Screen the unforeseen: Microbiome‐profiling for detection of zoonotic pathogens in wild rats
Bibliographic record
Abstract
Wild rats can host various zoonotic pathogens. Detection of these pathogens is commonly performed using molecular techniques targeting one or a few specific pathogens. However, this specific way of surveillance could lead to (emerging) zoonotic pathogens staying unnoticed. This problem may be overcome by using broader microbiome-profiling techniques, which enable broad screening of a sample's bacterial or viral composition. In this study, we investigated if 16S rRNA gene amplicon sequencing would be a suitable tool for the detection of zoonotic bacteria in wild rats. Moreover, we used virome-enriched (VirCapSeq) sequencing to detect zoonotic viruses. DNA from kidney samples of 147 wild brown rats (Rattus norvegicus) and 42 black rats (Rattus rattus) was used for 16S rRNA gene amplicon sequencing of the V3-V4 hypervariable region. Blocking primers were developed to reduce the amplification of rat host DNA. The kidney bacterial composition was studied using alpha- and beta-diversity metrics and statistically assessed using PERMANOVA and SIMPER analyses. From the sequencing data, 14 potentially zoonotic bacterial genera were identified from which the presence of zoonotic Leptospira spp. and Bartonella tribocorum was confirmed by (q)PCR or Sanger sequencing. In addition, more than 65% of all samples were dominated (>50% reads) by one of three bacterial taxa: Streptococcus (n = 59), Mycoplasma (n = 39) and Leptospira (n = 25). These taxa also showed the highest contribution to the observed differences in beta diversity. VirCapSeq sequencing in rat liver samples detected the potentially zoonotic rat hepatitis E virus in three rats. Although 16S rRNA gene amplicon sequencing was limited in its capacity for species level identifications and can be more difficult to interpret due to the influence of contaminating sequences in these low microbial biomass samples, we believe it has potential to be a suitable pre-screening method in the future to get a better overview of potentially zoonotic bacteria that are circulating in wildlife.
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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 | 0.001 | 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".