Dataset Information

A generic workflow for Single Locus Sequence Typing (SLST) design and subspecies characterization of microbiota.

ABSTRACT: We present TaxPhlAn, a new method and bioinformatics pipeline for design and analysis of single-locus sequence typing (SLST) markers to type and profile bacteria beyond the species-level in a complex microbial community background. TaxPhlAn can be applied to any group of phylogenetically-related bacteria, provided reference genomes are available. As TaxPhlAn requires the SLST targets identified to fit the phylogenetic pattern as determined through comprehensive evolutionary reconstruction of input genomes, TaxPhlAn allows for the identification and phylogenetic inference of new biodiversity. Here, we present a clinically relevant case study of high-resolution Staphylococcus profiling on skin of atopic dermatitis (AD) patients. We demonstrate that SLST enables profiling of cutaneous Staphylococcus members at (sub)species level and provides higher resolution than current 16S-based techniques. With the higher discriminative ability provided by our approach, we further show that the presence of Staphylococcus capitis on the skin together with Staphylococcus aureus associates with AD disease.

SUBMITTER: Ederveen THA

PROVIDER: S-EPMC6934516 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A generic workflow for Single Locus Sequence Typing (SLST) design and subspecies characterization of microbiota.

Ederveen Thomas H A THA Smits Jos P H JPH Hajo Karima K van Schalkwijk Saskia S Kouwenhoven Tessa A TA Lukovac Sabina S Wels Michiel M van den Bogaard Ellen H EH Schalkwijk Joost J Boekhorst Jos J Zeeuwen Patrick L J M PLJM van Hijum Sacha A F T SAFT

Scientific reports 20191227 1

We present TaxPhlAn, a new method and bioinformatics pipeline for design and analysis of single-locus sequence typing (SLST) markers to type and profile bacteria beyond the species-level in a complex microbial community background. TaxPhlAn can be applied to any group of phylogenetically-related bacteria, provided reference genomes are available. As TaxPhlAn requires the SLST targets identified to fit the phylogenetic pattern as determined through comprehensive evolutionary reconstruction of inp ...[more]

PMID: 31882601

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Project description:BackgroundRickettsiae closely related to the Malish strain, the reference Rickettsia conorii strain, include Indian tick typhus rickettsia (ITTR), Israeli spotted fever rickettsia (ISFR), and Astrakhan fever rickettsia (AFR). Although closely related genotypically, they are distinct serotypically. Using multilocus sequence typing (MLST), we have recently found that distinct serotypes may not always represent distinct species within the Rickettsia genus. We investigated the possibility of classifying rickettsiae closely related to R. conorii as R. conorii subspecies as proposed by the ad hoc committee on reconciliation of approaches to bacterial systematics. For this, we first estimated their genotypic variability by using MLST including the sequencing of 5 genes, of 31 rickettsial isolates closely related to R. conorii strain Malish, 1 ITTR isolate, 2 isolates and 3 tick amplicons of AFR, and 2 ISFR isolates. Then, we selected a representative of each MLST genotype and used multi-spacer typing (MST) and mouse serotyping to estimate their degree of taxonomic relatedness.ResultsAmong the 39 isolates or tick amplicons studied, four MLST genotypes were identified: i) the Malish type; ii) the ITTR type; iii) the AFR type; and iv) the ISFR type. Among these four MLST genotypes, the pairwise similarity in nucleotide sequence varied from 99.8 to 100%, 99.4 to 100%, 98.2 to 99.8%, 98.4 to 99.8%, and 99.2 to 99.9% for 16S rDNA, gltA, ompA, ompB, and sca4 genes, respectively. Representatives of the 4 MLST types were also classified within four types using MST genotyping as well as mouse serotyping.ConclusionAlthough homogeneous genotypically, strains within the R. conorii species show MST genotypic, serotypic, and epidemio-clinical dissimilarities. We, therefore, propose to modify the nomenclature of the R. conorii species through the creation of subspecies. We propose the names R. conorii subsp. conorii subsp. nov. (type strain = Malish, ATCC VR-613), R. conorii subspecies indica subsp. nov. (type strain = ATCC VR-597), R. conorii subspecies caspia subsp. nov. (type strain = A-167), and R. conorii subspecies israelensis subsp. nov. (type strain = ISTT CDC1). The description of R. conorii is emended to accomodate the four subspecies.

Project description:BackgroundMulti-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context.ResultsWe present a software tool, SRST, for quick and accurate retrieval of sequence types from short read sets, using inputs easily downloaded from public databases. SRST uses read mapping and an allele assignment score incorporating sequence coverage and variability, to determine the most likely allele at each MLST locus. Analysis of over 3,500 loci in more than 500 publicly accessible Illumina read sets showed SRST to be highly accurate at allele assignment. SRST output is compatible with common analysis tools such as eBURST, Clonal Frame or PhyloViz, allowing easy comparison between novel genome data and MLST data. Alignment, fastq and pileup files can also be generated for novel alleles.ConclusionsSRST is a novel software tool for accurate assignment of sequence types using short read data. Several uses for the tool are demonstrated, including quality control for high-throughput sequencing projects, plasmid MLST and analysis of genomic data during outbreak investigation. SRST is open-source, requires Python, BWA and SamTools, and is available from http://srst.sourceforge.net.

Dataset Information

A generic workflow for Single Locus Sequence Typing (SLST) design and subspecies characterization of microbiota.

Publications

A generic workflow for Single Locus Sequence Typing (SLST) design and subspecies characterization of microbiota.

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