Nucleotide specificity of the human terminal nucleotidyltransferase Gld2 (TUT2)
Bibliographic record
Abstract
The nontemplated addition of single or multiple nucleotides to RNA transcripts is an efficient means to control RNA stability and processing. Cytoplasmic RNA adenylation and the less well-known uridylation are post-transcriptional mechanisms regulating RNA maturation, activity, and degradation. Gld2 is a member of the noncanonical poly(A) polymerases, which include enzymes with varying nucleotide specificity, ranging from strictly ATP to ambiguous to exclusive UTP adding enzymes. Human Gld2 has been associated with transcript stabilizing miRNA monoadenylation and cytoplasmic mRNA polyadenylation. Most recent data have uncovered an unexpected miRNA uridylation activity, which promotes miRNA maturation. These conflicting data raise the question of Gld2 nucleotide specificity. Here, we biochemically characterized human Gld2 and demonstrated that it is a bona fide adenylyltransferase with only weak activity toward other nucleotides. Despite its sequence similarity with uridylyltransferases (TUT4, TUT7), Gld2 displays an 83-fold preference of ATP over UTP. Gld2 is a promiscuous enzyme, with activity toward miRNA, pre-miRNA, and polyadenylated RNA substrates. Apo-Gld2 activity is restricted to adding single nucleotides and processivity likely relies on additional RNA-binding proteins. A phylogeny of the PAP/TUTase superfamily suggests that uridylyltransferases, which are derived from distinct adenylyltransferase ancestors, arose multiple times during evolution via insertion of an active site histidine. A corresponding histidine insertion into the Gld2 active site alters substrate specificity from ATP to UTP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".