Multi-omics Analysis of the Maize Ear Diameter Mutant3 (zmed3) Provides Insights into Female Inflorescence Development

Multi-omics Analysis of the Maize Ear Diameter Mutant3 (zmed3) Provides Insights into Female Inflorescence Development | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics Analysis of the Maize Ear Diameter Mutant3 (zmed3) Provides Insights into Female Inflorescence Development Jing Liu, Tianxiao Yang, Zhuoya Gao, Jianxin Li, Tuan Li, Jialei Ou, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7194726/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted 14 You are reading this latest preprint version Abstract Background Ear is a crucial component of final yield in maize. Understanding how ear is developed is essential for maize genetic improvement and molecular breeding. Among the multiple factors influencing yield, ear development is particularly critical, as it is governed by the inflorescence meristem—a structure that directly shapes key yield-related traits such as ear size, kernel number, and row arrangement. Results Here, we analyzed the zmed3 mutant, which shows flattened ear tip and disordered kernel rows, is a single recessive mutation isolated from the Lx9801 breeding population. Using integrated transcriptomic, proteomic, and metabolomic analyses at the 4 mm stage of developing ears, we identified 1,589 differentially expressed genes (DEGs), 185 differentially accumulated proteins (DAPs), and 122 differentially accumulated metabolites (DAMs) in zmed3 mutants compared with normal siblings. These global omics changes were primarily associated with central carbon metabolism. Mutant zmed3 inflorescence meristems (IMs) were initially enlarged, switched to a more fasciated pattern, and finally leading to impaired spikelet meristems (SMs). Transcriptomics suggested activation of the jasmonic acid signaling pathway, potentially affecting spikelet cell elongation. Proteomics indicated disruption of the MAPK signaling pathway, likely affecting spikelet cell polarity. Metabolomics demonstrated deficiencies in the tricarboxylic acid cycle and phenylpropanoid synthesis pathway, which in turn alter meristem cells differentiation and cell wall remodeling. Multi-omics integration uncovered a regulatory network involving cell cycle initiation, jasmonic acid signaling, and metabolic flux homeostasis, and pinpointed several candidate genes for future functional characterization. Conclusions Our study not only identifies potential molecular mechanisms underlying maize ear development, but also pinpoints precise targets for genetic improvement. These findings deepen our understanding of inflorescence biology and provide a theoretical framework for optimizing yield-related traits, thereby offering actionable insights for the design of molecular breeding strategies to enhance maize productivity. maize ear transcriptome proteome metabolome candidate genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Maize ( Zea mays L. ) is a globally important crop that serves as a major source of human food, animal feed, and industrial raw materials. As the demand for maize continues to rise, improving grain yield remains a primary goal in breeding programs [ 1 ] . Yield is influenced by multiple factors, among which the ear development is vitally important. The ear inflorescence meristem, the precursor of ear, playing a decisive role in determining ear size and kernel number [ 2 ] . The ear inflorescence meristem in maize arises from the shoot apical meristem (SAM) through direct or indirect differentiation and gives rise to the branches and spikelets that shape the female ear architecture [ 3 ] . The SAM initiates inflorescence meristem (IM) formation by lifting suppression signals from axillary buds, promoting lateral cell differentiation [ 4 ] . As development progresses, the IM transitions into spikelet pair meristems (SPMs), regulated by ZAG1 , a C-class MADS-box transcription factor, and TSH4 . This transition involves ZAG1-mediated repression of WUSCHEL (WUS) to limit stem cell activity, and chromatin remodeling via ZAG1-TSH4 interaction, activating SPM-specific genes such as FEA3 [ 5 ] . At this stage, meristematic cells show reduced mitotic activity with divisions aligned along the rachis axis [ 6 ] . The auxin efflux carrier PIN1 localizes basipetally, forming auxin maxima at 4–6 cell intervals via a Turing-type reaction-diffusion mechanism [ 7 ] , establishing the bispiral phyllotaxis of spikelet meristems (SMs). Each SPM generates two SM primordia at the adaxial and abaxial sides of the rachis through perpendicular division planes. This process is tightly controlled by FEA3 , which senses mechanical stress to restrict SPM overproliferation and ensure spikelet pair determinacy [ 8 ] . During the terminal differentiation of SMs, downregulation of KN1 (KNOTTED1) triggers the exit from the proliferative phase. This process is accompanied by decreased Cyclin B1;1 (CycB1;1) expression and a sharp reduction in mitotic activity. Mitotic frequencies in differentiated SMs drop to as low as five divisions per hour [ 9 ] . Simultaneously, miR172-mediated degradation of APETALA2 ( AP2 ) transcription factors initiates carpel primordium specification: distal daughter cells form lemma/palea structures, while proximal cells, regulated by ZmYABBY14 , develop ovary and silk (style) primordia [ 10 ] . Ovary primordium expansion is mediated by α-expansin-induced cell wall loosening, while silk elongation occurs through gibberellin-induced polarized cell growth [ 11 ] . In second florets, dominant suppression by SI1 (Sterile Infertile1) limits development to rudimentary glumes, optimizing kernel set efficiency [ 12 ] . Recent studies have revealed the molecular mechanisms underlying ear deformity in maize, highlighting complex interactions among genes and signaling pathways [ 13 – 14 ] . One key regulator, KRN4 , located approximately 60 kb downstream of UB3 , enhances UB3 expression through cis-regulation, affecting ear row number. UB3 , a member of the SBP-box transcription factor family, is critical for axillary meristem initiation and directly influences tassel branching and ear row variation [ 5 ] . Another important factor, KNR6 , interacts with AGAP and exhibits protein kinase activity, phosphorylating AGAP to regulate ear length and kernel number per row. KNR6 -mediated phosphorylation activates AGAP, which modulates vesicular transport and maintains auxin homeostasis in the female inflorescence, influencing ear morphology [ 15 ] . Additionally, FEA4 ( FASCIATED EAR4 ), a TGA-class bZIP transcription factor, regulates the apical region of the inflorescence meristem. Its expression is controlled by ZmbHLH172 and ZmOFP28, emphasizing its role in shoot and inflorescence meristem development [ 6 ] . Finally, MSCA1 and its homologs are crucial for meristem development and ear morphogenesis, regulating the redox state of FEA4 to ensure proper development of the female inflorescence meristem [ 8 ] . The maize CLV-WUS pathway regulates ear development by maintaining meristem homeostasis. Crucially, several core pathway genes directly impact key aspects of maize ear morphogenesis. FEA2 , a CLV2-homologous LRR receptor protein, senses signals like ZmCLE7 and interacts with CT2 and ZmCRN [ 16 ] . Its weak allelic mutation can increase kernel row number. FEA3 , also a CLV2-homologous gene, independently senses ZmFCP1 signals to inhibit excessive meristem proliferation [ 15 ] . TD1 , a CLV1-homologous LRR kinase, collaborates with FEA2 to restrict meristem size. Mutation of TD1 causes thickening of the female ear [ 17 ] . CT2 , a G protein subunit, specifically transduces FEA2 signals and participates in jasmonic acid signaling [ 18 ] . UB2 / UB3 , SBP-box transcription factors, inhibit meristem activity by regulating hormone-related genes and responding to epigenetic regulation of the non-coding region KRN4 [ 19 ] . These genes collectively maintain the balance of meristematic stem cell proliferation and differentiation through multi-level mechanisms of signal perception, transduction, and transcriptional regulation. Their allelic variations provide important targets for high-yield maize breeding. In this study, we conducted transcriptomic, proteomic, and metabolomic profiling of zmed3 and its control Lx9801. By performing a multi-omics analysis with 4 mm developing ears, we aim to unravel the regulatory networks and metabolic pathways underlying the flattened ear phenotype in zmed3 . 2. Materials and Methods 2.1 Plant materials The zmed3 mutant was identified through a forward genetic screen of a maize breeding population derived from the elite inbred line Lx9801. Both zmed3 and Lx9801 were grown at the Yuanyang Experimental Base, Henan Agricultural University. Developing ears from self-pollinated zmed3 heterozygotes were collected at the 4 mm stage, immediately frozen in liquid nitrogen, and stored at − 80°C. 2.2 Ear morphology Developing ears at 2 mm and 4 mm stages were examined using a stereomicroscope, and 4 mm ears were further analyzed by scanning electron microscopy. Homozygous zmed3 plants were crossed as the female parent with inbred line B73, and the resulting F 1 plants were crossed with zmed3 to generate an BC 1 F 1 population. A chi-square test was performed on the BC 1 F 1 progeny. 2.3 Transcriptome Total RNA was extracted from 4 mm developing ear samples of maize zmed3 and Lx9801 (three biological replicates per group) using the RNAprep Pure Plant Kit (Tiangen). RNA quality (RIN), quantity (≥ 2 µg), and concentration (≥ 300 ng/µL) were assessed using an Agilent 2100 system, and genomic DNA contamination was checked via agarose gel electrophoresis. Libraries were constructed using the VAHTSTM Stranded mRNA-seq Library Prep Kit (Vazyme), including mRNA enrichment with Oligo dT beads, RNA fragmentation, cDNA synthesis, end repair, adapter ligation, PCR amplification, and circularization to generate single-stranded circular DNA libraries. Qualified libraries were sequenced on the MGI high-throughput platform, producing 150-bp paired-end reads with a total depth of 6 GB. Raw sequencing data (FASTQ) were filtered using SOAPnuke (v2.1.0) and bbduk to remove adapters, poly-A/poly-G tails, and low-quality reads. Clean reads were aligned to the maize B73 reference genome (v4) using STAR software (≤ 2 bp mismatches), and gene expression levels were quantified using FeatureCounts. Differentially expressed genes (DEGs) were identified with the edgeR package (FDR ≤ 0.05, |log 2 FC| ≥1). Log-transformed and centered expression data were analyzed by PCA using SIMCA software (v18.0.1). 2.4 Proteome Samples were ground in liquid nitrogen and homogenized with L3 lysis buffer (1% SDS, 7 M urea, 2 M thiourea) using ice-cold ultrasonication for debris removal. Proteins were purified by cold acetone precipitation overnight, then redissolved in 8 M urea. After reduction with 5 mM DTT (37°C, 45 min) and alkylation with 11 mM iodoacetamide (15 min, light-protected), tryptic digestion (Promega) was performed at 37°C overnight. Peptides were desalted using a C18 column (Millipore) and quantified with Pierce™ peptide assay kits. Peptides were separated via a NanoElute UHPLC system with a 60-min gradient (2–80% B; 300 nL/min) using mobile phases 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). Mass spectrometry was performed on a timsTOF Pro2 in ddaPASEF mode: positive ion detection (m/z 100–1700), ion mobility (1/K0: 0.7–1.4 Vs/cm²), quadrupole isolation width (2–3 Th), and collision energy scaled with ion mobility (20–59 eV). Raw data were processed using FragPipe (IonQuant module) for MaxLFQ label-free quantification. Filtering criteria included removal of non-specific peptides and contaminants, and retention of proteins with ≥ 1 unique peptide. Differentially accumulated proteins (DAPs) were identified with the edgeR package (|log 2 FC| ≥1, FDR ≤ 0.05). The expression matrix was log-transformed and centralized using SIMCA software, followed by PCA. Weighted gene co-expression network analysis (WGCNA; R package) was used to construct co-expression modules (β = 12, minModuleSize = 30). Protein-protein interactions (PPI) were analyzed using the STRING database (confidence score > 400), and functional annotations were derived through BLAST alignment against the maize B73 genome (v4). 2.5 Metabolome Metabolite extraction and analysis used a tissue sample (about 100 mg) ground by liquid nitrogen. After extraction and centrifugation with 80% methanol aqueous solution, the supernatant was diluted to 53% methanol concentration and centrifuged for the second time. The supernatant was subjected to LC-MS/MS analysis by Shimadzu UFLC HPLC system combined with Applied Biosystems 4500 QTRAP mass spectrometer (positive ion mode, controlled by Analyst 1.6 software). The identification of metabolites follows the Level 1 standard of Metabonomics Standards Initiative (MSI), and is confirmed by two orthogonal parameters: the accurate mass error is controlled to be less than 5 ppm, and the retention time of reference standard (± 0.2 min) or MS/MS fragment spectrum of the same system analysis is matched, and MSn fragment spectrum is provided for isomer/isobaric substance for structural analysis; Blank samples and dilution studies were used to eliminate pollutants, the original data, processing parameters and database (mzCloud, mzVault, Masslist) search criteria were ensured to be transparent, and new metabolites were stored in public databases such as MetaboLights as required. The quantitative analysis was normalized by QC samples (compounds with CV > 30% were eliminated), combined with mass deviation of 5 ppm, signal intensity deviation of 30% and minimum intensity threshold filtering, and the molecular formula was predicted by molecular ions and fragment ions and matched with the database. In the data analysis stage, the outliers are filtered by IQR, and the peak area data with missing value ≤ 50% is retained and the missing value is filled with half minimum value. After normalization by total ion current (TIC), SIMCA software is used for logarithmic transformation and centralized pretreatment, and PCA is carried out in turn to explore the data structure. Through cross-validation, permutation test and external validation set, we avoided over-fitting, and determined the differential metabolites by VIP score (> 1.0) combined with Benjamini-Hochberg corrected P value (FDR ≤ 0.05). GO and KEGG enrichment analysis uses agriGO v2.0 and KEGG tools, takes FDR ≤ 0.05 as the threshold, constructs venn diagram and PCA through Biocloud platform, realizes the visualization of enrichment bubble diagram and hierarchical clustering thermogram with the help of online tools, Morpheus and TBtools, and analyzes the correlation of metabolic pathways in combination with biological background. 2.6 GO and KEGG analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the identified Differentially Abundant Proteins (DAPs), DEGs, and Differential Metabolites (DAMs) using agriGO v2.0 ( http://systemsbiology.cau.edu.cn/agriGOv2/index.php ) and KEGG ( https://www.kegg.jp/ ) software [ 20 ] . Analyses were categorized into Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), with significance defined by FDR ≤ 0.05. Venn diagrams and PCA were generated using the Bemis cloud platform ( www.biocloud.net ), and enrichment bubble plots were created using online software ( http://www.bioinformatics.com.cn ). Clustering heatmaps were visualized with Morpheus ( https://software.broadinstitute.org/morpheus ) and TBtools ( https://github.com/CJ-Chen/TBtools ) [ 21 ] . 2.7 Multi-omic data integration A nine-quadrant diagram (|log 2 FC| ≥ 1) was created to visualize gene expression changes in the transcriptome and proteome of Lx9801 and zmed3 based on joint proteomics and metabolomics analysis using R (v3.5.1) software. KEGG enrichment analysis of multi-group data was performed using the Omicshare website, and a Venn diagram was generated to identify common KEGG pathways. Genes identified in both the proteome and transcriptome were co-located based on significantly enriched KEGG pathways. 2.8 RT-qPCR The genes located by transcriptome and metabolomics analysis were selected in the experiment. Real-time quantitative PCR (RT-qPCR) with UBQ9 as the reference gene was used to analyze the expression differences of co-located genes in Lx9801 and zmed3 . 3. Results 3.1 Phenotypic analysis A natural mutant, zmed3 , was identified in the elite inbred line Lx9801, widely used in Chinese maize breeding programs. Microscopic analysis revealed developmental anomalies in the female inflorescence of zmed3 compared to the wild type. At a gynoecium length of 2 mm, the inflorescence meristem (IM) of zmed3 showed pronounced degeneration and enlargement (Fig. 1A), progressing to flattening and disorganization of the floral meristem (FM) by 4 mm (Fig. 1B-C). Phenotypic characteristics show stability during the reproductive growth stage(Fig. 1D). Genetic crosses between zmed3 and B73 produced F 1 progeny with fully developed female spikes, confirming a recessive inheritance pattern. Backcrossing F 1 plants resulted in 2354 normal phenotypes and 2290 zmed3 mutants, consistent with a 1:1 segregation ratio (Fig. 1E). These results demonstrate that zmed3 female inflorescences undergo apical flattening and degeneration, with spike rows progressively disorganizing in a stably inherited manner. 3.2 Transcriptomic analysis To explore the transcript-level changes during ear development, transcriptome was conducted in both zmed3 and Lx9801 at the 4 mm stage. The base composition analysis showed that the A:T and C:G ratios were close to expected, indicating normal sequencing results (Supplementary Fig. 1A). Sequencing quality values for each base in the 5' to 3' direction of reads were within the normal range (Supplementary Fig. 1B). PCA of the transcriptome data clearly distinguished Lx9801 from zmed3 (Supplementary Fig. 1C). Correlation analysis indicated higher similarity between samples within the same material group than between groups, confirming the reliability of the data for further analysis (Fig. 2A). A total of 1,589 differentially expressed genes (DEGs) were identified, with 1,026 up-regulated and 563 down-regulated in zmed3 compared to Lx9801 (Fig. 2B). Hierarchical clustering of DEGs further separated Lx9801 and zmed3 (Supplementary Fig. 1D). Notably, the jasmonate-mediated signaling pathway was significantly activated in zmed3 , influencing the final morphology of the female spike by regulating inflorescence cell elongation and arrangement. Additionally, DEGs were enriched in metabolic pathways such as linoleic acid metabolism and tyrosine metabolism, highlighting the importance of unsaturated fatty acids and secondary metabolites in cell membrane fluidity and energy supply for the female panicle (Fig. 2C, D). Focusing on the MAPK signaling pathway, we observed altered expression levels of genes associated with jasmonic acid and ethylene. This pathway likely coordinates downstream gene expression to regulate inflorescence organ morphogenesis, further supporting the molecular mechanisms underlying female spike development in zmed3 . （Fig. 2E）. 3.3 Proteomic analysis To explore the protein-level changes during ear development, proteome was conducted in both zmed3 and Lx9801 at 4 mm stage. Mass spectrometry identified 101,684 peptides and 11,157 proteins, with 9,028 proteins quantified across both lines (Supplementary Fig. 2A). Analysis using Razor+Unique peptides showed a decreasing trend in the number of corresponding proteins as the number of independent peptides increased, indicating high confidence in identifying abundant proteins (Supplementary Fig. 2B). PCA of the proteomic data revealed clear separation in protein abundance between Lx9801 and zmed3 (Fig. 3A). Intra-group correlation analysis showed Pearson correlation coefficients above 0.95 for all biological replicates, confirming minimal variability and excellent reproducibility. Inter-group analysis highlighted significant differences between the two lines, ensuring the data's suitability for downstream analysis (Fig. 3B). Using stringent thresholds, 185 DAPs were identified, including 46 upregulated and 139 downregulated proteins in zmed3 (Fig. 3C). Z-score normalization and hierarchical clustering of DAPs demonstrated consistent expression patterns within groups and distinct differences between groups, further validating the accuracy of the differential protein screening (Fig. 3D). GO enrichment analysis revealed that ribosome-related proteins were significantly upregulated in zmed3 , suggesting enhanced protein anabolism, which may accelerate the cell cycle and promote the proliferation of inflorescence primordium cells. Additionally, abnormal expression of chromatin structure-related proteins indicates that epigenetic regulation could play a critical role in maintaining the inflorescence meristem (Fig. 4A). KEGG analysis further identified significant changes in key proteins of the MAPK signaling pathway, which regulates cell polarity by integrating auxin/cytokinin signals. Disruptions in this pathway could contribute to flattened ear (Fig. 4B). To explore proteins associated with ear flattening, WGCNA analysis was performed, filtering out proteins with stable or low expression across all samples. Scale-free topological fitting analysis optimized the soft threshold power at 18, with a fitting index of R²=0.82 (red-marked area). Using the dynamic mixed cutting method (deepSplit=2, minModuleSize=30), 28 coexpression modules were identified, with the turquoise module (containing 327 proteins) showing the strongest correlation with the phenotype (Fig. 4C,D). A static diagram revealed that proteins A0A804MWG6, B4F8D2, C0P455, P12339, and B6UIJ6 exhibited strong interactions with ribosomes (Fig. 4E). 3.4 Metabolomic analysis To explore the metabolite-level changes during ear development, metabolome was conducted in both zmed3 and Lx9801 at the 4 mm stage. After a rigorous quality filtering, 22,839 metabolites were retained, including 256 secondary qualitative metabolites (Supplementary Fig. 3A). PCA revealed a clear separation in the metabolic profiles between Lx9801 and zmed3 (Fig. 5A). Volcano plot analysis identified 60 significantly upregulated metabolites and 62 downregulated metabolites. Correlation analysis of these differentially expressed metabolites, based on P-values, demonstrated coordinated changes among the significant metabolites (Fig. 5B). Hierarchical clustering heatmap analysis further revealed distinct patterns of upregulated and downregulated metabolites, emphasizing the distinct metabolomic profiles between the two genotypes (Supplementary Fig. 3B). These shifts underscore metabolic adjustments during female ear development, which is a critical adaptive mechanism for enhancing fitness in response to developmental stresses. In zmed3 , alterations in key tricarboxylic acid (TCA) cycle intermediates, such as citric acid and oxaloacetic acid, significantly influenced stem cell differentiation and female ear development. Disruptions in phenylpropanoid biosynthesis, marked by the upregulation of ferulic acid and downregulation of erucinic acid, suggested impaired cell wall remodeling, potentially limiting cellular expansion (Fig. 5C). Additionally, dysregulation of glutathione metabolism induced oxidative stress, further compromising cellular function. These findings underscore the critical roles of these metabolic pathways in regulating ear development. To explore the metabolic regulatory network, pathway enrichment analysis was performed using the KEGG database for Zea mays, identifying intersections in metabolic pathways and highlighting potential key enzymes and metabolites (Supplementary Fig. 3C). This analysis offered valuable insights into the metabolic coordination underlying maize ear development and flattening. Furthermore, correlation analysis of the top 10 significantly upregulated and downregulated metabolites revealed their interplay and regulatory relationships during biological state transitions (Supplementary Fig. 3D), providing a comprehensive understanding of the metabolic dynamics in this developmental process. 3.5 Multi-omic joint analysis Through comprehensive transcriptome, proteome, and metabolome analysis, several key metabolic pathways and metabolites crucial for female ear development in maize were identified. The study first demonstrated the correlation between gene and protein expression using a nine-quadrant map, which revealed distinct gene-protein expression patterns: 48 genes were upregulated while their corresponding proteins were downregulated, and 173 genes were downregulated while their proteins were upregulated. These expression changes were primarily driven by miRNA-mediated post-translational regulation. Additionally, 366 genes and proteins were simultaneously upregulated, and 28 were simultaneously downregulated, indicating synchronized changes in these genes at both the transcriptional and translational levels (Fig. 6A). Further KEGG analysis revealed that key metabolic pathways, including glyoxylate and dicarboxylate metabolism, galactose metabolism, starch and sucrose metabolism, glycolysis/gluconeogenesis, tyrosine metabolism, phenylpropanoid biosynthesis, and cyanoamino acid metabolism, play crucial roles in the panicogenesis of zmed3 . In female maize ear development, metabolites such as citrate, oxaloacetate, and cis-aconitate—key intermediates in the TCA cycle—are involved in regulating energy homeostasis and cell differentiation, contributing to morphogenesis. Studies have shown that citric acid can specifically inhibit aconitase activity [22] , reducing mitochondrial oxidative phosphorylation efficiency, and directly delaying the cell division cycle of anthogenic basal stem cells by accumulating G1/S phase-blocking proteins [22] . Other metabolites, including 3-phospho-D-glycerate and L-glutamine phosphate, significantly affect protein synthesis and nitrogen cycling, thus influencing plant growth and development [23] . In galactose metabolism, the upregulation of D-fructose-6-phosphate and sucrose, along with the downregulation of raffinose and D-galactose, jointly regulate the intracellular redox state, affecting plant cell proliferation and division. Additionally, the upregulation of sucrose and downregulation of D-fructose-6-phosphate in starch and sucrose metabolism, along with the upregulation of glycerate and oxaloacetate and the downregulation of glycerate-3-phosphate in glycolysis/gluconeogenesis, significantly influence metabolite accumulation and conversion, thereby regulating ear development (Fig. 6B). Additionally, the upregulation of key metabolites in the tyrosine metabolism and phenylpropanoid biosynthesis pathways, such as tyrosine, 4-hydroxyphenylpyruvate, arginine, and ferulic acid, along with the downregulation of erucic acid, erucic acid malic acid, and p-coumaryl quinic acid, further influenced spike morphology by modulating cell differentiation and metabolic status. Specifically, the upregulation of ferulic acid and the downregulation of erucic acid malic acid suggest changes in the degree of cell wall crosslinking. In known pathways, ferulic acid enhances cell wall rigidity by promoting lignin monomer polymerization, while glycosylated products of erucic acid are involved in regulating cell expansion. The imbalance between these metabolites may decrease the stretchability of cob cells, ultimately contributing to the flat inflorescence phenotype. Furthermore, the upregulation of arginine in glutathione metabolism and the downregulation of reduced coenzyme II and oxyproline also influence cell differentiation and maintain redox balance. The joint analysis identified three core regulatory modules driving the abnormal development of female spikes in zmed3 . These include: 1) Ribosome and Cell Cycle Regulation: The upregulation of ribosome-related proteins accelerates cell proliferation, while disruptions in key nodes of the MAPK signaling pathway interfere with the establishment of cell polarity by integrating auxin/cytokinin signals. 2) Jasmonic Acid Signaling Pathway: The jasmonic acid-mediated signaling pathway is significantly activated and directly influences the morphogenesis of female spikes by regulating the elongation and spatial arrangement of inflorescence cells. 3) Metabolite Homeostasis: Abnormalities in key intermediate metabolites of the tricarboxylic acid (TCA) cycle, such as citric acid and oxaloacetic acid, impair stem cell differentiation. Additionally, dysregulation in the phenylpropanoid biosynthesis pathway—characterized by upregulation of ferulic acid and downregulation of erucic acid—limits cell expansion by interfering with cell wall remodeling and oxidative stress. These three modules work synergistically through multi-level and multi-pathway regulation, leading to the flattened spike phenotype and disordered inflorescence during the development of the female panicle in zmed3 . 3.6 Candidate gene mining Zm00001d041772 , Zm00001d002258 , Zm00001d043607 , Zm00001d043348 , and Zm00001d042353 genes exhibited either upregulation or downregulation in expression within the KEGG pathway during the co-analysis (Table 1). To validate these findings, RT-qPCR was performed on the roots, stems, and leaves of Lx9801 and zmed3 female spikes at the 4 mm developmental stage. Using Lx9801 as the control group for each tissue type, results showed that Zm00001d042353 was significantly upregulated in both leaves and female spikes of zmed3 . Specifically, expression of Zm00001d042353 was increased by 14.97% in leaves and 14.52% in female spikes compared to Lx9801 (Fig. 6C). Based on these results, Zm00001d042353 was identified as a key gene involved in the flattening and row disruption of female spikes in zmed3 . Table 1. Multi-omics analysis for KEGG pathways. KO-ID Pathway P-DEP P-DEG P-DEM P-DEM-NEG P-DEM-POS ko00630 Glyoxylate and dicarboxylate metabolism 0.005501117 0.00857 0.6340745 0.9879909 0.104062 ko00500 Starch and sucrose metabolism 0.006300258 0.00946 0.2032917 0.3896232 0.2813621 ko00010 Glycolysis / Gluconeogenesis 0.898608956 0.0000186 0.8447464 0.9272254 0.5921062 ko00350 Tyrosine metabolism 0.369693949 0.000735 0.888226 0.9879909 0.4170106 ko00906 Carotenoid biosynthesis 0.173559534 0.0206 0.9995991 0.9998589 0.9012326 ko00052 Galactose metabolism 0.570774283 0.236 0.0397617 0.05690359 0.3343912 ko00940 Phenylpropanoid biosynthesis 0.622544857 0.406 0.01981014 0.01996139 0.3366027 ko00480 Glutathione metabolism 0.127229931 0.00414 0.457331 0.8194249 0.2086767 KO-ID: KEGG Pathway ID; Pathway: KEGG Pathway description; P-DEP: p value of differential protein; P-DEG: p value of differential gene; P-DEM: p value of differential metabolites; P-DEM-NEG: P value of positive ions in differential metabolites; P-DEM-POS: P value of negative ions in differential metabolites. 4. Discussion 4.1 Mutant zmed3 phenotype As a crucial crop for China’s socio-economic stability, particularly in the context of diminishing arable land, maize requires significant advancements in production efficiency. Enhancing maize production is thus a critical agricultural priority. Ear-related traits, which are closely linked to key agronomic characteristics such as plant height, ear length, kernel rows, and hundred-kernel weight, are fundamental determinants of yield. Mutations affecting female ear development, including dg1 [ 24 ] , lrg1 [ 25 ] , and OsWUS [ 26 ] in rice, and ZmSPL10 , ZmSPL14 , ZmSPL26 [ 27 ] , and FEA4 [ 6 ] in maize, have provided valuable insights into the molecular mechanisms governing inflorescence architecture, including DNA binding, protein dimerization, and meristem determinacy. In this study, we characterized a natural maize mutant, zmed3 , derived from the inbred line Lx9801. At the 4 mm ear developmental stage, zmed3 exhibited a flattened ear and disordered kernel rows, suggesting defects in inflorescence meristem organization. Genetic analyses revealed that the mutant phenotype follows Mendelian inheritance patterns, confirming a stable and heritable genetic basis. 4.2 Key pathways and genes for spike morphology mutations Female ear development in maize is a complex process regulated by multiple molecular pathways, including cell cycle control, hormonal signaling, and metabolic homeostasis. Previous studies have identified several key genes that influence ear morphology, such as ZmCCS52B [ 28 ] , which regulates cell cycle progression, ZmBES1 [ 29 ] , which modulates hormone signaling, and FEA4 [ 6 ] , which maintains redox balance. Despite these insights, the integration of gene-protein interactions with metabolic pathways in shaping ear morphology remains poorly understood. To address this knowledge gap, we employed a multi-omics approach combining 4D-Label-free proteomics, RNA-seq, and untargeted metabolomics to analyze ear development in Lx9801 and zmed3 at the 4 mm stage. Our data revealed significant alterations in the MAPK signaling pathway, which integrates auxin and cytokinin signals to establish cell polarity during organ morphogenesis. Dysregulation of this pathway suggests a failure in cell polarity establishment, which may contribute to the flattened ear axis observed in zmed3 . Furthermore, the MAPK pathway’s involvement in extracellular matrix remodeling and cytoskeletal reorganization could exacerbate the observed inflorescence defects. Another key finding was the marked activation of the jasmonic acid (JA) signaling pathway, which we propose directly influences ear morphology by regulating cell elongation and arrangement. JA is a crucial signaling molecule with diverse physiological roles, including regulating plant growth and development, such as in plant regeneration [ 30 ] . The interplay between jasmonic acid and other hormones, such as auxin and cytokinin, likely amplifies its effect on inflorescence meristem development. Metabolomic analysis revealed significant disturbances in the TCA cycle, with abnormal levels of citric acid and oxaloacetate, which compromised cellular energy metabolism and stem cell differentiation. Additionally, dysregulation of the phenylpropane biosynthesis pathway, characterized by upregulation of ferulic acid and downregulation of sinapic acid, suggests impaired cell wall remodeling, potentially restricting cell expansion and altering inflorescence morphology. Disruption of glutathione metabolism further highlights the critical role of redox homeostasis in ear development. Together, these findings emphasize the importance of metabolic equilibrium and its complex interactions with signaling pathways in shaping maize ear architecture. In this study, it was found that MAPK and JA signaling pathways were abnormal in zmed3 mutant, while CLV-WUS pathway maintained the balance of meristem cells through FEA2 and other genes. Although the direct interaction between zmed3 and the core component of CLV-WUS has not been confirmed, the abnormal cell polarity (such as auxin-cytokinin signal integration) caused by the imbalance of MAPK pathway may affect the panicle axis morphology independently, which is spatially related to the meristem hyperproliferation phenotype of CLV-WUS pathway mutant at the development level. In addition, the activation of JA pathway or hormone cross-regulation through CT2 (such as stress signal crosstalk mediated by JA and CT2 ) indirectly interferes with hormone networks regulated by factors such as UB2 / UB3 , thus affecting meristem homeostasis. At present, these are only indirect conjectures based on pathway enrichment. Whether zmed3 indirectly relates to CLV-WUS pathway by regulating MAPK/JA pathway still needs to be verified by molecular interaction experiments to avoid over-interpretation of functional synergy as mechanism association. Zm00001d042353 encodes Sucrose-phosphate synthase, which regulates the effective supply of sucrose during plant growth and fiber elongation. It is worth discussing whether the abnormal expression of this gene will hinder sucrose synthesis and then adjust the phosphorylation state of MAPK by changing the level of JA hormone, which may eventually lead to abnormal cell elongation due to insufficient energy/carbon skeleton supply. 5. Conclusions In conclusion, the phenotypic variation in zmed3 arises from the synergistic effects of three core regulatory modules: (1) ribosome-mediated cell cycle control, (2) jasmonic acid signaling, and (3) metabolic homeostasis. Our study offers novel insights into the molecular mechanisms driving maize ear development, revealing a complex regulatory network that integrates gene expression, protein function, and metabolic activity. These findings not only deepen our understanding of inflorescence development but also highlight potential targets for molecular breeding strategies aimed at improving maize yield. Future research should focus on elucidating the crosstalk between these regulatory modules and exploring their broader applicability in other crops. Additionally, CRISPR/Cas9-based functional validation of the candidate genes identified in this study could further refine our understanding of their specific roles in ear development. By integrating multi-omics data with functional analyses, this study contributes to the expanding knowledge on crop development and provides a foundation for addressing global food security challenges in an era of climate change and resource scarcity. Abbreviations abbreviation description BP Biological Process CC Cellular Component zmed3 Ear Diameter Mutant3 DAMs differentially accumulated metabolites DAPs differentially accumulated proteins DEGs differentially expressed genes FDR false discovery rate FM floral meristem GO Gene Ontology IM inflorescence meristem JA Jasmonate KEGG Kyoto Encyclopedia of Genes and Genomes Maize Zea mays L. MAPK mitogen-activated protein kinase MF Molecular Function PCA principal component analysis PPI Protein-protein interactions RT-qPCR Real-time quantitative PCR SAM shoot apical meristem SM spikelet meristem SPM spikelet pair meristem TCA tricarboxylic acid TIC total ion current CLV-WUS CLAVATA-WUSCHEL Declarations Ethics approval and consent to participate Not applicable. Clinical trial number Not applicable Consent for publication Not applicable Availability of data and materials The data underlying this article will be shared on reasonable request to the corresponding author. The raw sequencing data were deposited in GSA database with the accession number: PRJCA041532. Competing interests The authors declare that they have no competing interests. Fundings This work was supported by the Key Research Project of the Shennong Laboratory (SN01-2022-02), Natural Science Foundation of Henan (252300421159), the Key research and development projects of Henan Province (241111114300), the National key research and development plan (2022YFD1201004) and the Agricultural Seed Joint Research Project of Henan Province (2022010204). Authors' contributions Weihua Li, Jihua Tang and Pengshuai Yan designed the research. Jing Liu performed the experiments. Jing Liu analyzed the data. Jing Liu drafted the manuscript. Tianxiao Yang, Weihua Li, Jihua Tang, and Pengshuai Yan revised the manuscript. All authors approved the final version of the manuscript. 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QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize. Frontiers in Plant Science, 15, 1371394. Feng, W., Liu, Y., Cao, Y., Zhao, Y., Zhang, H., Sun, F., Yang, Q., Li, W., Lu, Y., Zhang, X., Fu, F., and Yu, H. 2022. Maize ZmBES1/BZR1-3 and -9 Transcription Factors Negatively Regulate Drought Tolerance in Transgenic Arabidopsis. International Journal of Molecular Sciences, 23(11), 6025. Li, C., Xu, M., Cai, X., Han, Z., Si, J., & Chen, D. 2022. Jasmonate Signaling Pathway Modulates Plant Defense, Growth, and Their Trade-Offs. International journal of molecular sciences, 23(7), 3945. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Supplementary Table 1. Candidate gene primer information TableS2.xlsx FigS1.pdf Supplementary Fig. 1 Lx9801 and zmed3 Transcriptome data quality control analysis (A) Sequencing base content distribution map. (B) Sequencing mass distribution map. (C) PCA scatter plot. In the figure, the abscissa PC1 and the ordinate PC2 represent the scores of the first and second principal components respectively, each scatter represents a sample, and different colors of the scatter represent different groups. (D) DEGs aggregate heat maps with rows representing samples and columns representing DEGs. The shorter the aggregation branches, the higher the similarity. FigS2.pdf Supplementary Fig. 2 Lx9801 and zmed3 proteomic data quality control (A) Overview of proteome identification quantity. (B) Quantitative distribution map of peptide fragments. The abscissa represents the number of Razor+Unique peptide segments, and the ordinate represents the number of protein corresponding to Razor+Unique peptide segments. FigS3.pdf Supplementary Fig. 3 Metabolite data analysis of Lx9801 and efd1 (A) Donut Plot of metabolite classification. (B) Heat map of hierarchical clustering analysis. The abscissa represents different experimental groups, and the ordinate represents the different metabolites of this group. The color blocks in different positions represent the relative expression of metabolites in corresponding positions, and the change of color from blue to red represents the change of expression from low to high. S1 stands for LX 9801 and S2 stands for zmed3 . (C) Network analysis. Red dots represent a metabolic pathway, yellow dots represent the information of a substance-related regulatory enzyme, green dots represent the background substance of a metabolic pathway, purple dots represent the molecular module information of a substance, blue dots represent a chemical interaction of substances, and green squares represent the different substances obtained by this comparison. (D) Heatmap of correlation analysis for group. The horizontal and vertical axes represent the different metabolites of this group, the color blocks in different positions represent the correlation coefficient between metabolites in corresponding positions, red indicates positive correlation, blue indicates negative correlation, and the darker the color, the stronger the correlation. At the same time, the significant correlation is marked with an asterisk. 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(B) WT(Lx9801) and \u003cem\u003ezmed3\u003c/em\u003e female ear at 4mm (scale bar: 400 µm). (C) SEM of WT(Lx9801) and \u003cem\u003ezmed3 \u003c/em\u003efemale ear at 4mm (scale bar: 400 µm). (D) The mature development stage of WT (Lx9801) and \u003cem\u003ezmed3\u003c/em\u003e ear (scale bar: 5cm). (E) The number of normal phenotypes and mutant phenotypes in the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e population.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/efc14ec18dc85b88996a536c.jpeg"},{"id":88764176,"identity":"7a4cd21b-1e64-41c9-ae67-69667d4f57c0","added_by":"auto","created_at":"2025-08-11 08:37:41","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203934,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic analysis of the \u003cem\u003ezmed3\u003c/em\u003e mutant.\u003c/p\u003e\n\u003cp\u003e(A) Correlation diagram between samples. The change of color from red to green represents the change of correlation from high to low. (B) The volcano map of Differentially Expressed Gene (DEGs). The abscissa represents the difference multiple, with 2 as the base and 10 as the base, the ordinate represents the P value, and the red and green scattered points represent the up-and-down DEGs respectively. (C) DEGs GO enrichment bubble diagram. The abscissa represents the enrichment multiple, the ordinate represents the name of the GO entry, the change of the color of the point from blue to red represents the change of the P-value from big to small, and the size of the point represents the number of DEGs annotated by the corresponding entry. (D) DEGs KEGG enrichment bubble diagram the abscissa represents the enrichment multiple, the ordinate represents the name of KEGG metabolic pathway, the change of the color of the point from blue to red represents the change of P-value from big to small, and the size of the point represents the number of DEGs annotated by the corresponding entry. (E) The expression of various differentially expressed genes/proteins in Phytohomones under the MAPK pathway\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/80350d829d7a599b58c6004c.jpeg"},{"id":88764183,"identity":"7a84eec7-2f76-42d9-bd3d-f83fa09455be","added_by":"auto","created_at":"2025-08-11 08:37:41","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":271525,"visible":true,"origin":"","legend":"\u003cp\u003eProteomic analysis of the \u003cem\u003ezmed3\u003c/em\u003emutant.\u003c/p\u003e\n\u003cp\u003e(A) PCA scatter plot. The abscissa PC1 and the ordinate PC2 represent the scores of the first and second principal components respectively, each scatter represents a sample, and different colors of the scatter represent different groups. (B) Correlation diagram between samples. The change of color from red to blue represents the change of correlation from high to low. (C) The abscissa of the volcano diagram of DAPs represents the difference multiple, with 2 as the base and 10 as the base, the ordinate represents the P value, and the blue and yellow scattered points represent the up-and-down DAPS respectively. (D) DAPs aggregate heat maps with rows representing samples and columns representing DAPs. The shorter the aggregation branches, the higher the similarity.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/972f9ab33fcf6ae42bc8bac9.jpeg"},{"id":88764179,"identity":"173b4b7f-7040-40f5-b7a9-fdcc72d8790b","added_by":"auto","created_at":"2025-08-11 08:37:41","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":452288,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of differentially expressed proteins in Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A) DAPs GO enrichment analysis bubble chart. The abscissa represents the enrichment multiple, the ordinate represents the name of GO entry, the change of the color of the point from blue to red represents the change of P-value from big to small, and the size of the point represents the number of DAPs annotated by the corresponding entry. (B) DAPs KEGG enrichment analysis bubble diagram. The abscissa represents the enrichment factor, the ordinate represents the name of the KEGG metabolic pathway, the change in color of the dot from blue to red represents the change in P-value from large to small, and the size of the dot represents the number of DAPs annotated to the corresponding entry. (C) Schematic diagram of soft threshold selection. (D) Modular hierarchical clustering tree diagram. (E) Protein-protein interaction diagram. Each node in the interaction network represents DAPs, and the change of node color from red to blue represents the change of differential expression protein expression level from up to down.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/0574d4922ff5f8d864640ae8.jpeg"},{"id":88764182,"identity":"497a4b39-1284-462e-a078-76a2ec613dd5","added_by":"auto","created_at":"2025-08-11 08:37:41","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96815,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic analysis of the \u003cem\u003ezmed3\u003c/em\u003emutant.\u003c/p\u003e\n\u003cp\u003e(A) PCA scatter plot. In the figure, the abscissa PC1 and the ordinate PC2 represent the scores of the first and second principal components respectively, each scatter represents a sample, and different colors of the scatter represent different groups. (B) The volcano map of DAMs. The abscissa represents the difference multiple, with 2 as the base and 10 as the base, the ordinate represents the P value, and the red and green scattered points represent the up-and-down DAMs respectively. (C) DAMS GO enrichment bubble diagram. The abscissa represents the enrichment multiple, and the ordinate represents the name of the GO entry. The change of the color of the point from blue to red represents the change of the P-value from big to small, and the size of the point represents the number of DAMs annotated by the corresponding entry.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/f83fa5ed7c22a0be99d9cac3.jpeg"},{"id":88765499,"identity":"7440dac8-c421-4e63-863a-32b22333df46","added_by":"auto","created_at":"2025-08-11 08:45:41","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":185371,"visible":true,"origin":"","legend":"\u003cp\u003eCombined analysis among DAPs, DEGs, and DAMs.\u003c/p\u003e\n\u003cp\u003e(A) Nine-quadrant diagram of transcriptome and proteome. The abscissa is the difference multiple of proteome (log\u003csub\u003e2\u003c/sub\u003e FC), and the ordinate is the difference multiple of transcriptome (log\u003csub\u003e2\u003c/sub\u003e FC). At the top are the correlation coefficient and P value of proteome and transcriptome. Each point represents a gene or protein. mRNAs in quadrants 1, 9 are negatively correlated with the corresponding protein differential expression patterns; mRNAs in quadrants 3, 7 are positively correlated with the corresponding protein differential expression patterns; mRNAs in quadrants 2, 8 are differentially expressed, corresponding to no change in protein; proteins in quadrants 4, 6 are differentially expressed, corresponding to no change in mRNA, and co-expressed mRNAs and proteins in quadrant 5 are not differentially expressed. (B) Wayne diagram of differentially accumulated protein differentially expressed genes and differentially accumulated metabolites. (C) RT-qPCR analysis results. Relative expression analysis of genes in rhizomes and leaves during the development of female panicle to 4 mm. The abscissa represents different genes and their specific parts in the plant, and the ordinate represents the expression difference multiple of genes in \u003cem\u003ezmed3\u003c/em\u003e relative to Lx9801.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/aa5eb83f12e4596d8cbc4c1f.jpeg"},{"id":93956757,"identity":"951b2aaf-e6ba-44dc-a057-897331fa0157","added_by":"auto","created_at":"2025-10-20 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2362660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/82667127-1424-428e-8b00-79e1fd07ef42.pdf"},{"id":88764178,"identity":"62790a5c-bdff-42d1-a1b6-ef73397c633f","added_by":"auto","created_at":"2025-08-11 08:37:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11121,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. Candidate gene primer information\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/8045fa5a3bb66a79507ea2c8.xlsx"},{"id":88765495,"identity":"49db5b5b-9aac-41df-a688-a2558c50f688","added_by":"auto","created_at":"2025-08-11 08:45:41","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":119397,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/1a8ea5681d3dea7d6fd302d1.xlsx"},{"id":88765496,"identity":"37d8b9b5-f5f8-47a5-8f6a-7928c19d0cef","added_by":"auto","created_at":"2025-08-11 08:45:41","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1626521,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 1 Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e Transcriptome data quality control analysis\u003c/p\u003e\n\u003cp\u003e(A) Sequencing base content distribution map. (B) Sequencing mass distribution map. (C) PCA scatter plot. In the figure, the abscissa PC1 and the ordinate PC2 represent the scores of the first and second principal components respectively, each scatter represents a sample, and different colors of the scatter represent different groups. (D) DEGs aggregate heat maps with rows representing samples and columns representing DEGs. The shorter the aggregation branches, the higher the similarity.\u003c/p\u003e","description":"","filename":"FigS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/a3dbe8211d207110ddbc7d89.pdf"},{"id":88766139,"identity":"dbd0cab4-6371-4c75-88e9-841d63f4b753","added_by":"auto","created_at":"2025-08-11 08:53:41","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":447865,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 2 Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e proteomic data quality control\u003c/p\u003e\n\u003cp\u003e(A) Overview of proteome identification quantity. (B) Quantitative distribution map of peptide fragments. The abscissa represents the number of Razor+Unique peptide segments, and the ordinate represents the number of protein corresponding to Razor+Unique peptide segments.\u003c/p\u003e","description":"","filename":"FigS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/734344311a2d91bb42d44468.pdf"},{"id":88765498,"identity":"fca65e0a-b120-495a-8717-3c0e903c826f","added_by":"auto","created_at":"2025-08-11 08:45:41","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":882979,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig. 3 Metabolite data analysis of Lx9801 and efd1\u003c/p\u003e\n\u003cp\u003e(A) Donut Plot of metabolite classification. (B) Heat map of hierarchical clustering analysis. The abscissa represents different experimental groups, and the ordinate represents the different metabolites of this group. The color blocks in different positions represent the relative expression of metabolites in corresponding positions, and the change of color from blue to red represents the change of expression from low to high. S1 stands for LX 9801 and S2 stands for \u003cem\u003ezmed3\u003c/em\u003e. (C) Network analysis. Red dots represent a metabolic pathway, yellow dots represent the information of a substance-related regulatory enzyme, green dots represent the background substance of a metabolic pathway, purple dots represent the molecular module information of a substance, blue dots represent a chemical interaction of substances, and green squares represent the different substances obtained by this comparison. (D) Heatmap of correlation analysis for group. The horizontal and vertical axes represent the different metabolites of this group, the color blocks in different positions represent the correlation coefficient between metabolites in corresponding positions, red indicates positive correlation, blue indicates negative correlation, and the darker the color, the stronger the correlation. At the same time, the significant correlation is marked with an asterisk.\u003c/p\u003e","description":"","filename":"FigS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7194726/v1/33d3dbc824795c16bf2d3fa3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics Analysis of the Maize Ear Diameter Mutant3 (zmed3) Provides Insights into Female Inflorescence Development","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays L.\u003c/em\u003e) is a globally important crop that serves as a major source of human food, animal feed, and industrial raw materials. As the demand for maize continues to rise, improving grain yield remains a primary goal in breeding programs\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Yield is influenced by multiple factors, among which the ear development is vitally important. The ear inflorescence meristem, the precursor of ear, playing a decisive role in determining ear size and kernel number\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe ear inflorescence meristem in maize arises from the shoot apical meristem (SAM) through direct or indirect differentiation and gives rise to the branches and spikelets that shape the female ear architecture\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The SAM initiates inflorescence meristem (IM) formation by lifting suppression signals from axillary buds, promoting lateral cell differentiation\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. As development progresses, the IM transitions into spikelet pair meristems (SPMs), regulated by \u003cem\u003eZAG1\u003c/em\u003e, a C-class MADS-box transcription factor, and \u003cem\u003eTSH4\u003c/em\u003e. This transition involves ZAG1-mediated repression of WUSCHEL (WUS) to limit stem cell activity, and chromatin remodeling via ZAG1-TSH4 interaction, activating SPM-specific genes such as \u003cem\u003eFEA3\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. At this stage, meristematic cells show reduced mitotic activity with divisions aligned along the rachis axis\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The auxin efflux carrier PIN1 localizes basipetally, forming auxin maxima at 4\u0026ndash;6 cell intervals via a Turing-type reaction-diffusion mechanism\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, establishing the bispiral phyllotaxis of spikelet meristems (SMs). Each SPM generates two SM primordia at the adaxial and abaxial sides of the rachis through perpendicular division planes. This process is tightly controlled by \u003cem\u003eFEA3\u003c/em\u003e, which senses mechanical stress to restrict SPM overproliferation and ensure spikelet pair determinacy\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDuring the terminal differentiation of SMs, downregulation of \u003cem\u003eKN1\u003c/em\u003e (KNOTTED1) triggers the exit from the proliferative phase. This process is accompanied by decreased Cyclin B1;1 (CycB1;1) expression and a sharp reduction in mitotic activity. Mitotic frequencies in differentiated SMs drop to as low as five divisions per hour\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Simultaneously, miR172-mediated degradation of \u003cem\u003eAPETALA2\u003c/em\u003e (\u003cem\u003eAP2\u003c/em\u003e) transcription factors initiates carpel primordium specification: distal daughter cells form lemma/palea structures, while proximal cells, regulated by \u003cem\u003eZmYABBY14\u003c/em\u003e, develop ovary and silk (style) primordia\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Ovary primordium expansion is mediated by α-expansin-induced cell wall loosening, while silk elongation occurs through gibberellin-induced polarized cell growth\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In second florets, dominant suppression by \u003cem\u003eSI1\u003c/em\u003e (Sterile Infertile1) limits development to rudimentary glumes, optimizing kernel set efficiency\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent studies have revealed the molecular mechanisms underlying ear deformity in maize, highlighting complex interactions among genes and signaling pathways\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. One key regulator, \u003cem\u003eKRN4\u003c/em\u003e, located approximately 60 kb downstream of \u003cem\u003eUB3\u003c/em\u003e, enhances \u003cem\u003eUB3\u003c/em\u003e expression through cis-regulation, affecting ear row number. \u003cem\u003eUB3\u003c/em\u003e, a member of the SBP-box transcription factor family, is critical for axillary meristem initiation and directly influences tassel branching and ear row variation\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Another important factor, \u003cem\u003eKNR6\u003c/em\u003e, interacts with AGAP and exhibits protein kinase activity, phosphorylating AGAP to regulate ear length and kernel number per row. \u003cem\u003eKNR6\u003c/em\u003e-mediated phosphorylation activates AGAP, which modulates vesicular transport and maintains auxin homeostasis in the female inflorescence, influencing ear morphology\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Additionally, \u003cem\u003eFEA4\u003c/em\u003e (\u003cem\u003eFASCIATED EAR4\u003c/em\u003e), a TGA-class bZIP transcription factor, regulates the apical region of the inflorescence meristem. Its expression is controlled by ZmbHLH172 and ZmOFP28, emphasizing its role in shoot and inflorescence meristem development\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Finally, \u003cem\u003eMSCA1\u003c/em\u003e and its homologs are crucial for meristem development and ear morphogenesis, regulating the redox state of \u003cem\u003eFEA4\u003c/em\u003e to ensure proper development of the female inflorescence meristem\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe maize CLV-WUS pathway regulates ear development by maintaining meristem homeostasis. Crucially, several core pathway genes directly impact key aspects of maize ear morphogenesis. \u003cem\u003eFEA2\u003c/em\u003e, a CLV2-homologous LRR receptor protein, senses signals like \u003cem\u003eZmCLE7\u003c/em\u003e and interacts with \u003cem\u003eCT2\u003c/em\u003e and \u003cem\u003eZmCRN\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Its weak allelic mutation can increase kernel row number. \u003cem\u003eFEA3\u003c/em\u003e, also a CLV2-homologous gene, independently senses \u003cem\u003eZmFCP1\u003c/em\u003e signals to inhibit excessive meristem proliferation\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eTD1\u003c/em\u003e, a CLV1-homologous LRR kinase, collaborates with \u003cem\u003eFEA2\u003c/em\u003e to restrict meristem size. Mutation of \u003cem\u003eTD1\u003c/em\u003e causes thickening of the female ear\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eCT2\u003c/em\u003e, a G protein subunit, specifically transduces \u003cem\u003eFEA2\u003c/em\u003e signals and participates in jasmonic acid signaling\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eUB2\u003c/em\u003e/\u003cem\u003eUB3\u003c/em\u003e, SBP-box transcription factors, inhibit meristem activity by regulating hormone-related genes and responding to epigenetic regulation of the non-coding region \u003cem\u003eKRN4\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. These genes collectively maintain the balance of meristematic stem cell proliferation and differentiation through multi-level mechanisms of signal perception, transduction, and transcriptional regulation. Their allelic variations provide important targets for high-yield maize breeding.\u003c/p\u003e\u003cp\u003eIn this study, we conducted transcriptomic, proteomic, and metabolomic profiling of \u003cem\u003ezmed3\u003c/em\u003e and its control Lx9801. By performing a multi-omics analysis with 4 mm developing ears, we aim to unravel the regulatory networks and metabolic pathways underlying the flattened ear phenotype in \u003cem\u003ezmed3\u003c/em\u003e.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Plant materials\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003cp\u003eThe \u003cem\u003ezmed3\u003c/em\u003e mutant was identified through a forward genetic screen of a maize breeding population derived from the elite inbred line Lx9801. Both \u003cem\u003ezmed3\u003c/em\u003e and Lx9801 were grown at the Yuanyang Experimental Base, Henan Agricultural University. Developing ears from self-pollinated \u003cem\u003ezmed3\u003c/em\u003e heterozygotes were collected at the 4 mm stage, immediately frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ear morphology\u003c/h2\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003cp\u003eDeveloping ears at 2 mm and 4 mm stages were examined using a stereomicroscope, and 4 mm ears were further analyzed by scanning electron microscopy. Homozygous \u003cem\u003ezmed3\u003c/em\u003e plants were crossed as the female parent with inbred line B73, and the resulting F\u003csub\u003e1\u003c/sub\u003e plants were crossed with \u003cem\u003ezmed3\u003c/em\u003e to generate an BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e population. A chi-square test was performed on the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e progeny.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Transcriptome\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from 4 mm developing ear samples of maize \u003cem\u003ezmed3\u003c/em\u003e and Lx9801 (three biological replicates per group) using the RNAprep Pure Plant Kit (Tiangen). RNA quality (RIN), quantity (\u0026ge;\u0026thinsp;2 \u0026micro;g), and concentration (\u0026ge;\u0026thinsp;300 ng/\u0026micro;L) were assessed using an Agilent 2100 system, and genomic DNA contamination was checked via agarose gel electrophoresis. Libraries were constructed using the VAHTSTM Stranded mRNA-seq Library Prep Kit (Vazyme), including mRNA enrichment with Oligo dT beads, RNA fragmentation, cDNA synthesis, end repair, adapter ligation, PCR amplification, and circularization to generate single-stranded circular DNA libraries. Qualified libraries were sequenced on the MGI high-throughput platform, producing 150-bp paired-end reads with a total depth of 6 GB. Raw sequencing data (FASTQ) were filtered using SOAPnuke (v2.1.0) and bbduk to remove adapters, poly-A/poly-G tails, and low-quality reads. Clean reads were aligned to the maize B73 reference genome (v4) using STAR software (\u0026le;\u0026thinsp;2 bp mismatches), and gene expression levels were quantified using FeatureCounts. Differentially expressed genes (DEGs) were identified with the edgeR package (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge;1). Log-transformed and centered expression data were analyzed by PCA using SIMCA software (v18.0.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Proteome\u003c/h2\u003e\u003cp\u003eSamples were ground in liquid nitrogen and homogenized with L3 lysis buffer (1% SDS, 7 M urea, 2 M thiourea) using ice-cold ultrasonication for debris removal. Proteins were purified by cold acetone precipitation overnight, then redissolved in 8 M urea. After reduction with 5 mM DTT (37\u0026deg;C, 45 min) and alkylation with 11 mM iodoacetamide (15 min, light-protected), tryptic digestion (Promega) was performed at 37\u0026deg;C overnight. Peptides were desalted using a C18 column (Millipore) and quantified with Pierce\u0026trade; peptide assay kits. Peptides were separated via a NanoElute UHPLC system with a 60-min gradient (2\u0026ndash;80% B; 300 nL/min) using mobile phases 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). Mass spectrometry was performed on a timsTOF Pro2 in ddaPASEF mode: positive ion detection (m/z 100\u0026ndash;1700), ion mobility (1/K0: 0.7\u0026ndash;1.4 Vs/cm\u0026sup2;), quadrupole isolation width (2\u0026ndash;3 Th), and collision energy scaled with ion mobility (20\u0026ndash;59 eV). Raw data were processed using FragPipe (IonQuant module) for MaxLFQ label-free quantification. Filtering criteria included removal of non-specific peptides and contaminants, and retention of proteins with \u0026ge;\u0026thinsp;1 unique peptide. Differentially accumulated proteins (DAPs) were identified with the edgeR package (|log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge;1, FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05). The expression matrix was log-transformed and centralized using SIMCA software, followed by PCA. Weighted gene co-expression network analysis (WGCNA; R package) was used to construct co-expression modules (β\u0026thinsp;=\u0026thinsp;12, minModuleSize\u0026thinsp;=\u0026thinsp;30). Protein-protein interactions (PPI) were analyzed using the STRING database (confidence score\u0026thinsp;\u0026gt;\u0026thinsp;400), and functional annotations were derived through BLAST alignment against the maize B73 genome (v4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Metabolome\u003c/h2\u003e\u003cp\u003eMetabolite extraction and analysis used a tissue sample (about 100 mg) ground by liquid nitrogen. After extraction and centrifugation with 80% methanol aqueous solution, the supernatant was diluted to 53% methanol concentration and centrifuged for the second time. The supernatant was subjected to LC-MS/MS analysis by Shimadzu UFLC HPLC system combined with Applied Biosystems 4500 QTRAP mass spectrometer (positive ion mode, controlled by Analyst 1.6 software). The identification of metabolites follows the Level 1 standard of Metabonomics Standards Initiative (MSI), and is confirmed by two orthogonal parameters: the accurate mass error is controlled to be less than 5 ppm, and the retention time of reference standard (\u0026plusmn;\u0026thinsp;0.2 min) or MS/MS fragment spectrum of the same system analysis is matched, and MSn fragment spectrum is provided for isomer/isobaric substance for structural analysis; Blank samples and dilution studies were used to eliminate pollutants, the original data, processing parameters and database (mzCloud, mzVault, Masslist) search criteria were ensured to be transparent, and new metabolites were stored in public databases such as MetaboLights as required. The quantitative analysis was normalized by QC samples (compounds with CV\u0026thinsp;\u0026gt;\u0026thinsp;30% were eliminated), combined with mass deviation of 5 ppm, signal intensity deviation of 30% and minimum intensity threshold filtering, and the molecular formula was predicted by molecular ions and fragment ions and matched with the database.\u003c/p\u003e\u003cp\u003eIn the data analysis stage, the outliers are filtered by IQR, and the peak area data with missing value\u0026thinsp;\u0026le;\u0026thinsp;50% is retained and the missing value is filled with half minimum value. After normalization by total ion current (TIC), SIMCA software is used for logarithmic transformation and centralized pretreatment, and PCA is carried out in turn to explore the data structure. Through cross-validation, permutation test and external validation set, we avoided over-fitting, and determined the differential metabolites by VIP score (\u0026gt;\u0026thinsp;1.0) combined with Benjamini-Hochberg corrected P value (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05). GO and KEGG enrichment analysis uses agriGO v2.0 and KEGG tools, takes FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05 as the threshold, constructs venn diagram and PCA through Biocloud platform, realizes the visualization of enrichment bubble diagram and hierarchical clustering thermogram with the help of online tools, Morpheus and TBtools, and analyzes the correlation of metabolic pathways in combination with biological background.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.6 GO and KEGG analysis\u003c/h2\u003e\u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the identified Differentially Abundant Proteins (DAPs), DEGs, and Differential Metabolites (DAMs) using agriGO v2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://systemsbiology.cau.edu.cn/agriGOv2/index.php\u003c/span\u003e\u003cspan address=\"http://systemsbiology.cau.edu.cn/agriGOv2/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Analyses were categorized into Biological Process (BP), Molecular Function (MF), and Cellular Component (CC), with significance defined by FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05. Venn diagrams and PCA were generated using the Bemis cloud platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.biocloud.net\u003c/span\u003e\u003cspan address=\"http://www.biocloud.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and enrichment bubble plots were created using online software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Clustering heatmaps were visualized with Morpheus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://software.broadinstitute.org/morpheus\u003c/span\u003e\u003cspan address=\"https://software.broadinstitute.org/morpheus\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and TBtools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/CJ-Chen/TBtools\u003c/span\u003e\u003cspan address=\"https://github.com/CJ-Chen/TBtools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Multi-omic data integration\u003c/h2\u003e\u003cp\u003eA nine-quadrant diagram (|log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1) was created to visualize gene expression changes in the transcriptome and proteome of Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e based on joint proteomics and metabolomics analysis using R (v3.5.1) software. KEGG enrichment analysis of multi-group data was performed using the Omicshare website, and a Venn diagram was generated to identify common KEGG pathways. Genes identified in both the proteome and transcriptome were co-located based on significantly enriched KEGG pathways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.8 RT-qPCR\u003c/h2\u003e\u003cp\u003eThe genes located by transcriptome and metabolomics analysis were selected in the experiment. Real-time quantitative PCR (RT-qPCR) with UBQ9 as the reference gene was used to analyze the expression differences of co-located genes in Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Phenotypic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA natural mutant, \u003cem\u003ezmed3\u003c/em\u003e, was identified in the elite inbred line Lx9801, widely used in Chinese maize breeding programs. Microscopic analysis revealed developmental anomalies in the female inflorescence of \u003cem\u003ezmed3\u003c/em\u003e compared to the wild type. At a gynoecium length of 2 mm, the inflorescence meristem (IM) of \u003cem\u003ezmed3\u003c/em\u003e showed pronounced degeneration and enlargement (Fig. 1A), progressing to flattening and disorganization of the floral meristem (FM) by 4 mm (Fig. 1B-C). Phenotypic characteristics show stability during the reproductive growth stage(Fig. 1D). Genetic crosses between \u003cem\u003ezmed3\u003c/em\u003e and B73 produced F\u003csub\u003e1\u003c/sub\u003e progeny with fully developed female spikes, confirming a recessive inheritance pattern. Backcrossing F\u003csub\u003e1\u003c/sub\u003e plants resulted in 2354 normal phenotypes and 2290 \u003cem\u003ezmed3\u003c/em\u003e mutants, consistent with a 1:1 segregation ratio (Fig. 1E). These results demonstrate that \u003cem\u003ezmed3\u003c/em\u003e female inflorescences undergo apical flattening and degeneration, with spike rows progressively disorganizing in a stably inherited manner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Transcriptomic analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the transcript-level changes during ear development, transcriptome was conducted in both \u003cem\u003ezmed3\u003c/em\u003e and Lx9801 at the 4 mm stage. The base composition analysis showed that the A:T and C:G ratios were close to expected, indicating normal sequencing results (Supplementary Fig. 1A). Sequencing quality values for each base in the 5\u0026apos; to 3\u0026apos; direction of reads were within the normal range (Supplementary Fig. 1B). PCA of the transcriptome data clearly distinguished Lx9801 from \u003cem\u003ezmed3\u003c/em\u003e (Supplementary Fig. 1C). Correlation analysis indicated higher similarity between samples within the same material group than between groups, confirming the reliability of the data for further analysis (Fig. 2A). A total of 1,589 differentially expressed genes (DEGs) were identified, with 1,026 up-regulated and 563 down-regulated in \u003cem\u003ezmed3\u003c/em\u003e compared to Lx9801 (Fig. 2B). Hierarchical clustering of DEGs further separated Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e (Supplementary Fig. 1D). Notably, the jasmonate-mediated signaling pathway was significantly activated in \u003cem\u003ezmed3\u003c/em\u003e, influencing the final morphology of the female spike by regulating inflorescence cell elongation and arrangement. Additionally, DEGs were enriched in metabolic pathways such as linoleic acid metabolism and tyrosine metabolism, highlighting the importance of unsaturated fatty acids and secondary metabolites in cell membrane fluidity and energy supply for the female panicle (Fig. 2C, D). Focusing on the MAPK signaling pathway, we observed altered expression levels of genes associated with jasmonic acid and ethylene. This pathway likely coordinates downstream gene expression to regulate inflorescence organ morphogenesis, further supporting the molecular mechanisms underlying female spike development in \u003cem\u003ezmed3\u003c/em\u003e. （Fig. 2E）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eProteomic analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the protein-level changes during ear development, proteome was conducted in both \u003cem\u003ezmed3\u003c/em\u003e and Lx9801 at 4 mm stage. Mass spectrometry identified 101,684 peptides and 11,157 proteins, with 9,028 proteins quantified across both lines (Supplementary Fig. 2A). Analysis using Razor+Unique peptides showed a decreasing trend in the number of corresponding proteins as the number of independent peptides increased, indicating high confidence in identifying abundant proteins (Supplementary Fig. 2B). PCA of the proteomic data revealed clear separation in protein abundance between Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e (Fig. 3A). Intra-group correlation analysis showed Pearson correlation coefficients above 0.95 for all biological replicates, confirming minimal variability and excellent reproducibility. Inter-group analysis highlighted significant differences between the two lines, ensuring the data\u0026apos;s suitability for downstream analysis (Fig. 3B). Using stringent thresholds, 185 DAPs were identified, including 46 upregulated and 139 downregulated proteins in \u003cem\u003ezmed3\u003c/em\u003e (Fig. 3C). Z-score normalization and hierarchical clustering of DAPs demonstrated consistent expression patterns within groups and distinct differences between groups, further validating the accuracy of the differential protein screening (Fig. 3D).\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis revealed that ribosome-related proteins were significantly upregulated in \u003cem\u003ezmed3\u003c/em\u003e, suggesting enhanced protein anabolism, which may accelerate the cell cycle and promote the proliferation of inflorescence primordium cells. Additionally, abnormal expression of chromatin structure-related proteins indicates that epigenetic regulation could play a critical role in maintaining the inflorescence meristem (Fig. 4A). KEGG analysis further identified significant changes in key proteins of the MAPK signaling pathway, which regulates cell polarity by integrating auxin/cytokinin signals. Disruptions in this pathway could contribute to flattened ear (Fig. 4B). To explore proteins associated with ear flattening, WGCNA analysis was performed, filtering out proteins with stable or low expression across all samples. Scale-free topological fitting analysis optimized the soft threshold power at 18, with a fitting index of R\u0026sup2;=0.82 (red-marked area). Using the dynamic mixed cutting method (deepSplit=2, minModuleSize=30), 28 coexpression modules were identified, with the turquoise module (containing 327 proteins) showing the strongest correlation with the phenotype (Fig. 4C,D). A static diagram revealed that proteins A0A804MWG6, B4F8D2, C0P455, P12339, and B6UIJ6 exhibited strong interactions with ribosomes (Fig. 4E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Metabolomic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the metabolite-level changes during ear development, metabolome was conducted in both \u003cem\u003ezmed3\u003c/em\u003e and Lx9801 at the 4 mm stage. After a rigorous quality filtering, 22,839 metabolites were retained, including 256 secondary qualitative metabolites (Supplementary Fig. 3A). PCA revealed a clear separation in the metabolic profiles between Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e (Fig. 5A). Volcano plot analysis identified 60 significantly upregulated metabolites and 62 downregulated metabolites. Correlation analysis of these differentially expressed metabolites, based on P-values, demonstrated coordinated changes among the significant metabolites (Fig. 5B). Hierarchical clustering heatmap analysis further revealed distinct patterns of upregulated and downregulated metabolites, emphasizing the distinct metabolomic profiles between the two genotypes (Supplementary Fig. 3B). These shifts underscore metabolic adjustments during female ear development, which is a critical adaptive mechanism for enhancing fitness in response to developmental stresses.\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003ezmed3\u003c/em\u003e, alterations in key tricarboxylic acid (TCA) cycle intermediates, such as citric acid and oxaloacetic acid, significantly influenced stem cell differentiation and female ear development. Disruptions in phenylpropanoid biosynthesis, marked by the upregulation of ferulic acid and downregulation of erucinic acid, suggested impaired cell wall remodeling, potentially limiting cellular expansion (Fig. 5C). Additionally, dysregulation of glutathione metabolism induced oxidative stress, further compromising cellular function. These findings underscore the critical roles of these metabolic pathways in regulating ear development. To explore the metabolic regulatory network, pathway enrichment analysis was performed using the KEGG database for Zea mays, identifying intersections in metabolic pathways and highlighting potential key enzymes and metabolites (Supplementary Fig. 3C). This analysis offered valuable insights into the metabolic coordination underlying maize ear development and flattening. Furthermore, correlation analysis of the top 10 significantly upregulated and downregulated metabolites revealed their interplay and regulatory relationships during biological state transitions (Supplementary Fig. 3D), providing a comprehensive understanding of the metabolic dynamics in this developmental process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Multi-omic joint analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough comprehensive transcriptome, proteome, and metabolome analysis, several key metabolic pathways and metabolites crucial for female ear development in maize were identified. The study first demonstrated the correlation between gene and protein expression using a nine-quadrant map, which revealed distinct gene-protein expression patterns: 48 genes were upregulated while their corresponding proteins were downregulated, and 173 genes were downregulated while their proteins were upregulated. These expression changes were primarily driven by miRNA-mediated post-translational regulation. Additionally, 366 genes and proteins were simultaneously upregulated, and 28 were simultaneously downregulated, indicating synchronized changes in these genes at both the transcriptional and translational levels (Fig. 6A).\u003c/p\u003e\n\u003cp\u003eFurther KEGG analysis revealed that key metabolic pathways, including glyoxylate and dicarboxylate metabolism, galactose metabolism, starch and sucrose metabolism, glycolysis/gluconeogenesis, tyrosine metabolism, phenylpropanoid biosynthesis, and cyanoamino acid metabolism, play crucial roles in the panicogenesis of \u003cem\u003ezmed3\u003c/em\u003e. In female maize ear development, metabolites such as citrate, oxaloacetate, and cis-aconitate\u0026mdash;key intermediates in the TCA cycle\u0026mdash;are involved in regulating energy homeostasis and cell differentiation, contributing to morphogenesis. Studies have shown that citric acid can specifically inhibit aconitase activity\u003csup\u003e[22]\u003c/sup\u003e, reducing mitochondrial oxidative phosphorylation efficiency, and directly delaying the cell division cycle of anthogenic basal stem cells by accumulating G1/S phase-blocking proteins\u003csup\u003e[22]\u003c/sup\u003e. Other metabolites, including 3-phospho-D-glycerate and L-glutamine phosphate, significantly affect protein synthesis and nitrogen cycling, thus influencing plant growth and development\u003csup\u003e[23]\u003c/sup\u003e. In galactose metabolism, the upregulation of D-fructose-6-phosphate and sucrose, along with the downregulation of raffinose and D-galactose, jointly regulate the intracellular redox state, affecting plant cell proliferation and division. Additionally, the upregulation of sucrose and downregulation of D-fructose-6-phosphate in starch and sucrose metabolism, along with the upregulation of glycerate and oxaloacetate and the downregulation of glycerate-3-phosphate in glycolysis/gluconeogenesis, significantly influence metabolite accumulation and conversion, thereby regulating ear development (Fig. 6B).\u003c/p\u003e\n\u003cp\u003eAdditionally, the upregulation of key metabolites in the tyrosine metabolism and phenylpropanoid biosynthesis pathways, such as tyrosine, 4-hydroxyphenylpyruvate, arginine, and ferulic acid, along with the downregulation of erucic acid, erucic acid malic acid, and p-coumaryl quinic acid, further influenced spike morphology by modulating cell differentiation and metabolic status. Specifically, the upregulation of ferulic acid and the downregulation of erucic acid malic acid suggest changes in the degree of cell wall crosslinking. In known pathways, ferulic acid enhances cell wall rigidity by promoting lignin monomer polymerization, while glycosylated products of erucic acid are involved in regulating cell expansion. The imbalance between these metabolites may decrease the stretchability of cob cells, ultimately contributing to the flat inflorescence phenotype. Furthermore, the upregulation of arginine in glutathione metabolism and the downregulation of reduced coenzyme II and oxyproline also influence cell differentiation and maintain redox balance.\u003c/p\u003e\n\u003cp\u003eThe joint analysis identified three core regulatory modules driving the abnormal development of female spikes in \u003cem\u003ezmed3\u003c/em\u003e. These include: 1) Ribosome and Cell Cycle Regulation: The upregulation of ribosome-related proteins accelerates cell proliferation, while disruptions in key nodes of the MAPK signaling pathway interfere with the establishment of cell polarity by integrating auxin/cytokinin signals. 2) Jasmonic Acid Signaling Pathway: The jasmonic acid-mediated signaling pathway is significantly activated and directly influences the morphogenesis of female spikes by regulating the elongation and spatial arrangement of inflorescence cells. 3) Metabolite Homeostasis: Abnormalities in key intermediate metabolites of the tricarboxylic acid (TCA) cycle, such as citric acid and oxaloacetic acid, impair stem cell differentiation. Additionally, dysregulation in the phenylpropanoid biosynthesis pathway\u0026mdash;characterized by upregulation of ferulic acid and downregulation of erucic acid\u0026mdash;limits cell expansion by interfering with cell wall remodeling and oxidative stress. These three modules work synergistically through multi-level and multi-pathway regulation, leading to the flattened spike phenotype and disordered inflorescence during the development of the female panicle in \u003cem\u003ezmed3\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Candidate gene mining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZm00001d041772\u003c/em\u003e, \u003cem\u003eZm00001d002258\u003c/em\u003e, \u003cem\u003eZm00001d043607\u003c/em\u003e, \u003cem\u003eZm00001d043348\u003c/em\u003e, and \u003cem\u003eZm00001d042353\u003c/em\u003e genes exhibited either upregulation or downregulation in expression within the KEGG pathway during the co-analysis (Table 1). To validate these findings, RT-qPCR was performed on the roots, stems, and leaves of Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e female spikes at the 4 mm developmental stage. Using Lx9801 as the control group for each tissue type, results showed that \u003cem\u003eZm00001d042353\u003c/em\u003e was significantly upregulated in both leaves and female spikes of \u003cem\u003ezmed3\u003c/em\u003e. Specifically, expression of \u003cem\u003eZm00001d042353\u003c/em\u003e was increased by 14.97% in leaves and 14.52% in female spikes compared to Lx9801 (Fig. 6C). Based on these results, \u003cem\u003eZm00001d042353\u003c/em\u003e was identified as a key gene involved in the flattening and row disruption of female spikes in \u003cem\u003ezmed3\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTable 1. Multi-omics analysis for KEGG pathways.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKO-ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-DEP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-DEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-DEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-DEM-NEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-DEM-POS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eGlyoxylate and dicarboxylate metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.005501117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.00857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.6340745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.9879909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.104062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eStarch and sucrose metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.006300258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.00946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.2032917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.3896232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.2813621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eGlycolysis / Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.898608956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.0000186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.8447464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.9272254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.5921062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eTyrosine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.369693949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.000735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.888226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.9879909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.4170106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eCarotenoid biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.173559534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.0206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.9995991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.9998589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.9012326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eGalactose metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.570774283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.0397617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.05690359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.3343912\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003ePhenylpropanoid biosynthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.622544857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.01981014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.01996139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.3366027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003eko00480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.838%;\"\u003e\n \u003cp\u003eGlutathione metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.7254%;\"\u003e\n \u003cp\u003e0.127229931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6197%;\"\u003e\n \u003cp\u003e0.00414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0845%;\"\u003e\n \u003cp\u003e0.457331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.8194249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5563%;\"\u003e\n \u003cp\u003e0.2086767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eKO-ID: KEGG Pathway ID; Pathway: KEGG Pathway description; P-DEP: p value of differential protein; P-DEG: p value of differential gene; P-DEM: p value of differential metabolites; P-DEM-NEG: P value of positive ions in differential metabolites; P-DEM-POS: P value of negative ions in differential metabolites.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Mutant \u003cem\u003ezmed3\u003c/em\u003e phenotype\u003c/h2\u003e\u003cp\u003eAs a crucial crop for China\u0026rsquo;s socio-economic stability, particularly in the context of diminishing arable land, maize requires significant advancements in production efficiency. Enhancing maize production is thus a critical agricultural priority. Ear-related traits, which are closely linked to key agronomic characteristics such as plant height, ear length, kernel rows, and hundred-kernel weight, are fundamental determinants of yield. Mutations affecting female ear development, including \u003cem\u003edg1\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003elrg1\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, and \u003cem\u003eOsWUS\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e in rice, and \u003cem\u003eZmSPL10\u003c/em\u003e, \u003cem\u003eZmSPL14\u003c/em\u003e, \u003cem\u003eZmSPL26\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, and \u003cem\u003eFEA4\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e in maize, have provided valuable insights into the molecular mechanisms governing inflorescence architecture, including DNA binding, protein dimerization, and meristem determinacy. In this study, we characterized a natural maize mutant, \u003cem\u003ezmed3\u003c/em\u003e, derived from the inbred line Lx9801. At the 4 mm ear developmental stage, \u003cem\u003ezmed3\u003c/em\u003e exhibited a flattened ear and disordered kernel rows, suggesting defects in inflorescence meristem organization. Genetic analyses revealed that the mutant phenotype follows Mendelian inheritance patterns, confirming a stable and heritable genetic basis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Key pathways and genes for spike morphology mutations\u003c/h2\u003e\u003cp\u003eFemale ear development in maize is a complex process regulated by multiple molecular pathways, including cell cycle control, hormonal signaling, and metabolic homeostasis. Previous studies have identified several key genes that influence ear morphology, such as \u003cem\u003eZmCCS52B\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, which regulates cell cycle progression, \u003cem\u003eZmBES1\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, which modulates hormone signaling, and \u003cem\u003eFEA4\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, which maintains redox balance. Despite these insights, the integration of gene-protein interactions with metabolic pathways in shaping ear morphology remains poorly understood.\u003c/p\u003e\u003cp\u003eTo address this knowledge gap, we employed a multi-omics approach combining 4D-Label-free proteomics, RNA-seq, and untargeted metabolomics to analyze ear development in Lx9801 and \u003cem\u003ezmed3\u003c/em\u003e at the 4 mm stage. Our data revealed significant alterations in the MAPK signaling pathway, which integrates auxin and cytokinin signals to establish cell polarity during organ morphogenesis. Dysregulation of this pathway suggests a failure in cell polarity establishment, which may contribute to the flattened ear axis observed in \u003cem\u003ezmed3\u003c/em\u003e. Furthermore, the MAPK pathway\u0026rsquo;s involvement in extracellular matrix remodeling and cytoskeletal reorganization could exacerbate the observed inflorescence defects. Another key finding was the marked activation of the jasmonic acid (JA) signaling pathway, which we propose directly influences ear morphology by regulating cell elongation and arrangement. JA is a crucial signaling molecule with diverse physiological roles, including regulating plant growth and development, such as in plant regeneration\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The interplay between jasmonic acid and other hormones, such as auxin and cytokinin, likely amplifies its effect on inflorescence meristem development. Metabolomic analysis revealed significant disturbances in the TCA cycle, with abnormal levels of citric acid and oxaloacetate, which compromised cellular energy metabolism and stem cell differentiation. Additionally, dysregulation of the phenylpropane biosynthesis pathway, characterized by upregulation of ferulic acid and downregulation of sinapic acid, suggests impaired cell wall remodeling, potentially restricting cell expansion and altering inflorescence morphology. Disruption of glutathione metabolism further highlights the critical role of redox homeostasis in ear development. Together, these findings emphasize the importance of metabolic equilibrium and its complex interactions with signaling pathways in shaping maize ear architecture.\u003c/p\u003e\u003cp\u003eIn this study, it was found that MAPK and JA signaling pathways were abnormal in \u003cem\u003ezmed3\u003c/em\u003e mutant, while CLV-WUS pathway maintained the balance of meristem cells through \u003cem\u003eFEA2\u003c/em\u003e and other genes. Although the direct interaction between \u003cem\u003ezmed3\u003c/em\u003e and the core component of CLV-WUS has not been confirmed, the abnormal cell polarity (such as auxin-cytokinin signal integration) caused by the imbalance of MAPK pathway may affect the panicle axis morphology independently, which is spatially related to the meristem hyperproliferation phenotype of CLV-WUS pathway mutant at the development level. In addition, the activation of JA pathway or hormone cross-regulation through \u003cem\u003eCT2\u003c/em\u003e (such as stress signal crosstalk mediated by JA and \u003cem\u003eCT2\u003c/em\u003e) indirectly interferes with hormone networks regulated by factors such as \u003cem\u003eUB2\u003c/em\u003e/\u003cem\u003eUB3\u003c/em\u003e, thus affecting meristem homeostasis. At present, these are only indirect conjectures based on pathway enrichment. Whether \u003cem\u003ezmed3\u003c/em\u003e indirectly relates to CLV-WUS pathway by regulating MAPK/JA pathway still needs to be verified by molecular interaction experiments to avoid over-interpretation of functional synergy as mechanism association. \u003cem\u003eZm00001d042353\u003c/em\u003e encodes Sucrose-phosphate synthase, which regulates the effective supply of sucrose during plant growth and fiber elongation. It is worth discussing whether the abnormal expression of this gene will hinder sucrose synthesis and then adjust the phosphorylation state of MAPK by changing the level of JA hormone, which may eventually lead to abnormal cell elongation due to insufficient energy/carbon skeleton supply.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, the phenotypic variation in \u003cem\u003ezmed3\u003c/em\u003e arises from the synergistic effects of three core regulatory modules: (1) ribosome-mediated cell cycle control, (2) jasmonic acid signaling, and (3) metabolic homeostasis. Our study offers novel insights into the molecular mechanisms driving maize ear development, revealing a complex regulatory network that integrates gene expression, protein function, and metabolic activity. These findings not only deepen our understanding of inflorescence development but also highlight potential targets for molecular breeding strategies aimed at improving maize yield.\u003c/p\u003e\u003cp\u003eFuture research should focus on elucidating the crosstalk between these regulatory modules and exploring their broader applicability in other crops. Additionally, CRISPR/Cas9-based functional validation of the candidate genes identified in this study could further refine our understanding of their specific roles in ear development. By integrating multi-omics data with functional analyses, this study contributes to the expanding knowledge on crop development and provides a foundation for addressing global food security challenges in an era of climate change and resource scarcity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"469\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eabbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003edescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eBiological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eCellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003e\u003cem\u003ezmed3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003e\u003cem\u003eEar Diameter Mutant3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eDAMs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003edifferentially accumulated metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eDAPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003edifferentially accumulated proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003edifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003efalse discovery rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003efloral meristem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003einflorescence meristem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eJA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eJasmonate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eMaize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eZea mays L.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eMAPK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003emitogen-activated protein kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eprincipal component analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eProtein-protein interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eRT-qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eReal-time quantitative PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eSAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eshoot apical meristem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003espikelet meristem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eSPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003espikelet pair meristem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eTCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003etricarboxylic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eTIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003etotal ion current\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.0851%;\"\u003e\n \u003cp\u003eCLV-WUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61.9149%;\"\u003e\n \u003cp\u003eCLAVATA-WUSCHEL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article will be shared on reasonable request to the corresponding author.\u0026nbsp;The raw sequencing data were deposited in GSA database with the accession number: PRJCA041532.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key Research Project of the Shennong Laboratory (SN01-2022-02), Natural Science Foundation of Henan (252300421159), the Key research and development projects of Henan Province (241111114300), the National key research and development plan (2022YFD1201004) and the Agricultural Seed Joint Research Project of Henan Province (2022010204).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeihua Li, Jihua Tang and Pengshuai Yan designed the research. Jing Liu performed the experiments. Jing Liu analyzed the data. Jing Liu drafted the manuscript. Tianxiao Yang, Weihua Li, Jihua Tang, and Pengshuai Yan revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Key Research Project of the Shennong Laboratory, Natural Science Foundation of Henan, the Key research and development projects of Henan Province, the National key research and development plan, and the Agricultural Seed Joint Research Project of Henan Province for financially supporting this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePiperno, D. R., and Flannery, K. V. 2001. The earliest archaeological maize (Zea mays L.) from highland Mexico: new accelerator mass spectrometry dates and their implications. 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L., Xiong, M., Yang, F. Y., Yuan, X. Z., \u0026amp; He, G. H. 2020. MULTI-FLORET SPIKELET 2, a MYB Transcription Factor, Determines Spikelet Meristem Fate and Floral Organ Identity in Rice. Plant physiology, 184(2), 988\u0026ndash;1003.\u003c/li\u003e\n\u003cli\u003eRobil, J. M., and Tran, T. M. 2024. Bigger meristem, higher yield? The roles of REL2 and RELK in maize meristem function and yield enhancement. Plant Physiology, 197(1), kiae552.\u003c/li\u003e\n\u003cli\u003eZhao, Y., and Su, C. 2019. Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize. Scientific Reports, 9(1), 16112.\u003c/li\u003e\n\u003cli\u003eDong, Z., Wang, Y., Bao, J., Li, Y., Yin, Z., Long, Y., \u0026amp; Wan, X. 2023. The Genetic Structures and Molecular Mechanisms Underlying Ear Traits in Maize (Zea mays L.). Cells, 12(14), 1900.\u003c/li\u003e\n\u003cli\u003eJe, B. I., Xu, F., Wu, Q., Liu, L., Meeley, R., Gallagher, J. P., Corcilius, L., Payne, R. J., Bartlett, M. E., \u0026amp; Jackson, D. 2018. The CLAVATA receptor FASCIATED EAR2 responds to distinct CLE peptides by signaling through two downstream effectors. eLife, 7, e35673.\u003c/li\u003e\n\u003cli\u003eWang, Y., Bao, J., Wei, X., Wu, S., Fang, C., Li, Z., Qi, Y., Gao, Y., Dong, Z., \u0026amp; Wan, X. 2022. Genetic Structure and Molecular Mechanisms Underlying the Formation of Tassel, Anther, and Pollen in the Male Inflorescence of Maize (Zea mays L.). Cells, 11(11), 1753.\u003c/li\u003e\n\u003cli\u003eWu, Q., Regan, M., Furukawa, H., \u0026amp; Jackson, D. 2018. Role of heterotrimeric G\u0026alpha; proteins in maize development and enhancement of agronomic traits. PLoS genetics, 14(4), e1007374.\u003c/li\u003e\n\u003cli\u003eDu, Y., Liu, L., Peng, Y., Li, M., Li, Y., Liu, D., Li, X., \u0026amp; Zhang, Z. 2020. UNBRANCHED3 Expression and Inflorescence Development is Mediated by UNBRANCHED2 and the Distal Enhancer, KRN4, in Maize. PLoS genetics, 16(4), e1008764.\u003c/li\u003e\n\u003cli\u003eTian, T., Liu, Y., Yan, H., You, Q., Yi, X., Du, Z., Xu, W., and Su, Z. 2017. agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Research, 45(W1), W122-W129.\u003c/li\u003e\n\u003cli\u003eChen, C., Chen, H., Zhang, Y., Thomas, H. R., Frank, M. H., He, Y., \u0026amp; Xia, R. 2020. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Molecular plant, 13(8), 1194\u0026ndash;1202.\u003c/li\u003e\n\u003cli\u003eOverath, P., Czichos, J., and Haas, C. 1986. The effect of citrate/cis-aconitate on oxidative metabolism during transformation of Trypanosoma brucei. European Journal of Biochemistry, 160(1), 175-182.\u003c/li\u003e\n\u003cli\u003eLee, K. T., Liao, H. S., and Hsieh, M. H. 2023. Glutamine Metabolism, Sensing and Signaling in Plants. Plant and Cell Physiology, 64(12), 1466-1481.\u003c/li\u003e\n\u003cli\u003eYu, H., Ruan, B., Wang, Z., Ren, D., Zhang, Y., Leng, Y., Zeng, D., Hu, J., Zhang, G., Zhu, L., Gao, Z., Chen, G., Guo, L., Chen, W., and Qian, Q. 2017. Fine Mapping of a Novel defective glume 1 (dg1) Mutant, Which Affects Vegetative and Spikelet Development in Rice. Frontiers in Plant Science, 8, 486.\u003c/li\u003e\n\u003cli\u003eXu, Q., Yu, H., Xia, S., Cui, Y., Yu, X., Liu, H., Zeng, D., Hu, J., Zhang, Q., Gao, Z., Zhang, G., Zhu, L., Shen, L., Guo, L., Rao, Y., Qian, Q., and Ren, D. 2020. The C2H2 zinc-finger protein LACKING RUDIMENTARY GLUME 1 regulates spikelet development in rice. Science Bulletin, 65(9), 753-764.\u003c/li\u003e\n\u003cli\u003eTang, H., Fan, J., Wang, R., Zhu, J., Xiang, X., Dong, J., Zhou, L., and Wang, L. 2023. Changes in the expression pattern of OsWUS negatively regulate plant stature and panicle development in rice. G3, 13(7), jkad100.\u003c/li\u003e\n\u003cli\u003eKong, D., Jing, Y., Duan, Y., He, M., Ding, H., Li, H., Zhong, Z., Zheng, Z., Fan, X., Pan, X., Li, Y., Bai, M., Li, X., Luo, M., Xue, W., Zhang, X., Xu, X., Yuan, Y., Zou, T., Chen, L., \u0026hellip; Wang, H. 2024. ZmSPL10, ZmSPL14 and ZmSPL26 act together to promote stigmatic papilla formation in maize through regulating auxin signaling and ZmWOX3A expression. New Phytologist, 243(5), 1870-1886.\u003c/li\u003e\n\u003cli\u003eYang, H., Zhang, Z., Zhang, N., Li, T., Wang, J., Zhang, Q., Xue, J., Zhu, W., and Xu, S. 2024. QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize. Frontiers in Plant Science, 15, 1371394.\u003c/li\u003e\n\u003cli\u003eFeng, W., Liu, Y., Cao, Y., Zhao, Y., Zhang, H., Sun, F., Yang, Q., Li, W., Lu, Y., Zhang, X., Fu, F., and Yu, H. 2022. Maize ZmBES1/BZR1-3 and -9 Transcription Factors Negatively Regulate Drought Tolerance in Transgenic Arabidopsis. International Journal of Molecular Sciences, 23(11), 6025.\u003c/li\u003e\n\u003cli\u003eLi, C., Xu, M., Cai, X., Han, Z., Si, J., \u0026amp; Chen, D. 2022. Jasmonate Signaling Pathway Modulates Plant Defense, Growth, and Their Trade-Offs. International journal of molecular sciences, 23(7), 3945.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"maize ear, transcriptome, proteome, metabolome, candidate genes","lastPublishedDoi":"10.21203/rs.3.rs-7194726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7194726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEar is a crucial component of final yield in maize. Understanding how ear is developed is essential for maize genetic improvement and molecular breeding. Among the multiple factors influencing yield, ear development is particularly critical, as it is governed by the inflorescence meristem\u0026mdash;a structure that directly shapes key yield-related traits such as ear size, kernel number, and row arrangement.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHere, we analyzed the \u003cem\u003ezmed3\u003c/em\u003e mutant, which shows flattened ear tip and disordered kernel rows, is a single recessive mutation isolated from the Lx9801 breeding population. Using integrated transcriptomic, proteomic, and metabolomic analyses at the 4 mm stage of developing ears, we identified 1,589 differentially expressed genes (DEGs), 185 differentially accumulated proteins (DAPs), and 122 differentially accumulated metabolites (DAMs) in \u003cem\u003ezmed3\u003c/em\u003e mutants compared with normal siblings. These global omics changes were primarily associated with central carbon metabolism. Mutant \u003cem\u003ezmed3\u003c/em\u003e inflorescence meristems (IMs) were initially enlarged, switched to a more fasciated pattern, and finally leading to impaired spikelet meristems (SMs). Transcriptomics suggested activation of the jasmonic acid signaling pathway, potentially affecting spikelet cell elongation. Proteomics indicated disruption of the MAPK signaling pathway, likely affecting spikelet cell polarity. Metabolomics demonstrated deficiencies in the tricarboxylic acid cycle and phenylpropanoid synthesis pathway, which in turn alter meristem cells differentiation and cell wall remodeling. Multi-omics integration uncovered a regulatory network involving cell cycle initiation, jasmonic acid signaling, and metabolic flux homeostasis, and pinpointed several candidate genes for future functional characterization.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur study not only identifies potential molecular mechanisms underlying maize ear development, but also pinpoints precise targets for genetic improvement. These findings deepen our understanding of inflorescence biology and provide a theoretical framework for optimizing yield-related traits, thereby offering actionable insights for the design of molecular breeding strategies to enhance maize productivity.\u003c/p\u003e","manuscriptTitle":"Multi-omics Analysis of the Maize Ear Diameter Mutant3 (zmed3) Provides Insights into Female Inflorescence Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 08:37:36","doi":"10.21203/rs.3.rs-7194726/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-11T07:02:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-10T11:42:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222526153738686326435810820897716946080","date":"2025-08-07T16:51:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T04:13:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201383905874680615108312019356942210957","date":"2025-08-07T03:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109090706807755965556015039684037993857","date":"2025-08-07T00:03:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279619392182029898665982035392975351952","date":"2025-08-06T11:36:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229659001451150050174063302936438818247","date":"2025-08-06T09:23:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222552087205423976893186769493864936996","date":"2025-08-06T08:38:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-06T08:15:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-06T08:09:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-06T06:54:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-05T18:04:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-08-05T17:58:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"698d33b8-47e2-49ee-92ea-ca5cc3cc16aa","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:09:47+00:00","versionOfRecord":{"articleIdentity":"rs-7194726","link":"https://doi.org/10.1186/s12870-025-07439-0","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-10-14 15:58:11","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-08-11 08:37:36","video":"","vorDoi":"10.1186/s12870-025-07439-0","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07439-0","workflowStages":[]},"version":"v1","identity":"rs-7194726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7194726","identity":"rs-7194726","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}