Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry

Gelio Alves,David B Sacks,Steven K Drake,Aleksey Y Ogurtsov,Guanghui Wang,Marjan Gucek,Yi-Kuo Yu

doi:10.1007/s13361-018-1986-y

Abstract

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple “fingerprinting”; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.Graphical ᅟ

Highlights

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety [1,2,3]
To investigate the feasibility of microbial identification based on peptides identified, we examine in silico the peptidome similarities among microbes at different taxonomic level in our DB-1
We have proposed a workflow for microbial classification/identification by processing mass spectrometry (MS)/MS data acquired in high-resolution mass spectrometers

Summary

Introduction

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety [1,2,3]. Traditional methods for microbial identifications target only a limited number of microorganisms [4, 5] and often require 72 h or more to carry out [6,7,8]. G. Alves et al.: Identification of Microorganisms microorganismal identification protocols, the first step can take the longest. Alves et al.: Identification of Microorganisms microorganismal identification protocols, the first step can take the longest This time-consuming step involves preparing a culture of the collected sample in a selected medium, usually a blood culture, to test for the presence of any microbes and to amplify the concentration of microbes that might be present [7,8,9]. If the prepared culture tests positive for the presence of microbes, further tests are required to distinguish and identify within the sample each microbe present [7, 8, 10, 11]

Methods

Results

Conclusion