HLA-A, HSPA5, IGFBP5 and PSMA2 Are Restriction Factors for Zika Virus Growth in Astrocytic Cells
Bibliographic record
Abstract
mosquitoes Following its major outbreaks in 2013, 2014 and 2016, WHO declared it a Public Health Emergency of International Concern. Symptoms of ZIKV infection include acute fever, conjunctivitis, headache, muscle & joint pain and malaise. Cases of its transmission also have been reported via perinatal, sexual and transfusion transmission. ZIKV pathologies include meningo-encephalitis and myelitis in the central nervous system (CNS) and Guillain-Barré syndrome and acute transient polyneuritis in the peripheral nervous system (PNS). Drugs like azithromycin have been tested as inhibitors of ZIKV infection but no vaccines or treatments are currently available. Astrocytes are the most abundant cells in the CNS and among the first cells in CNS infected by ZIKV; (2) Methods: We previously used SOMAScan proteomics to study ZIKV-infected astrocytic cells. Here, we use mass spectrometric analyses to further explain dysregulations in the cellular expression profile of glioblastoma astrocytoma U251 cells. We also knocked down (KD) some of the U251 cellular proteins using siRNAs and observed the impact on ZIKV replication and infectivity; (3) Results & Conclusions: The top ZIKV dysregulated cellular networks were antimicrobial response, cell death, and energy production while top dysregulated functions were antigen presentation, viral replication and cytopathic impact. Th1 and interferon signaling pathways were among the top dysregulated canonical pathways. siRNA-mediated KD of HLA-A, IGFBP5, PSMA2 and HSPA5 increased ZIKV titers and protein synthesis, indicating they are ZIKV restriction factors. ZIKV infection also restored HLA-A expression in HLA-A KD cells by 48 h post-infection, suggesting interactions between this gene product and ZIKV.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".