A miR-137–Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing
Bibliographic record
Abstract
MiR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-Wide Association Studies implicate miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing. Using RNA-sequencing data from postmortem prefrontal cortex (N=522), we identified a co-expression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in-vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of co-expression prediction and associated them with fMRI activation in healthy volunteers (N1=214; N2=136; N3=2,075; N4=1,800) and with short-term treatment response in patients with schizophrenia (N=427). In 4,652 human subjects, we found that (i) schizophrenia risk genes are co-expressed in a biologically validated set enriched for miR-137 targets, (ii) increased expression of miR-137 target risk genes is mediated by low prefrontal miR-137 expression, (iii) alleles predicting greater gene-set co-expression are associated with greater prefrontal activation during emotion processing in three independent healthy cohorts (N1-2-3), in interaction with age (N4), (iv) these alleles predict less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia. The functional translation of miR-137 target gene expression linked with schizophrenia involves emotion processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".