Variant site strain typer (VaST): efficient strain typing using a minimal number of variant genomic sites.

BACKGROUND: Targeted PCR amplicon sequencing (TAS) techniques provide a sensitive, scalable, and cost-effective way to query and identify closely related bacterial species and strains. Typically, this is accomplished by targeting housekeeping genes that provide resolution down to the family, genera, and sometimes species level. Unfortunately, this level of resolution is not sufficient in many applications where strain-level identification of bacteria is required (biodefense, forensics, clinical diagnostics, and outbreak investigations). Adding more genomic targets will increase the resolution, but the challenge is identifying the appropriate targets. VaST was developed to address this challenge by finding the minimum number of targets that, in combination, achieve maximum strain-level resolution for any strain complex. The final combination of target regions identified by the algorithm produce a unique haplotype for each strain which can be used as a fingerprint for identifying unknown samples in a TAS assay. VaST ensures that the targets have conserved primer regions so that the targets can be amplified in all of the known strains and it also favors the inclusion of targets with basal variants which makes the set more robust when identifying previously unseen strains. RESULTS: We analyzed VaST's performance using a number of different pathogenic species that are relevant to human disease outbreaks and biodefense. The number of targets required to achieve full resolution ranged from 20 to 88% fewer sites than what would be required in the worst case and most of the resolution is achieved within the first 20 targets. We computationally and experimentally validated one of the VaST panels and found that the targets led to accurate phylogenetic placement of strains, even when the strains were not a part of the original panel design. CONCLUSIONS: VaST is an open source software that, when provided a set of variant sites, can find the minimum number of sites that will provide maximum resolution of a strain complex, and it has many different run-time options that can accommodate a wide range of applications. VaST can be an effective tool in the design of strain identification panels that, when combined with TAS technologies, offer an efficient and inexpensive strain typing protocol.

Metagenomic diagnostics for the simultaneous detection of multiple pathogens in human stool specimens from Côte d'Ivoire: a proof-of-concept study.

BACKGROUND: The intestinal microbiome is a complex community and its role in influencing human health is poorly understood. While conventional microbiology commonly attributes digestive disorders to a single microorganism, a metagenomic approach can detect multiple pathogens simultaneously and might elucidate the role of microbial communities in the pathogenesis of intestinal diseases. We present a proof-of-concept that a shotgun metagenomic approach provides useful information on the diverse composition of intestinal pathogens and antimicrobial resistance profiles in human stool samples. METHODS: In October 2012, we obtained stool specimens from patients with persistent diarrhea in south Côte d'Ivoire. Four stool samples were purposefully selected and subjected to microscopy, multiplex polymerase chain reaction (PCR), and a metagenomic approach. For the latter, we employed the National Center for Biotechnology Information nucleotide database and screened for 36 pathogenic organisms (bacteria, helminths, intestinal protozoa, and viruses) that may cause digestive disorders. We further characterized the bacterial population and the prevailing resistance patterns by comparing our metagenomic datasets with a genome-specific marker database and with a comprehensive antibiotic resistance database. RESULTS: In the four patients, the metagenomic approach identified between eight and 11 pathogen classes that potentially cause digestive disorders. For bacterial pathogens, the diagnostic agreement between multiplex PCR and metagenomics was high; yet, metagenomics diagnosed several bacteria not detected by multiplex PCR. In contrast, some of the helminth and intestinal protozoa infections detected by microscopy were missed by metagenomics. The antimicrobial resistance analysis revealed the presence of genes conferring resistance to several commonly used antibiotics. CONCLUSIONS: A metagenomic approach provides detailed information on the presence and diversity of pathogenic organisms in human stool samples. Metagenomic studies allow for in-depth molecular characterization such as the antimicrobial resistance status, which may be useful to develop setting-specific treatment algorithms. While metagenomic approaches remain challenging, the benefits of gaining new insights into intestinal microbial communities call for a broader application in epidemiologic studies. TRIAL REGISTRATION: ISRCTN86951400.