Revealing the composition of the eukaryotic microbiome of oyster spat by CRISPR-Cas Selective Amplicon Sequencing (CCSAS)
Bibliographic record
Abstract
BACKGROUND: The microbiome affects the health of plants and animals, including humans, and has many biological, ecological, and evolutionary consequences. Microbiome studies typically rely on sequencing ribosomal 16S RNA gene fragments, which serve as taxonomic markers for prokaryotic communities; however, for eukaryotic microbes this approach is compromised, because 18S rRNA gene sequences from microbial eukaryotes are swamped by contaminating host rRNA gene sequences. RESULTS: To overcome this problem, we developed CRISPR-Cas Selective Amplicon Sequencing (CCSAS), a high-resolution and efficient approach for characterizing eukaryotic microbiomes. CCSAS uses taxon-specific single-guide RNA (sgRNA) to direct Cas9 to cut 18S rRNA gene sequences of the host, while leaving protistan and fungal sequences intact. We validated the specificity of the sgRNA on ten model organisms and an artificially constructed (mock) community of nine protistan and fungal pathogens. The results showed that > 96.5% of host rRNA gene amplicons were cleaved, while 18S rRNA gene sequences from protists and fungi were unaffected. When used to assess the eukaryotic microbiome of oyster spat from a hatchery, CCSAS revealed a diverse community of eukaryotic microbes, typically with much less contamination from oyster 18S rRNA gene sequences than other methods using non-metazoan or blocking primers. However, each method revealed taxonomic groups that were not detected using the other methods, showing that a single approach is unlikely to uncover the entire eukaryotic microbiome in complex communities. To facilitate the application of CCSAS, we designed taxon-specific sgRNA for ~16,000 metazoan and plant taxa, making CCSAS widely available for characterizing eukaryotic microbiomes that have largely been neglected. CONCLUSION: CCSAS provides a high-through-put and cost-effective approach for resolving the eukaryotic microbiome of metazoa and plants with minimal contamination from host 18S rRNA gene sequences. Video Abstract.
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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".