Factors associated with resistance of HIV-1 reservoir viruses to neutralization by autologous IgG antibodies
Bibliographic record
Abstract
BACKGROUNDAntiretroviral therapy (ART) prevents HIV-1 replication but does not eliminate the latent reservoir, the source of viral rebound if treatment is stopped. Autologous neutralizing antibodies (aNAbs) can block in vitro outgrowth of a subset of reservoir viruses and therefore potentially affect viral rebound upon ART interruption.METHODSWe investigated aNAbs in 31 people with HIV-1 (PWH) on ART.RESULTSParticipants fell into 2 groups based on a high or low fraction of aNAb-resistant reservoir isolates, with most isolates being aNAb-resistant (IC50 > 100 μg/mL). Time on uninterrupted ART was associated with higher aNAb resistance. However, pharmacodynamic analysis predicted that many isolates would be partially inhibited at physiologic IgG concentrations, to the same degree as by single antiretroviral drugs. Steep dose-response curve slopes, an indication of cooperativity, were observed for the rare isolates that were very strongly inhibited (> 5 logs) by aNAbs. Resistance to aNAbs was not fully explained by declining in aNAb titers and may be driven partially by ADCC-mediated elimination of infected cells carrying aNAb-sensitive viruses over long time intervals, leaving only aNAb-resistant viruses, which can contribute to viral rebound.CONCLUSIONInhibition of reservoir viruses by aNAbs may be affected by dose-response curve slope, time on uninterrupted ART, waning of antibody responses, and selection against cells with aNAb-sensitive viruses.FUNDINGThis work was supported by NIH Martin Delaney Collaboratories for HIV Cure Research grant awards UM1AI164556, UM1AI164570, and UM1AI164560, and the Howard Hughes Medical Institute.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".