TrkB interacts with ErbB4 and regulates NRG1-induced NR2B phosphorylation in cortical neurons before synaptogenesis
Bibliographic record
Abstract
BACKGROUND: Neuregulin 1 (NRG1) and NMDARs play important roles in various neuronal functions including neural development. NMDARs also promote many cellular events such as proliferation and survival of neuroblasts before synapse formation. Although many recent studies have indicated that NRG1 regulates NMDAR function in cortical neurons, the effect of NRG1 on NMDAR activation before synapse formation is not well studied. RESULTS: NRG1 induces activation of NMDAR subunit NR2B, and tropomyosin-related kinase receptor B (TrkB), the receptor for BDNF via activation of phospholipase C-gamma (PLC-γ) in immature primary cortical neurons. Our data using TrkB inhibitor (K252a), TrkB siRNA and TrkB-/- neurons demonstrated that TrkB inhibition suppresses NRG1-induced NR2B activation in neurons. We found that NRG1 stimulation leads to GABAA receptor-mediated TrkB activation. Co-immunoprecipitation and proximity ligase assay showed that TrkB interacts with ErbB4 (NRG1 receptor) and the TrkB-ErbB4 interaction was increased following NRG1 treatment. A significant reduction in TrkB-ErbB4 interaction was observed in the prefrontal cortex of schizophrenia subjects. We found significant increase in released BDNF levels following NRG1 treatment, which was inhibited by ErbB4 inhibitor, AG1478. In addition, pretreatment with BDNF neutralizing antibody, but not control IgG abolished NRG1-induced increases in phospho-TrkB and phospho-NR2B levels. Moreover, studies using TrkB mutants showed that intercellular domain of TrkB is necessary for TrkB-ErbB4 interaction and NR2B activation. CONCLUSIONS: BDNF/TrkB signaling plays an important role in the NRG1-stimulated NR2B regulation. These findings could be of relevance to many neurodevelopmental disorders, as NRG1 and BDNF signaling pathways have been implicated in autism and schizophrenia.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".