Deep data mining reveals variable abundance and distribution of microbial reproductive manipulators within and among diverse host species

Bacterial symbionts that manipulate the reproduction of their hosts to increase their successful transmission are important factors in invertebrate ecology and evolution. In light of their use as a biological control agent, studying the genomic and phenotypic diversity of reproductive manipulators can improve efforts to control infectious diseases and contribute to our understanding of host-symbiont evolution. Despite the vast genomic and phenotypic diversity of reproductive manipulators, only a handful of Wolbachia strains are used as biological control agents because little is known about the broad scale infection frequencies of these bacteria in nature. Here we develop a data mining approach to quantify the number of arthropod and nematode host species available on the Sequence Read Archive (SRA) that are infected with Wolbachia and other reproductive manipulators such as Rickettsia and Spiroplasma. Across the entire database, we found reproductive manipulators infected 1733 arthropod and 103 nematode samples, representing 121 and 10 species, respectively. We estimated that Wolbachia infects approximately 24% of all arthropod species and 20% of all nematode species. In contrast, we estimated other reproductive manipulators infect 0-8% of arthropod and nematode species. We show that relative Wolbachia density within hosts, titer, is significantly lower than the titer of the other reproductive manipulators. Considering the fitness costs of high titers, low titer may contribute to enabling Wolbachia9s high prevalence across hosts species and mitigate impacts on host biology compared with other reproductive manipulator taxa. Our study demonstrates that data mining is a powerful tool for understanding host-symbiont co-evolution and opens an array of previously inaccessible questions for further analysis.