Topological Localization of Monomeric C-reactive Protein Determines Proinflammatory Endothelial Cell Responses
Bibliographic record
Abstract
The activation of endothelial cells (ECs) by monomeric C-reactive protein (mCRP) has been implicated in contributing to atherogenesis. However, the potent proinflammatory actions of mCRP on ECs in vitro appear to be incompatible with the atheroprotective effects of mCRP in a mouse model. Because mCRP is primarily generated within inflamed tissues and is rapidly cleared from the circulation, we tested whether these discrepancies can be explained by topological differences in response to mCRP within blood vessels. In a Transwell culture model, the addition of mCRP to apical (luminal), but not basolateral (abluminal), surfaces of intact human coronary artery EC monolayers evoked a significant up-regulation of MCP-1, IL-8, and IL-6. Such polarized stimulation of mCRP was observed consistently regardless of EC type or experimental conditions (e.g. culture of ECs on filters or extracellular matrix-coated surfaces). Accordingly, we detected enriched lipid raft microdomains, the major surface sensors for mCRP on ECs, in apical membranes, leading to the preferential apical binding of mCRP and activation of ECs through the polarized induction of the phospholipase C, p38 MAPK, and NF-κB signaling pathways. Furthermore, LPS and IL-1β induction of EC activation also exhibited topological dependence, whereas TNF-α did not. Together, these results indicate that tissue-associated mCRP likely contributes little to EC activation. Hence, topological localization is an important, but often overlooked, factor that determines the contribution of mCRP and other proinflammatory mediators to chronic vascular 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) | 0.000 | 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".