Dataset Information

Random whole metagenomic sequencing for forensic discrimination of soils.

ABSTRACT: Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

SUBMITTER: Khodakova AS

PROVIDER: S-EPMC4128759 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Random whole metagenomic sequencing for forensic discrimination of soils.

Khodakova Anastasia S AS Smith Renee J RJ Burgoyne Leigh L Abarno Damien D Linacre Adrian A

PloS one 20140811 8

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic pro ...[more]

PMID: 25111003

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Project description:Consumption of fresh bagged spinach contaminated with Shiga toxin-producing Escherichia coli (STEC) has led to severe illness and death; however current culture-based methods to detect foodborne STEC are time consuming. Since not all STEC strains are considered pathogenic to humans, it is crucial to incorporate virulence characterization of STEC in the detection method. In this study, we assess the comprehensiveness of utilizing a shotgun metagenomics approach for detection and strain-level identification by spiking spinach with a variety of genomically disparate STEC strains at a low contamination level of 0.1 CFU/g. Molecular serotyping, virulence gene characterization, microbial community analysis, and E. coli core gene single nucleotide polymorphism (SNP) analysis were performed on metagenomic sequence data from enriched samples. It was determined from bacterial community analysis that E. coli, which was classified at the phylogroup level, was a major component of the population in most samples. However, in over half the samples, molecular serotyping revealed the presence of indigenous E. coli which also contributed to the percent abundance of E. coli. Despite the presence of additional E. coli strains, the serotype and virulence genes of the spiked STEC, including correct Shiga toxin subtype, were detected in 94% of the samples with a total number of reads per sample averaging 2.4 million. Variation in STEC abundance and/or detection was observed in replicate spiked samples, indicating an effect from the indigenous microbiota during enrichment. SNP analysis of the metagenomic data correctly placed the spiked STEC in a phylogeny of related strains in cases where the indigenous E. coli did not predominate in the enriched sample. Also, for these samples, our analysis demonstrates that strain-level phylogenetic resolution is possible using shotgun metagenomic data for determining the genomic relatedness of a contaminating STEC strain to other closely related E. coli.

Dataset Information

Random whole metagenomic sequencing for forensic discrimination of soils.

Publications

Random whole metagenomic sequencing for forensic discrimination of soils.

Similar Datasets

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