Sex-specific proteomic analysis of epileptic brain tissues from Pten knockout mice and human refractory epilepsy
Bibliographic record
Abstract
RATIONALE: Epilepsy presents significant sex-based disparities in prevalence and manifestation. Epidemiological studies reveal that epilepsy is more prevalent in males, with lesional types being more common, whereas idiopathic generalized epilepsies are more frequently observed in females. These differences stress the importance of considering sex-specific factors in epilepsy diagnosis, treatment, and mechanistic research using preclinical models. To elucidate potential molecular differences that could explain these disparities and inform personalized treatment strategies, we conducted a proteomic analysis of epileptic brain tissues from both an experimental mouse model of genetic epilepsy and humans with drug-resistant epilepsy (DRE). METHODS: We employed mass spectrometry-based proteomic analysis on brain tissues from DRE patients and the Pten knockout (KO) mouse model of genetic epilepsy with focal cortical dysplasia. Mouse samples included hippocampi from adult wild-type (WT) and Pten KO mice (4-5 per group and sex). Human samples included the temporal cortex from 12 DRE adult patients (7 males, 5 females) and 5 non-epileptic (NE) controls (2 males, 3 females). Brain biopsies were collected with patients' informed consent under approved IRB protocols (Indiana University Health Biorepository). Proteomic profiles were analyzed using principal component analysis (PCA) along with volcano plots to identify significant changes in protein expression. The enrichment analysis of differentially expressed proteins was conducted by Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway. RESULTS: PCA revealed distinct clustering of brain proteomes between epilepsy and control cases in both human and mice, with 390 proteins showing significant differences in human and 437 proteins in mouse samples. These proteins are primarily associated with ion channels, synaptic processes, and neuronal energy regulation. In the mouse model, males have more pronounced proteomic changes than females, with enrichment in metabolic pathways and VEGF signaling pathway, indicating a more severe vascular permeability impairment in males. In human DRE cases, 118 proteins were significantly changed by comparing epileptic females to males. Pathway analysis revealed changes in metabolic pathways and the HIF-1 signaling pathway, indicating that altered neuronal activity and inflammation may lead to increased oxygen consumption. CONCLUSION: These findings highlight differences between epilepsy and control brain samples in both humans and mice. Sex-specific analysis revealed distinct pathway enrichments between females and males, with males exhibiting a broader range of proteomic alterations. While these observations suggest potential sex-related differences in proteomic profiles, larger studies are needed to further validate these patterns. This exploratory work provides initial insights into possible underlying mechanisms of epilepsy and suggests that sex may be an important consideration in future epilepsy studies, though more comprehensive studies are required to establish therapeutic interventions.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Proteomic analysis of epileptic brain tissue; a biomedical mechanism question.
The study analyzes sex-specific proteomic differences in epilepsy.
Sex-specific proteomics of epileptic tissue is biomedical disease research, not metaresearch.
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 | 0.001 | 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".