Forensic Human Identification Using Skin Microbiomes

Abstract

The human microbiome contributes significantly to the genetic content of the human body. Genetic and environmental factors help shape the microbiome, and as such, the microbiome can be unique to an individual. Previous studies have demonstrated the potential to use microbiome profiling for forensic applications; however, a method has yet to identify stable features of skin microbiomes that produce high classification accuracies for samples collected over reasonably long time intervals. A novel approach is described here to classify skin microbiomes to their donors by comparing two feature types: Propionibacterium acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. Supervised learning was used to attribute skin microbiomes from 14 skin body sites from 12 healthy individuals sampled at three time points over a >2.5-year period with accuracies of up to 100% for three body sites. Feature selection identified a reduced subset of markers from each body site that are highly individualizing, identifying 187 markers from 12 clades. Classification accuracies were compared in a formal model testing framework, and the results of this analysis indicate that learners trained on nucleotide diversity perform significantly better than those trained on presence/absence encodings. This study used supervised learning to identify individuals with high accuracy and associated stable features from skin microbiomes over a period of up to almost 3 years. These selected features provide a preliminary marker panel for future development of a robust and reproducible method for skin microbiome profiling for forensic human identification.IMPORTANCE A novel approach is described to attribute skin microbiomes, collected over a period of >2.5 years, to their individual hosts with a high degree of accuracy. Nucleotide diversities of stable clade-specific markers with supervised learning were used to classify skin microbiomes from a particular individual with up to 100% classification accuracy for three body sites. Attribute selection was used to identify 187 genetic markers from 12 clades which provide the greatest differentiation of individual skin microbiomes from 14 skin sites. This study performs skin microbiome profiling from a supervised learning approach and obtains high classification accuracy for samples collected from individuals over a relatively long time period for potential application to forensic human identification.

Keywords: forensic profiling; human identification; metagenomics; skin microbiome; supervised learning.

FIG 1

Proportional relative abundances of core skin microbiome taxa from 14 skin body sites. The core skin microbial taxa include prokaryotic, eukaryotic, and viral microbial species common to all samples (i.e., all individuals and time points) with ≥1% average relative abundance at each body site.

FIG 2

Maximum-likelihood phylogenies of P. acnes strains from all individuals and time points sampled from the antecubital fossa (Ac) (A) and cheek (Ch) (B). Phylogenetic trees were constructed using 200 P. acnes species-specific markers.

FIG 3

Classification accuracies of host individuals using P. acnes pangenome gene presence/absence features. (A and B) RMLR (A) and 1NN (B) classification, with (y axis) or without attribute selection (x axis), were used to attribute microbiomes from three time points (spanning >2.5 years) to their individual donor for 14 skin body sites. The red dashed lines represent the average predictive accuracies by random chance (10.1%).

FIG 4

PCA depicting the variance across skin microbiomes sampled from the antecubital fossa (Ac) (A) and cheek (Ch) (C) using the nucleotide diversity of shared clade-specific markers (242 and 252 markers, respectively) at each body site and using the nucleotide diversity from selected features using correlated feature selection to reduce the number of features to 27 markers at the Ac site (B) and 31 markers at the Ch site (D).

FIG 5

Classification accuracies of host individuals using nucleotide diversities of clade-specific markers shared by all individuals at each time point. (A and B) RMLR (A) and 1NN (B) classification, with (y axis) or without (x axis) attribute selection, were used to attribute microbiomes from three time points (spanning >2.5 years) to their individual donor for 14 skin body sites. The red dashed lines represent the average predictive accuracies by random chance (10.1%).

FIG 6

Comparison of classification accuracies from RMLR and 1NN analyses, with or without attribute selection (AttSelect), using P. acnes pangenome presence/absence features and nucleotide diversities of stable clade-specific markers. The red dashed lines represent the average predictive accuracies by random chance (10.1%).