Identifying rare spontaneous mutations through wildtype <i>E. coli</i> population sequencing
Bibliographic record
Abstract
Abstract Understanding the rate and types of mutations occurring in different populations is fundamental to our ability to predict and potentially manipulate evolution. Common techniques for microbial mutation rate estimation fall into 2 camps; rapid, locus specific, generally liquid-culture based fluctuation assays; and slow, genome-wide, generally solid-culture based mutation accumulation experiments. This constrains the hypotheses which can be tested, notably the mutagenic effects of liquid environments cannot be rapidly quantified at a genome-wide scale. One example of such effect is the negative association previously observed between population density and mutation rates at many marker loci using the fluctuation assay. Because homogeneous population density is specifically relevant in liquid culture, this association has not been tested at a truly genome-wide scale. We fill this methodological gap by developing a novel pipeline that relies on population sequencing to capture how mutation rates are affected by population density in liquid cultures. We simulate expected mutation counts in a growing population along with random sampling during sequencing to estimate the necessary sequencing coverage as ≥1,000-fold. We then carry out PCR-free sequencing of 95 wildtype E. coli populations at this coverage, calling rare variants with both reference-based and reference-free methods. These variant-calling methods are prone to different sources of error which can be minimised by considering only mutations called by both pipelines. This approach identifies 119 mutations across all cultures, with (non)coding/(non)synonymous and mutational spectrum profiles consistent with being samples of spontaneous mutation unbiased by selection. The distribution of these mutations also supports the motivating hypothesis, finding that mutation counts across the genome are strongly negatively associated with population density. This demonstrates the utility of population sequencing for the rapid testing of many previously inaccessible evolutionary biology hypotheses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".