Pre-treatment integrase inhibitor resistance is uncommon in antiretroviral therapy-naive individuals with HIV-1 subtype A1 and D infections in Uganda
Bibliographic record
Abstract
OBJECTIVE: Dolutegravir (DTG) is now a preferred component of first-line antiretroviral therapy (ART). However, prevalence data on natural resistance to integrase inhibitors [integrase strand transfer inhibitors (INSTIs)] in circulating non-subtype B HIV-1 in sub-Saharan Africa is scarce. Our objective is to report prevalence of pre-treatment integrase polymorphisms associated with resistance to INSTIs in an ART-naive cohort with diverse HIV-1 subtypes. DESIGN: We retrospectively examined HIV-1 integrase sequences from Uganda. METHODS: Plasma samples were derived from the Uganda AIDS Rural Treatment Outcomes (UARTO) cohort, reflecting enrollment from 2002 to 2010, prior to initiation of ART. HIV-1 integrase was amplified using nested-PCR and Sanger-sequenced (HXB2 4230-5093). Stanford HIVdb v8.8 was used to infer clinically significant INSTI-associated mutations. Human leukocyte antigen (HLA) typing was performed for all study participants. RESULTS: Plasma samples from 511 ART-naive individuals (subtype: 48% A1, 39% D) yielded HIV-1 integrase genotyping results. Six out of 511 participants (1.2%) had any major INSTI-associated mutations. Of these, two had E138T (subtype A1), three had E138E/K (subtype D), and one had T66T/I (subtype D). No participants had mutations traditionally associated with high levels of INSTI resistance. HLA genotypes A∗02:01/05/14, B∗44:15, and C∗04:07 predicted the presence of L74I, a mutation recently observed in association with long-acting INSTI cabotegravir virologic failure. CONCLUSION: We detected no HIV-1 polymorphisms associated with high levels of DTG resistance in Uganda in the pre-DTG era. Our results support widespread implementation of DTG but careful monitoring of patients on INSTI with virologic failure is warranted to determine if unique mutations predict failure for non-B subtypes of HIV-1.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".