Quantitative Analysis of the Brain Ubiquitylome in Alzheimer's Disease
Bibliographic record
Abstract
Several neurodegenerative diseases including Alzheimer's Disease (AD) are characterized by ubiquitin-positive pathological protein aggregates. Here, an immunoaffinity approach is utilized to enrich ubiquitylated isopeptides after trypsin digestion from five AD and five age-matched control postmortem brain tissues. Label-free MS-based proteomic analysis identifies 4291 unique ubiquitylation sites mapping to 1682 unique proteins. Differential enrichment analysis shows that over 800 ubiquitylation sites are significantly altered between AD and control cases. Of these, ≈80% are increased in AD, including seven poly ubiquitin linkages, which is consistent with proteolytic stress and high burden of ubiquitylated pathological aggregates in AD. The microtubule associated protein Tau, the core component of neurofibrillary tangles, has the highest number of increased sites of ubiquitylation per any protein in AD. Tau poly ubiquitylation from AD brain homogenates is confirmed by reciprocal co-immunoprecipitation and by affinity capture using tandem ubiquitin binding entities. Co-modified peptides, with both ubiquitylation and phosphorylation sites, are also enriched in AD. Notably, many of the co-modified peptides mapped to Tau within KXGS motifs in the microtubule binding region suggesting that crosstalk between phosphorylation and ubiquitylation occurs on Tau in AD. Overall, these findings highlight the utility of MS to map ubiquitylated substrates in human brain and provides insight into mechanisms underlying pathological protein posttranslational modification in AD.
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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) | 0.000 | 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".