SPARC and GluA1-Containing AMPA Receptors Promote Neuronal Health Following CNS Injury
Bibliographic record
Abstract
The proper formation and maintenance of functional synapses in the central nervous system (CNS) requires communication between neurons and astrocytes and the ability of astrocytes to release neuromodulatory molecules. Previously, we described a novel role for the astrocyte-secreted matricellular protein SPARC (Secreted Protein, Acidic and Rich in Cysteine) in regulating α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and plasticity at developing synapses. SPARC is highly expressed by astrocytes and microglia during CNS development but its level is reduced in adulthood. Interestingly, SPARC has been shown to be upregulated in CNS injury and disease. However, the role of SPARC upregulation in these contexts is not fully understood. In this study, we investigated the effect of chronic SPARC administration on glutamate receptors on mature hippocampal neuron cultures and following CNS injury. We found that SPARC treatment increased the number of GluA1-containing AMPARs at synapses and enhanced synaptic function. Furthermore, we determined that the increase in synaptic strength induced by SPARC could be inhibited by Philanthotoxin-433, a blocker of homomeric GluA1-containing AMPARs. We then investigated the effect of SPARC treatment on neuronal health in an injury context where SPARC expression is upregulated. We found that SPARC levels are increased in astrocytes and microglia following middle cerebral artery occlusion (MCAO) in vivo and oxygen-glucose deprivation (OGD) in vitro. Remarkably, chronic pre-treatment with SPARC prevented OGD-induced loss of synaptic GluA1. Furthermore, SPARC treatment reduced neuronal death through Philanthotoxin-433 sensitive GluA1 receptors. Taken together, this study suggests a novel role for SPARC and GluA1 in promoting neuronal health and recovery following CNS damage.
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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) | 0.001 | 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".