The Stress Kinase Mitogen-Activated Protein Kinase Kinase (Mkk)7 Is a Negative Regulator of Antigen Receptor and Growth Factor Receptor–Induced Proliferation in Hematopoietic Cells
Bibliographic record
Abstract
The dual specificity kinases mitogen-activated protein kinase (MAPK) kinase (MKK)7 and MKK4 are the only molecules known to directly activate the stress kinases stress-activated protein kinases (SAPKs)/c-Jun N-terminal kinases (JNKs) in response to environmental or mitogenic stimuli. To examine the physiological role of MKK7 in hematopoietic cells, we used a gene targeting strategy to mutate MKK7 in murine T and B cells and non-lymphoid mast cells. Loss of MKK7 in thymocytes and mature B cells results in hyperproliferation in response to growth factor and antigen receptor stimulation and increased thymic cellularity. Mutation of mkk7 in mast cells resulted in hyperproliferation in response to the cytokines interleukin (IL)-3 and stem cell factor (SCF). SAPK/JNK activation was completely abolished in the absence of MKK7, even though expression of MKK4 was strongly upregulated in mkk7(-/-) mast cell lines, and phosphorylation of MKK4 occurred normally in response to multiple stress stimuli. Loss of MKK7 did not affect activation of extracellular signal-regulated kinase (ERK)1/2 or p38 MAPK. mkk7(-/-) mast cells display reduced expression of JunB and the cell cycle inhibitor p16INK4a and upregulation of cyclinD1. Reexpression of p16INK4a in mkk7(-/-) mast cells abrogates the hyperproliferative response. Apoptotic responses to a variety of stimuli were not affected. Thus, MKK7 is an essential and specific regulator of stress-induced SAPK/JNK activation in mast cells and MKK7 negatively regulates growth factor and antigen receptor-driven proliferation in hematopoietic cells. These results indicate that the MKK7-regulated stress signaling pathway can function as negative regulator of cell growth in multiple hematopoietic lineages.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".