The genetic driver of Acute Necrotizing Encephalopathy, <i>RANBP2</i> , regulates the inflammatory response to Influenza A virus infection
Bibliographic record
Abstract
ABSTRACT Influenza virus infections can cause severe complications such as Acute Necrotizing Encephalopathy (ANE), which is characterised by rapid onset pathological inflammation following febrile infection. Heterozygous dominant mutations in the nucleoporin RANBP2/Nup358 predispose to influenza-triggered ANE1. The aim of our study was to determine whether RANBP2 plays a role in IAV-triggered inflammatory responses. We found that the depletion of RANBP2 in a human airway epithelial cell line increased IAV genomic replication by favouring the import of the viral polymerase subunits, PB1, PB2 and PA following viral transcription and translation. Additionally, RANBP2 knockdown enhanced the cytoplasmic export of viral RNA (vRNA) and disrupted segment stoichiometry, which associated with elevated production of the pro-inflammatory chemokines CXCL8, CXCL10, CCL2, CCL3 and CCL4 in human primary macrophages. Using CRISPR-Cas9 knock-in for the ANE1 disease variant RANBP2-T585M, we further demonstrate that this point mutation causes a loss-of-localisation phenotype that excludes RANBP2 from the nuclear envelope, which phenocopies RANBP2 knockdown by increasing IAV replication and driving pro-inflammatory cytokine expression following infection. Together, our results reveal that RANBP2 regulates influenza RNA replication and nuclear export, thereby restraining virus-induced hyperinflammation, and further suggest that ANE1 pathogenesis results from the impaired localisation of RANBP2 at the nuclear envelope.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".