1778. Epstein–Barr Virus Genetic Diversity: Evaluation of BZLF1 Variants among Bone Marrow Transplant Patients and Individuals with Infectious Mononucleosis
Bibliographic record
Abstract
Abstract Background Epstein–Barr virus (EBV) is associated with several diseases, including infectious mononucleosis (IM) and malignant disorders, including post-transplant lymphoproliferative disorder (PTLD). The relationship between strains of the virus and disease manifestations or illness severity is of interest. Such strains have been defined by genetic variations in the major viral genes. Data involving the patterns of genetic diversity of the virus in different populations are required. We examined the genetic diversity of the BZLF1 gene, which is a major lytic gene of the virus. Methods We sequenced the BZLF1 gene of EBV following amplification from DNA that was extracted from blood obtained from pediatric bone marrow transplant (BMT) patients and children and young adults with IM. Sequencing was done by Sanger methodology (dideoxy DNA sequencing) and the sequences were aligned with a reference strain of EBV using Geneious software. The variant burden and types of single nucleotide variants (SNV) were compared across the 3 exons of the BZLF1 gene. Results We sequenced the BZLF1 gene using 21 patients with IM (median age 14, age range 2–19 years) and 11 who underwent bone marrow transplantation (median age 6, range 3–13 years). Three of 11 BMT patients developed post-transplant lymphoproliferative disorder (PTLD). Among the 3 exons, exon 1 had the greatest diversity across both study groups. There was a tendency for less diversity among PTLD samples, with no sample containing >1 single nucleotide variant (SNV) in contrast to the other samples. In samples that contained SNVs, there was a non-statistically significant trend for more SNVs to occur among the IM samples compared with PTLD samples (median 4.5 and 0, respectively; P > 0.05). Additionally, 2/11 (18.2%) BMT sequences contained more than 1 SNV compared with 7/21 (33.3%) IM sequences (P > 0.05). Conclusion There was a tendency for more genetic diversity among samples from patients with IM compared with bone marrow transplant patients, notably those with PTLD. Further studies will determine if this tendency is due to selective pressures in the transplant setting, including but not limited to the use of antiviral agents directed at the lytic phase of EBV. Disclosures 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 | 0.001 | 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) | 0.002 | 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".