‘Re‐Wilding’ an Animal Model With Microbiota Shifts Immunity and Stress Gene Expression During Infection
Bibliographic record
Abstract
The frequency of emerging disease is growing with ongoing human activity facilitating new host-pathogen interactions. Novel infection outcomes can also be shaped by the host microbiota. Caenorhabditis elegans nematodes experimentally colonised by a wild microbiota community and infected by the widespread animal pathogen, Staphylococcus aureus, have been shown to suffer higher mortality than those infected by the pathogen alone. Understanding the host responses to such microbiota-pathogen ecological interactions is key to pinpointing the mechanism underlying severe infection outcomes. We conducted transcriptomic analyses of C. elegans colonised by its native microbiota, S. aureus and both in combination. Correlations between altered collagen gene expression and heightened mortality in co-colonised hosts suggest the microbiota modified host resistance to infection. Furthermore, microbiota colonised hosts showed increased expression of immunity genes and variable expression of stress response genes during infection. Changes in host immunity and stress response could encompass both causes and effects of severe infection outcomes. 'Re-wilding' this model nematode host with its native microbiota indicated that typically commensal microbes can mediate molecular changes in the host that are costly when challenged by a novel emerging pathogen.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".