Gld2 activity and RNA specificity is dynamically regulated by phosphorylation and interaction with QKI-7
Bibliographic record
Abstract
In the cell, RNA abundance is dynamically controlled by transcription and decay rates. Posttranscriptional nucleotide addition at the RNA 3' end is a means of regulating mRNA and RNA stability and activity, as well as marking RNAs for degradation. The human nucleotidyltransferase Gld2 polyadenylates mRNAs and monoadenylates microRNAs, leading to an increase in RNA stability. The broad substrate range of Gld2 and its role in controlling RNA stability make the regulation of Gld2 activity itself imperative. Gld2 activity can be regulated by post-translational phosphorylation via the oncogenic kinase Akt1 and other kinases, leading to either increased or almost abolished enzymatic activity, and here we confirm that Akt1 phosphorylates Gld2 in a cellular context. Another means to control Gld2 RNA specificity and activity is the interaction with RNA binding proteins. Known interactors are QKI-7 and CPEB, which recruit Gld2 to specific miRNAs and mRNAs. We investigate the interplay between five phosphorylation sites in the N-terminal domain of Gld2 and three RNA binding proteins. We found that the activity and RNA specificity of Gld2 is dynamically regulated by this network. Binding of QKI-7 or phosphorylation at S62 relieves the autoinhibitory function of the Gld2 N-terminal domain. Binding of QKI-7 to a short peptide sequence within the N-terminal domain can also override the deactivation caused by Akt1 phosphorylation at S116. Our data revealed that Gld2 substrate specificity and activity can be dynamically regulated to match the cellular need of RNA stabilization and turnover.
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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".