Culture‐independent tracking of <i>Vibrio cholerae</i> lineages reveals complex spatiotemporal dynamics in a natural population
Bibliographic record
Abstract
Populations of the bacterium Vibrio cholerae consist of dozens of distinct lineages, with primarily (but not exclusively) members of the pandemic generating lineage capable of causing the diarrhoeal disease cholera. Assessing the composition and temporal dynamics of such populations requires extensive isolation efforts and thus only rarely covers large geographic areas or timeframes exhaustively. We developed a culture-independent amplicon sequencing strategy based on the protein-coding gene viuB (vibriobactin utilization) to study the structure of a V. cholerae population over the course of a summer. We show that the 26 co-occurring V. cholerae lineages continuously compete for limited space on nutrient-rich particles where only a few of them can grow to large numbers. Differential abundance of lineages between locations and size-fractions associated with a particle-attached or free-swimming lifestyle could reflect adaptation to various environmental niches. In particular, a major V. cholerae lineage occasionally grows to large numbers on particles but remain undetectable using isolation-based methods, indicating selective culturability for some members of the species. We thus demonstrate that isolation-based studies may not accurately reflect the structure and complex dynamics of V. cholerae populations and provide a scalable high-throughput method for both epidemiological and ecological approaches to studying this species.
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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) | 0.000 | 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 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".