The impact of postsynaptic density 95 blocking peptide (Tat‐NR2B9c) and an iNOS inhibitor (1400W) on proteomic profile of the hippocampus in C57BL/6J mouse model of kainate‐induced epileptogenesis
Bibliographic record
Abstract
Antiepileptogenic agents that prevent the development of epilepsy following a brain insult remain the holy grail of epilepsy therapeutics. We have employed a label-free proteomic approach that allows quantification of large numbers of brain-expressed proteins in a single analysis in the mouse (male C57BL/6J) kainate (KA) model of epileptogenesis. In addition, we have incorporated two putative antiepileptogenic drugs, postsynaptic density protein-95 blocking peptide (PSD95BP or Tat-NR2B9c) and a highly selective inducible nitric oxide synthase inhibitor, 1400W, to give an insight into how such agents might ameliorate epileptogenesis. The test drugs were administered after the induction of status epilepticus (SE) and the animals were euthanized at 7 days, their hippocampi removed, and subjected to LC-MS/MS analysis. A total of 2,579 proteins were identified; their normalized abundance was compared between treatment groups using ANOVA, with correction for multiple testing by false discovery rate. Significantly altered proteins were subjected to gene ontology and KEGG pathway enrichment analyses. KA-induced SE was most robustly associated with an alteration in the abundance of proteins involved in neuroinflammation, including heat shock protein beta-1 (HSP27), glial fibrillary acidic protein, and CD44 antigen. Treatment with PSD95BP or 1400W moderated the abundance of several of these proteins plus that of secretogranin and Src substrate cortactin. Pathway analysis identified the glutamatergic synapse as a key target for both drugs. Our observations require validation in a larger-scale investigation, with candidate proteins explored in more detail. Nevertheless, this study has identified several mechanisms by which epilepsy might develop and several targets for novel drug development. OPEN PRACTICES: This article has been awarded Open Data. All materials and data are publicly accessible as supporting information. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".