Evidence map›Paper›PMID 28424085›Full record

ArticleGenome biology2017

The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy.

Andrej Trauner, Qingyun Liu, Laura E Via, Xin Liu, Xianglin Ruan, Lili Liang, Huimin Shi, Ying Chen, Ziling Wang, Ruixia Liang and 10 more

Abstract read
In one paragraph

Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

86 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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  18. Loss of theScience advances · 2025
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  20. Article

26 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Andrej TraunerDepartment of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, 4002, Basel, Switzerland.
Qingyun LiuKey Laboratory of Medical Molecular Virology of Ministries of Education and Health, Institutes of Biomedical Sciences and Institute of Medical Microbiology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Laura E ViaTuberculosis Research Section, Laboratory of Clinical Infectious Diseases, NIAID, NIH, Bethesda, MD, 20892, USA.
Xin LiuHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Xianglin RuanHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Lili LiangHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Huimin ShiHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Ying ChenHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Ziling WangHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Ruixia LiangHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Wei ZhangHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Wang WeiHenan Provincial Chest Hospital, Zhengzhou, 450003, Henan, China.
Jingcai GaoSino-US International Research Centers of Tuberculosis, Zhengzhou, 450003, Henan, China.
Gang SunKey Laboratory of Medical Molecular Virology of Ministries of Education and Health, Institutes of Biomedical Sciences and Institute of Medical Microbiology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Daniela BritesDepartment of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, 4002, Basel, Switzerland.
Kathleen EnglandTuberculosis Research Section, Laboratory of Clinical Infectious Diseases, NIAID, NIH, Bethesda, MD, 20892, USA.
Guolong ZhangSino-US International Research Centers of Tuberculosis, Zhengzhou, 450003, Henan, China. guolongzhang@hotmail.com.
Sebastien GagneuxDepartment of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, 4002, Basel, Switzerland. sebastien.gagneux@unibas.ch.
Clifton E BarryTuberculosis Research Section, Laboratory of Clinical Infectious Diseases, NIAID, NIH, Bethesda, MD, 20892, USA. cbarry@niaid.nih.gov.
Qian GaoKey Laboratory of Medical Molecular Virology of Ministries of Education and Health, Institutes of Biomedical Sciences and Institute of Medical Microbiology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China. qgao99@yahoo.com.

Funding

International Tuberculosis Research in ChinaZIAAI001081 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI BARRY, CLIFTON · 2010 to 2025
$12.2M
6 · The paper itself

Abstract

backgroundCombination therapy is one of the most effective tools for limiting the emergence of drug resistance in pathogens. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We address this question by studying the population dynamics of Mycobacterium tuberculosis within tuberculosis patients undergoing treatment with different combinations of antibiotics.

resultsBy combining very deep whole genome sequencing (~1000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy has a clear impact on the population dynamics: sufficient drug pressure bears a clear signature of purifying selection leading to apparent genetic stability. In contrast, M. tuberculosis populations subject to less drug pressure show markedly different dynamics, including cases of acquisition of additional drug resistance.

conclusionsOur findings show that for a pathogen like M. tuberculosis, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.

Indexed as

Host-Pathogen InteractionsAllelesAntitubercular AgentsDrug Resistance, BacterialDrug Therapy, CombinationGenetic VariationGenome, BacterialGenotypeHumansMarkov ChainsMicrobial Sensitivity TestsMycobacterium tuberculosisPolymorphism, Single NucleotideSelection, GeneticSequence Analysis, DNASputumAntitubercular AgentsCombination therapyDrug resistanceTuberculosisWhole genome sequencingWithin-host evolution

Identifiers

PMID28424085
PMCPMC5395877

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Registered trials

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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.