Transcriptomic profiles in major depressive disorder: the role of immunometabolic and cell-cycle-related pathways in depression with different levels of inflammation
Bibliographic record
Abstract
Transcriptomic profiles are important indicators for molecular mechanisms and pathways involved in major depressive disorder (MDD) and its different phenotypes, such as immunometabolic depression. We performed whole-transcriptome and pathway analyses on 139 individuals from the observational, case-control, BIOmarkers in DEPression (BIODEP) study, 105 with MDD and 34 controls. We divided MDD participants based on levels of inflammation, as measured by serum high-sensitivity C-reactive protein (CRP), in n = 39 'not inflamed' (CRP < 1 mg/L), n = 31 with 'elevated CRP' (1-3 mg/L), and n = 35 with 'low-grade inflammation' (>3 mg/L). We performed whole-blood RNA sequencing using Illumina NextSeq 550 and statistical analyses with the Deseq2 package for R statistics (RUV-corrected) and subsequent pathway analyses with Ingenuity Pathway Analysis. Immunometabolic pathways were activated in individuals with CRP > 1 mg/L, although surprisingly the CRP 1-3 group showed stronger immune activation than the CRP > 3 group. The main pathways identified in the comparison between CRP < 1 group and controls were cell-cycle-related, which may be protective against immunometabolic abnormalities in this 'non-inflamed' depressed group. We further divided MDD participants based on exposure and response to antidepressants (n = 47 non-responders, n = 37 responders, and n = 22 unmedicated), and identified specific immunomodulatory and neuroprotective pathways in responders (especially vs. non-responders), which could be relevant to treatment response. In further subgroup analyses, we found that the specific transcriptional profile of responders is independent of CRP levels, and that the inhibition of cell-cycle-related pathways in MDD with CRP < 1 mg/L is present only in those who are currently depressed, and not in the responders. The present study demonstrates immunometabolic and cell-cycle-related transcriptomic pathways associated with MDD and different (CRP-based and treatment-based) MDD phenotypes, while shedding light on potential molecular mechanisms that could prevent or facilitate an individual's trajectory toward immunometabolic depression and/or treatment-non-responsive depression. The recognition and integration of these mechanisms will facilitate a precision-medicine approach in MDD.
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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 | 0.001 |
| 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".