High-throughput small molecule screen identifies inhibitors of microsporidia invasion and proliferation in <i>C. elegans</i>
Bibliographic record
Abstract
Abstract Microsporidia are a diverse group of fungal-related obligate intracellular parasites that infect most animal phyla. Despite the emerging threat that microsporidia have become to humans and agricultural animals, few reliable treatment options exist. To identify novel chemical inhibitors of microsporidia infection, we developed a high-throughput screening method using Caenorhabditis elegans and the microsporidia species Nematocida parisii. We screened the Spectrum Collection of 2,560 FDA-approved compounds and natural products to identify compounds that prevent C. elegans progeny inhibition caused by N. parisii infection. We developed a semi-automated method for quantifying C. elegans progeny number in liquid culture, confirming 11 candidate microsporidia inhibitors. We show that five compounds prevent microsporidia infection by inhibiting spore firing, and demonstrate that one compound, dexrazoxane, slows infection progression. We also show that these compounds have activity against several other microsporidia species, including those which infect humans. Together, our results demonstrate the effectiveness of C. elegans as a model host for drug discovery against intracellular pathogens and provide a scalable high-throughput system for the identification and characterization of additional microsporidia inhibitors.
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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 | 0.000 | 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) | 0.001 | 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".