MSQuant, an open source platform for mass spectrometry-based quantitative proteomics

Selected Ion Extraction of Peptides with Heavy Isotopes and Hydrogen Loss Reduces the Type II Error in Plasma Proteomics

Naturally occurring peptides display a wide mass distribution after ionization due to the presence of heavy isotopes of C, H, N, O, and S and hydrogen loss. There is a crucial need for sensitive methods that collect as much information as possible about all plasma peptide forms. Statistical analysis of the delta mass distribution of peptide precursors from MS/MS spectra that were matched to 63,077 peptide sequences by X!TANDEM revealed Gaussian peaks representing heavy isotopes and hydrogen loss at integer delta mass values of -3, -2, -1, 0, +1, +2, +3, +4, and +5 Da. Human plasma samples were precipitated in acetonitrile, and the resulting proteins were collected over a quaternary amine resin, eluted with NaCl, digested with trypsin, and analyzed by nano liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with an orbital ion trap (OIT). Fragment spectra (MS/MS) generated from the OIT data were fit to human fully tryptic peptides by X!TANDEM, which led to the identification of 3,888 protein gene symbols represented by three or more peptides (n ≥ 3). The peptide counts to plasma proteins from experimental MS/MS spectra were corrected against 29 blank LC-ESI-MS/MS spectra and 30 million random MS/MS control spectra to yield 2,784 true positive proteins (n ≥ 3; q ≤ 0.01). Peptides identified by fragmenting ions with Gaussian heavy isotopes and hydrogen loss that were matched to known plasma proteins, such as albumin (ALB), were shown to be true positives and agreed with the peptide sequences identified in the monoisotopic peak. Accepting the ions from the monoisotopic peak alone (±0.1 Da) yielded only 382 plasma proteins (n ≥ 3; type I error q ≤ 0.01; type II error ∼86%). In contrast, accepting all ions within ±0.1 Da around the hydrogen loss, monoisotopic, and heavy isotopic peaks led to the identification of 963 proteins (n ≥ 3; q ≤ 0.01; type II error ∼60%). Using the power of the OIT to resolve the Gaussian peaks from heavy isotopes and hydrogen loss resulted in the identification of three times more proteins with high confidence and a much lower type II error than analyzing peptides from the monoisotopic peak alone. The resolving power of the OIT may be exploited to increase observation frequencies and provide greater proteomic coverage and statistical power in comparative proteomics studies.

Yeast Proteins: Proteomics, Extraction, Modification, Functional Characterization, and Structure: A Review

Proteins are essential for human tissues and organs, and they require adequate intake for normal physiological functions. With a growing global population, protein demand rises annually. Traditional animal and plant protein sources rely heavily on land and water, making it difficult to meet the increasing demand. The high protein content of yeast and the complete range of amino acids in yeast proteins make it a high-quality source of supplemental protein. Screening of high-protein yeast strains using proteomics is essential to increase the value of yeast protein resources and to promote the yeast protein industry. However, current yeast extraction methods are mainly alkaline solubilization and acid precipitation; therefore, it is necessary to develop more efficient and environmentally friendly techniques. In addition, the functional properties of yeast proteins limit their application in the food industry. To improve these properties, methods must be selected to modify the secondary and tertiary structures of yeast proteins. This paper explores how proteomic analysis can be used to identify nutrient-rich yeast strains, compares the process of preparing yeast proteins, and investigates how modification methods affect the function and structure of yeast proteins. It provides a theoretical basis for solving the problem of inadequate protein intake in China and explores future prospects.

15N Metabolic Labeling Quantification Workflow in Arabidopsis Using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using 15N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the 15N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector. Further, we discuss several important features of 15N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

Magnitude of Ubiquitination Determines the Fate of Epidermal Growth Factor Receptor Upon Ligand Stimulation

Receptor tyrosine kinases (RTK) bind growth factors and are critical for cell proliferation and differentiation. Their dysregulation leads to a loss of growth control, often resulting in cancer. Epidermal growth factor receptor (EGFR) is the prototypic RTK and can bind several ligands exhibiting distinct mitogenic potentials. Whereas the phosphorylation on individual EGFR sites and their roles for downstream signaling have been extensively studied, less is known about ligand-specific ubiquitination events on EGFR, which are crucial for signal attenuation and termination. We used a proteomics-based workflow for absolute quantitation combined with mathematical modeling to unveil potentially decisive ubiquitination events on EGFR from the first 30 seconds to 15 minutes of stimulation. Four ligands were used for stimulation: epidermal growth factor (EGF), heparin-binding-EGF like growth factor, transforming growth factor-α and epiregulin. Whereas only little differences in the order of individual ubiquitination sites were observed, the overall amount of modified receptor differed depending on the used ligand, indicating that absolute magnitude of EGFR ubiquitination, and not distinctly regulated ubiquitination sites, is a major determinant for signal attenuation and the subsequent cellular outcomes.

Biochemical and Computational Approaches for the Large-Scale Analysis of Protein Arginine Methylation by Mass Spectrometry

The absence of efficient mass spectrometry-based approaches for the large-scale analysis of protein arginine methylation has hindered the understanding of its biological role, beyond the transcriptional regulation occurring through histone modification. In the last decade, however, several technological advances of both the biochemical methods for methylated polypeptide enrichment and the computational pipelines for MS data analysis have considerably boosted this research field, generating novel insights about the extent and role of this post-translational modification. Here, we offer an overview of state-of-the-art approaches for the high-confidence identification and accurate quantification of protein arginine methylation by high-resolution mass spectrometry methods, which comprise the development of both biochemical and bioinformatics methods. The further optimization and systematic application of these analytical solutions will lead to ground-breaking discoveries on the role of protein methylation in biological processes.

The Application of an R Language-Based Platform cRacker for Phosphoproteomics Data Analysis

Phosphoproteomics has drawn great attention of biologist since phosphorylation is proven to play an important role in regulation of proteins. Mass spectrometry technology has helped with the development of phosphoproteomics due to its ability in generating large amount of detailed information after analyzing the phosphoproteome samples. However, interpreting the phosphoproteome data deprived from mass spectrometry can be time-consuming. Here, we introduced a free R language-based platform which can be used in accelerating phosphoproteome data analysis. This platform has integrated different functions and methods which are popularly used in phosphoproteome data analysis, so users can customize their analysis according to their demands.

WDR82/PNUTS-PP1 Prevents Transcription-Replication Conflicts by Promoting RNA Polymerase II Degradation on Chromatin

Transcription-replication (T-R) conflicts cause replication stress and loss of genome integrity. However, the transcription-related processes that restrain such conflicts are poorly understood. Here, we demonstrate that the RNA polymerase II (RNAPII) C-terminal domain (CTD) phosphatase protein phosphatase 1 (PP1) nuclear targeting subunit (PNUTS)-PP1 inhibits replication stress. Depletion of PNUTS causes lower EdU uptake, S phase accumulation, and slower replication fork rates. In addition, the PNUTS binding partner WDR82 also promotes RNAPII-CTD dephosphorylation and suppresses replication stress. RNAPII has a longer residence time on chromatin after depletion of PNUTS or WDR82. Furthermore, the RNAPII residence time is greatly enhanced by proteasome inhibition in control cells but less so in PNUTS- or WDR82-depleted cells, indicating that PNUTS and WDR82 promote degradation of RNAPII on chromatin. Notably, reduced replication is dependent on transcription and the phospho-CTD binding protein CDC73 after depletion of PNUTS/WDR82. Altogether, our results suggest that RNAPII-CTD dephosphorylation is required for the continuous turnover of RNAPII on chromatin, thereby preventing T-R conflicts.

Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes.

Fragment Mass Spectrum Prediction Facilitates Site Localization of Phosphorylation

Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been the most widely used technology for phosphoproteomics studies. As an alternative to database searching and probability-based phosphorylation site localization approaches, spectral library searching has been proved to be effective in the identification of phosphopeptides. However, incompletion of experimental spectral libraries limits the identification capability. Herein, we utilize MS/MS spectrum prediction coupled with spectral matching for site localization of phosphopeptides. In silico MS/MS spectra are generated from peptide sequences by deep learning/machine learning models trained with nonphosphopeptides. Then, mass shift according to phosphorylation sites, phosphoric acid neutral loss, and a "budding" strategy are adopted to adjust the in silico mass spectra. In silico MS/MS spectra can also be generated in one step for phosphopeptides using models trained with phosphopeptides. The method is benchmarked on data sets of synthetic phosphopeptides and is used to process real biological samples. It is demonstrated to be a method requiring only computational resources that supplements the probability-based approaches for phosphorylation site localization of singly and multiply phosphorylated peptides.

Update on Proteomic approaches to uncovering virus-induced protein alterations and virus -host protein interactions during the progression of viral infection

INTRODUCTION

Viruses induce profound changes in the cells they infect. Understanding these perturbations will assist in designing better therapeutics to combat viral infection. System-based proteomic assays now provide unprecedented opportunity to monitor large numbers of cellular proteins.

AREAS COVERED

This review will describe various quantitative and functional mass spectrometry-based methods, and complementary non-mass spectrometry-based methods, such as aptamer profiling and proximity extension assays, and examples of how each are used to delineate how viruses affect host cells, identify which viral proteins interact with which cellular proteins, and how these change during the course of a viral infection. PubMed was searched multiple times prior to manuscript submissions and revisions, using virus, viral, proteomics; in combination with each keyword. The most recent examples of published works from each search were then analyzed.

EXPERT OPINION

There has been exponential growth in numbers and types of proteomic analyses in recent years. Continued development of reagents that allow increased multiplexing and deeper proteomic probing of the cell, at quantitative and functional levels, enhancements that target more important protein modifications, and improved bioinformatics software tools and pathway prediction algorithms will accelerate this growth and usher in a new era of host proteome understanding.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.

Leishmania donovani 90 kD Heat Shock Protein - Impact of Phosphosites on Parasite Fitness, Infectivity and Casein Kinase Affinity

Leishmania parasites are thought to control protein activity at the post-translational level, e.g. by protein phosphorylation. In the pathogenic amastigote, the mammalian stage of Leishmania parasites, heat shock proteins show increased phosphorylation, indicating a role in stage-specific signal transduction. Here we investigate the impact of phosphosites in the L. donovani heat shock protein 90. Using a chemical knock-down/genetic complementation approach, we mutated 11 confirmed or presumed phosphorylation sites and assessed the impact on overall fitness, morphology and in vitro infectivity. Most phosphosite mutations affected the growth and morphology of promastigotes in vitro, but with one exception, none of the phosphorylation site mutants had a selective impact on the in vitro infection of macrophages. Surprisingly, aspartate replacements mimicking the negative charge of phosphorylated serines or threonines had mostly negative impacts on viability and infectivity. HSP90 is a substrate for casein kinase 1.2-catalysed phosphorylation in vitro. While several putative phosphosite mutations abrogated casein kinase 1.2 activity on HSP90, only Ser289 could be identified as casein kinase target by mass spectrometry. In summary, our data show HSP90 as a downstream client of phosphorylation-mediated signalling in an organism that depends on post-transcriptional gene regulation.

Dissecting Candida Pathobiology: Post-Translational Modifications on the Candida tropicalis Proteome

Candida tropicalis belongs to the non-albicans group of Candida, and causes epidermal, mucosal, or systemic candidiasis in immunocompromised individuals. Although the prevalence of candidiasis has increased worldwide and non-albicans Candida (NAC) are becoming more significant, there are very few studies that focus on the NAC biology. Proteins and their post-translational modifications (PTMs) are an integral aspect in the pathobiology of such medically important fungi. Previously, we had reported the largest proteomic catalog of C. tropicalis. Notably, PTMs can be identified from proteomics data without a priori enrichment for a particular PTM, thus allowing broad-scale omics analyses. In this study, we developed the "PTM-Pro," a graphical user interface-based tool for identification and summary of high-confidence PTM sites based on statistical threshold of users' choice. We mined available proteomic data of C. tropicalis, and using PTM-Pro identified nearly 600 high-confidence PTM sites. The PTMs identified include phosphorylation of serine, threonine, and tyrosine; acetylation, crotonylation, methylation, and succinylation of lysine. These PTMs reside on biologically significant molecules, including histones, enzymes, and transcription factors. To our knowledge, this is the first report of PTMs in C. tropicalis and lays a foundation for future investigations of C. tropicalis PTMs. In addition, the PTM-Pro offers a graphical user interface tool for research on PTM sites in the field of proteomics.

Bioinformatics Resources for Interpreting Proteomics Mass Spectrometry Data

Developments in mass spectrometry (MS) instrumentation have supported the advance of a variety of proteomic technologies that have enabled scientists to assess differences between healthy and diseased states. In particular, the ability to identify altered biological processes in a cell has led to the identification of novel drug targets, the development of more effective therapeutic drugs, and the growth of new diagnostic approaches and tools for personalized medicine applications. Nevertheless, large-scale proteomic data generated by modern mass spectrometers are extremely complex and necessitate equally complex bioinformatics tools and computational algorithms for their interpretation. A vast number of commercial and public resources have been developed for this purpose, often leaving the researcher perplexed at the overwhelming list of choices that exist. To address this challenge, the aim of this chapter is to provide a roadmap to the basic steps that are involved in mass spectrometry data acquisition and processing, and to describe the most common tools that are available for placing the results in biological context.

Spatiotemporal Proteomic Profiling of Human Cerebral Development

Mass spectrometry (MS) analysis of human post-mortem central nervous system (CNS) tissue and induced pluripotent stem cell (iPSC)-based directed differentiations offer complementary avenues to define protein signatures of neurodevelopment. Methodological improvements of formalin-fixed, paraffin-embedded (FFPE) protein isolation now enable widespread proteomic analysis of well-annotated archival tissue samples in the context of development and disease. Here, we utilize a shotgun label-free quantification (LFQ) MS method to profile magnetically enriched human cortical neurons and neural progenitor cells (NPCs) derived from iPSCs. We use these signatures to help define spatiotemporal protein dynamics of developing human FFPE cerebral regions. We show that the use of high resolution Q Exactive mass spectrometers now allow simultaneous quantification of >2700 proteins in a single LFQ experiment and provide sufficient coverage to define novel biomarkers and signatures of NPC maintenance and differentiation. Importantly, we show that this abbreviated strategy allows efficient recovery of novel cytoplasmic, membrane-specific and synaptic proteins that are shared between both in vivo and in vitro neuronal differentiation. This study highlights the discovery potential of non-comprehensive high-throughput proteomic profiling of unfractionated clinically well-annotated FFPE human tissue from a diverse array of development and diseased states.

Membrane potential independent transport of NH3 in the absence of ammonium permeases in Saccharomyces cerevisiae

Background

Microbial production of nitrogen containing compounds requires a high uptake flux and assimilation of the N-source (commonly ammonium), which is generally coupled with ATP consumption and negatively influences the product yield. In the industrial workhorse Saccharomyces cerevisiae, ammonium (NH₄⁺) uptake is facilitated by ammonium permeases (Mep1, Mep2 and Mep3), which transport the NH₄⁺ ion, resulting in ATP expenditure to maintain the intracellular charge balance and pH by proton export using the plasma membrane-bound H⁺-ATPase.

Results

To decrease the ATP costs for nitrogen assimilation, the Mep genes were removed, resulting in a strain unable to uptake the NH₄⁺ ion. Subsequent analysis revealed that growth of this ∆mep strain was dependent on the extracellular NH₃ concentrations. Metabolomic analysis revealed a significantly higher intracellular NH_X concentration (3.3-fold) in the ∆mep strain than in the reference strain. Further proteomic analysis revealed significant up-regulation of vacuolar proteases and genes involved in various stress responses.

Conclusions

Our results suggest that the uncharged species, NH₃, is able to diffuse into the cell. The measured intracellular/extracellular NH_X ratios under aerobic nitrogen-limiting conditions were consistent with this hypothesis when NH_x compartmentalization was considered. On the other hand, proteomic analysis indicated a more pronounced N-starvation stress response in the ∆mep strain than in the reference strain, which suggests that the lower biomass yield of the ∆mep strain was related to higher turnover rates of biomass components.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0381-1) contains supplementary material, which is available to authorized users.

Param-Medic: A Tool for Improving MS/MS Database Search Yield by Optimizing Parameter Settings

In shotgun proteomics analysis, user-specified parameters are critical to database search performance and therefore to the yield of confident peptide-spectrum matches (PSMs). Two of the most important parameters are related to the accuracy of the mass spectrometer. Precursor mass tolerance defines the peptide candidates considered for each spectrum. Fragment mass tolerance or bin size determines how close observed and theoretical fragments must be to be considered a match. For either of these two parameters, too wide a setting yields randomly high-scoring false PSMs, whereas too narrow a setting erroneously excludes true PSMs, in both cases, lowering the yield of peptides detected at a given false discovery rate. We describe a strategy for inferring optimal search parameters by assembling and analyzing pairs of spectra that are likely to have been generated by the same peptide ion to infer precursor and fragment mass error. This strategy does not rely on a database search, making it usable in a wide variety of settings. In our experiments on data from a variety of instruments including Orbitrap and Q-TOF acquisitions, this strategy yields more high-confidence PSMs than using settings based on instrument defaults or determined by experts. Param-Medic is open-source and cross-platform. It is available as a standalone tool ( http://noble.gs.washington.edu/proj/param-medic/ ) and has been integrated into the Crux proteomics toolkit ( http://crux.ms ), providing automatic parameter selection for the Comet and Tide search engines.

Quantitative Proteomics Using SILAC

The ability to enumerate all of the proteins in a cell is quickly becoming a reality. Quantitative proteomics adds an extra dimension to proteome-wide discovery experiments by enabling differential measurements of protein concentrations, characterization of protein turnover, increased stringency of co-immunoprecipitation reactions, as well as many other intriguing applications. One of the most widely used techniques that enable relative protein quantitation is stable isotope labeling by amino acids in cell culture (SILAC) (Ong et al., Mol Cell Proteomics 1(5):376-386, 2002). Over the past decade, SILAC has become the preferred approach for proteome-wide quantitation by mass spectrometry. This approach relies on the metabolic incorporation of isotopically enriched amino acids into the proteome of cells-the proteome of "light" (¹H, ¹²C, ¹⁴N) cells can then be compared to "heavy" (²H, ¹³C, ¹⁵N) cells as the isotopically labeled proteins and peptides are easily distinguished in a mass spectrometer. Since cellular uptake and response to isotopically different amino acid(s) is naïve, it is without impact on cell physiology. We provide a detailed step-by-step procedure for performing SILAC-based experiment for proteome-wide quantitation in this chapter.

Common errors in mass spectrometry-based analysis of post-translational modifications

Mass spectrometry (MS) is a powerful tool to analyze complex mixtures of proteins in a high-throughput fashion. Proteome analysis has already become a routine task in biomedical research with the emergence of proteomics core facilities in most research institutions. Post-translational modifications (PTMs) represent a mechanism by which complex biological processes are orchestrated dynamically at the systems level. MS is rapidly becoming popular to discover new modifications and novel sites of known PTMs, revolutionizing the current understanding of diverse signaling pathways and biological processes. However, MS-based analysis of PTMs has its own caveats and pitfalls that can lead to erroneous conclusions. Here, we review the most common errors in MS-based PTM analyses with the goal of adopting strategies that maximize correct interpretation in the context of biological questions that are being addressed. Finally, we provide suggestions that should help mass spectrometrists, bioinformaticians and biologists to perform and interpret MS-based PTM analyses more accurately.

Stable isotope dimethyl labelling for quantitative proteomics and beyond

Stable-isotope reductive dimethylation, a cost-effective, simple, robust, reliable and easy-to- multiplex labelling method, is widely applied to quantitative proteomics using liquid chromatography-mass spectrometry. This review focuses on biological applications of stable-isotope dimethyl labelling for a large-scale comparative analysis of protein expression and post-translational modifications based on its unique properties of the labelling chemistry. Some other applications of the labelling method for sample preparation and mass spectrometry-based protein identification and characterization are also summarized.This article is part of the themed issue 'Quantitative mass spectrometry'.

In Vivo Analysis of NH4+ Transport and Central Nitrogen Metabolism in Saccharomyces cerevisiae during Aerobic Nitrogen-Limited Growth

Ammonium is the most common N source for yeast fermentations. Although its transport and assimilation mechanisms are well documented, there have been only a few attempts to measure the in vivo intracellular concentration of ammonium and assess its impact on gene expression. Using an isotope dilution mass spectrometry (IDMS)-based method, we were able to measure the intracellular ammonium concentration in N-limited aerobic chemostat cultivations using three different N sources (ammonium, urea, and glutamate) at the same growth rate (0.05 h^-1). The experimental results suggest that, at this growth rate, a similar concentration of intracellular (IC) ammonium, about 3.6 mmol NH₄⁺/liter_IC, is required to supply the reactions in the central N metabolism, independent of the N source. Based on the experimental results and different assumptions, the vacuolar and cytosolic ammonium concentrations were estimated. Furthermore, we identified a futile cycle caused by NH₃ leakage into the extracellular space, which can cost up to 30% of the ATP production of the cell under N-limited conditions, and a futile redox cycle between Gdh1 and Gdh2 reactions. Finally, using shotgun proteomics with protein expression determined relative to a labeled reference, differences between the various environmental conditions were identified and correlated with previously identified N compound-sensing mechanisms.IMPORTANCE In our work, we studied central N metabolism using quantitative approaches. First, intracellular ammonium was measured under different N sources. The results suggest that Saccharomyces cerevisiae cells maintain a constant NH₄⁺ concentration (around 3 mmol NH₄⁺/liter_IC), independent of the applied nitrogen source. We hypothesize that this amount of intracellular ammonium is required to obtain sufficient thermodynamic driving force. Furthermore, our calculations based on thermodynamic analysis of the transport mechanisms of ammonium suggest that ammonium is not equally distributed, indicating a high degree of compartmentalization in the vacuole. Additionally, metabolomic analysis results were used to calculate the thermodynamic driving forces in the central N metabolism reactions, revealing that the main reactions in the central N metabolism are far from equilibrium. Using proteomics approaches, we were able to identify major changes, not only in N metabolism, but also in C metabolism and regulation.

Biochemical and cellular characterization of transcription factors binding to the hyperconserved core promoter-associated M4 motif

Background

The motif ACTAYRNNNCCCR (Y being C or T, R being A or G, and N any nucleotide), called M4, was discovered as a putative cis-regulatory element, present 520 times in human promoter regions. Of these, 317 (61 %) are conserved within promoter sequences of four related organisms: human, mouse, rat, and dog. Recent genome-wide studies have described M4 as a transcription factor (TF) binding site for THAP11 that does often overlap with SBS (STAF Binding Site) a second core-promoter associated TF binding module, which associates with the TFs STAF/ZNF143 and RBP-J. Human M4-promoter genes show enhanced expression in cells of hematopoietic origin, especially in B lymphoblasts and peripheral blood B and T cells. Apart from RBP-J that is well known to recruit ICN1 (the intracellular transcriptional mediator of activated Notch1), the functional role of the hyperconserved M4 cis-element in the context of transcriptional regulation of M4-genes in lymphoid cells remains poorly defined.

Results

Here, we present a quantitative proteomic investigation of the M4 motif TF binding landscape in lymphoid cell lines that is further validated by ChIP experiments and functional assays. Our data strongly suggest that THAP11 and Ikaros interact directly, while NFKB1 (NF-kappa B p50) and HCF-1 are binding indirectly to M4-promoters in vitro and in living cells.

Further analysis reveals that M4 is a bipartite composite cis-element, which is recognized by THAP11 via binding to the ACTAYR sequence module, thereby promoting ternary complex formation with HCF-1. Similarly, Ikaros binds to the CCCR module of the M4 motif and this interaction is crucial for recruiting NFKB1 to M4 harboring genes. Transient reporter assays in HEK293 and loss-of-function experiments in Molt4 T cells unequivocally demonstrate that binding of Ikaros and/or THAP11 to M4 bearing promoters is functionally important and therefore biologically relevant. Accordingly, this study validates our SILAC-based DNA protein interaction screening methodology as a valuable surrogate for a bona fide reverse ChIP technology.

Conclusions

The M4 motif (ACTAYRNNNCCCR) is a functional regulatory bipartite cis-element, which engages a THAP11/HCF-1 complex via binding to the ACTAYR module, while the CCCRRNRNRC subsequence part constitutes a binding platform for Ikaros and NFKB1.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3033-3) contains supplementary material, which is available to authorized users.

A phospho-dependent mechanism involving NCoR and KMT2D controls a permissive chromatin state at Notch target genes

The transcriptional shift from repression to activation of target genes is crucial for the fidelity of Notch responses through incompletely understood mechanisms that likely involve chromatin-based control. To activate silenced genes, repressive chromatin marks are removed and active marks must be acquired. Histone H3 lysine-4 (H3K4) demethylases are key chromatin modifiers that establish the repressive chromatin state at Notch target genes. However, the counteracting histone methyltransferase required for the active chromatin state remained elusive. Here, we show that the RBP-J interacting factor SHARP is not only able to interact with the NCoR corepressor complex, but also with the H3K4 methyltransferase KMT2D coactivator complex. KMT2D and NCoR compete for the C-terminal SPOC-domain of SHARP. We reveal that the SPOC-domain exclusively binds to phosphorylated NCoR. The balance between NCoR and KMT2D binding is shifted upon mutating the phosphorylation sites of NCoR or upon inhibition of the NCoR kinase CK2β. Furthermore, we show that the homologs of SHARP and KMT2D in Drosophila also physically interact and control Notch-mediated functions in vivo. Together, our findings reveal how signaling can fine-tune a committed chromatin state by phosphorylation of a pivotal chromatin-modifier.

Mass Spectrometry-Based Protein Quantification

Quantification of individual proteins and even entire proteomes is an important theme in proteomics research. Quantitative proteomics is an approach to obtain quantitative information about proteins in a sample. Compared to qualitative or semi-quantitative proteomics, this approach can provide more insight into the effects of a specific stimulus, such as a change in the expression level of a protein and its posttranslational modifications, or to a panel of proposed biomarkers in a given disease state. Proteomics methodologies, along with a variety of bioinformatics approaches, are a major tool in quantitative proteomics. As the theory and technological aspects underlying the proteomics methodologies will be extensively described in Chap. 20 , and protein identification as a prerequisite of quantification has been discussed in Chap. 17 , we will focus on the quantitative proteomics bioinformatics algorithms and software tools in this chapter. Our goal is to provide researchers and newcomers a rational framework to select suitable bioinformatics tools for data analysis, interpretation, and integration in protein quantification. Before doing so, a brief overview of quantitative proteomics is provided.

A Highly Effective Component Vaccine against Nontyphoidal Salmonella enterica Infections

Nontyphoidal Salmonella enterica (NTS) infections are a major burden to global public health, as they lead to diseases ranging from gastroenteritis to systemic infections and there is currently no vaccine available. Here, we describe a highly effective component vaccine against S. enterica serovar Typhimurium in both gastroenteritis and systemic murine infection models. We devised an approach to generate supernatants of S. enterica serovar Typhimurium, an organism that is highly abundant in virulence factors. Immunization of mice with this supernatant resulted in dramatic protection against a challenge with serovar Typhimurium, showing increased survival in the systemic model and decreased intestinal pathology in the gastrointestinal model. Protection correlated with specific IgA and IgG levels in the serum and specific secretory IgA levels in the feces of immunized mice. Initial characterization of the protective antigens in the bacterial culture supernatants revealed a subset of antigens that exhibited remarkable stability, a highly desirable characteristic of an effective vaccine to be used under suboptimal environmental conditions in developing countries. We were able to purify a subset of the peptides present in the supernatants and show their potential for immunization of mice against serovar Typhimurium resulting in a decreased level of colonization. This component vaccine shows promise with regard to protecting against NTS, and further work should significantly help to establish vaccines against these prevalent infections.

Salmonella enterica infections other than typhoid and paratyphoid fever are a major global health burden, as they cause high morbidity and mortality worldwide. Strategies that prevent Salmonella-related diseases are greatly needed, and there is a significant push for the development of vaccines against nontyphoidal Salmonella enterica serovars. In this work, we describe an S. Typhimurium supernatant-derived vaccine that is effective in reducing bacterial colonization in mouse models of gastroenteritis as well as invasive disease. This is a component vaccine that shows high stability to heat, a feature that is important for use under suboptimal conditions, such as those found in sub-Saharan Africa.

NADH-Cytochrome b5 Reductase 3 Promotes Colonization and Metastasis Formation and Is a Prognostic Marker of Disease-Free and Overall Survival in Estrogen Receptor-Negative Breast Cancer

Metastasis is the main cause of cancer-related deaths and remains the most significant challenge to management of the disease. Metastases are established through a complex multistep process involving intracellular signaling pathways. To gain insight to proteins central to specific steps in metastasis formation, we used a metastasis cell line model that allows investigation of extravasation and colonization of circulating cancer cells to lungs in mice. Using stable isotopic labeling by amino acids in cell culture and subcellular fractionation, the nuclear, cytosol, and mitochondria proteomes were analyzed by LC-MS/MS, identifying a number of proteins that exhibited altered expression in isogenic metastatic versus nonmetastatic cancer cell lines, including NADH-cytochrome b5 reductase 3 (CYB5R3), l-lactate dehydrogenase A (LDHA), Niemann-pick c1 protein (NPC1), and nucleolar RNA helicase 2 (NRH2). The altered expression levels were validated at the protein and transcriptional levels, and analysis of breast cancer biopsies from two cohorts of patients demonstrated a significant correlation between high CYB5R3 expression and poor disease-free and overall survival in patients with estrogen receptor-negative tumors (DFS: p = .02, OS: p = .04). CYB5R3 gene knock-down using siRNA in metastasizing cells led to significantly decreased tumor burden in lungs when injected intravenously in immunodeficient mice. The cellular effects of CYB5R3 knock-down showed signaling alterations associated with extravasation, TGFβ and HIFα pathways, and apoptosis. The decreased apoptosis of CYB5R3 knock-down metastatic cancer cell lines was confirmed in functional assays. Our study reveals a central role of CYB5R3 in extravasation/colonization of cancer cells and demonstrates the ability of our quantitative, comparative proteomic approach to identify key proteins of specific important biological processes that may also prove useful as potential biomarkers of clinical relevance. MS data are available via ProteomeXchange with identifier PXD001391.

Site-specific methylation of Notch1 controls the amplitude and duration of the Notch1 response

Physiologically, Notch signal transduction plays a pivotal role in differentiation; pathologically, Notch signaling contributes to the development of cancer. Transcriptional activation of Notch target genes involves cleavage of the Notch receptor in response to ligand binding, production of the Notch intracellular domain (NICD), and NICD migration into the nucleus and assembly of a coactivator complex. Posttranslational modifications of the NICD are important for its transcriptional activity and protein turnover. Deregulation of Notch signaling and stabilizing mutations of Notch1 have been linked to leukemia development. We found that the methyltransferase CARM1 (coactivator-associated arginine methyltransferase 1; also known as PRMT4) methylated NICD at five conserved arginine residues within the C-terminal transactivation domain. CARM1 physically and functionally interacted with the NICD-coactivator complex and was found at gene enhancers in a Notch-dependent manner. Although a methylation-defective NICD mutant was biochemically more stable, this mutant was biologically less active as measured with Notch assays in embryos of Xenopus laevis and Danio rerio. Mathematical modeling indicated that full but short and transient Notch signaling required methylation of NICD.

A search for protein biomarkers links olfactory signal transduction to social immunity

Background

The Western honey bee (Apis mellifera L.) is a critical component of human agriculture through its pollination activities. For years, beekeepers have controlled deadly pathogens such as Paenibacillus larvae, Nosema spp. and Varroa destructor with antibiotics and pesticides but widespread chemical resistance is appearing and most beekeepers would prefer to eliminate or reduce the use of in-hive chemicals. While such treatments are likely to still be needed, an alternate management strategy is to identify and select bees with heritable traits that allow them to resist mites and diseases. Breeding such bees is difficult as the tests involved to identify disease-resistance are complicated, time-consuming, expensive and can misidentify desirable genotypes. Additionally, we do not yet fully understand the mechanisms behind social immunity. Here we have set out to discover the molecular mechanism behind hygienic behavior (HB), a trait known to confer disease resistance in bees.

Results

After confirming that HB could be selectively bred for, we correlated measurements of this behavior with protein expression over a period of three years, at two geographically distinct sites, using several hundred bee colonies. By correlating the expression patterns of individual proteins with HB scores, we identified seven putative biomarkers of HB that survived stringent control for multiple hypothesis testing. Intriguingly, these proteins were all involved in semiochemical sensing (odorant binding proteins), nerve signal transmission or signal decay, indicative of the series of events required to respond to an olfactory signal from dead or diseased larvae. We then used recombinant versions of two odorant-binding proteins to identify the classes of ligands that these proteins might be helping bees detect.

Conclusions

Our data suggest that neurosensory detection of odors emitted by dead or diseased larvae is the likely mechanism behind a complex and important social immunity behavior that allows bees to co-exist with pathogens.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1193-6) contains supplementary material, which is available to authorized users.

Polarization of the endoplasmic reticulum by ER-septin tethering

Polarization of the plasma membrane (PM) into domains is an important mechanism to compartmentalize cellular activities and to establish cell polarity. Polarization requires formation of diffusion barriers that prevent mixing of proteins between domains. Recent studies have uncovered that the endoplasmic reticulum (ER) of budding yeast and neurons is polarized by diffusion barriers, which in neurons controls glutamate signaling in dendritic spines. The molecular identity of these barriers is currently unknown. Here, we show that a direct interaction between the ER protein Scs2 and the septin Shs1 creates the ER diffusion barrier in yeast. Barrier formation requires Epo1, a novel ER-associated subunit of the polarisome that interacts with Scs2 and Shs1. ER-septin tethering polarizes the ER into separate mother and bud domains, one function of which is to position the spindle in the mother until M phase by confining the spindle capture protein Num1 to the mother ER.

3DSpectra: A 3-dimensional quantification algorithm for LC-MS labeled profile data

Mass spectrometry-based proteomics can generate highly informative datasets, as profile three-dimensional (3D) LC-MS data: LC-MS separates peptides in two dimensions (time, m/z) minimizing their overlap, and profile acquisition enhances quantification. To exploit both data features, we developed 3DSpectra, a 3D approach embedding a statistical method for peptide border recognition. 3DSpectra efficiently accesses profile data by means of mzRTree, and makes use of a priori metadata, provided by search engines, to quantify the identified peptides. An isotopic distribution model, shaped by a bivariate Gaussian Mixture Model (GMM), which includes a noise component, is fitted to the peptide peaks using the expectation-maximization (EM) approach. The EM starting parameters, i.e., the centers and shapes of the Gaussians, are retrieved from the metadata. The borders of the peaks are delimited by the GMM iso-density curves, and noisy or outlying data are discarded from subsequent analysis. The 3DSpectra program was compared to ASAPRatio for a controlled mixture of Isotope-Coded Protein Labels (ICPL) labeled proteins, which were mixed at predefined ratios and acquired in enhanced profile mode, in triplicate. The 3DSpectra software showed significantly higher linearity, quantification accuracy, and precision than did ASAPRatio in this real use case simulation where the true ratios are known, and it also achieved wider peptide coverage and dynamic range.

BIOLOGICAL SIGNIFICANCE

Quantitative proteomics is pivotal for many systems biology related fields, such as biomarker discovery. The quantification quality provided by the adopted software is crucial for the success of protein differential expression studies. To determine the reliability of a quantitative computational method, we suggest evaluating performance parameters like accuracy and precision of the quantifications, robustness to outliers and proteome coverage. A quantitative comparison of these parameters is highly desirable since it enables to benchmark software performance. We applied this strategy to 3DSpectra, a 3-dimensional approach to spectra analysis for MS1 peptide quantification. It distinguishes peptide peaks from spurious peaks interfering in the survey scan. 3DSpectra was compared to ASAPRatio in terms of quantification quality performance parameters and showed an overall improvement.

Transcriptomics and proteomics in stem cell research

Stem cells are capable of self-renewal and differentiation, and the processes regulating these events are among the most comprehensively investigated topics in life sciences. In particular, the molecular mechanisms of the self-renewal, proliferation, and differentiation of stem cells have been extensively examined. Multi-omics integrative analysis, such as transcriptomics combined with proteomics, is one of the most promising approaches to the systemic investigation of stem cell biology. We reviewed the available information on stem cells by examining published results using transcriptomic and proteomic characterization of the different stem cell processes. Comprehensive understanding of these important processes can only be achieved using a systemic methodology, and employing such method will strengthen the study on stem cell biology and promote the clinical applications of stem cells.

A six-plex proteome quantification strategy reveals the dynamics of protein turnover

MS1 full scan based quantification is one of the most popular approaches for large-scale proteome quantification. Typically only three different samples can be differentially labeled and quantified in a single experiment. Here we present a two stages stable isotope labeling strategy which allows six different protein samples (six-plex) to be reliably labeled and simultaneously quantified at MS1 level. Briefly in the first stage, isotope lysine-d0 (K0) and lysine-d4 (K4) are in vivo incorporated into different protein samples during cell culture. Then in the second stage, three of K0 and K4 labeled protein samples are digested by lysine C and in vitro labeled with light (2CH₃), medium (2CD₂H), and heavy (2¹³CD₃) dimethyl groups, respectively. We demonstrated that this six-plex isotope labeling strategy could successfully investigate the dynamics of protein turnover in a high throughput manner.

Monitoring in vivo reversible cysteine oxidation in proteins using ICAT and mass spectrometry

Reversible thiol oxidation of cysteine residues occurs in many intracellular catalytic and signaling processes. Here we describe an optimized protocol, which can be completed in ∼5 d, to unambiguously identify specific cysteine residues that are transiently and reversibly oxidized by comparing two complex biological samples obtained from yeast cell cultures at the proteome level. After 'freezing' the in vivo thiol stage of cysteine residues by medium acidification, we first block reduced thiols in extracts with iodoacetamide (IAM), and then we sequentially reduce and label reversible oxidized thiols with the biotin-based heavy or light IAM derivatives, which are known as isotope-coded affinity tag (ICAT) reagents, so that the two samples can be compared at once after combination of the labeled extracts, trypsin digestion, streptavidin-affinity purification of peptides containing oxidized cysteines, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. For the same protein extracts, before cysteine-containing peptide enrichment, individual relative protein concentrations are obtained by stable-isotope dimethyl labeling.

Acetylation of histone H3 at lysine 64 regulates nucleosome dynamics and facilitates transcription

Post-translational modifications of proteins have emerged as a major mechanism for regulating gene expression. However, our understanding of how histone modifications directly affect chromatin function remains limited. In this study, we investigate acetylation of histone H3 at lysine 64 (H3K64ac), a previously uncharacterized acetylation on the lateral surface of the histone octamer. We show that H3K64ac regulates nucleosome stability and facilitates nucleosome eviction and hence gene expression in vivo. In line with this, we demonstrate that H3K64ac is enriched in vivo at the transcriptional start sites of active genes and it defines transcriptionally active chromatin. Moreover, we find that the p300 co-activator acetylates H3K64, and consistent with a transcriptional activation function, H3K64ac opposes its repressive counterpart H3K64me3. Our findings reveal an important role for a histone modification within the nucleosome core as a regulator of chromatin function and they demonstrate that lateral surface modifications can define functionally opposing chromatin states.

DOI:http://dx.doi.org/10.7554/eLife.01632.001

DNA is a very long molecule, so it needs to be packaged carefully to fit into the nucleus of a cell. To achieve this, the DNA is wrapped around proteins called histones to form a structure termed a nucleosome, which is the building block of a more compacted substance called chromatin. However, to express the genes in the DNA it is necessary to open up parts of the chromatin to give various enzymes access to the DNA.

Cells often chemically modify histones by adding acetyl or methyl groups, and these modifications are known to influence what proteins can bind to the nucleosomes, which ultimately influences what genes are expressed in the cell at a given time. It has been suspected for some time that histone modifications can also influence gene expression more directly, but there has been little evidence for this idea.

Now Di Cerbo et al. have studied what happens when acetyl or methyl groups are added to a specific site within a histone called H3K64, which is close to where the DNA wraps around this histone. These experiments showed that this site tends to be acetylated when a nearby gene is active, and to be unmodified or methylated when this gene is not active. It appears that the addition of the acetyl group makes this region of the chromatin less stable: this, in turn, makes it easier for the chromatin to be unpacked, thus giving access to the enzymes that transcribe the DNA and allowing transcription to take place. The work of Di Cerbo et al. shows that methylation and acetylation at the same site within a histone can define two opposing states of chromatin and DNA: an active state and a repressive state.

DOI:http://dx.doi.org/10.7554/eLife.01632.002

Creation of a federated database of blood proteins: a powerful new tool for finding and characterizing biomarkers in serum

Protein biomarkers offer major benefits for diagnosis and monitoring of disease processes. Recent advances in protein mass spectrometry make it feasible to use this very sensitive technology to detect and quantify proteins in blood. To explore the potential of blood biomarkers, we conducted a thorough review to evaluate the reliability of data in the literature and to determine the spectrum of proteins reported to exist in blood with a goal of creating a Federated Database of Blood Proteins (FDBP). A unique feature of our approach is the use of a SQL database for all of the peptide data; the power of the SQL database combined with standard informatic algorithms such as BLAST and the statistical analysis system (SAS) allowed the rapid annotation and analysis of the database without the need to create special programs to manage the data. Our mathematical analysis and review shows that in addition to the usual secreted proteins found in blood, there are many reports of intracellular proteins and good agreement on transcription factors, DNA remodelling factors in addition to cellular receptors and their signal transduction enzymes. Overall, we have catalogued about 12,130 proteins identified by at least one unique peptide, and of these 3858 have 3 or more peptide correlations. The FDBP with annotations should facilitate testing blood for specific disease biomarkers.

SILAC peptide ratio calculator: a tool for SILAC quantitation of peptides and post-translational modifications

This paper describes an algorithm to assist in relative quantitation of peptide post-translational modifications using stable isotope labeling by amino acids in cell culture (SILAC). The described algorithm first determines the normalization factor and then calculates SILAC ratios for a list of target peptide masses using precursor ion abundances. Four yeast histone mutants were used to demonstrate the effectiveness of this approach for quantitation of peptide post-translational modifications changes. The details of the algorithm's approach for normalization and peptide ratio calculation are described. The examples demonstrate the robustness of the approach as well as its utility to rapidly determine changes in peptide post-translational modifications within a protein.

Challenges and prospects for biomarker research: a current perspective from the developing world

Majority of deaths due to communicable and non-communicable diseases occur in the low and middle-income nations (LMNs), mainly due to the lack of early diagnoses and timely treatments. In such a scenario, biomarkers serve as an indispensible resource that can be used as indicators of biological processes, specific disease conditions or response to therapeutic interventions. Evaluation, diagnosis and management of diseases in developing world by following/extrapolating the findings obtained on the basis of the research work involving only the populations from the developed countries, could often be highly misleading due to existence of diverse patterns of diseases in developing countries compared to the developed world. Biomarker candidates identified from high-throughput integrated omics technologies have promising potential; however, their actual clinical applications are found to be limited, primarily due to the challenges of disease heterogeneity and pre-analytical variability associated with the biomarker discovery pipeline. Additionally, in the developing world, economic crunches, lack of awareness and education, paucity of biorepositories, enormous diversities in socio-epidemiological background, ethnicity, lifestyle, diet, exposure to various environmental risk factors and infectious agents, and ethical and social issues also cumulatively hinder biomarker discovery ventures. Establishment of standard operating procedures, comprehensive data repositories and exchange of scientific findings are crucial for reducing the variability and fragmentation of data. This review highlights the challenges associated with the discovery, validation and translational phases of biomarker research in LMNs with some of their amenable solutions and future prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Towards the phosphoproteome of trypanosomatids

The identification and localization of protein phosphorylation sites provide clues to what proteins or pathways might be activated in a given condition, helping to improve our understanding about signaling networks. Advances in strategies for enrichment of phosphorylated peptides/proteins, mass spectrometry (MS) instrumentation, and specific MS techniques for identification and quantification of post-translational modifications have allowed for large-scale mapping of phosphorylation sites, promoting the field of phosphoproteomics. The great promise of phosphoproteomics is to unravel the dynamics of signaling networks, a layer of the emerging field of systems biology. Until a few years ago only a small number of phosphorylation sites had been described. Following large-scale trends, recent phosphoproteomic studies have reported the mapping of thousands of phosphorylation sites in trypanosomatids. However, quantitative information about the regulation of such sites in different conditions is still lacking. In this chapter, we provide a historical overview of phosphoproteomic studies for trypanosomatids and discuss some challenges and perspectives in the field.

Combining pulsed SILAC labeling and click-chemistry for quantitative secretome analysis

Secreted proteins, such as cytokines, chemokines, and hormones, exhibit central functions in intercellular communication, which is crucial to maintain homeostasis in every multicellular organism. A common approach to identify secreted proteins is by proteomic analysis of culture media after conditioning with a cell type of interest. This is preferably done in serum-free conditions to enable the detection of low-abundance secretory factors that would otherwise be masked by serum proteins. However, serum starvation introduces the risk of bringing cells in a stressed or perturbed state. A superior approach employs the enrichment of newly synthesized and secreted proteins from serum-containing growth medium. This is achieved by the combination of two metabolic labels: stable isotope-labeled amino acids for reliable quantification, and azidohomoalanine (AHA), an azide-bearing analogue of methionine, for the enrichment of newly synthesized and secreted proteins. This approach has been used to compare secretomes of multiple cell lines or to analyze proteins that are secreted upon a specific stimulation. Here we describe in detail the enrichment and quantification of newly synthesized and secreted proteins.

Identification and validation of protein-protein interactions by combining co-immunoprecipitation, antigen competition, and stable isotope labeling

Co-immunoprecipitation (coIP) in combination with mass spectrometry (MS) is a powerful tool to identify potential protein-protein interactions. However, unspecifically precipitated proteins usually result in large numbers of false-positive identifications. Here we describe a detailed protocol particularly useful in plant sciences that is based on (15)N stable isotope labeling of cells, (14)N antigen titration, and coIP/MS to distinguish true from false protein-protein interactions.