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Proteotyping of Streptococcus pneumoniae, using tandem mass spectrometry for identification of biomarkers for species and strain differentiation

Hedvig E Jakobsson ; Lucia Gonzales-Siles ; Roger Karlsson ; Fredrik Boulund (Institutionen för matematiska vetenskaper, matematisk statistik) ; Francisco Salvà-Serra ; Erik Kristiansson (Institutionen för matematiska vetenskaper, Tillämpad matematik och statistik) ; Edward R.B. Moore
11th International Meeting on Microbial Epidemiological Markers (IMMEM XI) 9 - 12 March 2016, Estoril, Portugal (2016)
[Konferensbidrag, övrigt]

Background. Streptococcus pneumoniae (pneumococcus) is the leading cause of community-acquired pneumonia and a major cause of morbidity and mortality worldwide. S. pneumoniae is phenotypically and genotypically similar to commensal species of the upper respiratory tract of the Streptococcus mitis-Group (viridans streptococci), S. mitis, S. oralis, and S. pseudopneumoniae, causing problems of identification in clinical microbiology laboratories. We have applied state-of-the-art proteomics techniques for Streptococcus spp. proteotyping; to detecting and characterizing expressed protein biomarkers for species-level identification, determination of antibiotic resistance and virulence biomarkers and strain typing for epidemiological analyses. Material and methods. The proteins of intact bacteria or cell fractions are bound to a membrane surface, using patented (WO2006068619) Lipid-based Protein Immobilization (LPITM) technology. Peptides are generated from the bound proteins, using enzymatic digestion, separated and analyzed, using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The mass spectra profiles are compared to a database of reference peptide sequences. Subsequently, the identified peptides are compared to a database of reference genome sequences, all complete genomes of the NCBI Reference Sequence (RefSeq) Database. In this study, the type strains of the close-related mitis complex species S. pneumoniae (CCUG 28588T), S. mitis (CCUG 31611T), S. oralis (CCUG 13229T), S. psedopneumoniae (CCUG 49455T) and the more distantly-related S. pyogenes (CCUG 4207T) were analysed individually and in mixtures, to demonstrate proteotyping capability and differentiate closely related species,. Additionally, mixes containing different S. pneumoniae strains were analyzed. Results. Using proteotyping protocols, it was possible to detect and correctly identify S. pneumoniae from the closely related bacterial species, S. mitis, S. oralis S. psedopneumoniae and S. pyogenes, as well as different strains of S. pneumoniae by identification of unique discriminatory peptides. For successful proteotyping,a comprehensive and accurate genomic database is the key to obtaining reliable proteotyping data. Importantly, because of questionable classifications of sequenced genomes in the public databases, before incorporation of reference genomic sequence data for proteotyping, the genome sequences should be verified and confirmed for accurate classifications. Furthermore, it is also essential to include all relevant species with as many as 25 genomes in order to obtain a comprehensive coverage of coding sequences for accurate peptide matching and to be able to discriminate between the most closely related species. In this study, all genomes of the S. mitis-Group in the database were analyzed, using Average Nucleotide Identity Blast (ANIb) and S. mitis-Group strains that cannot be identified to the species level, using standard genotypic and phenotypic approaches, where characterized by proteotyping and whole genome sequencing to describe their taxonomy and to improve the database matching. Conclusions: Proteotyping, using LC-MS/MS, enabled the differentiation and identification of pneumococcus from its closely related species and sub-species-level strain discrimination, all from single MS analyses. The whole method will enhance the identification and characterization of microorganisms, allowing high-resolution discrimination of closely related species through the confident identification of new biomarkers, ultimately for cultivation-independent application to the analyses of clinical samples.

Invited presentation at the 11th International Meeting on Microbial Epidemiology Markers (IMMEM11). Navigating Microbial Genomes: Insights from the Next Generation. Estoril, PT; 20160309-12.

Denna post skapades 2017-03-31.
CPL Pubid: 248766


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)
Institutionen för matematiska vetenskaper, Tillämpad matematik och statistikInstitutionen för matematiska vetenskaper, Tillämpad matematik och statistik (GU)


Biokemi och molekylärbiologi

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