Population informative markers selected using Wright's fixation index and machine learning improves human identification using the skin microbiome

2021 ◽  
Author(s):  
Allison J. Sherier ◽  
August E. Woerner ◽  
Bruce Budowle
Keyword(s):  
Human Skin ◽  
Human Identification ◽  
Fixation Index ◽  
Support Vector ◽  
Skin Microbiome ◽  
Nucleotide Polymorphisms ◽  
Two Samples ◽  
Biological Evidence ◽  
Microbial Dna ◽  
Microbiome Profiling

Microbial DNA, shed from human skin, can be distinctive to its host and thus help individualize donors of forensic biological evidence. Previous studies have utilized single locus microbial DNA markers (e.g., 16S rRNA) to assess the presence/absence of personal microbiota to profile human hosts. However, since the taxonomic composition of the microbiome is in constant fluctuation, this approach may not be sufficiently robust for human identification (HID). Multi-marker approaches may be more powerful. Additionally, genetic differentiation, rather than taxonomic distinction, may be more individualizing. To this end, the non-dominant hands of 51 individuals were sampled in triplicate (n = 153). They were analyzed for markers in the hidSkinPlex, a multiplex panel comprising candidate markers for skin microbiome profiling. Single nucleotide polymorphisms (SNPs) with the highest Wright’s fixation index (F ST ) estimates were then selected for predicting donor identity using a support vector machine (SVM) learning model. F ST is an estimate of the genetic differences within and between populations. Three different SNP selection criteria were employed: SNPs with the highest-ranking F ST estimates 1) common between any two samples regardless of markers present (termed overall ); 2) each marker common between samples (termed per marker ); and 3) common to all samples used to train the SVM algorithm for HID (termed selected ). The SNPs chosen based on criteria for overall , per marker, and selected methods resulted in an accuracy of 92.00%, 94.77%, and 88.00%, respectively. The results support that estimates of F ST , combined with SVM, can notably improve forensic HID via skin microbiome profiling. IMPORTANCE There is a need for additional genetic information to help identify the source of biological evidence found at a crime scene. The human skin microbiome is a potentially abundant source of DNA that can enable the identification of a donor of biological evidence. With microbial profiling for human identification, there will be an additional source of DNA to identify individuals as well as to exclude individuals wrongly associated with biological evidence, thereby improving the utility of forensic DNA profiling to support criminal investigations.

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

2020 ◽  
Vol 8 (6) ◽  
pp. 873 ◽  
Author(s):  
Pamela Tozzo ◽  
Gabriella D’Angiolella ◽  
Paola Brun ◽  
Ignazio Castagliuolo ◽  
Sarah Gino ◽  
...  
Keyword(s):  
Human Identification ◽  
Spatial And Temporal Variability ◽  
Skin Microbiome ◽  
Forensic Evidence ◽  
Microbiome Analysis ◽  
Wide Range ◽  
Microbiome Research ◽  
Meta Analyses ◽  
High Degree ◽  
Microbiome Profiling

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.

scholarly journals Forensic Human Identification Using Skin Microbiomes

2017 ◽  
Vol 83 (22) ◽  
Author(s):  
Sarah E. Schmedes ◽  
August E. Woerner ◽  
Bruce Budowle
Keyword(s):  
Supervised Learning ◽  
Classification Accuracy ◽  
Formal Model ◽  
Human Identification ◽  
Attribute Selection ◽  
Skin Microbiome ◽  
Novel Approach ◽  
Long Time ◽  
Three Body ◽  
Microbiome Profiling

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.

scholarly journals Cutibacterium acnes (Propionibacterium acnes) 16S rRNA Genotyping of Microbial Samples from Possessions Contributes to Owner Identification

mSystems ◽  
2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Jiayue Yang ◽  
Tomoya Tsukimi ◽  
Mia Yoshikawa ◽  
Kenta Suzuki ◽  
Tomoki Takeda ◽  
...  
Keyword(s):  
16S Rrna ◽  
Human Skin ◽  
Rrna Gene ◽  
Skin Microbiome ◽  
Nucleotide Polymorphisms ◽  
Skin Microbiota ◽  
Content Type ◽  
Microbiome Profile ◽  
Cutibacterium Acnes ◽  
Genotype Composition

ABSTRACT The human skin surface harbors huge numbers of microbes. The skin microbiota interacts with its host and forms a skin microbiome profile that is specific for each individual. It has been reported that the skin microbiota that is left on an individual’s possessions can act as a sort of “fingerprint” and be used for owner identification. However, this approach needs to be improved to take into account any long-term instability of skin microbiota and contamination from nonspecific bacteria. Here, we took advantage of single-nucleotide polymorphisms (SNPs) in the 16S-encoding rRNA gene of Cutibacterium acnes, the most common and abundant bacterium on human skin, to perform owner identification. We first developed a high-throughput genotyping method based on next-generation sequencing to characterize the SNPs of the C. acnes 16S rRNA gene and found that the genotype composition of C. acnes 16S rRNA is individual specific. Owner identification accuracy of around 90% based on random forest machine learning was achieved by using a combination of C. acnes 16S rRNA genotype and skin microbiome profile data. Furthermore, our study showed that the C. acnes 16S rRNA genotype remained more stable over time than the skin microbiome profile. This characteristic of C. acnes was further confirmed by the analysis of publicly available human skin metagenome data. Our approach, with its high precision, good reproducibility, and low costs, thus provides new possibilities in the field of microbiome-based owner identification and forensics in general. IMPORTANCE Cutibacterium acnes is the most common and abundant bacterial species on human skin, and the gene that encodes its 16S rRNA has multiple single-nucleotide polymorphisms. In this study, we developed a method to efficiently determine the C. acnes 16S rRNA genotype composition from microbial samples taken from the hands of participants and from their possessions. Using the C. acnes 16S rRNA genotype composition, we could predict the owner of a possession with around 90% accuracy when the 16S rRNA gene-based microbiome profile was included. We also showed that the C. acnes 16S rRNA genotype composition was more stable over time than the skin microbiome profile and thus is more suitable for owner identification.

scholarly journals Divergence of a genomic island leads to the evolution of melanization in a halophyte root fungus

2021 ◽  
Author(s):  
Zhilin Yuan ◽  
Irina S. Druzhinina ◽  
John G. Gibbons ◽  
Zhenhui Zhong ◽  
Yves Van de Peer ◽  
...  
Keyword(s):  
Membrane Trafficking ◽  
Molecular Mechanisms ◽  
Genomic Island ◽  
Population Genomics ◽  
Growth Yield ◽  
Fixation Index ◽  
Nucleotide Polymorphisms ◽  
Evolutionary Mechanisms ◽  
Melanin Biosynthesis ◽  
Two Populations

AbstractUnderstanding how organisms adapt to extreme living conditions is central to evolutionary biology. Dark septate endophytes (DSEs) constitute an important component of the root mycobiome and they are often able to alleviate host abiotic stresses. Here, we investigated the molecular mechanisms underlying the beneficial association between the DSE Laburnicola rhizohalophila and its host, the native halophyte Suaeda salsa, using population genomics. Based on genome-wide Fst (pairwise fixation index) and Vst analyses, which compared the variance in allele frequencies of single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs), respectively, we found a high level of genetic differentiation between two populations. CNV patterns revealed population-specific expansions and contractions. Interestingly, we identified a ~20 kbp genomic island of high divergence with a strong sign of positive selection. This region contains a melanin-biosynthetic polyketide synthase gene cluster linked to six additional genes likely involved in biosynthesis, membrane trafficking, regulation, and localization of melanin. Differences in growth yield and melanin biosynthesis between the two populations grown under 2% NaCl stress suggested that this genomic island contributes to the observed differences in melanin accumulation. Our findings provide a better understanding of the genetic and evolutionary mechanisms underlying the adaptation to saline conditions of the L. rhizohalophila–S. salsa symbiosis.

scholarly journals Genomic Analyses of Globodera pallida, A Quarantine Agricultural Pathogen in Idaho

Pathogens ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 363
Author(s):  
Sulochana K. Wasala ◽  
Dana K. Howe ◽  
Louise-Marie Dandurand ◽  
Inga A. Zasada ◽  
Dee R. Denver
Keyword(s):  
Genetic Variation ◽  
Potato Production ◽  
Globodera Pallida ◽  
Fixation Index ◽  
Parasitic Nematodes ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Field Samples ◽  
Multiple Introduction

Globodera pallida is among the most significant plant-parasitic nematodes worldwide, causing major damage to potato production. Since it was discovered in Idaho in 2006, eradication efforts have aimed to contain and eradicate G. pallida through phytosanitary action and soil fumigation. In this study, we investigated genome-wide patterns of G. pallida genetic variation across Idaho fields to evaluate whether the infestation resulted from a single or multiple introduction(s) and to investigate potential evolutionary responses since the time of infestation. A total of 53 G. pallida samples (~1,042,000 individuals) were collected and analyzed, representing five different fields in Idaho, a greenhouse population, and a field in Scotland that was used for external comparison. According to genome-wide allele frequency and fixation index (Fst) analyses, most of the genetic variation was shared among the G. pallida populations in Idaho fields pre-fumigation, indicating that the infestation likely resulted from a single introduction. Temporal patterns of genome-wide polymorphisms involving (1) pre-fumigation field samples collected in 2007 and 2014 and (2) pre- and post-fumigation samples revealed nucleotide variants (SNPs, single-nucleotide polymorphisms) with significantly differentiated allele frequencies indicating genetic differentiation. This study provides insights into the genetic origins and adaptive potential of G. pallida invading new environments.

scholarly journals SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics

iScience ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 101925
Author(s):  
Shubham K. Jaiswal ◽  
Shitij Manojkumar Agarwal ◽  
Parikshit Thodum ◽  
Vineet K. Sharma
Keyword(s):  
Artificial Intelligence ◽  
Human Skin ◽  
Skin Microbiome ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

scholarly journals Complete Genome Sequence of Malassezia restricta CBS 7877, an Opportunist Pathogen Involved in Dandruff and Seborrheic Dermatitis

2019 ◽  
Vol 8 (6) ◽  
Author(s):  
Stanislas C. Morand ◽  
Morgane Bertignac ◽  
Agnes Iltis ◽  
Iris C. R. M. Kolder ◽  
Walter Pirovano ◽  
...  
Keyword(s):  
Human Skin ◽  
Genome Sequence ◽  
Complete Genome Sequence ◽  
Complete Genome ◽  
Seborrheic Dermatitis ◽  
Skin Microbiome ◽  
Basidiomycetous Yeasts ◽  
Content Type ◽  
Opportunist Pathogen ◽  
Malassezia Restricta

Malassezia restricta, one of the predominant basidiomycetous yeasts present on human skin, is involved in scalp disorders. Here, we report the complete genome sequence of the lipophilic Malassezia restricta CBS 7877 strain, which will facilitate the study of the mechanisms underlying its commensal and pathogenic roles within the skin microbiome.

scholarly journals Pre-digest of unprotected DNA by Benzonase improves the representation of living skin bacteria and efficiently depletes host DNA

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yacine Amar ◽  
Ilias Lagkouvardos ◽  
Rafaela L. Silva ◽  
Oluwaseun Ayodeji Ishola ◽  
Bärbel U. Foesel ◽  
...  
Keyword(s):  
Defense Mechanisms ◽  
Bacterial Load ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Skin Microbiome ◽  
Mock Community ◽  
Skin Bacteria ◽  
Α Diversity ◽  
Microbial Turnover ◽  
Microbial Dna

Abstract Background The identification of microbiota based on next-generation sequencing (NGS) of extracted DNA has drastically improved our understanding of the role of microbial communities in health and disease. However, DNA-based microbiome analysis cannot per se differentiate between living and dead microorganisms. In environments such as the skin, host defense mechanisms including antimicrobial peptides and low cutaneous pH result in a high microbial turnover, likely resulting in high numbers of dead cells present and releasing substantial amounts of microbial DNA. NGS analyses may thus lead to inaccurate estimations of microbiome structures and consequently functional capacities. Results We investigated in this study the feasibility of a Benzonase-based approach (BDA) to pre-digest unprotected DNA, i.e., of dead microbial cells, as a method to overcome these limitations, thus offering a more accurate assessment of the living microbiome. A skin mock community as well as skin microbiome samples were analyzed using 16S rRNA gene sequencing and metagenomics sequencing after DNA extraction with and without a Benzonase digest to assess bacterial diversity patterns. The BDA method resulted in less reads from dead bacteria both in the skin mock community and skin swabs spiked with either heat-inactivated bacteria or bacterial-free DNA. This approach also efficiently depleted host DNA reads in samples with high human-to-microbial DNA ratios, with no obvious impact on the microbiome profile. We further observed that low biomass samples generate an α-diversity bias when the bacterial load is lower than 105 CFU and that Benzonase digest is not sufficient to overcome this bias. Conclusions The BDA approach enables both a better assessment of the living microbiota and depletion of host DNA reads. Graphical abstract

Secretory Proteases of the Human Skin Microbiome

2021 ◽  
Author(s):  
Wisely Chua ◽  
Si En Poh ◽  
Hao Li
Keyword(s):  
Human Skin ◽  
Hydrolytic Enzymes ◽  
Microbial Interactions ◽  
Skin Microbiome ◽  
Protein Cleavage ◽  
Wide Range ◽  
Range Of Functions ◽  
Diverse Community ◽  
Secreted Proteases ◽  
Pathogenic Agents

The human skin is our outermost layer and serves as a protective barrier against external insults. Advances in next generation sequencing have enabled the discoveries of a rich and diverse community of microbes - bacteria, fungi and viruses that are residents of this surface. The genomes of these microbes also revealed the presence of many secretory enzymes. In particular, proteases which are hydrolytic enzymes capable of protein cleavage and degradation are of special interest in the skin environment which is enriched in proteins and lipids. In this minireview, we will focus on the roles of these skin-relevant microbial secreted proteases, both in terms of their widely studied roles as pathogenic agents in tissue invasion and host immune inactivation, and their recently discovered roles in inter-microbial interactions and modulation of virulence factors. From these studies, it has become apparent that while microbial proteases are capable of a wide range of functions, their expression is tightly regulated and highly responsive to the environments the microbes are in. With the introduction of new biochemical and bioinformatics tools to study protease functions, it will be important to understand the roles played by skin microbial secretory proteases in cutaneous health, especially the less studied commensal microbes with an emphasis on contextual relevance.

Sign in / Sign up

Export Citation Format

Share Document