Background: Ticks (order Ixodida) are ectoparasites, vectors and reservoirs of many infectious agents affecting humans and domestic animals. However, the lack of information on tick genomic diversity leaves significant gaps in the understanding of the evolution of ticks and associated bacteria. Results: We collected > 20,000 contemporary and historical (up to 60 years of preservation) tick samples representing a wide range of tick biodiversity across diverse geographic regions in China. Metagenomic sequencing was performed on individual ticks to obtain the complete or near-complete mitochondrial (mt) genome sequences from 46 tick species, among which mitochondrial genomes of 23 species were recovered for the first time. These new mt genomes data greatly expanded the diversity of many tick groups and revealed five cryptic species. Utilizing the same metagenomic sequence data we identified divergent and abundant bacteria in Haemaphysalis, Ixodes, Dermacentor and Carios ticks, including nine species of pathogenetic bacteria and potentially new species within the genus Borrelia. We also used these data to explore the evolutionary relationship between ticks and their associated bacteria, revealing a pattern of long-term co-divergence relationship between ticks and Rickettsia and Coxiella bacteria. Conclusions: In sum, our study provides important new information on the genetic diversity of ticks based on an analysis of mitochondrial DNA as well as on the prevalence of tick-borne pathogens in China. It also sheds new light on the long-term evolutionary and ecological relationships between ticks and their associated bacteria. Graphical

We report the isolation of a newly recognized phlebovirus, Hedi virus (HEDV), from Phlebotomus chinensis sandflies collected in Shanxi Province, China. The virus’ RNA is comprised of three segments. The greatest amino acid sequence similarity of the three gene segments between this virus and previously recognized phleboviruses is 40.85–63.52%, and the RNA-dependent RNA polymerase (RdRp) amino acid sequence has the greatest similarity (63.52%) to the Rift Valley fever virus (RVFV) ZH-548 strain. Phylogenetic analysis of the amino acid sequence of the virus RdRp indicated that HEDV is close to RVFV and distinct from other phleboviruses, forming its own evolutionary branch. We conclude that it is necessary to increase the monitoring of phleboviruses carried by sandflies in China.

Small mammals harbor a diverse array of potentially zoonotic pathogens. To date, however, metagenomic surveys of these species have primarily focused on viruses, with limited attention directed to bacterial and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome—defined as the set of microorganisms infecting the host—across 2408 individual samples, representing lung, spleen, and gut from 858 animals in Guangdong province, China, considering the impact of host species, tissue, season, and geographic location on pathogen diversity. We identified 76 potential pathogen species, comprising 29 RNA viruses, 12 DNA viruses, five bacteria, and 30 eukaryotic pathogens, including 33 that are newly discovered. Distinct tissue tropisms were identified, suggesting varied transmission routes. Individual animals carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Total pathogen richness was largely influenced by geographic region, followed by host species and season, while zoonotic pathogen richness was primarily driven by host species. Collectively, these data provide insights into the structure of the pathogen infectome and the drivers of pathogen diversity and transmission in these key mammalian disease reservoirs.

Dear Professor Remi N. Charrel and Professor Jerome Depaquit, we thank you for your interest in our research and for your kind suggestions

Understanding wildlife-pathogen interactions is crucial for mitigating zoonotic risk. Through meta-transcriptomic sequencing we profiled the infectomes of 1,922 samples from 67 mammalian species across China, uncovering a remarkable diversity of viral, bacterial, fungal, and parasitic pathogens. Of the 195 pathogens identified, 62 were novel, including a bi- segmented coronavirus in diseased lesser pandas, which we propose represents a new genus – Zetacoronavirus. The orders Carnivora and Rodentia exhibited the highest pathogen diversity and were implicated in numerous host-jumping events. Comparative analysis of diseased versus healthy animals revealed a trend of higher pathogen loads in the former, with possible differences in tissue tropisms. In total, 48 zoonotic and 17 epizootic pathogens were identified, with frequent cross-species transmission, emphasizing the potential for emerging public health threats. This study highlights the urgent need for wildlife pathogen surveillance to inform proactive disease management strategies.

Recent advances in large-scale sequence mining have expanded our knowledge of RNA virus diversity. Most genome mining approaches for detecting RNA viruses rely on identifying the conserved RNA-dependent RNA polymerase (RdRp) by scanning sequencing datasets with specialized profile Hidden Markov Models (pHMMs). Recently, several new pHMM databases for RdRp detection have been released, each following distinct design principles. However, their relative performance remains unclear, and their accessibility to users without advanced computational expertise is limited. Here, we introduce the RdRp Collaborative Analysis Tool with Collections of pHMMs (RdRpCATCH: https://github.com/dimitris-karapliafis/RdRpCATCH), a platform that consolidates publicly available RdRp pHMM resources into a single, user-friendly framework. RdRpCATCH enables the scanning of (meta)transcriptomic assemblies to discover RNA viruses and provides subsequent taxonomic annotation of detected contigs. A comparative analysis of RdRp pHMM databases reveals that most are highly effective at detecting the known diversity of RNA viruses while minimizing false positives, supporting their joint use within RdRpCATCH. RdRpCATCH is distributed as both a conda package and a web server application (https://rdrpcatch.bioinformatics.nl), facilitating access for researchers with diverse levels of computational expertise. By integrating multiple pHMM resources, this unified framework addresses fragmentation in the field and reduces technical barriers, enabling comprehensive viral discovery.