Mass Spectrometry-based Technologies Research Articles

Preferential Phosphorylation on Old Histones during Early Mitosis in Human Cells

How histone post-translational modifications (PTMs) are inherited through the cell cycle remains poorly understood. Canonical histones are made in the S phase of the cell cycle. Combining mass spectrometry-based technologies and stable isotope labeling by amino acids in cell culture, we question the distribution of multiple histone PTMs on old versus new histones in synchronized human cells. We show that histone PTMs can be grouped into three categories according to their distributions. Most lysine mono-methylation and acetylation PTMs are either symmetrically distributed on old and new histones or are enriched on new histones. In contrast, most di- and tri-methylation PTMs are enriched on old histones, suggesting that the inheritance of different PTMs is regulated distinctly. Intriguingly, old and new histones are distinct in their phosphorylation status during early mitosis in the following three human cell types: HeLa, 293T, and human foreskin fibroblast cells. The mitotic hallmark H3S10ph is predominantly associated with old H3 at early mitosis and becomes symmetric with the progression of mitosis. This same distribution was observed with other mitotic phosphorylation marks, including H3T3/T6ph, H3.1/2S28ph, and H1.4S26ph but not S28/S31ph on the H3 variant H3.3. Although H3S10ph often associates with the neighboring Lys-9 di- or tri-methylations, they are not required for the asymmetric distribution of Ser-10 phosphorylation on the same H3 tail. Inhibition of the kinase Aurora B does not change the distribution despite significant reduction of H3S10ph levels. However, K9me2 abundance on the new H3 is significantly reduced after Aurora B inhibition, suggesting a cross-talk between H3S10ph and H3K9me2.

Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data.

Protein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylation on a proteome-wide scale, computational approaches that can integrate static features with dynamic phosphoproteome data would greatly facilitate the prediction of biologically relevant kinase-specific substrates. Here, we propose a positive-unlabeled ensemble learning approach that integrates dynamic phosphoproteomics data with static kinase recognition motifs to predict novel substrates for kinases of interest. We extended a positive-unlabeled learning technique for an ensemble model, which significantly improves prediction sensitivity on novel substrates of kinases while retaining high specificity. We evaluated the performance of the proposed model using simulation studies and subsequently applied it to predict novel substrates of key kinases relevant to insulin signaling. Our analyses show that static sequence motifs and dynamic phosphoproteomics data are complementary and that the proposed integrated model performs better than methods relying only on static information for accurate prediction of kinase-specific substrates. Executable GUI tool, source code and documentation are freely available at https://github.com/PengyiYang/KSP-PUEL. pengyi.yang@nih.gov or jothi@mail.nih.gov Supplementary data are available at Bioinformatics online.

Site-Specific Proteomic Mapping Identifies Selectively Modified Regulatory Cysteine Residues in Functionally Distinct Protein Networks

S-Acylation, S-glutathionylation, S-nitrosylation, and S-sulfenylation are prominent, chemically distinct modifications that regulate protein function, redox sensing, and trafficking. Although the biological significance of these modifications is increasingly appreciated, their integration in the proteome remains unknown. Novel mass spectrometry-based technologies identified 2,596 predominately unique sites in 1,319 mouse liver proteins under physiological conditions. Structural analysis localized the modifications in unique, evolutionary conserved protein segments, outside commonly annotated functional regions. Contrary to expectations, propensity for modification did not correlate with biophysical properties that regulate cysteine reactivity. However, the in vivo chemical reactivity is fine-tuned for specificity, demonstrated by the nominal complementation between the four modifications and quantitative proteomics which showed that a reduction in S-nitrosylation is not correlated with increased S-glutathionylation. A comprehensive survey uncovered clustering of modifications within biologically related protein networks. The data provide the first evidence for the occurrence of distinct, endogenous protein networks that undergo redox signaling through specific cysteine modifications.

A Phosphoproteomics Approach to Identify Candidate Kinase Inhibitor Pathway Targets in Lymphoma-Like Primary Cell Lines

Mass spectrometry-based technologies are increasingly utilized in drug discovery. Phosphoproteomics in particular has allowed for the efficient surveying of phosphotyrosine signaling pathways involved in various diseases states, most prominently in cancer. We describe a phosphotyrosine-based proteomics screening approach to identify signaling pathways and tyrosine kinase inhibitor targets in highly tumorigenic human lymphoma-like primary cells. We identified several receptor tyrosine kinase pathways and validated SRC family kinases (SFKs) as potential drug targets for targeted selection of small molecule inhibitors. BMS-354825 (dasatinib) and SKI-606 (bosutinib), second and third generation clinical SFK/ABL inhibitors, were found to be potent cytotoxic agents against tumorigenic cells with low toxicity to normal pediatric stem cells. Both SFK inhibitors reduced ERK1/2 and AKT phosphorylation and induced apoptosis. This study supports the adaptation of high-end mass spectrometry techniques for the efficient identification of candidate tyrosine kinases as novel therapeutic targets in primary cancer cell lines.

Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer

Metabolic profiles of biofluids obtained by atmospheric pressure ionization mass spectrometry-based technologies contain hundreds to thousands of features, most of them remaining unknown or at least not characterized in analytical systems. We report here on the annotation of the human adult urinary metabolome and metabolite identification from electrospray ionization mass spectrometry (ESI-MS)-based metabolomics data sets. Features of biological interest were first of all annotated using the ESI-MS database of the laboratory. They were also grouped, thanks to software tools, and annotated using public databases. Metabolite identification was achieved using two complementary approaches: (i) formal identification by matching chromatographic retention times, mass spectra, and also product ion spectra (if required) of metabolites to be characterized in biological data sets to those of reference compounds and (ii) putative identification from biological data thanks to MS/MS experiments for metabolites not available in our chemical library. By these means, 384 metabolites corresponding to 1484 annotated features (659 in negative ion mode and 825 in positive ion mode) were characterized in human urine samples. Of these metabolites, 192 and 66 were formally and putatively identified, respectively, and 54 are reported in human urine for the first time. These lists of features could be used by other laboratories to annotate their ESI-MS metabolomics data sets.

Progress in Bioanalysis of Protein Histidine Phosphorylation

Acid-labile protein phosphorylation on histidine residues was hardly investigated by mass spectrometry-based technologies in the past. Artificial phosphorylation using potassium phosphoramidate now allows the generation of reference compounds and enables the systematic investigation of model substances generating confidence in experimental procedures.

CD proteome and beyond - technologies for targeting the immune cell surfaceome

Communication between cells of the immune system and the organism is dependent on information processing mediated by proteins of the cell surface. The cell surface proteome consists of a group of functionally diverse proteins, which not only enables but also limits the interaction capacities of cells within their particular microenvironment. Although these proteins represent a highly important proteome for immunological research, most routinely used technologies for their detection only allow for a fragmented view of the ensemble of cell surface located proteins. A major bottleneck is the limited availability of high quality antibodies against cell surface protein targets that altogether impedes a Systems Biology view on the cell surface proteome (surfaceome) and its concerted functions during signal processing. Recent developments in mass spectrometry-based technologies enable now complementary approaches for the qualitative and quantitative analysis of the surfaceome. Here, we highlight recent progress in the field towards the identification and quantification of the surfaceome as an important subproteome forming the information gateway of the cell.

Characterization of flavonol glycosides in individual Arabidopsis root tips by flow injection electrospray mass spectrometry

Developments in mass spectrometry-based technologies are offering insights into the complexity and dynamic nature of plant metabolism. However, the ability to generate reliable metabolic profiles at high spatial resolution is still limited by the need of most technologies for large sample sizes or time-intensive extraction and detection methods. Here we describe the use of flow injection electrospray mass spectrometry for the rapid identification and semi-quantitative analysis of flavonol glycosides in individual root tips. This method uncovered spatial and temporal differences in metabolic profiles that were masked in analyses of whole roots or seedlings, while showing that individual biological replicates can be extremely consistent.

Mitochondrial protein phosphorylation as a regulatory modality: implications for mitochondrial dysfunction in heart failure.

Phosphorylation of mitochondrial proteins has been recognized for decades, and the regulation of pyruvate- and branched-chain α-ketoacid dehydrogenases by an atypical kinase/phosphatase cascade is well established. More recently, the development of new mass spectrometry-based technologies has led to the discovery of many novel phosphorylation sites on a variety of mitochondrial targets. The evidence suggests that the major classes of kinase and several phosphatases may be present at the mitochondrial outer membrane, intermembrane space, inner membrane, and matrix, but many questions remain to be answered as to the location, timing, and reversibility of these phosphorylation events and whether they are functionally relevant. The authors review phosphorylation as a mitochondrial regulatory strategy and highlight its possible role in the pathophysiology of cardiac hypertrophy and failure.

Abstract PL01-03: Quantitative biology and biomarker discovery without immunoassays

Abstract Lack of antibody reagents and robust quantitative methods with sufficient sensitivity, specificity, reproducibility and throughput has significantly hampered our ability to understand dynamic, protein-based biological processes. My laboratory is addressing this serious barrier by developing targeted mass spectrometry-based technologies to screen and quantify low abundance proteins in a variety of biological contexts including human tissue and plasma. These assays are highly multiplexed, sensitive and specific and do not suffer from interferences that plague immunoassays. Targeted MS-based approaches are helping to usher in a new era of quantitative biology where proteins of interest and their modifications can be robustly measured in any biological context. We are applying these quantitative approaches in the context of a generalizable proteomics-based discovery-through-verification pipeline to identify early biomarkers of cardiovascular injury, breast cancer and infectious disease. These studies are beginning to demonstrate that modern proteomic technologies when coherently integrated can yield novel, credentialed protein biomarker candidates of sufficient merit to warrant real clinical evaluation. Citation Information: Cancer Prev Res 2011;4(10 Suppl):PL01-03.

Application of mass spectrometry-based chromatographic technologies in the diagnosis of Alzheimer’s disease

With the rapid growth of the elderly population in the world, the incident number of Alzheimer's disease (AD) is also increasing year by year. AD has become "the third biggest killer" to human health besides cardiovascular and cerebrovascular diseases and cancers. The diagnosis and treatment of diseases are of equally significance. Generally, the diagnosis of AD relies upon the typical clinical features, neuroimaging techniques, and the detection of disease-related biomarkers. In recent years, proteomics and mass spectrometry technology have developed rapidly, which can be used to find specific disease-related proteins as biomarkers for early diagnosis. This review focused on the proteomics-based screening of diagnostic biomarkers and the mass spectrometry-based chromatographic technologies applied in the diagnosis of AD. 34 references are reviewed.

Strategies for kinome profiling in cancer and potential clinical applications: chemical proteomics and array-based methods

Kinases are key enzymes involved in deregulated signal transduction associated with cancer development and progression. The advent of personalized medicine drives the development of new diagnostic tools for patient stratification and therapy selection Ginsburg and Willard (Transl Res 154:277-287, 2009). Since deregulation of kinase-mediated signal transduction is implied in tumorigenesis, the analysis of all kinases (the kinome) active in a particular tumor may yield tumor-specific information on aberrant cell signalling pathways. Tumor tissue kinase activity profiles may correlate with response to therapy and therefore may be used for future therapy selection. In this Trend paper we describe peptide array and mass spectrometry-based technologies and new developments for kinome profiling, and we present an outlook towards future implementation of therapy selection based on kinome profiling in clinical practice.

Mass Spectrometry-Based Technologies for High-Throughput Metabolomics

The metabolome is composed of a vast number of small-molecule metabolites that exhibit a diversity of physical and chemical properties and exist over a wide dynamic range in biological samples. Multiple analytical techniques, used in a complementary manner, are required to achieve high coverage of the metabolome. MS is playing a central role in metabolomics research. Herein, we present a brief overview of the MS-based technologies employed for high-throughput metabolomics. These technologies range from chromatography-MS techniques, such as GC-MS and LC-MS, to chromatography-free techniques, such as direct infusion, matrix-assisted and matrix-free laser desorption/ionization, imaging and some new ambient ionization approaches. Chemoinformatics and bioinformatics tools are widely available to facilitate successful metabolomics studies by turning the complex metabolomics data into biological information through streamlined data processing, analysis and interpretation.

New perspective for phage display as an efficient and versatile technology of functional proteomics

Phage display with antibody libraries has been widely used with versatile applications. However, phage display with cDNA libraries is rare and inefficient. Because of uncontrollable reading frames and stop codons in cDNA repertoires, high percentage of phage clones identified from conventional cDNA libraries are non-open reading frames (non-ORFs) encoding unnatural short peptides with minimal implications in protein networks. Consequently, phage display has not been used as a technology of functional proteomics to elucidate protein-protein interactions like yeast two-hybrid system and mass spectrometry-based technologies. Several strategies, including C-terminal display and ORF cDNA libraries, have been explored to circumvent the technical problem. The accumulative endeavors eventually led to the efficient elucidation of a large number of tubby- and phosphatidylserine-binding proteins in recent studies by ORF phage display with minimal reading frame issue. ORF phage display inherits all the versatile applications of antibody phage display, but enables efficient identification of real endogenous proteins with efficiency, sensitivity, and accuracy comparable to other technologies of functional proteomics. Its ELISA-like procedure can be conveniently adapted by individual laboratories or fully automated for high-throughput screening. Thus, ORF phage display is an efficient, sensitive, versatile, and convenient technology of functional proteomics for elucidation of global and pathway-specific protein-protein interactions, disease mechanisms, or therapeutic targets.

In Silico Analysis of Phosphoproteome Data Suggests a Rich-get-richer Process of Phosphosite Accumulation over Evolution

Recent phosphoproteome analyses using mass spectrometry-based technologies have provided new insights into the extensive presence of protein phosphorylation in various species and have raised the interesting question of how this protein modification was gained evolutionarily on such a large scale. We investigated this issue by using human and mouse phosphoproteome data. We initially found that phosphoproteins followed a power-law distribution with regard to their number of phosphosites: most of the proteins included only a few phosphosites, but some included dozens of phosphosites. The power-law distribution, unlike more commonly observed distributions such as normal and log-normal distributions, is considered by the field of complex systems science to be produced by a specific rich-get-richer process called preferential attachment growth. Therefore, we explored the factors that may have promoted the rich-get-richer process during phosphosite evolution. We conducted a bioinformatics analysis to evaluate the relationship of amino acid sequences of phosphoproteins with the positions of phosphosites and found an overconcentration of phosphosites in specific regions of protein surfaces and implications that in many phosphoproteins these clusters of phosphosites are activated simultaneously. Multiple phosphosites concentrated in limited spaces on phosphoprotein surfaces may therefore function biologically as cooperative modules that are resistant to selective pressures during phosphoprotein evolution. We therefore proposed a hypothetical model by which the modularization of multiple phosphosites has been resistant to natural selection and has driven the rich-get-richer process of the evolutionary growth of phosphosite numbers.

Biomarker Discovery in Clinical Proteomics: Strategies for Exposing Low Abundant Proteins

Disease specific proteins are highly valuable as clinical biomarkers, which can be used in early diagnosis, monitoring disease progressions and evaluating therapies. The identification and characterization of protein biomarkers in different physiological and pathological sources of interest under diverse milieu represent a key area of clinical proteomics. However, owing to the inherent complexities and the large dynamic ranges of proteins, there are many practical issues and challenges in discovering the low-abundance, disease-specific biomarkers from heterogeneous tissues and biofluids. Thus, an integrated approach for selective protein pre-fractionation, purification and separation, is necessary to detect low-abundant biomarkers in proteomics research. This review will summarize recent advancements in clinical sample preparation for removing high abundant proteins, as well as the improved two-dimensional gel electrophoresis- and mass spectrometry-based technologies for resolving low-abundant proteins. We will also elaborate the proteomic strategies for targeting protein sub-populations with special interests, such as high molecular weight protein complexes, membrane or its associated proteins, and organelle specific proteins etc. We will emphasize the use of these strategies to interrogate the current proteomic researches in clinical biomarker discovery.

Mass Spectrometric Identification of Proteotypic Peptides from Clinically Used Tumor Markers

Abstract Introduction With the rapid development of mass spectrometry-based technologies such as multiple reaction monitoring and heavy-isotope-labeled-peptide standards, quantitative analysis of biomarker proteins using mass spectrometry is rapidly progressing toward detection of target proteins/peptides from clinical samples. Proteotypic peptides are a few peptides that are repeatedly and consistently identified from a protein in a mixture and are used for quantitative analysis of the protein in a complex biological sample by mass spectrometry. Materials and Methods Using mass spectrometry, we identified peptide sequences and provided a list of tryptic peptides and glycopeptides as proteotypic peptides from five clinically used tumor markers, including prostate-specific antigen, carcinoembryonic antigen, Her-2, human chorionic gonadotropin, and CA125. Conclusion These proteotypic peptides have potential for targeted detection as well as heavy-isotope-peptide standards for quantitative analysis of marker proteins in clinical specimens using a highly specific, sensitive, and high-throughout mass spectrometry-based analysis method.

ITRAQPak: an R based analysis and visualization package for 8-plex isobaric protein expression data

The field of high throughput proteomics has spawned a number of mass spectrometry-based technologies, which enable the quantitative analysis of protein expression. One of these technologies is iTRAQ (trademarked by Applied Biosystems), which through the use of isobaric tags, enables the quantitation of up to eight complex protein samples in a single multiplexed analysis. Isobaric tagging methods are emerging as an important tool to study protein expression dynamics. In this report, we describe iTRAQPak, a free software package developed in the R statistical and visualization environment that can be applied to the analysis of 8-plex expression data. The utility of this package is demonstrated through its application to the analysis of 8-plex iTRAQ protein expression data obtained from cerebrospinal fluid samples from Alzheimer's disease subjects involved in a Phase I drug trial.

Mass spectrometry-based strategies for characterization of histones and their post-translational modifications

Due to the intimate interactions between histones and DNA, the characterization of histones has become the focus of great attention. A series of mass spectrometry-based technologies have been dedicated to the characterization and quantitation of different histone forms. This review focuses on the discussion of mass spectrometry-based strategies used for the characterization of histones and their post-translational modifications.

Transformations in pharmaceutical research and development, driven by innovations in multidimensional mass spectrometry-based technologies

In the pharmaceutical industry, there is a tremendous need for qualitative and quantitative analysis of target analytes such as peptides, proteins, drugs, metabolites, biomarkers, impurities, and degradation products in various mixtures including synthetic reactions, in vitro cultures, biological fluids, drug substances, finished products, and many others. To provide adequate specificity for analysis in these complex mixtures, multidimensional analytical techniques are required. Mass spectrometry plays a central role in many of these multidimensional approaches to mixture analysis because it provides an unparalleled combination of sensitivity and specificity that is useful for both molecular identification and quantitative applications. Recent innovations in mass spectrometry and industrial implementation of these advances have transformed many aspects of pharmaceutical research and development. Data that were previously unattainable, or were not collected due to exorbitant cost or time constraints, can now be obtained using mass spectrometry-based technologies. The impact of these innovations has been most dramatically felt in early stages of discovery, as more data are available to make critical decisions, such as selecting compounds for advancement to costly preclinical and clinical trials. New MS technologies have also accelerated the progression of drug candidates through development and toward regulatory approval. Here, five major categories of pharmaceutical applications of mass spectrometry are reviewed. They are new chemical entity characterization, biomacromolecule characterization, bioanalytical quantitation, metabolite identification, and impurity and degradation product identification. A brief historical perspective and evolution of technologies for each application area are presented. Those discussions are followed with a description of the current strategies for implementation of the tremendous capabilities of the state-of-the-art approaches, along with representative applications. In addition, emerging technologies for each application area are presented to indicate the future directions of instrumentation for mixture analysis in the pharmaceutical industry.