To the top

Page Manager: Webmaster
Last update: 9/11/2012 3:13 PM

Tell a friend about this page
Print version

Mass Spectrometry Proteot… - University of Gothenburg, Sweden Till startsida
To content Read more about how we use cookies on

Mass Spectrometry Proteotyping for detection, identification characterization and diagnostics of infectious bacteria in clinical respiratory-tract samples

Authors Lucia Gonzales-Siles
Roger Karlsson
C van Houten
L Bont
Fredrik Boulund
Erik Kristiansson
Edward R.B. Moore
Published in 11th International Meeting on Microbial Epidemiological Markers (IMMEM XI) 9 - 12 March 2016, Estoril, Portugal
Publication year 2016
Published at Department of Mathematical Sciences, Mathematical Statistics
Institute of Biomedicine, Department of Infectious Medicine
Language en
Subject categories Functional genomics, Molecular biology, Microbiology, Biological Systematics


Background. Lower respiratory tract infection (LRTI) is the leading cause of childhood deaths in most developing countries and the world (?) and are the most common causes of hospital and out-patient visits within the EU, comprising 1 of 3 admissions annually. In general, the over-prescription and use of broad-spectrum antibiotics are common practices that lead to the evolution and development of resistance in infectious bacteria and will lead to loss of time and resources in patient handling and adverse patient outcomes. Conventional approaches have depended upon cultivation of bacteria with subsequent testing for antibiotic sensitivity. Therefore, reliable and time-effective microbiological diagnostics are essential for more effective treatment of respiratory infections. In this project, we apply state-of-the-art proteomics techniques for identifications of pathogens and antibiotic resistance from clinical samples, without prior cultivation. Material and methods. Nasopharyngeal swab samples were collected, in commercial Amies medium supplemented with 5x STGG, as a part of the EU-TAILORED-Treatment project ( Samples were stored at -20°C until analyses. Different protocols for removal of human cells and mucus were tested, including non-ionic detergents, i.e., Igepal, Saponin, Urea-Chaps, as well as cytolysis. Samples were concentrated and analyzed by ‘proteotyping’ (1), using a Lipid-based Protein Immobilization (LPITM) technology (WO2006068619), in which intact bacterial cells or cell fractions are bound to a surface. Peptides were generated, using enzymatic digestion, and then separated and analyzed, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The mass spectra profiles were compared to a database of reference peptide sequences, consisting of all complete genomes of the NCBI Reference Sequence (RefSeq) Database. Results were confirmed by standard microbiology, including cultivation of bacteria in selective media, MALDI-TOF MS analyses and qPCR. Results. Proteotyping applied to clinical samples demonstrated that the number of viable bacteria and detected proteins determined were ten-times higher when nasal swabs were stored in Amies media supplemented with STGG 5X media compared to Amies media without STGG, after 1 and 2 months of storage at -70C. Among the different protocols tested to remove human biomaterial, all treatments proved effective to varying degrees, although the Igepal treatment was able to retain the highest number of discriminatory peptides. Using proteotyping, we were able to identify the pathogenic bacteria directly within clinical samples (nasopharyngeal and nasal swabs) that had been identified to be positive for respiratory infectious bacteria by standard methodologies at clinical bacteriology laboratories at Sahlgrenska University Hospital (Sweden) or Universitair Medisch Centrum Utrecht (Netherlands). Conclusions. Proteotyping of infectious bacteria, using tandem LC-MS/MS enabled the differentiation and identification of infectious bacteria in clinical samples from LRTIs. It has high levels of resolution and highly reproducible detection of protein biomarkers. Proteotyping identified biomarkers for species- and sub-species-level strain discrimination and antibiotic resistance, all from single MS analyses. 1) Karlsson et al., 2015. Syst. Appl. Microbiol. 38 :246-257.

Page Manager: Webmaster|Last update: 9/11/2012

The University of Gothenburg uses cookies to provide you with the best possible user experience. By continuing on this website, you approve of our use of cookies.  What are cookies?