Interplay between Zika Virus and Peroxisomes during Infection
Bibliographic record
Abstract
Zika virus (ZIKV) has emerged as an important human pathogen that can cause congenital defects in the fetus and neurological conditions in adults. The interferon (IFN) system has proven crucial in restricting ZIKV replication and pathogenesis. The canonical IFN response is triggered by the detection of viral RNA through RIG-I like receptors followed by activation of the adaptor protein MAVS on mitochondrial membranes. Recent studies have shown that a second organelle, peroxisomes, also function as a signaling platforms for the IFN response. Here, we investigated how ZIKV infection affects peroxisome biogenesis and antiviral signaling. We show that ZIKV infection depletes peroxisomes in human fetal astrocytes, a brain cell type that can support persistent infection. The peroxisome biogenesis factor PEX11B was shown to inhibit ZIKV replication, likely by increasing peroxisome numbers and enhancing downstream IFN-dependent antiviral signaling. Given that peroxisomes play critical roles in brain development and nerve function, our studies provide important insights into the roles of peroxisomes in regulating ZIKV infection and potentially neuropathogenesis.
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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 | 0.000 |
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".