Evidence map›Paper›PMID 28351427›Full record

ArticleBMC medicine2017

Use of whole-genome sequencing to distinguish relapse from reinfection in a completed tuberculosis clinical trial.

Adam A Witney, Anna L E Bateson, Amina Jindani, Patrick P J Phillips, David Coleman, Neil G Stoker, Philip D Butcher, Timothy D McHugh, RIFAQUIN Study Team

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
4.0field-weighted citation impact, top 6% 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

40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 59 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Review
  6. Article
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  13. Microbiology spectrum · 2024
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  14. Review
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  16. CD4bioRxiv : the preprint server for biology · 2023
    Article
  17. Article
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  19. Article
  20. Whole Genome Sequencing Assessing Impact of Diabetes Mellitus on Tuberculosis Mutations and Type of Recurrence in India.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022
    Article
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

9 authors at 2 institutions in 1 country.

Adam A WitneyInstitute for Infection and Immunity, St George's University of London, London, UK. awitney@sgul.ac.uk.
Anna L E BatesonUCL Centre for Clinical Microbiology, Royal Free Campus, UCL, London, UK.
Amina JindaniInstitute for Infection and Immunity, St George's University of London, London, UK.
Patrick P J PhillipsMRC Clinical Trials Unit at UCL, London, UK.
David ColemanInstitute for Infection and Immunity, St George's University of London, London, UK.
Neil G StokerInstitute for Infection and Immunity, St George's University of London, London, UK.
Philip D ButcherInstitute for Infection and Immunity, St George's University of London, London, UK.
Timothy D McHughUCL Centre for Clinical Microbiology, Royal Free Campus, UCL, London, UK.
RIFAQUIN Study Team
St George's, University of London · GBThe Royal Free Hospital · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundRIFAQUIN was a tuberculosis chemotherapy trial in southern Africa including regimens with high-dose rifapentine with moxifloxacin. Here, the application of whole-genome sequencing (WGS) is evaluated within RIFAQUIN for identifying new infections in treated patients as either relapses or reinfections. WGS is further compared with mycobacterial interspersed repetitive units-variable number tandem repeats (MIRU-VNTR) typing. This is the first report of WGS being used to evaluate new infections in a completed clinical trial for which all treatment and epidemiological data are available for analysis.

methodsDNA from 36 paired samples of Mycobacterium tuberculosis cultured from patients before and after treatment was typed using 24-loci MIRU-VNTR, in silico spoligotyping and WGS. Following WGS, the sequences were mapped against the reference strain H37Rv, the single-nucleotide polymorphism (SNP) differences between pairs were identified, and a phylogenetic reconstruction was performed.

resultsWGS indicated that 32 of the paired samples had a very low number of SNP differences (0-5; likely relapses). One pair had an intermediate number of SNP differences, and was likely the result of a mixed infection with a pre-treatment minor genotype that was highly related to the post-treatment genotype; this was reclassified as a relapse, in contrast to the MIRU-VNTR result. The remaining three pairs had very high SNP differences (>750; likely reinfections).

conclusionsWGS and MIRU-VNTR both similarly differentiated relapses and reinfections, but WGS provided significant extra information. The low proportion of reinfections seen suggests that in standard chemotherapy trials with up to 24 months of follow-up, typing the strains brings little benefit to an analysis of the trial outcome in terms of differentiating relapse and reinfection. However, there is a benefit to using WGS as compared to MIRU-VNTR in terms of the additional genotype information obtained, in particular for defining the presence of mixed infections and the potential to identify known and novel drug-resistance markers.

Indexed as

Genome, BacterialPolymorphism, Single NucleotideAntibiotics, AntitubercularBacterial Typing TechniquesDNA, BacterialGenotypeHumansMinisatellite RepeatsMycobacterium tuberculosisPhylogenyPolymerase Chain ReactionRecurrenceSequence Analysis, DNATuberculosisAntibiotics, AntitubercularDNA, BacterialClinical trialTuberculosisWhole genome sequencing

Identifiers

PMID28351427
PMCPMC5371199
OpenAlexW2598713477

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.