Identification of RNA-Protein Interaction Networks Involved in the Norovirus Life Cycle
Bibliographic record
Abstract
Human noroviruses are one of the major causes of acute gastroenteritis in the developed world, yet our understanding of their molecular mechanisms of genome translation and replication lags behind that for many RNA viruses. Due to the nonculturable nature of human noroviruses, many related members of the Caliciviridae family of small RNA viruses are often used as model systems to dissect the finer details of the norovirus life cycle. Murine norovirus (MNV) has provided one such system with which to study the basic mechanisms of norovirus translation and replication in cell culture. In this report we describe the use of riboproteomics to identify host factors that interact with the extremities of the MNV genome. This network of RNA-protein interactions contains many well-characterized host factors, including PTB, La, and DDX3, which have been shown to play a role in the life cycle of other RNA viruses. By using RNA coimmunoprecipitation, we confirmed that a number of the factors identified using riboproteomics are associated with the viral RNA during virus replication in cell culture. We further demonstrated that RNA inhibition-mediated knockdown of the intracellular levels of a number of these factors inhibits or slows norovirus replication in cell culture, allowing identification of new intracellular targets for this important group of pathogens.
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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 | 0.001 | 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".