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Record W4393867396 · doi:10.3389/fmala.2024.1363969

Antimalarial drug resistance profiling of Plasmodium falciparum infections in India using Ion Torrent deep sequencing

2024· article· en· W4393867396 on OpenAlexfundno aff
Sonal Kale, Swapna Uplekar, Nabamita Bandyopadhyay, Pavitra N. Rao, Surya K Sharma, Nikunj Tandel, Ankita Patel, Ranvir Singh, Aaron Dank, Sangamithra Ravishankaran, G. Sri Lakshmi Priya, Aswin Asokan, Alex Eapen, O. P. Singh, Jane M. Carlton, Prashant K. Mallick

Bibliographic record

VenueFrontiers in Malaria · 2024
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesYork University
KeywordsPlasmodium falciparumMalariaIon semiconductor sequencingDrug resistanceVirologyBiologyMicrobiologyDNA sequencingGeneticsImmunologyGene

Abstract

fetched live from OpenAlex

Introduction Tracking the emergence and spread of antimalarial drug resistance is critical for supporting progress towards the control and eventual elimination of malaria in South Asia, especially India. Plasmodium falciparum has evolved resistance to virtually every antimalarial drug, and significant progress has been made to identify the molecular genetic mechanisms involved in the most common types of resistance. Methods An amplicon sequencing protocol was used for molecular surveillance of antimalarial drug resistance in a total of 158 patient isolates collected from December 2012 to September 2015 from three sites in south, west and east India: Tamil Nadu, Gujarat, and Odisha respectively. Five full length Plasmodium falciparum genes whose mutant proteins are implicated in antimalarial drug resistance were investigated: Pfcrt for chloroquine, Pfdhfr for pyrimethamine, Pfdhps for sulfadoxine, Pfk13 for artemisinin and Pfmdr1 for resistance to multiple antimalarial drugs. Results We observed a high proportion of wild-type Pfcrt and Pfdhfr haplotypes from the P. falciparum-dominant site Rourkela, while mutant Pfcrt and Pfdhfr haplotypes were fixed at the P. vivax dominant sites Chennai and Nadiad. The wild-type Pfdhps haplotype was predominant across all study sites. We identified mutations in the propeller domain of Pfk13, although they are not associated with resistance to artemisinin. Finally, using samples taken from the same patient on day 2, day 7, and day 14 after artemisinin combination treatment, we were able to observe changes in allele frequency of drug resistance genes during the course of an infection. Discussion This is the first high-throughput deep sequencing study of five full-length P. falciparum genes in clinical isolates from three different study sites in India with varying transmission ecologies. Amplicon sequencing based on ion-torrent has the potential to track changes in the frequency of drug resistant alleles as a patient is undergoing drug therapy, and to identify new resistance mutations as they increase in frequency in the patient. This study showed possibility of whole gene sequencing, leads to in-depth molecular surveillance of multiple antimalarial resistant candidates and furthermore suggests investigations on reversal of resistant genotypes due to usage of artemisinin combination therapy in P. falciparum prevalent areas of the country.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes1
Has abstractyes

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