Abstract 267: Identification of Proteolytic Substrates for ADAMTS7 Using Terminal Amine Isotope Labeling of Substrates
Bibliographic record
Abstract
ADAMTS7 , a member of the disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) family, has been recently identified as a novel genetic locus for coronary artery disease. Although a handful of ADAMTS7 substrates have been identified through traditional yeast two-hybrid screening and standard proteomic profiling, the molecular mechanisms of ADAMTS7 contribution to atherosclerosis have not been fully explored in the context of its proteolytic action. Here we utilized terminal amine isotope labeling of substrates (TAILS), a cutting-edge proteomic approach to enrich N-terminal peptides, enable systemic characterization of proteolytic events and substrate identification for ADAMTS7. Briefly, primary vascular smooth muscle cells (SMC) from ADAMTS7 WT and KO mice were treated with TNFα to stimulated endogenous ADAMTS7 expression, followed by TAILS using dimethyl labeling in cellular proteome and secretome. In 3 biological replicates, we identified 1,720 and 1,616 quantified N-terminal peptides in SMC proteome and secretome, which composed of 415 and 122 unique N-termini of mature protein as well as 1,185 and 1,161 cleaved neo-N-termini driven by proteolytic processing. As expected, most of acetylated peptides in SMC proteome and secretome are natural N-termini of mature proteins (82% and 77%, respectively) while free N-terminal peptides are remarkably enriched with cleaved neo-N termini (85% and 96%, respectively). Importantly, 106 and 86 neo-N-termini (corresponding to 65 and 54 unique proteins) were enriched in ADAMTS7 WT SMC proteome and secretome respectively (WT/KO isotope ratio >2), suggesting that these proteins underwent elevated proteolysis in ADAMTS WT SMCs and thus are proteolytic substrate candidates for ADAMTS7. Interestingly, gene pathway analysis indicates that these proteins are enriched with genes involved in proteinaceous extracellular matrix and extracellular region, such as vimentin, fibulin and MMPs. Biochemistry studies are ongoing to validate these ADAMTS7 substrate candidates. In summary, our study presents a systemic profiling on proteolytic events in SMC cellular proteome and secretome and identifies candidate ADAMTS7 substrates relevant to smooth muscle function and vascular biology.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".