Gene Decay in Shigella as an Incipient Stage of Host-Adaptation
Bibliographic record
Abstract
BACKGROUND: Many facultative bacterial pathogens have undergone extensive gene decay processes, possibly due to lack of selection pressure during evolutionary conversion from free-living to intracellular lifestyle. Shigella, the causative agents of human shigellosis, have arisen from different E. coli-like ancestors independently by convergent paths. As these bacteria all have lost large numbers of genes by mutation or deletion, they can be used as ideal models for systematically studying the process of gene function loss in different bacteria living under similar selection pressures. METHODOLOGIES/PRINCIPAL FINDINGS: We compared the sequenced Shigella genomes and re-defined decayed genes (pseudogenes plus deleted genes) in these bacteria. Altogether, 85 genes are commonly decayed in the five analyzed Shigella strains and 1456 genes are decayed in at least one Shigella strain. Genes coding for carbon utilization, cell motility, transporter or membrane proteins are prone to be inactivated. Decayed genes tend to concentrate in certain operons rather than distribute averagely across the whole genome. Genes in the decayed operon accumulated more non-synonymous mutations than the rest genes and meanwhile have lower expression levels. CONCLUSIONS: Different Shigella lineages underwent convergent gene decay processes, and inactivation of one gene would lead to a lesser selection pressure for the other genes in the same operon. The pool of superfluous genes for Shigella may contain at least two thousand genes and the gene decay processes may still continue in Shigella until a minimum genome harboring only essential genes is reached.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".