In-Depth Understanding of Cytoplasmic Male Sterility by Metabolomics in Spring Stem Mustard (Brassica juncea var. tumida Tsen et Lee)

Abstract

Male sterility (MS) caused by aborted pollen is a vital germplasm resource. In this study, metabolomic, transcriptomic, and proteomic analyses were performed to investigate the molecular regulatory mechanism of cytoplasmic male sterility (CMS) in the flower buds of the male sterile line 09-05A and its maintainer line, 09-05B, of Brassica juncea. Our metabolomic analysis revealed that 41 metabolites involved in pollen development and male fertility formation were differentially accumulated between 09-05A and 09-05B at the bi-nucleate stage of B. juncea. Integrated omics indicated that the expression of key genes and proteins in the amino acid and phenylpropanoid metabolic pathways was remarkably downregulated in the flower buds of 09-05A. Furthermore, the abnormal expression of key transcription factor (TF) genes related to tapetum development and pollen wall formation may affect pollen development in the 09-05A CMS line. The results indicated that the downregulated expression level of critical genes and proteins in amino acid metabolism; phenylpropanoid synthesis; and TF genes, such as shikimate kinase, phenylalanine ammonia-lyase, and MYB103, may have led to impaired tapetum and pollen wall development, thereby causing pollen abortion in 09-05A. This study provides new insights into the metabolic and molecular regulatory mechanisms underlying the formation of CMS in B. juncea and lays a foundation for detailed studies on the identity and characteristics of MS-related genes.

1. Introduction

Heterosis, or hybrid vigor, is a natural phenomenon in which the hybrid offspring of a genetically diverse cross outperform the parents in multiple traits, including yield, uniformity, and tolerance to environmental challenges [1,2,3]. Hybridization is a promising avenue for enhancing the yield and quality of crops [4,5,6]. Male sterility (MS) and self-incompatibility are widespread in flowering plants and are used by breeders as the two main tools to achieve heterosis, which can greatly reduce labor and costs, while improving production efficiency [7,8,9]. The adoption of male sterile lines for the production of hybrid seeds has been extensively trialed and successfully implemented in various crops [10]. Recently, the cytoplasmic male sterility (CMS) technique has been widely utilized in Cruciferae crop breeding to produce high-yield hybrids in vegetables [11,12]. Previous research has shown that the formation of CMS primarily involves mutations or molecular rearrangements of genes in the mitochondrial or chloroplast genome [13]. To maximize the utilization of heterosis in plants, a comprehensive understanding of the metabolic and molecular mechanisms contributing to CMS formation is critical.

CMS is a maternally inherited trait resulting in the failure to produce functional pollen in higher plants [14]. Previous research has shown a connection between the manifestation of CMS and disruptions in the development of tapetum cells, pollen walls, and aberrant subcellular structures of pollen mother cells throughout anther development [15,16,17]. Notably, the premature and delayed degradation of the tapetum can trigger abnormal pollen development, thereby leading to CMS [18,19]. Research has also underscored that delayed tapetum programmed cell death (PCD), which prompts an imbalance in metabolic pathways, primarily causes anther abortion in the SaNa-1A CMS line of Brassica napus [15,19]. Furthermore, changes in the expression of genes involved in amino acid metabolism may influence pollen fertility during anther development [20]. For example, the expression level of the glutamine synthetase (GS) gene was reduced in the amino acid biosynthesis pathway in the pepper CMS line [21]. In addition, amino acid homeostasis was disrupted by the mutation of BoCYP704B1 in a male-sterile mutant [22]. During pollen development, the pollen wall, which is rich in lipids, requires enzymes that promote the metabolism of fatty acids and their derivatives for sporopollenin biosynthesis [23]. Previous studies have also found that sporopollenin biosynthesis gene mutants (CYP703A3, CYP704B2, and DPW) exhibited defective sporopollenin biosynthesis and abnormal pollen wall structures in rice [24,25,26]. The ABCG26 gene in rice is responsible for transporting lipidic precursors, and OsABCG26 gene knockdown leads to aborted pollen exine [27,28]. Moreover, transcription factors (TFs) are essential in the regulation of gene expression, and they play a vital role in tapetum development and pollen wall formation; their abnormal function is often caused CMS [29,30]. MYB TFs play important roles during the reproductive process in rice. For example, OsMYB103 is a positive regulator of tapetum degradation, and the downregulation of OsMYB106 causes pollen sterility and shortened plant height [31,32]. Several other TFs such as ACOS12, NAC19, and OsTDF1 also participate in tapetum PCD, implying a crucial role in anther development [33,34,35]. Moreover, TFs from the bZIP family, particularly bZIP18 and bZIP34 in Arabidopsis thaliana, have been documented to regulate pollen wall development [36,37]. Although CMS has received considerable interest in recent years, the complicated regulatory mechanisms of metabolic network pathways during various stages of plant pollen development still require more comprehensive investigation.

Mustard, which belongs to the large Brassicaceae family, is economically important within the group of Brassica crops [38]. This crop is rich in germplasm resources, and it has several cultivars, which can be used for medicinal and industrial purposes [39]. Understanding the molecular mechanism of MS is important to improve the utilization of heterosis in Brassica juncea breeding. The spring stem mustard CMS line system of B. juncea, which is known as 09-05A/B, exhibits shriveled anthers and completely abortive, abnormally degraded pollen grains, making it an ideal subject for studying the molecular mechanisms of CMS [40]. Our previous analysis on the developing buds of 09-05A/B had indicated that the abnormal degraded cellular components and morphologically altered pollen exine wall are the hallmarks of 09-05A pollen, and defective metabolic events, particularly carbohydrate and energy metabolism, impede pollen development in 09-05A, in accordance with RNA-seq and proteomic data [40]. Nonetheless, transcriptomics and proteomics can only provide information on gene- and protein-expression levels. Metabolomics has proven to be an excellent tool for revealing final-result phenomena and for exploring metabolic process mechanisms during pollen development [41,42,43]. Therefore, further comprehensive multi-omics research is essential to unravel the MS essence of MS in the 09-05A CMS line of B. juncea.

In the present study, the flower buds of the 09-05A oxa CMS line and its maintainer line, 09-05B, at the bi-nucleate stage of B. juncea were further analyzed by metabolome. Integrating with transcriptome and proteome analyses, the related interaction network and putative metabolic pathways were constructed on the basis of the candidate differentially accumulated metabolites (DAMs)/differentially expressed genes (DEGs)/differentially abundant proteins (DAPs). Meanwhile, TFs were identified and categorized on the basis of RNA-seq data using bioinformatics techniques to explore their regulatory roles in the metabolic pathways. The results can contribute to elucidating the metabolic and molecular mechanisms underlying CMS in crops, providing a theoretical foundation for a comprehensive understanding of complex metabolic regulation in plant-pollen development.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions

The CMS line 09-05A was developed via multiple backcrosses between the winter-stem mustard oxa CMS line [44] and spring-stem mustard maintainer line. The male sterile line 09-05A exhibits a comprehensive excellent performance, with the exception of its consistent infertility when compared with the maintainer line, 09-05B. Both the CMS line 09-05A and its maintainer line, 09-05B, were cultivated at the base of Ningbo Academy of Agricultural Sciences in Zhejiang Province, China, protected from extreme drought, plant diseases, and insect pests. Flower buds at the bi-nucleate stage (selected in accordance with the previous report [40]) from at least 10 plants of lines 09-05A or 09-05B were pooled to form a single biological replicate. Three independent biological replicates were performed for metabolomic, transcriptomic, and proteomic experiments. Flower materials were immediately frozen in liquid nitrogen upon collection and then stored at −80 °C for subsequent analysis.

2.2. Electron Microscopy

For scanning electron microscopy, individual mature pollen grains separated from the freshly opened flowers of 09-05A and 09-05B were spread on scanning electron microscopy carriers, coated with gold–palladium in an Eiko Model IB5 ion coater (Eiko Corporation, Tokyo, Japan) for 4–5 min, and observed under a scanning electron microscope (Model TM-1000, Hitachi, Chiyoda, Japan), as previously described [45].

2.3. Metabolite Extraction

Metabolite extraction analysis was performed according to the method previously reported, with little modification [46]. Flower buds at the bi-nucleate stage were freeze-dried under vacuum in a lyophilizer (Scientz-100F; Ningbo Scientz 125 Biotechnology Co. Ltd., Ningbo, China) and crushed using a mixer mill (MM 400, Retsch, Haan, Germany) with zirconia beads for 1.5 min at 30 Hz. Then, 50 mg of the lyophilized powder was extracted with 1.2 mL of 70% (v/v) methanol and vortexed six times for 30 s every 30 min. The extract was centrifuged at 12,000 rpm for 3 min, and the supernatant was passed through a 0.22 µm filter membrane (SCAA-104, ANPEL, Shanghai, China, http://www.anpel.com.cn/, accessed on 8 June 2023) before ultraperformance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis.

2.4. UPLC–MS/MS Conditions

All sample extracts were analyzed in triplicate using an UPLC–ESI-MS/MS system, as described previously [46] (UPLC, SHIMADZU Nexera X2, https://www.shimadzu.com.cn/, accessed on 8 June 2023; MS, Applied Biosystems 4500 Q TRAP, https://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html, accessed on 8 June 2023). Chromatographic separation was conducted on an Agilent SB-C18 column (1.8 µm; 2.1 mm × 100 mm; Agilent Technologies, Santa Clara, CA, USA). The mobile phase consisted of solvents A (0.1% formic acid in pure water) and B (acetonitrile with 0.1% formic acid). Sample measurements were performed with a gradient program that used 95% A and 5% B as the starting conditions. The linear gradient program for elution was as follows: (a) from 0.00 to 9.00 min, phase B was in the range of 5–95% and maintained at 95% for 1 min; (b) from 10.00 to 11.10 min, the proportion of phase B was reduced to 5% and held for 14 min. The flow rate was 0.35 mL per minute, the column oven was set to 40 °C, and the injection volume was 4 μL. The effluent was alternatively connected to an ESI triple-quadrupole linear ion trap (Q-TRAP)–MS.

2.5. ESI-Q-TRAP-MS/MS

For ESI source operation, instrument tuning and mass calibration, the parameters were set as previously described [46,47] as follows: source temperature of 550 °C; ion spray voltage of 5500 V (positive ion mode)/−4500 V (negative ion mode); ion source gas I, gas II, and curtain gas were set to 50, 60, and 25 psi, respectively; and the collision-activated dissociation was high. Instrument tuning and mass calibration were performed with 10 and 100 μmol/L polypropylene glycol solutions in triple quadrupole (QQQ) and linear ion trap modes, respectively. QQQ scans were acquired through multiple reaction monitoring (MRM) experiments, with collision gas (nitrogen) as the medium. Declustering potential and collision energy were applied to individual MRM transitions with further declustering potential and collision energy optimization. A specific set of MRM transitions was monitored for each period in accordance with the metabolites eluted within this period.

2.6. Qualitative and Quantitative Metabolite Analyses

Qualitative metabolite analysis was performed using the in-house Metware Database (Metware Biotechnology Co. Ltd., http://www.metware.cn, accessed on 30 June 2023, Wuhan, China) in accordance with secondary spectral information, as previously described [48]. Isotopic signals; repeated signals containing K⁺, Na⁺, and NH4⁺ ions; and repeated signals of other high-MW debris ions were removed during the analysis. Metabolites were quantified by MRM analysis and QQQ-MS. During instrumental analysis, a quality control (QC) sample was inserted after every 10th test and sample. The repeatability of the total ion flow detection method was determined by testing the spectra of various QC samples.

Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted on all metabolites to identify putative biomarkers by statistics function prcomp within R [49] (www.r-project.org, accessed on 30 June 2023). Metabolites with remarkably different metabolism were selected as biomarkers based on variable importance in projection (VIP) ≥1 and fold change ≥2 (upregulated) or ≤0.5 (downregulated). Volcano plot performed with R software 3.5.1 was used to analyze the main identified differential metabolites between the fertile and sterile lines.

2.7. Analysis of DEGs and Proteins

All transcriptomic and proteomic data were obtained from previously published studies [40]. A pairwise comparison of genes and proteins across the bi-nucleate stage was performed using the value of fragments per kilobase of exon million fragments mapped. DEGs and DAPs were identified using the following criterion: p-value < 0.05 and fold change calculated by 09-05A/09-05B >1.5 or <0.667 at the bi-nucleate stage [40]. In predicting and identifying the TFs in the DEGs, each unigene was annotated using the Interproscan (https://www.ebi.ac.uk/interpro/, accessed on 6 July 2023) and Pfam databases (http://pfam-legacy.xfam.org/, accessed on 6 July 2023).

2.8. Quantitative Real-Time PCR Analysis

On the basis of the results obtained by the integrated metabolome, transcriptome, and proteome analyses mentioned above, nine DEGs were randomly selected to further validate the reliability of the integrated analysis. The buds at the bi-nucleate stage from 09-05A and 09-05B were collected and used for RNA extraction. All the materials were immediately frozen in liquid nitrogen and stored at −80 °C. Total RNA was isolated using the TRIzol reagent (Invitrogen, Waltham, MA, USA) treated with DNAase. RNA was reverse-transcribed using a cDNA synthesis kit (TaKaRa, Tokyo, Japan), in accordance with the manufacturer’s instructions. QRT-PCR was performed using TOROGreen qPCR MasterMix (TOROIVD, Shanghai, China) on a qTOWER3G real-time PCR detection system (Analytik, Jena, Germany). The reaction system was as follows: 10 μL of 2× qPCR mix, 0.8 μL of 10 μmol gene primer, 3 μL of cDNA, and 5.4 μL of ddH₂O. The PCR cycling conditions comprised an initial polymerase activation step of 95 °C for 60 s, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. The housekeeping gene Bjactin was used as the reference gene for the quantitative validation of the expression data [50], and its coefficient of variation (CV%) was calculated based on the Ct data to reflect stable expression using Microsoft Excel 2021. The primers (Supplementary Table S1) were designed using Primer Premier 5.0 (Premier Biosoft International, Silicon Valley, CA, USA). Three biological replicates for each sample were obtained. The relative quantification method (2^−ΔΔCt) [51] was used to evaluate quantitative variation between 09-05A and 09-05B in Microsoft Excel 2021.

2.9. Functional Annotation

Pathway mapping of the identified metabolites, genes, and proteins was performed using the KEGG database (KEGG. http://www.genome.jp/kegg/, accessed on 8 July 2023). The mapped pathways were further subjected to pathway enrichment analysis on the web-based server Metabolite Sets Enrichment Analysis. The significantly enriched pathways were considered by a threshold of p ≤ 0.05.

2.10. Data Processing and Statistical Analysis

Hierarchical cluster analysis (HCA) of samples and metabolites and Pearson correlation coefficient (PCC) analysis between samples were carried out using the R package ComplexHeatmap [49] (www.r-project.org, accessed on 30 June 2023). The HCA and PCC results were presented as heatmaps. SPSS statistical software (version 22.0; IBM, Armonk, NY, USA) was used for the normality tests and homogeneity of variances analysis of the relative expression data obtained by QRT-PCR. Statistical significance was evaluated by one-way ANOVA, followed by Duncan’s multiple-range test in SPSS. A p-value less than 0.05 was considered statistically significant.

3. Results

3.1. Phenotypic Characterization of Pollen in 09-05A/B Lines of B. juncea

Scanning electron microscopy was performed to investigate the pollen morphology of the oxa CMS line 09-05A and its maintainer line, 09-05B, at the bi-nucleate stage, revealing significant deformities in a large fraction of 09-05A pollen (Figure 1D). Compared with the normally shaped pollen grains of 09-05B depicted in Figure 1A–C, the pollen grains of 09-05A were slipper-shaped and bilaterally sunken, lacking a uniform aperture distribution pattern (Figure 1E,F).

3.2. Metabolic Characteristics of Metabolites Identified in 09-05A/B Lines of B. juncea

To obtain a comprehensive understanding of pollen development at the metabolic level, the expression profiles of the identified metabolites at the bi-nucleate stage were analyzed using PCA (Figure 2A). The first two principal components, PC1 (40.74%) and PC2 (17.43%), accounted for the variance across the dataset. Furthermore, all samples of 09-05A were well separated from the 09-05B samples in PC1. OPLS-DA was used to identify systemic differences in the metabolite profiles between the fertile and sterile lines. In the OPLS-DA model, the predictive component explained 57.9% of the variance, with R2X, R2Y, and Q2 values of 0.579, 1, and 0.873, respectively (Supplementary Figure S1). Those metrics suggest a stable and efficient OPLS-DA model. The metabolites in 09-05A were differentiated on the basis of the VIP values derived from the OPLS-DA model. A total of 41 DAMs were identified in 09-05A compared with 09-05B, with 6 upregulated and 35 downregulated (Figure 2B and Supplementary Table S2). Notably, the predominance of downregulated DAMs in 09-05A indicates remarkable metabolite synthesis variation in the buds at the bi-nucleate stage, compared with those in 09-05B.

3.3. Comparative Analysis of DAMs in 09-05A/B Lines of B. juncea

The 41 identified DAMs were classified into eight categories: lipids (48.78%), amino acids and derivatives (9.76%), phenolic acids (7.31%), nucleotides and derivatives (4.88%), organic acids (4.88%), alkaloids (7.32%), flavonoids (4.88%), and others (12.19%) (Figure 3A). Figure 3B displays the top 20 substances exhibiting significant metabolite differences; L-lysine showed the highest increase, whereas quercetin-7-O-rutinoside showed the greatest decrease (Supplementary Table S2). To identify functionally enriched clusters, HCA was performed on the basis of the profiles of the DAMs. Our results demonstrated differential expression abundances between the 09-05A and 09-05B groups. In particular, the metabolic content of lipids, nucleotides, derivatives, glucosinolates, and organic acids was reduced in the flower buds of 09-05A compared with those of 09-05B at the bi-nucleate stage (Figure 4). To elucidate the relationships among these differential metabolites, we computed the PCC for each pairing among the 41 DAMs in the flower buds of lines 09-05A and 09-05B at the bi-nucleate stage. A total of 861 correlations were calculated, with values ranging from −0.6381 (between L-lysine and 4-aminobenzoic acid) to 1 (among N-hexyl glucoside, 4-methylamyl glucosinolate, 3-methylpentyl glucosinolate, and hexyl glucosinolate; and between 9-hydroxy-10 and -12, 15-octadecatrienoic acid, and (9Z,11E)-13-oxooctadeca-9,11-dienoic acid) (Figure 5 and Supplementary Table S3). In addition, of the 861 calculated correlations, 210 were negative, and the rest were positive (Figure 5). The high reliability and repeatability of these results indicate that subsequent analyses will accurately reflect the true differences in metabolite abundance between 09-05A and 09-05B.

3.4. KEGG Pathway Mapping of DAMs

A KEGG pathway analysis was performed to identify the metabolic pathways of DAMs in the 09-05A/B lines, assigning these DAMs to 20 significantly enriched pathways (p-value < 0.05) (Figure 6). The primary pathways identified in both comparison groups were “nitrogen metabolism”, “ABC transporters”, “lysine biosynthesis”, “glutathione metabolism”, “alanine, aspartate, and glutamate metabolism”, and “biosynthesis of amino acids” (Figure 6 and Supplementary Table S4). The analysis demonstrated that the biosynthesis and transport of metabolites in the 09-05A flowers buds were remarkably different from those in 09-05B at the bi-nucleate stage, and the amino acid-metabolism process might have played vital roles in 09-05A pollen abortion at the bi-nucleate stage.

3.5. Key Biological Pathway Integration of DAMs, DEGs, and DAPs in 09-05A/B Lines of B. juncea

The previous sections demonstrated significant differences in metabolite abundance between the buds of 09-05A and 09-05B at the bi-nucleate stage. A comparative transcriptomic and proteomic approach was utilized to identify genes and proteins differentially expressed in the buds of sterile and fertile lines at the bi-nucleate stage to elucidate the molecular regulation mechanism of male reproductive development in B. juncea. Using the KEGG database, the DAMs, DEGs, and DAPs associated with glycolysis; the tricarboxylic acid cycle; and amino acid metabolism were mapped into specific pathways (Figure 7 and Supplementary Table S5). In the present study, a downregulation in the expression level of the transketolase (TKT) gene and a decrease in the quantity of ATP phosphoribosyltransferase (HisG) protein in 09-05A were observed during histidine synthesis. Conversely, the expression level of genes for ribose 5-phosphate isomerase A (RPIA) and HisG was upregulated in the same line. During the synthesis of tryptophan and phenylalanine, the expression level of genes encoding shikimate kinase (SKI), 3-phosphoshikimate 1-carboxyvinyltransferase (AroA), anthranilate synthase component I, tryptophan synthase beta chain (TrpB), and arogenate dehydratase (ADT) in 09-05A was downregulated. Interestingly, other genes encoding TrpB and ADT were found to be upregulated in the same line, indicating that there are other regulatory factors participated in that metabolic pathway. The quantity of 3-deoxy-7-phosphoheptulonate synthase protein was found to be higher in 09-05A than in 09-05B at the bi-nucleate stage.

During the synthesis of valine and leucine, the expression level of genes for branched-chain amino acid aminotransferase (BCAT), 3-isopropylmalate dehydrogenase (LeuB), and 3-isopropylmalate/(R)-2-methylmalate dehydratase large subunit (LeuC) was downregulated in 09-05A. By contrast, the genes for LeuB and 3-isopropylmalate/(R)-2-methylmalate dehydratase small subunit (LeuD) were upregulated in the same line. In 09-05A, the protein levels of ketol-acid reductoisomerase (IlvC) and 2-isopropylmalate synthase (LeuA), which are essential for isoleucine and leucine synthesis, respectively, were found to be increased. In addition, during the synthesis of serine, glycine, and cysteine, the gene expression level of cystathionine gamma-synthase (MetB) and the protein levels of glycine hydroxymethyltransferase (GlyA) in 09-05A decreased. Conversely, in 09-05A, genes encoding cysteine synthase (CysK) and threonine aldolase (LtaE) showed increased expression compared with 09-05B. Moreover, in the synthesis of methionine, threonine, asparagine, and lysine, the gene expression level of asparagine synthase (AS) and 4-hydroxy-tetrahydrodipicolinate synthase (DapA) in 09-05A was downregulated. Yet, the expression level of genes for bifunctional aspartokinase/homoserine dehydrogenase 1 (ThrA), diaminopimelate epimerase (DapF), and diaminopimelate decarboxylase (LysA) was upregulated. The protein levels of 5-methyltetrahydropteroyltriglutamate-homocysteine methyltransferase (MetE) and 4-hydroxy-tetrahydrodipicolinate reductase (DapB) were decreased in 09-05A, whereas the abundance of homoserine kinase (ThrB), aspartate aminotransferase (ATAAT), aspartate-semialdehyde dehydrogenase (ASD), and DapF was increased.

During the synthesis of glutamine, proline, and arginine, the gene expression level of delta-1-pyrroline-5-carboxylate synthetase (P5CS) and N-acetyl-gamma-glutamyl-phosphate reductase (ArgC) was downregulated. In addition, the protein levels of glutamate-glyoxylate aminotransferase (GGAT) and GS decreased in 09-05A, whereas that of ATAAT increased. Comprehensive metabolic profiling revealed changes in metabolite concentrations in the 09-05A/B lines, showing decreases in N-monomethyl-L-arginine and S-allyl-L-cysteine in 09-05A compared with 09-05B (Supplementary Table S4). Conversely, lysine and glutamine levels increased by 12.92- and 12.95-fold, respectively, in 09-05A, indicating the differential accumulation of these primary metabolites compared with 09-05B (Figure 7 and Supplementary Table S4). Moreover, the levels of cadaverine (phosphoribosyltransferase pathway), deoxyguanosine (guanine metabolism), and deoxyadenosine (ATP synthesis) were lower in 09-05A than in 09-05B. Furthermore, the content of 4-aminobenzoic acid, which is crucial for folate biosynthesis, decreased in 09-05A. The integrated analysis suggests that downregulated DAMs, DEGs, and DAPs, which are involved in amino acid biosynthesis, secondary metabolite synthesis, and ATP metabolism, likely inhibited the critical metabolic pathways for pollen development in 09-05A.

In the phenylpropanoid biosynthesis pathway, genes encoding phenylalanine ammonia-lyase (PAL), caffeic acid 3-O-methyltransferase (COMT), and caffeoyl-CoA O-methyltransferase (CCoAOMT) were variably downregulated, and the protein levels of PAL and CCoAOMT increased in 09-05A relative to 09-05B at the bi-nucleate stage (Figure 8 and Supplementary Table S6). The gene expression and protein levels of 4-coumarate-CoA ligase (4CL), coumaroylquinate (coumaroylshikimate) 3′-monooxygenase (CYP98A), trans-cinnamate 4-monooxygenase (CYP73A), and peroxidase (POD) decreased in the 09-05A CMS line compared with 09-05B. Conversely, several genes encoding 4CL, CYP73A, and POD were upregulated, with corresponding increases in protein levels of 4CL and POD in 09-05A. The gene expression level of shikimate O-hydroxycinnamoyltransferase (Hct) was downregulated in 09-05A, although the protein levels of Hct and cytochrome P450 family 98 subfamily A polypeptide 8 (CYP980A8) increased. Therefore, the production of phenylpropanoids may have been affected by the downregulation of genes such as PAL, CYP98A, COMT, CCoAOMT, and POD. These findings indicate that disruptions in phenylpropanoid biosynthesis could impair sporopollenin synthesis and pollen wall formation in the 09-05A CMS line of B. juncea.

3.6. Identification of TFs in 09-05A/B Lines of B. juncea

TFs are crucial at various stages of pollen development and maturation [52]. To explore the functional and regulatory diversity in the flower buds of 09-05A versus 09-05B at the bi-nucleate stage, a pairwise differential analysis of TF gene expression levels was conducted. Of 7697 DEGs, 2605 were categorized into 385 TF families (Supplementary Table S7). These TFs were divided into two groups, namely upregulated and downregulated, with most showing opposite expression patterns between 09-05A and 09-05B (Figure 9). In most TF families, there were more downregulated than upregulated genes in 09-05A compared with 09-05B. The Pkinase family was the most represented (138 members: 50 upregulated and 88 downregulated), followed by the Myb_DNA-binding (93 members: 34 upregulated and 59 downregulated), Pkinase_Tyr (88 members: 23 upregulated and 65 downregulated), p450 (67 members: 36 upregulated and 31 downregulated), and NAM (50 members: 16 upregulated and 34 downregulated) families. Other notable families included HLH (31 members: 15 upregulated and 16 downregulated), WRKY (22 members: 14 upregulated and 8 downregulated), and BZIP (24 members: 7 upregulated and 17 downregulated), which are crucial in regulating genes involved in pollen development. These findings suggest that these TFs could be involved in regulating anther development and pollen abortion in the 09-05A CMS line of B. juncea at the bi-nucleate stage.

3.7. QRT-PCR Validation of Genes Identified in 09-05A/B Lines of B. juncea

Gene expressions related to tapetum development, pollen wall development, amino acid metabolism, and phenylpropanoid biosynthesis differed remarkably between the fertile and sterile lines of B. juncea at the bi-nucleate stage. The mRNA expression levels of nine genes involved in amino acid metabolism (SKI, AroA, MetB, and ArgC), phenylpropanoid biosynthesis (PAL2, POD-1, and POD-2), and tapetum and pollen wall development (MYB103 and ZIP34) were quantified using QRT-PCR (Figure 10). The average CV of the reference gene Bjactin in the buds of 09-05A/09-05B is 0.70%, indicating its stable gene expression in the two groups. The normality tests and homogeneity of variances analysis results showed that the statistical significance evaluated by one-way ANOVA is reliable (Supplementary Table S8). In summary, our QRT-PCR analysis showed the significant downregulation of ArgC and MYB103 mRNA in 09-05A compared with 09-05B at the bi-nucleate stage. The mRNA levels of SKI, AroA, MetB, PAL2, POD-1, POD-2, and ZIP34 were markedly downregulated in 09-05A. These findings confirm that the key genes involved in pollen development are significantly downregulated in the buds of 09-05A at the bi-nucleate stage, which is consistent with our integrated analyses.

4. Discussion

In plants, tapetum and pollen-wall development, along with amino acid and lipid metabolism and TFs, plays critical roles in regulating pollen abortion and MS [53,54,55,56]. In this study, the in-depth and combined analyses of the metabolome, transcriptome, and proteome revealed alterations in the amino acid biosynthesis pathway, phenylpropanoid biosynthesis pathway, and TF gene expression levels in the 09-05A CMS line compared with 09-05B (Figure 7, Figure 8 and Figure 9). Previous research has shown that the abnormal development of the tapetal layer in CMS lines disrupts amino acid synthesis, thereby leading to MS in pepper [21,57]. In addition, the key genes involved in pollen exine biosynthesis, specifically within the phenylpropanoid and fatty acid biosynthesis pathways, were significantly downregulated in R2P2CMS, resulting in the absence of pollen in the cabbage CMS line [58]. Moreover, numerous TFs have been identified as crucial for male fertility, regulating tapetal cell development, pollen exine formation, and callose dissolution [59,60,61]. Our integrative analysis of MS-related metabolites, genes, and proteins in 09-05A of B. juncea advances our understanding of the metabolic and molecular mechanisms underlying male reproductive development in this species.

4.1. Disrupted Amino Acid Homeostasis during Tapetum Development Is Responsible for MS in the 09-05A CMS Line

The tapetum, the innermost layer of the anther walls, provides indispensable nutrients for pollen grain development [23]. Previous research demonstrated that aberrant PCD in the tapetum disrupts amino acid and lipid metabolism, cell wall organization, and polysaccharide metabolism, resulting in pollen abortion and MS [62]. Amino acid metabolism is essential for male reproductive development and pollen viability [22,63]. Abnormal tapetum maturity in the CMS line, associated with reduced amino acid synthesis, likely contributes to CMS in pepper [21]. Studies have shown that inhibiting GS in glutamine biosynthesis leads to MS in rice and tomato anthers. In addition, genes encoding SKI and P5CS are downregulated in male sterile lines of A. thaliana, Chinese cabbage, and pepper, respectively [55,64,65,66]. In this study, the KEGG pathway enrichment analysis of metabolomics data for the 09-05A/B lines of B. juncea at the bi-nucleate stage identified key pathways in amino acid metabolism, including “lysine biosynthesis”, “glutathione metabolism”, and “biosynthesis of amino acids” (Figure 6). The metabolomic analysis revealed reduced levels of N-monomethyl-L-arginine and S-allyl-L-cysteine, which are amino acid derivatives, in the 09-05A CMS line (Supplementary Table S2). Based on our results, L-glutamine and L-lysine levels were markedly elevated in 09-05A during the bi-nucleate pollen stage of B. juncea (Figure 3B and Supplementary Table S2). RNA-seq and proteomic data mapped key DEGs/DAPs to over 10 amino acid synthesis pathways in 09-05A compared with 09-05B. In particular, genes such as TKT, MetB, GlyA, AS, ArgC, and P5CS were downregulated in the synthesis pathways of histidine, cysteine, serine, asparagine, arginine, and proline, respectively (Figure 7 and Supplementary Table S5). Concurrently, the gene expression level of SKI and AroA in the tryptophan and phenylalanine synthesis pathways and BCAT in the valine and leucine pathways was downregulated (Figure 7 and Supplementary Table S5). QRT-PCR analysis further confirmed significant decreases in the expression level of SKI, AroA, MetB, and ArgC in 09-05A at the bi-nucleate stage (Figure 10). The integrated analysis suggested that altered expression of genes, including SKI and P5CS, suppressed normal amino acid synthesis and accumulation, such as tryptophan, phenylalanine, cysteine, and arginine, in the 09-05A flower buds. Disrupted amino acid homeostasis likely affected the overall tapetum development, thereby leading to pollen abortion.

4.2. Suppression of Phenylpropanoid Metabolism Affects Sporopollenin Synthesis in the 09-05A CMS Line

The pollen exine, which is crucial for male fertility, provides a protective layer for the male gametophyte. It is also primarily composed of sporopollenin. This compound is synthesized and secreted by the tapetum [67]. Research indicates a close relationship between sporopollenin biosynthesis and phenylpropanoid metabolism [58,68,69]. The downregulation of phenylpropanoid biosynthesis pathways inhibited sporopollenin synthesis, contributing to MS in wheat line 4110S and Chinese cabbage line AB01 [70,71]. Key genes, including PAL, 4CL, CYP98A8, and POD, have been identified as integral to the phenylpropanoid synthesis pathway network and are essential for pollen exine development [72,73]. PAL, which is predominantly expressed in anthers, plays a critical role; its reduced activity is linked to exine defects and subsequent pollen abortion [74,75]. Studies suggest that a deficiency in 4CL delays the utilization of phenylalanine, leading to MS in citrus [76]. Moreover, cytochrome P450s are necessary for exine formation, and CYP78A7 and CYP98A8 were involved in phenylpropanoid biosynthesis and downregulated in the R2P2CMS line of cabbage [58]. In addition, studies have reported significantly lower POD and CAT enzyme activities in the male sterile line than in the fertile line of Capsicum annuum L. [77].

Our previous study found that microspores in the 09-05A CMS line were defective, and they exhibited altered pollen walls at the bi-nucleate stage [40]. In this study, pollen grains from 09-05A displayed deformities, including slipper-shaped and bilaterally sunken structures, and a disrupted even aperture distribution pattern, compared with 09-05B (Figure 1). Our present comprehensive omics analysis revealed that the expression level of the genes SKI, AroA, and PAT, involved in phenylalanine synthesis, was downregulated in 09-05A, thereby inhibiting phenylalanine production (Figure 7 and Supplementary Table S5). In addition, PAL, 4CL, CYP98A, and POD showed variable levels of downregulation in the phenylpropanoid metabolic pathway in 09-05A compared with 09-05B (Figure 8 and Supplementary Table S6). QRT-PCR analysis indicated a significant decrease in the expression level of PAL2, POD-1, and POD-2 in 09-05A compared with 09-05B at the bi-nucleate stage (Figure 10). These results indicate that the downregulated expression level of key genes or functional loss of proteins involved in phenylpropanoid metabolism could have inhibited sporopollenin synthesis in pollen, thereby contributing to exine defects and MS in the 09-05A CMS line.

Our metabolomic analysis revealed reductions in 20 lipids, namely 10 lysophosphatidylcholines, 5 lysophosphatidylethanolamines, and 5 free fatty acids, in the 09-05A CMS line compared with 09-05B (Figure 3A and Supplementary Table S2). Previous research has shown that lipid metabolic balance is crucial for pollen fertility. Mutations in the lipid metabolism genes ACOS5, CYP704B1, and MS2 have been linked to decreased sporopollenin biosynthesis, resulting in exine defects and MS in A. thaliana and rice [78,79,80]. However, in our study, remarkable changes in the expression of key lipid metabolism genes and proteins were not observed. Future studies could utilize lipid metabolomics, enhanced transcriptomic, and targeted proteomic analyses to elucidate the molecular mechanisms by which abnormal lipid metabolism affects pollen development and contributes to MS in B. juncea.

4.3. Abnormal Expression of Key TFs Affects Tapetum Development and Pollen Wall Formation in the 09-05A CMS Line

Transcriptional regulation is a fundamental process that controls plant growth and development by affecting gene expression. In flowering plants, TFs play crucial roles in regulating pollen development and MS [7,81,82]. A genetic pathway formed by five TFs—dysfunctional tapetum 1 (DYT1), tapetal development and function 1 (TDF1), aborted microspores (AMS), MYB103, and MS1—has been proven to regulate tapetum function, influencing developing microspores by controlling tapetal PCD, pollen exine formation, and callose dissolution [60,83]. DYT1 and TDF1 encode a bHLH TF and a putative R2R3 MYB TF, respectively. Defects in these TF genes result in a dysfunctional tapetum and the absence of pollen [33,84,85]. In the AMS mutant, abnormal tapetal development leads to disordered pollen wall formation. Similarly, the MYB103 mutant exhibits a fully male sterile phenotype characterized by early tapetum degeneration, defective exine, and pollen coat [86,87]. In addition, AtMYB58, AtMYB20, and AtMYB85 have been reported to activate phenylalanine biosynthesis, which is involved in controlling pollen wall formation [88,89]. HvMS1, encoding a plant homeodomain finger TF expressed in the anther tapetum, is essential for pollen exine formation, and the overexpression of HvMS1 in barley causes complete MS [90,91]. Mutations in WRKY2, WRKY34, and VQ20 simultaneously led to MS, with associated defects in pollen development and germination [92]. T-DNA insertion mutants in AtbZIP34 exhibited morphological defects in pollen exine shape, which resulted in reduced pollen germination efficiency [36].

In our study, PAL gene expression, which is involved in phenylpropanoid synthesis, was remarkably lower in the 09-05A CMS line than in the 09-05B CMS line (Figure 8 and Supplementary Table S6). Concurrently, a remarkable change in the number of differentially expressed TFs in the MYB, WRKY, and BZIP families was observed, with key TFs, such as MYB85, MYB58, MYB103, WRKY2, and ZIP34, which are critical to tapetum and pollen wall development and are downregulated in 09-05A (Figure 9 and Supplementary Table S7). The QRT-PCR results showed significant decreases in MYB103 and ZIP34 mRNA levels in 09-05A compared with 09-05B at the bi-nucleate stage (Figure 10). These findings suggest that the downregulation of key TF genes, including MYB103, WRKY2, and ZIP34, which are involved in tapetum and pollen wall development, negatively impacts pollen formation, leading to pollen abortion and MS in 09-05A of B. juncea. The key TFs identified in this study provide ideal candidates for exploring the molecular regulatory mechanisms of floral development in B. juncea through genetic approaches.

5. Conclusions

Metabolomic, transcriptomic, and proteomic analyses were conducted on the 09-05A/B flower buds of B. juncea at the bi-nucleate stage to elucidate the molecular regulatory mechanisms underlying MS. At the metabolomic level, 41 metabolites displayed significant changes, with DAMs in amino acid metabolism potentially causing the shrinking of pollen grains in the 09-05A CMS line. Omics analyses revealed that downregulated DEGs/DEPs in amino acid and phenylpropanoid biosynthesis pathways were closely associated with suppressed amino acid and sporopollenin syntheses in 09-05A of B. juncea. Furthermore, the gene expression level of key TFs, such as MYB103, WRKY2, and ZIP34, which regulate tapetum development and pollen wall formation, were significantly downregulated in the 09-05A CMS line. Based on these results, we hypothesize that the downregulated expression level of key genes/proteins in amino acid metabolism, phenylpropanoid synthesis, and TF regulation disrupts amino acid homeostasis and sporopollenin synthesis, impairing tapetum and pollen wall development and leading to CMS in 09-05A. This research provides essential information on the molecular regulatory mechanisms of MS in B. juncea through integrated metabolomic, transcriptomic, and proteomic analyses, thereby advancing our understanding of germplasm innovation and the utilization of heterosis in plants.