Identification of unusually disulphide-bonded insulin forms using mass spectrometry and thermolysin cleavage
Bibliographic record
Abstract
ABSTRACT Insulin is an essential hormone made by the pancreatic beta-cells in the islets of Langerhans. Beta-cells produce more insulin protein than virtually all other cellular proteins combined. Dysfunction in the process of insulin synthesis can lead to disease, including rare forms of monogenic diabetes. Specifically, aberrant intra-insulin and inter-insulin disulphide bonds have been implicated in the pathology of type 1 diabetes and type 2 diabetes, respectively. In type 1 diabetes, misprocessed insulin isoforms may be neoepitopes that kick-start and/or exacerbate the auto-immune response. In type 2 diabetes, aberrant disulphides form insulin dimers that can clog the endoplasmic reticulum and contribute to beta cell dysfunction. To facilitate the study of novel and known insulin neoepitopes and dimers, we present an unbiased and rapid technique for identifying insulin disulphide patterns from pancreatic islet extracts. The basis of this method is the cleavage between insulin’s cysteine residues with the metalloprotease, thermolysin, and subsequent identification of cysteine containing fragments and their partner peptides by LC-MS/MS. Using this technique, we identify 6 aberrant disulphide bonded insulin species, including a previously described type 1 diabetes neoepitope, as well as inter-chain disulphide bonded insulin dimers. Furthermore, using the endoplasmic stress inducer, thapsigargin, we observe increased disulphide errors in a patient donor sample. This approach lays foundations to identify the scope and cause of aberrant insulin disulphide formation 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 | 0.001 | 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".