Proteomic Analysis of Lipid Microdomains from Lipopolysaccharide-Activated Human Endothelial Cells
Bibliographic record
Abstract
The endothelium plays a critical role in orchestrating the inflammatory response seen during sepsis. Many of the inflammatory effects of Gram-negative sepsis are elicited by lipopolysaccharide (LPS), a glycolipid component of bacterial cell walls. Lipid-rich microdomains have been shown to concentrate components of the LPS signaling system. However, much remains to be learned about which proteins are constituents of lipid microdomains, and how these are regulated following cell activation. Progress in this area would be accelerated by employing global proteomic analyses, but the hydrophobicity of membrane proteins presents an analytical barrier to the effective application of such approaches. Herein, we describe a method to isolate detergent-resistant membranes from endothelial cells, and prepare these samples for proteomic analysis in a way that is compatible with subsequent separations and mass spectrometric (MS) analysis. In the application of these sample preparation and MS analyses, 358 proteins from the lipid-rich microdomains of LPS-activated endothelial cell membranes have been identified of which half are classified as membrane proteins by Gene Ontology. We also demonstrate that the sample preparation method used for solubilization and trypsin digestion of lipid-rich microdomains renders the membrane spanning sequences of transmembrane proteins accessible for endoproteolytic hydrolysis. This analysis sets the analytical foundation for an in-depth probing of LPS signaling in endothelial cells.
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) | 0.000 | 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 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".