Automated algorithm for rapid, scalable determination of hospital acquired bacterial infections using whole genome sequencing

Project Abstract The goal of this project is to develop a rapidcost effective whole genome sequencing based method to determine if a hospital acquired infectionHAItransmission event has occurredHAIsparticularly those caused by multi drug resistantMDRorganismsare recognized as a widespread challengeaffecting one inpatients treated in healthcare facilitiesYet there is currently no rapid and robust method for confirming when a suspected transmission has occurredgold standard approaches for determining relatedness of bacterial infectionssuch as pulsed field gel electrophoresisare not employed on a regular basis due to their high cost and slow turnaround timeWhole genome sequencingWGShas recently been successfully used in HAI investigations to provide high accuracy determination of clonalityyet the standard single nucleotide polymorphismSNPbased computational method is slow and requires significant analysis by a skilled computational biologistThus WGS is currently not widely available as a tool for HAI determinationDay Zero Diagnostics is developing a computational method to analyze the WGS data of bacterial infections with a novel algorithm that rapidly computes the genetic relatedness of samples to determine if a transmission event has occurredThe methodcalled ksimcalculates the genomic similarity between samples by comparing their kmerssubsequences of length k in the WGS dataThe method has several advantages over the SNP methodit is fasttaking only a few minutes to compare two samplesit is automatednot requiring trained personneland it is scalable to large datasets of thousands of samplesThis method can be deployed as a service for hospital transmission investigationsand has the capability to become the basis for proactive identification of potential transmission events in large scale ongoing sequencing effortsThe objective of this Phase I SBIR project is to further develop ksimwhich has shown promising initial results but needs further optimization to yield robust results across different bacterial speciesAimwill optimize the algorithm forspecies commonly involved in MDR HAIsrefining ksim to distinguish between core and accessory genomic regionsand optimizing parameters using published hospital outbreak datasetsAimwill demonstrate a proof of concept validation of the ksim methodtesting it onnew suspected hospital cluster casesand demonstrating its applicability for proactive identification of outbreaks by determining the genetic relatedness ofclinical samples from a large single hospital longitudinal WGS databaseThe resulting optimized algorithm will be useful for a large proportion of HAIsand is readily expandable to additional pathogens through further developmentThe successful delivery of this proposal has the potential to make WGS easily available for routine use in transmission investigationswhich would dramatically improve the capabilities of infection control procedures to manage and prevent HAIs PROJECT NARRATIVE Hospital acquired infectionsHAIare recognized as a widespread concernaffectingof patients in the United Statesand multidrug resistant pathogens can lead to particularly deadly hospital outbreaksyet current methods for determining when a hospital transmission has occurred are slowcostlyor difficult to automateThis project will develop a novel computational method that uses whole genome sequencing data to rapidly identify transmission eventsIf successfulour rapid and fully automated approach will provide cost effective high resolution transmission information as soon ashours from receipt of bacterial samplesa window of time that can have a major impact on the cost and magnitude of interventions a hospital might employ