Identifying cellular RNA-binding proteins during infection uncovers a role for MKRN2 in influenza mRNA trafficking
Bibliographic record
Abstract
Utilisation of RNA-binding proteins (RBPs) is an important aspect of post-transcriptional regulation of viral RNA. Viruses such as influenza A viruses (IAV) interact with RBPs to regulate processes including splicing, nuclear export and trafficking, while also encoding RBPs within their genomes, such as NP and NS1. But with almost 1000 RBPs encoded within the human genome it is still unclear what role, if any, many of these proteins play during viral replication. Using the RNA interactome capture (RIC) technique, we isolated RBPs from IAV infected cells to unravel the RBPome of mRNAs from IAV infected human cells. This led to the identification of one particular RBP, MKRN2, that associates with and positively regulates IAV mRNA. Through further validation, we determined that MKRN2 is involved in the nuclear-cytoplasmic trafficking of IAV mRNA potentially through an association with the RNA export mediator GLE1. In the absence of MKRN2, IAV mRNAs accumulate in the nucleus of infected cells, which may lead to their degradation by the nuclear RNA exosome complex. MKRN2, therefore, appears to be required for the efficient nuclear export of IAV mRNAs in human cells.
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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 | 0.001 |
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".