Gilthead Sea Bream Gut Bacteriome as a valuable tool for seafood provenance analysis

The increasing demand for high-quality seafood, underscores the significant challenges posed by rampant seafood fraud. This study aimed to identify regional capture biomarkers by using the gut bacteriome of Sparus aurata specimens through state-of-the-art long-read sequencing allied to machine learning tools. The gut bacteriomes of animals from four different fishing areas on the Portuguese coast were sequenced.The alpha and beta diversity analyses were shown to enable Center-South gut bacteriome differentiation from other fishing areas due to higher abundance of species of the phyla Pseudomonadota, Bacteroidota, and Bacillota and classes such as Alphaproteobacteria, Betaproteobacteria and Bacilli.The gradient boosting machine (GBM) model selected by the H2O automatic machine learning pipeline presented a high global accuracy during training and validation phases, identifying Center-South and South sample provenance with 100 % and 71.1 % accuracy, respectively. By integrating the most important OTUs to the GBM model with the regional biomarkers identified through point biserial correlation analysis (indicspecies packages), a reduced set of five provenance biomarkers was identified, belonging to Gammaproteobacteria, Betaproteobacteria, and Bacilli classes, possibly highlighting the anthropogenic activities surrounding the fishing areas and local environmental abiotic factors.This study highlights the extensive and valuable information obtained by long-read sequencing and couples it with the potential of machine learning algorithms to ultimately demonstrate its efficiency in providing efficient and highly accurate seafood provenance biomarkers. This study also reports the likely influence of industrial and recreational activities, population density, and water management facilities on the gut bacteriome of S. aurata.