Homotypic fibrillization of TMEM106B across diverse neurodegenerative diseases
Bibliographic record
Abstract
Misfolding and aggregation of disease-specific proteins, resulting in the formation of filamentous cellular inclusions, is a hallmark of neurodegenerative disease with characteristic filament structures, or conformers, defining each proteinopathy. Here we show that a previously unsolved amyloid fibril composed of a 135 amino acid C-terminal fragment of TMEM106B is a common finding in distinct human neurodegenerative diseases, including cases characterized by abnormal aggregation of TDP-43, tau, or α-synuclein protein. A combination of cryoelectron microscopy and mass spectrometry was used to solve the structures of TMEM106B fibrils at a resolution of 2.7 Å from postmortem human brain tissue afflicted with frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP, n = 8), progressive supranuclear palsy (PSP, n = 2), or dementia with Lewy bodies (DLB, n = 1). The commonality of abundant amyloid fibrils composed of TMEM106B, a lysosomal/endosomal protein, to a broad range of debilitating human disorders indicates a shared fibrillization pathway that may initiate or accelerate neurodegeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".