4-Methylcytosine DNA modification is critical for global epigenetic regulation and virulence in the human pathogen <i>Leptospira interrogans</i>
Bibliographic record
Abstract
In bacteria, DNA methylation can be facilitated by 'orphan' DNA methyltransferases lacking cognate restriction endonucleases, but whether and how these enzymes control key cellular processes are poorly understood. The effects of a specific modification, 4-methylcytosine (4mC), are even less clear, as this epigenetic marker is unique to bacteria and archaea, whereas the bulk of epigenetic research is currently performed on eukaryotes. Here, we characterize a 4mC methyltransferase from the understudied pathogen Leptospira spp. Inactivating this enzyme resulted in complete abrogation of CTAG motif methylation, leading to genome-wide dysregulation of gene expression. Mutants exhibited growth defects, decreased adhesion to host cells, higher susceptibility to LPS-targeting antibiotics, and, importantly, were no longer virulent in an acute infection model. Further investigation resulted in the discovery of at least one gene, that of an ECF sigma factor, whose transcription was altered in the methylase mutant and, subsequently, by mutation of the CTAG motifs in the promoter of the gene. The genes that comprise the regulon of this sigma factor were, accordingly, dysregulated in the methylase mutant and in a strain overexpressing the sigma factor. Our results highlight the importance of 4mC in Leptospira physiology, and suggest the same of other understudied species.
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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".