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
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
R. Karlsson
Goteborgs Universitet
Nanoxis Consulting AB
Sahlgrenska Universitetssjukhuset
L. Gonzales-Siles
Goteborgs Universitet
Goteborgs universitet, Institutionen for biomedicin
Anna Johnning
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
N. Karami
Goteborgs Universitet
Goteborgs universitet, Institutionen for biomedicin
O. Al-Bayati
Sahlgrenska Universitetssjukhuset
C. Ahren
Goteborgs Universitet
Goteborgs universitet, Institutionen for biomedicin
E. R. B. Moore
Goteborgs Universitet
Goteborgs universitet, Institutionen for biomedicin
Sahlgrenska Universitetssjukhuset
Erik Kristiansson
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Molecular and Cellular Proteomics
1535-9476 (ISSN) 1535-9484 (eISSN)
Vol. 16 6 1052-1063Subject Categories (SSIF 2011)
Microbiology
DOI
10.1074/mcp.M116.061721