Skin Microbiome Analysis for Forensic Human Identification: What Do We Know So Far?

Pamela Tozzo,Luciana Caenazzo,Sarah Gino,Paola Brun,Gabriella D’Angiolella,Ignazio Castagliuolo

doi:10.3390/microorganisms8060873

Abstract

Microbiome research is a highly transdisciplinary field with a wide range of applications and methods for studying it, involving different computational approaches and models. The fact that different people host radically different microbiota highlights forensic perspectives in understanding what leads to this variation and what regulates it, in order to effectively use microbes as forensic evidence. This narrative review provides an overview of some of the main scientific works so far produced, focusing on the potentiality of using skin microbiome profiling for human identification in forensics. This review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The examined literature clearly ascertains that skin microbial communities, although personalized, vary systematically across body sites and time, with intrapersonal differences over time smaller than interpersonal ones, showing such a high degree of spatial and temporal variability that the degree and nature of this variability can constitute in itself an important parameter useful in distinguishing individuals from one another. Even making the effort to organically synthesize all results achieved until now, it is quite evident that these results are still the pieces of a puzzle, which is not yet complete.

Highlights

The mistaken belief that the human body hosts microbes that outnumber our somatic and germ cells by an estimated 10-fold led to an increasing interest in characterizing human microbiota—namely, all the bacteria inhabiting the human body in health or disease [1]—to better understand the balance between human and microbial components, predominantly bacteria, with fungi, viruses, and protists [2]
This study showed that, considering classifier accuracy as a function of nearest neighbor (NN) and reverse nearest neighbor (rNN) distance, the proportion of correctly classified samples was consistently greater using the Euclidean distance on nucleotide diversity than the proportion obtained with the patristic distance approach (54% and 63% accuracy, respectively)
The skin is the largest organ of the human body and it is colonized by millions of microorganisms, whose composition is influenced by many factors, both endogenous, depending on the host, and exogenous, depending on the environment

Summary

Introduction

The mistaken belief that the human body hosts microbes that outnumber our somatic and germ cells by an estimated 10-fold led to an increasing interest in characterizing human microbiota—namely, all the bacteria inhabiting the human body in health or disease [1]—to better understand the balance between human and microbial components, predominantly bacteria, with fungi, viruses, and protists [2]. It was demonstrated that the microbial cells which colonize the human body (i.e., microbiota) are at least as abundant as our somatic ones, with a more realistic estimated ratio of bacteria to human cells of about 1.3 [3]. Considering that the human body harbors 500–1000 different species of bacteria and each bacterial strain has a genome containing thousands of genes, it is clear that the total DNA content of microbes inhabiting our bodies, which is the microbiome [1], offers much more genetic diversity than the human genome [4,5]. Using NGS to sequence total DNA extracts from any sample allows both the sequencing of the whole genome of a given microorganism and the examination of whole communities of microbes, with the possibility of rapidly and efficiently identifying all different bacterial taxa and strains, thereby obtaining an overview of the resident microbial population [9,10]

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Discussion

Conclusion