Design and application of a novel two-amplicon approach for defining eukaryotic microbiota
Bibliographic record
Abstract
BACKGROUND: Due to a lack of systematic diagnostics, our understanding of the diversity and role of eukaryotic microbiota in human health is limited. While studies have shown fungal communities to be significant modulators of human health, information on the prevalence of taxa such as protozoa and helminths has been limited to a small number of species for which targeted molecular diagnostics are available. To probe the diversity of eukaryotic microbes and their relationships with other members of the microbiota, we applied in silico and experimental approaches to design a novel two-amplicon surveillance tool, based on sequencing regions of ribosomal RNA genes and their internal transcribed spacers. We subsequently demonstrated the utility of our approach by characterizing the eukaryotic microbiota of 46 hospitalized Malawian children suffering from Severe Acute Malnutrition (SAM). RESULTS: Through in silico analysis and validation on a diverse panel of eukaryotes, we identified 18S rRNA variable genetic regions 4 and 5 (18S V4 V5), together with a region encoding 28S rRNA variable genetic region 2 and the internal transcribed spacers (transITS), as optimal for the systematic classification of eukaryotes. Sequencing of these regions revealed protozoa in all stool samples from children with SAM and helminths in most, including several eukaryotes previously implicated in malnutrition and diarrheal disease. Clinical comparisons revealed no association between protozoan parasites and diarrhea or HIV reactivity. However, the presence of Blastocystis correlated with bacterial alpha diversity and increased abundance of specific taxa, including Sporobacter, Cellulosibacter, Oscillibacter, and Roseburia. CONCLUSION: We suggest this novel two-amplicon based strategy will prove an effective tool to deliver new insights into the role of eukaryotic microbiota in health and disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".