Characterization of ADAT2/3 molecules in <i>Trypanosoma cruzi</i> and regulation of mucin gene expression by tRNA editing
Bibliographic record
Abstract
Adenosine-to-inosine conversion at position 34 (A34-to-I) of certain tRNAs is essential for expanding their decoding capacity. This reaction is catalyzed by the adenosine deaminase acting on tRNA (ADAT) complex, which in Eukarya is formed by two subunits: ADAT2 and ADAT3. We herein identified and thoroughly characterized the ADAT molecules from the protozoan pathogen Trypanosoma cruzi, the causative agent of Chagas Disease. TcADAT2 and TcADAT3 spontaneously form a catalytically active complex, as shown by expression in engineered bacteria and/or by the increased ex vivo tRNA A-to-I deamination activity of T. cruzi epimastigotes overexpressing TcADAT subunits. Importantly, enhanced TcADAT2/3 activity in transgenic parasites caused a shift in their in vivo tRNAThrAGU signature, which correlated with significant changes in the expression of the Thr-rich TcSMUG proteins. To our knowledge, this is the first evidence indicating that T. cruzi tRNA editing can be modulated in vivo, in turn post-transcriptionally changing the expression of specific genes. Our findings suggest tRNA editing/availability as a forcible step in controlling gene expression and driving codon adaptation in T. cruzi. Moreover, we unveil certain differences between parasite and mammalian host tRNA editing and processing, such as cytosine-to-uridine conversion at position 32 of tRNAThrAGU in T. cruzi, that may be exploited for the identification of novel druggable targets of intervention.
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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".