Post-translational modifications of hnRNP A1 differentially modulate retroviral IRES-mediated translation initiation
Bibliographic record
Abstract
The full-length mRNAs of the human immunodeficiency virus type-1 (HIV-1), the human T-cell lymphotropic virus type-1 (HTLV-1), and the mouse mammary tumor virus (MMTV) harbor IRESs. The activity of the retroviral-IRESs requires IRES-transacting factors (ITAFs), being hnRNP A1, a known ITAF for the HIV-1 IRES. In this study, we show that hnRNP A1 is also an ITAF for the HTLV-1 and MMTV IRESs. The MMTV IRES proved to be more responsive to hnRNP A1 than either the HTLV-1 or the HIV-1 IRESs. The impact of post-translational modifications of hnRNP A1 on HIV-1, HTLV-1 and MMTV IRES activity was also assessed. Results show that the HIV-1 and HTLV-1 IRESs were equally responsive to hnRNP A1 and its phosphorylation mutants S4A/S6A, S4D/S6D and S199A/D. However, the S4D/S6D mutant stimulated the activity from the MMTV-IRES to levels significantly higher than the wild type hnRNP A1. PRMT5-induced symmetrical di-methylation of arginine residues of hnRNP A1 enabled the ITAF to stimulate the HIV-1 and HTLV-1 IRESs while reducing the stimulatory ability of the ITAF over the MMTV IRES. We conclude that retroviral IRES activity is not only dependent on the recruited ITAFs but also relies on how these proteins are modified at the post-translational level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".