TAILS N-terminomics of human platelets reveals pervasive metalloproteinase-dependent proteolytic processing in storage
Bibliographic record
Abstract
Proteases, and specifically metalloproteinases, have been linked to the loss of platelet function during storage before transfusion, but the underlying mechanisms remain unknown. We used a dedicated N-terminomics technique, iTRAQ terminal amine isotopic labeling of substrates (TAILS), to characterize the human platelet N-terminome, proteome, and posttranslational modifications throughout platelet storage over 9 days under blood-banking conditions. From the identified 2938 proteins and 7503 unique peptides, we characterized N-terminal methionine excision, co- and posttranslational Nα acetylation, protein maturation, and proteolytic processing of proteins in human platelets. We also identified for the first time 10 proteins previously classified by the Human Proteome Organization as "missing" in the human proteome. Most N termini (77%) were internal neo-N termini (105 were novel potential alternative translation start sites, and 2180 represented stable proteolytic products), thus highlighting a prominent yet previously uncharacterized role of proteolytic processing during platelet storage. Protease inhibitor studies revealed metalloproteinases as being primarily responsible for proteolytic processing (as opposed to degradation) during storage. System-wide identification of metalloproteinase and other proteinase substrates and their respective cleavage sites suggests novel mechanisms of the effect of proteases on protein activity and platelet function during storage. All data sets and metadata are available through ProteomeXchange with the data set identifier PXD000906.
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 | 0.000 |
| 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".