MEF2A is a Regulator of Interferon-Mediated Inflammation
Bibliographic record
Abstract
Abstract Interferons (IFN) are antiviral cytokines induced by recognition of viral nucleic acids or cellular replicative stress. In particular, the accumulation of cytosolic nucleic acids or the accumulation of DNA lesions has the potential to activate cGAS/STING-mediated IFN induction if not properly constrained. While these responses can be harnessed as tumor therapies, the excessive induction of IFNs and deleterious IFN-mediated inflammation is associated with tissue damage in non-malignant disease. We sought to identify non-canonical IFN-modulatory transcription factors (TF) by examining factors with known genetic alterations that associate with both malignant and inflammatory diseases. Here, we identify MEF2A as a negative regulator of the production of IFNs at homeostasis. The loss of MEF2A function results in the spontaneous induction of type I and III IFNs and downstream ISG expression. We demonstrate that IFN production following MEF2A depletion was dependent on DNA replication and cell cycle progression with CDK2 inhibition ablating this response. Furthermore, DNA lesions that induced DNA damage responses following MEF2A loss resulted in cGAS/STING pathway activation and genetic deletion of STING maintained homeostatic levels of type I IFN in the absence of this TF factor. Thus, our study is first to connect MEF2A with protection from maladaptive type I IFN responses due to genomic instability across various cell types. Overall, our study reveals that therapeutic modulation of MEF2A or other members of its family could be beneficial for the management of viral, malignant, and autoinflammatory diseases.
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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 | 0.000 |
| 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".