A unique cerebellar pattern of microglia activation in a mouse model of encephalopathy of prematurity
Bibliographic record
Abstract
Preterm infants often show pathologies of the cerebellum, which are associated with impaired motor performance, lower IQ and poor language skills at school ages. Using a mouse model of inflammation-induced encephalopathy of prematurity driven by systemic administration of pro-inflammatory IL-1β, we sought to uncover causes of cerebellar damage. In this model, IL-1β is administered between postnatal day (P) 1 to day 5, a timing equivalent to the last trimester for brain development in humans. Structural MRI analysis revealed that systemic IL-1β treatment induced specific reductions in gray and white matter volumes of the mouse cerebellar lobules I and II (5% false discovery rate [FDR]) from P15 onwards. Preceding these MRI-detectable cerebellar volume changes, we observed damage to oligodendroglia, with reduced proliferation of OLIG2+ cells at P10 and reduced levels of the myelin proteins myelin basic protein (MBP) and myelin-associated glycoprotein (MAG) at P10 and P15. Increased density of IBA1+ cerebellar microglia were observed both at P5 and P45, with evidence for increased microglial proliferation at P5 and P10. Comparison of the transcriptome of microglia isolated from P5 cerebellums and cerebrums revealed significant enrichment of pro-inflammatory markers in microglia from both regions, but cerebellar microglia displayed a unique type I interferon signaling dysregulation. Collectively, these data suggest that perinatal inflammation driven by systemic IL-1β leads to specific cerebellar volume deficits, which likely reflect oligodendrocyte pathology downstream of microglial activation. Further studies are now required to confirm the potential of protective strategies aimed at preventing sustained type I interferon signaling driven by cerebellar microglia as an important therapeutic target.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".