Species level and SNP profiling of skin microbiome improve the specificity in identifying forensic fluid and individual

Abstract

Human skin possesses individual and body fluid-specific microbial signatures potentially useful for forensic identification. Previous studies mostly attribute individuals based on the relative abundance of microbiota at single time point, however fluctuations in taxonomy and phylogenetic structure may cause this to be unreliable. In this study, we assessed the skin microbiome of individuals at consecutive time-point from fingers, palm, arm and forehead sites using full-length 16S rRNA gene sequencing. At the species level, hand samples (fingers, palm, arm) differed significantly from forehead microbes. Additionally, skin flora of the present study differed significantly from the dominant species that have been reported for saliva, feces, and vaginal secretions samples. ANOSIM analysis of all skin samples showed that inter-individual differences were greater than intra-individual differences, yet accuracy of individual identification was only 52.5 %. At the microbial gene level, three machine learning models based on single nucleotide polymorphism (SNP) profiles of Cutibacterium acnes resulted in accurate classification of more than 97.5 % individuals. These results indicate that consideration of bacterial SNP profiling may provide new directions for forensic identification and may have potential applications in body fluid identification and individual identification in forensic.