Manipulation of mitochondrial poly(A) polymerase family proteins in Trypanosoma brucei impacts mRNA termini processing
Bibliographic record
Abstract
RNA-specific nucleotidyltransferases (rNTrs) add nontemplated nucleotides to the 3′ end of RNA. Two noncanonical rNTRs that are thought to be poly(A) polymerases (PAPs) have been identified in the mitochondria of trypanosomes – KPAP1 and KPAP2. KPAP1 is the primary polymerase that adds adenines (As) to trypanosome mitochondrial mRNA 3′ tails, while KPAP2 is a non-essential putative polymerase whose role in the mitochondria is ambiguous. Here, we elucidate the effects of manipulations of KPAP1 and KPAP2 on the 5′ and 3′ termini of transcripts and their 3′ tails. Using glycerol gradients followed by immunoblotting, we present evidence that KPAP2 is found in protein complexes of up to about 1600 kDa. High-throughput sequencing of mRNA termini showed that KPAP2 overexpression subtly changes an edited transcript’s 3′ tails, though not in a way consistent with general PAP activity. Next, to identify possible roles of posttranslational modifications on KPAP1 regulation, we mutated two KPAP1 arginine methylation sites to either mimic methylation or hypomethylation. We assessed their effect on 3′ mRNA tail characteristics and found that the two mutants generally had opposing effects, though some of these were transcript-specific. We present results suggesting that while methylation increases KPAP1 substrate binding and/or initial nucleotide additions, unmethylated KPAP1is more processive. We also present a comprehensive review of UTR termini, and evidence that tail addition activity may change as mRNA editing is initiated. Together, this work furthers our understanding of the role of KPAP1 and KPAP2 on trypanosome mitochondrial mRNA 3′ tail addition, as well as provides more information on mRNA termini processing in general.
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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) | 0.001 | 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 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".