Sphingosine Kinases Promote Ebola Virus Infection and Can Be Targeted to Inhibit Filoviruses, Coronaviruses, and Arenaviruses Using Late Endocytic Trafficking to Enter Cells
Bibliographic record
Abstract
Entry of enveloped viruses in host cells requires the fusion of viral and host cell membranes, a process that is facilitated by viral fusion proteins protruding from the viral envelope. These viral fusion proteins need to be triggered by host factors, and for some viruses, this event occurs inside endosomes and/or lysosomes. Consequently, these 'late-penetrating viruses' must be internalized and delivered to entry-conducive intracellular vesicles. Because endocytosis and vesicular trafficking are tightly regulated cellular processes, late-penetrating viruses also depend on specific host proteins for efficient delivery to the site of fusion, suggesting that these could be targeted for antiviral therapy. In this study, we investigated a role for sphingosine kinases (SKs) in viral entry and found that chemical inhibition of sphingosine kinase 1 (SK1) and/or SK2 and knockdown of SK1/2 inhibited entry of Ebola virus (EBOV) into host cells. Mechanistically, inhibition of SK1/2 prevented EBOV from reaching late-endosomes and lysosomes that contain the EBOV receptor, Niemann Pick C1 (NPC1). Furthermore, we present evidence that suggests that the trafficking defect caused by SK1/2 inhibition occurs independently of sphingosine-1-phosphate (S1P) signaling through cell-surface S1P receptors. Lastly, we found that chemical inhibition of SK1/2 prevents entry of other late-penetrating viruses, including arenaviruses and coronaviruses, and inhibits infection by replication-competent EBOV and SARS-CoV-2 in Huh7.5 cells. In sum, our results highlight an important role played by SK1/2 in endocytic trafficking, which can be targeted to inhibit entry of late-penetrating viruses and could serve as a starting point for the development of broad-spectrum antiviral therapeutics.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".