Time-course global proteome analyses reveal an inverse correlation between Aβ burden and immunoglobulin M levels in the APPNL-F mouse model of Alzheimer disease
Bibliographic record
Abstract
Alzheimer disease (AD) stands out amongst highly prevalent diseases because there is no effective treatment nor can the disease be reliably diagnosed at an early stage. A hallmark of AD is the accumulation of aggregation-prone amyloid β peptides (Aβ), the main constituent of amyloid plaques. To identify Aβ-dependent changes to the global proteome we used the recently introduced APPNL-F mouse model of AD, which faithfully recapitulates the Aβ pathology of the disease, and a workflow that interrogated the brain proteome of these mice by quantitative mass spectrometry at three different ages. The elevated Aβ burden in these mice was observed to cause almost no changes to steady-state protein levels of the most abundant >2,500 brain proteins, including 12 proteins encoded by well-confirmed AD risk loci. The notable exception was a striking reduction in immunoglobulin heavy mu chain (IGHM) protein levels in homozygote APPNL-F/NL-F mice, relative to APPNL-F/wt littermates. Follow-up experiments revealed that IGHM levels generally increase with age in this model. Although discovered with brain samples, the relative IGHM depletion in APPNL-F/NL-F mice was validated to manifest systemically in the blood, and did not extend to other blood proteins, including immunoglobulin G. Results presented are consistent with a cause-effect relationship between the excessive accumulation of Aβ and the selective depletion of IGHM levels, which may be of relevance for understanding the etiology of the disease and ongoing efforts to devise blood-based AD diagnostics.
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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 | 0.001 | 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".