A universal set of primers to study animal associated microeukaryotic communities
Bibliographic record
Abstract
Abstract Background Unlike the study of bacterial microbiomes, the study of the microeukaryotes associated with animals has largely been restricted to visual identification or molecular targeting of particular groups. The application of high-throughput sequencing (HTS) approaches, such as those used to look at bacteria, has been restricted because the barcoding gene traditionally used to study microeukaryotic ecology and distribution in the environment, the Small Subunit of the Ribosomal RNA gene (18S rRNA), is also present in the animal host. As a result, when host-associated microbial eukaryotes are analyzed by HTS, the obtained reads tend to be dominated by host sequences. Results We have done an in-silico validation against the SILVA 18S rRNA reference database of contrametazoan primers that cover the V4 region of the 18S rRNA, and compared these with universal V4 18S rRNA primers that are widely used by the microbial ecology community. We observe that the contrametazoan primers recover only 2.6% of all the metazoan sequences present in SILVA, while the universal primers recover up to 20%. Among metazoans, the contrametazoan primers are predicted to amplify 74% of Porifera sequences and 4% and 15% of ctenophore and Cnidaria, respectively, while amplifying almost no sequences within Bilateria. We tested these predictions in-vivo, and observed that contrametazoan primers amplify the 18SrRNA from two ctenophore species, but reduce significantly the metazoan signal from material derived from coral and from human samples. When compared in-vivo against universal primers, contrametazoan primers worked in 8 out of 9 samples, providing at worst a 2-fold decrease in the number of metazoan reads, and at best a 2800-fold decrease. Conclusions We have validated an easy, inexpensive, and near-universal method for the study of microeukaryotes associated with animal hosts using 18S rRNA Illumina metabarcoding. This method will contribute to a better understanding of microbial communities, as they related to the wellbeing of animals and humans.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".