Abstract
Revealing individual characteristics is supportive for identifying individuals in forensic crime. As saliva is one of the most common biological samples used in crime scenes, it is important to make full use of the rich individual information contained in saliva. The aim of this study was to explore the application of the microbiome in forensic science by analysing differences in the salivary microbiome and metabolome of healthy individuals with different dietary habits. We performed 16S rDNA sequencing analysis based on oral saliva samples collected from 12 vegetarians, 12 seafood omnivores and 12 beef and lamb omnivores. Non-targeted metabolomics analyses were also performed based on saliva samples from healthy individuals. The results showed that the dominant flora of vegetarians was dominated by Neisseria (belonging to the phylum Proteobacteria), while seafood omnivores and beef and lamb omnivores were dominated by Streptococcus (belonging to the phylum Firmicutes). NDMS-based and cluster analyses showed that vegetarian dieters were significantly differentiated from meat dieters (seafood omnivores and beef and lamb omnivores), which may be related to the fact that high-fiber diets can create a different salivary flora structure. Variants were also detected in salivary metabolic pathways, including positive correlations with Lipid metabolism, Amino acid metabolism, Carbohydrate metabolism, and Nucleotide metabolism in vegetarians, and correlations in seafood omnivores. In order to select salivary microorganisms and metabolic markers that can distinguish different dietary profiles, a random forest classifier model was constructed in this study, and the results showed that individuals with different dietary profiles could be successfully distinguished based on the core genera and metabolites such as Streptococcus, Histidinyl-Valine. Our study provides a supportive basis for the application of salivary polyomics in order to reveal the dietary characteristics of individuals for forensic investigation and crime solving.
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