Metatranscriptomic analysis of common mosquito vector species in the Canadian Prairies
Bibliographic record
Abstract
The microbiome plays vital roles in the life history of mosquitoes, including their development, immunity, longevity, and vector competence. Recent advances in sequencing technologies have allowed for detailed exploration into the diverse microorganisms harbored by these medically important insects. Although these meta-studies have cataloged the microbiomes of mosquitoes in several continents, much of the information currently available for North America is limited to the state of California. In this study, we collected >35,000 mosquitoes throughout Manitoba, Canada, over a 3-year period and then harnessed RNA sequencing and targeted reverse transcriptase-PCR to characterize the microbiomes of the eight most pervasive and important vector and pest species. The consensus microbiome of each species was overwhelmingly composed of viruses but also included fungi, bacteria, protozoa, and parasitic invertebrates. The microbial assemblages were heterogeneous between species, even within the same genus. We detected notable pathogens, including the causal agents of Cache Valley Fever, avian malaria, and canine heartworm. The remaining microbiome consisted largely of putatively insect-specific viruses that are not well characterized, including 17 newly discovered viruses from 10 different families. Future research should focus on evaluating the potential application of these viruses in biocontrol, as biomarkers, and/or in disrupting mosquito vectorial capacity. Interestingly, we also detected viruses that naturally infect honeybees and thrips, which were presumably acquired indirectly through nectar foraging behaviors. Overall, we provide the first comprehensive catalog of the microorganisms harbored by the most common and important mosquito vectors and pests in the Canadian Prairies. IMPORTANCE: Mosquitoes are the most dangerous animals on the planet, responsible for over 800,000 deaths per year globally. This is because they carry and transmit a plethora of human disease-causing microorganisms, such as West Nile virus and the malaria parasite. Recent innovations in nucleic acid sequencing technologies have enabled researchers unparalleled opportunities to characterize the suite of microorganisms harbored by different mosquito species, including the causal agents of disease. In our study, we carried out 3 years of intensive mosquito surveillance in Canada. We collected and characterized the microorganisms harbored by >35,000 mosquitoes, including the identification of the agents of Cache Valley fever, avian malaria, and canine heartworm. We also detected insect-specific viruses and discovered 17 new viruses that have never been reported. This study, which is the first of its kind in Canada and one of only a handful globally, will greatly aid in future infectious disease research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".