Abstract
Francisella tularensis, the bacterium that causes the zoonosis tularemia, and its genetic near neighbor species, can be difficult or impossible to cultivate from complex samples. Thus, there is a lack of genomic information for these species that has, among other things, limited the development of robust detection assays for F. tularensis that are both specific and sensitive. The objective of this study was to develop and validate approaches to capture, enrich, sequence, and analyze Francisella DNA present in DNA extracts generated from complex samples. RNA capture probes were designed based upon the known pan genome of F. tularensis and other diverse species in the family Francisellaceae. Probes that targeted genomic regions also present in non-Francisellaceae species were excluded, and probes specific to particular Francisella species or phylogenetic clades were identified. The capture-enrichment system was then applied to diverse, complex DNA extracts containing low-level Francisella DNA, including human clinical tularemia samples, environmental samples (i.e., animal tissue and air filters), and whole ticks/tick cell lines, which was followed by sequencing of the enriched samples. Analysis of the resulting data facilitated rigorous and unambiguous confirmation of the detection of F. tularensis or other Francisella species in complex samples, identification of mixtures of different Francisella species in the same sample, analysis of gene content (e.g., known virulence and antimicrobial resistance loci), and high-resolution whole genome-based genotyping. The benefits of this capture-enrichment system include: even very low target DNA can be amplified; it is culture-independent, reducing exposure for research and/or clinical personnel and allowing genomic information to be obtained from samples that do not yield isolates; and the resulting comprehensive data not only provide robust means to confirm the presence of a target species in a sample, but also can provide data useful for source attribution, which is important from a genomic epidemiology perspective.