Metabolic preference assay for rapid diagnosis of bloodstream infections

Abstract

Bloodstream infections (BSIs) cause >500,000 infections and >80,000 deaths per year in North America. The length of time between the onset of symptoms and administration of appropriate antimicrobials is directly linked to mortality rates. It currently takes 2–5 days to identify BSI pathogens and measure their susceptibility to antimicrobials – a timeline that directly contributes to preventable deaths. To address this, we demonstrate a rapid metabolic preference assay (MPA) that uses the pattern of metabolic fluxes observed in ex-vivo microbial cultures to identify common pathogens and determine their antimicrobial susceptibility profiles. In a head-to-head race with a leading platform (VITEK 2, BioMérieux) used in diagnostic laboratories, MPA decreases testing timelines from 40 hours to under 20. If put into practice, this assay could reduce septic shock mortality and reduce the use of broad spectrum antibiotics.

Method for quantifying the metabolic boundary fluxes of cell cultures in large cohorts by high resolution hydrophilic liquid chromatography mass spectrometry

“…via the arginine catabolic mobile element) 29 . Thus, metabolic profiling of S. aureus could potentially be harnessed as a tool for clinical diagnostics 9 . Currently, most S. aureus metabolomics studies have been conducted using a laboratory strains 30 and relatively small collections of clinical isolates 31 .…”

“…These applications include 1) constructing/refining flux balance models 4,5 , 2) screening large libraries of isolates for auxotrophy or other metabolic variability 33,34 , 3) as a tool to accelerate bioengineering projects, particularly those that are seeking to use saturation mutagenesis and other high-throughput methods to maximize metabolic outputs in microbial systems 7,8 , and 4) as a strategy for rapidly screening inhibitor libraries to identify new antimicrobial lead compounds. Moreover, we have recently shown that microbial boundary fluxes can be used as a diagnostic readout for differentiating microbial pathogens and measuring their antibiotic susceptibility profiles 9 . In summary, the method we present here has broad applicability to any large cohort study of in vitro culture systems where the primary objective is to quantify the rates at which metabolites are consumed or secreted to the culture medium.…”

“…Our methods for quantifying boundary fluxes have been described in detail elsewhere 9 . Briefly, metabolites present in microbial cultures were collected prior to, and after incubating samples for 4 hours in Mueller-Hinton growth medium.…”

“…Quantifying boundary fluxes can provide a comprehensive understanding of cellular function by enabling flux balance models of cell metabolism to be optimized 5 , nutritional dependencies to be mapped 6 , and metabolic networks of model organisms to be optimized for industrial applications 7,8 . Moreover, we have recently shown that metabolic boundary fluxes can be used as a clinical microbiology assay for identifying microbial species and measuring antibiotic susceptibility profiles 9 . One critical distinction between boundary flux analysis and routine metabolomics is the complexity of the samples.…”

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry

“… 7 , 8 Moreover, we have recently shown that metabolic boundary fluxes can be used as a clinical microbiology assay for identifying microbial species and measuring antibiotic susceptibility profiles. 9 One critical distinction between boundary flux analysis and routine metabolomics is the complexity of the samples. Whereas intracellular metabolic phenotypes affect thousands of molecules, 10 cell media can contain 100 or fewer organic molecules.…”