The <i>Caenorhabditis elegans</i> bacterial microbiome influences microsporidia infection through nutrient limitation and inhibiting parasite invasion
Bibliographic record
Abstract
Abstract Microsporidia are eukaryotic obligate intracellular parasites that infect most animals including humans. To understand how the microbiome can impact microsporidia infection, we tested how bacterial isolates that naturally occur with Caenorhabditis elegans influence infection by the microsporidian Nematocida parisii . Nematodes exposed to two of these bacteria, Chryseobacterium scopthalmum and Sphingobacterium multivorum , exhibit reduced pathogen loads. Using untargeted metabolomics, we show that unsaturated fatty acid levels are disrupted by growth on these bacteria and that supplementation with the polyunsaturated fatty acid linoleic acid can restore full parasite growth in animals cultured on S. multivorum . We also found that two isolates, Pseudomonas lurida and Pseudomonas mendocina, secrete molecules that inactivate N. parisii spores. We determined that P. lurida inhibits N. parisii through the production of massetolides. We then measured 53 additional Pseudomonas strains, 64% of which significantly reduced N. parisii infection. A mixture of Pseudomonas species can greatly limit the amount of infection in C. elegans populations over many generations. Our findings suggest that interactions between bacteria and N. parisii are common and that these bacteria both modulate host metabolism and produce compounds that inhibit microsporidia infection.
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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) | 0.002 | 0.001 |
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".