Fusion and cell entry by oncogenic sheep retroviruses
Bibliographic record
Abstract
Jaagsiekte sheep retrovirus (JSRV) and enzootic nasal tumour virus (ENTV) are two highly related oncogenic retroviruses that induce a contagious cancer in sheep and goats respectively.Both JSRV and ENTV use hyaluronidase-2 (Hyal2) as a cell entry receptor, yet JSRV induces lung tumours and ENTV causes tumours in the nasal epithelium.Unlike most acutely transforming retroviruses, the genomes of JSRV and ENTV do not contain an oncogene derived from the host cell; instead, the viral envelope protein (Env) functions as an active oncogene in addition of mediating cell entry.JSRV and ENTV Env are first synthesized as precursors that are cleaved by a cellular protease into two functional subunits: the surface (SU) subunit that contains the receptor binding domain and the transmembrane (TM) subunit that mediates membrane fusion.While most of previous studies have focused on the oncogenic properties of these proteins, little was known about how they mediate membrane fusion and viral entry.The goal of my Ph.D study was to investigate the mechanisms of fusion and cell entry by JSRV and ENTV Env as well as their regulations.We found that, although most retroviruses are believed to use a pH-independent pathway for entry, JSRV requires an acidic pH for entry and fusion and that its fusogenicity was negatively regulated by its cytoplasmic tail.Unexpectedly, ENTV Env requires an unusually low pH (<4.5) for fusion activation.While an irreversible inhibitor, Bafilomycin A1, which prevents acidification in the endosomes and lysosomes inhibited entry of JSRV and ENTV, ENTV Env-mediated infection was considerably enhanced in the presence of lysosomotropic agents or leupeptin, suggesting that JSRV and ENTV likely fuse in distinct cellular compartments.Importantly, we found that SU also modulates the fusion activity of JSRV and ENTV Env, despite that TM dictates the ix
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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".