Lipopolysaccharide and TNF-α Produce Very Similar Changes in Gene Expression in Human Endothelial Cells
Bibliographic record
Abstract
Intracellular signaling pathways regulated by Toll-like receptor 4 (TLR4) and tumor necrosis factor-alpha (TNF-alpha) both activate NFkappaB. This suggests that lipopolysaccharide (LPS) and TNF-alpha should alter transcription of a common set of genes. We tested this hypothesis by treating first passage human umbilical endothelial cells (HUVEC) for 6 h with LPS (50 ng/ml+1 microg/ml CD14) or TNF-alpha (10 ng/ml) and analyzing changes in gene expression by microarray analysis (Affymetrix GeneChips). LPS and TNF-alpha increased expression of 191 common genes and decreased expression of 102 genes. Regulated transcripts encoded for a large number of chemokines, adhesion molecules, procoagulant factors, and molecules that affect cell integrity. Based on the microarray analysis and subsequent confirmation of specific genes by Northern analysis, all 203 genes altered by LPS were altered by TNF-alpha. An additional 17 genes were induced only by TNF-alpha and the expression of 46 was reduced. There were, however, some differences in the kinetics of changes. We also showed that endogenous CD14 was present on these early passage cells and exogenous CD14 was not necessary for most of the LPS response. An autocrine effect from LPS induced expression of TNF-alpha also was ruled out by blocking TNF-alpha with monoclonal antibodies. In conclusion, LPS induces a robust alteration in gene expression in HUVEC that is very similar to that induced by TNF-a. This LPS effect on endothelium could play an important role in the innate immune response.
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 | 0.001 |
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".