Predominant role of IPS-1 over TRIF adaptor proteins in early innate immune response against Zika virus in mice
Bibliographic record
Abstract
Toll-like receptors and RNA helicases are involved in the control of RNA virus infection through production of type I interferons (IFNs). To delineate the relative contributions of these signalling pathways in the innate immune response and the control of Zika virus (ZIKV) pathogenesis, the impact of a deficiency in TRIF and/or IPS-1 adaptor proteins was investigated in mice. Mice were infected intravenously with ZIKV and monitored for clinical signs for 14 days. Groups of mice were sacrificed on days 1, 3 and 7 post-infection (p.i.) and viral RNA was measured by digital droplet PCR in serum, spleen, brain and eyes. Some mice were sacrificed at 12 h p.i. for determination of the levels of IFN-α/–β (ELISA), cytokines/chemokines (Luminex) and total/phosphorylated IRF3 and IRF7 (Western blotting). All groups of mice infected with ZIKV exhibited no clinical signs of infection. However, IPS-1−/− and TRIF−/−xIPS-1−/− mice developed higher viraemia than WT and TRIF−/− groups on days 1, 3 and 7. TRIF−/−xIPS-1−/− mice presented higher viral RNA levels in spleen, brain and eyes over time than TRIF−/−, IPS-1−/− and WT groups. At 12 h, IFN-α/-β and cytokine/chemokine levels in spleen were significantly decreased in IPS-1−/− and TRIF−/−xIPS-1−/− compared to WT and TRIF−/−. On day 1 p.i., IFN-β levels were significantly reduced in spleen of TRIF−/−xIPS-1−/− mice compared to all other groups. These data suggest that IPS-1 plays a more important role than TRIF in the early type I IFN response and that both IPS-1 and TRIF are involved at later stages of ZIKV infection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".