Type I toxin-antitoxin systems contribute to mobile genetic elements maintenance in <i>Clostridioides difficile</i> and can be used as a counter-selectable marker for chromosomal manipulation
Bibliographic record
Abstract
ABSTRACT Toxin-antitoxin (TA) systems are widespread on mobile genetic elements as well as in bacterial chromosomes. According to the nature of the antitoxin and its mode of action for toxin inhibition, TA systems are subdivided into different types. The first type I TA modules were recently identified in the human enteropathogen Clostridioides (formerly Clostridium ) difficile . In type I TA, synthesis of the toxin protein is prevented by the transcription of an antitoxin RNA during normal growth. Here, we report the characterization of five additional type I TA systems present within phiCD630-1 and phiCD630-2 prophage regions of C. difficile 630. Toxin genes encode 34 to 47 amino acid peptides and their ectopic expression in C. difficile induces growth arrest. Growth is restored when the antitoxin RNAs, transcribed from the opposite strand, are co-expressed together with the toxin genes. In addition, we show that type I TA modules located within the phiCD630-1 prophage contribute to its stability and mediate phiCD630-1 heritability. Type I TA systems were found to be widespread in genomes of C. difficile phages, further suggesting their functional importance. We have made use of a toxin gene from one of type I TA modules of C. difficile as a counter-selectable marker to generate an efficient mutagenesis tool for this bacterium. This tool enabled us to delete all identified toxin genes within the phiCD630-1 prophage, thus allowing investigation of the role of TA in prophage maintenance. Furthermore, we were able to delete the large 49 kb phiCD630-2 prophage region using this improved procedure.
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 | 0.000 |
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".