Altered tRNA expression profile associated with codon-specific proteomic changes in the suicide brain
Bibliographic record
Abstract
Suicide is a major public health concern, and the number of deaths by suicide has been increasing in recent years in the US. There are various biological risk factors for suicide, but causal molecular mechanisms remain unknown, suggesting that investigation of novel mechanisms and integrative approaches are necessary. Transfer (t)RNAs and their modifications, including cytosine methylation (m 5 C), have received little attention regarding their role in normal or diseased brain function, though they are dynamic mediators of protein synthesis. tRNA regulation is highly interconnected with proteomic and metabolomic outcomes, suggesting that investigating these multiple levels of molecular regulation together may elucidate more information on neural function and suicide risk. In the current study, we used an integrative ‘omics’ approach to probe tRNA dysregulation, including tRNA expression and tRNA m 5 C, proteomics, and amino acid metabolomics in prefrontal cortex from 98 subjects who died by suicide during an episode of major depressive disorder (MDD) and neurotypical controls. While no changes were detected in amino acid content, results showed increased tRNA Gly GCC expression in the suicide brain that is not driven by changes in m 5 C. Proteomics revealed increased expression of proteins with high glycine codon GGC content, demonstrating a strong association between isoacceptor-specific tRNA expression and proteomic outcomes in the suicide brain, which is in line with previous work linking tRNA Gly with alterations in glycine-rich proteins in a translational rodent model of depression. Further, we confirmed using a rodent model that tRNA Gly GCC overexpression was sufficient to increase the expression of proteins with high glycine codon GGC content that were upregulated in the suicide brain. By characterizing the effects of MDD-suicide in human PFC tissue, we now begin to elucidate a novel molecular signature with downstream consequences for psychiatric outcomes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".