Streptococcus pyogenes in human plasma: adaptive mechanisms analyzed by mass spectrometry-based proteomics.
ABSTRACT Streptococcus pyogenes is a major bacterial pathogen and a potent inducer of inflammation causing plasma leakage at the site of infection. A combination of label-free quantitative mass spectrometry-based proteomics strategies were used to measure how the intracellular proteome homeostasis of S. pyogenes is influenced by the presence of human plasma, identifying and quantifying 842 proteins. In plasma the bacterium modifies its production of 213 proteins, and the most pronounced change was the complete down-regulation of proteins required for fatty acid biosynthesis. Fatty acids are transported by albumin (HSA) in plasma. S. pyogenes expresses HSA-binding surface proteins, and HSA carrying fatty acids reduced the amount of fatty acid biosynthesis proteins to the same extent as plasma. The results clarify the function of HSA-binding proteins in S. pyogenes and underline the power of the quantitative mass spectrometry strategy used here to investigate bacterial adaptation to a given environment.
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ABSTRACT: Streptococcus pyogenes is a major human bacterial pathogen responsible for severe and invasive disease associated with high mortality rates. The bacterium interacts with several human blood plasma proteins and clarifying these interactions and their biological consequences will help to explain the progression from mild to severe infections. In this study, we used a combination of mass spectrometry (MS) based techniques to comprehensively quantify the components of the S. pyogenes-plasma protein interaction network. From an initial list of 181 interacting human plasma proteins defined using liquid chromatography (LC)-MS/MS analysis we further subdivided the interacting protein list using selected reaction monitoring (SRM) depending on the level of enrichment and protein concentration on the bacterial surface. The combination of MS methods revealed several previously characterized interactions between the S. pyogenes surface and human plasma along with many more, so far uncharacterised, possible plasma protein interactions with S. pyogenes. In follow-up experiments, the combination of MS techniques was applied to study differences in protein binding to a S. pyogenes wild type strain and an isogenic mutant lacking several important virulence factors, and a unique pair of invasive and non-invasive S. pyogenes isolates from the same patient. Comparing the plasma protein-binding properties of the wild type and the mutant and the invasive and non-invasive S. pyogenes bacteria revealed considerable differences, underlining the significance of these protein interactions. The results also demonstrate the power of the developed mass spectrometry method to investigate host-microbial relationships with a large proteomics depth and high quantitative accuracy.Molecular BioSystems 02/2014; · 3.35 Impact Factor
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ABSTRACT: Early diagnosis of severe infectious diseases is essential for timely implementation of lifesaving therapies. In a search for novel biomarkers in sepsis diagnosis we focused on polymorphonuclear neutrophils (PMNs). Notably, PMNs have their protein cargo readily stored in granules and following systemic stimulation an immediate increase of neutrophil-borne proteins can be observed into the circulation of sepsis patients. We applied a combination of mass spectrometry (MS) based approaches, LC-MS/MS and selected reaction monitoring (SRM), to characterise and quantify the neutrophil proteome in healthy or disease conditions. With this approach we identified a neutrophil-derived protein abundance pattern in blood plasma consisting of 20 proteins that can be used as a protein signature for severe infectious diseases. Our results also show that SRM is highly sensitive, specific, and reproducible and, thus, a promising technology to study a complex, dynamic and multifactorial disease such as sepsis.Thrombosis and Haemostasis 08/2014; 112(5). · 5.76 Impact Factor
- Nature Biotechnology 03/2014; 32(3):219-23. · 39.08 Impact Factor