The Q43L mutant of neuregulin 2β is a pan-ErbB receptor antagonist
Bibliographic record
Abstract
The ErbB4 receptor tyrosine kinase possesses both tumour suppressor and oncogenic activities. Thus pharmacological agents are needed to help elucidate ErbB4 functions. However, limitations of existing ErbB4 agonists and antagonists have led us to seek novel ErbB4 antagonists. The Q43L mutant of the ErbB4 agonist NRG2β (neuregulin 2β) stimulates ErbB4 tyrosine phosphorylation, yet fails to stimulate ErbB4 coupling to cell proliferation. Thus in the present paper we hypothesize that NRG2β/Q43L may be an ErbB4 antagonist. NRG2β/Q43L competitively antagonizes agonist stimulation of ErbB4 coupling to cell proliferation. NRG2β/Q43L stimulates less ErbB4 tyrosine phosphorylation than does NRG2β. In addition, NRG2β stimulation of cell proliferation requires PI3K (phosphoinositide 3-kinase) activity and NRG2β stimulates greater Akt phosphorylation than does NRG2β/Q43L. Moreover, EGFR [EGF (epidermal growth factor) receptor] kinase activity (but not that of ErbB4) is critical for coupling ErbB4 to proliferation. Experiments utilizing ErbB4 splicing isoforms and mutants suggest that NRG2β and NRG2β/Q43L may differentially stimulate ErbB4 coupling to the transcriptional co-regulator YAP (Yes-associated protein). Finally, NRG2β/Q43L competitively antagonizes agonist stimulation of EGFR and ErbB2/ErbB3, indicating that NRG2β/Q43L is a pan-ErbB antagonist. Thus we postulate that NRG2β/Q43L and other antagonistic ligands stimulate ErbB tyrosine phosphorylation on a set of residues distinct from that stimulated by agonists, thus suggesting a novel mechanism of ErbB receptor regulation. Moreover, NRG2β/Q43L and related ligand-based antagonists establish a paradigm for the discovery of anti-ErbB therapeutics.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".