A bioinformatics approach to identifyingWolbachiainfections in arthropods

Abstract

Wolbachiais the most widespread endosymbiont, infecting >20% of arthropod species, and capable of drastically manipulating the host’s reproductive mechanisms. Conventionally, diagnosis has relied on PCR amplification; however, PCR is not always a reliable diagnostic technique due to primer specificity, strain diversity, degree of infection and/or tissue sampled. Here, we look for evidence ofWolbachiainfection across a wide array of arthropod species using a bioinformatic approach to detect theWolbachiagenesftsZ, wsp,and thegroEoperon in next-generation sequencing samples available through the NCBI Sequence Read Archive. For samples showing signs of infection, we attempted to assemble entireWolbachiagenomes, and in order to better understand the relationships between hosts and symbionts, phylogenies were constructed using the assembled gene sequences. Out of the 34 species with positively identified infections, eight species of arthropod had not previously been recorded to harborWolbachiainfection. All putative infections cluster with known representative strains belonging to supergroup A or B, which are known to only infect arthropods. This study presents an efficient bioinformatic approach for post-sequencing diagnosis and analysis ofWolbachiainfection in arthropods.