The Epstein-Barr Virus Protein, Latent Membrane Protein 2A, Co-opts Tyrosine Kinases Used by the T Cell Receptor
Bibliographic record
Abstract
Epstein-Barr virus (EBV) is the causative agent of infectious mononucleosis and is associated with several human malignancies. The EBV protein latent membrane protein 2A (LMP2A) promotes viral latency in memory B cells by interfering with B cell receptor signaling and provides a survival signal for mature B cells that have lost expression of surface immunoglobulin. The latter function has suggested that LMP2A may enhance the survival of EBV-positive tumors. EBV is associated with several T cell malignancies and, since LMP2A has been detected in several of these disorders, we examined the ability of LMP2A to transmit signals and interfere with T cell receptor signaling in T cells. We show that LMP2A is tyrosine-phosphorylated in Jurkat TAg T cells, which requires expression of the Src family tyrosine kinases, Lck and Fyn. Lck and Fyn are recruited to the tyrosine-phosphorylated Tyr112 site in LMP2A, whereas phosphorylation of an ITAM motif in LMP2A creates a binding site for the ZAP-70/Syk tyrosine kinases. LMP2A also associates through its two PPPPY motifs with AIP4, a NEDD4 family E3 ubiquitin ligase; this interaction results in ubiquitylation of LMP2A and serves to regulate the stability of LMP2A and LMP2A-kinase complexes. Furthermore, stable expression of LMP2A in Jurkat T cells down-regulated T cell receptor levels and attenuated T cell receptor signaling. Thus, through recruiting tyrosine kinases involved in T cell receptor activation, LMP2A may provide a survival signal for EBV-positive T cell tumors, whereas LMP2A-associated NEDD4 E3 ligases probably titer the strength of this signal.
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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) | 0.002 | 0.001 |
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".