Amyloid accumulation drives proteome-wide alterations in mouse models of Alzheimer’s disease like pathology
Bibliographic record
Abstract
Summary Amyloid beta (Aβ) peptides impair multiple cellular pathways in the brain and play a causative role in Alzheimer’s disease (AD) pathology, but how the brain proteome is remodeled during this process is unknown. To identify new protein networks associated with AD-like pathology, we performed global quantitative proteomic analysis in three mouse models at pre- and post-symptomatic ages. Our analysis revealed a robust and consistent increase in Apolipoprotein E (ApoE) levels in nearly all transgenic brain regions with increased Aβ levels. Taken together with prior findings on ApoE driving Aβ accumulation, this analysis points to a pathological dysregulation of the ApoE-Aβ axis. We also found dysregulation of protein networks involved in excitatory synaptic transmission consistent with AD pathophysiology. Targeted analysis of the AMPA receptor complex revealed a specific loss of TARPγ-2, a key AMPA receptor trafficking protein. Expression of TARPγ-2 in vivo in hAPP transgenic mice led to a restoration of AMPA currents. This database of proteome alterations represents a unique resource for the identification of protein alterations responsible for AD. Highlights Proteomic analysis of mouse brains with AD-like pathology reveals stark remodeling Proteomic evidence points to a dysregulation of ApoE levels associated with Aβ clearance rather than production Co-expression analysis found distinctly impaired synapse and mitochondria modules In-depth analyses of AMPAR complex points to loss of TARPγ-2, which may compromise synapses in AD eTOC Blurb Proteome-wide profiling of brain tissue from three mouse models of AD-like pathology reveals Aβ, brain region, and age dependent alterations of protein levels. This resource provides a new global protein expression atlas for the Alzheimer’s disease research community.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".