# Transcriptomic and Metabolomic Joint Analysis Revealing Different Metabolic Pathways and Genes Dynamically Regulating Bitter Gourd (Momordica charantia L.) Fruit Growth and Development in Different Stages

## TL;DR

This study explores how bitter gourd fruit grows by combining gene and metabolite data, revealing key metabolic pathways and genes involved in different growth stages.

## Contribution

The study identifies specific metabolic pathways and genes dynamically regulating bitter gourd fruit growth across developmental stages.

## Key findings

- Fruit growth follows a 'slow-fast-slow' pattern with distinct metabolic activity changes.
- Glycolysis, fructose/mannose metabolism, and flavonoid biosynthesis pathways regulate growth stages.
- 53 genes and 12 metabolites were identified, with gene expression validated by quantitative PCR.

## Abstract

Insights into dynamic regulatory factors in various stages of growth and development can guide strategies for precision and targeted breeding. Bitter gourd, as a vegetable product with medicinal value, plays a role in both agricultural and medical fields. In this study, phenotypic observations, metabolomic and transcriptomic analyses, and differential gene expression patterns, along with a correlation analysis, were conducted in different stages of fruit growth and development. The results revealed that the growth rate of fruit’s fresh weight, length, diameter, and flesh thickness during the first seven days was slow, and that it then rapidly increased after the seventh day, and finally slowed once more after 17 days, indicating that the overall process followed a “slow–fast–slow” pattern. Transcriptomic and metabolomic analyses identified several differentially expressed genes and metabolites, and joint analyses revealed that each of the glycolysis/gluconeogenesis, fructose and mannose metabolism and flavonoid biosynthesis pathways individually play significant roles in the dynamic regulation of fruit growth and development during the early, middle, and late stages. Among these, 53 differentially expressed genes (DEGs) and 12 differentially expressed metabolites (DEMs) were found in these pathways. A total of 12 randomly selected DEGs were analyzed using quantitative PCR, and the results showed that gene expression levels were generally consistent with transcriptomic sequencing results, exhibiting dynamic changes with varying expression levels. Correlation analysis revealed that 11 DEMs were positively correlated with four traits except for arbutin, while eight DEGs were related to all traits, including six significantly positive and two significantly negative correlations. These findings enhance our understanding of the regulatory network governing yield and quality and provide substantial evidence to support improvements in breeding programs.