Emerging challenges of whole-genome-sequencing–powered epidemiological surveillance of globally distributed clonal groups of bacterial infections, giving Acinetobacter baumannii ST195 as an example

Carbapenem-resistant Acinetobacter baumannii (CRAB) outbreaks in hospital settings challenge the treatment of patients and infection control. Understanding the relatedness of clinical isolates is important in distinguishing outbreak isolates from sporadic cases. This study investigated 11 CRAB isolates from a hospital outbreak by whole-genome sequencing (WGS), utilizing various bioinformatics tools for outbreak analysis. The results of multilocus sequence typing (MLST), single nucleotide polymorphism (SNP) analysis, and phylogenetic tree analysis by WGS through web-based tools were compared, and repetitive element polymerase chain reaction (rep-PCR) typing was performed. Through the WGS of 11 A. baumannii isolates, three clonal lineages were identified from the outbreak. The coexistence of blaOXA-23, blaOXA-66, blaADC-25, and armA with additional aminoglycoside-inactivating enzymes, predicted to confer multidrug resistance, was identified in all isolates. The MLST Oxford scheme identified three types (ST191, ST369, and ST451), and, through whole-genome MLST and whole-genome SNP analyses, different clones were found to exist within the MLST types. wgSNP showed the highest discriminatory power with the lowest similarities among the isolates. Using the various bioinformatics tools for WGS, CRAB outbreak analysis was applicable and identified three discrete clusters differentiating the separate epidemiologic relationships among the isolates.

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

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.1098 ◽

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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Investigation of a Carbapenemase-producing Acinetobacter baumannii outbreak using whole genome sequencing versus a standard epidemiologic investigation

Antimicrobial Resistance and Infection Control ◽

10.1186/s13756-018-0437-7 ◽

2018 ◽

Vol 7 (1) ◽

Cited By ~ 4

Author(s):

Chloe Bogaty ◽

Laura Mataseje ◽

Andrew Gray ◽

Brigitte Lefebvre ◽

Simon Lévesque ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Acinetobacter Baumannii ◽

Genome Sequencing ◽

Whole Genome ◽

Epidemiologic Investigation

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Genetic Characterization of Brucella spp.: Whole Genome Sequencing-Based Approach for the Determination of Multiple Locus Variable Number Tandem Repeat Profiles

Frontiers in Microbiology ◽

10.3389/fmicb.2021.740068 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ana Pelerito ◽

Alexandra Nunes ◽

Teresa Grilo ◽

Joana Isidro ◽

Catarina Silva ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

In Silico ◽

Variable Number Tandem Repeat ◽

Epidemiological Studies ◽

Variable Number ◽

Epidemiological Surveillance ◽

Future Application ◽

Whole Genome

Brucellosis is an important zoonosis that is emerging in some regions of the world, gaining increased relevance with the inclusion of the causing agent Brucella spp. in the class B bioterrorism group. Until now, multi-locus VNTR Analysis (MLVA) based on 16 loci has been considered as the gold standard for Brucella typing. However, this methodology is laborious, and, with the rampant release of Brucella genomes, the transition from the traditional MLVA to whole genome sequencing (WGS)-based typing is on course. Nevertheless, in order to avoid a disruptive transition with the loss of massive genetic data obtained throughout the last decade and considering that the transition timings will vary considerably among different countries, it is important to determine WGS-based MLVA alleles of the nowadays sequenced genomes. On this regard, we aimed to evaluate the performance of a Python script that had been previously developed for the rapid in silico extraction of the MLVA alleles, by comparing it to the PCR-based MLVA procedure over 83 strains from different Brucella species. The WGS-based MLVA approach detected 95.3% of all possible 1,328 hits (83 strains×16 loci) and showed an agreement rate with the PCR-based MLVA procedure of 96.4% for MLVA-16. According to our dataset, we suggest the use of a minimal depth of coverage of ~50x and a maximum number of ~200 contigs as guiding “boundaries” for the future application of the script. In conclusion, the evaluated script seems to be a very useful and robust tool for the in silico determination of MLVA profiles of Brucella strains, allowing retrospective and prospective molecular epidemiological studies, which are important for maintaining an active epidemiological surveillance of brucellosis.

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Extensively drug-resistant Acinetobacter baumannii isolated from intensive care units in northern Italy: a genomic approach to characterize new sequence types

Future Microbiology ◽

10.2217/fmb-2019-0083 ◽

2019 ◽

Vol 14 (15) ◽

pp. 1281-1292 ◽

Cited By ~ 2

Author(s):

Giovanni Lorenzin ◽

Erika Scaltriti ◽

Franco Gargiulo ◽

Francesca Caccuri ◽

Giorgio Piccinelli ◽

...

Keyword(s):

Intensive Care ◽

Whole Genome Sequencing ◽

Acinetobacter Baumannii ◽

Intensive Care Units ◽

Genome Sequencing ◽

Multilocus Sequence Typing ◽

Whole Genome ◽

Drug Resistant ◽

Extensively Drug Resistant ◽

Sequence Types

Aim: This study aims to characterize clinical strains of Acinetobacter baumannii with an extensively drug-resistant phenotype. Methods: VITEK® 2, Etest® method and broth microdilution method for colistin were used. PCR analysis and multilocus sequence typing Pasteur scheme were performed to identify bla-OXA genes and genetic relatedness, respectively. Whole-genome sequencing analysis was used to characterize three isolates. Results: All the isolates were susceptible only to polymyxins. blaOXA-23-like gene was the only acquired carbapenemase gene in 88.2% of the isolates. Multilocus sequence typing identified various sequence types: ST2, ST19, ST195, ST577 and ST632. Two new sequence types, namely, ST1279 and ST1280, were detected by whole-genome sequencing. Conclusion: This study showed that carbapenem-resistant A. baumannii isolates causing infections in intensive care units almost exclusively produce OXA-23, underlining their frequent spread in Italy.

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Population Structure of Mycobacterium bovis in Germany: a Long-Term Study Using Whole-Genome Sequencing Combined with Conventional Molecular Typing Methods

Journal of Clinical Microbiology ◽

10.1128/jcm.01573-20 ◽

2020 ◽

Vol 58 (11) ◽

Author(s):

Thomas A. Kohl ◽

Katharina Kranzer ◽

Sönke Andres ◽

Thierry Wirth ◽

Stefan Niemann ◽

...

Keyword(s):

Population Structure ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Mycobacterium Bovis ◽

Variable Number Tandem Repeat ◽

Epidemiological Surveillance ◽

Whole Genome ◽

Content Type ◽

Long Term Study ◽

Wide Range

ABSTRACT Mycobacterium bovis is the primary cause of bovine tuberculosis (bTB) and infects a wide range of domestic animal and wildlife species and humans. In Germany, bTB still emerges sporadically in cattle herds, free-ranging wildlife, diverse captive animal species, and humans. In order to understand the underlying population structure and estimate the population size fluctuation through time, we analyzed 131 M. bovis strains from animals (n = 38) and humans (n = 93) in Germany from 1999 to 2017 by whole-genome sequencing (WGS), mycobacterial interspersed repetitive-unit–variable-number tandem-repeat (MIRU-VNTR) typing, and spoligotyping. Based on WGS data analysis, 122 out of the 131 M. bovis strains were classified into 13 major clades, of which 6 contained strains from both human and animal cases and 7 only strains from human cases. Bayesian analyses suggest that the M. bovis population went through two sharp anticlimaxes, one in the middle of the 18th century and another one in the 1950s. WGS-based cluster analysis grouped 46 strains into 13 clusters ranging in size from 2 to 11 members and involving strains from distinct host types, e.g., only cattle and also mixed hosts. Animal strains of four clusters were obtained over a 9-year span, pointing toward autochthonous persistent bTB infection cycles. As expected, WGS had a higher discriminatory power than spoligotyping and MIRU-VNTR typing. In conclusion, our data confirm that WGS and suitable bioinformatics constitute the method of choice to implement prospective molecular epidemiological surveillance of M. bovis. The population of M. bovis in Germany is diverse, with subtle, but existing, interactions between different host groups.

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

mBio ◽

10.1128/mbio.00444-16 ◽

2016 ◽

Vol 7 (3) ◽

Cited By ~ 123

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.

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Efficient whole genome sequencing of influenza A viruses

10.1101/749234 ◽

2019 ◽

Author(s):

Marina Escalera-Zamudio ◽

Ana Georgina Cobián-Güemes ◽

Blanca Taboada ◽

Irma López-Martínez ◽

Joel Armando Vázquez-Pérez ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Influenza A Virus ◽

Genome Sequencing ◽

Influenza A ◽

Genetic Material ◽

Influenza Viruses ◽

Epidemiological Surveillance ◽

Clinical Samples ◽

Whole Genome ◽

Influenza A Viruses

ABSTRACTThe constant threat of emergence for novel pathogenic influenza A viruses with pandemic potential, makes full-genome characterization of circulating influenza viral strains a high priority, allowing detection of novel and re-assorting variants. Sequencing the full-length genome of influenza A virus traditionally required multiple amplification rounds, followed by the subsequent sequencing of individual PCR products. The introduction of high-throughput sequencing technologies has made whole genome sequencing easier and faster. We present a simple protocol to obtain whole genome sequences of hypothetically any influenza A virus, even with low quantities of starting genetic material. The complete genomes of influenza A viruses of different subtypes and from distinct sources (clinical samples of pdmH1N1, tissue culture-adapted H3N2 viruses, or avian influenza viruses from cloacal swabs) were amplified with a single multisegment reverse transcription-PCR reaction and sequenced using Illumina sequencing platform. Samples with low quantity of genetic material after initial PCR amplification were re-amplified by an additional PCR using random primers. Whole genome sequencing was successful for 66% of the samples, whilst the most relevant genome segments for epidemiological surveillance (corresponding to the hemagglutinin and neuraminidase) were sequenced with at least 93% coverage (and a minimum 10x) for 98% of the samples. Low coverage for some samples is likely due to an initial low viral RNA concentration in the original sample. The proposed methodology is especially suitable for sequencing a large number of samples, when genetic data is urgently required for strains characterization, and may also be useful for variant analysis.

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