Mass Spectrometry-based Protein Profiling to Determine the Cause of Lysosomal Storage Diseases of Unknown Etiology
Bibliographic record
Abstract
Diagnosis of lysosomal storage diseases (LSDs) can be problematic in atypical cases where clinical phenotype may overlap with other genetically distinct disorders. In addition, LSDs may result from mutations in genes not yet implicated in disease. Thus, there are individuals that are diagnosed with apparent LSD based upon clinical criteria where the gene defect remains elusive. The objective of this study was to determine whether comparative proteomics approaches could provide useful insights into such cases. Most LSDs arise from mutations in genes encoding lysosomal proteins that contain mannose 6-phosphate, a carbohydrate modification that acts as a signal for intracellular targeting to the lysosome. We purified mannose 6-phosphorylated proteins by affinity chromatography and estimated relative abundance of individual proteins in the mixture by spectral counting of peptides detected by tandem mass spectrometry. Our rationale was that proteins that are decreased or absent in patients compared with controls could represent candidates for the primary defect, directing biochemical or genetics studies. On a survey of brain autopsy specimens from 23 patients with either confirmed or possible lysosomal disease, this approach identified or validated the genetic basis for disease in eight cases. These results indicate that this protein expression approach is useful for identifying defects in cases of undiagnosed lysosomal disease, and we demonstrated that it can be used with more accessible patient samples, e.g. cultured cells. Furthermore this approach was instrumental in the identification or validation of mutations in two lysosomal proteins, CLN5 and sulfamidase, in the adult form of neuronal ceroid lipofuscinosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".