Genetic Diversity of Epstein–Barr Virus Lytic Gene BZLF-1 among Patients with and Without Post-transplant Lymphoproliferative Disorder
Bibliographic record
Abstract
The relationship between genetic variants of the Epstein–Barr virus (EBV) and disease outcomes is of interest. This was explored in this study, with a focus on the lytic gene BZLF1. This is one the major genes of the virus which has been used to define newer subtypes of EBV. Of interest was the extent to which genetic diversity was associated with the presence of EBV-related post-transplant lymphoproliferative disorder (PTLD) among organ transplant recipients. DNA was extracted from peripheral blood mononuclear cells from transplant patients (with and without biopsy-proven PTLD) and persons with infectious mononucleosis (IM). Following amplification of the BZLF1 gene, dideoxy DNA sequencing was done on all 3 exons of the gene. Nucleotide sequences were aligned and compared with the EBV reference strain, B95.8. Data were descriptively summarized and medians and proportions compared using a non-parametric procedure and Fisher’s exact test, respectively. For this report, we examined the presence of mutations that resulted in a specific protein change (non-synonymous mutations). Sequences from 22 patients were studied; 6 sequences from patients with PTLD, 7 from transplant patients without PTLD and 9 from patients with IM. Most variations were in exon 1, where the median numbers of non-synonymous Single-nucleotide variations (SNVs) were: IM 0 (range 0–6), transplant without PTLD 0 (range 0–6) and PTLD patients 5 (range 0–8). Among transplant patients, 4 of 6 PTLD patients (66.7%) had at least 1 mutation compared with 1/7 non-PTLD patients (14.3%) (P = 0.1). Furthermore, PTLD patients were more likely to have SNVs compared with transplant patients without PTLD (23/78 vs. 6/91, P = 0.0001), where the denominator is the nucleotide count within the exon. A pattern of 5 SNVs within each sample was seen in 22% of IM patents, 0% of transplant patients without PTLD and 50% of PTLD patients. We have documented differences in the EBV BZLF1 variants among the study groups. A polymorphism pattern was identified among the disease states. Differences were observed between transplant patients with and without PTLD; thus providing the impetus for further research to define cause and effect relationships and whether the presence of BZLF1 variants might indicate an increased likelihood of PTLD. All authors: No reported disclosures.
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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) | 0.001 | 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".