Current Proteomics Research Articles

Proteomic analyses of age related changes in A.BY/SnJ mouse hearts

BackgroundA.BY/SnJ mice are used to study pathological alterations in the heart due to enteroviral infections. Since age is a well-known factor influencing the susceptibility of mice to infection, response to stress and manifestation of cardiovascular diseases, the myocardial proteome of A.BY/SnJ mice aged 1 and 4 months was comparatively studied using two dimensional-differential in-gel electrophoresis (2D-DIGE) and liquid chromatography tandem mass spectrometry (LC-MS/MS).ResultsComplementary analyses by 2D-DIGE and gel-free LC-MS/MS revealed 96 distinct proteins displaying age associated alterations in their levels. Proteins related to protein transport, and transport chain, lipid metabolism and fatty acid transport showed significant changes in 4 months old mouse hearts compared to juvenile hearts. Proteins involved in lipid metabolism and transport were identified at significantly higher levels in older mice and dysregulation of proteins of the respiratory transport chain were observed.ConclusionThe current proteomics study discloses age dependent changes occurring in the hearts already in young mice of the strain A.BY/SnJ. Besides alterations in protein transport, we provide evidence that a decrease of ATP synthase in murine hearts starts already in the first months of life, leading to well-known low expression levels manifested in old mice thereby raising the possibility of reduced energy supply. In the first few months of murine life this seems to be compensated by an increased lipid metabolism. The functional alterations described should be considered during experimental setups in disease related studies.

Proteomics as a tool for studying osteoarthritis

s / Osteoarthritis and Cartilage 21 (2013) S1–S8 S5 engineered constructs will alleviate the problems concerning the present treatment of cartilage diseases. I-14 PROTEOMICS AS A TOOL FOR STUDYING OSTEOARTHRITIS D. de Seny. GIGA Res., Univ. of Liege, CHU Liege, Liege, Belgium Proteomics is the large-scale study of proteins expressed by cells, tissues or organ. It describes the dynamics of cell regulation by detecting molecular events related to the development of a disease. It is a multidisciplinary approach using medical, biological, bioanalytical and bioinformatics knowledge. One of the most promising applications of proteomics studies is identification of new biomarkers as indicators of pathogenic processes or pharmacologic responses to a therapeutic intervention. However, several practical considerations need to be raised to set up a robust and sensitive strategy for biomarker discovery in osteoarthritis (OA). OA is a heterogeneous, complex joint pathology. Progression of OA is slow and periodic with intermittent episodes of inflammatory flares and remission periods. Therefore, biomarkers are discontinuously released in biological fluids. This presentation will focus on current proteomic approaches and their limitations in identifying the so-called soluble or “wet biomarkers” measured in blood, serum, plasma, urine and synovial fluid. The presence of a high quantity of proteins in such complex biological fluid make biomarkers discovery highly challenging. Nevertheless, current approaches exist to extend accessible concentration range and deep proteome detection. Comparison between a “targeted proteomic approach” based in the evaluation of single disease-related molecule levels and a “non-targeted proteomic approach” based on the simultaneous evaluation of hundreds proteins in a blind experiment using gel-based methods or mass spectrometry will be reviewed. Based on the assumption that a combination of biomarkers is more specific and sensitive for disease diagnosis than single marker, non-targeted proteomic technologies became highly attractive over the last decade. However, the gap between biomarkers discovery and clinical implementation remains important, as only few biomarkers have already been validated. All these practical considerations will be illustrated by current proteomics studies. I-15 REHABILITATION FOR CARTILAGE DEFECT IN EARLY KNEE OA M. Risberg. NAR, Dept. of Sport Med., Norwegian Sch. Sport Sci., Oslo,

Food Peptidomics: Large scale analysis of small bioactive peptides — A pilot study

Food peptidomics deals in part with the identification and quantification of nutritionally relevant peptides which are called bioactive peptides. This category of peptides comprises large, medium to small peptides. However, small peptides (2-6 amino acids) represent by far the largest category. Such molecules sit at the interface of both the world of proteomics and small molecule. The purpose of this study was to evaluate the feasibility of developing an LC-MSMS based method to measure such small peptides at a large scale that is representative of the hundreds of known small bioactive peptides. In order to do that we selected a very complex and homogeneous peptide set in terms of chemical and physical properties. This peptide set comprised only di, tri- and tetrapeptides made out of the three branched chain amino acids (valine, leucine and isoleucine). Results showed that at least 60% of these 117 peptides can be uniquely identified although many are isobaric and co-eluting. Moreover, identical results were obtained when spiked into a complex matrix, i.e. hydrolyzed whey protein. In conclusion, these results support the feasibility of a large scale approach and open the door to further development for all potential small bioactive peptides known so far. Bioactive peptides are a key category of molecules for functional food application. Most known bioactive peptides are small (less than 5 amino acids) and hence represent a challenge in terms of analysis when using current proteomics techniques. Therefore development of the food peptidomics field through high throughput large scale assays for these molecules is mandatory in the future to better conduct research in this field.

Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimer's disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.

Data-warehousing of protein-protein interactions indicates that pathogens preferentially target hub and bottleneck proteins

When pathogens come into contact with their respective host, they immediately encounter the broad diversity of the current cellular proteome which usually consists of several thousand different proteins spanning a concentration range of at least seven orders of magnitude (Beck et al., 2011). Pathogens exploit the host, its metabolism and proteome for their own replication. The resulting expenses for the host provoke the evolution of immune mechanisms. Immune reactions are initiated by receptors which recognize conserved pathogen-associated patterns and signal via highly interconnected networks of co-receptors, adapter proteins, and transcription factors to induce the expression of anti-pathogenic host proteins. Pathogens in turn develop corresponding countermeasures like pathogen-encoded immune antagonists and bacterial effector proteins. These facts exemplify the complexity and richness of the interactions between host- and pathogen-derived proteomes.

Proteomics quantification of protein nitration

Protein tyrosine nitration is a post-translational modification (PTM) that can occur in biological systems under conditions of oxidative stress. This PTM can impact protein structure and function and has been linked to diseases such as Alzheimer’s disease, cardiomyopathy, and arthritis. In order to understand the role that 3-nitrotyrosine (3NT) plays in disease states, a better understanding at the macromolecular level is necessary. Proteomics is a powerful approach that can simultaneously measure hundreds to thousands of proteins in normal or diseased states and thus can be helpful in the analysis of 3NT-modified proteins. Recently, some attention has been focused on the development of proteomic workflows that provide enrichment, characterization, and relative quantification of 3NT-modified proteins. These approaches rely on gel electrophoresis, liquid chromatography, and mass spectrometry (MS) techniques. This review provides an overview of current proteomics approaches for 3NT-modified proteins and highlights current MS-based methods for quantification of this PTM.

Clinical proteomics for diagnosis and typing of systemic amyloidoses

Amyloidoses are characterized by deposition of misfolded proteins as β-pleated sheet fibrils in organs. Despite the similar morphologic appearance of fibrils, at least 28 different proteins have been identified as causative agents of amyloidosis in humans, 14 of which responsible for systemic forms. Correct identification of the amyloidogenic proteins in each patient is crucial for clinical management, in order to avoid misdiagnosis, inappropriate treatment, and to assess the prognosis. Amyloidosis, being essentially a protein deposition disorder, is an attractive venue for the application of proteomics methodologies; among the different possible analytic goals, the most important is the unequivocal diagnosis and typing of the amyloid deposits. Amyloidosis typing has been traditionally based on a multidisciplinary approach, requiring detailed clinical evaluation and immunohistochemical studies together with biochemical and genetic tests. However, drawbacks of immunohistochemistry-based techniques have driven the search for alternative methods for direct amyloid typing. In particular, MS-based proteomics, recently introduced in the clinical practice with or without the previous 2DE separation of proteins, has revolutionized amyloid typing. This review provides a description of current proteomics methods for the identification of the amyloidogenic proteins, with special attention to the most innovative MS-based techniques.

Organic Silicone Sol–Gel Polymer as a Noncovalent Carrier of Receptor Proteins for Label-Free Optical Biosensor Application

Optical biosensing techniques have become of key importance for label-free monitoring of biomolecular interactions in the current proteomics era. Together with an increasing emphasis on high-throughput applications in functional proteomics and drug discovery, there has been demand for facile and generally applicable methods for the immobilization of a wide range of receptor proteins. Here, we developed a polymer platform for microring resonator biosensors, which allows the immobilization of receptor proteins on the surface of waveguide directly without any additional modification. A sol-gel process based on a mixture of three precursors was employed to prepare a liquid hybrid polysiloxane, which was photopatternable for the photocuring process and UV imprint. Waveguide films were prepared on silicon substrates by spin coating and characterized by atomic force microscopy for roughness, and protein adsorption. The results showed that the surface of the polymer film was smooth (rms = 0.658 nm), and exhibited a moderate hydrophobicity with the water contact angle of 97°. Such a hydrophobic extent could provide a necessary binding strength for stable immobilization of proteins on the material surface in various sensing conditions. Biological activity of the immobilized Staphylococcal protein A and its corresponding biosensing performance were demonstrated by its specific recognition of human Immunoglobulin G. This study showed the potential of preparing dense, homogeneous, specific, and stable biosensing surfaces by immobilizing receptor proteins on polymer-based optical devices through the direct physical adsorption method. We expect that such polymer waveguide could be of special interest in developing low-cost and robust optical biosensing platform for multidimensional arrays.

Comparative Ranking of Human Chromosomes Based on Post-Genomic Data

The goal of the Human Proteome Project (HPP) is to fully characterize the 21,000 human protein-coding genes with respect to the estimated two million proteins they encode. As such, the HPP aims to create a comprehensive, detailed resource to help elucidate protein functions and to advance medical treatment. Similarly to the Human Genome Project (HGP), the HPP chose a chromosome-centric approach, assigning different chromosomes to different countries. Here we introduce a scoring method for chromosome ranking based on several characteristics, including relevance to health problems, existing published knowledge, and current transcriptome and proteome coverage. The score of each chromosome was computed as a weighted combination of indexes reflecting the aforementioned characteristics. The approach is tailored to the chromosome-centric HPP (C-HPP), and is advantageous in that it takes into account currently available information. We ranked the human chromosomes using the proposed score, and observed that Chr Y, Chr 13, and Chr 18 were top-ranked, whereas the scores of Chr 19, Chr 11, and Chr 17 were comparatively low. For Chr 18, selected for the Russian part of C-HPP, about 25% of the encoded genes were associated with diseases, including cancers and neurodegenerative and psychiatric diseases, as well as type 1 diabetes and essential hypertension. This ranking approach could easily be adapted to prioritize research for other sets of genes, such as metabolic pathways and functional categories.

1102 PROTEOMICS IN CARDIOVASCULAR DISEASE

Proteomics is the study of the expression of all or at least a large number of proteins in cells, tissues or biofluids. Proteomics looks at products that are downstream of genomic and transcriptomic processes and therefore has the potential to represent actual disease state. Proteomic techniques are being used to study pathophysiology and to gain mechanistic insight in the development of cardiovascular diseases. In parallel, clinical proteomics has been developed to provide novel biomarkers of cardiovascular risk and disease that could be used in patient care. A number of proteomic platforms are available for research purposes. They are based on separation of peptides and proteins often by combining two different methods, for example liquid chromatography and mass spectrometry. The high costs of sample analysis and the limited standardisation of methods across different techniques and laboratories pose challenges to widespread clinical use of current proteomics. Another practical challenge is related to sample stability which often requires pre-analytical treatment for example with protease inhibitors. Also, many techniques rely on removal of larger proteins in a pre-analytical step which may impact on the amount of information that can be gained from these studies. Despite these technical challenges proteomic studies have been successfully used to identify biomarkers in a range of cardiovascular disorders including coronary artery disease, pre-eclampsia and chronic kidney disease. It is now the time to move from small clinical studies to larger prospective studies in parallel to mechanistic proteomic work to understand the potential of proteomics for pathophysiology and clinical practice.

Find pairs: the module for protein quantification of the PeakQuant software suite.

Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.

Minireview: Progress and Challenges in Proteomics Data Management, Sharing, and Integration

The proteome represents the identity, expression levels, interacting partners, and posttranslational modifications of proteins expressed within any given cell. Proteomic studies aim to census the quantitative and qualitative factors regulating the biological relationships of proteins acting in concert as functional cellular networks. In the field of endocrinology, proteomics has been of considerable value in determining the function and mechanism of action of endocrine signaling molecules in the cell membrane, cytoplasm, and nucleus and for the discovery of proteins as candidates for clinical biomarkers. The volume of data that can be generated by proteomics methodologies, up to gigabytes of data within a few hours, brings with it its own logistical hurdles and presents significant challenges to realizing the full potential of these datasets. In this minireview, we describe selected current proteomics methodologies and their application in basic and translational endocrinology before focusing on mass spectrometry as a model for current progress and challenges in data analysis, management, sharing, and integration.

Topograph, a Software Platform for Precursor Enrichment Corrected Global Protein Turnover Measurements

Defects in protein turnover have been implicated in a broad range of diseases, but current proteomics methods of measuring protein turnover are limited by the software tools available. Conventional methods require indirect approaches to differentiate newly synthesized protein when synthesized from partially labeled precursor pools. To address this, we have developed Topograph, a software platform which calculates the fraction of peptides that are from newly synthesized proteins and their turnover rates. A unique feature of Topograph is the ability to calculate amino acid precursor pool enrichment levels which allows for accurate calculations when the precursor pool is not fully labeled, and the approach used by Topograph is applicable regardless of the stable isotope label used. We validate the Topograph algorithms using data acquired from a mouse labeling experiment and demonstrate the influence that precursor pool corrections can have on protein turnover measurements.

Affinity proteomics: the role of specific binding reagents in human proteome analysis

Affinity proteomics is the field of proteome analysis based on the use of antibodies and other binding reagents as protein-specific detection probes. In this review, the particular strengths of affinity methods for determination of protein localization, functional characterization, biomarker discovery and intracellular applications, and their resulting impact in basic and clinical research are highlighted. An additional focus is on the requirements for systematic binder generation and current large-scale binder projects, including bioinformatic frameworks for epitope selection and for documentation of available binding reagents and their performance. In addition to current affinity proteomics methods and applications, including arrays of proteins, binders, lysates and tissues, approaches coupling mass spectrometry-based proteomics and affinity proteomics are reviewed.

Label-free mass spectrometry-based proteomics for biomarker discovery and validation

For most diseases, better biomarkers are urgently needed to enable (early) detection, diagnosis, prognosis, stratification for therapy and response monitoring. Proteomics delineates gene products that carry out the majority of cellular functions, and thereby may not only yield insight into altered signaling pathways in disease, but also yield novel biomarkers. In recent years, great progress has been made in mass spectrometry-based analysis of clinical tissues and biofluids, with identification and quantification of thousands of proteins now becoming increasingly routine. However, biomarker validation and clinical translation has turned out to be challenging. In this review, we summarize current mass spectrometry-based proteomics strategies for biomarker discovery and verification using selected reaction monitoring, with a focus on progress and recent applications in clinical material using label-free approaches.

Proteomic Analysis of Stem Cell Differentiation and Early Development

Genomics methodologies have advanced to the extent that it is now possible to interrogate the gene expression in a single cell but proteomics has traditionally lagged behind and required much greater cellular input and was not quantitative. Coupling protein with gene expression data is essential for understanding how cell behavior is regulated. Advances primarily in mass spectrometry have, however, greatly improved the sensitivity of proteomics methods over the last decade and the outcome of proteomic analyses can now also be quantified. Nevertheless, it is still difficult to obtain sufficient tissue from staged mammalian embryos to combine proteomic and genomic analyses. Recent developments in pluripotent stem cell biology have in part addressed this issue by providing surrogate scalable cell systems in which early developmental events can be modeled. Here we present an overview of current proteomics methodologies and the kind of information this can provide on the biology of human and mouse pluripotent stem cells.

Low-Molecular-Mass Antioxidants in Parasites

Parasitic infections continue to be a major problem for global human health. Vaccines are practically not available and chemotherapy is highly unsatisfactory. One approach toward a novel antiparasitic drug development is to unravel pathways that may be suited as future targets. Parasitic organisms show a remarkable diversity with respect to the nature and functions of their main low-molecular-mass antioxidants and many of them developed pathways that do not have a counterpart in their mammalian hosts. Work of the last years disclosed the individual antioxidants employed by parasites and their distinct pathways. Entamoeba, Trichomonas, and Giardia directly use cysteine as main low-molecular-mass thiol but have divergent cysteine metabolisms. Malarial parasites rely exclusively on cysteine uptake and generate glutathione (GSH) as main free thiol as do metazoan parasites. Trypanosomes and Leishmania have a unique trypanothione-based thiol metabolism but employ individual mechanisms for their cysteine supply. In addition, some trypanosomatids synthesize ovothiol A and/or ascorbate. Various essential parasite enzymes such as trypanothione synthetase and trypanothione reductase in Trypanosomatids and the Schistosoma thioredoxin GSH reductase are currently intensively explored as drug target molecules. Essentiality is a prerequisite but not a sufficient property of an enzyme to become a suited drug target. The availability of an appropriate in vivo screening system and many other factors are equally important. The current organism-wide RNA-interference and proteome analyses are supposed to reveal many more interesting candidates for future drug development approaches directed against the parasite antioxidant defense systems.

Mechanisms of Plant Salt Response: Insights from Proteomics

Soil salinity is a major abiotic stress that limits plant growth and agriculture productivity. To cope with salt stress, plants have evolved complex salt-responsive signaling and metabolic processes at the cellular, organ, and whole-plant levels. Investigation of the physiological and molecular mechanisms underlying plant salinity tolerance will provide valuable information for effective engineering strategies. Current proteomics provides a high-throughput approach to study sophisticated molecular networks in plants. In this review, we describe a salt-responsive protein database by an integrated analysis of proteomics-based studies. The database contains 2171 salt-responsive protein identities representing 561 unique proteins. These proteins have been identified from leaves, roots, shoots, seedlings, unicells, grains, hypocotyls, radicles, and panicles from 34 plant species. The identified proteins provide invaluable information toward understanding the complex and fine-tuned plant salt-tolerance mechanisms in photosynthesis, reactive oxygen species (ROS) scavenging, ion homeostasis, osmotic modulation, signaling transduction, transcription, protein synthesis/turnover, cytoskeleton dynamics, and cross-tolerance to different stress conditions.

Chemoproteomic approaches to drug target identification and drug profiling

Chemoproteomics represents a new research discipline at the interface of medicinal chemistry, biochemistry, and cell biology focused on studying the molecular mechanisms of action of drugs and other bioactive small molecules. Research strategies frequently combine phenotypic screening with subsequent target identification, and aim at a proteome-wide characterization of drug-induced changes in cellular protein expression and post-translational modifications. In recent years quantitative mass spectrometry has taken center stage in many of these approaches. This review describes experimental strategies in current chemical proteomics research, discusses recent examples of successful applications, and highlights areas in drug discovery where chemical proteomics-based assays using native endogenous proteins are expected to have substantial impact.

In-depth Identification of Pathways Related to Cisplatin-induced Hepatotoxicity through an Integrative Method Based on an Informatics-assisted Label-free Protein Quantitation and Microarray Gene Expression Approach

Cisplatin is used widely for treatment of a variety of cancer diseases. Recently, however, the use of cisplatin is restricted because of its adverse effects such as hepatotoxicity. There is no study with current proteomics technology to evaluate cisplatin-induced hepatotoxicity, even if some studies have reported on the hepatotoxicity. In this study, proteomic as well as genomic analyses have been used for identification of proteins and genes that respond to cisplatin treatment in rat primary hepatocytes. To investigate the hepatotoxic effects of cisplatin, rat primary hepatocytes were treated with an IC(20) concentration for 24 h. From proteomic analysis based on label-free quantitation strategy, cisplatin induced 76 up-regulated and 19 down-regulated proteins among 325 distinct proteins. In the mRNA level, genomic analysis revealed 72 up-regulated and 385 down-regulated genes in the cisplatin-treated group. Based on these two analyses, 19 pathways were commonly altered, whereas seven pathways were identified only by proteomic analysis, and 19 pathways were identified only by genomic analysis. Overall, this study explained the mechanism of cisplatin-induced hepatotoxicity with two points of view: well known pathways including drug metabolism, fatty acid metabolism, and glycolysis/TCA cycle and little known pathways including urea cycle and inflammation metabolism, for hepatotoxicity of other toxic agents. Up-regulated proteins detected by proteomic analysis in the cisplatin-treated group: FBP1 (fructose 1,6-bisphosphatase 1), FASN (fatty acid synthase), CAT (catalase), PRDX1 (peroxiredoxin-1), HSPD1 (60-kDa heat shock protein), MDH2 (malate dehydrogenase 2), and ARG1 (arginase 1), and also down-regulated proteins in the cisplatin-treated group: TPM1 (tropomyosin 1), TPM3 (tropomyosin 3), and CTSB (cathepsin B), were confirmed by Western blot analysis. In addition, up-regulated mRNAs detected by microarray analysis in the cisplatin-treated group: GSTA2, GSTT2, YC2, TXNRD1, CYP2E1, CYP2C13, CYP2D1, ALDH17, ARG1, ARG2, and IL-6, and also down-regulated mRNAs: CYP2C12, CYP26B1, TPM1, and TPM3, were confirmed by RT-PCR analysis. In case of PRDX1, FASN, and ARG1, they were further confirmed by immunofluorescence analysis. Through the integrated proteomic and genomic approaches, the present study provides the first pathway map related to cisplatin-induced hepatotoxicity, which may provide new insight into the mechanism of hepatotoxicity.