Cross talk between EBV and telomerase: the role of TERT and NOTCH2 in the switch of latent/lytic cycle of the virus
Bibliographic record
Abstract
Epstein-Barr virus (EBV)-associated malignancies, as well as lymphoblastoid cell lines (LCLs), obtained in vitro by EBV infection of B cells, express latent viral proteins and maintain their ability to grow indefinitely through inappropriate activation of telomere-specific reverse transcriptase (TERT), the catalytic component of telomerase. Our previous studies demonstrated that high levels of TERT expression in LCLs prevent the activation of EBV lytic cycle, which is instead triggered by TERT silencing. As lytic infection promotes the death of EBV-positive tumor cells, understanding the mechanism(s) by which TERT affects the latent/lytic status of EBV may be important for setting new therapeutic strategies. BATF, a transcription factor activated by NOTCH2, the major NOTCH family member in B cells, negatively affects the expression of BZLF1, the master regulator of viral lytic cycle. We therefore analyzed the interplay between TERT, NOTCH and BATF in LCLs and found that high levels of endogenous TERT are associated with high NOTCH2 and BATF expression levels. In addition, ectopic expression of TERT in LCLs with low levels of endogenous telomerase was associated with upregulation of NOTCH2 and BATF at both mRNA and protein levels. By contrast, infection of LCLs with retroviral vectors expressing functional NOTCH2 did not alter TERT transcript levels. Luciferase reporter assays, demonstrated that TERT significantly activated NOTCH2 promoter in a dose-dependent manner. We also found that NF-κB pathway is involved in TERT-induced NOTCH2 activation. Lastly, pharmacologic inhibition of NOTCH signaling triggers the EBV lytic cycle, leading to the death of EBV-infected cells. Overall, these results indicate that TERT contributes to preserve EBV latency in B cells mainly through the NOTCH2/BAFT pathway, and suggest that NOTCH2 inhibition may represent an appealing therapeutic strategy against EBV-associated malignancies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".