Skin locations inference and body fluid identification from skin microbial patterns for forensic applications.

ABSTRACT

Given that microbiological analysis can be an alternative method that overcomes the shortcomings of traditional forensic technology, and skin samples may be the most common source of cases, the analysis of skin microbiome was investigated in this study. High-throughput sequencing targeting the V3-V4 region of 16S rRNA gene was performed to reveal the skin microbiome of healthy individuals in Guangdong Han. The bacterial diversity of the palm, navel, groin and plantar of the same individual was analyzed. The overall classification based on 16S rRNA gene amplicons revealed that the microbial composition of skin samples from different anatomical parts was different, and the dominant bacterial genus of the navel, plantar, groin and palm skin were dominated by Cutibacterium, Staphylococcus, Corynebacterium and Staphylococcus, respectively. PCoA analysis showed that the skin at these four anatomical locations could only be grouped into three clusters. A predictive model based on random forest algorithm showed the potential to accurately distinguish these four anatomical locations, which indicated that specific bacteria with low abundance were the key taxa. In addition, the skin microbiome in this study is significantly different from the dominant microbiome in saliva and vaginal secretions identified in our previous study, and can be distinguished from these two tissue fluids. In conclusion, the present findings on the community and microbial structure details of the human skin may reveal its potential application value in assessing the location of skin samples and the type of body fluids in forensic medicine.