Mechanistic convergence of depression and suicidality on astrocyte fatty acid metabolism
Bibliographic record
Abstract
Abstract Genome-wide association studies (GWAS) show conceptual promise to identify novel mechanisms of major depressive disorder (MDD), but have not yet achieved this potential. One explanation is that MDD risk acts through complex expression networks, and GWAS-identified genes represent important components of these networks but in isolation are insufficient for their functional annotation. In this study, we aimed to identify and characterize the expression networks through which GWAS-identified MDD risk genes operate. We generated and characterized seeded co-expression networks of 252 MDD risk genes over 11 brain regions. We used principal component regression and Mendelian randomization to identify a relation between the networks of two such genes ( FADS1 and ZKSCAN8 ) and suicidal ideation. These networks were primarily expressed in astrocytes, enriched for functions related to fatty acid metabolism, and could define MDD-altered astrocyte states. We then identified FGFR3 to EPHA4 signaling as a putative downstream effector of these astrocyte states on synaptic function. Finally through transcriptomic and genetic analyses, we identify PPARA as a putative therapeutic target of these mechanisms in MDD. Our study defines a tractable pathway to translate genetic findings into therapeutically actionable mechanisms.
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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 | 0.000 | 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".