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Record W3081162216 · doi:10.1093/cid/ciaa1236

Mutations in an Aquaglyceroporin as a Proven Marker of Antimony Clinical Resistance in the Parasite <i>Leishmania donovani</i>

2020· article· en· W3081162216 on OpenAlexfundno aff
Jade-Éva Potvin, Philippe Leprohon, Marine Queffeulou, Shyam Sundar, Marc Ouellette

Bibliographic record

VenueClinical Infectious Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsCRISPRLeishmania donovaniDrug resistanceGeneBiologyGenome editingLeishmaniasisGeneticsGenomeVirologyVisceral leishmaniasis

Abstract

fetched live from OpenAlex

BACKGROUND: Antimonial drugs have long been the mainstay to treat visceral leishmaniasis. Their use has been discontinued in the Indian subcontinent because of drug resistance, but they are still clinically useful elsewhere. The goal of this study was to find markers of antimony resistance in Leishmania donovani clinical isolates and validate experimentally their role in resistance. METHODS: The genomes of sensitive and antimony-resistant clinical isolates were sequenced. The role of a specific gene in contributing to resistance was studied by CRISPR-Cas9-mediated gene editing and intracellular drug sensitivity assays. RESULTS: Both gene copy number variations and single nucleotide variants were associated with antimony resistance. A homozygous insertion of 2 nucleotides was found in the gene coding for the aquaglyceroporin AQP1 in both resistant isolates. Restoring the wild-type AQP1 open reading frame re-sensitized the 2 independent resistant isolates to antimonials. Alternatively, editing the genome of a sensitive isolate by incorporating the 2-nucleotide insertion in its AQP1 gene led to antimony-resistant parasites. CONCLUSIONS: Through genomic analysis and CRISPR-Cas9-mediated genome editing we have proven the role of the AQP1 mutations in antimony clinical resistance in L. donovani.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.080
GPT teacher head0.431
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2020
Admission routes1
Has abstractyes

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