Identification of AL proteins from 10 λ-AL amyloidosis patients by mass spectrometry extracted from abdominal fat and heart tissue
Bibliographic record
Abstract
Background Systemic AL amyloidosis arises from the misfolding of patient-specific immunoglobulin light chains (LCs). Potential drivers of LC amyloid formation are mutational changes and post-translational modifications (PTMs). However, little information is available on the exact primary structure of the AL proteins and their precursor LCs.Objective We analyse the exact primary structure of AL proteins extracted from 10 λ AL amyloidosis patients and their corresponding precursor LCs.Materials and Methods By cDNA sequencing of the precursor LC genes in combination with mass spectrometry of the AL proteins, the exact primary structure and PTMs were determined. This information was used to analyse their biochemical properties.Results All AL proteins comprise the VL and a small part of the CL with a common C-terminal truncation region. While all AL proteins retain the conserved native disulphide bond of the VL, we found no evidence for presence of other common PTMs. The analysis of the biochemical properties revealed that the isoelectric point of the VL is significantly increased due to introduced mutations.Conclusion Our data imply that mutational changes influence the surface charge properties of the VL and that common proteolytic processes are involved in the generation of the cleavage sites of AL proteins.
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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 | 0.001 |
| 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) | 0.001 | 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".