Diet- and salinity-induced modifications of the gut microbiota are associated with differential physiological responses to ranavirus infection in <i>Rana sylvatica</i>
Bibliographic record
Abstract
Abstract Greater knowledge of how host–microbiome interactions vary with anthropogenic environmental change and influence pathogenic infections is needed to better understand stress-mediated disease outcomes. We investigated how increasing salinization in freshwaters (e.g. due to road de-icing salt runoff) and associated increases in growth of nutritional algae influenced gut bacterial assembly, host physiology and responses to ranavirus exposure in larval wood frogs (Rana sylvatica). Elevating salinity and supplementing a basic larval diet with algae increased larval growth and also increased ranavirus loads. However, larvae given algae did not exhibit elevated kidney corticosterone levels, accelerated development or weight loss post-infection, whereas larvae fed a basic diet did. Thus, algal supplementation reversed a potentially maladaptive stress response to infection observed in prior studies in this system. Algae supplementation also reduced gut bacterial diversity. Notably, we observed higher relative abundances of Firmicutes in treatments with algae—a pattern consistent with increased growth and fat deposition in mammals—that may contribute to the diminished stress responses to infection via regulation of host metabolism and endocrine function. Our study informs mechanistic hypotheses about the role of microbiome mediation of host responses to infection that can be tested in future experiments in this host–pathogen system. This article is part of the theme issue ‘Amphibian immunity: stress, disease and ecoimmunology’.
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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 | 0.001 |
| 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".