Chronic neurodegeneration induces type I interferon synthesis via STING, shaping microglial phenotype and accelerating disease progression
Bibliographic record
Abstract
Abstract Type I interferons (IFN‐I) are the principal antiviral molecules of the innate immune system and can be made by most cell types, including central nervous system cells. IFN‐I has been implicated in neuroinflammation during neurodegeneration, but its mechanism of induction and its consequences remain unclear. In the current study, we assessed expression of IFN‐I in murine prion disease (ME7) and examined the contribution of the IFN‐I receptor IFNAR1 to disease progression. The data indicate a robust IFNβ response, specifically in microglia, with evidence of IFN‐dependent genes in both microglia and astrocytes. This IFN‐I response was absent in stimulator of interferon genes (STING−/−) mice. Microglia showed increased numbers and activated morphology independent of genotype, but transcriptional signatures indicated an IFNAR1‐dependent neuroinflammatory phenotype. Isolation of microglia and astrocytes demonstrated disease‐associated microglial induction of Tnfα, Tgfb1, and of phagolysosomal system transcripts including those for cathepsins, Cd68, C1qa, C3, and Trem2, which were diminished in IFNAR1 and STING deficient mice. Microglial increases in activated cathepsin D, and CD68 were significantly reduced in IFNAR1−/− mice, particularly in white matter, and increases in COX‐1 expression, and prostaglandin synthesis were significantly mitigated. Disease progressed more slowly in IFNAR1−/− mice, with diminished synaptic and neuronal loss and delayed onset of neurological signs and death but without effect on proteinase K‐resistant PrP levels. Therefore, STING‐dependent IFN‐I influences microglial phenotype and influences neurodegenerative progression despite occurring secondary to initial degenerative changes. These data expand our mechanistic understanding of IFN‐I induction and its impact on microglial function during chronic 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 | 0.001 |
| 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".