Dataset Information

Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics.

ABSTRACT: 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 ?-lactamases in an extended spectrum ?-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.

SUBMITTER: Boulund F

PROVIDER: S-EPMC5461537 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics.

Boulund Fredrik F Karlsson Roger R Gonzales-Siles Lucia L Johnning Anna A Karami Nahid N Al-Bayati Omar O Åhrén Christina C Moore Edward R B ERB Kristiansson Erik E

Molecular & cellular proteomics : MCP 20170418 6

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. Her ...[more]

PMID: 28420677

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Project description:The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Project description:Two-dimensional (2D) liquid chromatography (LC)-tandem mass spectrometry (MS/MS) are typically employed for deep bottom-up proteomics, and the state-of-the-art 2D-LC-MS/MS has approached over 8000 protein identifications (IDs) from mammalian cell lines or tissues in 1-3 days of mass spectrometer time. Capillary zone electrophoresis (CZE)-MS/MS has been suggested as an alternative to LC-MS/MS for bottom-up proteomics. CZE-MS/MS and LC-MS/MS are complementary in protein/peptide ID from complex proteome digests because CZE and LC are orthogonal for peptide separation. In addition, the migration time of peptides from CZE-MS can be predicted accurately, which is invaluable for evaluating the confidence of peptide ID from the database search and even guiding the database search. However, the number of protein IDs from complex proteomes using CZE-MS/MS is still much lower than the state of the art using 2D-LC-MS/MS. In this work, for the first time, we established a strong cation exchange (SCX)-reversed phase LC (RPLC)-CZE-MS/MS platform for deep bottom-up proteomics. The platform identified around 8200 protein groups and 65,000 unique peptides from a mouse brain proteome digest in 70 h. The data represents the largest bottom-up proteomics dataset using CZE-MS/MS and provides a valuable resource for further improving the tool for prediction of peptide migration time in CZE. The peak capacity of the orthogonal SCX-RPLC-CZE platform was estimated to be around 7000. SCX-RPLC-CZE-MS/MS produced comparable numbers of protein and peptide IDs with 2D-LC-MS/MS (8200 vs. 8900 protein groups, 65,000 vs. 70,000 unique peptides) from the mouse brain proteome digest using comparable instrument time. This is the first time that CZE-MS/MS showed its capability to approach comparable performance to the state-of-the-art 2D-LC-MS/MS for deep proteomic sequencing. SCX-RPLC-CZE-MS/MS and 2D-LC-MS/MS showed good complementarity in protein and peptide IDs and combining those two methods improved the number of protein group and unique peptide IDs by nearly 10% and over 40%, respectively, compared with 2D-LC-MS/MS alone.

Dataset Information

Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics.

Publications

Typing and Characterization of Bacteria Using Bottom-up Tandem Mass Spectrometry Proteomics.

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