Next generation sequencing, insect microbiomes, and the confounding effect of <i>Wolbachia</i>: a case study using spotted-wing drosophila (<i>Drosophila suzukii</i>) (Diptera: Drosophilidae)
Bibliographic record
Abstract
Next generation sequencing (NGS) increasingly is being used to characterize the gut microbiome of insects to provide insights into the ecology and biology of the host, but results may be confounded by co-occurring infections of bacteria genus Wolbachia Hertig, 1936 in the cells of the host. We illustrate this issue using spotted-wing drosophila, Drosophila suzukii (Matsumura, 1931) (Diptera: Drosophilidae), as an example. With an assay based on polymerase chain reactions, we detected Wolbachia in 20% of flies collected from sites in British Columbia, Alberta, and Newfoundland and Labrador, Canada. With NGS, we determined that the total microbiome of infected flies was dominated by Wolbachia (mean of 98.8%) with mean values of bacterial richness and diversity 1.4- and 22-fold lower than that of co-occurring, uninfected flies (mean of 0.6% Wolbachia). We review options available to address the confounding factor of Wolbachia, which vary with the presence of infections in the population, the prevalence of infected individuals in the population, and the titre of Wolbachia in infected individuals. Understanding this issue and how it can be resolved is of broad importance, given that an estimated 40% of terrestrial arthropod species harbour Wolbachia 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".