Identification of Occupations in Different Populations Based on Skin Microbial Characteristics.

Abstract

The composition and diversity of the skin microbiome are affected by several factors, including the working environment, which plays an active role in shaping microbial communities in human skin. Previous studies have shown that residual microbial communities on personal items can be used to identify their owners. However, few studies have used the skin microbiome to identify occupations in different populations or evaluate whether the skin microbiome can be used as a tool for forensic investigations. Here, we collected palm and cuff swabs from three occupational groups-cooks, medical staff, and students-and performed next-generation sequencing targeting the 16S rRNA gene to characterise the microbial communities associated with each profession. We found that different occupational environments resulted in different skin microbial community compositions. Actinobacteria and Firmicutes were the dominant phyla in the student samples. Compared with the other two occupations, cooks had the highest relative abundances of Bacteroides and Cyanobacteria. Additionally, cuff samples from medical staff had the highest relative abundances of Proteobacteria. Principal co-ordinate analysis results indicated that the samples were roughly divided into three clusters according to their occupation. Furthermore, linear discriminant analysis effect size results showed that cooks, medical staff, and students had their own unique biomarkers, cooks exhibited seven shared biomarkers between palm and cuff samples, medical staff showed 2, while students demonstrated the highest congruence which was 13 shared biomarkers. This suggested that some palm skin microbial communities could be transferred to the cuffs through contact friction. Thus, there were also microbial communities present in cuff samples that could be used to identify the owner's occupations, suggesting that skin microorganisms left on personal items via daily contact could also be used to provide information about an individual's occupation. Finally, we constructed a random forest model based on the composition and relative abundance of the microbiota to infer the subject's occupation, achieving an accuracy of 76.92% for the palm testing dataset and 73.33% for the cuff testing dataset; all of the cuff sample datasets showed an accuracy of 70.97%. These findings suggested that an individual's occupation can be inferred not only from the skin microbiota but also from the microbiota left on the cuffs of the individual's clothes. Further studies are needed; however, these results demonstrate the potential of the skin microbiota as a forensic tool for predicting population occupations.