A zebrafish-based in vivo model of Zika virus infection unveils alterations of the glutamatergic neuronal development and NS4A as a key viral determinant of neuropathogenesis
Bibliographic record
Abstract
Infection of pregnant women by Zika virus (ZIKV) is associated with severe neurodevelopmental defects in newborns through poorly defined mechanisms. Here, we established a zebrafish in vivo model of ZIKV infection to circumvent limitations of existing mammalian models. Leveraging the unique tractability of this system, we gained unprecedented access to the ZIKV-infected brain at early developmental stages. The infection of zebrafish larvae with ZIKV phenocopied the disease in mammals including a reduced head area and neural progenitor cells (NPC) infection and depletion. Moreover, transcriptomic analyses of NPCs isolated from ZIKV-infected embryos revealed a distinct dysregulation of genes involved in survival and neuronal differentiation, including downregulation of the expression of the glutamate transporter vglut1, resulting in an altered glutamatergic network in the brain. Mechanistically, ectopic expression of ZIKV protein NS4A in the larvae recapitulated the morphological defects observed in infected animals, identifying NS4A as a key determinant of neurovirulence and a promising antiviral target for developing therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".