Xenogeneic Silencing and Bacterial Genome Evolution: Mechanisms for DNA Recognition Imply Multifaceted Roles of Xenogeneic Silencers
Bibliographic record
Abstract
Horizontal gene transfer (HGT) is a major driving force for bacterial evolution. To avoid the deleterious effects due to the unregulated expression of newly acquired foreign genes, bacteria have evolved specific proteins named xenogeneic silencers to recognize foreign DNA sequences and suppress their transcription. As there is considerable diversity in genomic base compositions among bacteria, how xenogeneic silencers distinguish self- from nonself DNA in different bacteria remains poorly understood. This review summarizes the progress in studying the DNA binding preferences and the underlying molecular mechanisms of known xenogeneic silencer families, represented by H-NS of Escherichia coli, Lsr2 of Mycobacterium, MvaT of Pseudomonas, and Rok of Bacillus. Comparative analyses of the published data indicate that the differences in DNA recognition mechanisms enable these xenogeneic silencers to have clear characteristics in DNA sequence preferences, which are further correlated with different host genomic features. These correlations provide insights into the mechanisms of how these xenogeneic silencers selectively target foreign DNA in different genomic backgrounds. Furthermore, it is revealed that the genomic AT contents of bacterial species with the same xenogeneic silencer family proteins are distributed in a limited range and are generally lower than those species without any known xenogeneic silencers in the same phylum/class/genus, indicating that xenogeneic silencers have multifaceted roles on bacterial genome evolution. In addition to regulating horizontal gene transfer, xenogeneic silencers also act as a selective force against the GC to AT mutational bias found in bacterial genomes and help the host genomic AT contents maintained at relatively low levels.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".