Evidence map›Paper›PMID 37956210›Full record

ArticlePLoS neglected tropical diseases2023

Wide reference databases for typing Trypanosoma cruzi based on amplicon sequencing of the minicircle hypervariable region.

Fanny Rusman, Anahí G Díaz, Tatiana Ponce, Noelia Floridia-Yapur, Christian Barnabé, Patricio Diosque, Nicolás Tomasini

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Deep Sequencing of mHVRs for Trypanosoma cruzi Genotyping.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
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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

7 authors.

Fanny RusmanUnidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental Dr. Miguel Ángel Basombrío, Universidad Nacional de Salta-CONICET, Salta, Salta, Argentina.
Anahí G DíazUnidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental Dr. Miguel Ángel Basombrío, Universidad Nacional de Salta-CONICET, Salta, Salta, Argentina.
Tatiana PonceUnidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental Dr. Miguel Ángel Basombrío, Universidad Nacional de Salta-CONICET, Salta, Salta, Argentina.
Noelia Floridia-YapurUnidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental Dr. Miguel Ángel Basombrío, Universidad Nacional de Salta-CONICET, Salta, Salta, Argentina.
Christian BarnabéInstitut de Recherche pour le Développement (IRD), UMR INTERTRYP IRD-CIRAD, University of Montpellier, Montpellier, France.
Patricio DiosqueUnidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental Dr. Miguel Ángel Basombrío, Universidad Nacional de Salta-CONICET, Salta, Salta, Argentina.
Nicolás TomasiniUnidad de Epidemiología Molecular (UEM), Instituto de Patología Experimental Dr. Miguel Ángel Basombrío, Universidad Nacional de Salta-CONICET, Salta, Salta, Argentina.ORCID 0000-0002-0518-1942

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTrypanosoma cruzi, the etiological agent of Chagas Disease, exhibits remarkable genetic diversity and is classified into different Discrete Typing Units (DTUs). Strain typing techniques are crucial for studying T. cruzi, because their DTUs have significant biological differences from one another. However, there is currently no methodological strategy for the direct typing of biological materials that has sufficient sensitivity, specificity, and reproducibility. The high diversity and copy number of the minicircle hypervariable regions (mHVRs) makes it a viable target for typing. METHODOLOGY/PRINCIPAL

findingsApproximately 24 million reads obtained by amplicon sequencing of the mHVR were analyzed for 62 strains belonging to the six main T. cruzi DTUs. To build reference databases of mHVR diversity for each DTU and to evaluate this target as a typing tool. Strains of the same DTU shared more mHVR clusters than strains of different DTUs, and clustered together. Different identity thresholds were used to build the reference sets of the mHVR sequences (85% and 95%, respectively). The 95% set had a higher specificity and was more suited for detecting co-infections, whereas the 85% set was excellent for identifying the primary DTU of a sample. The workflow's capacity for typing samples obtained from cultures, a set of whole-genome data, under various simulated PCR settings, in the presence of co-infecting lineages and for blood samples was also assessed. CONCLUSIONS/SIGNIFICANCE: We present reference databases of mHVR sequences and an optimized typing workflow for T. cruzi including a simple online tool for deep amplicon sequencing analysis (https://ntomasini.github.io/cruzityping/). The results show that the workflow displays an equivalent resolution to that of the other typing methods. Owing to its specificity, sensitivity, relatively low cost, and simplicity, the proposed workflow could be an alternative for screening different types of samples.

Indexed as

Chagas DiseaseTrypanosoma cruziGenetic VariationGenotypeHumansPolymerase Chain ReactionReproducibility of ResultsSequence Analysis, DNA

Identifiers

PMID37956210
PMCPMC10681310

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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.