Updated: May 25
A Novel Multi-Scale Outbreak Detection and Strain Identification Capability for Genome Sequence-Based Infection Control: An MRSA Example.
J. C. Littlefair, B. Uttley, D. Frampton, J. F. Peden, N. J. Saunders
The exclusion (as well as inclusion) of strains is vital for outbreak investigation and infection prevention and control of healthcare-associated infection, for both health resource management and patient-care. Current genome-sequence based diagnostics strategies have limitations in their analytical capabilities and scalability which restrict their utility for proactive diagnostic surveillance and to direct real-time infection control. MLST is of limited value in the context of dominant epidemic or more virulent clones; such as MRSA where up to 90% of isolates fall into a small number of common STs. High-resolution SNP-based analysis can be pursued when there is an appropriate reference genome, but the greater the diversity between the sample and reference genome the lower the coverage, resolution, and accuracy. Further, the similarity of members of clonal clusters within hospital and surrounding community and health-care environments can be high. Thus, the detection and differentiation of hospital-associated acquisition and transmission from strains entering the hospital is non-trivial and requires improved sequence analysis to resolve.
GenPAx has developed a novel analysis pipeline that can perform multi-scale analysis for both outbreak detection and high-resolution determination of strain identity and relatedness (and implied transmission connections). This can be run without prior knowledge of potential outbreaks or associated strains, and can detect single instances of known high-virulence and highly-resistant clones. Using a well-established dataset of over 600 London-based MRSA we achieved clearly higher resolution and improved determination of connected isolates than using traditional methods: the number of sites used for identification within the study was more than doubled, and the dynamic range of differences and resolution of associated strains was greatly increased. While highly similar strains some with less than 10 nt difference were still identified, other previously linked strains became clearly separated, indicating that the number and size of hospital and health-care associated cross-infections was substantially lower than previously thought. There was also a better match between the size of the largest cluster and the number of epidemiologically determined interactions. Thus, we have shown that this next generation of bacterial genomics resources can substantially increase the future diagnostic utility of genome sequencing for hospital infection control.