Quantitative proteomics by metabolic labeling of model organisms

Rapid headspace solid-phase microextraction sheets with direct analysis in real time mass spectrometry (SPMESH-DART-MS) of derivatized volatile phenols in grape juices and wines

Volatile phenols possess "smoky, spicy" aromas and are routinely measured in grapes, wines and other foodstuffs for quality control. Routine analyses of volatile phenols rely on gas chromatography - mass spectrometry (GC-MS), but slow throughput of GC-MS can cause challenges during times of surge demand, i.e. following 'smoke taint' events involving forest fires near vineyards. Parallel extraction of headspace volatiles onto sorbent sheets (HS-SPMESH) followed by direct analysis in real time mass spectrometry (DART-MS) is a rapid alternative to conventional GC-MS approaches. However, HS-SPMESH extraction is poorly suited for lower volatility odorants, including volatile phenols. This work reports development and validation of an HS-SPMESH-DART-MS approach for five volatile phenols (4-ethylphenol, 4-ethylguiacol, guaiacol, 4-methylguaiacol, and cresols). Prior to HS-SPMESH extraction, volatile phenols were acetylated to facilitate their extraction. A unique feature of this work was the use of d₆-Ac₂O as a derivatizing agent to overcome issues with isobaric interferences inherent to chromatography-free MS techniques. The use of alkaline conditions during derivatization resulted in cumulative measurement of both free and bound forms of volatile phenols. The validated HS-SPMESH-DART-MS method achieved a throughput of 24 samples in ∼60 min (including derivatization and extraction time) with low limits of detection (<1 μg L^-1) and good repeatability (3-6% RSD) in grape and wine matrices. Validation experiments with smoke-tainted grape samples indicated good correlation between total (free + bound) volatile phenols measured by HS-SPMESH-DART-MS and a gold standard GC-MS method.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

LFQ-Based Peptide and Protein Intensity Differential Expression Analysis

Testing for significant differences in quantities at the protein level is a common goal of many LFQ-based mass spectrometry proteomics experiments. Starting from a table of protein and/or peptide quantities from a given proteomics quantification software, many tools and R packages exist to perform the final tasks of imputation, summarization, normalization, and statistical testing. To evaluate the effects of packages and settings in their substeps on the final list of significant proteins, we studied several packages on three public data sets with known expected protein fold changes. We found that the results between packages and even across different parameters of the same package can vary significantly. In addition to usability aspects and feature/compatibility lists of different packages, this paper highlights sensitivity and specificity trade-offs that come with specific packages and settings.

SILAC-Based Quantitative Proteomic Analysis of Drosophila Embryos

The fruit fly Drosophila melanogaster represents a classic genetic model organism that is amenable to a plethora of comprehensive analyses including proteomics. SILAC-based quantitative proteomics is a powerful method to investigate the translational and posttranslational regulation ongoing in cells, tissues, organs, and whole organisms. Here we describe a protocol for routine SILAC labeling of Drosophila adults within one generation to produce embryos with a labeling efficiency of over 92%. In combination with genetic selection markers, this method permits the quantification of translational and posttranslational changes in embryos mutant for developmental and disease-related genes.

Advances in stable isotope labeling: dynamic labeling for spatial and temporal proteomic analysis

The field of proteomics is continually improving, requiring the development of new quantitative methods. Stable isotope labeling in cell culture (SILAC) is a metabolic labeling technique originating in the early 2000s. By incorporating isotopically labeled amino acids into the media used for cell culture, unlabeled versus labeled cells can be differentiated by the mass spectrometer. Traditional SILAC labeling has been expanded to pulsed applications allowing for a new quantitative dimension of proteomics - temporal analysis. The complete introduction of Heavy SILAC labeling chased with surplus unlabeled medium mimics traditional pulse-chase experiments and allows for the loss of heavy signal to track proteomic changes over time. In a similar fashion, pulsed SILAC (pSILAC) monitors the initial incorporation of a heavy label across a period of time, which allows for the rate of protein label integration to be assessed. These innovative techniques have aided in inspiring numerous SILAC-based temporal and spatial labeling applications, including super SILAC, spike-in SILAC, spatial SILAC, and a revival in label multiplexing. This review reflects upon the evolution of SILAC and the pulsed SILAC application, introduces advances in SILAC labeling, and proposes future perspectives for this novel and exciting field.

Quantitative Proteomics Using Isobaric Labeling: A Practical Guide

In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines

Circadian rhythms are oscillations of behavior, physiology and metabolism in many organisms. Recent advancements in omics technology make it possible for genome-wide profiling of circadian rhythms. Here, we conducted a comprehensive analysis of seven existing algorithms commonly used for circadian rhythm detection. Using gold-standard circadian and non-circadian genes, we systematically evaluated the accuracy and reproducibility of the algorithms on empirical datasets generated from various omics platforms under different experimental designs. We also carried out extensive simulation studies to test each algorithm's robustness to key variables, including sampling patterns, replicates, waveforms, signal-to-noise ratios, uneven samplings and missing values. Furthermore, we examined the distributions of the nominal $P$-values under the null and raised issues with multiple testing corrections using traditional approaches. With our assessment, we provide method selection guidelines for circadian rhythm detection, which are applicable to different types of high-throughput omics data.

Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression

Cancer is one of the largest contributors to the burden of chronic disease in the world and is the second leading cause of death globally. It is associated with episodes of low-oxygen stress (hypoxia or ischemia/reperfusion) that promotes cancer progression and therapeutic resistance. Efforts have been made in the past using traditional proteomic approaches to decipher oxygen deprivation stress-related mechanisms of the disease initiation and progression and to identify key proteins as a therapeutic target for the treatment and prevention. Despite the potential benefits of proteomic in translational research for the discovery of new drugs, the therapeutic outcome with this approach has not met expectations in clinical trials. This is mainly due to the disease complexity which possess a multifaceted molecular pathology. Therefore, novel strategies to identify and characterize clinically important sets of modulators and molecular events for multi-target drug discovery are needed. Here, we review important past and current studies on proteomics in cancer with an emphasis on recent pioneered labeling approaches in mass spectrometry (MS)-based systematic quantitative analysis to improve clinical success. We also discuss the results of the selected innovative publications that integrate advanced proteomic technologies (e.g. MALDI-MSI, pSILAC/SILAC/iTRAQ/TMT-LC-MS/MS, MRM-MS) for comprehensive analysis of proteome dynamics in different biosystems, including cell type, cell species, and subcellular proteome (i.e. secretome and chromatome). Finally, we discuss the future direction and challenges in the application of these technological advancements in mass spectrometry within the context of cancer and hypoxia.

Noise Exposures Causing Hearing Loss Generate Proteotoxic Stress and Activate the Proteostasis Network

Exposure to excessive sound causes noise-induced hearing loss through complex mechanisms and represents a common and unmet neurological condition. We investigate how noise insults affect the cochlea with proteomics and functional assays. Quantitative proteomics reveals that exposure to loud noise causes proteotoxicity. We identify and confirm hundreds of proteins that accumulate, including cytoskeletal proteins, and several nodes of the proteostasis network. Transcriptomic analysis reveals that a subset of the genes encoding these proteins also increases acutely after noise exposure, including numerous proteasome subunits. Global cochlear protein ubiquitylation levels build up after exposure to excess noise, and we map numerous posttranslationally modified lysines residues. Several collagen proteins decrease in abundance, and Col9a1 specifically localizes to pillar cell heads. After two weeks of recovery, the cochlea selectively elevates the abundance of the protein synthesis machinery. We report that overstimulation of the auditory system drives a robust cochlear proteotoxic stress response.

THE PRESENT AND FUTURE OF THE MASS SPECTROMETRY-BASED INVESTIGATION OF THE EXOSOME LANDSCAPE

Exosomes are critical intercellular messengers released upon the fusion of multivesicular bodies with the cellular plasma membrane that deliver their cargo in the form of extracellular vesicles. Containing numerous nonrandomly packed functional proteins, lipids, and RNAs, exosomes are vital intercellular messengers that contribute to the physiologic processes of the healthy organism. During the post-genome era, exosome-oriented proteomics have garnered great interest. Since its establishment, mass spectrometry (MS) has been indispensable for the field of proteomics research and has advanced rapidly to interrogate biological samples at a higher resolution and sensitivity. Driven by new methodologies and more advanced instrumentation, MS-based approaches have revolutionized our understanding of protein biology. As the access to online proteomics database platforms has blossomed, experimental data processing occurs with more speed and accuracy. Here, we review recent advances in the technological progress of MS-based proteomics and several new detection strategies for MS-based proteomics research. We also summarize the use of integrated online databases for proteomics research in the era of big data. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Accumulation of "Old Proteins" and the Critical Need for MS-based Protein Turnover Measurements in Aging and Longevity

Aging and age-related diseases are accompanied by proteome remodeling and progressive declines in cellular machinery required to maintain protein homeostasis (proteostasis), such as autophagy, ubiquitin-mediated degradation, and protein synthesis. While many studies have focused on capturing changes in proteostasis, the identification of proteins that evade these cellular processes has recently emerged as an approach to studying the aging proteome. With advances in proteomic technology, it is possible to monitor protein half-lives and protein turnover at the level of individual proteins in vivo. For large-scale studies, these technologies typically include the use of stable isotope labeling coupled with MS and comprehensive assessment of protein turnover rates. Protein turnover studies have revealed groups of highly relevant long-lived proteins (LLPs), such as the nuclear pore complexes, extracellular matrix proteins, and protein aggregates. Here, the role of LLPs during aging and age-related diseases and the methods used to identify and quantify their changes are reviewed. The methods available to conduct studies of protein turnover, used in combination with traditional proteomic methods, will enable the field to perform studies in a systems biology context, as changes in proteostasis may not be revealed in studies that solely measure differential protein abundances.

SILAC-based quantitative proteomic analysis of Drosophila gastrula stage embryos mutant for fibroblast growth factor signalling

Quantitative proteomic analyses in combination with genetics provide powerful tools in developmental cell signalling research. Drosophila melanogaster is one of the most widely used genetic models for studying development and disease. Here we combined quantitative proteomics with genetic selection to determine changes in the proteome upon depletion of Heartless (Htl) Fibroblast-Growth Factor (FGF) receptor signalling in Drosophila embryos at the gastrula stage. We present a robust, single generation SILAC (stable isotope labelling with amino acids in cell culture) protocol for labelling proteins in early embryos. For the selection of homozygously mutant embryos at the pre-gastrula stage, we developed an independent genetic marker. Our analyses detected quantitative changes in the global proteome of htl mutant embryos during gastrulation. We identified distinct classes of downregulated and upregulated proteins, and network analyses indicate functionally related groups of proteins in each class. In addition, we identified changes in the abundance of phosphopeptides. In summary, our quantitative proteomic analysis reveals global changes in metabolic, nucleoplasmic, cytoskeletal and transport proteins in htl mutant embryos.

Decoding communication patterns of the innate immune system by quantitative proteomics

The innate immune system is a collective network of cell types involved in cell recruitment and activation using a robust and refined communication system. Engagement of receptor-mediated intracellular signaling initiates communication cascades by conveying information about the host cell status to surrounding cells for surveillance and protection. Comprehensive profiling of innate immune cells is challenging due to low cell numbers, high dynamic range of the cellular proteome, low abundance of secreted proteins, and the release of degradative enzymes (e.g., proteases). However, recent advances in mass spectrometry-based proteomics provides the capability to overcome these limitations through profiling the dynamics of cellular processes, signaling cascades, post-translational modifications, and interaction networks. Moreover, integration of technologies and molecular datasets provide a holistic view of a complex and intricate network of communications underscoring host defense and tissue homeostasis mechanisms. In this Review, we explore the diverse applications of mass spectrometry-based proteomics in innate immunity to define communication patterns of the innate immune cells during health and disease. We also provide a technical overview of mass spectrometry-based proteomic workflows, with a focus on bottom-up approaches, and we present the emerging role of proteomics in immune-based drug discovery while providing a perspective on new applications in the future.

Hippocampal proteomic changes of susceptibility and resilience to depression or anxiety in a rat model of chronic mild stress

Chronic stressful occurrences are documented as a vital cause of both depression and anxiety disorders. However, the stress-induced molecular mechanisms underlying the common and distinct pathophysiology of these disorders remains largely unclear. We utilized a chronic mild stress (CMS) rat model to differentiate and subgroup depression-susceptible, anxiety-susceptible, and insusceptible rats. The hippocampus was analyzed for differential proteomes by combining mass spectrometry and the isobaric tags for relative and absolute quantitation (iTRAQ) labeling technique. Out of 2593 quantified proteins, 367 were aberrantly expressed. These hippocampal protein candidates might be associated with susceptibility to stress-induced depression or anxiety and stress resilience. They provide the potential protein systems involved in various metabolic pathways as novel investigative protein targets. Further, independent immunoblot analysis identified changes in Por, Idh2 and Esd; Glo1, G6pdx, Aldh2, and Dld; Dlat, Ogdhl, Anxal, Tpp2, and Sdha that were specifically associated to depression-susceptible, anxiety-susceptible, or insusceptible groups respectively, suggesting that identical CMS differently impacted the mitochondrial and metabolic processes in the hippocampus. Collectively, the observed alterations to protein abundance profiles of the hippocampus provided significant and novel insights into the stress regulation mechanism in a CMS rat model. This might serve as the molecular basis for further studies that would contributed to a better understanding of the similarities and differences in pathophysiologic mechanisms underlying stress-induced depression or anxiety, and stress resiliency.

Mass Spectrometry-Based Proteomics of Fungal Pathogenesis, Host-Fungal Interactions, and Antifungal Development

The prevalence of fungal diseases is increasing on a global scale, ranging from acute to systemic infections caused by commensal or pathogenic microorganisms, often associated with the immune status of the host. Morbidity and mortality rates remain high and our ability to treat fungal infections is challenged by a limited arsenal of antifungal agents and the emergence of drug resistant pathogens. There is a high demand for new approaches to elucidate the fungal mechanisms of pathogenesis and the interplay between host and pathogen to discover novel treatment options. Moreover, the need for improved drug efficacy and reduced host toxicity requires the identification and characterization of antifungal biological targets and molecular mechanisms of action. Mass spectrometry (MS)-based proteomics is a rapidly advancing field capable of addressing these priorities by providing comprehensive information on the dynamics of cellular processes, modifications, and interactions. In this Review, we focus on applications of MS-based proteomics in a diverse array of fungal pathogens and host systems to define and distinguish the molecular details of fungal pathogenesis and host–fungal interactions. We also explore the emerging role of MS-based proteomics in the discovery and development of novel antifungal therapies and provide insight into the future of MS-based proteomics in fungal biology.

Investigating the Role of Mitochondria in Type 2 Diabetes - Lessons from Lipidomics and Proteomics Studies of Skeletal Muscle and Liver

Mitochondrial dysfunction is discussed as a key player in the pathogenesis of type 2 diabetes mellitus (T2Dm), a highly prevalent disease rapidly developing as one of the greatest global health challenges of this century. Data however about the involvement of mitochondria, central hubs in bioenergetic processes, in the disease development are still controversial. Lipid and protein homeostasis are under intense discussion to be crucial for proper mitochondrial function. Consequently proteomics and lipidomics analyses might help to understand how molecular changes in mitochondria translate to alterations in energy transduction as observed in the healthy and metabolic diseases such as T2Dm and other related disorders. Mitochondrial lipids integrated in a tool covering proteomic and functional analyses were up to now rarely investigated, although mitochondrial lipids might provide a possible lynchpin in the understanding of type 2 diabetes development and thereby prevention. In this chapter state-of-the-art analytical strategies, pre-analytical aspects, potential pitfalls as well as current proteomics and lipidomics-based knowledge about the pathophysiological role of mitochondria in the pathogenesis of type 2 diabetes will be discussed.

Recent Trends in Plant Protein Complex Analysis in a Developmental Context

Because virtually all proteins interact with other proteins, studying protein-protein interactions (PPIs) is fundamental in understanding protein function. This is especially true when studying specific developmental processes, in which proteins often make developmental stage- or tissue specific interactions. However, studying these specific PPIs in planta can be challenging. One of the most widely adopted methods to study PPIs in planta is affinity purification coupled to mass spectrometry (AP/MS). Recent developments in the field of mass spectrometry have boosted applications of AP/MS in a developmental context. This review covers two main advancements in the field of affinity purification to study plant developmental processes: increasing the developmental resolution of the harvested tissues and moving from affinity purification to affinity enrichment. Furthermore, we discuss some new affinity purification approaches that have recently emerged and could have a profound impact on the future of protein interactome analysis in plants.

Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining

There are more than 3.7 million published articles on the biological functions or disease implications of proteins, constituting an important resource of proteomics knowledge. However, it is difficult to summarize the millions of proteomics findings in the literature manually and quantify their relevance to the biology and diseases of interest. We developed a fully automated bioinformatics framework to identify and prioritize proteins associated with any biological entity. We used the 22 targeted areas of the Biology/Disease-driven (B/D)-Human Proteome Project (HPP) as examples, prioritized the relevant proteins through their Protein Universal Reference Publication-Originated Search Engine (PURPOSE) scores, validated the relevance of the score by comparing the protein prioritization results with a curated database, computed the scores of proteins across the topics of B/D-HPP, and characterized the top proteins in the common model organisms. We further extended the bioinformatics workflow to identify the relevant proteins in all organ systems and human diseases and deployed a cloud-based tool to prioritize proteins related to any custom search terms in real time. Our tool can facilitate the prioritization of proteins for any organ system or disease of interest and can contribute to the development of targeted proteomic studies for precision medicine.

Protein Turnover in Aging and Longevity

Progressive loss of proteostasis is a hallmark of aging that is marked by declines in various components of proteostasis machinery, including: autophagy, ubiquitin-mediated degradation, protein synthesis, and others. While declines in proteostasis have historically been observed as changes in these processes, or as bulk changes in the proteome, recent advances in proteomic methodologies have enabled the comprehensive measurement of turnover directly at the level of individual proteins in vivo. These methods, which utilize a combination of stable-isotope labeling, mass spectrometry, and specialized software analysis, have now been applied to various studies of aging and longevity. Here we review the role of proteostasis in aging and longevity, with a focus on the proteomic methods available to conduct protein turnover in aging models and the insights these studies have provided thus far.

Cell-free synthesis of stable isotope-labeled internal standards for targeted quantitative proteomics

High-sensitivity mass spectrometry approaches using selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) methods are powerful tools for targeted quantitative proteomics-based investigation of dynamics in specific biological systems. Both high-sensitivity detection of low-abundance proteins and their quantification using this technique employ stable isotope-labeled peptide internal standards. Currently, there are various ways for preparing standards, including chemical peptide synthesis, cellular protein expression, and cell-free protein or peptide synthesis. Cell-free protein synthesis (CFPS) or in vitro translation (IVT) systems in particular provide high-throughput and low-cost preparation methods, and various cell types and reconstituted forms are now commercially available. Herein, we review the use of such systems for precise and reliable protein quantification.

Metabolic Labeling of Microorganisms with Stable Heavy Nitrogen Isotopes (15N)

Quantitative Proteomics based on stable isotope labeling by ¹⁵N is a versatile method in determination of changes in microbial protein expression profiles. Once set up, the workflow relies on defined chemical or commercially available complex media leading to universal heavy labeling by the stable isotope ¹⁵N. This chapter provides a detailed protocol for labeling of different bacteria and the strategy for evaluating the incorporation rate of the heavy stable isotopes in the sample is discussed.

Exploring ribosome composition and newly synthesized proteins through proteomics and potential biomedical applications

INTRODUCTION

Protein synthesis is the outcome of tightly regulated gene expression which is responsive to a variety of conditions. Efforts are ongoing to monitor individual stages of protein synthesis to ensure maximum efficiency and accuracy. Due to post-transcriptional regulation mechanisms, the correlation between translatome and proteome is higher than between transcriptome and proteome. However, the most accurate approach to assess the key modulators and final protein expression is directly by using proteomics. Areas covered: This review covers various proteomic strategies that were used to better understand post-transcriptional regulation, specifically during and early after translation. The methods that identify both regulatory proteins associated with translational components and newly synthesized proteins are discussed. Expert commentary: Emerging proteomic approaches make it possible to monitor protein dynamics in cells, tissues and whole animals. The ability to detect alteration in protein abundance soon after their synthesis enables earlier recognition of disease causing factors and candidates to prevent/rectify disease phenotype.

A Comprehensive Guide for Performing Sample Preparation and Top-Down Protein Analysis

Methodologies for the global analysis of proteins in a sample, or proteome analysis, have been available since 1975 when Patrick O′Farrell published the first paper describing two-dimensional gel electrophoresis (2D-PAGE). This technique allowed the resolution of single protein isoforms, or proteoforms, into single ‘spots’ in a polyacrylamide gel, allowing the quantitation of changes in a proteoform′s abundance to ascertain changes in an organism′s phenotype when conditions change. In pursuit of the comprehensive profiling of the proteome, significant advances in technology have made the identification and quantitation of intact proteoforms from complex mixtures of proteins more routine, allowing analysis of the proteome from the ‘Top-Down’. However, the number of proteoforms detected by Top-Down methodologies such as 2D-PAGE or mass spectrometry has not significantly increased since O’Farrell’s paper when compared to Bottom-Up, peptide-centric techniques. This article explores and explains the numerous methodologies and technologies available to analyse the proteome from the Top-Down with a strong emphasis on the necessity to analyse intact proteoforms as a better indicator of changes in biology and phenotype. We arrive at the conclusion that the complete and comprehensive profiling of an organism′s proteome is still, at present, beyond our reach but the continuing evolution of protein fractionation techniques and mass spectrometry brings comprehensive Top-Down proteome profiling closer.

Physiological Effects of Ac4ManNAz and Optimization of Metabolic Labeling for Cell Tracking

Metabolic labeling techniques are powerful tools for cell labeling, tracking and proteomic analysis. However, at present, the effects of the metabolic labeling agents on cell metabolism and physiology are not known. To address this question, in this study, we analyzed the effects of cells treated with Ac4ManNAz through microarray analysis and analyses of membrane channel activity, individual bio-physiological properties, and glycolytic flux. According to the results, treatment with 50 μM Ac4ManNAz led to the reduction of major cellular functions, including energy generation capacity, cellular infiltration ability and channel activity. Interestingly, 10 μM Ac4ManNAz showed the least effect on cellular systems and had a sufficient labeling efficiency for cell labeling, tracking and proteomic analysis. Based on our results, we suggest 10 μM as the optimum concentration of Ac4ManNAz for in vivo cell labeling and tracking. Additionally, we expect that our approach could be used for cell-based therapy for monitoring the efficacy of molecule delivery and the fate of recipient cells.

A timeline of stable isotopes and mass spectrometry in the life sciences

This review retraces the role of stable isotopes and mass spectrometry in the life sciences. The timeline is divided into four segments covering the years 1920-1950, 1950-1980, 1980-2000, and 2000 until today. For each period methodic progress and typical applications are discussed. Application of stable isotopes is driven by improvements of mass spectrometry, chromatography, and related fields in sensitivity, mass accuracy, structural specificity, complex sample handling ability, data output, and data evaluation. We currently experience the vision of omics-type analyses, that is, the comprehensive identification and quantification of a complete compound class within one or a few analytical runs. This development is driven by stable isotopes without competition by radioisotopes. In metabolic studies as classic field of isotopic tracer experiments, stable isotopes and radioisotopes were competing solutions, with stable isotopes as the long-term junior partner. Since the 1990s the number of metabolic studies with radioisotopes decreases, whereas stable isotope studies retain their slow but stable upward tendency. Unique fields of stable isotopes are metabolic tests in newborns, metabolic experiments in healthy controls, newborn screening for inborn errors, quantification of drugs and drug metabolites in doping control, natural isotope fractionation in geology, ecology, food authentication, or doping control, and more recently the field of quantitative omics-type analyses. There, cells or whole organisms are systematically labeled with stable isotopes to study proteomic differences or specific responses to stimuli or genetic manipulation. The duo of stable isotopes and mass spectrometry will probably continue to grow in the life sciences, since it delivers reference-quality quantitative data with molecular specificity, often combined with informative isotope effects. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:58-85, 2017.

Kinase Assay-Linked Phosphoproteomics: Discovery of Direct Kinase Substrates

Dissection of direct kinase-substrate relationships provides invaluable information about phosphorylation pathways and can highlight both pathogenic mechanisms and possible drug targets for diseases in which abnormal kinase activity is linked to onset and progression. Here, we describe a mass spectrometry-based strategy to define the direct substrates of a kinase of interest. The kinase assay-linked phosphoproteomics approach examines putative kinase substrates both in vitro and in vivo to produce a list of highly confident substrates.

Synthesis of d-labeled and unlabeled ethyl succinic anhydrides and application to quantitative analysis of peptides by isotope differential mass spectrometry

Ethyl succinic anhydride and its d₅-labeled version have been synthesized and applied to quantitative analysis of peptides in combination with MALDI or ESI mass spectrometry. These modifiers react with amino groups in the N-termini and lysine side chains in proteins, and therefore the combination of these modifiers was shown to be a useful tool for quantification of peptides and hence for proteomics research.

Identifying proteins that bind to specific RNAs - focus on simple repeat expansion diseases

RNA–protein complexes play a central role in the regulation of fundamental cellular processes, such as mRNA splicing, localization, translation and degradation. The misregulation of these interactions can cause a variety of human diseases, including cancer and neurodegenerative disorders. Recently, many strategies have been developed to comprehensively analyze these complex and highly dynamic RNA–protein networks. Extensive efforts have been made to purify in vivo-assembled RNA–protein complexes. In this review, we focused on commonly used RNA-centric approaches that involve mass spectrometry, which are powerful tools for identifying proteins bound to a given RNA. We present various RNA capture strategies that primarily depend on whether the RNA of interest is modified. Moreover, we briefly discuss the advantages and limitations of in vitro and in vivo approaches. Furthermore, we describe recent advances in quantitative proteomics as well as the methods that are most commonly used to validate robust mass spectrometry data. Finally, we present approaches that have successfully identified expanded repeat-binding proteins, which present abnormal RNA–protein interactions that result in the development of many neurological diseases.

The Use of Advanced Mass Spectrometry to Dissect the Life-Cycle of Photosystem II

Photosystem II (PSII) is a photosynthetic membrane-protein complex that undergoes an intricate, tightly regulated cycle of assembly, damage, and repair. The available crystal structures of cyanobacterial PSII are an essential foundation for understanding PSII function, but nonetheless provide a snapshot only of the active complex. To study aspects of the entire PSII life-cycle, mass spectrometry (MS) has emerged as a powerful tool that can be used in conjunction with biochemical techniques. In this article, we present the MS-based approaches that are used to study PSII composition, dynamics, and structure, and review the information about the PSII life-cycle that has been gained by these methods. This information includes the composition of PSII subcomplexes, discovery of accessory PSII proteins, identification of post-translational modifications and quantification of their changes under various conditions, determination of the binding site of proteins not observed in PSII crystal structures, conformational changes that underlie PSII functions, and identification of water and oxygen channels within PSII. We conclude with an outlook for the opportunity of future MS contributions to PSII research.

Proteomic and Phosphoproteomic Insights into a Signaling Hub Role for Cdc14 in Asexual Development and Multiple Stress Responses in Beauveria bassiana

Cdc14 is a dual-specificity phosphatase that regulates nuclear behavior by dephosphorylating phosphotyrosine and phosphoserine/phosphothreonine in fungi. Previously, Cdc14 was shown to act as a positive regulator of cytokinesis, asexual development and multiple stress responses in Beauveria bassiana, a fungal insect pathogen. This study seeks to gain deep insight into a pivotal role of Cdc14 in the signaling network of B. bassiana by analyzing the Cdc14-specific proteome and phosphoproteome generated by the 8-plex iTRAQ labeling and MS/MS analysis of peptides and phosphopeptides. Under normal conditions, 154 proteins and 86 phosphorylation sites in 67 phosphoproteins were upregulated in Δcdc14 versus wild-type, whereas 117 proteins and 85 phosphorylation sites in 58 phosphoproteins were significantly downregulated. Co-cultivation of Δcdc14 with NaCl (1 M), H₂O₂ (3 mM) and Congo red (0.15 mg/ml) resulted in the upregulation / downregulation of 23/63, 41/39 and 79/79 proteins and of 127/112, 52/47 and 105/226 phosphorylation sites in 85/92, 45/36 and 79/146 phosphoproteins, respectively. Bioinformatic analyses revealed that Cdc14 could participate in many biological and cellular processes, such as carbohydrate metabolism, glycerophospholipid metabolism, the MAP Kinase signaling pathway, and DNA conformation, by regulating protein expression and key kinase phosphorylation in response to different environmental cues. These indicate that in B. bassiana, Cdc14 is a vital regulator of not only protein expression but also many phosphorylation events involved in developmental and stress-responsive pathways. Fourteen conserved and novel motifs were identified in the fungal phosphorylation events.

Proteomic approaches in research of cyanobacterial photosynthesis

Oxygenic photosynthesis in cyanobacteria, algae, and plants is carried out by a fabulous pigment-protein machinery that is amazingly complicated in structure and function. Many different approaches have been undertaken to characterize the most important aspects of photosynthesis, and proteomics has become the essential component in this research. Here we describe various methods which have been used in proteomic research of cyanobacteria, and demonstrate how proteomics is implemented into on-going studies of photosynthesis in cyanobacterial cells.

Quantitative and Systems-Based Approaches for Deciphering Bacterial Membrane Interactome and Gene Function

High-throughput genomic and proteomic methods provide a concise description of the molecular constituents of a cell, whereas systems biology strives to understand the way these components function as a whole. Recent developments, such as genome editing technologies and protein epitope-tagging coupled with high-sensitivity mass-spectrometry, allow systemic studies to be performed at an unprecedented scale. Available methods can be successfully applied to various goals, both expanding fundamental knowledge and solving applied problems. In this review, we discuss the present state and future of bacterial cell envelope interactomics, with a specific focus on host-pathogen interactions and drug target discovery. Both experimental and computational methods will be outlined together with examples of their practical implementation.

Studies on quantitative phosphopeptide analysis by matrix-assisted laser desorption/ionization mass spectrometry without label, chromatography or calibration curves

RATIONALE

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry combined with isotope labeling methods are effective for protein and peptide quantification, but limited in their multiplexing capacity, cost-effectiveness and dynamic range. This study investigates MALDI-MS-based quantification of peptide phosphorylation without labeling, and aims to overcome the shot-to-shot variability of MALDI using a mathematical transformation and extended data acquisition times.

METHODS

A linear relationship between the reciprocal of phosphopeptide mole fraction and the reciprocal of phosphorylated-to-unphosphorylated signal ratio is derived, and evaluated experimentally using three separate phosphopeptide systems containing phosphorylated serine, threonine and tyrosine residues: mixtures of phosphopeptide and its des-phospho-analog with known stoichiometry measured by vacuum MALDI-linear ion trap mass spectrometry and fit to the linear model. The model is validated for quantifying in vitro phosphorylation assays with inhibition studies on Cdk2/cyclinA.

RESULTS

Dynamic range of picomoles to femtomoles, good accuracy (deviations of 1.5-3.0% from expected values) and reproducibility (relative standard deviation (RSD) = 4.3-6.3%) are achieved. Inhibition of cyclin-dependent kinase phosphorylation by the classical inhibitors olomoucine and r-roscovitine was evaluated and IC50 values found to be in agreement with reported literature values. These results, achieved with single-point calibration, without isotope or chromatography, compare favorably to those arrived at using isotope dilution (p > 0.5 for accuracy).

CONCLUSIONS

The mathematical relationship derived here can be applied to a method that we term Double Reciprocal Isotope-free Phosphopeptide Quantification (DRIP-Q), as a strategy for quantification of in vitro phosphorylation assays, the first MALDI-based, isotope- and calibration curve-free method of its type. These results also pave the way for further systematic studies investigating the effect of peptide composition and experimental conditions on quantitative, label-free MALDI.

Gelatin quantification by oxygen-18 labeling and liquid chromatography-high-resolution mass spectrometry

Combined with high-performance liquid chromatography (HPLC) and linear-ion trap/Orbitrap high-resolution mass spectrometry, trypsin-catalyzed (16)O-to-(18)O exchange was used to establish an accurate quantitative method for bovine or porcine gelatin. The sophisticated modifications for these two mammalian gelatins were unambiguously identified by accurate mass and tandem mass spectrometry. Eighteen marker peptides were successfully identified for the bovine and porcine gelatin, respectively. The gelatins were subjected to (18)O or (16)O labeling in the presence of trypsin and mixed together in various ratios for quantification. All of the (18)O-labeled peptides were also confirmed by accurate mass and tandem mass spectrometry. The 10 marker peptides with the strongest signals were chosen to calculate the average ratios of (18)O-labeled and (16)O-labeled gelatin. The measured ratios of (18)O-labeled and (16)O-labeled peptides were very close to the mixing ratios of 20:1, 5:1, 1:1, and 1:5 with low standard deviation values. The samples with a mixing ratio of 1:1 (18)O-labeled and (16)O-labeled peptides were determined to 1.00 and 0.99 with standard deviations of 0.02 and 0.04 for bovine and porcine gelatins, respectively, indicating the high accuracy of this method. Trypsin-catalyzed (18)O labeling was proved to be an excellent internal calibrant for gelatins. When combined with HPLC and high-resolution mass spectrometry, it is an accurate and sensitive quantitative method for gelatin in the food industry.

Quantitative proteomics in the field of microbiology

Quantitative proteomics has become an indispensable analytical tool for microbial research. Modern microbial proteomics covers a wide range of topics in basic and applied research from in vitro characterization of single organisms to unravel the physiological implications of stress/starvation to description of the proteome content of a cell at a given time. With the techniques available, ranging from classical gel-based procedures to modern MS-based quantitative techniques, including metabolic and chemical labeling, as well as label-free techniques, quantitative proteomics is today highly successful in sophisticated settings of high complexity such as host-pathogen interactions, mixed microbial communities, and microbial metaproteomics. In this review, we will focus on the vast range of techniques practically applied in current research with an introduction of the workflows used for quantitative comparisons, a description of the advantages/disadvantages of the various methods, reference to hallmark publications and presentation of applications in current microbial research.

Spore proteomics: the past, present and the future

Endospores are metabolically dormant, multi-layered cellular structures formed by Gram-positive bacteria belonging to the genera Bacillus, Clostridium and related organisms. Their external layers are composed of proteins which in part play a role in the resistance behaviour of spores to varied chemical and environmental assaults. Thus, protein analysis is of major interest in spore biology. Spore proteomic studies have been carried out previously but these studies have focused on the soluble coat protein fraction. Using gel-based techniques, protein identification and analysis were performed. Mass spectrometry-driven proteomics has opened new avenues to resolve in particular the insoluble part of the spore layer proteomes. Mass spectrometry-based qualitative and quantitative proteomics methods expand the knowledge about both the actual composition and the amount of proteins in their various layers. The techniques can also be used to study the integrity of the layers as well as spore biology in general. This notion is explored concisely in this mini-review.

In-vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism

Circadian clocks are endogenous oscillators that drive the rhythmic expression of a broad array of genes, orchestrating metabolism and physiology. Recent evidence indicates that post-transcriptional and post-translational mechanisms play essential roles in modulating temporal gene expression for proper circadian function, particularly for the molecular mechanism of the clock. Due to technical limitations in large-scale, quantitative protein measurements, it remains unresolved to what extent the circadian clock regulates metabolism by driving rhythms of protein abundance. Therefore, we aimed to identify global circadian oscillations of the proteome in the mouse liver by applying in vivo SILAC mouse technology in combination with state of the art mass spectrometry. Among the 3000 proteins accurately quantified across two consecutive cycles, 6% showed circadian oscillations with a defined phase of expression. Interestingly, daily rhythms of one fifth of the liver proteins were not accompanied by changes at the transcript level. The oscillations of almost half of the cycling proteome were delayed by more than six hours with respect to the corresponding, rhythmic mRNA. Strikingly we observed that the length of the time lag between mRNA and protein cycles varies across the day. Our analysis revealed a high temporal coordination in the abundance of proteins involved in the same metabolic process, such as xenobiotic detoxification. Apart from liver specific metabolic pathways, we identified many other essential cellular processes in which protein levels are under circadian control, for instance vesicle trafficking and protein folding. Our large-scale proteomic analysis reveals thus that circadian post-transcriptional and post-translational mechanisms play a key role in the temporal orchestration of liver metabolism and physiology.

The circadian clock is an evolutionary system that allows organisms to anticipate and thus adapt to daily changes in the environment. In mammals, the circadian clock is found in virtually every tissue regulating rhythms of metabolism and physiology. While a lot of studies have focused in how circadian clocks regulate gene expression little is known about daily control of protein abundance. Here we applied state of the art mass spectrometry in combination with quantitative proteomics to investigate global circadian oscillations of the proteome in the mouse liver. We found that approximately 6% of the liver proteins are cycling daily and interestingly the majority of these oscillations diverge from the behavior of their transcripts. Our data indicates that post-transcriptional mechanisms play an essential role in shaping the phase of rhythmic proteins downstream of transcription regulation to ultimately drive rhythms of metabolism. Moreover, the contribution of post-transcriptional regulation seems to differ among distinct metabolic pathways. Overall we not only found circadian oscillations in the abundance of proteins involved in liver specific metabolic pathways but also in essential cellular processes.

Viruses and antiviral immunity in Drosophila

Viral pathogens present many challenges to organisms, driving the evolution of a myriad of antiviral strategies to combat infections. A wide variety of viruses infect invertebrates, including both natural pathogens that are insect-restricted, and viruses that are transmitted to vertebrates. Studies using the powerful tools in the model organism Drosophila have expanded our understanding of antiviral defenses against diverse viruses. In this review, we will cover three major areas. First, we will describe the tools used to study viruses in Drosophila. Second, we will survey the major viruses that have been studied in Drosophila. And lastly, we will discuss the well-characterized mechanisms that are active against these diverse pathogens, focusing on non-RNAi mediated antiviral mechanisms. Antiviral RNAi is discussed in another paper in this issue.

SILAC and alternatives in studying cellular proteomes of plants

Quantitative proteomics by metabolic labeling has a high impact on the growing field of plant systems biology. SILAC has been pioneered and optimized for plant cell culture systems allowing for SILAC-based quantitative experiments in specialized experimental setups. In comparison to other model organisms, the application of SILAC to whole plants is challenging. As autotrophic organisms, plants under their natural growth conditions can hardly be fully labeled with stable isotope-coded amino acids. The metabolic labeling with inorganic nitrogen is therefore the method of choice for most whole-plant physiological questions. Plants can easily metabolize different inorganic nitrogen isotopes. The incorporation of the labeled inorganic nitrogen then results in proteins and metabolites with distinct molecular mass, which can be detected on a mass spectrometer. In comparative quantitative experiments, similarly as in SILAC experiments, treated and untreated samples are differentially labeled by nitrogen isotopes and jointly processed, thereby minimizing sample-to-sample variation. In recent years, heavy nitrogen labeling has become a widely used strategy in quantitative proteomics and novel approaches were developed for metabolite identification. Here we present a typical hydroponics setup, the workflow for processing of samples, mass spectrometry and data analysis for large-scale metabolic labeling experiments of whole plants.

Deducing intracellular distributions of metabolic pathways from genomic data

In the recent years, a large number of genomes from a variety of different organisms have been sequenced. Most of the sequence data has been publicly released and can be assessed by interested users. However, this wealth of information is currently underexploited by scientists not directly involved in genome annotation. This is partially because sequencing, assembly, and automated annotation can be done much faster than the identification, classification, and prediction of the intracellular localization of the gene products. This part of the annotation process still largely relies on manual curation and addition of contextual information. Users of genome databases who are unfamiliar with the types of data available from (whole) genomes might therefore find themselves either overwhelmed by the vast amount and multiple layers of data or dissatisfied with less-than-meaningful analyses of the data.In this chapter we present procedures and approaches to identify and characterize gene models of enzymes involved in metabolic pathways based on their similarity to known sequences. Furthermore we describe how to predict the subcellular location of the proteins using publicly available prediction servers and how to interpret the obtained results. The strategies we describe are generally applicable to organisms with primary plastids such as land plants or green algae. Additionally, we describe strategies suitable for those groups of algae with secondary plastids (for instance diatoms), which are characterized by a different cellular topology and a larger number of intracellular compartments compared to plants.

A pipeline for 15N metabolic labeling and phosphoproteome analysis in Arabidopsis thaliana

Within the past two decades, the biological application of mass spectrometric technology has seen great advances in terms of innovations in hardware, software, and reagents. Concurrently, the burgeoning field of proteomics has followed closely (Yates et al., Annu Rev Biomed Eng 11:49-79, 2009)-and with it, importantly, the ability to globally assay altered levels of posttranslational modifications in response to a variety of stimuli. Though many posttranslational modifications have been described, a major focus of these efforts has been protein-level phosphorylation of serine, threonine, and tyrosine residues (Schreiber et al., Proteomics 8:4416-4432, 2008). The desire to examine changes across signal transduction cascades and networks in their entirety using a single mass spectrometric analysis accounts for this push-namely, preservation and enrichment of the transient yet informative phosphoryl side group. Analyzing global changes in phosphorylation allows inferences surrounding cascades/networks as a whole to be made. Towards this same end, much work has explored ways to permit quantitation and combine experimental samples such that more than one replicate or experimental condition can be identically processed and analyzed, cutting down on experimental and instrument variability, in addition to instrument run time. One such technique that has emerged is metabolic labeling (Gouw et al., Mol Cell Proteomics 9:11-24, 2010), wherein biological samples are labeled in living cells with nonradioactive heavy isotopes such as (15)N or (13)C. Since metabolic labeling in living organisms allows one to combine the material to be processed at the earliest possible step, before the tissue is homogenized, it provides a unique and excellent method for comparing experimental samples in a high-throughput, reproducible fashion with minimal technical variability. This chapter describes a pipeline used for labeling living Arabidopsis thaliana plants with nitrogen-15 ((15)N) and how this can be used, in conjunction with a technique for enrichment of phosphorylated peptides (phosphopeptides), to determine changes in A. thaliana's phosphoproteome on an untargeted, global scale.

Isotopic ratio outlier analysis global metabolomics of Caenorhabditis elegans

We demonstrate the global metabolic analysis of Caenorhabditis elegans stress responses using a mass-spectrometry-based technique called isotopic ratio outlier analysis (IROA). In an IROA protocol, control and experimental samples are isotopically labeled with 95 and 5% (13)C, and the two sample populations are mixed together for uniform extraction, sample preparation, and LC-MS analysis. This labeling strategy provides several advantages over conventional approaches: (1) compounds arising from biosynthesis are easily distinguished from artifacts, (2) errors from sample extraction and preparation are minimized because the control and experiment are combined into a single sample, (3) measurement of both the molecular weight and the exact number of carbon atoms in each molecule provides extremely accurate molecular formulas, and (4) relative concentrations of all metabolites are easily determined. A heat-shock perturbation was conducted on C. elegans to demonstrate this approach. We identified many compounds that significantly changed upon heat shock, including several from the purine metabolism pathway. The metabolomic response information by IROA may be interpreted in the context of a wealth of genetic and proteomic information available for C. elegans . Furthermore, the IROA protocol can be applied to any organism that can be isotopically labeled, making it a powerful new tool in a global metabolomics pipeline.