Neuropathological signatures revealed by transcriptomic and proteomic analysis in Pten-deficient mouse models
Bibliographic record
Abstract
PTEN hamartoma tumour syndrome is characterised by mutations in the human PTEN gene. We performed transcriptomic and proteomic analyses of neural tissues and primary cultures from heterozygous and homozygous Pten-knockout mice. The somatosensory cortex of heterozygous Pten-knockout mice was enriched in immune response and oligodendrocyte development Gene Ontology (GO) terms. Parallel proteomic analysis revealed differentially expressed proteins (DEPs) related to dendritic spine development, keratinisation and hamartoma signatures. However, primary astrocytes (ASTs) from heterozygous Pten-knockout mice were enriched in the extracellular matrix GO term, while primary cortical neurons (PCNs) were enriched in immediate-early genes. In ASTs from homozygous Pten-knockout mice, cilium-related activity was enriched, while PCNs exhibited downregulation of forebrain neuron generation and differentiation, implying an altered excitatory/inhibitory balance. By integrating DEPs with pre-filtered differentially expressed genes, we identified the enrichment of traits of intelligence, cognitive function and schizophrenia, while DEPs in ASTs were significantly associated with intelligence and depression.
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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) | 0.000 | 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".