Suppressed oligodendrocyte steroidogenesis in multiple sclerosis: Implications for regulation of neuroinflammation
Bibliographic record
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS). Neurosteroids are reported to exert anti-inflammatory effects in several neurological disorders. We investigated the expression and actions of the neurosteroid, dehydroepiandrosterone (DHEA), and its more stable 3β-sulphated ester, DHEA-S, in MS and associated experimental models. CNS tissues from patients with MS and animals with experimental autoimmune encephalomyelitis (EAE) displayed reduced DHEA concentrations, accompanied by diminished expression of the DHEA-synthesizing enzyme CYP17A1 in oligodendrocytes (ODCs), in association with increased expression of inflammatory genes including interferon (IFN)-γ and interleukin (IL)-1β. CYP17A1 was expressed variably in different human neural cell types but IFN-γ exposure selectively reduced CYP17A1 detection in ODCs. DHEA-S treatment reduced IL-1β and -6 release from activated human myeloid cells with minimal effect on lymphocyte viability. Animals with EAE receiving DHEA-S treatment showed reduced Il1b and Ifng transcript levels in spinal cord compared to vehicle-treated animals with EAE. DHEA-S treatment also preserved myelin basic protein immunoreactivity and reduced axonal loss in animals with EAE, relative to vehicle-treated EAE animals. Neurobehavioral deficits were reduced in DHEA-S-treated EAE animals compared with vehicle-treated animals with EAE. Thus, CYP17A1 expression in ODCs and its product DHEA were downregulated in the CNS during inflammatory demyelination while DHEA-S provision suppressed neuroinflammation, demyelination, and axonal injury that was evident as improved neurobehavioral performance. These findings indicate that DHEA production is an immunoregulatory pathway within the CNS and its restoration represents a novel treatment approach for neuroinflammatory diseases.
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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".