Integrative Utilization of Transcriptomics and Metabolomics Sheds Light on Disparate Growth Performance of Whiteleg Shrimp, Litopenaeus vannamei

Abstract

Litopenaeus vannamei is a key economic species in aquaculture, yet the molecular mechanisms underlying its growth variability remain unclear. This study conducted transcriptomic and metabolomic analyses of fast-growing (NL) and slow-growing (NS) shrimp under identical conditions. A total of 1280 differentially expressed genes (DEGs) related to protein processing, ribosomes, and oxidative phosphorylation, along with 5297 differentially abundant metabolites (DMs) involved in arginine biosynthesis, amino acid metabolism, and pantothenate and CoA biosynthesis, were identified and analyzed. An integrative analysis revealed that the NL shrimp exhibited an enhanced retinol, glutathione, riboflavin, and purine metabolism, which implies a higher tolerance to environmental stress. In contrast, the NS shrimp showed increased fatty acid degradation and an accelerated TCA cycle. This suggests that NS shrimp might require a substantial amount of energy to cope with environmental changes, consequently resulting in increased energy expenditures. This study provides significant insights into the molecular mechanisms underlying the growth disparity in L. vannamei, offering valuable data for future research aimed at optimizing shrimp growth performance and enhancing aquaculture productivity.

Keywords: Litopenaeus vannamei; growth trait; metabolomics; transcriptomics.

Figure 1

Growth performance of Litopenaeus vannamei. (A) Image showing varying growth rates in shrimp. (B) Statistical analysis of body weight (n = 5), with “**” indicating a significance level of p < 0.01.

Figure 2

Metabolomics data quality assessment. (A) PCA score plot for positive ion mode samples. (B) PCA score plot for negative ion mode samples. (C) OPLS-DA score plot for positive ion mode. (D) OPLS-DA score plot for negative ion mode. Q² and R²: coordinates where regression line intersects Y axis; same below. (E) OPLS-DA validation for positive ion mode. (F) OPLS-DA validation in negative ion mode.

Figure 3

Metabolomic analysis of Litopenaeus vannamei in NS and NL groups. (A) Heatmap of hierarchical clustering of differentially abundant metabolites (DMs) between NS and NL groups. (B) KEGG pathway analysis of differentially expressed metabolites between NL and NS groups. (C) Hierarchical clustering heatmap of differentially abundant metabolites. (D) Bubble map of metabolic pathways associated with differentially abundant metabolites.

Figure 4

Transcriptomic analysis of Litopenaeus vannamei in NS and NL groups. (A) Volcano plot of differentially expressed genes (DEGs) between NS and NL groups. (B) Validation of RNA-Seq gene expression data, presented as means ± SDs (n = 3). (C) Gene Ontology (GO) functional enrichment of DEGs. (D) KEGG pathway enrichment of DEGs.

Figure 5

A correlation analysis between the transcriptome and metabolome of Litopenaeus vannamei. (A) A heatmap showing the gene–metabolite correlations. The columns represent genes, and the rows represent metabolites. Red indicates positive correlations, and blue indicates negative correlations. “***” denotes p < 0.001. (B) An integrated metabolic network map of the DEGs and metabolites between the NS and NL groups. The DEGs are italicized, with orange (upregulated) and blue (downregulated) in the NL group. The DMs are in Romania, with red (upregulated) and green (downregulated) in the NL group.