Antidepressant treatment effects on hippocampal protein turnover: Molecular and spatial insights from mass spectrometry
Bibliographic record
Abstract
A major challenge in managing depression is that antidepressant drugs take a long time to exert their therapeutic effects. For the development of faster-acting treatments, it is crucial to get an improved understanding of the molecular mechanisms underlying antidepressant mode of action. Here, we used a novel mass spectrometry-based workflow to investigate how antidepressant treatment affects brain protein turnover at single-cell and subcellular resolution. We combined stable isotope metabolic labeling, quantitative Tandem Mass Spectrometry (qTMS) and Multi-isotope Imaging Mass Spectrometry (MIMS) to simultaneously quantify and image protein synthesis and turnover in hippocampi of mice treated with the antidepressant paroxetine. We identified changes in turnover of individual hippocampal proteins that reveal altered metabolism-mitochondrial processes and report on subregion-specific turnover patterns upon paroxetine treatment. This workflow can be used to investigate brain protein turnover changes in vivo upon pharmacological interventions at a resolution and precision that has not been possible with other methods to date. Our results reveal acute paroxetine effects on brain protein turnover and shed light on antidepressant mode of action.
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".