Assessing the Diversity and Distribution of Apicomplexans in Host and Free-Living Environments Using High-Throughput Amplicon Data and a Phylogenetically Informed Reference Framework
Bibliographic record
Abstract
Apicomplexans are a group of microbial eukaryotes that contain some of the most well-studied parasites, including the causing agents of toxoplasmosis and malaria, and emergent diseases like cryptosporidiosis or babesiosis. Decades of research have illuminated the pathogenic mechanisms, molecular biology, and genomics of model apicomplexans, but we know little about their diversity and distribution in natural environments. In this study we analyze the distribution of apicomplexans across a range of both host-associated and free-living environments. Using publicly available small subunit (SSU) rRNA gene databases, high-throughput environmental sequencing (HTES) surveys, and our own generated HTES data, we developed an apicomplexan reference database, which includes the largest apicomplexan SSU rRNA tree available to date and encompasses comprehensive sampling of this group and their closest relatives. This tree allowed us to identify and correct incongruences in the molecular identification of apicomplexan sequences. Analyzing the diversity and distribution of apicomplexans in HTES studies with this curated reference database also showed a widespread, and quantitatively important, presence of apicomplexans across a variety of free-living environments. These data allow us to describe a remarkable molecular diversity of this group compared with our current knowledge, especially when compared with that identified from described apicomplexan species. This is most striking in marine environments, where potentially the most diverse apicomplexans apparently exist, but have not yet been formally recognized. The new database will be useful for microbial ecology and epidemiological studies, and provide valuable reference for medical and veterinary diagnosis especially in cases of emerging, zoonotic, and cryptic infections.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".