Applied and Environmental MicrobiologyMicrobiologyPeer-reviewed journal article
Microbiome determinants of productivity in whiteleg shrimp aquaculture
Xiaoyu ShaniD, Kunying Li, Patrizia Stadler, Martha BorboriD, Guillermo ReyesiD, Ramiro SolórzanoiD, Esmeralda Chamorro LegardaiD, Bonny BayotiD, Otto X. CorderoiD
Abstract
Aquaculture holds immense promise for addressing the food needs of our growing global population. Yet, a quantitative understanding of the factors that control its efficiency and productivity has remained elusive. In this study, we address this knowledge gap by focusing on the microbiome determinants of productivity, more specifically animal survival and growth, for one of the most predominant animal species in global aquaculture, whiteleg shrimp (Penaeus vannamei). Through analysis of the shrimp-associated microbiome from 610 aquaculture samples across Asia and Latin America, we established the presence of core phylogenetic groups, widely prevalent across aquaculture conditions in disparate geographic locations and including both pathogenic and beneficial microbes. Focusing on early stages of growth (larval hatcheries), we showed that the composition of the microbiome alone can predict approximately 50% of the variation in shrimp larvae survival rates. Taxa associated with high survival rates share recently acquired genes that appear to be specific to aquaculture conditions. These genes are involved in the biosynthesis of growth factors and in protein degradation, underscoring the potential role of beneficial microorganisms in nutrient assimilation. In contrast, the predictability of the microbiome on the adult shrimp weight in grow-out farms is weaker (10-20%), akin to observations in the context of livestock. In conclusion, our study unveils a novel avenue for predicting productivity in shrimp aquaculture based on microbiome analysis. This paves the way for targeted manipulation of the microbiome as a strategic approach to enhancing aquaculture efficiency from the earliest developmental stages.
- organism: non-human primate
- assay: metagenomics
- research focus: ecology & biodiversity
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 14
- FWCI
- 4.72
- Field percentile
- top 4%
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