Within-host competition sparks pathogen molecular evolution and perpetual microbiota dysbiosis
Bibliographic record
Abstract
Abstract Pathogens newly invading a host must compete with resident microbiota. This within-host microbial warfare could lead to more severe disease outcomes or constrain the evolution of virulence. Using experimental evolution of a widespread pathogen ( Staphylococcus aureus ) and a native microbiota community in C. elegans nematode hosts, we show that a competitively superior pathogen displaced microbiota and reduced species richness, whilst maintaining virulence across generations. Conversely, pathogen populations and microbiota passaged separately caused more host harm relative to their respective ancestral controls. We find the evolved increase in virulence exhibited by pathogen populations passaged independently (compared to ancestral controls) was partly mediated by enhanced expression of the global virulence regulator agr and increased biofilm formation. Whole genome sequencing revealed shifts in the mode of selection from directional (on pathogens evolving alone) to fluctuating (on pathogens evolving with a host microbiota), with competitive interactions driving early diversification among pathogen populations. Metagenome sequencing of the evolved microbiota shows that evolution in infected hosts caused a significant reduction in community stability, along with restrictions on the co- existence of some species based on nutrient competition. Our study reveals how microbial competition during emerging infection determines the patterns and processes of evolution with major consequences for host health.
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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 | 0.001 |
| Research integrity | 0.001 | 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".