EBNA3C Can Modulate the Activities of the Transcription Factor Necdin in Association with Metastasis Suppressor Protein Nm23-H1
Bibliographic record
Abstract
Previous studies have demonstrated the interaction between the Epstein-Barr virus (EBV) nuclear antigen 3C (EBNA3C) and the metastatic suppressor Nm23-H1 both in vitro and in vivo (C. Subramanian, M. A. Cotter II, and E. S. Robertson, Nat. Med. 7:350-355, 2001). Importantly EBNA3C can reverse the ability of Nm23-H1 to suppress migration of human cells in vitro. EBNA3C contributes to EBV-associated human cancers by regulating transcription of a number of cellular and viral promoters as well as targeting and altering the transcription activities of the metastasis suppressor Nm23-H1. Furthermore, Necdin is a cellular protein which is highly induced in terminally differentiated cells; it contributes to the regulation of cell growth and is also known to interact with viral oncoproteins. In this report, we show that Nm23-H1 and EBNA3C can modulate the biological functions of Necdin in the context of EBV infection and transformation. The levels of Necdin were consistently lower in EBV-positive cells, and EBNA3C could change the subcellular localization of Necdin as well as rescue cells from the antiangiogenic and antiproliferative effects mediated by Necdin. We also show that Necdin directly interacts with Nm23-H1, resulting in modulation of the biochemical function of Nm23-H1 as well as the biological function of Necdin. Both EBNA3C and Nm23-H1 were able to rescue not only Necdin-mediated transcriptional repression of the downstream vascular endothelial growth factor promoter but also Necdin-mediated growth suppression and antiangiogenic effects on cancer cells. The majority of this response was mediated through amino acid residues 191 to 222 of Necdin, which are also known to be important for nuclear matrix targeting. These studies suggest a role for Necdin in the regulation of downstream cellular targets in a hypoxic environment in virus-associated human cancers.
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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".