Roving methyltransferases generate a mosaic epigenetic landscape and influence evolution in Bacteroides fragilis group
Bibliographic record
Abstract
Three types of DNA methyl modifications have been detected in bacterial genomes, and mechanistic studies have demonstrated roles for DNA methylation in physiological functions ranging from phage defense to transcriptional control of virulence and host-pathogen interactions. Despite the ubiquity of methyltransferases and the immense variety of possible methylation patterns, epigenomic diversity remains unexplored for most bacterial species. Members of the Bacteroides fragilis group (BFG) reside in the human gastrointestinal tract as key players in symbiotic communities but also can establish anaerobic infections that are increasingly multi-drug resistant. In this work, we utilize long-read sequencing technologies to perform pangenomic (n = 383) and panepigenomic (n = 268) analysis of clinical BFG isolates cultured from infections seen at the NIH Clinical Center over four decades. Our analysis reveals that single BFG species harbor hundreds of DNA methylation motifs, with most individual motif combinations occurring uniquely in single isolates, implying immense unsampled methylation diversity within BFG epigenomes. Mining of BFG genomes identified more than 6000 methyltransferase genes, approximately 1000 of which were associated with intact prophages. Network analysis revealed substantial gene flow among disparate phage genomes, implying a role for genetic exchange between BFG phages as one of the ultimate sources driving BFG epigenome diversity.
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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 | 0.001 | 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".