Characterisation and zoonotic risk of tick viruses in public datasets

Abstract Tick-borne viruses remain a substantial zoonotic risk worldwide, so knowledge of the diversity of tick viruses has potential health consequences. Despite their importance, large amounts of sequences in public datasets from tick meta-genomic and –transcriptomic projects remain unannotated, sequence data that could contain undocumented viruses. Through data mining and bioinformatic analyses of more than 37,800 public meta-genomic and -transcriptomic datasets, we found 83 unannotated contigs exhibiting high identity with known tick viruses. These putative viral contigs were classified into three RNA viral families ( Alphatetraviridae , Orthomyxoviridae , Chuviridae ) and one DNA viral family ( Asfaviridae ). After manual checking of quality and dissimilarity toward other sequences in the dataset, these 83 contigs were reduced to five putative novel Alphatetra-like viral contigs, four putative novel Orthomyxo-like viral contigs, and one Chu-like viral contig which clustered with known tick-borne viruses, forming a separate clade within the viral families. We further attempted to assess which previously known tick viruses likely represent zoonotic risks and thus deserve further investigation. We ranked the human infection potential of 136 known tick-borne viruses using a genome composition-based machine learning model. We found five high-risk tick-borne viruses (Langat virus, Lonestar tick chuvirus 1, Grotenhout virus, Taggert virus, and Johnston Atoll virus) that have not been known to infect human and two viral families ( Nairoviridae and Phenuiviridae ) that contain a large proportion of potential zoonotic tick-borne viruses. This adds to the knowledge of tick virus diversity and highlights the importance of surveillance of newly emerging tick-borne diseases. Importance Ticks are important hosts of pathogens. Despite this, numerous tick-borne viruses are still unknown or poorly characterised. To overcome this, we re-examined currently known tick-borne viruses and identified putative novel viruses associated with ticks in public datasets. Using genome-based machine learning approach, we predicted five high-risk tick-borne viruses that have not yet been reported to cause human infections. Additionally, we highlighted two viral families, Nairoviridae and Phenuiviridae , which are potential public health threats. Our analysis also revealed 10 putative novel RNA viral contigs clustered with known tick-borne viruses. Our study highlights the importance of monitoring ticks and the viruses they carry in endemic areas to prevent and control zoonotic infectious disease outbreaks. To achieve this, we advocate for a multidisciplinary approach within a One Health and EcoHealth framework that considers the relationship between zoonotic disease outbreaks and their hosts, humans, and the environment.