Discovery of a novel in expands nematode host distribution.

Bioinformatics sequence data mining can reveal hidden microbial symbionts that might normally be filtered and removed as contaminants. Data mining can be helpful to detect , a widespread bacterial endosymbiont in insects and filarial nematodes whose distribution in plant-parasitic nematodes (PPNs) remains underexplored. To date, has only been reported a few PPNs, yet nematode-infecting may have been widespread in the evolutionary history of the phylum based on evidence of horizontal gene transfers, suggesting there may be undiscovered infections in PPNs. The goal of this study was to more broadly sample PPN strains in tylenchid nematodes to enable further comparative genomic analyses that may reveal role and identify targets for biocontrol. Published whole-genome shotgun assemblies and their raw sequence data from 33 spp. assemblies, seven spp. assemblies, and seven spp. assemblies were analyzed to look for . No was found in spp. and spp., but among seven genome assemblies for spp., an assembly from the Netherlands was found to have a large like sequence that, when re-assembled from reads, formed a complete, circular genome. Detailed analyses comparing read coverage, GC content, pseudogenes, and phylogenomic patterns clearly demonstrated that the represented a novel strain (hereafter, denoted Het). Phylogenomic tree construction with PhyloBayes showed Het was most closely related to another PPN Tex, while 16S rRNA gene analysis showed it clustered with other assembled from sequence databases. Pseudogenes in Het suggested relatedness to the PPN clade, as did the lack of significantly enriched GO terms compared to PPN strains. It remains unclear whether the lack of in other published isolates represents the true absence of the endosymbiont from some hosts.