Microglia-mediated neuron death requires TNF and is exacerbated by mutant Huntingtin
Bibliographic record
Abstract
Microglia, the resident immune cells of the brain, regulate the balance of inflammation in the central nervous system under healthy and pathogenic conditions. Huntington’s disease (HD) is a chronic neurodegenerative disease characterized by activated microglia and elevated concentrations of pro-inflammatory cytokines within the brain. Chronic hyperactivation of microglia is associated with brain pathology and eventual neuron death. However, it is unclear which specific cytokines are required for neuron death and whether HD neurons may be hypersensitive to neuroinflammation. We assessed the profile of microglia-secreted proteins in response to LPS and IFNγ, and a conditioned media paradigm was used to examine the effects of these secreted proteins on cultured neuronal cells. ST Hdh Q7/Q7 and ST Hdh Q111/Q111 neuronal cells were used to model wild-type and HD neurons, respectively. We determined that ST Hdh Q111/Q111 cells were hypersensitive to pro-inflammatory factors secreted by microglia, and that TNF was required to induce neuronal death. Microglia-mediated neuronal death could be effectively halted through the use of JAK-STAT or TNF inhibitors which supported the requirement for TNF as well as IFNγ in the process of secondary neurotoxicity. Further data derived from human HD patients as well as HD mice were suggestive of enhanced receptor density for TNF (TNFR1) and IFNγ (IFNGR) which could sensitize the HD brain to these cytokines. This highlights several potential mechanisms by which microglia may induce neuronal death and suggests that these mechanisms may be upregulated in the brain of HD patients.
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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".