Abstract

The fast and reliable characterization of bacterial and fungal pathogens plays an important role in infectious disease control and tracking of outbreak agents. DNA based methods are the gold standard for epidemiological investigations, but they are still comparatively expensive and time-consuming. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a fast, reliable and cost-effective technique now routinely used to identify clinically relevant human pathogens. It has been used for subspecies differentiation and typing, but its use for epidemiological tasks, e. g. for outbreak investigations, is often hampered by the complexity of data analysis. We have analysed publicly available MALDI-TOF mass spectra from a large outbreak of Shiga-Toxigenic Escherichia coli in northern Germany using a general purpose software tool for the analysis of complex biological data. The software was challenged with depauperate spectra and reduced learning group sizes to mimic poor spectrum quality and scarcity of reference spectra at the onset of an outbreak. With high quality formic acid extraction spectra, the software’s built in classifier accurately identified outbreak related strains using as few as 10 reference spectra (99.8% sensitivity, 98.0% specificity). Selective variation of processing parameters showed impaired marker peak detection and reduced classification accuracy in samples with high background noise or artificially reduced peak counts. However, the software consistently identified mass signals suitable for a highly reliable marker peak based classification approach (100% sensitivity, 99.5% specificity) even from low quality direct deposition spectra. The study demonstrates that general purpose data analysis tools can effectively be used for the analysis of bacterial mass spectra.

Highlights

  • Characterization of bacterial and fungal pathogens is essential in effective infectious disease control and tracking of outbreak agents

  • The frequency of peak presence in the lists of most significant peaks from ten independent computational runs and a rank score, calculated as Si(11 − rank in the list of most significant peaks of runi), were determined for each peak among the top ten most significant peaks in the analyses with binary matrices from formic acid extraction (FAE) spectra processed with a signal to noise ratio (SNR) cut-off of 4

  • Pair wise Pearson correlation and Pearson distances between binary signal vectors representing peak presence or absence from nonoutbreak related E. coli (NOREC) FAE spectra processed with a SNR cut-off of 4 were determined in R to identify correlated peaks

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Summary

Introduction

Characterization of bacterial and fungal pathogens is essential in effective infectious disease control and tracking of outbreak agents. In the last few years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) fingerprinting has been extensively used to identify clinically relevant human pathogens, including bacteria [1,2,3,4,5], yeasts [6,7] and filamentous fungi [1,8]. Microbial identification is based on the analysis of whole cell mass spectra (mainly representing highly abundant ribosomal proteins) that are compared to reference spectra of well characterized isolates or probed for the presence of known genus- and species-specific mass signals [9]. Critical issues for the further development of MALDI-TOF MS in medical microbiology are the maintenance of reliable reference databases and the identification of discriminatory mass signals to increase phylogenetic resolution within closely related taxa. Identification algorithms and databases have continuously been expanded and improved by the major suppliers of MALDI-TOF MS based microbial identification systems, but subspecies-level discrimination has mainly been pursued with in-house algorithms and workflows

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