HIV-1 Tat neurotoxicity is prevented by matrix metalloproteinase inhibitors
Bibliographic record
Abstract
The release of potentially neurotoxic molecules by HIV-infected brain macrophages is accompanied by neuronal injury and death that results in the development of HIV-associated dementia (HAD). Among the potential neurotoxins implicated in the development of HAD is the HIV-1 transactivating protein, Tat. To investigate the mechanism by which Tat causes neurotoxicity, brain-derived Tat sequences from nondemented (Tat-ND) and demented (Tat-HAD) AIDS patients, which differed primarily in the augmenting region of Tat, were expressed in U937 monoblastoid cells and primary human macrophages. Cells expressing Tat-HAD protein exhibited elevated matrix metalloproteinase (MMP)-2 and -7 release and activation, but cells expressing Tat-ND did not exhibit enhanced MMP expression. Conditioned media from Tat-HAD-transfected cells caused significantly greater neuronal death (15.4 +/- 4.3%) than did Tat-ND (4.4 +/- 2.1%) or nontransfected (2.1 +/- 0.8%) cell-derived conditioned media. The neurotoxicity induced by Tat-HAD was inhibited by anti-MMP-2 or -7 antibodies (p < 0.005) but not by antibodies against MMP-9 or Tat. Similarly, scid/nod mice receiving striatal implants of Tat-HAD-transfected cells exhibited greater neurobehavioral abnormalities and neuronal loss (p < 0.005) than did animals receiving Tat-ND or nontransfected cells, which were reduced by treatment with the MMP inhibitor prinomastat (p < 0.005). These findings indicate that Tat causes neuronal death through an indirect mechanism that is Tat sequence dependent and involves the induction of MMPs.
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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".