Screen the unforeseen: Microbiome‐profiling for detection of zoonotic pathogens in wild rats. Issue 6 (30th November 2022) - de Cock, Marieke; Fonville, Manoj; de Vries, Ankje; Bossers, Alex; van den Bogert, Bartholomeus; Hakze‐van der Honing, Renate; Koets, Ad; Sprong, Hein; van der Poel, Wim; Maas, Miriam
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.