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

Quantification of Adaptive Immune Responses Against Protein-Binding Interfaces in the Streptococcal M1 Protein

Bacterial or viral antigens can contain subdominant protein regions that elicit weak antibody responses upon vaccination or infection although there is accumulating evidence that antibody responses against subdominant regions can enhance the protective immune response. One proposed mechanism for subdominant protein regions is the binding of host proteins that prevent antibody production against epitopes hidden within the protein binding interfaces. Here, we used affinity purification combined with quantitative mass spectrometry (AP-MS) to examine the level of competition between antigen-specific antibodies and host-pathogen protein interaction networks using the M1 protein from Streptococcus pyogenes as a model system. As most humans have circulating antibodies against the M1 protein, we first used AP-MS to show that the M1 protein interspecies protein network formed with human plasma proteins is largely conserved in naïve mice. Immunizing mice with the M1 protein generated a time-dependent increase of anti-M1 antibodies. AP-MS analysis comparing the composition of the M1-plasma protein network from naïve and immunized mice showed significant enrichment of 292 IgG peptides associated with 56 IgG chains in the immune mice. Despite the significant increase of bound IgGs, the levels of interacting plasma proteins were not significantly reduced in the immune mice. The results indicate that the antigen-specific polyclonal IgG against the M1 protein primarily targets epitopes outside the other plasma protein binding interfaces. In conclusion, this study demonstrates that AP-MS is a promising strategy to determine the relationship between antigen-specific antibodies and host-pathogen interaction networks that could be used to define subdominant protein regions of relevance for vaccine development.

Procoagulant Activity of Blood and Microvesicles Is Disturbed by Pneumococcal Pneumolysin, Which Interacts with Coagulation Factors

The coagulation and contact systems are parts of the innate immune system as they prevent bleeding and dissemination of pathogens and also contribute to microbial killing by inflammatory reactions and the release of antimicrobial peptides. Here, we investigated the influence of Streptococcus pneumoniae on the coagulation and contact system. S. pneumoniae (pneumococci), but no other investigated streptococcal species, impairs coagulation of blood by autolysis and release of pneumolysin. Defective blood coagulation results from the lysis of tissue factor-producing mononuclear cells and their procoagulant microvesicles, which are the main trigger for blood coagulation during sepsis. In addition, pneumolysin binds coagulation and contact system factors, but this does not result in activation. Thus, pneumococci modulate activation of the coagulation system by releasing pneumolysin, which could potentiate lung injury during pneumonia.

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

Streptococcus pyogenes Forms Serotype- and Local Environment-Dependent Interspecies Protein Complexes

Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surfaces of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes forms serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modeling of one of the M proteins, M28, revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by the binding of monomeric IgA and C4b-binding protein (C4BP). This indicates that an upsurge of C4BP in the local microenvironment due to damage to the mucosal membrane drives the binding of C4BP and monomeric IgA to M28. These results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat human immune surveillance during both mucosal and systemic infections.

IMPORTANCEStreptococcus pyogenes (group A Streptococcus [GAS]), is a human-specific Gram-positive bacterium. Each year, the bacterium affects 700 million people globally, leading to 160,000 deaths. The clinical manifestations of S. pyogenes are diverse, ranging from mild and common infections like tonsillitis and impetigo to life-threatening systemic conditions such as sepsis and necrotizing fasciitis. S. pyogenes expresses multiple virulence factors on its surface to localize and initiate infections in humans. Among all these expressed virulence factors, the M protein is the most important antigen. In this study, we perform an in-depth characterization of the human protein interactions formed around one of the foremost human pathogens. This strategy allowed us to decipher the protein interaction networks around different S. pyogenes strains on a global scale and to compare and visualize how such interactions are mediated by M proteins.

Protocol for Proteome Analysis of Group A Streptococcus

This chapter describes a protocol for the separation of different cell fractions from Streptococcus pyogenes and sample preparation for analysis by mass spectrometry. The presented approach can be used for the analysis of all subcellular proteomes from S. pyogenes and enables identification of drug or vaccine targets within a certain cellular fraction. Here, proteins integral to the plasma membrane are of particular interest for the development of new antimicrobial therapies.

Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions

A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.

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.

Rapid determination of quaternary protein structures in complex biological samples

The understanding of complex biological systems is still hampered by limited knowledge of biologically relevant quaternary protein structures. Here, we demonstrate quaternary structure determination in biological samples using a combination of chemical cross-linking, high-resolution mass spectrometry and high-accuracy protein structure modeling. This approach, termed targeted cross-linking mass spectrometry (TX-MS), relies on computational structural models to score sets of targeted cross-linked peptide signals acquired using a combination of mass spectrometry acquisition techniques. We demonstrate the utility of TX-MS by creating a high-resolution quaternary model of a 1.8 MDa protein complex composed of a pathogen surface protein and ten human plasma proteins. The model is based on a dense network of cross-link distance constraints obtained directly in a mixture of human plasma and live bacteria. These results demonstrate that TX-MS can increase the applicability of flexible backbone docking algorithms to large protein complexes by providing rich cross-link distance information from complex biological samples.

Protein structure determination in complex biological samples is still challenging. Here, the authors develop a computational modeling-guided cross-linking mass spectrometry method, obtaining a high-resolution model of a 1.8 MDa protein assembly from cross-links detected in a mixture of human plasma and bacteria.

Proteomic analysis at the sites of clinical infection with invasive Streptococcus pyogenes

Invasive Streptococcus pyogenes infections are rare, with often-unexplained severity. Prompt diagnosis is desirable, as deaths can occur rapidly following onset and there is an increased, but preventable, risk to contacts. Here, proteomic analyses of clinical samples from invasive human S. pyogenes infections were undertaken to determine if novel diagnostic targets could be detected, and to augment our understanding of disease pathogenesis. Fluid samples from 17 patients with confirmed invasive S. pyogenes infection (empyema, septic arthritis, necrotising fasciitis) were analysed by proteomics for streptococcal and human proteins; 16/17 samples had detectable S. pyogenes DNA. Nineteen unique S. pyogenes proteins were identified in just 6/17 samples, and 15 of these were found in a single pleural fluid sample including streptococcal inhibitor of complement, trigger factor, and phosphoglycerate kinase. In contrast, 469 human proteins were detected in patient fluids, 177 (38%) of which could be identified as neutrophil proteins, including alpha enolase and lactotransferrin which, together, were found in all 17 samples. Our data suggest that streptococcal proteins are difficult to detect in infected fluid samples. A vast array of human proteins associated with leukocyte activity are, however, present in samples that deserve further evaluation as potential biomarkers of infection.

Streptococcus pyogenes Infection and the Human Proteome with a Special Focus on the Immunoglobulin G-cleaving Enzyme IdeS

Infectious diseases are characterized by a complex interplay between host and pathogen, but how these interactions impact the host proteome is unclear. Here we applied a combined mass spectrometry-based proteomics strategy to investigate how the human proteome is transiently modified by the pathogen Streptococcus pyogenes, with a particular focus on bacterial cleavage of IgG in vivo. In invasive diseases, S. pyogenes evokes a massive host response in blood, whereas superficial diseases are characterized by a local leakage of several blood plasma proteins at the site of infection including IgG. S. pyogenes produces IdeS, a protease cleaving IgG in the lower hinge region and we find highly effective IdeS-cleavage of IgG in samples from local IgG poor microenvironments. The results show that IdeS contributes to the adaptation of S. pyogenes to its normal ecological niches. Additionally, the work identifies novel clinical opportunities for in vivo pathogen detection.

Comprehensive Mass Spectrometric Survey of Streptococcus pyogenes Subcellular Proteomes

Streptococcus pyogenes is a major global health burden causing a wide variety of diseases. Because a vaccine against this bacterium is still lacking, vaccine candidates or antimicrobial therapies are urgently needed. Here we use an invasive and clinically relevant streptococcal M1 serotype to characterize the bacterial proteome in-depth. An elaborate fractionation technique is employed to separate the different cell fractions, followed by shotgun mass-spectrometry analysis, allowing us to confirm the expression of nearly two-thirds (1022) of the 1690 open reading frames predicted for the streptococcal M1 reference proteome. In contrast with other studies, we present the entire isolated membrane proteome, which opens up a whole new source for drug targets. We show both the unique and most prevalent proteins for each cellular fraction and analyze the presence of predicted cell-wall-anchored proteins and lipoproteins. With our approach, we also identify a variety of novel proteins whose presence has not been reported in previous proteome studies. Proteins of interest, potential virulence factors, and drug or vaccine targets are discussed for each cellular fraction. Overall, the results of this work represent the so-far widest proteomic approach to characterize the protein composition and localization in S. pyogenes.

Quantitative multiplexed proteomics of Taenia solium cysts obtained from the skeletal muscle and central nervous system of pigs

In human and porcine cysticercosis caused by the tapeworm Taenia solium, the larval stage (cysts) can infest several tissues including the central nervous system (CNS) and the skeletal muscles (SM). The cyst’s proteomics changes associated with the tissue localization in the host tissues have been poorly studied. Quantitative multiplexed proteomics has the power to evaluate global proteome changes in response to different conditions. Here, using a TMT-multiplexed strategy we identified and quantified over 4,200 proteins in cysts obtained from the SM and CNS of pigs, of which 891 were host proteins. To our knowledge, this is the most extensive intermixing of host and parasite proteins reported for tapeworm infections.Several antigens in cysticercosis, i.e., GP50, paramyosin and a calcium-binding protein were enriched in skeletal muscle cysts. Our results suggested the occurrence of tissue-enriched antigen that could be useful in the improvement of the immunodiagnosis for cysticercosis. Using several algorithms for epitope detection, we selected 42 highly antigenic proteins enriched for each tissue localization of the cysts. Taking into account the fold changes and the antigen/epitope contents, we selected 10 proteins and produced synthetic peptides from the best epitopes. Nine peptides were recognized by serum antibodies of cysticercotic pigs, suggesting that those peptides are antigens. Mixtures of peptides derived from SM and CNS cysts yielded better results than mixtures of peptides derived from a single tissue location, however the identification of the ‘optimal’ tissue-enriched antigens remains to be discovered. Through machine learning technologies, we determined that a reliable immunodiagnostic test for porcine cysticercosis required at least five different antigenic determinants.

Human and porcine cysticercosis caused by Taenia solium is a parasite disease still endemic in developing countries. The cysts can be located in different host tissues, including different organs of the central nervous system and the skeletal muscles. The molecular mechanisms associated with the tissue localization of the cysts are not well understood. Here, we described the proteome changes of the cysts obtained from different host tissues from infected pigs using quantitative multiplex proteomics. We explored the diversity of host proteins identified in the cyst’s protein extracts and we also explored the immune-localization of several host-related proteins within the cysts, and propose their possible function. We identified several proteins and antigens enriched for a given tissue localization. Several synthetic peptides designed from these tissue-enriched antigens were tested trough ELISA. Using a combination of peptide mixtures and machine learning technologies we were able to distinguish non cysticercotic and cysticercotic pig’s sera. The tissue-enriched proteins/antigens could be useful for the development of improved immuno-diagnostic tests capable of discriminate the tissue-localization of the cysts.

A Proteomic Signature of Dormancy in the Actinobacterium Micrococcus luteus

Dormancy is a protective state in which diverse bacteria, including Mycobacterium tuberculosis, Staphylococcus aureus, Treponema pallidum (syphilis), and Borrelia burgdorferi (Lyme disease), curtail metabolic activity to survive external stresses, including antibiotics. Evidence suggests dormancy consists of a continuum of interrelated states, including viable but nonculturable (VBNC) and persistence states. VBNC and persistence contribute to antibiotic tolerance, reemergence from latent infections, and even quorum sensing and biofilm formation. Previous studies indicate that the protein mechanisms regulating persistence and VBNC states are not well understood. We have queried the VBNC state of Micrococcus luteus NCTC 2665 (MI-2665) by quantitative proteomics combining gel electrophoresis, high-performance liquid chromatography, and tandem mass spectrometry to elucidate some of these mechanisms. MI-2665 is a nonpathogenic actinobacterium containing a small (2.5-Mb), high-GC-content genome which exhibits a well-defined VBNC state induced by nutrient deprivation. The MI-2665 VBNC state demonstrated a loss of protein diversity accompanied by increased levels of 18 proteins that are conserved across actinobacteria, 14 of which have not been previously identified in VNBC. These proteins implicate an anaplerotic strategy in the transition to VBNC, including changes in the glyoxylate shunt, redox and amino acid metabolism, and ribosomal regulatory processes. Our data suggest that MI-2665 is a viable model for dissecting the protein mechanisms underlying the VBNC stress response and provide the first protein-level signature of this state. We expect that this protein signature will enable future studies deciphering the protein mechanisms of dormancy and identify novel therapeutic strategies effective against antibiotic-tolerant bacterial infections.IMPORTANCE Dormancy is a protective state enabling bacteria to survive antibiotics, starvation, and the immune system. Dormancy is comprised of different states, including persistent and viable but nonculturable (VBNC) states that contribute to the spread of bacterial infections. Therefore, it is imperative to identify how bacteria utilize these different dormancy states to survive antibiotic treatment. The objective of our research is to eliminate dormancy as a route to antibiotic tolerance by understanding the proteins that control dormancy in Micrococcus luteus NCTC 2665. This bacterium has unique advantages for studying dormancy, including a small genome and a well-defined and reproducible VBNC state. Our experiments implicate four previously identified and 14 novel proteins upregulated in VBNC that may regulate this critical survival mechanism.

Targeted Proteomics and Absolute Protein Quantification for the Construction of a Stoichiometric Host-Pathogen Surface Density Model

Sepsis is a systemic immune response responsible for considerable morbidity and mortality. Molecular modeling of host-pathogen interactions in the disease state represents a promising strategy to define molecular events of importance for the transition from superficial to invasive infectious diseases. Here we used the Gram-positive bacterium Streptococcus pyogenes as a model system to establish a mass spectrometry based workflow for the construction of a stoichiometric surface density model between the S. pyogenes surface, the surface virulence factor M-protein, and adhered human blood plasma proteins. The workflow relies on stable isotope labeled reference peptides and selected reaction monitoring mass spectrometry analysis of a wild-type strain and an M-protein deficient mutant strain, to generate absolutely quantified protein stoichiometry ratios between S. pyogenes and interacting plasma proteins. The stoichiometry ratios in combination with a novel targeted mass spectrometry method to measure cell numbers enabled the construction of a stoichiometric surface density model using protein structures available from the protein data bank. The model outlines the topology and density of the host-pathogen protein interaction network on the S. pyogenes bacterial surface, revealing a dense and highly organized protein interaction network. Removal of the M-protein from S. pyogenes introduces a drastic change in the network topology, validated by electron microscopy. We propose that the stoichiometric surface density model of S. pyogenes in human blood plasma represents a scalable framework that can continuously be refined with the emergence of new results. Future integration of new results will improve the understanding of protein-protein interactions and their importance for bacterial virulence. Furthermore, we anticipate that the general properties of the developed workflow will facilitate the production of stoichiometric surface density models for other types of host-pathogen interactions.

SCM, the M Protein of Streptococcus canis Binds Immunoglobulin G

The M protein of Streptococcus canis (SCM) is a virulence factor and serves as a surface-associated receptor with a particular affinity for mini-plasminogen, a cleavage product of the broad-spectrum serine protease plasmin. Here, we report that SCM has an additional high-affinity immunoglobulin G (IgG) binding activity. The ability of a particular S. canis isolate to bind to IgG significantly correlates with a scm-positive phenotype, suggesting a dominant role of SCM as an IgG receptor. Subsequent heterologous expression of SCM in non-IgG binding S. gordonii and Western Blot analysis with purified recombinant SCM proteins confirmed its IgG receptor function. As expected for a zoonotic agent, the SCM-IgG interaction is species-unspecific, with a particular affinity of SCM for IgGs derived from human, cats, dogs, horses, mice, and rabbits, but not from cows and goats. Similar to other streptococcal IgG-binding proteins, the interaction between SCM and IgG occurs via the conserved Fc domain and is, therefore, non-opsonic. Interestingly, the interaction between SCM and IgG-Fc on the bacterial surface specifically prevents opsonization by C1q, which might constitute another anti-phagocytic mechanism of SCM. Extensive binding analyses with a variety of different truncated SCM fragments defined a region of 52 amino acids located in the central part of the mature SCM protein which is important for IgG binding. This binding region is highly conserved among SCM proteins derived from different S. canis isolates but differs significantly from IgG-Fc receptors of S. pyogenes and S. dysgalactiae sub. equisimilis, respectively. In summary, we present an additional role of SCM in the pathogen-host interaction of S. canis. The detailed analysis of the SCM-IgG interaction should contribute to a better understanding of the complex roles of M proteins in streptococcal pathogenesis.

Analysis of Bacterial Surface Interactions with Mass Spectrometry-Based Proteomics

Host-pathogen protein-protein interaction networks are highly complex and dynamic. In this experimental protocol we describe a method to isolate host proteins attached to the bacterial surface followed by quantitative mass spectrometry based proteomics analysis. This technique provides an overview of the host-pathogen interaction network, which can be used to guide directed perturbations of the system, and to select target of specific interest for further studies.

Genomic Landscape of Intrahost Variation in Group A Streptococcus: Repeated and Abundant Mutational Inactivation of the fabT Gene Encoding a Regulator of Fatty Acid Synthesis

To obtain new information about Streptococcus pyogenes intrahost genetic variation during invasive infection, we sequenced the genomes of 2,954 serotype M1 strains recovered from a nonhuman primate experimental model of necrotizing fasciitis. A total of 644 strains (21.8%) acquired polymorphisms relative to the input parental strain. The fabT gene, encoding a transcriptional regulator of fatty acid biosynthesis genes, contained 54.5% of these changes. The great majority of polymorphisms were predicted to deleteriously alter FabT function. Transcriptome-sequencing (RNA-seq) analysis of a wild-type strain and an isogenic fabT deletion mutant strain found that between 3.7 and 28.5% of the S. pyogenes transcripts were differentially expressed, depending on the growth temperature (35°C or 40°C) and growth phase (mid-exponential or stationary phase). Genes implicated in fatty acid synthesis and lipid metabolism were significantly upregulated in the fabT deletion mutant strain. FabT also directly or indirectly regulated central carbon metabolism genes, including pyruvate hub enzymes and fermentation pathways and virulence genes. Deletion of fabT decreased virulence in a nonhuman primate model of necrotizing fasciitis. In addition, the fabT deletion strain had significantly decreased survival in human whole blood and during phagocytic interaction with polymorphonuclear leukocytes ex vivo We conclude that FabT mutant progeny arise during infection, constitute a metabolically distinct subpopulation, and are less virulent in the experimental models used here.

Listeria monocytogenes remodels the cell surface in the blood-stage

After crossing the intestinal barrier, the bacterial pathogen Listeria monocytogenes disseminates via the blood to the liver, spleen, brain and placenta. Transcriptomic studies have shown that L. monocytogenes changes expression of many genes during this blood-stage. However, no comparable data at the protein level are known. As main interactors with the environment, we focused in surface proteins produced by L. monocytogenes in an ex vivo bovine blood model. Bacteria exposed to blood alter selectively the amount of several surface proteins compared with bacteria grown in laboratory media. Increased levels were detected for Lmo0514 and Internalin A, two surface proteins covalently bound to peptidoglycan, and the moonlighting protein alcohol-acetaldehyde dehydrogenase, also known as Lap for 'Listeria adhesion protein'. Lmo0514, induced by L. monocytogenes inside epithelial cells, is required for survival in plasma and for virulence in mice at early infection stages. Lmo0514 is also important to cope with low pH stress. By contrast, L. monocytogenes down-regulates other surface proteins following exposure to blood and plasma such as Internalin I. These data provide evidence for remodelling of the L. monocytogenes cell surface during the blood-stage, which it could facilitate pathogen dissemination to deep organs.

Development of Phage-Based Antibody Fragment Reagents for Affinity Enrichment of Bacterial Immunoglobulin G Binding Proteins

Disease and death caused by bacterial infections are global health problems. Effective bacterial strategies are required to promote survival and proliferation within a human host, and it is important to explore how this adaption occurs. However, the detection and quantification of bacterial virulence factors in complex biological samples are technically demanding challenges. These can be addressed by combining targeted affinity enrichment of antibodies with the sensitivity of liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS). However, many virulence factors have evolved properties that make specific detection by conventional antibodies difficult. We here present an antibody format that is particularly well suited for detection and analysis of immunoglobulin G (IgG)-binding virulence factors. As proof of concept, we have generated single chain fragment variable (scFv) antibodies that specifically target the IgG-binding surface proteins M1 and H of Streptococcus pyogenes. The binding ability of the developed scFv is demonstrated against both recombinant soluble protein M1 and H as well as the intact surface proteins on a wild-type S. pyogenes strain. Additionally, the capacity of the developed scFv antibodies to enrich their target proteins from both simple and complex backgrounds, thereby allowing for detection and quantification with LC-SRM MS, was demonstrated. We have established a workflow that allows for affinity enrichment of bacterial virulence factors.

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.

DIANA--algorithmic improvements for analysis of data-independent acquisition MS data

MOTIVATION

Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification.

RESULTS

We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples.

AVAILABILITY AND IMPLEMENTATION

DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

A comprehensive analysis of the Streptococcus pyogenes and human plasma protein interaction network

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.

To harm or not to harm? On the evolution and expression of virulence in group A streptococci

Group A streptococci (GAS) cause three different types of infection (sensu lato) with distinct levels of virulence: asymptomatic colonization, superficial symptomatic infection, and invasive infection. To address why this pattern with several infection types has evolved, we combine mechanistic understanding from infection medicine with recent theory from evolutionary ecology. We propose that asymptomatic colonization and superficial symptomatic infection exploit different states of the host epithelium to maximize transmission between hosts in different epidemiological conditions, whereas the ability of the bacteria to cause invasive infection is a non-adaptive side effect of traits required for superficial symptomatic infection.

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.

A novel role for pro-coagulant microvesicles in the early host defense against streptococcus pyogenes

Previous studies have shown that stimulation of whole blood or peripheral blood mononuclear cells with bacterial virulence factors results in the sequestration of pro-coagulant microvesicles (MVs). These particles explore their clotting activity via the extrinsic and intrinsic pathway of coagulation; however, their pathophysiological role in infectious diseases remains enigmatic. Here we describe that the interaction of pro-coagulant MVs with bacteria of the species Streptococcus pyogenes is part of the early immune response to the invading pathogen. As shown by negative staining electron microscopy and clotting assays, pro-coagulant MVs bind in the presence of plasma to the bacterial surface. Fibrinogen was identified as a linker that, through binding to the M1 protein of S. pyogenes, allows the opsonization of the bacteria by MVs. Surface plasmon resonance analysis revealed a strong interaction between pro-coagulant MVs and fibrinogen with a K_D value in the nanomolar range. When performing a mass-spectrometry-based strategy to determine the protein quantity, a significant up-regulation of the fibrinogen-binding integrins CD18 and CD11b on pro-coagulant MVs was recorded. Finally we show that plasma clots induced by pro-coagulant MVs are able to prevent bacterial dissemination and possess antimicrobial activity. These findings were confirmed by in vivo experiments, as local treatment with pro-coagulant MVs dampens bacterial spreading to other organs and improved survival in an invasive streptococcal mouse model of infection. Taken together, our data implicate that pro-coagulant MVs play an important role in the early response of the innate immune system in infectious diseases.

The coagulation system is much more than a passive bystander in our defense against exogenous microorganisms. Over the last years there has been a growing body of evidence pointing to an integral part of coagulation in innate immunity and a special focus has been on bacterial entrapment in a fibrin network. However, thus far, pro-coagulant MVs have not been discussed in this context, though it is known that their numbers can dramatically increase in many pathological conditions, including severe infectious diseases. In the present study we see a significant increase of pro-coagulant MVs in an invasive streptococcal mouse model, suggesting that their release is an immune response to the infection. We find that pro-coagulant MVs bind to Streptococcus pyogenes and promote clotting, entrapment, and killing of the bacteria in a fibrin network. As a proof of concept pro-coagulant MVs were applied as local treatment in the streptococcal infection model, and it was demonstrated that this led to a significantly improved survival in mice.

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.

An automated pipeline for high-throughput label-free quantitative proteomics

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

Automated selected reaction monitoring software for accurate label-free protein quantification

Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

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.