Distinct Mechanisms Determine the Patterns of Differential Activation of H-Ras, N-Ras, K-Ras 4B, and M-Ras by Receptors for Growth Factors or Antigen
Bibliographic record
Abstract
Although GTPases of the Ras family have been implicated in many aspects of the regulation of cells, little is known about the roles of individual family members. Here, we analyzed the mechanisms of activation of H-Ras, N-Ras, K-Ras 4B, and M-Ras by two types of external stimuli, growth factors and ligation of the antigen receptors of B or T lymphocytes (BCRs and TCRs). The growth factors interleukin-3, colony-stimulating factor 1, and epidermal growth factor all preferentially activated M-Ras and K-Ras 4B over H-Ras or N-Ras. Preferential activation of M-Ras and K-Ras 4B depended on the presence of their polybasic carboxy termini, which directed them into high-buoyant-density membrane domains where the activated receptors, adapters, and mSos were also present. In contrast, ligation of the BCR or TCR resulted in activation of H-Ras, N-Ras, and K-Ras 4B, but not M-Ras. This pattern of activation was not influenced by localization of the Ras proteins to membrane domains. Activation of H-Ras, N-Ras, and K-Ras 4B instead depended on the presence of phospholipase C-gamma and RasGRP. Thus, the molecular mechanisms leading to activation of Ras proteins vary with the stimulus and can be influenced by either colocalization with activated receptors or differential sensitivity to the exchange factors activated by a stimulus.
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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 | 0.001 | 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 | 0.001 |
| 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".