Developmental disruption to the cortical transcriptome and synaptosome in a model of <i>SETD1A</i> loss-of-function
Bibliographic record
Abstract
Large-scale genomic studies of schizophrenia implicate genes involved in the epigenetic regulation of transcription by histone methylation and genes encoding components of the synapse. However, the interactions between these pathways in conferring risk to psychiatric illness are unknown. Loss-of-function (LoF) mutations in the gene encoding histone methyltransferase, SETD1A, confer substantial risk to schizophrenia. Among several roles, SETD1A is thought to be involved in the development and function of neuronal circuits. Here, we employed a multi-omics approach to study the effects of heterozygous Setd1a LoF on gene expression and synaptic composition in mouse cortex across five developmental timepoints from embryonic day 14 to postnatal day 70. Using RNA sequencing, we observed that Setd1a LoF resulted in the consistent downregulation of genes enriched for mitochondrial pathways. This effect extended to the synaptosome, in which we found age-specific disruption to both mitochondrial and synaptic proteins. Using large-scale patient genomics data, we observed no enrichment for genetic association with schizophrenia within differentially expressed transcripts or proteins, suggesting they derive from a distinct mechanism of risk from that implicated by genomic studies. This study highlights biological pathways through which SETD1A LOF may confer risk to schizophrenia. Further work is required to determine whether the effects observed in this model reflect human pathology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".