Systems-Level Analysis of Proteolytic Events in Increased Vascular Permeability and Complement Activation in Skin Inflammation
Bibliographic record
Abstract
During inflammation, vascular permeability is increased by various proteolytic events, such as the generation of bradykinin, that augment local tissue responses by enabling tissue penetration of serum proteins, including complement and acute-phase proteins. Proteases also govern inflammatory responses by processing extracellular matrix proteins and soluble bioactive mediators. We quantified changes in the proteome and the nature of protein amino termini (the N-terminome) and the altered abundance of murine proteases and inhibitors during skin inflammation. Through analysis of the N-terminome by iTRAQ-TAILS, we identified cotranslational and posttranslational αN-acetylation motifs, quantitative increases in protein abundance, and qualitative changes in the proteolytic signature during inflammation. Of the proteins identified in normal skin, about half were cleaved, and phorbol ester-induced inflammation increased the proportion of cleaved proteins, including chemokines and complement proteins, that were processed at previously uncharacterized sites. In response to phorbol ester-induced inflammation, mice deficient in matrix metalloproteinase 2 (MMP2) showed reduced accumulation of serum proteins in the skin and exhibited different proteolytic networks from those of wild-type mice. We found that the complement 1 (C1) inhibitor attenuated the increase in serum protein accumulation in inflamed skin. Cleavage and inactivation of the C1 inhibitor by MMP2 increased complement activation and bradykinin generation in wild-type mice, leading to increased vessel permeability during inflammation, which was diminished in Mmp2(-/-) mice. Thus, our systems-level analysis of proteolysis dissected cleavage events associated with skin inflammation and demonstrated that loss of a single protease could perturb the proteolytic signaling network and enhance inflammation.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".