Leaping over the blood-brain barrier: DNA methylation as a link between peripheral and central immune systems
Bibliographic record
Abstract
Abstract Most existing DNA methylation (DNAm) studies have used peripheral surrogate tissues to research molecular mechanisms underlying brain disorders and diseases. Initial studies comparing brain to blood primarily at the individual CpG level analysis have generally pointed to a limited overlap of epigenetic patterns, consistent with DNAm being largely tissue- and even cell type-specific. Expanding on these studies, we employed a more complex analysis strategy aimed to 1) identify single DNAm sites associated with deconvolution-estimated brain cell type proportions, the principal measure in this study, in both the frontal brain and peripheral blood, 2) combine blood DNAm sites to predict brain cell type proportions through multivariate models, and 3) examine the association of blood DNAm, age, and epigenetic age acceleration (EAA) on brain cell type proportions. Epigenome-wide association studies for seven brain cell type proportions in matched frontal brain and peripheral blood samples (n=104) revealed that ∼10% of brain cell type-associated DNAm sites had correlating DNAm levels in peripheral blood (p<0.05). However, only three peripheral blood DNAm sites were significantly associated with endothelial and stromal brain cell type proportions (adjusted p<0.05). Brain cell type proportion predictions trained with machine learning approaches using peripheral blood DNAm showed the strongest, although still modest correlations with microglia proportions estimated through cell deconvolution using brain DNAm. Further, deconvolution- estimated blood immune cell type proportions showed a nominally significant association with estimated brain stromal cell proportions, driven primarily by NK cells; however, this association was dependent on chronological age and did not survive age-residualization. Lastly, brain EAA was not associated with brain and blood cell type proportions (adjusted p<0.05). Collectively, these results suggested that in the context of broad tissue-specificity of DNAm patterns, DNAm levels in peripheral blood might actually inform on some immune brain cell type proportions. The correlations between DNAm profiles specific to immune cell types in blood and brain were consistent with a potential link between peripheral immune and central nervous system immune functions.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".