Genome Analysis of <i>Pseudoloma neurophilia</i>: A Microsporidian Parasite of Zebrafish (<i>Danio rerio</i>)
Bibliographic record
Abstract
Microsporidia are highly successful parasites that infect virtually all known animal lineages, including the model Danio rerio (zebrafish). The widespread use of this aquatic model for biomedical research has resulted in an unexpected increase in infections from the microsporidium Pseudoloma neurophilia, which can lead to significant physical, behavioral, and immunological modifications, resulting in nonprotocol variation during experimental procedures. Here, we seek to obtain insights into the biology of P. neurophilia by investigating its genome content, which was obtained from only 29 nanograms of DNA using the MiSeq technology and paired-end Illumina sequencing. We found that the genome of P. neurophilia is phylogenetically and genetically related to other fish-microsporidians, but features unique to this intracellular parasite are also found. The small 5.25-Mb genome assembly includes 1,139 unique open-reading frames and an unusually high number of transposable elements for such a small genome. Investigations of intragenomic diversity also provided strong indications that the mononucleate nucleus of this species is diploid. Overall, our study provides insights into the dynamics of microsporidian genomes and a solid sequence reference to be used in future studies of host-parasite interactions using the zebrafish D. rerio and P. neurophilia as a model.
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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 | 0.001 | 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) | 0.000 | 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 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".