Dysregulation of Ephrin receptor and PPAR signaling pathways in neural progenitor cells infected by Zika virus
Bibliographic record
Abstract
Zika virus (ZIKV) infection is a serious public threat with cases reported in about 70 countries and territories. One of the most serious consequences of ZIKV infection is congenital microcephaly in babies. Congenital microcephaly has been suggested to result from infection of neural progenitor cells (NPCs) in the developing fetal brain. However, the molecular and cellular mechanisms underlying microcephaly development remains to be fully elucidated. In this study, we employed quantitative proteomics to determine protein expression profile that occur during viral replication in NPCs. Bioinformatics analysis of the protein expression changes resulted in the identification of a wide range of cell signaling pathways. Specifically, pathways involved in neurogenesis and embryonic development were markedly altered, along with those associated with cell cycle, apoptosis, lipid metabolism and oxidative stress. Notably, the differential regulation of Ephrin Receptor and PPAR signaling pathways, as revealed by quantitative proteomics and validated by qPCR array, underscores the need to explore these pathways in disease development. Collectively, these results indicate that ZIKV-induced pathogenesis involves complex virus-host reactions; the findings reported here could help shed light on the mechanisms underlying ZIKV-induced microcephaly and ZIKV replication in NPCs.
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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".