Genomic Instability in Regions Adjacent to a Highly Conserved<i>pch</i>Prophage in<i>Escherichia coli</i>O157:H7 Generates Diversity in Expression Patterns of the LEE Pathogenicity Island
Bibliographic record
Abstract
The LEE pathogenicity island has been acquired on multiple occasions within the different lineages of enteropathogenic and enterohemorrhagic Escherichia coli. In each lineage, LEE expression is regulated by complex networks of pathways, including core pathways shared by all lineages and lineage-specific pathways. Within the O157:H7 lineage of enterohemorrhagic E. coli, strain-to-strain variation in LEE expression has been observed, implying that expression patterns can diversify even within highly related subpopulations. Using comparative genomics of E. coli O157:H7 subpopulations, we have identified one source of strain-level variation affecting LEE expression. The variation occurs in prophage-dense regions of the genome that lie immediately adjacent to the late regions of the pch prophage carrying pchA, pchB, pchC, and a newly identified pch gene, pchX. Genomic segments extending from the holin S region to the pchA, pchB, pchC, and pchX genes of their respective prophage are highly conserved but are nonetheless embedded within adjacent genomic segments that are extraordinarily variable, termed pch adjacent genomic regions (pch AGR). Despite the remarkable degree of variation, the pattern of variation in pch AGR is highly correlated with the distribution of phylogenetic markers on the backbone of the genome. Quantitative analysis of transcription from the LEE1 promoter further revealed that variation in the pch AGR has substantial effects on absolute levels and patterns of LEE1 transcription. Variation in the pch AGR therefore serves as a mechanism to diversify LEE expression patterns, and the lineage-specific pattern of pch AGR variation could ultimately influence ecological or virulence characteristics of subpopulations within each lineage.
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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".