Incomplete immunity in a natural animal-microbiota interaction selects for higher pathogen virulence
Bibliographic record
Abstract
Incomplete immunity in recovered hosts is predicted to favor more virulent pathogens upon re-infection in the population. 1 The microbiota colonizing animals can generate a similarly long-lasting, partial immune response, allowing for infection but dampened disease severity. 2 We tracked the evolutionary trajectories of a widespread pathogen ( Pseudomonas aeruginosa ), experimentally passaged through populations of nematodes immune-primed by a natural microbiota member ( P. berkeleyensis ). This bacterium can induce genes regulated by a mitogen-activated protein kinase (MAPK) signaling pathway effective at conferring protection against pathogen-induced death despite infection. 3 Across host populations, this incomplete immunity selected for pathogens more than twice as likely to kill as those evolved in non-primed (i.e., naive) or immune-compromised (mutants with a knockout of the MAPK ortholog) control populations. Despite the higher virulence, pathogen molecular evolution in immune-primed hosts was slow and constrained. In comparison, evolving pathogens in immune-compromised hosts were characterized by substantial genomic differentiation and attenuated virulence. These findings directly attribute the incomplete host immunity induced from microbiota as a significant force shaping the virulence and evolutionary dynamics of novel infectious diseases.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".