Sequence‐Dependent Structural Dynamics of Primate Adenosine‐to‐Inosine Editing Substrates
Bibliographic record
Abstract
Humans have the highest level of adenosine-to-inosine (A-to-I) editing amongst primates, yet the reasons for this difference remain unclear. Sequence analysis of the Alu Sg elements (A-to-I RNA substrates) corresponding to the Nup50 gene in human, chimp, and rhesus reveals subtle sequence variations surrounding the edit sites. We have developed three constructs that represent human (HuAp5), chimp (ChAp5), and rhesus (RhAp5) Nup50 Alu Sg A-to-I editing substrates. Here, 2-aminopurine (2-Ap) was substituted for edited adenosine (A5) so as to monitor the fluorescence intensity with respect to temperature. UV and steady-state fluorescence (SSF) T(M) plots indicate that local and global unfolding are coincident, with the human construct displaying a T(M) of approximately 70°C, compared to 60°C for chimp and 54°C for rhesus. However, time-resolved fluorescence (TRF) resolves three different fluorescence lifetimes that we assign to folded, intermediate(s), and unfolded states. The TRF data fit well to a two-intermediate model, whereby both intermediates (M, J) are in equilibrium with each other, and the folded/unfolded states. Our model suggests that, at 37°C, human state J and the folded state will be the most heavily populated in comparison to the other primate constructs. In order for adenosine deaminase acting on RNA (ADAR) to efficiently dock, a stable duplex must be present that corresponds to the human construct, globally. Next, the enzyme must "flip out" the base of interest to facilitate the A-to-I conversion; a nucleotide in an intermediate-like position would enhance this conformational change. Our experiments demonstrate that subtle variations in RNA sequence might contribute to the high A-to-I editing levels found in humans.
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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 | 0.000 | 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) | 0.000 | 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".