Phosphoproteome analysis of an early onset mouse model (<scp>T</scp>g<scp>CRND</scp>8) of <scp>A</scp>lzheimer's disease reveals temporal changes in neuronal and glia signaling pathways
Bibliographic record
Abstract
Sustained exposure to soluble amyloid β (Aβ42 ) oligomers is predicted to impair synaptic function in the hippocampal-entorhinal circuit, signaling synaptic loss and precipitating cognitive impairment in Alzheimer's disease. Regional changes in overall patterns of protein phosphorylation are likely crucial to promote transition from a presymptomatic to a symptomatic state in response to accumulating Aβ42. Here, we used unbiased proteomic approaches to compare the phosphoproteome of presymptomatic and symptomatic TgCRND8 mice and identify network disruptions in signaling pathways implicated in the manifestation of behavioral indices of learning and memory impairment. Phosphopeptide enrichment with triple isotopic dimethylation labeling combined with online multidimensional separation and MS was used to profile phosphoproteome changes in 2- and 6-month-old TgCRND8 mice and congenic littermate controls. We identified 1026 phosphopeptides representing 1168 phosphorylation sites from 476 unique proteins. Of these, 595 phosphopeptides from 293 unique proteins were reliably quantified and 139 phosphopeptides were found to change significantly in the hippocampus of TgCRND8 mice following conversion from a presymptomatic to a symptomatic state.
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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 | 0.001 |
| 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".