Automated workflow for large-scale selected reaction monitoring experiments

Complement Activation Occurs at the Surface of Platelets Activated by Streptococcal M1 Protein and This Results in Phagocytosis of Platelets

Platelets circulate the bloodstream and principally maintain hemostasis. Disturbed hemostasis, a dysregulated inflammatory state, and a decreased platelet count are all hallmarks of severe invasive Streptococcus pyogenes infection, sepsis. We have previously demonstrated that the released M1 protein from S. pyogenes activates platelets, and this activation is dependent on the binding of M1 protein, fibrinogen, and M1-specific IgG to platelets in susceptible donors. In this study, we characterize the M1-associated protein interactions in human plasma and investigate the acquisition of proteins to the surface of activated platelets and the consequences for platelet immune function. Using quantitative mass spectrometry, M1 protein was determined to form a protein complex in plasma with statistically significant enrichment of fibrinogen, IgG3, and complement components, especially C1q. Using flow cytometry, these plasma proteins were also confirmed to be acquired to the platelet surface, resulting in complement activation on M1-activated human platelets. Furthermore, we demonstrated an increased phagocytosis of M1-activated platelets by monocytes, which was not observed with other physiological platelet agonists. This reveals a novel mechanism of complement activation during streptococcal sepsis, which contributes to the platelet consumption that occurs in sepsis.

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.

Targeted Proteomics for Multiplexed Verification of Markers of Colorectal Tumorigenesis

Targeted proteomic methods can accelerate the verification of multiple tumor marker candidates in large series of patient samples. We utilized the targeted approach known as selected/multiple reaction monitoring (S/MRM) to verify potential protein markers of colorectal adenoma identified by our group in previous transcriptomic and quantitative shotgun proteomic studies of a large cohort of precancerous colorectal lesions. We developed SRM assays to reproducibly detect and quantify 25 (62.5%) of the 40 selected proteins in an independent series of precancerous and cancerous tissue samples (19 adenoma/normal mucosa pairs; 17 adenocarcinoma/normal mucosa pairs). Twenty-three proteins were significantly up-regulated (n = 17) or downregulated (n = 6) in adenomas and/or adenocarcinomas, as compared with normal mucosa (linear fold changes ≥ ±1.3, adjusted p value <0.05). Most changes were observed in both tumor types (up-regulation of ANP32A, ANXA3, SORD, LDHA, LCN2, NCL, S100A11, SERPINB5, CDV3, OLFM4, and REG4; downregulation of ARF6 and PGM5), and a five-protein biomarker signature distinguished neoplastic tissue from normal mucosa with a maximum area under the receiver operating curve greater than 0.83. Other changes were specific for adenomas (PPA1 and PPA2 up-regulation; KCTD12 downregulation) or adenocarcinoma (ANP32B, G6PD, RCN1, and SET up-regulation; downregulated AKR1B1, APEX1, and PPA1). Some changes significantly correlated with a few patient- or tumor-related phenotypes. Twenty-two (96%) of the 23 proteins have a potential to be released from the tumors into the bloodstream, and their detectability in plasma has been previously reported. The proteins identified in this study expand the pool of biomarker candidates that can be used to develop a standardized precolonoscopy blood test for the early detection of colorectal tumors.

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.

RePLiCal: A QconCAT Protein for Retention Time Standardization in Proteomics Studies

This study introduces a new reversed-phase liquid chromatography retention time (RT) standard, RePLiCal (Reversed-phase liquid chromatography calibrant), produced using QconCAT technology. The synthetic protein contains 27 lysine-terminating calibrant peptides, meaning that the same complement of standards can be generated using either Lys-C or trypsin-based digestion protocols. RePLiCal was designed such that each constituent peptide is unique with respect to all eukaryotic proteomes, thereby enabling integration into a wide range of proteomic analyses. RePLiCal has been benchmarked against three commercially available peptide RT standard kits and outperforms all in terms of LC gradient coverage. RePLiCal also provides a higher number of calibrant points for chromatographic retention time standardization and normalization. The standard provides stable RTs over long analysis times and can be readily transferred between different LC gradients and nUHPLC instruments. Moreover, RePLiCal can be used to predict RTs for other peptides in a timely manner. Furthermore, it is shown that RePLiCal can be used effectively to evaluate trapping column performance for nUHPLC instruments using trap-elute configurations, to optimize gradients to maximize peptide and protein identification rates, and to recalibrate the m/z scale of mass spectrometry data post-acquisition.

Modeling and systematic analysis of biomarker validation using selected reaction monitoring

Background

Discovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for the discovery of biomarkers. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline. Validation is typically done by triple quadrupole (QQQ) mass spectrometry (MS) running in selected reaction monitoring (SRM) mode. Although the individual modules of this pipeline have been studied, there is little work on integrating the components from a systematic point of view.

Results

This paper analyzes the SRM experiment pipeline in a systematic fashion, by modeling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. Sample complexity, purification, peptide ionization, and peptide specificity are among the parameters of the SRM experiment that are studied. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment.

Conclusions

The model presented in this paper can be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM. On the other hand, the model would be beneficial for optimization of the work flow as well as identification of the bottlenecks of the pipeline. Also, it creates the required infrastructure for predicting the performance of the SRM pipeline for a specific setting of the parameters.

Electronic supplementary material

The online version of this article (doi:10.1186/s13637-014-0017-y) contains supplementary material, which is available to authorized users.

A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins

Transformation of engineered Escherichia coli into a robust microbial factory is contingent on precise control of metabolism. Yet, the throughput of omics technologies used to characterize cell components has lagged far behind our ability to engineer novel strains. To expand the utility of quantitative proteomics for metabolic engineering, we validated and optimized targeted proteomics methods for over 400 proteins from more than 20 major pathways in E. coli metabolism. Complementing these methods, we constructed a series of synthetic genes to produce concatenated peptides (QconCAT) for absolute quantification of the proteins and made them available through the Addgene plasmid repository (www.addgene.org). To facilitate high sample throughput, we developed a fast, analytical-flow chromatography method using a 5.5-min gradient (10 min total run time). Overall this toolkit provides an invaluable resource for metabolic engineering by increasing sample throughput, minimizing development time and providing peptide standards for absolute quantification of E. coli proteins.

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.

Characterization of lacrimal proline-rich protein 4 (PRR4) in human tear proteome

This study was initiated considering the lack of comprehensive characteristics profile of PRR4 in tears of healthy subjects. Therefore, detailed characterizations of PRR4 from basal tears employing in-gel and in-solution digestions for MS systems are presented herein. First, pooled tear samples (n = 10) were utilized to identify PRR4-rich region/spots in 1DE/2DE gels employing LC-MALDI-MS and 1DE-LC-ESI-LTQ-Orbitrap-MS systems. PRR4-rich region and ten spots with vast polymorphisms (Mr : 17-30 kDa, pI: 3.0-6.6) were identified in 1DE and 2DE gels, respectively. In addition, combinations of four types of PTMs, which are methylation, acetylation, oxidation, and pyroglutamate formation, were identified in these ten PRR4 spots. Furthermore, a targeted data-acquisition approach was utilized to identify PRR4 isoforms in individual tear samples (n = 61) by in-solution digestion combined with a LC-ESI-LTQ-Orbitrap-MS system. Importantly, a new PRR4 isoform designated as PRR4-N3 in addition to PRR4 (gi154448886) and pHL E1F1 (gi1050983) was identified. Moreover, different combinations of these three PRR4 isoforms identified in the individual tear samples could be categorized into six distinguished groups. Conclusively, these findings provide fundamental insight into the complex characteristics profile of PRR4 isoforms and their PTMs in tears of healthy individuals.

Comparison of targeted peptide quantification assays for reductive dehalogenases by selective reaction monitoring (SRM) and precursor reaction monitoring (PRM)

Targeted absolute protein quantification yields valuable information about physiological adaptation of organisms and is thereby of high interest. Especially for this purpose, two proteomic mass spectrometry-based techniques namely selective reaction monitoring (SRM) and precursor reaction monitoring (PRM) are commonly applied. The objective of this study was to establish an optimal quantification assay for proteins with the focus on those involved in housekeeping functions and putative reductive dehalogenase proteins from the strictly anaerobic bacterium Dehalococcoides mccartyi strain CBDB1. This microbe is small and slow-growing; hence, it provides little biomass for comprehensive proteomic analysis. We therefore compared SRM and PRM techniques. Eleven peptides were successfully quantified by both methods. In addition, six peptides were solely quantified by SRM and four by PRM, respectively. Peptides were spiked into a background of Escherichia coli lysate and the majority of peptides were quantifiable down to 500 amol absolute on column by both methods. Peptide quantification in CBDB1 lysate resulted in the detection of 15 peptides using SRM and 14 peptides with the PRM assay. Resulting quantification of five dehalogenases revealed copy numbers of <10 to 115 protein molecules per cell indicating clear differences in abundance of RdhA proteins during growth on hexachlorobenzene. Our results indicated that both methods show comparable sensitivity and that the combination of the mass spectrometry assays resulted in higher peptide coverage and thus more reliable protein quantification.

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.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Analysis of protein isoforms: can we do it better?

Protein isoforms/splice variants can play important roles in various biological processes and can potentially be used as biomarkers or therapeutic targets/mediators. Thus, there is a need for efficient and, importantly, accurate methods to distinguish and quantify specific protein isoforms. Since protein isoforms can share a high percentage of amino acid sequence homology and dramatically differ in their cellular concentration, the task for accuracy and efficiency in methodology and instrumentation is challenging. The analysis of intact proteins has been perceived to provide a more accurate and complete result for isoform identification/quantification in comparison to analysis of the corresponding peptides that arise from protein enzymatic digestion. Recently, novel approaches have been explored and developed that can possess the accuracy and reliability important for protein isoform differentiation and isoform-specific peptide targeting. In this review, we discuss the recent development in methodology and instrumentation for enhanced detection of protein isoforms as well as the examples of their biological importance.

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.