The Role of Micrornas in Regulating Redox Modulation in Bipolar Disorder
Bibliographic record
Abstract
Introduction Alterations in redox modulation are consistently reported in bipolar disorder (BD). MicroRNAs are targeted regulators of gene expression. Objectives and aims We aimed to examine if microRNAs that target redox modulators can discriminate between BD and healthy controls. Methods Data from brains of individuals with and without BD were obtained from Array Collection datasets. MicroRNAs targeting redox modulators were assessed for their ability to discriminate BD from the control group using machine-learning algorithms. Methylation of microRNAs, expression of their transcription factors and redox targets were assessed with ANCOVA with FDR correction. For validation, we acquired plasma samples belonging to 2 families of individuals with and without BD (n = 9). Plasma microRNAs were sequenced using the Ion Total RNA Sequencing Kit (Thermo Fisher Scientific), and microRNAs identified from the in silico analysis were examined in the validation dataset. Results We identified 5 miRNAs (hsa-miR-299, hsa-miR-125a, hsa-miR-145, hsa-miR-30b, hsa-miR424) that were common in two of the four in silico datasets. Target genes glutathione peroxidase 4, ATP5A1, ATP5G1, NDUFS1, NDUFC2, and catalase were expressed at different levels between BD and the control group. Furthermore, our results showed that transcription factors CTCF and USF1 might control the expression of hsa-miR-145, while methylation differences were not found. Finally, hsa-miR-30b was significantly increased in the plasma of patients with BD compared to controls in the validation experiment. Conclusions Our study demonstrates that microRNAs may have an important role in the initiation of redox changes in BD. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".