Unlocking microbiota potential: the role of organic copper in enhancing healthy white shrimp (Penaeus vannamei) farming

Abstract

Background: Microbiota sequencing has emerged a powerful tool for advancing aquatic nutrition research. However, few studies have comprehensively investigated the host microbiota's response to trace minerals. This study examined the role of organic copper supplementation in promoting the health of farmed white shrimp (Penaeus vannamei) from a microbiota perspective.

Results: In an 8-week feeding trial, shrimp were fed diets supplemented with no copper, 30 mg/kg inorganic copper (CuSO₄·5H₂O) or organic copper (Cu-proteinate). The apparent digestibility coefficients of copper and zinc, along with carbon and nitrogen assimilation, were determined. The V3-V4 region of the 16S rRNA gene was sequenced from feeds, intestines, gills, and water samples. Shrimp that fed the organic copper diet demonstrated healthier physiological status, higher apparent digestibility coefficients of both copper and zinc, as well as greater accumulation of copper, zinc, carbon, and nitrogen. The organic copper group exhibited distinct microbial diversity and a more complex microbial co-occurrence network, characterized by enhanced natural connectivity and robustness. Keystone taxa, including Vibrio, Candidatus_Bacilloplasma, and Photobacterium, contributed to network stability. Taxa associated with nutrient metabolism, including Butyricicoccus, Lactobacillus, and genera in the family Lachnospiraceae, Prevotellaceae, Rikenellaceae and Ruminococcaceae, were significantly enriched, correlating well with improved nutritional profiles. In accordance, functional annotation revealed that the organic copper group exhibited higher abundances of functional modules associated with nutrient and energy metabolism such as carbon and nitrogen cycling. Furthermore, host-selective pressure shaped the unique microbiota composition in the intestine and gill, which differed from the surrounding water and water source, with the gill microbiota potentially serving as a transitional bridge shaping the intestinal microbiota.

Conclusions: More stable host microbiota, enriched nutrient-metabolizing taxa, and enhanced ecological cycling in this study provide a potential strategy for innovative aqua-feed development. Our findings offer novel microbiota-centric insights into the role of organic copper in healthy shrimp farming.

Keywords: Penaeus vannamei; Healthy farming; Host microbiota; Microbiota community stability; Microbiota functions; Microecological niches; Organic copper.

Fig. 1

Effects of organic copper on the nutritional composition and physiological health of white shrimp. Z-score normalization was applied to each index when constructing heatmaps. Clustering in the heatmap was performed using the Bray–Curtis distance with the average linkage method. The means ± SD are provided in Table S2. ^a,b,c Values not sharing the same superscript letter of each row are significantly different (P < 0.05). ADC, apparent digestibility coefficient; ATP7b like, copper-transporting ATPase 2 like; C, carbon; Cu, copper; N, nitrogen; Zn, zinc. Italic text indicates qPCR results. Health status-related indicators were illustrated in Fig. S1. Group information: C0, no Cu supplementation; S30, 30 mg/kg inorganic Cu supplementation; O30, 30 mg/kg organic Cu supplementation

Fig. 2

The merged plot of the top 10 phylum (A) and genus of all groups (B) in Fig. S5. Taxa not assigned to genus level are preceded by a letter (c__, class and f__, family) indicating the level to which they are assigned. Clustering was performed using the Bray–Curtis distance with the average linkage method. The numbers on the cells are the average relative abundance (%). ‘ − ’ indicates the feature did not exist in that group. WS: water source. Sample groups were designated using prefixes to denote sample types: ‘I’ for intestinal samples, ‘G’ for gill samples, and ‘W’ for water samples. Treatment groups consisted of C0 (control, no Cu supplementation), S30 (30 mg/kg inorganic Cu supplementation), and O30 (30 mg/kg organic Cu supplementation)

Fig. 3

Heatmap (A) and Venn plots (B–D) of core features of the intestine, gill, water, and WS. Taxa not assigned to genus level are preceded by a letter (c__, class and f__, family) indicating their assigned level. Clustering was performed using the Bray–Curtis distance with the average linkage method. The numbers on the cells are the average relative abundance (%). ‘ − ’ indicates the feature was not core in that group. WS: water source. Sample groups were designated using prefixes to denote sample types: ‘I’ for intestinal samples, ‘G’ for gill samples, and ‘W’ for water samples. Treatment groups consisted of C0 (control, no Cu supplementation), S30 (30 mg/kg inorganic Cu supplementation), and O30 (30 mg/kg organic Cu supplementation)

Fig. 4

Alpha diversity analysis across the sample types of intestine, gill, water, and WS. Z-score normalization was applied to each alpha diversity index. Clustering in the heatmap was performed using the Bray–Curtis distance with the average linkage method. The means ± SD are provided in Table S4. ^a,b,c Values not sharing the same superscript letter of each column are significantly different (P < 0.05). PD: phylogenetic diversity; WS: water source. Group information: C0, no Cu supplementation; S30, 30 mg/kg inorganic Cu supplementation; O30, 30 mg/kg organic Cu supplementation

Fig. 5

Principal coordinate analysis (PCoA) plots across all sample types (A) and UPGMA clustering trees (B) based on unweighted UniFrac distances across all groups. R² and P values were the results of the overall permutational MANOVA test. WS: water source. Sample groups were designated using prefixes to denote sample types: ‘I’ for intestinal samples, ‘G’ for gill samples, and ‘W’ for water samples. Treatment groups consisted of C0 (control, no Cu supplementation), S30 (30 mg/kg inorganic Cu supplementation), and O30 (30 mg/kg organic Cu supplementation)

Fig. 6

Microbiota co-occurrence network analysis of Diets C0, S30 and O30. Each diet group contained the intestine, gill, and water sample types. Clustering of A and B was performed using the Bray–Curtis distance with the average linkage method based on the microbial co-occurrence networks parameters provided in Table 2. A: nodes colored by the phylum taxonomy. B: nodes colored by the niche breadth of microbial community (NBMC) taxonomy. Labels represent the key features; label colors represent the NBMC of key features. C: PCA plot based on the NBMC of the nodes. D: Sankey diagram: key features and their ecological functions based on the FAPROTAX database. c__, class and f__, family. E: natural connectivity of the network. Numbers within the graph represent the proportion of decrease in connectivity. Sample groups were designated using prefixes to denote sample types: ‘I’ for intestinal samples, ‘G’ for gill samples, and ‘W’ for water samples. Treatment groups consisted of C0 (control, no Cu supplementation), S30 (30 mg/kg inorganic Cu supplementation), and O30 (30 mg/kg organic Cu supplementation)

Fig. 7

Significant association features between microbial taxa and diets, physiological indicators, and potential ecological functions of these features (FAPROTAX database). Color legends were calculated using − log(q-value) × sign(coefficient). Cells indicating significant associations are colored (red or blue) and overlaid with + or − symbols to indicate a higher ( +) or lower ( −) relative abundance of microbial taxa when two Diets are compared (the left 3 columns, the references of Diets are brown), or positive ( +) or negative ( −) correlation between the relative abundance of microbial taxa and physiological indicators. E_, Eubacterium_. Taxa not assigned to genus level are preceded by a letter (o__, order; f__, family) indicating their assigned level. Group information: C0, no Cu supplementation; S30, 30 mg/kg inorganic Cu supplementation; O30, 30 mg/kg organic Cu supplementation

Fig. 8

Heatmap of the abundance of potential ecological functions based on the FAPROTAX database. A: summary of annotated functions. B: annotated functions. The numbers on the cells of A and B are the average relative abundance (%) of features with relevant functions. Z-score normalization was applied to each index. C: PCA analysis based on the functional abundance composition shown in B. ‘ − ’ indicates the feature did not exist in that group. Sample groups were designated using prefixes to denote sample types: ‘I’ for intestinal samples, ‘G’ for gill samples, and ‘W’ for water samples. Treatment groups consisted of C0 (control, no Cu supplementation), S30 (30 mg/kg inorganic Cu supplementation), and O30 (30 mg/kg organic Cu supplementation)

Fig. 9

The boxplot of differential KEGG pathways of interest among groups. ^a,b,c Value boxes not sharing the same superscript letter are significantly different (Q < 0.25). Sample groups were designated using prefixes to denote sample types: ‘I’ for intestinal samples, ‘G’ for gill samples, and ‘W’ for water samples. Treatment groups consisted of C0 (control, no Cu supplementation), S30 (30 mg/kg inorganic Cu supplementation), and O30 (30 mg/kg organic Cu supplementation)