Tick-borne viral pathogen contigs.

Background

We set out to investigate the utility of publicly available tick transcriptomic data to identify and characterize known and recently described tick-borne viruses, using de novo assembly and subsequent protein database alignment and taxonomical binning.

Methodology/principal findings

A total of 127 virus contigs were recovered from 35 transcriptomes, originating from cell lines (40%), colony-reared ticks (25.7%) or field-collected ticks (34.2%). Generated virus contigs encompass DNA (n = 2) and RNA (n = 13) virus families, with 3 and 28 taxonomically distinct isolates, respectively. Known human and animal pathogens comprise 32.8% of the contigs, where Beiji nairovirus (BJNV) was the most prevalent tick-borne pathogenic virus, identified in 22.8% of the transcriptomes. Other pathogens included Nuomin virus (NUMV) (2.8%), African swine fever virus (ASFV) (5.7%), African horse sickness virus 3 (AHSV-3) (2.8%) and Alongshan virus (ALSV) (2.8%).

Conclusions

Previously generated transcriptome data can be leveraged for detecting tick-borne viruses, as exemplified by new descriptions of ALSV and BJNV in new geographic locations and other viruses previously detailed in screening reports. Monitoring pathogens using publicly available data might facilitate biosurveillance by directing efforts to regions of preliminary spillover and identifying targets for screening. Metadata availability is crucial for further assessments of detections.