Identification of a Feline Leukemia Virus Variant That Can Use THTR1, FLVCR1, and FLVCR2 for Infection
Bibliographic record
Abstract
The pathogenic subgroup C feline leukemia virus (FeLV-C) arises in infected cats as a result of mutations in the envelope (Env) of the subgroup A FeLV (FeLV-A). To better understand emergence of FeLV-C and potential FeLV intermediates that may arise, we characterized FeLV Env sequences from the primary FY981 FeLV isolate previously derived from an anemic cat. Here, we report the characterization of the novel FY981 FeLV Env that is highly related to FeLV-A Env but whose variable region A (VRA) receptor recognition sequence partially resembles the VRA sequence from the prototypical FeLV-C/Sarma Env. Pseudotype viruses bearing FY981 Env were capable of infecting feline, human, and guinea pig cells, suggestive of a subgroup C phenotype, but also infected porcine ST-IOWA cells that are normally resistant to FeLV-C and to FeLV-A. Analysis of the host receptor used by FY981 suggests that FY981 can use both the FeLV-C receptor FLVCR1 and the feline FeLV-A receptor THTR1 for infection. However, our results suggest that FY981 infection of ST-IOWA cells is not mediated by the porcine homologue of FLVCR1 and THTR1 but by an alternative receptor, which we have now identified as the FLVCR1-related protein FLVCR2. Together, our results suggest that FY981 FeLV uses FLVCR1, FLVCR2, and THTR1 as receptors. Our findings suggest the possibility that pathogenic FeLV-C arises in FeLV-infected cats through intermediates that are multitropic in their receptor use.
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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".