Up-frameshift proteins and their distinct roles in HIV-1 RNA metabolism
Bibliographic record
Abstract
HIV-1 co-opts host cell proteins at every step of its replication cycle to ensure proper replication.Our work identified that the HIV-1 genomic RNA is not a substrate for nonsense-mediated mRNA decay (NMD) even though it has multiple open reading frames as well as a long 3'UTR.We demonstrate that Up-frameshift protein 1 (UPF1) is involved in HIV-1 genomic RNA stability such that overexpression of UPF1 increases HIV-1 genomic RNA levels and Gag translation.Moreover, the role of UPF1 in HIV-1 is NMD-independent, is observed in both nuclear and cytoplasmic compartments and does not require binding to UPF2.Furthermore, the shuttling function of UPF1 is required for HIV-1 genomic RNA export since a UPF1 nuclear export mutant sequesters the genomic RNA in the nucleus and a nuclear localization mutant does not immunoprecipitate with the HIV-1 genomic RNA.UPF1's role in HIV-1 genomic RNA export is observed in both Rev-dependent and -independent conditions.In addition, UPF1 is found in complex with Rev, CRM1, Nup62 and DDX3, cellular proteins with already characterized roles in HIV-1 genomic RNA export.Lastly, we also identified UPF2 as a negative regulator, such that its binding to UPF1 results in the nuclear sequestration of the HIV-1 genomic RNA.We have identified a possible mechanism to explain how HIV-1 escapes the RNA quality control mechanism of NMD by co-opting UPF1 function for efficient HIV-1 genomic RNA export, stability and translation.iii
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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 | 0.001 | 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".