Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel.

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S. Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool's output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

Time-to-Detection of Inducible Macrolide Resistance in Mycobacterium abscessus Subspecies and Its Association with the Erm(41) Sequevar.

Mutations in the erm(41) gene of M.abscessus group organisms are associated with differences in inducible macrolide resistance, with current recommendations being to hold rapidly growing isolates for up to 14 days in order to ensure that resistance which develops more slowly can be detected. This study aimed to determine the ideal incubation time for accurate identification of inducible macrolide resistance as well as to determine if there was an association between the time taken to detect inducible resistance in M.abscessus group organisms and their erm(41) sequevar. We amplified and sequenced the erm(41) genes of a total of 104 M.abscessus group isolates and determined their sequevars. The isolates were tested for phenotypic clarithromycin resistance at days 7, 10, 14 and 21, using Trek Diagnostics Sensititre RAPMYCO microbroth dilution plates. Associations between erm(41) gene sequevars and time to detection of resistance were evaluated using Fisher's exact test in R. The samples included in this study fell into 14 sequevars, with the majority of samples falling into Sequevar02 (16), Sequevar06 (15), Sequevar08 (7) and Sequvar 15 (31), and several isolates that were in small clusters, or unique. The majority (82.7%) of samples exhibiting inducible macrolide resistance were interpreted as resistant by day 7. Two isolates in Sequevar02, which has a T28C mutation that is associated with sensitivity, showed intermediate resistance at day 14, though the majority (13) were sensitive at day 14. The majority of isolates with inducible macrolide resistance fell into Sequevars 06,08 and 15, none of which contain the T28C mutation. These sequevars were analyzed to determine if there was any correlation between sequevar and time to detection of resistance. None was found. Based on these findings, we recommend the addition of a day 7 read to the CLSI guidelines to improve turn-around-times for these isolates. It is also recommended that erm(41) gene sequencing be added to routine phenotypic testing for the resolution of cases with difficult-to-interpret phenotypic results.