scholarly journals Whole-Genome Sequencing for Routine Pathogen Surveillance in Public Health: a Population Snapshot of InvasiveStaphylococcus aureusin Europe

mBio ◽  
2016 ◽  
Vol 7 (3) ◽  
Author(s):  
David M. Aanensen ◽  
Edward J. Feil ◽  
Matthew T. G. Holden ◽  
Janina Dordel ◽  
Corin A. Yeats ◽  
...  
Keyword(s):  
Public Health ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Bacterial Pathogens ◽  
Epidemiological Surveillance ◽  
Data Sets ◽  
Whole Genome ◽  
Bioinformatic Tools ◽  
Road Map

ABSTRACTThe implementation of routine whole-genome sequencing (WGS) promises to transform our ability to monitor the emergence and spread of bacterial pathogens. Here we combined WGS data from 308 invasiveStaphylococcus aureusisolates corresponding to a pan-European population snapshot, with epidemiological and resistance data. Geospatial visualization of the data is made possible by a generic software tool designed for public health purposes that is available at the project URL (http://www.microreact.org/project/EkUvg9uY?tt=rc). Our analysis demonstrates that high-risk clones can be identified on the basis of population level properties such as clonal relatedness, abundance, and spatial structuring and by inferring virulence and resistance properties on the basis of gene content. We also show thatin silicopredictions of antibiotic resistance profiles are at least as reliable as phenotypic testing. We argue that this work provides a comprehensive road map illustrating the three vital components for future molecular epidemiological surveillance: (i) large-scale structured surveys, (ii) WGS, and (iii) community-oriented database infrastructure and analysis tools.IMPORTANCEThe spread of antibiotic-resistant bacteria is a public health emergency of global concern, threatening medical intervention at every level of health care delivery. Several recent studies have demonstrated the promise of routine whole-genome sequencing (WGS) of bacterial pathogens for epidemiological surveillance, outbreak detection, and infection control. However, as this technology becomes more widely adopted, the key challenges of generating representative national and international data sets and the development of bioinformatic tools to manage and interpret the data become increasingly pertinent. This study provides a road map for the integration of WGS data into routine pathogen surveillance. We emphasize the importance of large-scale routine surveys to provide the population context for more targeted or localized investigation and the development of open-access bioinformatic tools to provide the means to combine and compare independently generated data with publicly available data sets.

scholarly journals SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

2021 ◽  
Author(s):  
Einar Gabbasov ◽  
Miguel Moreno-Molina ◽  
Iñaki Comas ◽  
Maxwell Libbrecht ◽  
Leonid Chindelevitch
Keyword(s):  
Public Health ◽  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Bacterial Pathogens ◽  
Superior Performance ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Multiple Strains

AbstractThe occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited.In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains.We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens, and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.Author summaryWhen multiple strains of a pathogenic organism are present in a patient, it may be necessary to not only detect this, but also to identify the individual strains. However, this problem has not yet been solved for bacterial pathogens processed via whole-genome sequencing. In this paper, we propose the SplitStrains algorithm for detecting multiple strains in a sample, identifying their proportions, and inferring their sequences, in the case of Mycobacterium tuberculosis. We test it on both simulated and real data, with encouraging results. We believe that our work opens new horizons in public health microbiology by allowing a more precise detection, identification and quantification of multiple infecting strains within a sample.

scholarly journals Comparing serotyping with whole-genome sequencing for subtyping of non-typhoidal Salmonella enterica: a large-scale analysis of 37 serotypes with a public health impact in the USA

2020 ◽  
Vol 6 (9) ◽  
Author(s):  
Ehud Elnekave ◽  
Samuel L. Hong ◽  
Seunghyun Lim ◽  
Timothy J. Johnson ◽  
Andres Perez ◽  
...  
Keyword(s):  
Public Health ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Health Impact ◽  
Public Health Impact ◽  
Whole Genome ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
The Usa

Serotyping has traditionally been used for subtyping of non-typhoidal Salmonella (NTS) isolates. However, its discriminatory power is limited, which impairs its use for epidemiological investigations of source attribution. Whole-genome sequencing (WGS) analysis allows more accurate subtyping of strains. However, because of the relative newness and cost of routine WGS, large-scale studies involving NTS WGS are still rare. We aimed to revisit the big picture of subtyping NTS with a public health impact by using traditional serotyping (i.e. reaction between antisera and surface antigens) and comparing the results with those obtained using WGS. For this purpose, we analysed 18 282 sequences of isolates belonging to 37 serotypes with a public health impact that were recovered in the USA between 2006 and 2017 from multiple sources, and were available at the National Center for Biotechnology Information (NCBI). Phylogenetic trees were reconstructed for each serotype using the core genome for the identification of genetic subpopulations. We demonstrated that WGS-based subtyping allows better identification of sources potentially linked with human infection and emerging subpopulations, along with providing information on the risk of dissemination of plasmids and acquired antimicrobial resistance genes (AARGs). In addition, by reconstructing a phylogenetic tree with representative isolates from all serotypes (n=370), we demonstrated genetic variability within and between serotypes, which formed monophyletic, polyphyletic and paraphyletic clades. Moreover, we found (in the entire data set) an increased detection rate for AARGs linked to key antimicrobials (such as quinolones and extended-spectrum cephalosporins) over time. The outputs of this large-scale analysis reveal new insights into the genetic diversity within and between serotypes; the polyphyly and paraphyly of certain serotypes may suggest that the subtyping of NTS to serotypes may not be sufficient. Moreover, the results and the methods presented here, leading to differentiation between genetic subpopulations based on their potential risk to public health, as well as narrowing down the possible sources of these infections, may be used as a baseline for subtyping of future NTS infections and help efforts to mitigate and prevent infections in the USA and globally.

scholarly journals SeqSero2: Rapid and Improved Salmonella Serotype Determination Using Whole-Genome Sequencing Data

2019 ◽  
Vol 85 (23) ◽  
Author(s):  
Shaokang Zhang ◽  
Hendrik C. den Bakker ◽  
Shaoting Li ◽  
Jessica Chen ◽  
Blake A. Dinsmore ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Draft Genome ◽  
The United States ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Link Type

ABSTRACT SeqSero, launched in 2015, is a software tool for Salmonella serotype determination from whole-genome sequencing (WGS) data. Despite its routine use in public health and food safety laboratories in the United States and other countries, the original SeqSero pipeline is relatively slow (minutes per genome using sequencing reads), is not optimized for draft genome assemblies, and may assign multiple serotypes for a strain. Here, we present SeqSero2 (github.com/denglab/SeqSero2; denglab.info/SeqSero2), an algorithmic transformation and functional update of the original SeqSero. Major improvements include (i) additional sequence markers for identification of Salmonella species and subspecies and certain serotypes, (ii) a k-mer based algorithm for rapid serotype prediction from raw reads (seconds per genome) and improved serotype prediction from assemblies, and (iii) a targeted assembly approach for specific retrieval of serotype determinants from WGS for serotype prediction, new allele discovery, and prediction troubleshooting. Evaluated using 5,794 genomes representing 364 common U.S. serotypes, including 2,280 human isolates of 117 serotypes from the National Antimicrobial Resistance Monitoring System, SeqSero2 is up to 50 times faster than the original SeqSero while maintaining equivalent accuracy for raw reads and substantially improving accuracy for assemblies. SeqSero2 further suggested that 3% of the tested genomes contained reads from multiple serotypes, indicating a use for contamination detection. In addition to short reads, SeqSero2 demonstrated potential for accurate and rapid serotype prediction directly from long nanopore reads despite base call errors. Testing of 40 nanopore-sequenced genomes of 17 serotypes yielded a single H antigen misidentification. IMPORTANCE Serotyping is the basis of public health surveillance of Salmonella. It remains a first-line subtyping method even as surveillance continues to be transformed by whole-genome sequencing. SeqSero allows the integration of Salmonella serotyping into a whole-genome-sequencing-based laboratory workflow while maintaining continuity with the classic serotyping scheme. SeqSero2, informed by extensive testing and application of SeqSero in the United States and other countries, incorporates important improvements and updates that further strengthen its application in routine and large-scale surveillance of Salmonella by whole-genome sequencing.

scholarly journals Revolutionising Public Health Reference Microbiology using Whole Genome Sequencing: Salmonella as an exemplar

2015 ◽  
Author(s):  
Philip Ashton ◽  
Satheesh Nair ◽  
Tansy Peters ◽  
Rediat Tewolde ◽  
Martin Day ◽  
...  
Keyword(s):  
Public Health ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
High Throughput Sequencing ◽  
Low Cost ◽  
Bacterial Pathogens ◽  
Reference Laboratory ◽  
Whole Genome ◽  
The United Kingdom ◽  
Primary Test

Advances in whole genome sequencing (WGS) platforms and DNA library preparation have led to the development of methods for high throughput sequencing of bacterial genomes at a relatively low cost (Loman et al. 2012; Medini et al. 2008). WGS offers unprecedented resolution for determining degrees of relatedness between strains of bacterial pathogens and has proven a powerful tool for microbial population studies and epidemiological investigations (Harris et al. 2010; Lienau et al. 2011; Holt et al. 2009; Ashton, Peters, et al. 2015). The potential utility of WGS to public health microbiology has been highlighted previously (Koser et al. 2012; Kwong et al. 2013; Reuter et al. 2013; Joensen et al. 2014; Nair et al. 2014; Bakker et al. 2014; D Auria et al. 2014). Here we report, for the first time, the routine use of WGS as the primary test for identification, surveillance and outbreak investigation by a national reference laboratory. We present data on how this has revolutionised public health microbiology for one of the most common bacterial pathogens in the United Kingdom, the Salmonellae.

0306 Exploring the feasibility of using copy number variants as genetic markers through large-scale whole genome sequencing experiments

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 146-146
Author(s):  
D. M. Bickhart ◽  
L. Xu ◽  
J. L. Hutchison ◽  
J. B. Cole ◽  
D. J. Null ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genetic Markers ◽  
Genome Sequencing ◽  
Copy Number ◽  
Large Scale ◽  
Copy Number Variants ◽  
Whole Genome

scholarly journals Expanding U.S. Laboratory Capacity for Neisseria gonorrhoeae Antimicrobial Susceptibility Testing and Whole-Genome Sequencing through the CDC's Antibiotic Resistance Laboratory Network

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Ellen N. Kersh ◽  
Cau D. Pham ◽  
John R. Papp ◽  
Robert Myers ◽  
Richard Steece ◽  
...  
Keyword(s):  
Public Health ◽  
Antibiotic Resistance ◽  
Neisseria Gonorrhoeae ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Antimicrobial Susceptibility ◽  
Susceptibility Testing ◽  
Whole Genome ◽  
Content Type ◽  
Laboratory Capacity

ABSTRACT U.S. gonorrhea rates are rising, and antibiotic-resistant Neisseria gonorrhoeae (AR-Ng) is an urgent public health threat. Since implementation of nucleic acid amplification tests for N. gonorrhoeae identification, the capacity for culturing N. gonorrhoeae in the United States has declined, along with the ability to perform culture-based antimicrobial susceptibility testing (AST). Yet AST is critical for detecting and monitoring AR-Ng. In 2016, the CDC established the Antibiotic Resistance Laboratory Network (AR Lab Network) to shore up the national capacity for detecting several resistance threats including N. gonorrhoeae. AR-Ng testing, a subactivity of the CDC’s AR Lab Network, is performed in a tiered network of approximately 35 local laboratories, four regional laboratories (state public health laboratories in Maryland, Tennessee, Texas, and Washington), and the CDC’s national reference laboratory. Local laboratories receive specimens from approximately 60 clinics associated with the Gonococcal Isolate Surveillance Project (GISP), enhanced GISP (eGISP), and the program Strengthening the U.S. Response to Resistant Gonorrhea (SURRG). They isolate and ship up to 20,000 isolates to regional laboratories for culture-based agar dilution AST with seven antibiotics and for whole-genome sequencing of up to 5,000 isolates. The CDC further examines concerning isolates and monitors genetic AR markers. During 2017 and 2018, the network tested 8,214 and 8,628 N. gonorrhoeae isolates, respectively, and the CDC received 531 and 646 concerning isolates and 605 and 3,159 sequences, respectively. In summary, the AR Lab Network supported the laboratory capacity for N. gonorrhoeae AST and associated genetic marker detection, expanding preexisting notification and analysis systems for resistance detection. Continued, robust AST and genomic capacity can help inform national public health monitoring and intervention.

scholarly journals Whole-Genome Sequencing for Bacterial Strain Typing Using the iSeq100 Platform

2020 ◽  
Vol 41 (S1) ◽  
pp. s434-s434
Author(s):  
Grant Vestal ◽  
Steven Bruzek ◽  
Amanda Lasher ◽  
Amorce Lima ◽  
Suzane Silbert
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Outbreak Detection ◽  
Epidemiological Surveillance ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Dna Libraries ◽  
Patient Health ◽  
Hospital Acquired ◽  
Reference Genomes

Background: Hospital-acquired infections pose a significant threat to patient health. Laboratories are starting to consider whole-genome sequencing (WGS) as a molecular method for outbreak detection and epidemiological surveillance. The objective of this study was to assess the use of the iSeq100 platform (Illumina, San Diego, CA) for accurate sequencing and WGS-based outbreak detection using the bioMérieux EPISEQ CS, a novel cloud-based software for sequence assembly and data analysis. Methods: In total, 25 isolates, including 19 MRSA isolates and 6 ATCC strains were evaluated in this study: A. baumannii ATCC 19606, B. cepacia ATCC 25416, E. faecalis ATCC 29212, E. coli ATCC 25922, P. aeruginosa ATCC 27853 and S. aureus ATCC 25923. DNA extraction of all isolates was performed on the QIAcube (Qiagen, Hilden, Germany) using the DNEasy Ultra Clean Microbial kit extraction protocol. DNA libraries were prepared for WGS using the Nextera DNA Flex Library Prep Kit (Illumina) and sequenced at 2×150-bp on the iSeq100 according to the manufacturer’s instructions. The 19 MRSA isolates were previously characterized by the DiversiLab system (bioMérieux, France). Upon validation of the iSeq100 platform, a new outbreak analysis was performed using WGS analysis using EPISEQ CS. ATCC sequences were compared to assembled reference genomes from the NCBI GenBank to assess the accuracy of the iSeq100 platform. The FASTQ files were aligned via BowTie2 version 2.2.6 software, using default parameters, and FreeBayes version 1.1.0.46-0 was used to call homozygous single-nucleotide polymorphisms (SNPs) with a minimum coverage of 5 and an allele frequency of 0.87 using default parameters. ATCC sequences were analyzed using ResFinder version 3.2 and were compared in silico to the reference genome. Results: EPISEQ CS classified 8 MRSA isolates as unrelated and grouped 11 isolates into 2 separate clusters: cluster A (5 isolates) and cluster B (6 isolates) with similarity scores of ≥99.63% and ≥99.50%, respectively. This finding contrasted with the previous characterization by DiversiLab, which identified 3 clusters of 2, 8, and 11 isolates, respectively. The EPISEQ CS resistome data detected the mecA gene in 18 of 19 MRSA isolates. Comparative analysis of the ATCCsequences to the reference genomes showed 99.9986% concordance of SNPs and 100.00% concordance between the resistance genes present. Conclusions: The iSeq100 platform accurately sequenced the bacterial isolates and could be an affordable alternative in conjunction with EPISEQ CS for epidemiological surveillance analysis and infection prevention.Funding: NoneDisclosures: None

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