Stability of APOBEC3F in the Presence of the APOBEC3 Antagonist HIV-1 Vif Increases at the Expense of Co-Expressed APOBEC3H Haplotype I
Bibliographic record
Abstract
The seven human APOBEC3 enzymes (APOBEC3A through H, excluding E) are host restriction factors. Most of the APOBEC3 enzymes can restrict HIV-1 replication with different efficiencies. The HIV-1 Vif protein combats APOBEC3-mediated restriction by inducing ubiquitination and degradation in the proteasome. APOBEC3F and APOBEC3G can hetero-oligomerize, which increases their restriction capacity and resistance to Vif. Here we determined if APOBEC3C, APOBEC3F, or APOBEC3G could hetero-oligomerize with APOBEC3H haplotype I. APOBEC3H haplotype I has a short half-life in cells due to ubiquitination and degradation by host proteins, but is also resistant to Vif. We hypothesized that hetero-oligomerization with APOBEC3H haplotype I may result in less Vif-mediated degradation of the interacting APOBEC3 and stabilize APOBEC3H haplotype I, resulting in more efficient HIV-1 restriction. Although we found that all three APOBEC3s could interact with APOBEC3H haplotype I, only APOBEC3F affected APOBEC3H haplotype I by surprisingly accelerating its proteasomal degradation. However, this increased APOBEC3F levels in cells and virions in the absence or presence of Vif and enabled APOBEC3F-mediated restriction of HIV-1 in the presence of Vif. Altogether, the data suggest that APOBEC3 enzymes can co-regulate each other at the protein level and that they cooperate to ensure HIV-1 inactivation rather than evolution.
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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".