Mosquito Microbiome Dynamics, a Background for Prevalence and Seasonality of West Nile Virus
Bibliographic record
Abstract
Symbiotic microbial communities augment host phenotype, including defense against pathogen carriage and infection. We sampled the microbial communities in eleven adult mosquito host species from six regions in southern Ontario, Canada over three years. Of the factors examined, we found that mosquito species was the largest driver of the microbiota, with remarkable phylosymbiosis between host and microbiota. Seasonal shifts of the microbiome were consistently repeated over the three-year period, while region had little impact. Both host species and seasonal shifts in microbiota were associated with patterns of West Nile virus (WNV) in these mosquitoes. The highest prevalence of WNV, with a seasonal spike each year in August, was in the Culex pipiens/restuans complex, and high WNV prevalence followed a decrease in relative abundance of Wolbachia in this species. Indeed, mean temperature, but not precipitation, was significantly correlated with Wolbachia abundance. This suggests that at higher temperatures Wolbachia abundance is reduced leading to greater susceptibility to WNV in the subsequent generation of C. pipiens/restuans hosts. Different mosquito genera harbored significantly different bacterial communities, and presence or abundance of Wolbachia was primarily associated with these differences. We identified several operational taxonomic units (OTUs) of Wolbachia that drive overall microbial community differentiation among mosquito taxa, locations and timepoints. Distinct Wolbachia OTUs were consistently found to dominate microbiomes of Cx. pipiens/restuans, and of Coquilletidia perturbans. Seasonal fluctuations of several other microbial taxa included Bacillus cereus, Enterococcus, Methylobacterium, Asaia, Pantoea, Acinetobacter johnsonii, Pseudomonas, and Mycoplasma. This suggests that microbiota may explain some of the variation in vector competence previously attributed to local environmental processes, especially because Wolbachia is known to affect carriage of viral pathogens.
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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 | 0.000 | 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) | 0.001 | 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".