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Comparative Analysis of Two Helicobacter pylori Strains using Genomics and Mass Spectrometry-Based Proteomics

Overview of attention for article published in Frontiers in Microbiology, November 2016
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Title
Comparative Analysis of Two Helicobacter pylori Strains using Genomics and Mass Spectrometry-Based Proteomics
Published in
Frontiers in Microbiology, November 2016
DOI 10.3389/fmicb.2016.01757
Pubmed ID
Authors

Roger Karlsson, Kaisa Thorell, Shaghayegh Hosseini, Diarmuid Kenny, Carina Sihlbom, Åsa Sjöling, Anders Karlsson, Intawat Nookaew

Abstract

Helicobacter pylori, a gastroenteric pathogen believed to have co-evolved with humans over 100,000 years, shows significant genetic variability. This motivates the study of different H. pylori strains and the diseases they cause in order to identify determinants for disease evolution. In this study, we used proteomics tools to compare two H. pylori strains. Nic25_A was isolated in Nicaragua from a patient with intestinal metaplasia, and P12 was isolated in Europe from a patient with duodenal ulcers. Differences in the abundance of surface proteins between the two strains were determined with two mass spectrometry-based methods, label-free quantification (MaxQuant) or the use of tandem mass tags (TMT). Each approach used a lipid-based protein immobilization (LPI(TM)) technique to enrich peptides of surface proteins. Using the MaxQuant software, we found 52 proteins that differed significantly in abundance between the two strains (up- or downregulated by a factor of 1.5); with TMT, we found 18 proteins that differed in abundance between the strains. Strain P12 had a higher abundance of proteins encoded by the cag pathogenicity island, while levels of the acid response regulator ArsR and its regulatory targets (KatA, AmiE, and proteins involved in urease production) were higher in strain Nic25_A. Our results show that differences in protein abundance between H. pylori strains can be detected with proteomic approaches; this could have important implications for the study of disease progression.

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The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 38%
Student > Master 3 14%
Student > Bachelor 2 10%
Professor > Associate Professor 2 10%
Librarian 1 5%
Other 1 5%
Unknown 4 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 33%
Biochemistry, Genetics and Molecular Biology 3 14%
Computer Science 2 10%
Engineering 2 10%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 5 24%