Genomic inversions in <i>Escherichia coli</i> alter gene expression and are associated with nucleoid protein binding sites
Bibliographic record
Abstract
Genomic reorganization, such as rearrangements and inversions, influences how genetic information is organized within the bacterial genomes. Inversions, in particular, facilitate genome evolution through gene gain and loss, and can alter gene expression. Previous studies have investigated the impact inversions have on gene expression induced inversions targeting specific genes or examine inversions between distantly related species. This fails to encompass a genome-wide perspective of naturally occurring inversions and their post-adaptation impact on gene expression. Here, we used bioinformatic techniques and multiple RNA-seq datasets to investigate the short- and long-range impact inversions have on genomic gene expression within Escherichia coli. We observed differences in gene expression between homologous inverted and non-inverted genes even after long-term exposure to adaptive selection. In 4% of inversions representing 33 genes, differential gene expression between inverted and non-inverted homologs was detected, with greater than two-thirds (71%) of differentially expressed inverted genes having 9.4–85.6-fold higher gene expression. The identified inversions had more overlap than expected with nucleoid-associated protein binding sites, which assist in the regulation of genomic gene expression. Some inversions can drastically impact gene expression, even between different strains of E. coli, and could provide a mechanism for the diversification of genetic content through controlled expression changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".