AUF1/hnRNP D is a novel protein partner of the EBER1 noncoding RNA of Epstein-Barr virus
Bibliographic record
Abstract
Epstein-Barr virus (EBV)-infected cells express two noncoding RNAs called EBV-encoded RNA (EBER) 1 and EBER2. Despite their high abundance in the nucleus (about 10(6) copies), the molecular function of these noncoding RNAs has remained elusive. Here, we report that the insertion into EBER1 of an RNA aptamer that binds the bacteriophage MS2 coat protein allows the isolation of EBER1 and associated protein partners. By combining MS2-mediated selection with stable isotope labeling of amino acids in cell culture (SILAC) and analysis by mass spectrometry, we identified AUF1 (AU-rich element binding factor 1)/hnRNP D (heterogeneous nuclear ribonucleoprotein D) as an interacting protein of EBER1. AUF1 exists as four isoforms generated by alternative splicing and is best known for its role in destabilizing mRNAs upon binding to AU-rich elements (AREs) in their 3' untranslated region (UTR). Using UV crosslinking, we demonstrate that predominantly the p40 isoform of AUF1 interacts with EBER1 in vivo. Electrophoretic mobility shift assays show that EBER1 can compete for the binding of the AUF1 p40 isoform to ARE-containing RNA. Given the high abundance of EBER1 in EBV-positive cells, EBER1 may disturb the normal homeostasis between AUF1 and ARE-containing mRNAs or compete with other AUF1-interacting targets in cells latently infected by EBV.
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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) | 0.001 | 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 | 0.001 |
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".