ADAR regulates APOL1 via A-to-I RNA editing by inhibition of MDA5 activation in a paradoxical biological circuit
Bibliographic record
Abstract
high-risk genotype develop kidney disease. As APOL1 gene expression correlates closely with the degree of kidney cell injury in both cell and animal models, the mechanisms regulating APOL1 expression may be critical determinants of risk allele penetrance. The APOL1 messenger RNA includes Alu elements at the 3' untranslated region that can form a double-stranded RNA structure (Alu-dsRNA) susceptible to posttranscriptional adenosine deaminase acting on RNA (ADAR)-mediated adenosine-to-inosine (A-to-I) editing, potentially impacting gene expression. We studied the effects of ADAR expression and A-to-I editing on APOL1 levels in podocytes, human kidney tissue, and a transgenic APOL1 mouse model. In interferon-γ (IFN-γ)-stimulated human podocytes, ADAR down-regulates APOL1 by preventing melanoma differentiation-associated protein 5 (MDA5) recognition of dsRNA and the subsequent type I interferon (IFN-I) response. Knockdown experiments showed that recognition of APOL1 messenger RNA itself is an important contributor to the MDA5-driven IFN-I response. Mathematical modeling suggests that the IFN-ADAR-APOL1 network functions as an incoherent feed-forward loop, a biological circuit capable of generating fast, transient responses to stimuli. Glomeruli from human kidney biopsies exhibited widespread editing of APOL1 Alu-dsRNA, while the transgenic mouse model closely replicated the edited sites in humans. APOL1 expression in mice was inversely correlated with Adar1 expression under IFN-γ stimuli, supporting the idea that ADAR regulates APOL1 levels in vivo. ADAR-mediated A-to-I editing is an important regulator of APOL1 expression that could impact both penetrance and severity of APOL1-associated kidney disease.
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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 | 0.001 | 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".