Deletion of tumor necrosis factor-α ameliorates neurodegeneration in Sandhoff disease mice
Bibliographic record
Abstract
Sandhoff disease (SD) is a lysosomal storage disorder caused by a lack of a functional β-subunit of the β-hexosaminidase A and B enzymes, leading to the accumulation of gangliosides in the central nervous system (CNS). The Hexb-/- mouse model of SD shows a progressive neurodegenerative phenotype similar to the human equivalent. Previous studies have revealed that Hexb-/- mice suffer from chronic neuroinflammation characterized by microglial activation and expansion. Tumor necrosis factor-α (TNFα), a key modulator of the CNS immune response in models of neurodegeneration, is a hallmark of this activation. In this study, we explore the role of TNFα in the development and progression of SD in mice, by creating a Hexb-/- Tnfα-/- double-knockout mouse. Our results revealed that the double-knockout mice have an ameliorated disease course, with an extended lifespan, enhanced sensorimotor coordination and improved neurological function. TNFα-deficient SD mice also show decreased levels of astrogliosis and reduced neuronal cell death, with no alterations in neuronal storage of gangliosides. Interestingly, temporal microglia activation appears similar between the Hexb-/- Tnfα-/- and SD mice. Evidence is provided for the TNFα activation of the JAK2/STAT3 pathway as a mechanism for astrocyte activation in the disease. Bone marrow transplantation experiments reveal that both CNS-derived and bone marrow-derived TNFα have a pathological effect in SD mouse models, with CNS-derived TNFα playing a larger role. This study reveals TNFα as a neurodegenerative cytokine mediating astrogliosis and neuronal cell death in SD and points to TNFα as a potential therapeutic target to attenuate neuropathogenesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".