<i>miR-142-3p</i> regulates cortical oligodendrocyte gene co-expression networks associated with tauopathy
Bibliographic record
Abstract
Oligodendrocytes exist in a heterogenous state and are implicated in multiple neuropsychiatric diseases including dementia. Cortical oligodendrocytes are a glial population uniquely positioned to play a key role in neurodegeneration by synchronizing circuit connectivity but molecular pathways specific to this role are lacking. We utilized oligodendrocyte-specific translating ribosome affinity purification and RNA-seq (TRAP-seq) to transcriptionally profile adult mature oligodendrocytes from different regions of the central nervous system. Weighted gene co-expression network analysis reveals distinct region-specific gene networks. Two of these mature myelinating oligodendrocyte gene networks uniquely define cortical oligodendrocytes and differentially regulate cortical myelination (M8) and synaptic signaling (M4). These two cortical oligodendrocyte gene networks are enriched for genes associated with dementia including MAPT and include multiple gene targets of the regulatory microRNA, miR-142-3p. Using a combination of TRAP-qPCR, miR-142-3p overexpression in vitro, and miR-142-null mice, we show that miR-142-3p negatively regulates cortical myelination. In rTg4510 tau-overexpressing mice, cortical myelination is compromised, and tau-mediated neurodegeneration is associated with gene co-expression networks that recapitulate both the M8 and M4 cortical oligodendrocyte gene networks identified from normal cortex. We further demonstrate overlapping gene networks in mature oligodendrocytes present in normal cortex, rTg4510 and miR-142-null mice, and existing datasets from human tauopathies to provide evidence for a critical role of miR-142-3p-regulated cortical myelination and oligodendrocyte-mediated synaptic signaling in neurodegeneration.
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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".