The Sole Mycobacterium smegmatis MazF Toxin Targets tRNALys to Impart Highly Selective, Codon-Dependent Proteome Reprogramming
Bibliographic record
Abstract
Survival of mycobacteria, both free-living and host-dependent pathogenic species, is dependent on their ability to evade being killed by the stresses they routinely encounter. Toxin-antitoxin (TA) systems are unique to bacteria and archaea are thought to function as stress survival proteins. Here we study the activity of the endoribonuclease toxin derived from the MazEF TA system in Mycobacterium smegmatis, designated MazEF-ms. We first enlisted a specialized RNA-seq method, 5’ RNA-seq, to identify the primary RNA target(s) of the MazF-ms toxin. Just two tRNA species, tRNALys-UUU and tRNALys-CUU, were targeted for cleavage by MazF-ms at a single site within their anticodon sequence (UUU and CUU) to render these tRNAs nonfunctional. The 5’ RNA-seq dataset also revealed hallmarks of ribosome stalling at Lys AAA codons because both Lys tRNAs were depleted by MazF-ms. Stalled ribosomes were then cleaved on their 5’ side by another unknown RNase, resulting in very selective degradation of only those mRNAs harboring ribosomes stalled at Lys codons. This highly surgical, codon-dependent degradation of mRNA transcripts was validated using quantitative mass spectrometry of proteins that were newly synthesized during MazF-ms expression. The M. smegmatis proteome was altered as predicted, Lys AAA codon-rich proteins were downregulated while Lys AAA codon deficient proteins were upregulated. Analysis of specific subsets of proteins that were up- or down-regulated were consistent with the growth-arrested phenotype of MazF-ms expressing cells. Curiously, the tRNA target and mechanism of action of MazF-ms paralleled that of one atypical MazF toxin in M. tuberculosis, suggesting manipulation of the levels of lysine tRNAs as the preferred conduit for reprogramming the proteomes via ribosome stalling at rare AAA codons in these GC-rich mycobacteria.
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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".