Glycyrrhizic acid alters Kaposi sarcoma–associated herpesvirus latency, triggering p53-mediated apoptosis in transformed B lymphocytes
Bibliographic record
Abstract
Kaposi sarcoma-associated herpesvirus (KSHV) is linked with all clinical forms of Kaposi sarcoma and several lymphoproliferative disorders. Like other herpesviruses, KSHV becomes latent in the infected cells, expressing only a few genes that are essential for the establishment and maintenance of its latency and for the survival of the infected cells. Inhibiting the expression of these latent genes should lead to eradication of herpesvirus infection. All currently available drugs are ineffective against latent infection. Here we show, for the first time to our knowledge, that latent infection with KSHV in B lymphocytes can be terminated by glycyrrhizic acid (GA), a triterpenoid compound earlier shown to inhibit the lytic replication of other herpesviruses. We demonstrate that GA disrupts latent KSHV infection by downregulating the expression of latency-associated nuclear antigen (LANA) and upregulating the expression of viral cyclin and selectively induces cell death of KSHV-infected cells. We show that reduced levels of LANA lead to p53 reactivation, an increase in ROS, and mitochondrial dysfunction, which result in G1 cell cycle arrest, DNA fragmentation, and oxidative stress-mediated apoptosis. Latent genes are involved in KSHV-induced oncogenesis, and strategies to interfere with their expression might prove useful for eradicating latent KSHV infection and have future therapeutic implications.
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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 | 0.000 |
| 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) | 0.001 | 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".