Rapid Open-Source SNP-Based Clustering Offers an Alternative to Core Genome MLST for Outbreak Tracing in a Hospital Setting

Whole genomes from bacteria collected at diagnostic units around the world 2020

The Two Weeks in the World research project has resulted in a dataset of 3087 clinically relevant bacterial genomes with pertaining metadata, collected from 59 diagnostic units in 35 countries around the world during 2020. A relational database is available with metadata and summary data from selected bioinformatic analysis, such as species prediction and identification of acquired resistance genes.

Optimizing hierarchical tree dissection parameters using historic epidemiologic data as 'ground truth'

Hierarchical clustering of pathogen genotypes is widely used to complement epidemiologic investigations of outbreaks. Investigators must dissect trees to obtain genetic partitions that provide epidemiologists with meaningful information. Statistical approaches to tree dissection often require a user-defined parameter to predict the optimal partition number and augmenting this parameter can drastically impact resultant partition memberships. Here, we demonstrate how to optimize a given tree dissection parameter to maximize accuracy irrespective of the tree dissection method used. We hierarchically clustered 1,873 genotypes of the foodborne pathogen Cyclospora spp., including 587 possessing links to historic outbreaks. We dissected the resulting tree using a statistical method requiring users to select the value of a 'stringency parameter' (s), with a recommended value of 95% to 99.5%. We dissected this hierarchical tree across s-values from 94% to 99.5% (at increments of 0.25%), to identify a value that maximized partitioning accuracy, defined as the degree to which genetic partitions conform to known epidemiologic groupings. We show that s-values of 96.5% and 96.75% yield the highest accuracy (> 99.9%) when clustering Cyclospora sp. isolates with known epidemiologic linkages. In practice, the optimized s-value will generate robust genetic partitions comprising isolates likely derived from a common food source, even when the epidemiologic grouping is not known prior to genetic clustering. While the s-value is specific to the tree dissection method used here, the optimization approach described could be applied to any parameter/method used to dissect hierarchical trees.

Rapid phylogenetic analysis using open reading frame content patterns acquired by Oxford Nanopore sequencing

AIMS

Phylogenetic analysis based on core genome single nucleotide polymorphisms (cgSNPs) using whole-genome sequencing (WGS) is increasingly used in epidemiological investigations of bacteria. The approach, however, is both resource intensive and time-consuming. Oxford Nanopore Technologies (ONT) sequencing is capable of real-time data analysis but the high error rate hampers its application in cgSNP-based phylogenetic analysis. Here, we developed a cgSNP-independent phylogenetic analysis method using ONT read assemblies by focusing on open reading frame (ORF) content patterns.

METHODS AND RESULTS

WGS data of 66 Enterobacter hormaechei strains acquired by both ONT and Illumina sequencing and 162 strains obtained from NCBI database were converted to binary sequences based on the presence or absence of ORFs using BLASTn. Phylogenetic trees calculated from binary sequences (ORF trees) were compared with cgSNP trees derived from Illumina sequences. Clusters of closely related strains in the cgSNP trees formed comparable clusters in the ORF trees built with binary sequences, and the tree topologies between them were similar based on Fowlkes-Mallows index.

CONCLUSIONS

The ORF-based phylogenetic analysis using ONT sequencing may be useful in epidemiological investigations and offer advantages over the cgSNP-based approach.

SIGNIFICANCE AND IMPACT OF THE STUDY

Conversion of assembled WGS data to binary sequences based on the presence or absence of ORFs circumvents read error concerns with ONT sequencing. Since ONT sequencing generates data in real-time and does not require major investment, this ORF-based phylogenetic analysis method has the potential to enable phylogenetic and epidemiological analysis at the point of care.