Body fluid prediction from microbial patterns for forensic application. (September 2017)

• Record Type: Journal Article
• Authors: Hanssen, Eirik Nataas; Avershina, Ekaterina; Rudi, Knut; Gill, Peter; Snipen, Lars
• Is Part Of: Forensic science international. Volume 30(2017)
• Page Start: 10
• Page End: 17
• Publication Date: 2017-09
• Subjects: 00-01 -- 99-00***; Body fluid prediction -- Microbiome -- Massively parallel sequencing -- Principal component analysis -- Linear discriminant analysis; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2017.05.009
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: ⬢ We used standard 16S rRNA sequencing and pattern recognition for body fluid prediction. ⬢ We used saliva on skin as a study model. ⬢ There is a strong association between the obtained taxonomic profiles and body fluid. ⬢ In a cross-validation 94% of the samples were correctly classified as either saliva or skin. ⬢ The method needs optimization and standardization before being ready for casework. Abstract: The association of a DNA profile with a certain body fluid can be of essential importance in the evaluation of biological evidence. Several alternative methods for body fluid prediction have been proposed to improve the currently used presumptive tests. Most of them measure gene expression. Here we present a novel approach based on microbial taxonomic profiles obtained by standard 16S rRNA gene sequencing. We used saliva deposited on skin as a forensically relevant study model, but the same principle can be applied for predicting other bacteria rich body fluids. For classification we used standard pattern recognition based on principal component analysis in combination with linear discriminant analysis. A cross-validation of the experimental data shows that the new method is able to successfully classify samples from saliva deposited on skin and samples from pure skin in 94% of the cases. We found that there is a person-effect influencing the result, especially from skin, indicating that a reference sample of pure skin microbiota from the same person could improve accuracy. In addition the pattern recognition methods could be further optimized. Although there is room for improvement, this study shows the potential of microbial profiles as a new forensic tool for body fluid prediction.