Systems-Level Analysis of Proteolytic Events in Increased Vascular Permeability and Complement Activation in Skin Inflammation
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it