Host genome drives the microbiota enrichment of beneficial microbes in shrimp: exploring the hologenome perspective

Abstract

Background: Pacific Whiteleg shrimp (Litopenaeus vannamei) is an important model for breeding programs to improve global aquaculture productivity. However, the interaction between host genetics and microbiota in enhancing productivity remains poorly understood. We investigated the effect of two shrimp genetic lines, Fast-Growth (Gen1) and Disease-Resistant (Gen2), on the microbiota of L. vannamei.

Results: Using genome-wide SNP microarray analysis, we confirmed that Gen1 and Gen2 represented distinct genetic populations. After confirming that the rearing pond did not significantly influence the microbiota composition, we determined that genetic differences explained 15.8% of the microbiota variability, with a stronger selective pressure in the hepatopancreas than in the intestine. Gen1, which exhibited better farm productivity, fostered a microbiota with greater richness, diversity, and resilience than Gen2, along with a higher abundance of beneficial microbes. Further, we demonstrated that a higher abundance of beneficial microbes was associated with healthier shrimp vs. diseased specimens, suggesting that Gen1 could improve shrimp's health and productivity by promoting beneficial microbes. Finally, we determined that the microbiota of both genetic lines was significantly different from their wild-type counterparts, suggesting farm environments and selective breeding programs strongly alter the natural microbiome.

Conclusions: This study highlights the importance of exploring the hologenome perspective, where integrating host genetics and microbiome composition can enhance breeding programs and farming practices.

Keywords: Breeding; Genetic line; Metagenomics; Microbiome; Microbiota-host interactions.

Fig. 1

Experimental design for sample collection in the shrimp farm Camarones El Renacimiento. Satellite overview of sample collection sites in Sinaloa, Mexico (CNES/Airbus©, 2020). Ponds A and B were located separated from each other and contained shrimps from the same genetic line (Gen1), while pond C was located next to pond A and contained shrimps from the second genetic line (Gen2). Below the map we show a diagram of the anatomical location of the dissected hepatopancreas and intestine

Fig. 2

Genetic variability between both genetic lines. A ADMIXTURE analysis (K = 2) of nine shrimp samples from each pond. The colors red and green indicate the two genetic populations represented in the samples. Samples from Ponds A and B correspond to the shrimps from Gen1, while samples from Pond C correspond to shrimps from Gen2. B Multidimensional scaling analysis (MDS) with samples tagged by genetic line

Fig. 3

Beta and alpha diversity and niche breadth analyses of microbiota composition. Unweighted principal coordinate analysis (PCoA) of UniFrac distances representing the microbiota variability in samples tagged by A pond, B organ, and C genetic line. The ANOSIM R and p values are indicated above each graph. Boxplots showing the distribution for D Chao1, E Shannon index, and F Niche breadth estimation for the microbiota in both genetic lines. All graphs consider both organs from pond A and C. Statistical differences between groups were evaluated with a Mann–Whitney test using a 95% confidence level of p < 0.05

Fig. 4

Significantly enriched taxas in each genetic line using LEfSe analysis. A hepatopancreas and B intestine. The heat maps represent the relative abundance of each bacteria in all samples

Fig. 5

Distribution of the relative abundance of beneficial microbes for each organ, genetic line and health status. The boxplots compare the relative abundance (log10) of beneficial microbes at species level between Gen1 and Gen2 in A hepatopancreas, B intestine, and C between the intestine of healthy and diseased shrimps. The statistical differences were determined with a Wilcoxon test (p < 0.05)

Fig. 6

Genetic and microbiota variability between both genetic lines and wild-type shrimps. A Multidimensional scaling analysis (MDS) representing the genetic variability in samples tagged by genetic line and wild-type origin. B Unweighted principal coordinate analysis (PCoA) of UniFrac distances representing the microbiota variability with samples tagged by genetic line and wild-type origin. The ANOSIM R and p values are indicated above the graph

Fig. 7

Graphic representation of the insights obtained in this study. Some image elements were created in BioRender [89] https://BioRender.com/vivea2r