Network analysis of S-nitrosylated synaptic proteins demonstrates unique roles in health and disease
Bibliographic record
Abstract
Nitric oxide can covalently modify cysteine thiols on target proteins to alter that protein's function in a process called S-nitrosylation (SNO). S-nitrosylation of synaptic proteins plays an integral part in neurotransmission. Here we review the function of the SNO-proteome at the synapse and whether clusters of SNO-modification may predict synaptic dysfunction associated with disease. We used a systematic search strategy to concatenate SNO-proteomic datasets from normal human or murine brain samples. Identified SNO-modified proteins were then filtered against proteins reported in the Synaptome Database, which provides a detailed and experimentally verified annotation of all known synaptic proteins. Subsequently, we performed an unbiased network analysis of all known SNO-synaptic proteins to identify clusters of SNO proteins commonly involved in biological processes or with known disease associations. The resulting SNO networks were significantly enriched in biological processes related to metabolism, whereas significant gene-disease associations were related to Schizophrenia, Alzheimer's, Parkinson's and Huntington's disease. Guided by an unbiased network analysis, the current review presents a thorough discussion of how clustered changes to the SNO-proteome influence health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".