Recommendations To Address the Difficulties Encountered When Determining Linezolid Resistance from Whole-Genome Sequencing Data

ABSTRACT Mutations associated with linezolid resistance within the V domain of 23S rRNA are annotated using an Escherichia coli numbering system. The 23S rRNA gene varies in length, nucleotide sequence, and copy number among bacterial species. Consequently, this numbering system is not intuitive and can lead to confusion when mutation sites are being located using whole-genome sequencing data. Using the mutation G2576T as an example, we demonstrate the difficulties associated with using the E. coli numbering system.

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Short-Read Whole-Genome Sequencing for Laboratory-Based Surveillance of Bordetella pertussis

Journal of Clinical Microbiology ◽

10.1128/jcm.02436-16 ◽

2017 ◽

Vol 55 (5) ◽

pp. 1446-1453 ◽

Cited By ~ 1

Author(s):

Alex Marchand-Austin ◽

Raymond S. W. Tsang ◽

Jennifer L. Guthrie ◽

Jennifer H. Ma ◽

Gillian H. Lim ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Bordetella Pertussis ◽

Vaccine Effectiveness ◽

Vaccine Antigen ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Short Read ◽

Content Type

ABSTRACTBordetella pertussisis a Gram-negative bacterium that causes respiratory infections in humans. Ongoing molecular surveillance ofB. pertussisacellular vaccine (aP) antigens is critical for understanding the interaction between evolutionary pressures, disease pathogenesis, and vaccine effectiveness. Methods currently used to characterize aP components are relatively labor-intensive and low throughput. To address this challenge, we sought to derive aP antigen genotypes from minimally processed short-read whole-genome sequencing data generated from 40 clinicalB. pertussisisolates and analyzed using the SRST2 bioinformatic package. SRST2 was able to identify aP antigen genotypes for all antigens with the exception of pertactin, possibly due to low read coverage in GC-rich low-complexity regions of variation. Two main genotypes were observed in addition to a singular third genotype that contained an 84-bp deletion that was identified by SRST2 despite the issues in allele calling. This method has the potential to generate large pools ofB. pertussismolecular data that can be linked to clinical and epidemiological information to facilitate research of vaccine effectiveness and disease severity in the context of emerging vaccine antigen-deficient strains.

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Whole-Genome Sequencing Data for Serotyping Escherichia coli—It's Time for a Change!

Journal of Clinical Microbiology ◽

10.1128/jcm.01448-15 ◽

2015 ◽

Vol 53 (8) ◽

pp. 2402-2403 ◽

Cited By ~ 9

Author(s):

Claire Jenkins

Keyword(s):

Escherichia Coli ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Content Type ◽

Link Type ◽

Article Type ◽

User Friendly

The accessibility of whole-genome sequencing (WGS) presents the opportunity for national reference laboratories to provide a state-of-the-art public health surveillance service. The replacement of traditional serology-based typing ofEscherichia coliby WGS is supported by user-friendly, freely available data analysis Web tools. Anarticle in this issueof theJournal of Clinical Microbiology(K. G. Joensen, A. M. M. Tetzschner, A. Iguchi, F. M. Aarestrup, and F. Scheutz, J Clin Microbiol, 53:2410–2426, 2015,http://dx.doi.org/10.1128/JCM.00008-15) describes SerotypeFinder, an essential guide to serotypingE. coliin the 21st century.

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Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data

mSystems ◽

10.1128/msystems.00656-19 ◽

2020 ◽

Vol 5 (3) ◽

Cited By ~ 3

Author(s):

Nenad Macesic ◽

Oliver J. Bear Don’t Walk ◽

Itsik Pe’er ◽

Nicholas P. Tatonetti ◽

Anton Y. Peleg ◽

...

Keyword(s):

Machine Learning ◽

Klebsiella Pneumoniae ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Genomic Data ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Pr Genes ◽

Content Type

ABSTRACT Polymyxins are used as treatments of last resort for Gram-negative bacterial infections. Their increased use has led to concerns about emerging polymyxin resistance (PR). Phenotypic polymyxin susceptibility testing is resource intensive and difficult to perform accurately. The complex polygenic nature of PR and our incomplete understanding of its genetic basis make it difficult to predict PR using detection of resistance determinants. We therefore applied machine learning (ML) to whole-genome sequencing data from >600 Klebsiella pneumoniae clonal group 258 (CG258) genomes to predict phenotypic PR. Using a reference-based representation of genomic data with ML outperformed a rule-based approach that detected variants in known PR genes (area under receiver-operator curve [AUROC], 0.894 versus 0.791, P = 0.006). We noted modest increases in performance by using a bacterial genome-wide association study to filter relevant genomic features and by integrating clinical data in the form of prior polymyxin exposure. Conversely, reference-free representation of genomic data as k-mers was associated with decreased performance (AUROC, 0.692 versus 0.894, P = 0.015). When ML models were interpreted to extract genomic features, six of seven known PR genes were correctly identified by models without prior programming and several genes involved in stress responses and maintenance of the cell membrane were identified as potential novel determinants of PR. These findings are a proof of concept that whole-genome sequencing data can accurately predict PR in K. pneumoniae CG258 and may be applicable to other forms of complex antimicrobial resistance. IMPORTANCE Polymyxins are last-resort antibiotics used to treat highly resistant Gram-negative bacteria. There are increasing reports of polymyxin resistance emerging, raising concerns of a postantibiotic era. Polymyxin resistance is therefore a significant public health threat, but current phenotypic methods for detection are difficult and time-consuming to perform. There have been increasing efforts to use whole-genome sequencing for detection of antibiotic resistance, but this has been difficult to apply to polymyxin resistance because of its complex polygenic nature. The significance of our research is that we successfully applied machine learning methods to predict polymyxin resistance in Klebsiella pneumoniae clonal group 258, a common health care-associated and multidrug-resistant pathogen. Our findings highlight that machine learning can be successfully applied even in complex forms of antibiotic resistance and represent a significant contribution to the literature that could be used to predict resistance in other bacteria and to other antibiotics.

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Application of Whole-Genome Sequencing Data for O-Specific Antigen Analysis andIn SilicoSerotyping of Pseudomonas aeruginosa Isolates

Journal of Clinical Microbiology ◽

10.1128/jcm.00349-16 ◽

2016 ◽

Vol 54 (7) ◽

pp. 1782-1788 ◽

Cited By ~ 17

Author(s):

Sandra Wingaard Thrane ◽

Véronique L. Taylor ◽

Ole Lund ◽

Joseph S. Lam ◽

Lars Jelsbak

Keyword(s):

Pseudomonas Aeruginosa ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Specific Antigen ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Chronic Infections ◽

Content Type

Accurate typing methods are required for efficient infection control. The emergence of whole-genome sequencing (WGS) technologies has enabled the development of genome-based methods applicable for routine typing and surveillance of bacterial pathogens. In this study, we developed thePseudomonas aeruginosaserotyper (PAst) program, which enabledin silicoserotyping ofP. aeruginosaisolates using WGS data. PAst has been made publically available as a web service and aptly facilitates high-throughput serotyping analysis. The program overcomes critical issues such as the loss ofin vitrotypeability often associated withP. aeruginosaisolates from chronic infections and quickly determines the serogroup of an isolate based on the sequence of the O-specific antigen (OSA) gene cluster. Here, PAst analysis of 1,649 genomes resulted in successful serogroup assignments in 99.27% of the cases. This frequency is rarely achievable by conventional serotyping methods. The limited number of nontypeable isolates found using PAst was the result of either a complete absence of OSA genes in the genomes or the artifact of genomic misassembly. With PAst,P. aeruginosaserotype data can be obtained from WGS information alone. PAst is a highly efficient alternative to conventional serotyping methods in relation to outbreak surveillance of serotype O12 and other high-risk clones, while maintaining backward compatibility to historical serotype data.

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Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data

European Journal of Clinical Microbiology & Infectious Diseases ◽

10.1007/s10096-020-04043-y ◽

2020 ◽

Author(s):

Louise Gade Dahl ◽

Katrine Grimstrup Joensen ◽

Mark Thomas Østerlund ◽

Kristoffer Kiil ◽

Eva Møller Nielsen

Keyword(s):

Antimicrobial Resistance ◽

Whole Genome Sequencing ◽

Campylobacter Jejuni ◽

Genome Sequencing ◽

Resistance Genes ◽

Point Mutations ◽

23S Rrna ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

Abstract Campylobacter jejuni is recognised as the leading cause of bacterial gastroenteritis in industrialised countries. Although the majority of Campylobacter infections are self-limiting, antimicrobial treatment is necessary in severe cases. Therefore, the development of antimicrobial resistance (AMR) in Campylobacter is a growing public health challenge and surveillance of AMR is important for bacterial disease control. The aim of this study was to predict antimicrobial resistance in C. jejuni from whole-genome sequencing data. A total of 516 clinical C. jejuni isolates collected between 2014 and 2017 were subjected to WGS. Resistance phenotypes were determined by standard broth dilution, categorising isolates as either susceptible or resistant based on epidemiological cutoffs for six antimicrobials: ciprofloxacin, nalidixic acid, erythromycin, gentamicin, streptomycin, and tetracycline. Resistance genotypes were identified using an in-house database containing reference genes with known point mutations and the presence of resistance genes was determined using the ResFinder database and four bioinformatical methods (modified KMA, ABRicate, ARIBA, and ResFinder Batch Upload). We identified seven resistance genes including tet(O), tet(O/32/O), ant(6)-Ia, aph(2″)-If, blaOXA, aph(3′)-III, and cat as well as mutations in three genes: gyrA, 23S rRNA, and rpsL. There was a high correlation between phenotypic resistance and the presence of known resistance genes and/or point mutations. A correlation above 98% was seen for all antimicrobials except streptomycin with a correlation of 92%. In conclusion, we found that WGS can predict antimicrobial resistance with a high degree of accuracy and have the potential to be a powerful tool for AMR surveillance.

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Hymenobacter terrestris sp. nov. and Hymenobacter lapidiphilus sp. nov., isolated from regoliths in Antarctica

INTERNATIONAL JOURNAL OF SYSTEMATIC AND EVOLUTIONARY MICROBIOLOGY ◽

10.1099/ijsem.0.004540 ◽

2020 ◽

Vol 70 (12) ◽

pp. 6364-6372

Author(s):

Ivo Sedláček ◽

Roman Pantůček ◽

Michal Zeman ◽

Pavla Holochová ◽

Ondrej Šedo ◽

...

Keyword(s):

16S Rrna ◽

16S Rrna Gene ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Type Strain ◽

Whole Genome Sequencing Data ◽

Rrna Gene ◽

Whole Genome ◽

Content Type ◽

Link Type

A group of four psychrotrophic bacterial strains was isolated on James Ross Island (Antarctica) in 2013. All isolates, originating from different soil samples, were collected from the ice-free northern part of the island. They were rod-shaped, Gram-stain-negative, and produced moderately slimy red-pink pigmented colonies on R2A agar. A polyphasic taxonomic approach based on 16S rRNA gene sequencing, whole-genome sequencing, MALDI-TOF MS, rep-PCR analyses, chemotaxonomic methods and extensive biotyping was used to clarify the taxonomic position of these isolates. Phylogenetic analysis based on 16S rRNA gene sequences showed that the isolates belonged to the genus Hymenobacter . The closest relative was Hymenobacter humicola CCM 8763T, exhibiting 98.3 and 98.9% 16S rRNA pairwise similarity with the reference isolates P5342T and P5252T, respectively. Average nucleotide identity, digital DNA–DNA hybridization and core gene distances calculated from the whole-genome sequencing data confirmed that P5252T and P5342T represent two distinct Hymenobacter species. The menaquinone systems of both strains contained MK-7 as the major respiratory quinone. The predominant polar lipids for both strains were phosphatidylethanolamine and one unidentified glycolipid. The major components in the cellular fatty acid composition were summed feature 3 (C16:1 ω7c/C16:1ω6c), C16:1ω5c, summed feature 4 (anteiso-C17:1 B/iso-C17:1 I), anteiso-C15:0 and iso-C15 : 0 for all isolates. Based on the obtained results, two novel species are proposed, for which the names Hymenobacter terrestris sp. nov. (type strain P5252T=CCM 8765T=LMG 31495T) and Hymenobacter lapidiphilus sp. nov. (type strain P5342T=CCM 8764T=LMG 30613T) are suggested.

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Use of whole-genome sequencing data to analyze 23S rRNA-mediated azithromycin resistance

International Journal of Antimicrobial Agents ◽

10.1016/j.ijantimicag.2016.10.023 ◽

2017 ◽

Vol 49 (2) ◽

pp. 252-254 ◽

Cited By ~ 12

Author(s):

Steven R. Johnson ◽

Yonatan Grad ◽

A. Jeanine Abrams ◽

Kevin Pettus ◽

David L. Trees

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

23S Rrna ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Azithromycin Resistance

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Mechanisms of Linezolid Resistance among Coagulase-Negative Staphylococci Determined by Whole-Genome Sequencing

mBio ◽

10.1128/mbio.00894-14 ◽

2014 ◽

Vol 5 (3) ◽

Cited By ~ 22

Author(s):

Ryan Tewhey ◽

Bing Gu ◽

Theodoros Kelesidis ◽

Carmen Charlton ◽

April Bobenchik ◽

...

Keyword(s):

United States ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

The United States ◽

23S Rrna ◽

Whole Genome ◽

Coagulase Negative Staphylococci ◽

Content Type ◽

Linezolid Resistance ◽

Methylase Gene

ABSTRACT Linezolid resistance is uncommon among staphylococci, but approximately 2% of clinical isolates of coagulase-negative staphylococci (CoNS) may exhibit resistance to linezolid (MIC, ≥8 µg/ml). We performed whole-genome sequencing (WGS) to characterize the resistance mechanisms and genetic backgrounds of 28 linezolid-resistant CoNS (21 Staphylococcus epidermidis isolates and 7 Staphylococcus haemolyticus isolates) obtained from blood cultures at a large teaching health system in California between 2007 and 2012. The following well-characterized mutations associated with linezolid resistance were identified in the 23S rRNA: G2576U, G2447U, and U2504A, along with the mutation C2534U. Mutations in the L3 and L4 riboproteins, at sites previously associated with linezolid resistance, were also identified in 20 isolates. The majority of isolates harbored more than one mutation in the 23S rRNA and L3 and L4 genes. In addition, the cfr methylase gene was found in almost half (48%) of S. epidermidis isolates. cfr had been only rarely identified in staphylococci in the United States prior to this study. Isolates of the same sequence type were identified with unique mutations associated with linezolid resistance, suggesting independent acquisition of linezolid resistance in each isolate. IMPORTANCE Linezolid is one of a limited number of antimicrobials available to treat drug-resistant Gram-positive bacteria, but resistance has begun to emerge. We evaluated the genomes of 28 linezolid-resistant staphylococci isolated from patients. Multiple mutations in the rRNA and associated proteins previously associated with linezolid resistance were found in the isolates investigated, underscoring the multifocal nature of resistance to linezolid in Staphylococcus. Importantly, almost half the S. epidermidis isolates studied harbored a plasmid-borne cfr RNA methylase gene, suggesting that the incidence of cfr may be higher in the United States than previously documented. This finding has important implications for infection control practices in the United States. Further, cfr is commonly detected in bacteria isolated from livestock, where the use of phenicols, lincosamides, and pleuromutilins in veterinary medicine may provide selective pressure and lead to maintenance of this gene in animal bacteria.

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