Genetic determinants of HIV-1 subtype C Nef-mediated SERINC3 down-regulation
Bibliographic record
Abstract
BACKGROUND: Nef-mediated down-regulation of the host restriction factors SERINC3 and SERINC5 significantly enhances HIV-1 infectivity. Natural Nef polymorphisms that affect SERINC3 down-regulation are not as well-characterised as those that affect SERINC5 down-regulation, particularly in HIV-1 subtype C infection. We therefore aimed to identify genetic determinants of SERINC3 down-regulation by subtype C Nef. In addition, we investigated the role of SERINC3 down-regulation activity in disease progression and its contribution to overall Nef function, using Nef fitness model-derived E values as a proxy for overall Nef function in vivo. METHODS: SERINC3 down-regulation activity of 107 participant-derived Nef clones was measured using a flow cytometry-based assay in a T cell line. The relationship between SERINC3 down-regulation activity and viral load set point or rate of CD4 + T cell decline during untreated HIV infection was analysed by linear regression. Quantile regression was used to assess the contribution of SERINC3 down-regulation activity to overall Nef function. Individual Nef amino acids associated with a significantly altered SERINC3 down-regulation activity were identified using codon-by-codon Mann Whitney U tests. RESULTS: SERINC3 down-regulation activity was not a significant predictor of viral load set point nor rate of CD4 + T cell decline. SERINC3 down-regulation activity was a significant predictor of estimated Nef fitness (E values) in univariate analysis (p < 0.0001) and remained significant in multivariate analyses adjusting for other Nef functions that were measured for the same Nef clones (p < 0.02). A total of 30 amino acids were identified to be associated with differential Nef-mediated ability to down-regulate SERINC3 (p < 0.05 and q < 0.3), with 63% of these residues being in the N-terminal domain. CONCLUSION: Although SERINC3 down-regulation did not associate significantly with markers of HIV disease progression, our results nevertheless suggest that SERINC3 down-regulation contributes significantly to overall Nef function and fitness. The identification of Nef amino acids associated with differential SERINC3 down-regulation ability may be useful for rational design of therapeutics and vaccines targeting the Nef region.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".