Proteomic analysis reveals distinct cerebrospinal fluid signatures across genetic frontotemporal dementia subtypes
Bibliographic record
Abstract
We used an untargeted mass spectrometric approach, tandem mass tag proteomics, for the identification of proteomic signatures in genetic frontotemporal dementia (FTD). A total of 238 cerebrospinal fluid (CSF) samples from the Genetic FTD Initiative were analyzed, including samples from 107 presymptomatic (44 C9orf72, 38 GRN, and 25 MAPT) and 55 symptomatic (27 C9orf72, 17 GRN, and 11 MAPT) mutation carriers as well as 76 mutation-negative controls ('noncarriers'). We found shared and distinct proteomic alterations in each genetic form of FTD. Among the proteins significantly altered in symptomatic mutation carriers compared with noncarriers, we found that a set of proteins including neuronal pentraxin 2 and fatty acid binding protein 3 changed across all three genetic forms of FTD and patients with Alzheimer's disease from previously published datasets. We observed differential changes in lysosomal proteins among symptomatic mutation carriers with marked abundance decreases in MAPT carriers but not other carriers. Further, we identified mutation-associated proteomic changes already evident in presymptomatic mutation carriers. Weighted gene coexpression network analysis combined with gene ontology annotation revealed clusters of proteins enriched in neurodegeneration and glial responses as well as synapse- or lysosome-related proteins indicating that these are the central biological processes affected in genetic FTD. These clusters correlated with measures of disease severity and were associated with cognitive decline. This study revealed distinct proteomic changes in the CSF of patients with genetic FTD, providing insights into the pathological processes involved in the disease. In addition, we identified proteins that warrant further exploration as diagnostic and prognostic biomarker candidates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".