Interactions between microsporidia and other members of the microbiome
Bibliographic record
Abstract
The microbiome is the collection of microbes that are associated with a host. Microsporidia are intracellular eukaryotic parasites that can infect most types of animals. In the last decade, there has been much progress to define the relationship between microsporidia and the microbiome. In this review, we cover an increasing number of reports suggesting that microsporidia are common components of the microbiome in both invertebrates and vertebrates. These microsporidia infections can range from mutualistic to pathogenic, causing several physiological phenotypes, including death. Infection with microsporidia often causes a disruption in the normal microbiome, with both increases and decreases of bacterial, fungal, viral, and protozoan species being observed. This impact on the microbiome can occur through upregulation and downregulation of innate immunity as well as morphological changes to tissues that impact interactions with these microbes. Other microbes, particularly bacteria, can inhibit microsporidia and have been exploited to control microsporidia infections. These bacteria can function through regulating immunity, secreting anti-microsporidia compounds, and, in engineered versions, expressing double-stranded RNA targeting microsporidia genes. We end this review by discussing potential future directions to further understand the complex interactions between microsporidia and the other members of the microbiome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".