Extracellular vesicle storm during the course of Ebola virus infection in primates
Bibliographic record
Abstract
Introduction: Ebola virus (EBOV) is an RNA virus of the Filoviridae family that is responsible for outbreaks of hemorrhagic fevers in primates with a lethality rate as high as 90%. EBOV primarily targets host macrophages leading to cell activation and systemic cytokine storm, and fatal infection is associated with an inhibited interferon response, and lymphopenia. The EBOV surface glycoprotein (GP) has been shown to directly induce T cell depletion and can be secreted outside the virion via extracellular vesicles (EVs), though most studies are limited to epithelial cells and underlying mechanisms remain poorly elucidated. Methods: To assess the role of GP on EBOV-induced dysregulation of host immunity, we first utilized EBOV virus-like particles (VLPs) expressing VP40 and NP either alone (Bald-VLP) or in conjunction with GP (VLP-GP) to investigate early inflammatory responses in THP-1 macrophages and in a murine model. We then sought to decipher the role of non-classical inflammatory mediators such as EVs over the course of EBOV infection in two EBOV-infected rhesus macaques by isolating and characterizing circulatory EVs throughout disease progression using size exclusion chromatography, nanoparticle tracking-analysis, and LC-MS/MS. Results: While all VLPs could induce inflammatory mediators and recruit small peritoneal macrophages, pro-inflammatory cytokine and chemokine gene expression was exacerbated by the presence of GP. Further, quantification of EVs isolated from infected rhesus macaques revealed that the concentration of vesicles peaked in circulation at the terminal stage, at which time EBOV GP could be detected in host-derived exosomes. Moreover, comparative proteomics conducted across EV populations isolated from serum at various time points before and after infection revealed differences in host-derived protein content that were most significantly pronounced at the endpoint of infection, including significant expression of mediators of TLR4 signaling. Discussion: These results suggest a dynamic role for EVs in the modification of disease states in the context of EBOV. Overall, our work highlights the importance of viral factors, such as the GP, and host derived EVs in the inflammatory cascade and pathogenesis of EBOV, which can be collectively further exploited for novel antiviral development.
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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 | 0.001 |
| 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".