A Multi-Omics Therapeutic Approach using SAHA, SP600125, and Exercise to Modulate BDNF levels in Major Depressive Disorder
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Bibliographic record
Abstract
Major depressive disorder (MDD) is a serious mental health condition associated with decreased quality of life and is correlated with other medical comorbidities like diabetes and hypertension. Despite its widespread impact, there is not an objective method of diagnosis or treatment. However, many studies have outlined the potential of exercise in altering Brain-derived neurotrophic factor (BDNF) to alleviate MDD symptoms. To investigate this association, a targeted literature search was conducted on databases such as OVID, PubMed and Science Direct, including studies published in the past 10 years. BDNF exists in two forms, pro-BDNF and mature BDNF, with its mature form more involved in improving cognitive function. Pro and mature BDNF can be regulated through the enzyme histone deacetylase 2 (HDAC2), an epigenetic silencer preventing its transcription. Consistent evidence was found for BDNF being regulated by multiple molecular pathways influenced by exercise and that BDNF is a potential target in MDD, playing a role in synaptic plasticity, neurogenesis, and mood regulation. As such, a multi-omics approach was explored to examine the mechanism behind exercise and BDNF, within metabolomics, epigenomics, transcriptomics, and proteomics in the context of MDD. Our proposed experiment ties together the multi-omics approach by exploring the effects of exercise metabolite β-hydroxybutyric (BHB), and pharmacological agents including SAHA and SP600125 on BDNF levels in mice models. Vorinostat (SAHA), a BHB mimic, is an HDAC2 inhibitor that upregulates both forms of BDNF. To minimize the pro-apoptotic response triggered by excess pro-BDNF binding to P75NTR and activating c-Jun N-terminal kinase (JNK), SP600125 (a JNK inhibitor) can be coupled with SAHA to inhibit pro-BDNF-JNK pathways. These markers can be further analyzed through ChIP-seq and ELISA. This research holds promise in informing novel therapies for MDD and furthering current knowledge in BDNF multi-omics interactions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it