Identification of Occupations in Different Populations Based on Skin Microbial Characteristics

Area of Science:

• Microbiology
• Forensic Science
• Human Microbiome Research

Background:

• The working environment significantly shapes the human skin microbiome.
• Previous research indicates microbial communities on personal items can identify owners.
• Limited studies have explored using skin microbiomes for occupation identification or forensic applications.

Purpose of the Study:

• To characterize skin microbial communities across different occupations.
• To evaluate the potential of skin and cuff microbiomes for occupation identification.
• To assess the feasibility of using microbial data as a forensic tool.

Main Methods:

• Collected palm and cuff swabs from cooks, medical staff, and students.
• Performed next-generation sequencing of the 16S rRNA gene to analyze microbial communities.
• Utilized principal coordinate analysis and linear discriminant analysis effect size for community comparison and biomarker identification.
• Developed a random forest model to predict occupation based on microbial data.

Main Results:

• Distinct skin microbial community compositions were observed across the three occupational groups.
• Specific microbial biomarkers were identified for cooks, medical staff, and students.
• Microbial transfer from palm to cuffs was evident, with cuff samples also showing occupational signatures.
• A random forest model achieved prediction accuracies of up to 76.92% for palm and 73.33% for cuff samples.

Conclusions:

• Occupation significantly influences the skin microbiome composition.
• Both skin and clothing cuff microbiomes can serve as indicators of an individual's occupation.
• The findings support the potential of skin microbiota as a novel forensic tool for occupation prediction.