Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania

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Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania. / Kumburu, Happiness H; Sonda, Tolbert; van Zwetselaar, Marco; Leekitcharoenphon, Pimlapas; Lukjancenko, Oksana; Mmbaga, Blandina T; Alifrangis, Michael; Lund, Ole; Aarestrup, Frank M; Kibiki, Gibson S.

In: The Journal of antimicrobial chemotherapy, Vol. 74, No. 6, 01.06.2019, p. 1484-1493.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Kumburu, HH, Sonda, T, van Zwetselaar, M, Leekitcharoenphon, P, Lukjancenko, O, Mmbaga, BT, Alifrangis, M, Lund, O, Aarestrup, FM & Kibiki, GS 2019, 'Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania', The Journal of antimicrobial chemotherapy, vol. 74, no. 6, pp. 1484-1493. https://doi.org/10.1093/jac/dkz055

APA

Kumburu, H. H., Sonda, T., van Zwetselaar, M., Leekitcharoenphon, P., Lukjancenko, O., Mmbaga, B. T., Alifrangis, M., Lund, O., Aarestrup, F. M., & Kibiki, G. S. (2019). Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania. The Journal of antimicrobial chemotherapy, 74(6), 1484-1493. https://doi.org/10.1093/jac/dkz055

Vancouver

Kumburu HH, Sonda T, van Zwetselaar M, Leekitcharoenphon P, Lukjancenko O, Mmbaga BT et al. Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania. The Journal of antimicrobial chemotherapy. 2019 Jun 1;74(6):1484-1493. https://doi.org/10.1093/jac/dkz055

Author

Kumburu, Happiness H ; Sonda, Tolbert ; van Zwetselaar, Marco ; Leekitcharoenphon, Pimlapas ; Lukjancenko, Oksana ; Mmbaga, Blandina T ; Alifrangis, Michael ; Lund, Ole ; Aarestrup, Frank M ; Kibiki, Gibson S. / Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania. In: The Journal of antimicrobial chemotherapy. 2019 ; Vol. 74, No. 6. pp. 1484-1493.

Bibtex

@article{4304a86ceb2d4388818772d383af89dc,
title = "Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania",
abstract = "BACKGROUND: Reliable phenotypic antimicrobial susceptibility testing can be a challenge in clinical settings in low- and middle-income countries. WGS is a promising approach to enhance current capabilities.AIM: To study diversity and resistance determinants and to predict and compare resistance patterns from WGS data of Acinetobacter baumannii with phenotypic results from classical microbiological testing at a tertiary care hospital in Tanzania.METHODS AND RESULTS: MLST using Pasteur/Oxford schemes yielded eight different STs from each scheme. Of the eight, two STs were identified to be global clones 1 (n = 4) and 2 (n = 1) as per the Pasteur scheme. Resistance testing using classical microbiology determined between 50% and 92.9% resistance across all drugs. Percentage agreement between phenotypic and genotypic prediction of resistance ranged between 57.1% and 100%, with coefficient of agreement (κ) between 0.05 and 1. Seven isolates harboured mutations at significant loci (S81L in gyrA and S84L in parC). A number of novel plasmids were detected, including pKCRI-309C-1 (219000 bp) carrying 10 resistance genes, pKCRI-43-1 (34935 bp) carrying two resistance genes and pKCRI-49-1 (11681 bp) and pKCRI-28-1 (29606 bp), each carrying three resistance genes. New ampC alleles detected included ampC-69, ampC-70 and ampC-71. Global clone 1 and 2 isolates were found to harbour ISAba1 directly upstream of the ampC gene. Finally, SNP-based phylogenetic analysis of the A. baumannii isolates revealed closely related isolates in three clusters.CONCLUSIONS: The validity of the use of WGS in the prediction of phenotypic resistance can be appreciated, but at this stage is not sufficient for it to replace conventional antimicrobial susceptibility testing in our setting.",
author = "Kumburu, {Happiness H} and Tolbert Sonda and {van Zwetselaar}, Marco and Pimlapas Leekitcharoenphon and Oksana Lukjancenko and Mmbaga, {Blandina T} and Michael Alifrangis and Ole Lund and Aarestrup, {Frank M} and Kibiki, {Gibson S}",
note = "{\textcopyright} The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.",
year = "2019",
month = jun,
day = "1",
doi = "10.1093/jac/dkz055",
language = "English",
volume = "74",
pages = "1484--1493",
journal = "Journal of Antimicrobial Chemotherapy",
issn = "0305-7453",
publisher = "Oxford University Press",
number = "6",

}

RIS

TY - JOUR

T1 - Using WGS to identify antibiotic resistance genes and predict antimicrobial resistance phenotypes in MDR Acinetobacter baumannii in Tanzania

AU - Kumburu, Happiness H

AU - Sonda, Tolbert

AU - van Zwetselaar, Marco

AU - Leekitcharoenphon, Pimlapas

AU - Lukjancenko, Oksana

AU - Mmbaga, Blandina T

AU - Alifrangis, Michael

AU - Lund, Ole

AU - Aarestrup, Frank M

AU - Kibiki, Gibson S

N1 - © The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.

PY - 2019/6/1

Y1 - 2019/6/1

N2 - BACKGROUND: Reliable phenotypic antimicrobial susceptibility testing can be a challenge in clinical settings in low- and middle-income countries. WGS is a promising approach to enhance current capabilities.AIM: To study diversity and resistance determinants and to predict and compare resistance patterns from WGS data of Acinetobacter baumannii with phenotypic results from classical microbiological testing at a tertiary care hospital in Tanzania.METHODS AND RESULTS: MLST using Pasteur/Oxford schemes yielded eight different STs from each scheme. Of the eight, two STs were identified to be global clones 1 (n = 4) and 2 (n = 1) as per the Pasteur scheme. Resistance testing using classical microbiology determined between 50% and 92.9% resistance across all drugs. Percentage agreement between phenotypic and genotypic prediction of resistance ranged between 57.1% and 100%, with coefficient of agreement (κ) between 0.05 and 1. Seven isolates harboured mutations at significant loci (S81L in gyrA and S84L in parC). A number of novel plasmids were detected, including pKCRI-309C-1 (219000 bp) carrying 10 resistance genes, pKCRI-43-1 (34935 bp) carrying two resistance genes and pKCRI-49-1 (11681 bp) and pKCRI-28-1 (29606 bp), each carrying three resistance genes. New ampC alleles detected included ampC-69, ampC-70 and ampC-71. Global clone 1 and 2 isolates were found to harbour ISAba1 directly upstream of the ampC gene. Finally, SNP-based phylogenetic analysis of the A. baumannii isolates revealed closely related isolates in three clusters.CONCLUSIONS: The validity of the use of WGS in the prediction of phenotypic resistance can be appreciated, but at this stage is not sufficient for it to replace conventional antimicrobial susceptibility testing in our setting.

AB - BACKGROUND: Reliable phenotypic antimicrobial susceptibility testing can be a challenge in clinical settings in low- and middle-income countries. WGS is a promising approach to enhance current capabilities.AIM: To study diversity and resistance determinants and to predict and compare resistance patterns from WGS data of Acinetobacter baumannii with phenotypic results from classical microbiological testing at a tertiary care hospital in Tanzania.METHODS AND RESULTS: MLST using Pasteur/Oxford schemes yielded eight different STs from each scheme. Of the eight, two STs were identified to be global clones 1 (n = 4) and 2 (n = 1) as per the Pasteur scheme. Resistance testing using classical microbiology determined between 50% and 92.9% resistance across all drugs. Percentage agreement between phenotypic and genotypic prediction of resistance ranged between 57.1% and 100%, with coefficient of agreement (κ) between 0.05 and 1. Seven isolates harboured mutations at significant loci (S81L in gyrA and S84L in parC). A number of novel plasmids were detected, including pKCRI-309C-1 (219000 bp) carrying 10 resistance genes, pKCRI-43-1 (34935 bp) carrying two resistance genes and pKCRI-49-1 (11681 bp) and pKCRI-28-1 (29606 bp), each carrying three resistance genes. New ampC alleles detected included ampC-69, ampC-70 and ampC-71. Global clone 1 and 2 isolates were found to harbour ISAba1 directly upstream of the ampC gene. Finally, SNP-based phylogenetic analysis of the A. baumannii isolates revealed closely related isolates in three clusters.CONCLUSIONS: The validity of the use of WGS in the prediction of phenotypic resistance can be appreciated, but at this stage is not sufficient for it to replace conventional antimicrobial susceptibility testing in our setting.

U2 - 10.1093/jac/dkz055

DO - 10.1093/jac/dkz055

M3 - Journal article

C2 - 30843063

VL - 74

SP - 1484

EP - 1493

JO - Journal of Antimicrobial Chemotherapy

JF - Journal of Antimicrobial Chemotherapy

SN - 0305-7453

IS - 6

ER -

ID: 218274827