Chromosomal barcoding of <i>E. coli</i> populations reveals lineage diversity dynamics at high resolution
Bibliographic record
Abstract
Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages have so far prevented a detailed elucidation of evolutionary dynamics in large bacterial populations. Here, we overcome this hurdle by developing a chromosomal barcoding technique that allows simultaneous tracking of ∼450,000 distinct lineages in E. coli. We used this technique to gather insights into the evolutionary dynamics of large (>10 7 cells) E. coli populations propagated for ∼420 generations in the presence of sub-inhibitory concentrations of common antibiotics. By deep sequencing the barcodes, we reconstructed trajectories of individual lineages at high frequency resolution (< 10 −5 ). Using quantitative tools from ecology, we found that populations lost lineage diversity at distinct rates corresponding to their antibiotic regimen. Additionally, by quantifying the reproducibility of these dynamics across replicate populations, we found that some lineages had similar fates over independent experiments. Combined with an analysis of individual lineage trajectories, these results suggest how standing genetic variation and new mutations may contribute to adaptation to sub-inhibitory antibiotic levels. Altogether, our results demonstrate the power of high-resolution barcoding in studying the dynamics of bacterial evolution.
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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 | 0.000 |
| 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".