Networking and Specificity-Changing DNA Methyltransferases in Helicobacter pylori
Bibliographic record
Abstract
Epigenetic DNA base methylation plays important roles in gene expression regulation. We here describe a gene expression regulation network consisting of many DNA methyltransferases each frequently changing its target sequence-specificity. Our object Helicobacter pylori, a bacterium responsible for most incidence of stomach cancer, carries a large and variable repertoire of sequence-specific methyltransferases. By knocking out a dozen of them, we revealed they form a network controlling methylome, transcriptome and adaptive phenotype sets. The methyltransferases interact with each other in a hierarchical way, sometimes regulated positively by one methyltransferase but negatively with another. Motility, oxidative stress tolerance and DNA damage repair are likewise regulated by multiple methyltransferases. Their regulation sometimes involves translation start and stop codons suggesting coupling of methylation, transcription and translation. The methyltransferases frequently change their sequence-specificity through gene conversion of their target recognition domain and switch their target sets to remodel the network. The emerging picture of a metamorphosing gene regulation network, or firework, consisting of epigenetic systems ever-changing their specificity in search for adaptation, provides a new paradigm in understanding global gene regulation and adaptive evolution.
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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".