Evidence map›Paper›PMID 27194699›Full record

ArticleProceedings. Biological sciences2016

The emergence of latent infection in the early evolution of Mycobacterium tuberculosis.

Rebecca H Chisholm, Mark M Tanaka

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

4 citing papers in PubMed.

  1. Pan and Core Genome Analysis of 183Antibiotics (Basel, Switzerland) · 2021
    Article
  2. Review
  3. Review
  4. Controlled fire use in early humans might have triggered the evolutionary emergence of tuberculosis.Proceedings of the National Academy of Sciences of the United States of America · 2016
    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

2 authors.

Rebecca H ChisholmSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2052, Australia Evolution and Ecology Research Centre, University of New South Wales, Sydney 2052, Australia rebecca.chisholm@unsw.edu.au.ORCID 0000-0002-7830-793X
Mark M TanakaSchool of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney 2052, Australia Evolution and Ecology Research Centre, University of New South Wales, Sydney 2052, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mycobacterium tuberculosis has an unusual natural history in that the vast majority of its human hosts enter a latent state that is both non-infectious and devoid of any symptoms of disease. From the pathogen perspective, it seems counterproductive to relinquish reproductive opportunities to achieve a détente with the host immune response. However, a small fraction of latent infections reactivate to the disease state. Thus, latency has been argued to provide a safe harbour for future infections which optimizes the persistence of M. tuberculosis in human populations. Yet, if a pathogen begins interactions with humans as an active disease without latency, how could it begin to evolve latency properties without incurring an immediate reproductive disadvantage? We address this question with a mathematical model. Results suggest that the emergence of tuberculosis latency may have been enabled by a mechanism akin to cryptic genetic variation in that detrimental latency properties were hidden from natural selection until their expression became evolutionarily favoured.

Indexed as

Genetic FitnessAsymptomatic InfectionsHumansModels, GeneticMycobacterium tuberculosisTuberculosisVirulenceadaptive dynamicscryptic genetic variationlatencyniche constructionvirulence

Identifiers

PMID27194699
PMCPMC4892801

What OpenQuestion holds

Textmetadata
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.