Vulnerability of the Myelin‐Axon Interface Uncovered by Subcellular Proteomics and Imaging of Alzheimer's Human Brain
Bibliographic record
Abstract
BACKGROUND: Myelin ensheathment is essential for rapid axonal electrical conduction, metabolic support and neuronal plasticity. In Alzheimer's disease (AD), disruptions in both myelin and axonal structures occur; however, the underlying mechanisms remain poorly understood. METHOD: To investigate the molecular and cellular mechanisms of myelin-axon disruption, we developed novel proximity labeling subcellular proteomics of the myelin-axon interface in postmortem human brains and AD-model mice. We developed new computational algorithm to uncover cell-cell-communication between myelin-axon and their disruption in AD humans. We applied super-resolution expansion microscopy and high-resolution confocal imaging to AD human postmortem brains and AD-model mice to reveal myelin-axon disruption and pathological changes. RESULT: Using proximity labeling subcellular proteomics of the myelin-axon interface in postmortem human brains and AD-model mice, we discovered dysregulated signaling pathways and ligand-receptor interactions, including those involved in β-amyloid processing, axonal outgrowth and lipid metabolism. Expansion microscopy confirmed the subcellular expression of top proteomic hits and revealed β-amyloid aggregation within internodal peri-axonal space and paranodal/juxtaparanodal channels. Although no overt changes in myelin coverage were observed, we found reduced paranode density and aberrant myelination and paranode positioning around amyloid plaque-associated dystrophic axons. CONCLUSION: These findings highlight the myelin-axon interface as a critical site of protein aggregation and disrupted neuro-glial communication in AD. The molecular architecture of the myelin-axon interface uncovered in this study provides a foundation for future mechanistic investigations in health and disease.
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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 | 0.000 | 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) | 0.000 | 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".