Elevated TNF-α Leads to Neural Circuit Instability in the Absence of Interferon Regulatory Factor 8
Bibliographic record
Abstract
Interferon regulatory factor 8 (IRF8) is a transcription factor necessary for the maturation of microglia, as well as other peripheral immune cells. It also regulates the transition of microglia and other immune cells to a pro-inflammatory phenotype. Irf8 is also a known risk gene for multiple sclerosis and lupus, and it has recently been shown to be downregulated in schizophrenia. While most studies have focused on IRF8-dependent regulation of immune cell function, little is known about how it impacts neural circuits. Here, we show by RNAseq from Irf8−/− male and female mouse brains that several genes involved in regulation of neural activity are dysregulated. We then show that these molecular changes are reflected in heightened neural excitability and a profound increase in susceptibility to lethal seizures in male and female Irf8−/− mice. Finally, we identify that TNF-α is elevated specifically in microglia in the CNS, and genetic or acute pharmacological blockade of TNF-α in the Irf8−/− CNS rescued the seizure phenotype. These results provide important insights into the consequences of IRF8 signaling and TNF-α on neural circuits. Our data further suggest that neuronal function is impacted by loss of IRF8, a factor involved in neuropsychiatric and neurodegenerative diseases. SIGNIFICANCE STATEMENT Here, we identify a previously unknown and key role for interferon regulator factor 8 (IRF8) in regulating neural excitability and seizures. We further determine that these effects on neural circuits are through elevated TNF-α in the CNS. As IRF8 has most widely been studied in the context of regulating the development and inflammatory signaling in microglia and other immune cells, we have uncovered a novel function. Further, IRF8 is a risk gene for multiple sclerosis and lupus, IRF8 is dysregulated in schizophrenia, and elevated TNF-α has been identified in a multitude of neurologic conditions. Thus, elucidating these IRF8 and TNF-α-dependent effects on brain circuit function has profound implications for understanding underlying, therapeutically relevant mechanisms of disease.
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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) | 0.002 | 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".