Bovine leukemia virus long terminal repeat variability: identification of single nucleotide polymorphisms in regulatory sequences
Bibliographic record
Abstract
Limited data are available on the incidence of variations in nucleotide sequences of long terminal repeat (LTR) regions of Bovine Leukemia Virus (BLV). Consequently, the possible impact of SNPs on BLV LTR function are poorly elucidated. Thus, a detailed and representative study of full-length LTR sequences obtained from sixty-four BLV isolates from different geographical regions of Poland, Moldova, Croatia, Ukraine and Russia were analyzed for their genetic variability. Overlap extension PCR, sequencing and Bayesian phylogenetic reconstruction of LTR sequences were performed. These analyses were followed by detailed sequence comparison, estimation of genetic heterogeneity and identification of transcription factor binding site (TFBS) modifications. Phylogenetic analysis of curated LTR sequences and those available in the GenBank database reflected the acknowledged env gene classification of BLV into 10 genotypes, and further clustered analysed sequences into three genotypes - G4, G7 and G8. Additional molecular studies revealed the presence of 97 point mutations distributed at 89 positions throughout all 64 LTR sequences. The highest rate of variability was noted in U3 and U5 subregions. However, the variability in regulatory sequences (V R ) was assessed as lower than the variability within non-regulatory sequences (V NR ) for both, U3 and U5 subregions. In contrast, V R value for R subregion, as well as for the total LTR, was higher than the V NR suggesting the existence of positive selection. Twelve unique SNPs for these LTR sequences localized in regulatory and non-regulatory elements were identified. The presence of different types of substitutions lead to the abrogation of present or to the creation of additional TFBS. This study represents the largest study of LTR genetic variability of BLV field isolates from Eastern part of Europe. Phylogenetic analysis of LTRs supports the clustering BLV variants based on their geographic origin. The SNP screening showed variations modifying LTR regulatory sequences, as well as altering TFBS. These features warrant further exploration as they could be related to proviral load and distinctive regulation of BLV transcription and replication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".