Evidence map›Paper›PMID 27518286›Full record

ArticlePloS one2016

Analysis of Mycobacterium tuberculosis Genotypic Lineage Distribution in Chile and Neighboring Countries.

Jaime Lagos, David Couvin, Loredana Arata, Javier Tognarelli, Carolina Aguayo, Tamara Leiva, Fabiola Arias, Juan Carlos Hormazabal, Nalin Rastogi, Jorge Fernández

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Molecular epidemiology ofJournal of clinical tuberculosis and other mycobacterial diseases · 2024
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Frontiers in veterinary science · 2021
    Article
  8. Article
  9. Circulation ofPathogens and global health · 2019
    Review
  10. Genetic Diversity and Molecular Epidemiology ofThe American journal of tropical medicine and hygiene · 2019
    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

10 authors.

Jaime LagosSubdepartment of Molecular Genetics, Public Health Institute of Chile, Santiago, Chile.
David CouvinWHO Supranational TB Reference Laboratory, TB and Mycobacteria Unit, Institut Pasteur de la Guadeloupe, Guadeloupe, France.
Loredana ArataSubdepartment of Molecular Genetics, Public Health Institute of Chile, Santiago, Chile.
Javier TognarelliSubdepartment of Molecular Genetics, Public Health Institute of Chile, Santiago, Chile.
Carolina AguayoSubdepartment of Molecular Genetics, Public Health Institute of Chile, Santiago, Chile.
Tamara LeivaMycobacteria Laboratory, Public Health Institute of Chile, Santiago, Chile.
Fabiola AriasMycobacteria Laboratory, Public Health Institute of Chile, Santiago, Chile.
Juan Carlos HormazabalSubdepartment of Infectious Disease, Public Health Institute of Chile, Santiago, Chile.
Nalin RastogiWHO Supranational TB Reference Laboratory, TB and Mycobacteria Unit, Institut Pasteur de la Guadeloupe, Guadeloupe, France.
Jorge FernándezSubdepartment of Molecular Genetics, Public Health Institute of Chile, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis (MTB), remains a disease of high importance to global public health. Studies into the population structure of MTB have become vital to monitoring possible outbreaks and also to develop strategies regarding disease control. Although Chile has a low incidence of MTB, the current rates of migration have the potential to change this scenario. We collected and analyzed a total of 458 M. tuberculosis isolates (1 isolate per patient) originating from all 15 regions of Chile. The isolates were genotyped using the spoligotyping method and the data obtained were analyzed and compared with the SITVIT2 database. A total of 169 different patterns were identified, of which, 119 patterns (408 strains) corresponded to Spoligotype International Types (SITs) and 50 patterns corresponded to orphan strains. The most abundantly represented SITs/lineages were: SIT53/T1 (11.57%), SIT33/LAM3 (9.6%), SIT42/LAM9 (9.39%), SIT50/H3 (5.9%), SIT37/T3 (5%); analysis of the spoligotyping minimum spanning tree as well as spoligoforest were suggestive of a recent expansion of SIT42, SIT50 and SIT37; all of which potentially evolved from SIT53. The most abundantly represented lineages were LAM (40.6%), T (34.1%) and Haarlem (13.5%). LAM was more prevalent in the Santiago (43.6%) and Concepción (44.1%) isolates, rather than the Iquique (29.4%) strains. The proportion of X lineage was appreciably higher in Iquique and Concepción (11.7% in both) as compared to Santiago (1.6%). Global analysis of MTB lineage distribution in Chile versus neighboring countries showed that evolutionary recent lineages (LAM, T and Haarlem) accounted together for 88.2% of isolates in Chile, a pattern which mirrored MTB lineage distribution in neighboring countries (n = 7378 isolates recorded in SITVIT2 database for Peru, Brazil, Paraguay, and Argentina; and published studies), highlighting epidemiological advantage of Euro-American lineages in this region. Finally, we also observed exclusive emergence of patterns SIT4014/X1 and SIT4015 (unknown lineage signature) that have hitherto been found exclusively in Chile, indicating that conditions specific to Chile, along with the unique genetic makeup of the Chilean population, might have allowed for a possible co-evolution leading to the success of these emerging genotypes.

Indexed as

ArgentinaBacterial Typing TechniquesBrazilChileGenetic VariationGenotypeHumansIncidenceMycobacterium tuberculosisParaguayPeruPhylogenyTuberculosis

Identifiers

PMID27518286
PMCPMC4982630

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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.