ArticleBMC microbiology2013
Discrimination of multilocus sequence typing-based Campylobacter jejuni subgroups by MALDI-TOF mass spectrometry.
Article in BMC microbiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 50 citations in OpenAlex.
- Comparative Analysis ofPathogens (Basel, Switzerland) · 2023Article
- Proteotyping ofMicroorganisms · 2023Article
- Use of MALDI-TOF MS to Discriminate between Aflatoxin B1-Producing and Non-Producing Strains ofMolecules (Basel, Switzerland) · 2022Article
- Investigation of MALDI-TOF Mass Spectrometry for Assessing the Molecular Diversity ofDiagnostics (Basel, Switzerland) · 2021Article
- MALDI-TOF Mass Spectrometry and Specific Biomarkers: Potential New Key for Swift Identification of Antimicrobial Resistance in Foodborne Pathogens.Microorganisms · 2019Review
- Differentiation ofEuropean journal of microbiology & immunology · 2019Article
- Proteotyping as alternate typing method to differentiate Campylobacter coli clades.Scientific reports · 2019Article
- Proteotyping ofFrontiers in microbiology · 2019Article
- Typing and Species Identification of Clinical Klebsiella Isolates by Fourier Transform Infrared Spectroscopy and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.Journal of clinical microbiology · 2018Article
- Subtyping of Campylobacter jejuni ssp. doylei Isolates Using Mass Spectrometry-based PhyloProteomics (MSPP).Journal of visualized experiments : JoVE · 2016Article
- Association ofEuropean journal of microbiology & immunology · 2016Article
- Recent development of mass spectrometry and proteomics applications in identification and typing of bacteria.Proteomics. Clinical applications · 2016Review
- Principal component analysis of MALDI TOF MS mass spectra separates M. abscessus (sensu stricto) from M. massiliense isolates.BMC microbiology · 2016Article
- Personalized Proteomics: The Future of Precision Medicine.Proteomes · 2016Article
- A Designed Experiments Approach to Optimizing MALDI-TOF MS Spectrum Processing Parameters Enhances Detection of Antibiotic Resistance in Campylobacter jejuni.Frontiers in microbiology · 2016Article
- Mass Spectrometry-based PhyloProteomics (MSPP): A novel microbial typing Method.Scientific reports · 2015Article
- Evaluation of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for identification and clustering of Neisseria gonorrhoeae.BMC microbiology · 2015Article
- Emerging rapid resistance testing methods for clinical microbiology laboratories and their potential impact on patient management.BioMed research international · 2014Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCampylobacter jejuni, the most common bacterial pathogen causing gastroenteritis, shows a wide genetic diversity. Previously, we demonstrated by the combination of multi locus sequence typing (MLST)-based UPGMA-clustering and analysis of 16 genetic markers that twelve different C. jejuni subgroups can be distinguished. Among these are two prominent subgroups. The first subgroup contains the majority of hyperinvasive strains and is characterized by a dimeric form of the chemotaxis-receptor Tlp7(m+c). The second has an extended amino acid metabolism and is characterized by the presence of a periplasmic asparaginase (ansB) and gamma-glutamyl-transpeptidase (ggt).
resultsPhyloproteomic principal component analysis (PCA) hierarchical clustering of MALDI-TOF based intact cell mass spectrometry (ICMS) spectra was able to group particular C. jejuni subgroups of phylogenetic related isolates in distinct clusters. Especially the aforementioned Tlp7(m+c)(+) and ansB+/ ggt+ subgroups could be discriminated by PCA. Overlay of ICMS spectra of all isolates led to the identification of characteristic biomarker ions for these specific C. jejuni subgroups. Thus, mass peak shifts can be used to identify the C. jejuni subgroup with an extended amino acid metabolism.
conclusionsAlthough the PCA hierarchical clustering of ICMS-spectra groups the tested isolates into a different order as compared to MLST-based UPGMA-clustering, the isolates of the indicator-groups form predominantly coherent clusters. These clusters reflect phenotypic aspects better than phylogenetic clustering, indicating that the genes corresponding to the biomarker ions are phylogenetically coupled to the tested marker genes. Thus, PCA clustering could be an additional tool for analyzing the relatedness of bacterial isolates.
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