Effect of ketamine and esketamine on RNA expression and its relevance for depression: A systematic review
Bibliographic record
Abstract
Treatment-resistant depression (TRD) remains a challenge in psychiatry, necessitating novel therapeutic strategies beyond traditional monoaminergic antidepressants. Ketamine and its S-enantiomer esketamine have demonstrated rapid and robust antidepressant effects in TRD, probably through mechanisms involving glutamatergic modulation, neuroplasticity, and anti-inflammatory properties. However, the molecular underpinnings of these effects are not yet understood. This systematic review aimed to synthesize evidence from human and in vitro studies evaluating transcriptional changes associated with ketamine and esketamine treatment, to identify potential biomarkers and clarify molecular pathways relevant to their antidepressant properties. A systematic search conducted on PubMed and Scopus identified 12 studies assessing RNA expression following ketamine or esketamine treatment in patients with unipolar or bipolar depression or in human-derived cell models. Eligibility and quality were evaluated using PRISMA guidelines and a modified version of Downs and Black checklist. Twelve studies met inclusion criteria, only one of which explored the effect of esketamine, while all others focused on racemic ketamine. Five studies examined peripheral blood gene expression in patients with TRD, while seven assessed mRNA changes in vitro using human-derived cells. Transcriptome and candidate gene expression studies revealed modulation of genes and pathways related to glutamatergic signaling (GRM2, GRIN2D), immune regulation (STAT3, CCL22, IL6), and neuroplasticity (IGF2). No consistent peripheral biomarkers emerged, but transcriptional profiling revealed dynamic molecular responses to ketamine and esketamine. Ketamine and esketamine induce diverse transcriptional changes implicating neuroplastic, inflammatory, and metabolic pathways. Transcriptomic profiling offers a promising approach for uncovering biomarkers and mechanisms of antidepressant response, warranting further multi-omics, large-scale studies.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".