Insights into the molecular mechanisms of browning tolerance in luffa: a transcriptome and metabolome analysis

Abstract

Introduction: Enzymatic browning significantly affects the edible, nutritional, and commercial value of luffa. Investigating the expression and regulation of key enzyme genes involved in the browning process is crucial for understanding the molecular mechanisms underlying luffa browning.

Methods: Fruit samples were collected at 15 (S1), 20 (S2), and 45 days (S3) after flowering from two contrasting luffa varieties: the browning-sensitive Long-quan-yi (LQY) and the browning-tolerant Jiang-du (JD). RNA-sequencing technology, combined with ultra-performance liquid chromatography electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS), was used to obtain transcriptome and metabolome data, which were subsequently analyzed using a series of bioinformatics approaches. Quantitative polymerase chain reaction (q-PCR) was used to validate gene expression.

Results: Compared with JD, the ROS levels and PPO activity were elevated in LQY. In the polyphenol metabolic pathway, 24 key enzyme genes including CuAO, PPO, and TDC, were identified. In the flavonoid metabolic pathway, 57 key structural genes, such as PAL, C3H, and 4CL, were identified. These genes showed different expression patterns between the two luffa varieties. Differentially expressed genes were mainly involved in the regulation of 34 MYB, 15 bHLH, 19 WD40, and 14 WRKY transcription factors. Further metabolomics analysis showed that the levels of polyphenol metabolites were upregulated in LQY, whereas the levels of flavonoid metabolites were upregulated in JD.

Discussion: This study integrated transcriptomic and metabolomics data to identify key genes, transcription factors and metabolic pathways associated with luffa browning. q-PCR analysis was performed to validate the expression of POD and MYB genes. These findings provide a theoretical foundation for further investigation into the molecular mechanisms underlying luffa browning and offer potential targets for genetic improvement or breeding strategies to enhance luffa quality.

Keywords: antioxidant enzyme; browning; flavonoid metabolism; metabolome; transcription factor; transcriptome.

Figure 1

Transcriptome analysis revealed distinct patterns of gene expression between varieties and across developmental stages. (A) Principal component analysis (PCA) of transcriptome revealed clear clustering within each variety and distinct separation between the two varieties. (B) Number of differentially expressed genes (DEGs) in pairwise comparisons between stages within each variety. (C) Heatmap analysis of the relative expression of all DEGs revealed variety-specific differences between LQY and JD. (D) Functional enrichment analysis of DEGs between varieties at the same developmental stage.

Figure 2

K-means cluster analysis and functional analysis of DEGs across developmental stages between LQY and JD. The first column represents the gene expression change trend of DEGs in each module, the second column presents the expression heatmap of genes in the module, the third column displays the enriched pathways of the genes in the module, and the fourth column shows the hub genes from the protein-protein interaction (PPI) network analysis of the genes in the module.

Figure 3

Expression changes of structural genes in polyphenol metabolic pathways at different developmental stages between LQY and JD. The heatmaps show the expression profiles of enzyme genes involved in tyrosol and hydroxytyrosol synthesis. PPO, polyphenol oxidase; DDC, DOPA decarboxylase; CuAO, copper-containing primary amine oxidase; ALDH, alcohol dehydrogenase; TDC, tyrosine decarboxylase; PAR, phenyl-acetaldehyde reductase.

Figure 4

Heatmaps displaying the expression patterns of transcription factor (TF) families associated with luffa browning in LQY and JD across different developmental stages. These include 34 MYB, 15 bHLH, 19 WD40, and 14 WRKY TFs.

Figure 5

K-means clustering of DAMs identified distinct patterns of metabolic accumulation between species. The first column represents the accumulation change trend of DAMs in each module. The second column shows the top five metabolic pathways in the module. The third column displays the interaction between key metabolites and genes in each module.

Figure 6

Quantitative real–time polymerase chain reaction (q-PCR) analysis of the expression of POD and MYB genes in luffa fruit samples across developmental stages. The x-axis represents different fruit samples, while the y-axis represents relative gene expression levels. Error bars indicate standard deviation. Statistical significance is denoted by asterisks (*p-value < 0.05, **p-value < 0.01, ***p-value < 0.001), while “NS” indicates non-significant differences (Student’s t-test).

Figure 7

Schematic diagram demonstrating the physiological and molecular differences between browning-sensitive LQY and browning-tolerant JD luffa varieties.