Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics
Journal article, 2017

Methods for rapid and reliable microbial identification are essential in modern healthcare. The ability to detect and correctly identify pathogenic species and their resistance phenotype is necessary for accurate diagnosis and efficient treatment of infectious diseases. Bottom-up tandem mass spectrometry (MS) proteomics enables rapid characterization of large parts of the expressed genes of microorganisms. However, the generated data are highly fragmented, making downstream analyses complex. Here we present TCUP, a new computational method for typing and characterizing bacteria using proteomics data from bottom-up tandem MS. TCUP compares the generated protein sequence data to reference databases and automatically finds peptides suitable for characterization of taxonomic composition and identification of expressed antimicrobial resistance genes. TCUP was evaluated using several clinically relevant bacterial species (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae), using both simulated data generated by in silico peptide digestion and experimental proteomics data generated by liquid chromatography-tandem mass spectrometry (MS/MS). The results showed that TCUP performs correct peptide classifications at rates between 90.3 and 98.5% at the species level. The method was also able to estimate the relative abundances of individual species in mixed cultures. Furthermore, TCUP could identify expressed beta-lactamases in an extended spectrum beta-lactamase-producing (ESBL) E.coli strain, even when the strain was cultivated in the absence of antibiotics. Finally, TCUP is computationally efficient, easy to integrate in existing bioinformatics workflows, and freely available under an open source license for both Windows and Linux environments.

Author

Fredrik Boulund

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

R. Karlsson

University of Gothenburg

Nanoxis Consulting AB

Sahlgrenska University Hospital

L. Gonzales-Siles

University of Gothenburg

Anna Johnning

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

University of Gothenburg

N. Karami

University of Gothenburg

O. Al-Bayati

Sahlgrenska University Hospital

C. Ahren

University of Gothenburg

E. R. B. Moore

University of Gothenburg

Sahlgrenska University Hospital

Erik Kristiansson

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Molecular and Cellular Proteomics

1535-9476 (ISSN) 1535-9484 (eISSN)

Vol. 16 6 1052-1063

Subject Categories

Microbiology

DOI

10.1074/mcp.M116.061721

More information

Latest update

9/6/2018 1