Identification of natural products and synthetic analogs which inhibit microsporidia spores and prevent infection
Bibliographic record
Abstract
Abstract Microsporidiosis, a disease caused by microsporidia, affects many animals, with symptoms ranging from diarrhea to death, especially in immunocompromised individuals. Current treatments, such as albendazole and fumagillin, are limited in efficacy. To address this problem, we utilized Caenorhabditis elegans infected with its natural microsporidian Nematocida parisii to evaluate 4,080 structurally diverse compounds from the Boston University Center for Molecular Discovery (BU-CMD) chemical library. From this screen we identified 34 compounds that effectively inhibited N. parisii infection and restored the reproductive capacity of C. elegans . All 17 compounds we validated prevented N. parisii infection in C. elegans , and 10 were capable of suppressing microsporidia invasion by inactivating mature spores. Additionally, five of the identified compounds were also effective against Pancytospora epiphaga , a species related to human - infecting microsporidia. Together this work identifies and characterizes compounds which inhibit microsporidia infection. Importance Microsporidia are a large group of microbial parasites that infect many animals, including humans. Many agriculturally important animals such as honey bees, shrimp, crabs, and fish are infected by microsporidia, and these infections often result in smaller animals, fewer offspring, and death. Only two drugs are commonly used to treat microsporidia infections; due to some species being resistant and host toxicity there is a need to identify other microsporidia inhibitors. In this study, we screened over 4000 small molecules using a model round worm infected with microsporidia. We identified several dozen inhibitors and characterized how these compounds prevented infection by determining which stage of the parasite they act upon. Together our work identified and characterized compounds that could be used as a starting point to design better microsporidia inhibitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".