Exploiting the gut microbiota to predict the origins and quality traits of cultured sea cucumbers

Abstract

Summary Nowadays, the true economic and nutritional value of food is underpinned by both origin and quality traits, more often expressed as increased quality benefits derived from the origin source. Gut microbiota contribute to food metabolism and host health, therefore, it may be suitable as a qualifying indicator of origin and quality of economic species. Here, we investigated relationships between the gut microbiota of the sea cucumber (Apostichopus japonicus), a valuable aquaculture species in Asia, with their origins and quality metrics. Based on data from 287 intestinal samples, we generated the first biogeographical patterns for A. japonicus gut microbiota from origins across China. Importantly, A. japonicus origins were predicted using the random forest model that was constructed using 20 key gut bacterial genera, with 97.6% accuracy. Furthermore, quality traits such as saponin, fat and taurine were also successfully predicted by random forest models based on gut microbiota, with approximately 80% consistency between predicted and true values. We showed that substantial variations existed in the gut microbiota and quality variables in A. japonicus across different origins, and we also demonstrated the great potential of gut microbiota to track A. japonicus origins and predict their quality traits.

Host Species Influence the Gut Microbiota of Endemic Cold-Water Fish in Upper Yangtze River

“…On the contrary, neutral theories consider that stochastic processes (e.g., birth, death, immigration, and limited dispersal) shape the microbial community structure ( Rosindell et al, 2011 ; Zhou and Ning, 2017 ). Numerous studies have reported that stochastic processes play an important role in shaping the microbial community structure of the environment ( Chen et al, 2019 ), and in animals ( Zhao et al, 2022 ). However, it is challenging to quantify the relative importance of stochastic processes due to the diversity and complexity of microorganisms.…”

Accurate prediction of huanglongbing occurrence in citrus plants by machine learning-based analysis of symbiotic bacteria

“…In addition, supervised learning methods have identified robust and reproducible features relevant for diagnosing shrimp diseases according to meta-analyses of gut microbiota ( Sha et al., 2022 ). The origin and quality of sea cucumber cultured in diverse geographic regions has also been accurately predicted using random forest models for gut microbiota ( Zhao et al., 2022 ). Furthermore, supervised machine learning approaches have accurately predicted environmental health variables following analyses of microbiome data ( Zhou et al., 2022b ).…”

Succession and assembly mechanisms of seawater prokaryotic communities along an extremely wide salinity gradient

“…The primers, 341F and 806R, were applied to amplify the V3‐V4 regions of prokaryotic 16S rRNA gene from each extracted DNA sample (Berg et al, 2012). The processes of PCR and sequencing library construction were consisting to our previous study (Zhao et al, 2022). These libraries were sequenced at BIOZERON Biotech.…”

“…Network analysis can reveal the consequences of microbial community changes, but the underlying mechanisms behind these changes remain unclear. In accordance with the microbial ecological theories, the balance of two ecological processes, namely deterministic and stochastic, shapes the assembly of microbial communities (Zhao et al, 2022). When communities are governed by deterministic processes, the presence of species can be predictable because species occupy specific ecological niches (Vanwonterghem et al, 2014).…”