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 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".