The upregulation of chlorophyll a–b binding protein (tr|K7TXI5) and ATP synthase subunit gamma (tr|K7WFV1) suggests an adaptive mechanism to sustain photosynthetic electron transport and ATP generation under dehydration. On the other hand, chlorophyll a–b binding proteins stabilize photosystem II and delay chlorophyll degradation, maintaining light-harvesting efficiency during moderate stress [66,67], while enhanced ATP synthase activity supports energy supply for repair and osmolyte synthesis [68,69]. These proteomic adjustments explain the transient maintenance of photosynthetic activity observed under 10% PEG and its collapse under 25% PEG, reflecting the threshold at which cellular energy regulation can no longer compensate for severe osmotic inhibition.

The composition of minerals, such as antioxidants and proteins, is also affected by drought. Plants display various responses at the cellular level, which are related to their biochemistry [70]. Proteomic evaluation to identify differentially regulated proteins has been conducted in response to osmotic stress. Drought-stressed plants tolerate stress by adjusting their structural and biochemical form through the accumulation of proteins [71]. Proteins are the “workhorses” of cells in living organisms, responsible for a wide range of functions and modifications to the internal and/or external cellular conditions that cause temporal changes in the proteome expression, thus affecting their growth and are encoded in leaf growth regulatory genes of varying functional classes that regulate cellular processes [71,72]. Changes in proteomic expression in drought-affected plants such as wheat [73] and rice [74,75] have been observed before. The proteomic analysis highlights differentially expressed proteins associated with significant biological processes, molecular processes, and several metabolic pathways [70].

Various studies use gel-based proteomic separation to make sense of abiotic stress-responsive mechanisms. Likewise, in our study, we used the same technique to analyze the fractions of soluble leaf extracts by 1DE with Coomassie brilliant blue for staining. We observed a similarity in protein expression, banding patterns, and abundance. The protein profile for the control (lanes 2 and 5) exhibited partial expression, whilst the 10% PEG (lanes 3 and 6) protein profile exhibited less abundant proteins at 70 and 20 kDa. Additionally, newly pronounced proteins were observed between 250 and 200 kDa in the 10% PEG protein profile. The 25% PEG protein profile presented less abundant proteins between 70 and 50 kDa (~68 kDa), simultaneously presenting uniquely pronounced proteins 15 and 10 kDa (~14 kDa) (Figure 6A). Our results coincide with those of Lukhele [76], who found that higher protein abundance is not always found in non-stressed plants than in stressed ones. Similarly, our protein expression profiles resulted in both less and more abundant proteins. Under PEG-mediated osmotic stress, Thamaga [77] noted a decrease in protein synthesis and total soluble proteins in Z. mays leaves. In contrast, our results demonstrate that PEG-induced osmotic stress stimulated the abundance of several proteins in Z. mays leaves.

We further performed functional annotation and subcellular localization for 50 proteins of the 439 differentially identified proteins, in which the majority were related to photosynthesis, ATP synthesis, and protein biosynthesis (Figure 6C). The subcellular localization further revealed that most of the proteins were actively synthesized in the chloroplast and cytoplasm. Essential proteins such as ATP synthase were exhibited in all of the treatments’ protein profiles. The control protein profile exhibited stress-response heat shock 70 kDa proteins (tr|C4J410; tr|A0A1D6MUE8), which are predicted to prevent aggregation and regulate processes important for plant survival, assist in the folding of proteins and promote the anti-stress ability of the cell [70,78]. It is common for stressed plants to accumulate ROS in their cells, which leads to oxidative stress. Antioxidant enzymes are then induced to prevent excess ROS accumulation and contribute to cell survival [38,79]. In our study, Calvin cycle enzymes that play major roles in carboxylation, such as fructose biphosphate aldolase (tr|B4FAL9) and hydrogen peroxide (tr|B4FRD6), were identified in the 10% PEG treatment protein profile. These enzymes are vital for carboxylation; therefore, their downregulation resulted in reduced photosynthesis rates [70]. The 10% PEG protein profile also exhibited peptide-methionine (tr|B6TNT5), which produces polyamines and ethylene for cell division, morphogenesis, and plant senescence; pyruvate kinase (PK) (tr|B4FYH2), which catalyzes ADP into ATP under stress and is recognized to yield more energy for tolerance; glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (tr|B4F8L7), which alleviates ROS-induced cell damage under stress; and adenosyl homocysteinase (tr|A0A804NLS4), which regulates intracellular methylation and improves drought tolerance by slowing membrane damage [79,80].

To strengthen the relevance of our proteomic findings, the differentially expressed proteins (DEPs) were cross-referenced with their corresponding gene loci in the maize genome using UniProt, MaizeGDB, and NCBI databases. Various key proteins identified, including LEA (tr|B4F9K0), HSP70 (tr|C4J410), GAPDH (tr|B4F8L7), and pyruvate kinase (tr|B4FYH2), correspond to genes previously associated with drought tolerance in maize, sorghum, and rice through transcriptomic and QTL mapping studies [81,82,83,84,85,86]. For instance, the ZmLEA3 and ZmHSP70-1 genes have been reported to enhance osmotic adjustment and protect cellular proteins under dehydration stress [81,82], while ZmGAPDH contributes to ROS detoxification and energy balance during drought spells [83]. Similarly, pyruvate kinase genes (ZmPK1) have been associated with improved carbon metabolism and drought adaptation [84]. These associations affirm that the proteomic responses observed in this study are underpinned by stress-responsive genes already recognized for their functional role in drought tolerance. Despite the present study focusing primarily on protein identification and functional characterization, future integrative analyses linking DEPs with genomic markers, promoter regions, and SNP variations will be essential to translate these molecular signatures into breeding tools for marker-assisted selection and genetic improvement of drought-tolerant maize cultivars [85,86].

The 25% PEG protein profile exhibited LEA protein (tr|B4F9K0), which is a water-soluble protein synthesized in high desiccation-tolerant plants, as well as HSPs: 17.5 kDa class II HSP (tr|B4f9k4) and 17.4 kDa class III HSP (tr|B4F9K4), which are components for stress response and ATP binding, hydrolysis activity, and dependent protein folding [84,87]. Soluble proteins are linked with amino acids that are produced in response to heat or drought stress to maintain the cell’s normal functionality [88]. In sunflowers, osmotic stress similarly induced low-MW HSPs [89]. The 10% and 25% PEG treatments both expressed glutathione peroxidase (GPX) (tr|COP3R8), which is responsible for the oxidative stress response. By increasing glutathione content under stress, wheat revealed that it could withstand drought stress, and this response was deemed common even in barley [40,57]. Under unfavorable conditions, GPX acts synergistically with other antioxidant enzymes to eliminate harmful free radicals [80]. Therefore, its identification in our study provides insight that it is crucial for ROS-scavenging in stressed seedlings to confer tolerance. These observations suggest that drought stress primarily affects proteins involved in metabolism and stress adaptation. This descriptive functional profiling provides an overview of stress-responsive pathways. In future studies, more detailed enrichment or abundance-based analyses are required to support these patterns.

To further understand the functional interactions and coordination among these proteins under osmotic stress, we performed protein–protein interaction (PPI) network analysis which revealed insights into the molecular mechanisms of maize adaptation to varying osmotic stress levels [90]. Under mild stress conditions (10% PEG) (Figure 7A), the formation of a dense and highly interconnected PPI network suggests a well-coordinated cellular response. Key proteins involved in translation, protein folding, and primary metabolism form central hubs, indicating that maize maintains cellular homeostasis through enhanced protein synthesis and stability. This observation aligns with the findings of Li et al. [67], who reported that drought-tolerant maize cultivars exhibit robust translational machinery and proteostasis mechanisms under stress conditions. Conversely, under severe osmotic stress (25% PEG) (Figure 7B), the fragmented PPI network reflects disrupted protein coordination and reduced translational capacity. The limited connectivity of proteins involved in energy metabolism and stress-response pathways suggests a compromised ability to sustain cellular functions under high stress. This is consistent with a study by Zhao et al. [91], who observed that maize subjected to combined drought and heat stress exhibited a disorganized PPI network, correlating with decreased stress tolerance. Moreover, the central role of molecular chaperones and heat shock proteins in the PPI network underscores their importance in maintaining protein integrity and function during stress. These findings are supported by Palotai et al. [92], who highlighted the critical role of chaperones as integrators of cellular networks, particularly under stress conditions. Collectively, the PPI network analysis reveals that maize employs a coordinated and robust molecular strategy to cope with mild osmotic stress, characterized by enhanced protein synthesis and stability. However, under severe stress, the disruption of this network indicates a threshold beyond which maize’s adaptive capacity is compromised. These insights contribute to our understanding of the molecular basis of drought tolerance in maize and can inform strategies for developing more resilient cultivars. Overall, the proteomic adjustments observed in maize seedlings provide a molecular association between the morphological reductions in leaf and root growth and the physiological impairments in photosynthesis, stomatal conductance, and RWC, highlighting an integrated drought stress response. Our findings demonstrate that maize seedlings employ a multi-layered drought response, integrating morphological adjustments, metabolic reprogramming, and chaperone-mediated stress protection. The overrepresentation of PPIases and metabolic enzymes adds mechanistic relevance to the proteomic findings, linking molecular and phenotypic resilience to underlying biochemical pathways. This integrative understanding enhances the relevance of the results and provides a framework for future research on the development of drought-tolerant maize varieties.

5. Conclusions

The responses of plants to drought are complex and can be observed at the morphological, physiological, and molecular levels in relation to stress duration and severity. This study evaluated the morphological, physiological, and proteomic responses of Z. mays to PEG-induced osmotic stress. Exposure to drought-induced osmotic stress hampered the morpho-physiological growth of the Z. mays seedlings. Our study successfully linked the Z. mays seedling response to the interactive damage for all studied traits, such as plant height leaf and root growth parameters, as well as stomatal morphology, relative water content (RWC) and leaf gaseous exchange parameters. The results fairly demonstrated variations in the response of the treated plants when compared to the control plants, thus reflecting the wider range of adaptation capabilities that maize uses to survive stress. Additionally, proteomic analysis carried out in maize leaves to monitor the osmotic stress-responsive proteins and a variety of proteins were positively identified. Among them, the most differentially expressed response proteins were related to cellular components (cellular anatomical entity), molecular function (catalytic activity), and biological processes (metabolic and cellular processes), respectively. Our overall analyses of Z. mays revealed the negative effect of osmotic stress on plant growth and development, along with novel insights into the physiological and proteomic level response mechanisms. Our findings could thus be used as a tool to bridge the gap in future studies to improve osmotic stress tolerance in maize. Additionally, the cross-referencing of drought-responsive proteins with their encoding genes provides a functional basis for understanding the genetic control of stress adaptation. Future studies combining proteomic data with genomic and marker-based analyses will be critical for translating these molecular signatures into practical tools for maize breeding and drought-resilient cultivar development.