Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data

Plasma-based proteomics analysis of molecular pathways in canine diabetes mellitus after astaxanthin supplementation

The hyperglycemic state in diabetes mellitus induces oxidative stress and inflammation, contributing to diabetic tissue damage and associated complications. Astaxanthin, a potent antioxidant carotenoid, has been investigated for its potential to prevent and manage diabetes across various species; however, its effect on client-owned dogs remains poorly studied. This study explored the impact of astaxanthin supplementation on canine diabetes mellitus using a proteomics approach. A total of 18 client-owned dogs were enrolled: 6 dogs with diabetes mellitus and 12 clinically healthy dogs. The diabetic dogs received their standard treatment regimen along with daily oral supplementation of 12 mg of astaxanthin (1.5-2.4 mg/kg) for 90 days. Plasma samples were collected at the beginning and end of the study period for proteomics analysis. After astaxanthin supplementation, significant alterations in the expression of proteins associated with the complement system, coagulation cascade, JAK-STAT signaling, and protein kinase C signaling (all of which contribute to inflammation and oxidative stress) were observed. Astaxanthin exhibited potential for reducing diabetes-associated complications, such as insulin resistance, vascular dysfunction, nephropathy, and cardiac issues, even though it did not affect clinical parameters (hematology, plasma biochemistry, blood glucose, and serum fructosamine). These findings suggest that astaxanthin may be a valuable complementary therapy for managing diabetes-related complications in canines.

Metaproteomics-informed stoichiometric modeling reveals the responses of wetland microbial communities to oxygen and sulfate exposure

Climate changes significantly impact greenhouse gas emissions from wetland soil. Specifically, wetland soil may be exposed to oxygen (O2) during droughts, or to sulfate (SO42-) as a result of sea level rise. How these stressors - separately and together - impact microbial food webs driving carbon cycling in the wetlands is still not understood. To investigate this, we integrated geochemical analysis, proteogenomics, and stoichiometric modeling to characterize the impact of elevated SO42- and O2 levels on microbial methane (CH4) and carbon dioxide (CO2) emissions. The results uncovered the adaptive responses of this community to changes in SO42- and O2 availability and identified altered microbial guilds and metabolic processes driving CH4 and CO2 emissions. Elevated SO42- reduced CH4 emissions, with hydrogenotrophic methanogenesis more suppressed than acetoclastic. Elevated O2 shifted the greenhouse gas emissions from CH4 to CO2. The metabolic effects of combined SO42- and O2 exposures on CH4 and CO2 emissions were similar to those of O2 exposure alone. The reduction in CH4 emission by increased SO42- and O2 was much greater than the concomitant increase in CO2 emission. Thus, greater SO42- and O2 exposure in wetlands is expected to reduce the aggregate warming effect of CH4 and CO2. Metaproteomics and stoichiometric modeling revealed a unique subnetwork involving carbon metabolism that converts lactate and SO42- to produce acetate, H2S, and CO2 when SO42- is elevated under oxic conditions. This study provides greater quantitative resolution of key metabolic processes necessary for the prediction of CH4 and CO2 emissions from wetlands under future climate scenarios.

Increased specificity of Fasciola hepatica excretory-secretory antigens combining negative selection on hydroxyapatite and salt precipitation

A single and rapid method to obtain an antigenic fraction of excretory-secretory antigens (ESAs) from Fasciola hepatica suitable for serodiagnosis of fascioliasis is reported. The procedure consists in the negative selection of F. hepatica ESAs by hydroxyapatite (HA) chromatography (HAC; fraction HAC-NR) followed by antigen precipitation with 50% ammonium sulphate (AS) and subsequent recovery by means of a Millex-GV or equivalent filter (Fi-SOLE fraction). Tested in indirect ELISA, the Fi-SOLE antigens detected natural infections by F. hepatica with 100% sensitivity and 98.9% specificity in sheep, and 97.7% sensitivity and 97.7% specificity in cattle, as determined by ROC analysis. The SDS-PAGE and proteomic nano-UHPLC-Tims-QTOF MS/MS analysis of fractions showed that the relative abundance of L-cathepsins and fragments thereof was 57% in fraction HAC-NR and 93.8% in fraction Fi-SOLE. The second most abundant proteins in fraction HAC-NR were fatty-acid binding proteins (11.9%). In contrast, free heme, and heme:MF6p/FhHDM-1 complexes remained strongly bond to the HA particles during HAC. Interestingly, phosphorylcholine (PC)-bearing antigens, which are a frequent source of cross-reactivity, were detected with an anti-PC mAb (BH8) in ESAs and fraction HAC-NR but were almost absent in fraction Fi-SOLE.

Enzyme-mediated depletion of methylthioadenosine restores T cell function in MTAP-deficient tumors and reverses immunotherapy resistance

Chromosomal region 9p21 containing tumor suppressors CDKN2A/B and methylthioadenosine phosphorylase (MTAP) is one of the most frequent genetic deletions in cancer. 9p21 loss is correlated with reduced tumor-infiltrating lymphocytes (TILs) and resistance to immune checkpoint inhibitor (ICI) therapy. Previously thought to be caused by CDKN2A/B loss, we now show that it is loss of MTAP that leads to poor outcomes on ICI therapy and reduced TIL density. MTAP loss causes accumulation of methylthioadenosine (MTA) both intracellularly and extracellularly and profoundly impairs T cell function via the inhibition of protein arginine methyltransferase 5 (PRMT5) and by adenosine receptor agonism. Administration of MTA-depleting enzymes reverses this immunosuppressive effect, increasing TILs and drastically impairing tumor growth and importantly, synergizes well with ICI therapy. As several studies have shown ICI resistance in 9p21/MTAP null/low patients, we propose that MTA degrading therapeutics may have substantial therapeutic benefit in these patients by enhancing ICI effectiveness.

JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations

Recent advances in mass spectrometry (MS)-based proteomics allow the measurement of turnover rates of thousands of proteins using dynamic labeling methods, such as pulse stable isotope labeling by amino acids in cell culture (pSILAC). However, when applying the pSILAC strategy to multicellular animals (e.g., mice), the labeling process is significantly delayed by native amino acids recycled from protein degradation in vivo, raising a challenge of defining accurate protein turnover rates. Here, we report JUMPt, a software package using a novel ordinary differential equation (ODE)-based mathematical model to determine reliable rates of protein degradation. The uniqueness of JUMPt is to consider amino acid recycling and fit the kinetics of the labeling amino acid (e.g., Lys) and whole proteome simultaneously to derive half-lives of individual proteins. Multiple settings in the software are designed to enable simple to comprehensive data inputs for precise analysis of half-lives with flexibility. We examined the software by studying the turnover of thousands of proteins in the pSILAC brain and liver tissues. The results were largely consistent with the proteome turnover measurements from previous studies. The long-lived proteins are enriched in the integral membrane, myelin sheath, and mitochondrion in the brain. In summary, the ODE-based JUMPt software is an effective proteomics tool for analyzing large-scale protein turnover, and the software is publicly available on GitHub (https://github.com/JUMPSuite/JUMPt) to the research community.

A systems biology approach for studying neurodegenerative diseases

Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.

Peptide mass mapping in bioapatites isolated from animal bones

Bioapatite ceramics produced from biogenic sources provide highly attractive materials for the preparation of artificial replacements since such materials are not only more easily accepted by living organisms, but bioapatite isolated from biowaste such as xenogeneous bones also provides a low-cost material. Nevertheless, the presence of organic compounds in the bioapatite may lead to a deterioration in its quality and may trigger an undesirable immune response. Therefore, procedures which ensure the elimination of organic compounds through bioapatite isolation are being subjected to intense investigation and the presence of remaining organic impurities is being determined through the application of various methods. Since current conclusions concerning the conditions suitable for the elimination of organic compounds remain ambiguous, we used the mass spectrometry-based proteomic approach in order to determine the presence of proteins or peptides in bioapatite samples treated under the most frequently employed conditions, i.e., the alkaline hydrothermal process and calcination at 500 °C. Since we also investigated the presence of proteins or peptides in treated bioapatite particles of differing sizes, we discovered that both calcination and the size of the bioapatite particles constitute the main factors influencing the presence of proteins or peptides in bioapatite. In fact, while intact proteins were detected even in calcinated bioapatite consisting of particles >250 µm, no proteins were detected in the same material consisting of particles <40 µm. Therefore, we recommend the use of powdered bioapatite for the preparation of artificial replacements since it is more effectively purified than apatite in the form of blocks. In addition, we observed that while alkaline hydrothermal treatment leads to the non-specific cleavage of proteins, it does not ensure the full degradation thereof.

Mitochondrial protein sulfenation during aging in the rat brain

There is accumulating evidence that cysteine sulfenation (cys-SOH) in proteins plays an important role in cellular response to oxidative stress. The purpose of the present study was to identify mitochondrial proteins that undergo changes in cys-SOH during aging. Studies were conducted in rats when they were 5 or 30 months of age. Following blocking of free protein thiols with N-ethylmaleimide, protein sulfenic acids were reduced by arsenite to free thiol groups that were subsequently labeled with biotin-maleimide. Samples were then comparatively analyzed by two-dimensional Western blots, and proteins showing changes in sulfenation were selectively identified by mass spectrometry peptide sequencing. As a result, five proteins were identified. Proteins showing an age-related decrease in sulfenation include pyruvate carboxylase and pyruvate dehydrogenase; while those showing an age-related increase in sulfenation include aconitase, mitofilin, and tubulin (α-1). Results of the present study provide a general picture of mitochondrial protein sulfenation in brain oxidative stress and implicate the involvement of protein sulfenation in overall decline of mitochondrial function during brain aging.

Post-ischemic administration of 5-methoxyindole-2-carboxylic acid at the onset of reperfusion affords neuroprotection against stroke injury by preserving mitochondrial function and attenuating oxidative stress

We previously reported that 5-methoxyindole-2-carboxylic acid (MICA) could induce preconditioning effect in the ischemic brain of rat. In the present study, we addressed the question of whether MICA could also trigger a postconditioning effect in ischemic stroke. To this end, MICA (100 mg/kg body weight) was injected intraperitoneally at the onset of 24 h reperfusion following 1 h ischemia in rat brain. Results indicate that stroked animals treated with MICA showed less brain infarction volume than that of vehicle-treated animals. Further experiments revealed that brain mitochondrial complexes I and IV showed elevated enzymatic activities in MICA treated group and the elevation in complex I activity was likely contributed by seemingly enhanced expression of many complex I subunits, which was determined by mass spectral peptide sequencing. When compared with vehicle-treated rats, the preservation of complexes I and IV activities was shown to be accompanied by enhanced mitochondrial membrane potential, increased ATP production, and decreased caspase-3 activity. Additional studies also indicate the involvement of NQO1 upregulation by the Nrf2 signaling pathway in this MICA postconditioning paradigm. Consequently, attenuated oxidative stress in the MICA treated group reflected by decrease in H2O2 production and protein carbonylation and lipid peroxidation was detected. Taken together, the present study demonstrates that MICA can also induce a postconditioning effect in the ischemic brain of rat and the underlying mechanism likely involves preservation of mitochondrial function, upregulation of cellular antioxidative capacity, and attenuation of oxidative stress.

Comparative Sperm Proteomics in Mouse Species with Divergent Mating Systems

Sexual selection is the pervasive force underlying the dramatic divergence of sperm form and function. Although it has been demonstrated that testis gene expression evolves rapidly, exploration of the proteomic basis of sperm diversity is in its infancy. We have employed a whole-cell proteomics approach to characterize sperm divergence among closely related Mus species that experience different sperm competition regimes and exhibit pronounced variation in sperm energetics, motility and fertilization capacity. Interspecific comparisons revealed significant abundance differences amongst proteins involved in fertilization capacity, including those that govern sperm-zona pellucida interactions, axoneme components and metabolic proteins. Ancestral reconstruction of relative testis size suggests that the reduction of zona pellucida binding proteins and heavy-chain dyneins was associated with a relaxation in sperm competition in the M. musculus lineage. Additionally, the decreased reliance on ATP derived from glycolysis in high sperm competition species was reflected in abundance decreases in glycolytic proteins of the principle piece in M. spretus and M. spicilegus. Comparison of protein abundance and stage-specific testis expression revealed a significant correlation during spermatid development when dynamic morphological changes occur. Proteins underlying sperm diversification were also more likely to be subject to translational repression, suggesting that sperm composition is influenced by the evolution of translation control mechanisms. The identification of functionally coherent classes of proteins relating to sperm competition highlights the utility of evolutionary proteomic analyses and reveals that both intensified and relaxed sperm competition can have a pronounced impact on the molecular composition of the male gamete.

Production of Cow's Milk Free from Beta-Casein A1 and Its Application in the Manufacturing of Specialized Foods for Early Infant Nutrition

Beta-casein (BC) is frequently expressed as BC A2 and BC A1 in cow’s milk. Gastrointestinal digestion of BC A1 results in the release of the opioid peptide beta-casomorphin 7 (BCM7) which is less likely to occur from BC A2. This work was aimed to produce milk containing BC A2 with no BC A1 (BC A2 milk) using genetically selected CSN2 A2A2 Jersey cows. Additionally, we aimed to develop an infant formula (IF) suitable for healthy full-term infants during the first six months of life based on BC A2 milk. The concentration of BCM7 released from BC A2 IF, from commercially available IFs as well as from human milk and raw cow’s milk was evaluated after simulated gastrointestinal digestion (SGID). BC A2 IF presented the lowest mean relative abundance of BC A1 (IF 1 = 0.136 ± 0.010), compared with three commercially available IFs (IF 2 = 0.597 ± 0.020; IF 3 = 0.441 ± 0.014; IF 4 = 0.503 ± 0.011). Accordingly, SGID of whole casein fraction from BC A2 IF resulted in a significantly lower release of BCM7 (IF 1 = 0.860 ± 0.014 µg/100 mL) compared to commercially available IFs (IF 2 = 2.625 ± 0.042 µg/100 mL; IF 3 = 1.693 ± 0.012 µg/100 mL; IF 4 = 1.962 ± 0.067 µg/100 mL). Nevertheless, BCM7 levels from BC A2 IF were significantly higher than those found in SGID hydrolysates of BC A2 raw milk (0.742 ± 0.008 µg/100 mL). Interestingly, results showed that BCM7 was also present in human milk in significantly lower amounts (0.697 ± 0.007 µg/100 mL) than those observed in IF 1 and BC A2 milk. This work demonstrates that using BC A2 milk in IF formulation significantly reduces BCM7 formation during SGID. Clinical implications of BC A2 IF on early infant health and development need further investigations.

msBiodat analysis tool, big data analysis for high-throughput experiments

Background

Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions.

Results

In this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes.

Conclusion

The msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.hr

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0104-6) contains supplementary material, which is available to authorized users.

Integrated analysis of proteome and transcriptome changes in the mucopolysaccharidosis type VII mouse hippocampus

Mucopolysaccharidosis type VII (MPS VII) is a lysosomal storage disease caused by the deficiency of β-glucuronidase. In this study, we compared the changes relative to normal littermates in the proteome and transcriptome of the hippocampus in the C57Bl/6 mouse model of MPS VII, which has well-documented histopathological and neurodegenerative changes. A completely different set of significant changes between normal and MPS VII littermates were found in each assay. Nevertheless, the functional annotation terms generated by the two methods showed agreement in many of the processes, which also corresponded to known pathology associated with the disease. Additionally, assay-specific changes were found, which in the proteomic analysis included mitochondria, energy generation, and cytoskeletal differences in the mutant, while the transcriptome differences included immune, vesicular, and extracellular matrix changes. In addition, the transcriptomic changes in the mutant hippocampus were concordant with those in a MPS VII mouse caused by the same mutation but on a different background inbred strain.

Computational Methods in Mass Spectrometry-Based Proteomics

This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics.

Proteomic Analysis of Dhh1 Complexes Reveals a Role for Hsp40 Chaperone Ydj1 in Yeast P-Body Assembly

P-bodies (PB) are ribonucleoprotein (RNP) complexes that aggregate into cytoplasmic foci when cells are exposed to stress. Although the conserved mRNA decay and translational repression machineries are known components of PB, how and why cells assemble RNP complexes into large foci remain unclear. Using mass spectrometry to analyze proteins immunoisolated with the core PB protein Dhh1, we show that a considerable number of proteins contain low-complexity sequences, similar to proteins highly represented in mammalian RNP granules. We also show that the Hsp40 chaperone Ydj1, which contains an low-complexity domain and controls prion protein aggregation, is required for the formation of Dhh1-GFP foci on glucose depletion. New classes of proteins that reproducibly coenrich with Dhh1-GFP during PB induction include proteins involved in nucleotide or amino acid metabolism, glycolysis, transfer RNA aminoacylation, and protein folding. Many of these proteins have been shown to form foci in response to other stresses. Finally, analysis of RNA associated with Dhh1-GFP shows enrichment of mRNA encoding the PB protein Pat1 and catalytic RNAs along with their associated mitochondrial RNA-binding proteins. Thus, global characterization of PB composition has uncovered proteins important for PB assembly and evidence suggesting an active role for RNA in PB function.

Toward the complete characterization of host cell proteins in biotherapeutics via affinity depletions, LC-MS/MS, and multivariate analysis

Host cell protein (HCP) impurities are generated by the host organism during the production of therapeutic recombinant proteins, and are difficult to remove completely. Though commonly present in small quantities, if levels are not controlled, HCPs can potentially reduce drug efficacy and cause adverse patient reactions. A high resolution approach for thorough HCP characterization of therapeutic monoclonal antibodies is presented herein. In this method, antibody samples are first depleted via affinity enrichment (e.g., Protein A, Protein L) using milligram quantities of material. The HCP-containing flow-through is then enzymatically digested, analyzed using nano-UPLC-MS/MS, and proteins are identified through database searching. Nearly 700 HCPs were identified from samples with very low total HCP levels (< 1 ppm to ∼ 10 ppm) using this method. Quantitation of individual HCPs was performed using normalized spectral counting as the number of peptide spectrum matches (PSMs) per protein is proportional to protein abundance. Multivariate analysis tools were utilized to assess similarities between HCP profiles by: 1) quantifying overlaps between HCP identities; and 2) comparing correlations between individual protein abundances as calculated by spectral counts. Clustering analysis using these measures of dissimilarity between HCP profiles enabled high resolution differentiation of commercial grade monoclonal antibody samples generated from different cell lines, cell culture, and purification processes.

Controlled Measurement and Comparative Analysis of Cellular Components in E. coli Reveals Broad Regulatory Changes in Response to Glucose Starvation

How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.

Bacteria frequently experience starvation conditions in their natural environments. Yet how they modify their physiology in response to these conditions remains poorly understood. Here, we performed a detailed, two-week starvation experiment in E. coli. We exhaustively monitored changes in cellular components, such as RNA and protein abundances, over time. We subsequently compared and contrasted these measurements using novel computational approaches we developed specifically for analyzing gene-expression time-course data. Using these approaches, we could identify systematic trends in the E. coli starvation response. In particular, we found that cells systematically limit mRNA and protein production, degrade proteins involved in energy-intensive processes, and maintain or increase the amount of proteins involved in energy production. Thus, the bacteria assume a cellular state in which their ongoing energy use is limited while they are poised to take advantage of any nutrients that may become available.

Can "normal" protein expression ranges be estimated with high-throughput proteomics?

Although biological science discovery often involves comparing conditions to a normal state, in proteomics little is actually known about normal. Two Human Proteome studies featured in Nature offer new insights into protein expression and an opportunity to assess how high-throughput proteomics measures normal protein ranges. We use data from these studies to estimate technical and biological variability in protein expression and compare them to other expression data sets from normal tissue. Results show that measured protein expression across same-tissue replicates vary by ±4- to 10-fold for most proteins. Coefficients of variation (CV) for protein expression measurements range from 62% to 117% across different tissue experiments; however, adjusting for technical variation reduced this variability by as much as 50%. In addition, the CV could also be reduced by limiting comparisons to proteins with at least 3 or more unique peptide identifications as the CV was on average 33% lower than for proteins with 2 or fewer peptide identifications. We also selected 13 housekeeping proteins and genes that were expressed across all tissues with low variability to determine their utility as a reference set for normalization and comparative purposes. These results present the first step toward estimating normal protein ranges by determining the variability in expression measurements through combining publicly available data. They support an approach that combines standard protocols with replicates of normal tissues to estimate normal protein ranges for large numbers of proteins and tissues. This would be a tremendous resource for normal cellular physiology and comparisons of proteomics studies.

Neuronal Activity Promotes Glioma Growth through Neuroligin-3 Secretion

Active neurons exert a mitogenic effect on normal neural precursor and oligodendroglial precursor cells, the putative cellular origins of high-grade glioma (HGG). By using optogenetic control of cortical neuronal activity in a patient-derived pediatric glioblastoma xenograft model, we demonstrate that active neurons similarly promote HGG proliferation and growth in vivo. Conditioned medium from optogenetically stimulated cortical slices promoted proliferation of pediatric and adult patient-derived HGG cultures, indicating secretion of activity-regulated mitogen(s). The synaptic protein neuroligin-3 (NLGN3) was identified as the leading candidate mitogen, and soluble NLGN3 was sufficient and necessary to promote robust HGG cell proliferation. NLGN3 induced PI3K-mTOR pathway activity and feedforward expression of NLGN3 in glioma cells. NLGN3 expression levels in human HGG negatively correlated with patient overall survival. These findings indicate the important role of active neurons in the brain tumor microenvironment and identify secreted NLGN3 as an unexpected mechanism promoting neuronal activity-regulated cancer growth.

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other ‘omics’ studies.

Absolute quantitation of protein posttranslational modification isoform

Mass spectrometry has been widely applied in characterization and quantification of proteins from complex biological samples. Because the numbers of absolute amounts of proteins are needed in construction of mathematical models for molecular systems of various biological phenotypes and phenomena, a number of quantitative proteomic methods have been adopted to measure absolute quantities of proteins using mass spectrometry. The liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with internal peptide standards, i.e., the stable isotope-coded peptide dilution series, which was originated from the field of analytical chemistry, becomes a widely applied method in absolute quantitative proteomics research. This approach provides more and more absolute protein quantitation results of high confidence. As quantitative study of posttranslational modification (PTM) that modulates the biological activity of proteins is crucial for biological science and each isoform may contribute a unique biological function, degradation, and/or subcellular location, the absolute quantitation of protein PTM isoforms has become more relevant to its biological significance. In order to obtain the absolute cellular amount of a PTM isoform of a protein accurately, impacts of protein fractionation, protein enrichment, and proteolytic digestion yield should be taken into consideration and those effects before differentially stable isotope-coded PTM peptide standards are spiked into sample peptides have to be corrected. Assisted with stable isotope-labeled peptide standards, the absolute quantitation of isoforms of posttranslationally modified protein (AQUIP) method takes all these factors into account and determines the absolute amount of a protein PTM isoform from the absolute amount of the protein of interest and the PTM occupancy at the site of the protein. The absolute amount of the protein of interest is inferred by quantifying both the absolute amounts of a few PTM-site-independent peptides in the total cellular protein and their peptide yields. The PTM occupancy determination is achieved by measuring the absolute amounts of both PTM and non-PTM peptides from the highly purified protein sample expressed in transgenic organisms or directly isolated from an organism using affinity purification. The absolute amount of each PTM isoform in the total cellular protein extract is finally calculated from these two variables. Following this approach, the ion intensities given by mass spectrometers are used to calculated the peptide amounts, from which the amounts of protein isoforms are then deduced. In this chapter, we describe the principles underlying the experimental design and procedures used in AQUIP method. This quantitation method basically employs stable isotope-labeled peptide standards and affinity purification from a tagged recombinant protein of interest. Other quantitation strategies and purification techniques related to this method are also discussed.

Urinary proteome profile predictive of disease activity in rheumatoid arthritis

Current serum biomarkers for rheumatoid arthritis (RA) are not highly sensitive or specific to changes of disease activities. Thus, other complementary biomarkers have been needed to improve assessment of RA activities. In many diseases, urine has been studied as a window to provide complementary information to serum measures. Here, we conducted quantitative urinary proteome profiling using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and identified 134 differentially expressed proteins (DEPs) between RA and osteoarthritis (OA) urine samples. By integrating the DEPs with gene expression profiles in joints and mononuclear cells, we initially selected 12 biomarker candidates related to joint pathology and then tested their altered expression in independent RA and OA samples using enzyme-linked immunosorbent assay. Of the initial candidates, we selected four DEPs as final candidates that were abundant in RA patients and consistent with those observed in LC-MS/MS analysis. Among them, we further focused on urinary soluble CD14 (sCD14) and examined its diagnostic value and association with disease activity. Urinary sCD14 had a diagnostic value comparable to conventional serum measures and an even higher predictive power for disease activity when combined with serum C-reactive protein. Thus, our urinary proteome provides a diagnostic window complementary to current serum parameters for the disease activity of RA.

Protein-to-mRNA ratios are conserved between Pseudomonas aeruginosa strains

Recent studies have shown that the concentrations of proteins expressed from orthologous genes are often conserved across organisms and to a greater extent than the abundances of the corresponding mRNAs. However, such studies have not distinguished between evolutionary (e.g., sequence divergence) and environmental (e.g., growth condition) effects on the regulation of steady-state protein and mRNA abundances. Here, we systematically investigated the transcriptome and proteome of two closely related Pseudomonas aeruginosa strains, PAO1 and PA14, under identical experimental conditions, thus controlling for environmental effects. For 703 genes observed by both shotgun proteomics and microarray experiments, we found that the protein-to-mRNA ratios are highly correlated between orthologous genes in the two strains to an extent comparable to protein and mRNA abundances. In spite of this high molecular similarity between PAO1 and PA14, we found that several metabolic, virulence, and antibiotic resistance genes are differentially expressed between the two strains, mostly at the protein but not at the mRNA level. Our data demonstrate that the magnitude and direction of the effect of protein abundance regulation occurring after the setting of mRNA levels is conserved between bacterial strains and is important for explaining the discordance between mRNA and protein abundances.

Machine learning applications in proteomics research: how the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.

Application of machine learning to proteomics data: classification and biomarker identification in postgenomics biology

Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes.

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.

Proteomics screen to reveal molecular changes mediated by C722G missense mutation in CHRM2 gene

Previously, we reported a missense mutation (C722G) in the M2-muscarinic acetylcholine receptor (CHRM2) gene associated with familial dilated cardiomyopathy. However, the exact molecular mechanisms by the related protein changes of CHRM2-C722G mutation induced are still unclear. CHRM2 and CHRM2-C722G lentiviral vector was infected to CHO cells. Proteomic analysis by label-free shotgun strategy and the STRING 9.0 software were performed. A total of 102 proteins with at least 2-fold change in the CHRM2-C722G group were identified, 42 proteins were up-regulated, whereas 57 were down-regulated. These altered proteins belong to three broad functional categories: (i) metabolic (e.g. Cytosolic acyl coenzyme A thioester hydrolase, Malate dehydrogenase); (ii) cytoskeletal (e.g. Actin-related protein, Myosin light polypeptide 6 and Alpha-actinin-1) and (iii) stress response (e.g. heat shock protein 70, Ras-related protein Rab-10). Interestingly, the marked differences in the expression of selected eight proteins (change >4.0-fold), were connected with many proteins related to apoptosis and immune/inflammatory response such as: FOS, BAX, MYC, TP53 and IL6. This novel study demonstrated for the first time a full-scale screening of the proteomics research by CHRM2-C722G mutation and profiled 102 changed proteins, of which, eight might be critical in cardiac dysfunction for future mapping.

SIGNIFICANCE

It was a full-scale screening of the proteomics research by CHRM2-C722G mutation. These proteins might serve as valuable biomarkers that could predict the presence of a precursor field. These proteins might serve to further explore the pathophysiological mechanisms in familial DCM patients with C176W mutation.

The proteomic response to mutants of the Escherichia coli RNA degradosome

The Escherichia coli RNA degradosome recognizes and degrades RNA through the coordination of four main protein components, the endonuclease RNase E, the exonuclease PNPase, the RhlB helicase and the metabolic enzyme enolase. To help our understanding of the functions of the RNA degradosome, we quantified expression changes of >2300 proteins using mass spectrometry based shotgun proteomics in E. coli strains deficient in rhlB, eno, pnp (which displays temperature sensitive growth), or rne(1-602) which encodes a C-terminal truncation mutant of RNase E and is deficient in degradosome assembly. Global protein expression changes are most similar between the pnp and rhlB mutants, confirming the functional relationship between the genes. We observe down-regulation of protein chaperones including GroEL and DnaK (which associate with the degradosome), a decrease in translation related proteins in Δpnp, ΔrhlB and rne(1-602) cells, and a significant increase in the abundance of aminoacyl-tRNA synthetases. Analysis of the observed proteomic changes points to a shared motif, CGCTGG, that may be associated with RNA degradosome targets. Further, our data provide information on the expression modulation of known degradosome-associated proteins, such as DeaD and RNase G, as well as other RNA helicases and RNases - suggesting or confirming functional complementarity in some cases. Taken together, our results emphasize the role of the RNA degradosome in the modulation of the bacterial proteome and provide the first large-scale proteomic description of the response to perturbation of this major pathway of RNA degradation.

Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions

BACKGROUND

Proteogenomic mapping is an approach that uses mass spectrometry data from proteins to directly map protein-coding genes and could aid in locating translational regions in the human genome. In concert with the ENcyclopedia of DNA Elements (ENCODE) project, we applied proteogenomic mapping to produce proteogenomic tracks for the UCSC Genome Browser, to explore which putative translational regions may be missing from the human genome.

RESULTS

We generated ~1 million high-resolution tandem mass (MS/MS) spectra for Tier 1 ENCODE cell lines K562 and GM12878 and mapped them against the UCSC hg19 human genome, and the GENCODE V7 annotated protein and transcript sets. We then compared the results from the three searches to identify the best-matching peptide for each MS/MS spectrum, thereby increasing the confidence of the putative new protein-coding regions found via the whole genome search. At a 1% false discovery rate, we identified 26,472, 24,406, and 13,128 peptides from the protein, transcript, and whole genome searches, respectively; of these, 481 were found solely via the whole genome search. The proteogenomic mapping data are available on the UCSC Genome Browser at http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeUncBsuProt.

CONCLUSIONS

The whole genome search revealed that ~4% of the uniquely mapping identified peptides were located outside GENCODE V7 annotated exons. The comparison of the results from the disparate searches also identified 15% more spectra than would have been found solely from a protein database search. Therefore, whole genome proteogenomic mapping is a complementary method for genome annotation when performed in conjunction with other searches.

Computational approaches to protein inference in shotgun proteomics

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programming and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.

Role of Pseudomonas aeruginosa peptidoglycan-associated outer membrane proteins in vesicle formation

Gram-negative bacteria produce outer membrane vesicles (OMVs) that package and deliver proteins, small molecules, and DNA to prokaryotic and eukaryotic cells. The molecular details of OMV biogenesis have not been fully elucidated, but peptidoglycan-associated outer membrane proteins that tether the outer membrane to the underlying peptidoglycan have been shown to be critical for OMV formation in multiple Enterobacteriaceae. In this study, we demonstrate that the peptidoglycan-associated outer membrane proteins OprF and OprI, but not OprL, impact production of OMVs by the opportunistic pathogen Pseudomonas aeruginosa. Interestingly, OprF does not appear to be important for tethering the outer membrane to peptidoglycan but instead impacts OMV formation through modulation of the levels of the Pseudomonas quinolone signal (PQS), a quorum signal previously shown by our laboratory to be critical for OMV formation. Thus, the mechanism by which OprF impacts OMV formation is distinct from that for other peptidoglycan-associated outer membrane proteins, including OprI.

Bioinformatic challenges in targeted proteomics

Selected reaction monitoring mass spectrometry is an emerging targeted proteomics technology that allows for the investigation of complex protein samples with high sensitivity and efficiency. It requires extensive knowledge about the sample for the many parameters needed to carry out the experiment to be set appropriately. Most studies today rely on parameter estimation from prior studies, public databases, or from measuring synthetic peptides. This is efficient and sound, but in absence of prior data, de novo parameter estimation is necessary. Computational methods can be used to create an automated framework to address this problem. However, the number of available applications is still small. This review aims at giving an orientation on the various bioinformatical challenges. To this end, we state the problems in classical machine learning and data mining terms, give examples of implemented solutions and provide some room for alternatives. This will hopefully lead to an increased momentum for the development of algorithms and serve the needs of the community for computational methods. We note that the combination of such methods in an assisted workflow will ease both the usage of targeted proteomics in experimental studies as well as the further development of computational approaches.

Translational research in infectious disease: current paradigms and challenges ahead

In recent years, the biomedical community has witnessed a rapid scientific and technologic evolution after the development and refinement of high-throughput methodologies. Concurrently and consequentially, the scientific perspective has changed from the reductionist approach of meticulously analyzing the fine details of a single component of biology to the "holistic" approach of broadmindedly examining the globally interacting elements of biological systems. The emergence of this new way of thinking has brought about a scientific revolution in which genomics, proteomics, metabolomics, and other "omics" have become the predominant tools by which large amounts of data are amassed, analyzed, and applied to complex questions of biology that were previously unsolvable. This enormous transformation of basic science research and the ensuing plethora of promising data, especially in the realm of human health and disease, have unfortunately not been followed by a parallel increase in the clinical application of this information. On the contrary, the number of new potential drugs in development has been decreasing steadily, suggesting the existence of roadblocks that prevent the translation of promising research into medically relevant therapeutic or diagnostic application. In this article, we will review, in a noninclusive fashion, several recent scientific advancements in the field of translational research, with a specific focus on how they relate to infectious disease. We will also present a current picture of the limitations and challenges that exist for translational research, as well as ways that have been proposed by the National Institutes of Health to improve the state of this field.

Secretomics for skeletal muscle cells: a discovery of novel regulators?

Metabolic tissues, including skeletal muscle, adipose tissue and the digestive system, dynamically secrete various factors depending on the metabolic state, communicate with each other and orchestrate functions to maintain body homeostasis. Skeletal muscle secretes cytokines such as interleukin-6 (IL-6), IL-15, fibroblast growth factor-21 (FGF21) and IL-8. These compounds, myokines, play important roles in biological homeostasis such as energy metabolism, angiogenesis and myogenesis. New technological advances have allowed secretomics - analysis of the secretome - to be performed. The application of highly sensitive mass spectrometry makes qualitative and quantitative analysis of the secretome of skeletal muscle possible. Secretory proteins derived from skeletal muscle cells under various conditions were analyzed, and many important factors were suggested. In-depth studies of the secretome from metabolic cells in various conditions are strongly recommended. This study will provide information on methods of novel communication between metabolic tissues.

Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry

Proteomic analysis of T cells emerging from quiescence identifies dynamic network-level changes in key cellular processes. Disruption of two such processes, ribosome biogenesis and RNA splicing, reveals that the programs controlling cell growth and cell-cycle entry are separable.

The authors conduct a proteomic and protein interaction network analysis of human T lymphocytes during entry into the first cell cycle.

Inhibiting the induction of eIF6 (60S ribosome biogenesis) causes T cells to enter the cell cycle without growing in size.

Inhibiting the induction of SF3B2/SF3B4 (U2/U12-dependent RNA splicing) allows an increase in cell size without entering the cell cycle.

These results provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells.

Regulating the transition of cells such as T lymphocytes from quiescence (G₀) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G₀. We also provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells. We employed a proteomic profiling method to identify large-scale changes in chromatin/nuclear matrix-bound and unbound proteins in human T lymphocytes during the transition from G₀ into the first cell cycle and mapped them to form functionally annotated, dynamic protein interaction networks. Inhibiting the induction of two proteins involved in two of the most significantly upregulated cellular processes, ribosome biogenesis (eIF6) and hnRNA splicing (SF3B2/SF3B4), showed, respectively, that human T cells can enter the cell cycle without growing in size, or increase in size without entering the cell cycle.

Label-free protein quantitation using weighted spectral counting

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein's probability of correct identification.The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O (i)) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O (i)-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.

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.

N-Glycosylation pattern of recombinant human CD82 (KAI1), a tumor-associated membrane protein

The membrane glycoprotein CD82 (KAI1) has attracted increasing attention as a suppressor of cell migration, related tumor invasion, as well as metastasis. The glycosylation of CD82 has been shown to be involved in a correlative cell adhesion and motility. However, the N-glycosylation pattern of CD82 has not been described yet. In the current study, a detailed characterization of the recombinant human CD82 N-linked glycosylation pattern was conducted by employing an integrative proteomic and glycomic approach, including glycosidase and protease digestions, glycan permethylation, MS analyses, site-directed mutagenesis, and lectin blots. The results reveal three N-glycosylation sites, and further demonstrate a putative glycosylation site at Asn(157) for the first time. A highly heterogeneous pattern of N-linked glycans is described, which express distinct carbohydrate epitopes, such as bisecting N-acetylglucosamine, (α-2,6) N-acetylneuraminic acid, and core fucose. These epitopes are highly associated with various biological functions, including cell adhesion and cancer metastasis, and can possibly influence the anti-cancer inhibition ability of CD82.

Protein expression regulation under oxidative stress

Oxidative stress is known to affect both translation and protein turnover, but very few large scale studies describe protein expression under stress. We measure protein concentrations in Saccharomyces cerevisiae over the course of 2 h in response to a mild oxidative stress induced by diamide, providing detailed time-resolved information for 815 proteins, with additional data for another ~1,100 proteins. For the majority of proteins, we discover major differences between the global transcript and protein response. Although mRNA levels often return to baseline 1 h after treatment, protein concentrations continue to change. Integrating our data with features of translation and protein degradation, we are able to predict expression patterns for 41% of the proteins in the core data set. Predictive features include, among others, targeting by RNA-binding proteins (Lhp1 and Khd1), RNA secondary structures, RNA half-life, and translation efficiency under unperturbed conditions and in response to oxidative reagents, but not chaperone binding. We are able to both describe general dynamics of protein concentration changes and suggest possible regulatory mechanisms for individual proteins.

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.

In vivo versus in vitro protein abundance analysis of Shigella dysenteriae type 1 reveals changes in the expression of proteins involved in virulence, stress and energy metabolism

BACKGROUND

Shigella dysenteriae serotype 1 (SD1) causes the most severe form of epidemic bacillary dysentery. Quantitative proteome profiling of Shigella dysenteriae serotype 1 (SD1) in vitro (derived from LB cell cultures) and in vivo (derived from gnotobiotic piglets) was performed by 2D-LC-MS/MS and APEX, a label-free computationally modified spectral counting methodology.

RESULTS

Overall, 1761 proteins were quantitated at a 5% FDR (false discovery rate), including 1480 and 1505 from in vitro and in vivo samples, respectively. Identification of 350 cytoplasmic membrane and outer membrane (OM) proteins (38% of in silico predicted SD1 membrane proteome) contributed to the most extensive survey of the Shigella membrane proteome reported so far. Differential protein abundance analysis using statistical tests revealed that SD1 cells switched to an anaerobic energy metabolism under in vivo conditions, resulting in an increase in fermentative, propanoate, butanoate and nitrate metabolism. Abundance increases of transcription activators FNR and Nar supported the notion of a switch from aerobic to anaerobic respiration in the host gut environment. High in vivo abundances of proteins involved in acid resistance (GadB, AdiA) and mixed acid fermentation (PflA/PflB) indicated bacterial survival responses to acid stress, while increased abundance of oxidative stress proteins (YfiD/YfiF/SodB) implied that defense mechanisms against oxygen radicals were mobilized. Proteins involved in peptidoglycan turnover (MurB) were increased, while β-barrel OM proteins (OmpA), OM lipoproteins (NlpD), chaperones involved in OM protein folding pathways (YraP, NlpB) and lipopolysaccharide biosynthesis (Imp) were decreased, suggesting unexpected modulations of the outer membrane/peptidoglycan layers in vivo. Several virulence proteins of the Mxi-Spa type III secretion system and invasion plasmid antigens (Ipa proteins) required for invasion of colonic epithelial cells, and release of bacteria into the host cell cytosol were increased in vivo.

CONCLUSIONS

Global proteomic profiling of SD1 comparing in vivo vs. in vitro proteomes revealed differential expression of proteins geared towards survival of the pathogen in the host gut environment, including increased abundance of proteins involved in anaerobic energy respiration, acid resistance and virulence. The immunogenic OspC2, OspC3 and IpgA virulence proteins were detected solely under in vivo conditions, lending credence to their candidacy as potential vaccine targets.

Spectral counting assessment of protein dynamic range in cerebrospinal fluid following depletion with plasma-designed immunoaffinity columns

Background

In cerebrospinal fluid (CSF), which is a rich source of biomarkers for neurological diseases, identification of biomarkers requires methods that allow reproducible detection of low abundance proteins. It is therefore crucial to decrease dynamic range and improve assessment of protein abundance.

Results

We applied LC-MS/MS to compare the performance of two CSF enrichment techniques that immunodeplete either albumin alone (IgYHSA) or 14 high-abundance proteins (IgY14). In order to estimate dynamic range of proteins identified, we measured protein abundance with APEX spectral counting method.

Both immunodepletion methods improved the number of low-abundance proteins detected (3-fold for IgYHSA, 4-fold for IgY14). The 10 most abundant proteins following immunodepletion accounted for 41% (IgY14) and 46% (IgYHSA) of CSF protein content, whereas they accounted for 64% in non-depleted samples, thus demonstrating significant enrichment of low-abundance proteins. Defined proteomics experiment metrics showed overall good reproducibility of the two immunodepletion methods and MS analysis. Moreover, offline peptide fractionation in IgYHSA sample allowed a 4-fold increase of proteins identified (520 vs. 131 without fractionation), without hindering reproducibility.

Conclusions

The novelty of this study was to show the advantages and drawbacks of these methods side-to-side. Taking into account the improved detection and potential loss of non-target proteins following extensive immunodepletion, it is concluded that both depletion methods combined with spectral counting may be of interest before further fractionation, when searching for CSF biomarkers. According to the reliable identification and quantitation obtained with APEX algorithm, it may be considered as a cheap and quick alternative to study sample proteomic content.

Quantitative phosphoproteomics strategies for understanding protein kinase-mediated signal transduction pathways

Protein phosphorylation is a central regulatory mechanism of cell signaling pathways. This highly controlled biochemical process is involved in most cellular functions, and defects in protein kinases and phosphatases have been implicated in many diseases, highlighting the importance of understanding phosphorylation-mediated signaling networks. However, phosphorylation is a transient modification, and phosphorylated proteins are often less abundant. Therefore, the large-scale identification and quantification of phosphoproteins and their phosphorylation sites under different conditions are one of the most interesting and challenging tasks in the field of proteomics. Both 2D gel electrophoresis and liquid chromatography-tandem mass spectrometry serve as key phosphoproteomic technologies in combination with prefractionation, such as enrichment of phosphorylated proteins/peptides. Recently, new possibilities for quantitative phosphoproteomic analysis have been offered by technical advances in sample preparation, enrichment, separation, instrumentation, quantification and informatics. In this article, we present an overview of several strategies for quantitative phosphoproteomics and discuss how phosphoproteomic analysis can help to elucidate signaling pathways that regulate various cellular processes.