Evidence map›Paper›PMID 26351358›Full record

ArticleThe Journal of infectious diseases2016

Excessive Cytolytic Responses Predict Tuberculosis Relapse After Apparently Successful Treatment.

Jacqueline M Cliff, Jang-Eun Cho, Ji-Sook Lee, Katharina Ronacher, Elizabeth C King, Paul van Helden, Gerhard Walzl, Hazel M Dockrell

Open access · hybridAbstract read
In one paragraph

Article in The Journal of infectious diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.1field-weighted citation impact, top 8% 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

24 citing papers in PubMed, 40 citations in OpenAlex.

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  16. Review
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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

8 authors at 2 institutions in 2 countries.

Jacqueline M CliffTB Centre and Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom.
Jang-Eun ChoTB Centre and Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom.
Ji-Sook LeeTB Centre and Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom.
Katharina RonacherDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences/MRC Centre for Molecular and Cellular Biology/DST NRF Centre of Excellence for Biomedical TB Research, Faculty of Health Sciences, Stellenbosch University, Cape Town, South Africa.
Elizabeth C KingTB Centre and Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom.
Paul van HeldenDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences/MRC Centre for Molecular and Cellular Biology/DST NRF Centre of Excellence for Biomedical TB Research, Faculty of Health Sciences, Stellenbosch University, Cape Town, South Africa.
Gerhard WalzlDivision of Molecular Biology and Human Genetics, Department of Biomedical Sciences/MRC Centre for Molecular and Cellular Biology/DST NRF Centre of Excellence for Biomedical TB Research, Faculty of Health Sciences, Stellenbosch University, Cape Town, South Africa.
Hazel M DockrellTB Centre and Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, United Kingdom.
London School of Hygiene & Tropical Medicine · GBStellenbosch University · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrently, there are no tools to accurately predict tuberculosis relapse. This study aimed to determine whether patients who experience tuberculosis relapse have different immune responses to mycobacteria in vitro than patients who remain cured for 2 years.

methodsPatients with an initial episode of pulmonary tuberculosis were recruited in South Africa. Diluted blood, collected at diagnosis and after 2 and 4 weeks of treatment, was cultured with live Mycobacterium tuberculosis for 6 days, and cellular RNA was frozen. Gene expression in samples from 10 patients who subsequently experienced relapse, confirmed by strain genotyping, was compared to that in samples from patients who remained cured, using microarrays.

resultsAt diagnosis, expression of 668 genes was significantly different in samples from patients who experienced relapse, compared with expression in patients who remained successfully cured; these differences persisted for at least 4 weeks. Gene ontology and biological pathways analyses revealed significant upregulation of genes involved in cytotoxic cell-mediated killing. Results were confirmed by real-time quantitative reverse-transcription polymerase chain reaction analysis in a wider patient cohort.

conclusionsThese data show that patients who will subsequently experience relapse exhibit altered immune responses, including excessively robust cytolytic responses to M. tuberculosis in vitro, at the time of diagnosis, compared with patients who will achieve durable cure. Together with microbiological and clinical indices, these differences could be exploited in drug development.

Indexed as

RecurrenceAdultAntitubercular AgentsBCG VaccineBiomarkersBlood CellsFemaleGene Expression RegulationHumansMaleMiddle AgedMycobacterium bovisMycobacterium tuberculosisRNA, MessengerTuberculosis, PulmonaryYoung AdultAntitubercular AgentsBCG VaccineBiomarkersRNA, Messengerblooddrug developmentmicroarraypatienttranscriptomics

Identifiers

PMID26351358
PMCPMC4704670
OpenAlexW2197951368

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.