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Record W2086729134 · doi:10.1371/journal.pone.0009263

HIV Drug Resistance Surveillance Using Pooled Pyrosequencing

2010· article· en· W2086729134 on OpenAlexafffund
Hezhao Ji, Nathalie Massé, Shaun Tyler, Binhua Liang, Yang Li, Harriet Merks, Morag Graham, Paul Sandstrom, James Brooks

Bibliographic record

VenuePLoS ONE · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersGovernment of Canada
KeywordsPyrosequencingSanger sequencingBiologyGeneticsPopulationGenotypeAmpliconDrug resistanceSequence analysisSingle-nucleotide polymorphismVirologyMutationGenePolymerase chain reactionMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Surveillance for HIV transmitted drug resistance (TDR) is performed using HIV genotype results from individual specimens. Pyrosequencing, through its massive parallel sequencing ability, can analyze large numbers of specimens simultaneously. Instead of using pyrosequencing conventionally, to sequence a population of viruses within an individual, we interrogated a single combined pool of surveillance specimens to demonstrate that it is possible to determine TDR rates in HIV protease from a population of individuals. METHODOLOGY/PRINCIPAL FINDINGS: The protease region from 96 treatment naïve, HIV+ serum specimens was genotyped using standard Sanger sequencing method. The 462 bp protease amplicons from these specimens were pooled in equimolar concentrations and re-sequenced using the GS FLX Titanium system. The nucleotide (NT) and amino acid (AA) differences from the reference sequence, along with TDR mutations, detected by each method were compared. In the protease sequence, there were 212 nucleotide and 81 AA differences found using conventional sequencing and 345 nucleotide and 168 AA differences using pyrosequencing. All nucleotide and amino acid polymorphisms found at frequencies >/=5% in pyrosequencing were detected using both methods with the rates of variation highly correlated. Using Sanger sequencing, two TDR mutations, M46L and I84V, were each detected as mixtures at a frequency of 1.04% (1/96). These same TDR mutations were detected by pyrosequencing with a prevalence of 0.29% and 0.34% respectively. Phylogenetic analysis established that the detected low frequency mutations arose from the same single specimens that were found to contain TDR mutations by Sanger sequencing. Multiple clinical protease DR mutations present at higher frequencies were concordantly identified using both methods. CONCLUSIONS/SIGNIFICANCE: We show that pyrosequencing pooled surveillance specimens can cost-competitively detect protease TDR mutations when compared with conventional methods. With few modifications, the method described here can be used to determine population rates of TDR in both protease and reverse transcriptase. Furthermore, this pooled pyrosequencing technique may be generalizable to other infectious agents where a survey of DR rates is required.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.242
Teacher spread0.210 · 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

Citations33
Published2010
Admission routes2
Has abstractyes

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