Extracellular matrix composition determines the transcriptional response to epidermal growth factor receptor activation
Bibliographic record
Abstract
The transcriptional response to epidermal growth factor (EGF) was examined in a cultured cell model of adhesion. Gene expression was monitored in human embryonic kidney cells (HEK293) after attachment of cells to the extracellular matrix (ECM) proteins, laminin, and fibronectin, by using complementary DNA microarrays printed with 1,718 individual human genes. Cluster analysis revealed that the influence of EGF on gene expression, either positive or negative, was largely independent of ECM composition. However, clusters of EGF-regulated genes were identified that were diagnostic of the type of ECM proteins to which cells were attached. In these clusters, attachment of cells to a laminin or fibronectin substrata specifically modified the direction of gene expression changes in response to EGF stimulation. For example, in HEK293 cells attached to fibronectin, EGF stimulated an increase in the expression of some genes; however, genes in the same group were nonresponsive or even suppressed in cells attached to laminin. Many of the genes regulated by EGF and ECM proteins in this manner are involved in ECM and cytoskeletal architecture, protein synthesis, and cell cycle control, indicating that cell responses to EGF stimulation can be dramatically affected by ECM composition.
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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 | 0.001 | 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".