Proteolytic processing of SDF-1α reveals a change in receptor specificity mediating HIV-associated neurodegeneration
Bibliographic record
Abstract
Proteolytic cleavage of constitutively expressed proteins can generate peptides with novel bioactive properties. Matrix metalloproteinase (MMP)-2 cleaves the 4 amino-terminal residues of the chemokine, stromal cell-derived factor (SDF)-1alpha, yielding a highly neurotoxic molecule, SDF(5-67), which fails to bind to its cognate receptor, CXCR4. Herein, we detected SDF(5-67) in brain monocytoid cells of HIV-infected persons, particularly in those with HIV-associated dementia. SDF(5-67) activated cell type-specific expression of proinflammatory genes including IL-1beta, TNFalpha, indoleamine 2',3'-dioxygenase (IDO), and IL-10 in both astrocytic and monocytoid cells (P < 0.05). Unlike SDF-1alpha, SDF(5-67) caused neuronal membrane perturbations with ensuing neurotoxicity and apoptosis (P < 0.05) through engagement of an inducible receptor. CXCR3 antagonists and siRNA-mediated knockdown of CXCR3 inhibited SDF(5-67)-stimulated neurophysiological changes, neuronal death, and neuroimmune activation (P < 0.05). Moreover SDF(5-67) bound directly to CXCR3 in a competitive manner, mediated by its amino terminus. In vivo neuroinflammation, neuronal loss, and neurobehavioral abnormalities caused by SDF(5-67) (P < 0.05) were prevented by a CXCR3 antagonist. These studies reveal additive neuropathogenic properties exerted by a proteolytically cleaved chemokine as consequences of a change in receptor specificity, culminating in neurodegeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".