Evidence map›Paper›PMID 30843063›Full record

ArticleThe Journal of antimicrobial chemotherapy2019

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

Happiness H Kumburu, Tolbert Sonda, Marco van Zwetselaar, Pimlapas Leekitcharoenphon, Oksana Lukjancenko, Blandina T Mmbaga, Michael Alifrangis, Ole Lund, Frank M Aarestrup, Gibson S Kibiki

Open access · bronzeAbstract read
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
2.4field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 66 citations in OpenAlex.

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  17. Molecular epidemiology and antimicrobial resistance patterns of carbapenem-resistantFrontiers in cellular and infection microbiology · 2023
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  18. Antimicrobial resistance genes ofAIMS microbiology · 2023
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  19. Whole-Genome Sequencing of ST2Antibiotics (Basel, Switzerland) · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Happiness H KumburuKilimanjaro Christian Medical University College, Moshi, Tanzania.
Tolbert SondaKilimanjaro Christian Medical University College, Moshi, Tanzania.
Marco van ZwetselaarKilimanjaro Clinical Research Institute, Moshi, Tanzania.
Pimlapas LeekitcharoenphonDTU-Food, Technical University of Denmark, Copenhagen, Denmark.
Oksana LukjancenkoDTU-Food, Technical University of Denmark, Copenhagen, Denmark.
Blandina T MmbagaKilimanjaro Christian Medical University College, Moshi, Tanzania.
Michael AlifrangisCentre for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen and Department of Infectious Diseases, Copenhagen University Hospital, Copenhagen, Denmark.
Ole LundDTU-Bioinformatics, Technical University of Denmark, Copenhagen, Denmark.
Frank M AarestrupDTU-Food, Technical University of Denmark, Copenhagen, Denmark.
Gibson S KibikiEast African Health Research Commission, Bujumbura, Burundi.
Kilimanjaro Christian Medical Centre · TZTechnical University of Denmark · DKUniversity of Copenhagen · DK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReliable 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.

aimTo 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

resultsMLST 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.

conclusionsThe 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.

Indexed as

Drug Resistance, Multiple, BacterialAcinetobacter baumanniiAcinetobacter InfectionsAdultAgedAged, 80 and overAnti-Bacterial AgentsBacterial ProteinsChildFemaleGenome, BacterialHumansMaleMiddle AgedMutationTanzaniaAnti-Bacterial AgentsBacterial Proteins

Identifiers

PMID30843063
PMCPMC6524488
OpenAlexW2921054405

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.