Association between genetic clades and cancer prevalence suggested by French-wide study of oncogenic small ruminant beta-retroviruses diversity
Bibliographic record
Abstract
Abstract ENTV (Enzootic Nasal Tumor Virus) and JSRV (Jaagsiekte Sheep Retrovirus) are β-retroviruses responsible for respiratory cancers in sheep and goats. In this study, we analyzed the genetic features of the sheep and goat β-retrovirus (29 JSRV and 24 ENTV strains) circulating in France to identify molecular signatures associated with disease severity in flocks. We developed a highly specific PCR to amplify and sequence exogenous targeted regions or near full length proviruses based on limited discriminating motifs along their genomes. The phylogenetic reconstructions based on the LTR and env regions suggest that one major strain is circulating on the French territory for ENTV-1 and ENTV-2 while not clustering with already published Spanish, Canadian or Chinese strains. JSRV strains circulating in French sheep flocks were distributed in 2 distinct genetic clades clustering with sequences originating from North America, Africa and United-Kingdom. JSRV clade I was found to be associated with a higher incidence of cancer in French flocks. Specific motifs spanning the entire JSRV genome particularly in the LTRs and in the intracytoplasmic domain of the envelope were detected between the two genetic subtypes. This work represents the first nationwide study describing the circulation of the three closely related β-oncogenic retrovirus JSRV, ENTV-1 and ENTV-2 in French sheep and goat flocks. Better characterization of strain genetics is a critical step in monitoring circulating β retrovirus, especially those associated with higher cancer incidence in small ruminants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".