2DDB - a bioinformatics solution for analysis of quantitative proteomics data

A quantitative Streptococcus pyogenes-human protein-protein interaction map reveals localization of opsonizing antibodies

A fundamental challenge in medical microbiology is to characterize the dynamic protein–protein interaction networks formed at the host–pathogen interface. Here, we generate a quantitative interaction map between the significant human pathogen, Streptococcus pyogenes, and proteins from human saliva and plasma obtained via complementary affinity-purification and bacterial-surface centered enrichment strategies and quantitative mass spectrometry. Perturbation of the network using immunoglobulin protease cleavage, mixtures of different concentrations of saliva and plasma, and different S. pyogenes serotypes and their isogenic mutants, reveals how changing microenvironments alter the interconnectivity of the interaction map. The importance of host immunoglobulins for the interaction with human complement proteins is demonstrated and potential protective epitopes of importance for phagocytosis of S. pyogenes cells are localized. The interaction map confirms several previously described protein–protein interactions; however, it also reveals a multitude of additional interactions, with possible implications for host–pathogen interactions involving other bacterial species.

Characterizing host-pathogen protein interactions can help elucidate the molecular basis of bacterial infections. Here, the authors use an integrative proteomics approach to generate a quantitative map of protein interactions between Streptococcus pyogenes and human saliva and plasma.

The Human Salivary Antimicrobial Peptide Profile according to the Oral Microbiota in Health, Periodontitis and Smoking

Antimicrobial peptides (AMPs) are a diverse family of peptides that defend the mucosal surfaces of the oral cavity and other locations. Many AMPs have multiple functions and properties that influence aspects of innate defense and colonization by microorganisms. The human oral cavity is home to the second-most diverse microbiome, and the health of the mouth is influenced by the presence of these bacteria as well as by extrinsic factors such as periodontitis and smoking. This study hypothesized that the AMP profile is different in the presence of extrinsic factors and that this would also be reflected in the bacteria present. The AMP profile was analyzed by quantitative selected-reaction-monitoring mass spectrometry analysis and 40 bacterial species were quantified by DNA-DNA hybridization in saliva donated by 41 individuals. Periodontal status was assessed through dental examination and smoking status through medical charting. Periodontal health (in nonsmokers) was associated with a higher abundance of ribonuclease 7, protachykinin 1, β-defensin 128, lipocalin 1, bactericidal permeability-increasing protein fold-containing family B member 3, and bone-marrow proteoglycan. Nonsmoking periodontal disease was associated with an abundance of neutrophil defensin 1 and cathelicidin. However, 7 AMPs were overabundant in periodontal disease in smokers: adrenomedullin, eosinophil peroxidase, 3 different histones, myeloperoxidase, and neutrophil defensin 1. There were no differentially abundant AMPs in smokers versus nonsmokers with periodontal health. Correlation network inference of healthy nonsmokers, healthy smokers, nonsmoking periodontitis, or smoking periodontitis donors demonstrated very different networks growing in complexity with increasing numbers of stressors. The study highlights the importance of the interaction between the oral cavity and its resident microbiota and how this may be influenced by periodontal disease and smoking.

Saliva-Induced Clotting Captures Streptococci: Novel Roles for Coagulation and Fibrinolysis in Host Defense and Immune Evasion

Streptococcal pharyngitis is among the most common bacterial infections, but the molecular mechanisms involved remain poorly understood. Here we investigate the interactions among three major players in streptococcal pharyngitis: streptococci, plasma, and saliva. We find that saliva activates the plasma coagulation system through both the extrinsic and the intrinsic pathways, entrapping the bacteria in fibrin clots. The bacteria escape the clots by activating host plasminogen. Our results identify a potential function for the intrinsic pathway of coagulation in host defense and a corresponding role for fibrinolysis in streptococcal immune evasion.

Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics

The plasma proteome is highly dynamic and variable, composed of proteins derived from surrounding tissues and cells. To investigate the complex processes that control the composition of the plasma proteome, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in murine plasma. The strategy relies on the construction of a comprehensive protein tissue atlas from cells and highly vascularized organs using shotgun mass spectrometry. The protein tissue atlas was transformed to a spectral library for highly reproducible quantification of tissue-specific proteins directly in plasma using SWATH-like data-independent mass spectrometry analysis. We show that the method can determine drastic changes of tissue-specific protein profiles in blood plasma from mouse animal models with sepsis. The strategy can be extended to several other species advancing our understanding of the complex processes that contribute to the plasma proteome dynamics.

Sepsis can lead to multiple organ failure that could potentially be reflected by change in plasma protein abundance. Here the authors describe a proteomics strategy that allows the determination of plasma proteins tissue origin in a quantitative manner for use as biomarkers—illustrated in a mouse model of sepsis.

Quantitative proteogenomics of human pathogens using DIA-MS

The increasing number of bacterial genomes in combination with reproducible quantitative proteome measurements provides new opportunities to explore how genetic differences modulate proteome composition and virulence. It is challenging to combine genome and proteome data as the underlying genome influences the proteome. We present a strategy to facilitate the integration of genome data from several genetically similar bacterial strains with data-independent analysis mass spectrometry (DIA-MS) for rapid interrogation of the combined data sets. The strategy relies on the construction of a composite genome combining all genetic data in a compact format, which can accommodate the fusion with quantitative peptide and protein information determined via DIA-MS. We demonstrate the method by combining data sets from whole genome sequencing, shotgun MS and DIA-MS from 34 clinical isolates of Streptococcus pyogenes. The data structure allows for fast exploration of the data showing that undetected proteins are on average more amenable to amino acid substitution than expressed proteins. We identified several significantly differentially expressed proteins between invasive and non-invasive strains. The work underlines how integration of whole genome sequencing with accurately quantified proteomes can further advance the interpretation of the relationship between genomes, proteomes and virulence. This article is part of a Special Issue entitled: Computational Proteomics.

Targeted mass spectrometry analysis of neutrophil-derived proteins released during sepsis progression

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.

Functional and structural properties of a novel protein and virulence factor (Protein sHIP) in Streptococcus pyogenes

Streptococcus pyogenes is a significant bacterial pathogen in the human population. The importance of virulence factors for the survival and colonization of S. pyogenes is well established, and many of these factors are exposed to the extracellular environment, enabling bacterial interactions with the host. In the present study, we quantitatively analyzed and compared S. pyogenes proteins in the growth medium of a strain that is virulent to mice with a non-virulent strain. Particularly, one of these proteins was present at significantly higher levels in stationary growth medium from the virulent strain. We determined the three-dimensional structure of the protein that showed a unique tetrameric organization composed of four helix-loop-helix motifs. Affinity pull-down mass spectrometry analysis in human plasma demonstrated that the protein interacts with histidine-rich glycoprotein (HRG), and the name sHIP (streptococcal histidine-rich glycoprotein-interacting protein) is therefore proposed. HRG has antibacterial activity, and when challenged by HRG, sHIP was found to rescue S. pyogenes bacteria. This and the finding that patients with invasive S. pyogenes infection respond with antibody production against sHIP suggest a role for the protein in S. pyogenes pathogenesis.

GeneTopics--interpretation of gene sets via literature-driven topic models

Background

Annotation of a set of genes is often accomplished through comparison to a library of labelled gene sets such as biological processes or canonical pathways. However, this approach might fail if the employed libraries are not up to date with the latest research, don't capture relevant biological themes or are curated at a different level of granularity than is required to appropriately analyze the input gene set. At the same time, the vast biomedical literature offers an unstructured repository of the latest research findings that can be tapped to provide thematic sub-groupings for any input gene set.

Methods

Our proposed method relies on a gene-specific text corpus and extracts commonalities between documents in an unsupervised manner using a topic model approach. We automatically determine the number of topics summarizing the corpus and calculate a gene relevancy score for each topic allowing us to eliminate non-specific topics. As a result we obtain a set of literature topics in which each topic is associated with a subset of the input genes providing directly interpretable keywords and corresponding documents for literature research.

Results

We validate our method based on labelled gene sets from the KEGG metabolic pathway collection and the genetic association database (GAD) and show that the approach is able to detect topics consistent with the labelled annotation. Furthermore, we discuss the results on three different types of experimentally derived gene sets, (1) differentially expressed genes from a cardiac hypertrophy experiment in mice, (2) altered transcript abundance in human pancreatic beta cells, and (3) genes implicated by GWA studies to be associated with metabolite levels in a healthy population. In all three cases, we are able to replicate findings from the original papers in a quick and semi-automated manner.

Conclusions

Our approach provides a novel way of automatically generating meaningful annotations for gene sets that are directly tied to relevant articles in the literature. Extending a general topic model method, the approach introduced here establishes a workflow for the interpretation of gene sets generated from diverse experimental scenarios that can complement the classical approach of comparison to reference gene sets.

Surface proteins of group G Streptococcus in different phases of growth: patterns of production and implications for the host-bacteria relationship

Group G Streptococcus (GGS) is a human bacterial pathogen expressing surface proteins FOG and protein G (PG) which interact with several host defence systems, including the complement and contact systems. Selected reaction monitoring mass spectrometry, electron microscopy and protein binding assays were used to track the amounts of FOG and PG intracellularly and on the bacterial surface during different phases of growth. Large and increasing amounts of PG were present on the surface in the stationary growth phase, and this was due to de novo production. In contrast, the amount of FOG did not change substantially during this phase. Apart from PG, a number of housekeeping proteins also increased in abundance in the stationary phase. These results show that GGS protein production is active during the stationary phase and that the bacteria actively remodel their surface and enter a less pro-inflammatory state in this phase.

Proteome-wide selected reaction monitoring assays for the human pathogen Streptococcus pyogenes

Selected reaction monitoring mass spectrometry (SRM-MS) is a targeted proteomics technology used to identify and quantify proteins with high sensitivity, specificity and high reproducibility. Execution of SRM-MS relies on protein-specific SRM assays, a set of experimental parameters that requires considerable effort to develop. Here we present a proteome-wide SRM assay repository for the gram-positive human pathogen group A Streptococcus. Using a multi-layered approach we generated SRM assays for 10,412 distinct group A Streptococcus peptides followed by extensive testing of the selected reaction monitoring assays in >200 different group A Streptococcus protein pools. Based on the number of SRM assay observations we created a rule-based selected reaction monitoring assay-scoring model to select the most suitable assays per protein for a given cellular compartment and bacterial state. The resource described here represents an important tool for deciphering the group A Streptococcus proteome using selected reaction monitoring and we anticipate that concepts described here can be extended to other pathogens.

Selected reaction monitoring mass spectrometry (SRM-MS) can quantify dynamic changes in protein expression with high sensitivity. Karlsson et al. define optimal detection parameters for 10,412 distinct group A Streptococcus pyogenes peptides, which facilitates proteome-wide SRM-MS studies in this bacterium.

Characterization of released polypeptides during an interferon-γ-dependent antibacterial response in airway epithelial cells

When pathogenic bacteria breach the epithelial lining at mucosal surfaces, rapidly available innate immune mechanisms are critical to halt the infection. In the present study, we characterized the production of antibacterial polypeptides released by epithelial cells. IFN-γ, but neither TNF nor IL-1β alone, induced release of antibacterial activity to a cell culture medium, causing a lytic appearance of killed bacteria as revealed by electron microscopy. Addition of the protein streptococcal inhibitor of complement, derived from Streptococcus pyogenes, known for its ability to neutralize antimicrobial polypeptides (AMPs), reduced the antibacterial activity of the medium. Characterization of the antibacterial incubation medium using mass spectrometric approaches and ELISAs, displayed presence of several classical AMPs, antibacterial chemokines, as well as complement factors and proteases that may interfere with bacterial killing. Many were constitutively produced, that is, being released by cells incubated in a medium alone. While a combination of IFN-γ and TNF did not increase bacterial killing, the presence of TNF boosted the amounts and detectable number of AMPs, including antibacterial chemokines. However, the methods applied in the study failed to single out certain AMPs as critical mediators, but rather demonstrate the broad range of molecules involved. Since many AMPs are highly amphiphatic in nature (i.e., cationic and hydrophobic), it is possible that difficulties in optimizing recovery present limitations in the context investigated. The findings demonstrate that epithelial cells have a constitutive production of AMPs and that IFN-γ is an important inducer of an antibacterial response in which is likely to be a critical part of the innate host defense against pathogenic bacteria at mucosal surfaces.

Automated workflow for large-scale selected reaction monitoring experiments

Targeted proteomics allows researchers to study proteins of interest without being drowned in data from other, less interesting proteins or from redundant or uninformative peptides. While the technique is mostly used for smaller, focused studies, there are several reasons to conduct larger targeted experiments. Automated, highly robust software becomes more important in such experiments. In addition, larger experiments are carried out over longer periods of time, requiring strategies to handle the sometimes large shift in retention time often observed. We present a complete proof-of-principle software stack that automates most aspects of selected reaction monitoring workflows, a targeted proteomics technology. The software allows experiments to be easily designed and carried out. The steps automated are the generation of assays, generation of mass spectrometry driver files and methods files, and the import and analysis of the data. All data are normalized to a common retention time scale, the data are then scored using a novel score model, and the error is subsequently estimated. We also show that selected reaction monitoring can be used for label-free quantification. All data generated are stored in a relational database, and the growing resource further facilitates the design of new experiments. We apply the technology to a large-scale experiment studying how Streptococcus pyogenes remodels its proteome under stimulation of human plasma.

openBIS: a flexible framework for managing and analyzing complex data in biology research

Background

Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.

Results

We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.

Conclusions

openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

Streptococcus pyogenes in human plasma: adaptive mechanisms analyzed by mass spectrometry-based proteomics

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.

Identification of the active site of DS-epimerase 1 and requirement of N-glycosylation for enzyme function

Dermatan sulfate is a highly sulfated polysaccharide and has a variety of biological functions in development and disease. Iduronic acid domains in dermatan sulfate, which are formed by the action of two DS-epimerases, have a key role in mediating these functions. We have identified the catalytic site and three putative catalytic residues in DS-epimerase 1, His-205, Tyr-261, and His-450, by tertiary structure modeling and amino acid conservation to heparinase II. These residues were systematically mutated to alanine or more conserved residues, which resulted in complete loss of epimerase activity. Based on these data and the close relationship between lyase and epimerase reactions, we propose a model where His-450 functions as a general base abstracting the C5 proton from glucuronic acid. Subsequent cleavage of the glycosidic linkage by Tyr-261 generates a 4,5-unsaturated hexuronic intermediate, which is protonated at the C5 carbon by His-205 from the side of the sugar plane opposite to the side of previous proton abstraction. Concomitant recreation of the glycosidic linkage ends the reaction, generating iduronic acid. In addition, we show that proper N-glycosylation of DS-epimerase 1 is required for enzyme activity. This study represents the first description of the structural basis for epimerization by a glycosaminoglycan epimerase.

Two-dimensional separation of human plasma proteins using iterative free-flow electrophoresis

Blood plasma is the most complex human-derived proteome, containing other tissue proteomes as subsets. This proteome has only been partially characterized due to the extremely wide dynamic range of the plasma proteins of more than ten orders of magnitude. Thus, the reduction in sample complexity prior to mass spectrometric analysis is particularly important and alternative separation methodologies are required to more effectively mine the lower abundant plasma proteins. Here, we demonstrated a novel separation approach using 2-D free-flow electrophoresis (FFE) separating proteins and peptides in solution according to their pI prior to LC-MS/MS. We used the combination of sequential protein and peptide separation by first separating the plasma proteins into specific FFE fractions. Tryptic digests of the separated proteins were generated and subsequently separated using FFE. The protein separation medium was optimized to segregate albumin into specific fractions containing only few other proteins. An optimization of throughput for the protein separation reduced the separation time of 1 mL of plasma to approximately 3 h providing sufficient material for digestion and the subsequent peptide separation. Our approach revealed low-abundant proteins (e.g., L-selectin at 17 ng/mL and vascular endothelial-cadherin precursor at 30 ng/mL) and several tissue leakage products, thus providing a powerful orthogonal separation step in the proteomics workflow.

Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology

Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01.

Optimized Peptide Separation and Identification for Mass Spectrometry Based Proteomics via Free-Flow Electrophoresis

Multidimensional LC-MS based shotgun proteomics experiments at the peptide level have traditionally been carried out by ion exchange in the first dimension and reversed-phase liquid chromatography in the second. Recently, it has been shown that isoelectric focusing (IEF) is an interesting alternative approach to ion exchange separation of peptides in the first dimension. Here we present an improved protocol for peptide separation by continuous free-flow electrophoresis (FFE) as the first dimension in a two-dimensional peptide separation work flow. By the use of a flat pI gradient and a mannitol and urea based separation media we were able to perform high-throughput proteome analysis with improved interfacing between FFE and RPLC-MS/MS. The developed protocol was applied to a cytosolic fraction from Schneider S2 cells from Drosophila melanogaster, resulting in the identification of more than 10,000 unique peptides with high probability. To improve the accuracy of the peptide identification following FFE-IEF we incorporated the pI information as an additional parameter into a statistical model for discrimination between correct and incorrect peptide assignments to MS/MS spectra.