Response of iPSC-derived neurons from individuals with treatment-resistant depression to (2 R,6 R)-hydroxynorketamine and reelin: an exploratory study
Bibliographic record
Abstract
Treatment-resistant depression (TRD) is associated with worse clinical outcomes and longer course of illness. However, TRD is more difficult to model in animal phenotypes, suggesting that other experimental and translational models must be considered to properly address and research novel therapeutics. Reelin, an endogenous glycoprotein downregulated in depression, has shown rapid antidepressant-like effects akin to those of the N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine. Interestingly, the antidepressant-like effects of both ketamine and reelin affect mechanistic target of rapamycin complex 1 (mTORC1) activity and that of its related downstream signalers. (2 R,6 R)-hydroxynorketamine (HNK) is a major metabolite of ketamine that, at therapeutic levels, appears to activate mTORC1 without antagonizing NMDARs. To model the effects of (2 R,6 R)-HNK and reelin on neurons from TRD participants, induced pluripotent stem cells (iPSCs) were reprogrammed from peripheral blood mononuclear cells collected from five females with TRD (mean=40.2 yrs) and then differentiated into cortical neurons. In iPSC-derived neurons from TRD participants, 50 nM reelin and 1 µM (2 R,6 R)-HNK had similar effects on the protein expression of GluA1, PSD-95, Dab1, Synapsin I, and p-ERK, with concentration-dependent increases observed at one hour that significantly decreased by 24 h. RNA sequencing revealed similar changes in gene expression between 50 nM reelin and 1 µM (2 R,6 R)-HNK at one hour, although only reelin upregulated mTORC1 signaling. While this work remains preliminary, the results suggest that iPSC-derived neurons could provide a valuable in vitro model to study TRD and hold promise for evaluating novel therapeutics such as (2 R,6 R)-HNK and reelin.Clinicaltrials.gov: NCT02484456.
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".