<i>In vivo</i>modification of tRNA with an artificial nucleobase leads to full disease remission in an animal model of multiple sclerosis
Bibliographic record
Abstract
Queuine is a modified pyrrolopyrimidine nucleobase derived exclusively from bacteria. It post-transcriptionally replaces guanine 34 in transfer RNA isoacceptors for Asp, Asn, His and Tyr, in almost all eukaryotic organisms, through the activity of the ancient tRNA guanine transglycosylase (TGT) enzyme. tRNA hypomodification with queuine is a characteristic of rapidly-proliferating, non-differentiated cells. Autoimmune diseases, including multiple sclerosis, are characterised by the rapid expansion of T cells directed to self-antigens. Here, we demonstrate the potential medicinal relevance of targeting the modification of tRNA in the treatment of a chronic multiple sclerosis model—murine experimental autoimmune encephalomyelitis. Administration of a de novo designed eukaryotic TGT substrate (NPPDAG) led to an unprecedented complete reversal of clinical symptoms and a dramatic reduction of markers associated with immune hyperactivation and neuronal damage after five daily doses. TGT is essential for the therapeutic effect, since animals deficient in TGT activity were refractory to therapy. The data suggest that exploitation of the eukaryotic TGT enzyme is a promising approach for the treatment of multiple sclerosis.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".