HIV Drug Resistance Surveillance Using Pooled Pyrosequencing
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".