HIV‐1 Tat through phosphorylation of NMDA receptors potentiates glutamate excitotoxicity
Bibliographic record
Abstract
Toxic effects of HIV-1 proteins contribute to altered function and decreased survival of select populations of neurons in HIV-1-infected brain. One such HIV-1 protein, Tat, can activate calcium release from IP3-sensitive intracellular pools, induce calcium influx in neural cells, and, as a result, can increase neuronal cell death. Here, we provide evidence that Tat potentiates excitatory amino acid (glutamate and NMDA) triggered calcium flux, as well as glutamate- and staurosporine-mediated neurotoxicity. Calcium flux in cultured rat hippocampal neurons triggered by the transient application of glutamate or NMDA was facilitated by pre-exposure to Tat. Facilitation of glutamate-triggered calcium flux by Tat was prevented by inhibitors of ADP-ribosylation of G(i)/G(o) proteins (pertussis toxin), protein kinase C (H7 and bisindolymide), and IP3-mediated calcium release (xestospongin C), but was not prevented by an activator of G(s) (cholera toxin) or an inhibitor of protein kinase A (H89). Facilitation of NMDA-triggered calcium flux by Tat was reversed by inhibitors of tyrosine kinase (genestein and herbimycin A) and by an inhibitor of NMDA receptor function (zinc). Tat increased 32P incorporation into NMDA receptor subunits NR2A and NR2B and this effect was blocked by genestein. Subtoxic concentrations of Tat combined with subtoxic concentrations of glutamate or staurosporine increased neuronal cell death significantly. Together, these findings suggest that NMDA receptors play an important role in Tat neurotoxicity and the mechanisms identified may provide additional therapeutic targets for the treatment of HIV-1 associated dementia.
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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 | 0.001 |
| 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".