Evidence map›Paper›PMID 39906217›Full record

ArticleFrontiers in cellular and infection microbiology2024

Whole-genome sequencing reveals transmission pattern and drug resistance of

Feng Ding, Wanfei Liu, Chi Wu, Wensi Zhang, Shuyan Chen, Wenjie Lai, Jiayao Qu, Qiang Lin, Shuihua Lu, Jiuxin Qu

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In one paragraph

Article in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

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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

2 citing papers in PubMed.

  1. Molecular Characterization of theAntibiotics (Basel, Switzerland) · 2026
    Article
  2. Review
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.

Feng Ding *National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Wanfei Liu *Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Chi Wu *National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Wensi ZhangDepartment of Clinical Laboratory, Shenzhen Third People's Hospital, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.
Shuyan ChenNational Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Wenjie LaiNational Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Jiayao QuNational Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Qiang LinShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Shuihua LuNational Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Jiuxin QuNational Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Tuberculosis (TB) remains a serious global public health problem. The Methods: A retrospective study on 286 samples from 184 TB patients collected between 2015 and 2018 in Shenzhen Third People's Hospital was conducted using whole-genome sequencing. Drug susceptibility testing (DST) was performed using both phenotypic DST (pDST) and molecular DST (mDST). Sample diversity was evaluated by SNPs and transmission clusters were identified based on SNP differences of 12 or fewer in genetic clusters. Results: Except four samples identified as non-tuberculous mycobacteria, 282 MTB samples (181 patients) underwent mDST, with 244 samples (162 patients) undergoing pDST. The overall multidrug-resistant rate in patients was 22.31% in pDST (12.00% for new patients and 40.82% for retreatment patients) and 34.48% in mDST (20.41% for new patients and 58.21% for retreatment patients). Totally 92 transmission clusters were identified, encompassing 70.21% samples (57.46% patients), with 5 clusters containing samples (15, 5.32%) from different patients (9, 4.97%), indicating recent transmission. The drug-resistant mutations in 36 of 45 transmission clusters (80.00%) were identical in all samples, suggesting the transmission of drug resistance. Patients with multiple samples were categorized into simultaneous sampling (SS) and continuous sampling (CS) groups, revealing significant differences in treatment types, treatment outcomes, residential addresses, and drug resistance types. mDST showed greater accuracy than pDST in SS and CS groups. A novel method based on heterozygous SNPs and two-sample Kolmogorov-Smirnov test were developed and identified 12 (4.26%) samples as mixed infection samples. Six of 12 patients had mixed and pure samples together, and major strains of mixed samples were closer to corresponding pure strains than minor strains. Conclusions: This retrospective study, conducted at the only municipal hospital specializing in infectious diseases in Shenzhen, provides the opportunity to understand drug resistance of TB patients, which mainly are refractory patients. The study revealed transmission patterns of MTB, analyzed mixed infections, and tracked changes in MTB strains during short/long-term treatment.

Indexed as

Drug Resistance, Multiple, BacterialMycobacterium tuberculosisTuberculosisTuberculosis, Multidrug-ResistantWhole Genome SequencingAdolescentAdultAgedAntitubercular AgentsChinaFemaleGenome, BacterialHumansMaleMicrobial Sensitivity TestsMiddle AgedAntitubercular Agentsdrug resistant mutationsMycobacterium tuberculosisphenotypic DSTtransmission patternwhole genome sequencing

Identifiers

PMID39906217
PMCPMC11790449

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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.