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1.  Contribution of oncoproteomics to cancer biomarker discovery 
Molecular Cancer  2007;6:25.
Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.
PMCID: PMC1852117  PMID: 17407558
2.  Proteomics and Opportunities for Clinical Translation in Urologic Disease 
The Journal of urology  2009;182(3):835-843.
Proteomics is a rapidly growing new discipline that has the potential to increase and improve the understanding of protein function and interaction in the context of systems biology. As a translational science it has the potential to identify many new therapeutic targets and diagnostic and prognostic biomarkers of disease. Proteomics approaches consist of a combination of powerful technologies: protein/peptide separation, identification and bioinformatic detection and quantitation based on powerful computational data processing tools. In this article, we present an overview of current proteomics technologies, a review of proteomics applications in urology and a perspective on the future of proteomics in clinical medicine.
Materials and Methods
A literature search was performed on the basic concepts of proteomics and technologies commonly used in this field. Advantages, challenges and limitations of current proteomics approaches were discussed. Proteomics applications in the field of urology were presented.
The proteomic approaches to answer clinical questions have only recently been introduced. Many different technologies have been employed in this field, which is moving from simple description into quantitative clinical applications.
Proteomics offers new approaches to the study of diseases of the genitourinary tract and the potential to identify clinically relevant biomarkers and new therapeutic targets.
PMCID: PMC4370209  PMID: 19616261
Mass Spectrometry; Liquid Chromatography; Biomarker; Urinary Proteomics
3.  A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development 
PLoS Medicine  2008;5(6):e123.
The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer.
Methods and Findings
Plasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer.
Our findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.
Samir Hanash and colleagues identify proteins that are increased at an early stage of pancreatic tumor development in a mouse model and may be a useful tool in detecting early tumors in humans.
Editors' Summary
Cancers are life-threatening, disorganized masses of cells that can occur anywhere in the human body. They develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer is detected when it is still small and has not metastasized, surgery can often provide a cure. Unfortunately, many cancers are detected only when they are large enough to press against surrounding tissues and cause pain or other symptoms. By this time, surgical removal of the original (primary) tumor may be impossible and there may be secondary cancers scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. One cancer type for which late detection is a particular problem is pancreatic adenocarcinoma. This cancer rarely causes any symptoms in its early stages. Furthermore, the symptoms it eventually causes—jaundice, abdominal and back pain, and weight loss—are seen in many other illnesses. Consequently, pancreatic cancer has usually spread before it is diagnosed, and most patients die within a year of their diagnosis.
Why Was This Study Done?
If a test could be developed to detect pancreatic cancer in its early stages, the lives of many patients might be extended. Tumors often release specific proteins—“cancer biomarkers”—into the blood, a bodily fluid that can be easily sampled. If a protein released into the blood by pancreatic cancer cells could be identified, it might be possible to develop a noninvasive screening test for this deadly cancer. In this study, the researchers use a “proteomic” approach to identify potential biomarkers for early pancreatic cancer. Proteomics is the study of the patterns of proteins made by an organism, tissue, or cell and of the changes in these patterns that are associated with various diseases.
What Did the Researchers Do and Find?
The researchers started their search for pancreatic cancer biomarkers by studying the plasma proteome (the proteins in the fluid portion of blood) of mice genetically engineered to develop cancers that closely resemble human pancreatic tumors. Through the use of two techniques called high-resolution mass spectrometry and acrylamide isotopic labeling, the researchers identified 165 proteins that were present in larger amounts in plasma collected from mice with early and/or advanced pancreatic cancer than in plasma from control mice. Then, to test whether any of these protein changes were relevant to human pancreatic cancer, the researchers analyzed blood samples collected from patients with pancreatic cancer. These samples, they report, contained larger amounts of some of these proteins than blood collected from patients with chronic pancreatitis, a condition that has similar symptoms to pancreatic cancer. Finally, using blood samples collected during a clinical trial, the Carotene and Retinol Efficacy Trial (a cancer-prevention study), the researchers showed that the measurement of five of the proteins present in increased amounts at an early stage of tumor development in the mouse model discriminated between people with pancreatic cancer and matched controls up to 13 months before cancer diagnosis.
What Do These Findings Mean?
These findings suggest that in-depth proteomic analysis of genetically engineered mouse models of human cancer might be an effective way to identify biomarkers suitable for the early detection of human cancers. Previous attempts to identify such biomarkers using human samples have been hampered by the many noncancer-related differences in plasma proteins that exist between individuals and by problems in obtaining samples from patients with early cancer. The use of a mouse model of human cancer, these findings indicate, can circumvent both of these problems. More specifically, these findings identify a panel of proteins that might allow earlier detection of pancreatic cancer and that might, therefore, extend the life of some patients who develop this cancer. However, before a routine screening test becomes available, additional markers will need to be identified and extensive validation studies in larger groups of patients will have to be completed.
Additional Information.
Please access these Web sites via the online version of this summary at
The MedlinePlus Encyclopedia has a page on pancreatic cancer (in English and Spanish). Links to further information are provided by MedlinePlus
The US National Cancer Institute has information about pancreatic cancer for patients and health professionals (in English and Spanish)
The UK charity Cancerbackup also provides information for patients about pancreatic cancer
The Clinical Proteomic Technologies for Cancer Initiative (a US National Cancer Institute initiative) provides a tutorial about proteomics and cancer and information on the Mouse Proteomic Technologies Initiative
PMCID: PMC2504036  PMID: 18547137
4.  Bioinformatics in microbial biotechnology – a mini review 
The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics – sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics – identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack of available gene-functionality from the wet-lab data, the lack of computer algorithms to explore vast amount of data with unknown functionality, limited availability of protein-protein and protein-DNA interactions, and the lack of knowledge of temporal and transient behavior of genes and pathways.
PMCID: PMC1182391  PMID: 15985162
5.  System-wide Perturbation Analysis with Nearly Complete Coverage of the Yeast Proteome by Single-shot Ultra HPLC Runs on a Bench Top Orbitrap* 
Molecular & Cellular Proteomics : MCP  2011;11(3):M111.013722.
Yeast remains an important model for systems biology and for evaluating proteomics strategies. In-depth shotgun proteomics studies have reached nearly comprehensive coverage, and rapid, targeted approaches have been developed for this organism. Recently, we demonstrated that single LC-MS/MS analysis using long columns and gradients coupled to a linear ion trap Orbitrap instrument had an unexpectedly large dynamic range of protein identification (Thakur, S. S., Geiger, T., Chatterjee, B., Bandilla, P., Frohlich, F., Cox, J., and Mann, M. (2011) Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, 10.1074/mcp.M110.003699). Here we couple an ultra high pressure liquid chromatography system to a novel bench top Orbitrap mass spectrometer (Q Exactive) with the goal of nearly complete, rapid, and robust analysis of the yeast proteome. Single runs of filter-aided sample preparation (FASP)-prepared and LysC-digested yeast cell lysates identified an average of 3923 proteins. Combined analysis of six single runs improved these values to more than 4000 identified proteins/run, close to the total number of proteins expressed under standard conditions, with median sequence coverage of 23%. Because of the absence of fractionation steps, only minuscule amounts of sample are required. Thus the yeast model proteome can now largely be covered within a few hours of measurement time and at high sensitivity. Median coverage of proteins in Kyoto Encyclopedia of Genes and Genomes pathways with at least 10 members was 88%, and pathways not covered were not expected to be active under the conditions used. To study perturbations of the yeast proteome, we developed an external, heavy lysine-labeled SILAC yeast standard representing different proteome states. This spike-in standard was employed to measure the heat shock response of the yeast proteome. Bioinformatic analysis of the heat shock response revealed that translation-related functions were down-regulated prominently, including nucleolar processes. Conversely, stress-related pathways were up-regulated. The proteomic technology described here is straightforward, rapid, and robust, potentially enabling widespread use in the yeast and other biological research communities.
PMCID: PMC3316726  PMID: 22021278
6.  SP3 In-depth Bioinformatics Analysis of Proteomics Data: Problems and Solutions 
The principal goal in proteomics is to extract biologically or clinically meaningful information from large-scale studies in order to provide new insights into fundamental biological processes or find new means to diagnose or treat disease. Many labs now have methods and machinery in place that make possible the robust generation of many thousands of protein identifications each day. This, however, has exposed a new major bottleneck in the proteomics workflow—the problem of analyzing the wealth of protein identifications to find the relatively few proteins that actually render support for biological hypotheses or that have potential medical relevance.
This presentation shows the application of a new bioinformatics tool that almost entirely removes this bottleneck. The new technology was specifically developed to help researchers gain a fast overview of biologically relevant features in vast protein datasets and rapidly zoom in on single proteins or subsets of proteins of particular interest. In a matter of minutes, the output from the MS database search software was turned into information that was biologically more meaningful. This was done by means of sequential steps that: (1) collapsed all protein redundancies into non-redundant lists; (2) filtered/sorted these lists based on experimental observations and biological sequence annotation that was automatically added; and (3) compared/combined lists of annotated proteins to elucidate differences and overlaps between multiple experimental datasets.
The presentation will show through examples how the new technology can be used in proteomics experiments in order to accelerate the otherwise tedious process of making biological sense of lists of protein accession codes. We present the efficient data mining and categorizing of several large datasets of proteins, including data uploaded from the PRIDE database and HUPO projects.
PMCID: PMC2291907
7.  Microbial proteomics: a mass spectrometry primer for biologists 
It is now more than 10 years since the publication of the first microbial genome sequence and science is now moving towards a post genomic era with transcriptomics and proteomics offering insights into cellular processes and function. The ability to assess the entire protein network of a cell at a given spatial or temporal point will have a profound effect upon microbial science as the function of proteins is inextricably linked to phenotype. Whilst such a situation is still beyond current technologies rapid advances in mass spectrometry, bioinformatics and protein separation technologies have produced a step change in our current proteomic capabilities. Subsequently a small, but steadily growing, number of groups are taking advantage of this cutting edge technology to discover more about the physiology and metabolism of microorganisms. From this research it will be possible to move towards a systems biology understanding of a microorganism. Where upon researchers can build a comprehensive cellular map for each microorganism that links an accurately annotated genome sequence to gene expression data, at a transcriptomic and proteomic level.
In order for microbiologists to embrace the potential that proteomics offers, an understanding of a variety of analytical tools is required. The aim of this review is to provide a basic overview of mass spectrometry (MS) and its application to protein identification. In addition we will describe how the protein complexity of microbial samples can be reduced by gel-based and gel-free methodologies prior to analysis by MS. Finally in order to illustrate the power of microbial proteomics a case study of its current application within the Bacilliaceae is given together with a description of the emerging discipline of metaproteomics.
PMCID: PMC1971468  PMID: 17697372
8.  Initial characterization of the human central proteome 
BMC Systems Biology  2011;5:17.
On the basis of large proteomics datasets measured from seven human cell lines we consider their intersection as an approximation of the human central proteome, which is the set of proteins ubiquitously expressed in all human cells. Composition and properties of the central proteome are investigated through bioinformatics analyses.
We experimentally identify a central proteome comprising 1,124 proteins that are ubiquitously and abundantly expressed in human cells using state of the art mass spectrometry and protein identification bioinformatics. The main represented functions are proteostasis, primary metabolism and proliferation. We further characterize the central proteome considering gene structures, conservation, interaction networks, pathways, drug targets, and coordination of biological processes. Among other new findings, we show that the central proteome is encoded by exon-rich genes, indicating an increased regulatory flexibility through alternative splicing to adapt to multiple environments, and that the protein interaction network linking the central proteome is very efficient for synchronizing translation with other biological processes. Surprisingly, at least 10% of the central proteome has no or very limited functional annotation.
Our data and analysis provide a new and deeper description of the human central proteome compared to previous results thereby extending and complementing our knowledge of commonly expressed human proteins. All the data are made publicly available to help other researchers who, for instance, need to compare or link focused datasets to a common background.
PMCID: PMC3039570  PMID: 21269460
9.  Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data 
PLoS ONE  2009;4(7):e6195.
Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.
Methodology/Principal Findings
To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.
Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents.
PMCID: PMC2704375  PMID: 19593435
10.  Methods and approaches for the comprehensive characterization and quantification of cellular proteomes using mass spectrometry 
Physiological genomics  2007;33(1):3-11.
Proteomics has been proposed as one of the key technologies in the postgenomic era. So far, however, the comprehensive analysis of cellular proteomes has been a challenge because of the dynamic nature and complexity of the multitude of proteins in cells and tissues. Various approaches have been established for the analyses of proteins in a cell at a given state, and mass spectrometry (MS) has proven to be an efficient and versatile tool. MS-based proteomics approaches have significantly improved beyond the initial identification of proteins to comprehensive characterization and quantification of proteomes and their posttranslational modifications (PTMs). Despite these advances, there is still ongoing development of new technologies to profile and analyze cellular proteomes more completely and efficiently. In this review, we focus on MS-based techniques, describe basic approaches for MS-based profiling of cellular proteomes and analysis methods to identify proteins in complex mixtures, and discuss the different approaches for quantitative proteome analysis. Finally, we briefly discuss novel developments for the analysis of PTMs. Altered levels of PTM, sometimes in the absence of protein expression changes, are often linked to cellular responses and disease states, and the comprehensive analysis of cellular proteome would not be complete without the identification and quantification of the extent of PTMs of proteins.
PMCID: PMC2771641  PMID: 18162499
quantitative proteomics; isotopic labeling; phosphoproteomics
11.  Clinical Proteomics: Present and Future Prospects 
Clinical Biochemist Reviews  2006;27(2):99-116.
Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.
PMCID: PMC1579414  PMID: 17077880
12.  Challenges and Solutions in Proteomics 
Current Genomics  2007;8(1):21-28.
The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.
PMCID: PMC2474689  PMID: 18645629
Proteomic profiling; high-throughput analysis; biomarkers; bioinformatic tools; iProXpress; sequence library; pathway discovery; stage specific proteins
13.  Bench-to-bedside review: Future novel diagnostics for sepsis - a systems biology approach 
Critical Care  2013;17(5):231.
The early, accurate diagnosis and risk stratification of sepsis remains an important challenge in the critically ill. Since traditional biomarker strategies have not yielded a gold standard marker for sepsis, focus is shifting towards novel strategies that improve assessment capabilities. The combination of technological advancements and information generated through the human genome project positions systems biology at the forefront of biomarker discovery. While previously available, developments in the technologies focusing on DNA, gene expression, gene regulatory mechanisms, protein and metabolite discovery have made these tools more feasible to implement and less costly, and they have taken on an enhanced capacity such that they are ripe for utilization as tools to advance our knowledge and clinical research. Medicine is in a genome-level era that can leverage the assessment of thousands of molecular signals beyond simply measuring selected circulating proteins. Genomics is the study of the entire complement of genetic material of an individual. Epigenetics is the regulation of gene activity by reversible modifications of the DNA. Transcriptomics is the quantification of the relative levels of messenger RNA for a large number of genes in specific cells or tissues to measure differences in the expression levels of different genes, and the utilization of patterns of differential gene expression to characterize different biological states of a tissue. Proteomics is the large-scale study of proteins. Metabolomics is the study of the small molecule profiles that are the terminal downstream products of the genome and consists of the total complement of all low-molecular-weight molecules that cellular processes leave behind. Taken together, these individual fields of study may be linked during a systems biology approach. There remains a valuable opportunity to deploy these technologies further in human research. The techniques described in this paper not only have the potential to increase the spectrum of diagnostic and prognostic biomarkers in sepsis, but they may also enable the discovery of new disease pathways. This may in turn lead us to improved therapeutic targets. The objective of this paper is to provide an overview and basic framework for clinicians and clinical researchers to better understand the 'omics technologies' to enhance further use of these valuable tools.
PMCID: PMC4057467  PMID: 24093155
14.  Study of phosphorylation events for cancer diagnoses and treatment 
The activation of signaling cascades in response to extracellular and intracellular stimuli to control cell growth, proliferation and survival, is orchestrated by protein kinases via phosphorylation. A critical issue is the study of the mechanisms of cancer cells for the development of more effective drugs. With the application of the new proteomic technologies, together with the advancement in the sequencing of the human proteome, patients will therefore be benefited by the discovery of novel therapeutic and/or diagnostic protein targets. Furthermore, the advances in proteomic approaches and the Human Proteome Organization (HUPO) have opened a new door which is helpful in the identification of patients at risk and towards improving current therapies. Modification of the signaling-networks via mutations or abnormal protein expression underlies the cause or consequence of many diseases including cancer. Resulting data is used to reveal connections between genes proteins and compounds and the related molecular pathways for underlining disease states. As a delegate of HUPO, for human proteome on children assays and studies, we, at Hospital Universitario Niño Jesús, are seeking to support the human proteome in this context. Clinical goals have to be clearly established and proteomics experts have to set up the appropriate proteomic strategy, which coupled to bioinformatics will make it possible to achieve new therapies for patients with poor prognosis. We envision to combine our up-coming data to the HUPO organization in order to support international efforts to advance the cure of cancer disease.
PMCID: PMC4460185  PMID: 26055493
Diagnosis; Oncology; Proteomics; Treatment
15.  Redox Proteomics in Selected Neurodegenerative Disorders: From Its Infancy to Future Applications 
Antioxidants & Redox Signaling  2012;17(11):1610-1655.
Several studies demonstrated that oxidative damage is a characteristic feature of many neurodegenerative diseases. The accumulation of oxidatively modified proteins may disrupt cellular functions by affecting protein expression, protein turnover, cell signaling, and induction of apoptosis and necrosis, suggesting that protein oxidation could have both physiological and pathological significance. For nearly two decades, our laboratory focused particular attention on studying oxidative damage of proteins and how their chemical modifications induced by reactive oxygen species/reactive nitrogen species correlate with pathology, biochemical alterations, and clinical presentations of Alzheimer's disease. This comprehensive article outlines basic knowledge of oxidative modification of proteins and lipids, followed by the principles of redox proteomics analysis, which also involve recent advances of mass spectrometry technology, and its application to selected age-related neurodegenerative diseases. Redox proteomics results obtained in different diseases and animal models thereof may provide new insights into the main mechanisms involved in the pathogenesis and progression of oxidative-stress-related neurodegenerative disorders. Redox proteomics can be considered a multifaceted approach that has the potential to provide insights into the molecular mechanisms of a disease, to find disease markers, as well as to identify potential targets for drug therapy. Considering the importance of a better understanding of the cause/effect of protein dysfunction in the pathogenesis and progression of neurodegenerative disorders, this article provides an overview of the intrinsic power of the redox proteomics approach together with the most significant results obtained by our laboratory and others during almost 10 years of research on neurodegenerative disorders since we initiated the field of redox proteomics. Antioxid. Redox Signal. 17, 1610–1655.
I. Introduction
II. Protein (/Lipid) Oxidation and Protein Dysfunction
A. Protein carbonyls
B. Protein nitration
1. Peroxynitrite (ONOO−)
2. Nitrogen dioxide (NO2)
C. HNE adduction to proteins
D. Importance of clearance and detoxification systems
1. The proteasome, parkin, ubiquitin carboxy-terminal hydrolase-L1, and HSPs
2. Superoxide dismutase
3. Catalase
4. Peroxiredoxins
5. Trx and Trx reductase
6. Glutathione reductase
7. Vitamins in neurodegeneration
8. Involvement of iron in neurodegeneration
E. Role of iron in neurodegeneration
1. Fe homeostasis in AD
2. Fe homeostasis in PD
3. Fe homeostasis in ALS
4. Fe homeostasis in HD
F. Some known consequences of protein oxidation
III. Overview of Redox Proteomics
A. Global, gel-based approaches
B. Targeted, gel-free approach
1. Enrichment of PCO modified proteins
2. Enrichment of HNE modified proteins
3. Enrichment of 3-NT modified proteins
IV. Application of Redox Proteomics to Selected Neurodegenerative Disorders
A. Alzheimer's disease
1. PCO in AD
2. Identification of carbonylated proteins in brain of subjects with AD
a. Sample: the brain
b. Energy dysfunction
c. Excitotoxicity
d. Proteosomal dysfunction
e. Neuritic abnormalities
f. APP regulation, tau hyperphosphorylation, and cell cycle regulation
g. Synaptic abnormalities and LTP
h. pH maintenance
i. Mitochondrial abnormalities
3. Carbonylated proteins in brain of subjects with amnestic MCI
4. EAD carbonylated proteins
5. PCAD vs. amnestic MCI protein carbonylation in brain
6. Protein-bound HNE in brain and progression of Alzheimer's disease
7. Protein-bound 3-NT in brain and progression of Alzheimer's disease
8. Nitrated brain proteins in MCI
9. Nitrated proteins in EAD
B. Parkinson disease
1. Redox proteomics in PD
C. Amyotrophic lateral sclerosis
1. Redox proteomics studies in ALS transgenic mice
D. Huntington disease
1. Redox proteomics-transgenic mouse model of HD
2. Proteomics of HD brain
E. Down syndrome
1. Redox proteomics in DS transgenic mice
V. Conclusions and Future Directions
PMCID: PMC3448942  PMID: 22115501
16.  The expanding proteome of the molecular chaperone HSP90 
Cell Cycle  2012;11(7):1301-1308.
The molecular chaperone HSP90 maintains the activity and stability of a diverse set of “client” proteins that play key roles in normal and disease biology. Around 20 HSP90 inhibitors that deplete the oncogenic clientele have entered clinical trials for cancer. However, the full extent of the HSP90-dependent proteome, which encompasses not only clients but also proteins modulated by downstream transcriptional responses, is still incompletely characterized and poorly understood. Earlier large-scale efforts to define the HSP90 proteome have been valuable but are incomplete because of limited technical sensitivity. Here, we discuss previous large-scale surveys of proteome perturbations induced by HSP90 inhibitors in light of a significant new study using state-of-the-art stable isotope labeling by amino acids (SILAC) technology combined with more sensitive high-resolution mass spectrometry (MS) that extends the catalog of proteomic changes in inhibitor-treated cancer cells. Among wide-ranging changes, major functional responses include downregulation of protein kinase activity and the DNA damage response alongside upregulation of the protein degradation machinery. Despite this improved proteomic coverage, there was surprisingly little overlap with previous studies. This may be due in part to technical issues but is likely also due to the variability of the HSP90 proteome with the inhibitor conditions used, the cancer cell type and the genetic status of client proteins. We suggest future proteomic studies to address these factors, to help distinguish client protein components from indirect transcriptional components and to address other key questions in fundamental and translational HSP90 research. Such studies should also reveal new biomarkers for patient selection and novel targets for therapeutic intervention.
PMCID: PMC3350876  PMID: 22421145
HSP90; HSP90 proteome; HSP90 inhibitors; HSP90 biomarkers; cancer
17.  Computational Vaccinology: An Important Strategy to Discover New Potential S. mansoni Vaccine Candidates 
The flatworm Schistosoma mansoni is a blood fluke parasite that causes schistosomiasis, a debilitating disease that occurs throughout the developing world. Current schistosomiasis control strategies are mainly based on chemotherapy, but many researchers believe that the best long-term strategy to control schistosomiasis is through immunization with an antischistosomiasis vaccine combined with drug treatment. Several papers on Schistosoma mansoni vaccine and drug development have been published in the past few years, representing an important field of study. The advent of technologies that allow large-scale studies of genes and proteins had a remarkable impact on the screening of new and potential vaccine candidates in schistosomiasis. In this postgenomic scenario, bioinformatic technologies have emerged as important tools to mine transcriptomic, genomic, and proteomic databases. These new perspectives are leading to a new round of rational vaccine development. Herein, we discuss different strategies to identify potential S. mansoni vaccine candidates using computational vaccinology.
PMCID: PMC3196198  PMID: 22013383
18.  Nucleic Acids for Ultra-Sensitive Protein Detection 
Sensors (Basel, Switzerland)  2013;13(1):1353-1384.
Major advancements in molecular biology and clinical diagnostics cannot be brought about strictly through the use of genomics based methods. Improved methods for protein detection and proteomic screening are an absolute necessity to complement to wealth of information offered by novel, high-throughput sequencing technologies. Only then will it be possible to advance insights into clinical processes and to characterize the importance of specific protein biomarkers for disease detection or the realization of “personalized medicine”. Currently however, large-scale proteomic information is still not as easily obtained as its genomic counterpart, mainly because traditional antibody-based technologies struggle to meet the stringent sensitivity and throughput requirements that are required whereas mass-spectrometry based methods might be burdened by significant costs involved. However, recent years have seen the development of new biodetection strategies linking nucleic acids with existing antibody technology or replacing antibodies with oligonucleotide recognition elements altogether. These advancements have unlocked many new strategies to lower detection limits and dramatically increase throughput of protein detection assays. In this review, an overview of these new strategies will be given.
PMCID: PMC3574740  PMID: 23337338
nucleic acid; biosensor; protein; biomarker; PCR; aptamer
19.  A dynamic model of proteome changes reveals new roles for transcript alteration in yeast 
By characterizing dynamic changes in yeast protein abundance following osmotic shock, this study shows that the correlation between protein and mRNA differs for transcripts that increase versus decrease in abundance, and reveals physiological reasons for these differences.
The correlation between protein and mRNA change is very high at transcripts that increase in abundance, but negligible at reduced transcripts following NaCl shock.Modeling and experimental data suggest that reducing levels of high-abundance transcripts helps to direct translational machinery to newly made transcripts.The transient burst of transcript increase serves to accelerate changes in protein abundance.Post-transcriptional regulation of protein abundance is pervasive, although most of the variance in protein change is explained by changes in mRNA abundance.
Natural microenvironments change rapidly, and living creatures must respond quickly and efficiently to thrive within this flux. At all cellular levels—signaling, transcription, translation, metabolism, cell growth, and division—the response is dynamic and coordinated. Some aspects of this response, such as dynamic changes of the transcriptome, are well understood. But other aspects, like the response of the proteome, have remained obscured primarily because of previous limitations in technology. Without coordinated time-course data, it has remained impossible to correctly characterize the correlations and dependencies between these two essential levels of cell biology.
This work presents an extended picture of the coordinated response of the transcriptome and proteome as cells respond to an abrupt environmental change. To assay proteomic dynamics, we developed a strategy for large-scale, multiplexed quantitation using isobaric tags and high mass accuracy mass spectrometry. This sensitive yet efficient platform allows for the expedient collection of quantitative time-course proteomic data at six time points, sufficiently reproducible to permit meaningful interpretation of variation across biological replicates. Time-course transcriptome data were generated from paired biological samples, allowing us to examine the relationships between changes in mRNA and protein for each gene in terms of direction and intensity, as well as the characteristics of the temporal profiles for each gene.
It was immediately obvious that a single measure of correlation across the entire data set was a meaningless metric. We therefore analyzed relationships between mRNA and protein for different subsets of data. In response to osmotic shock, hundreds of transcripts are highly induced, and their temporal pattern reveals a transient peak of maximal induction, which resolves into a new elevated level as cells acclimate (Figure 2). For this group of genes, there is extremely high correlation between peak mRNA change and protein change (R2∼0.8). But the dynamics of the molecules differ: while mRNA levels transiently overshoot their final levels, proteins gradually rise in abundance toward their new, elevated state. We observed, however, that a measure of efficiency connects the two profiles. The time it takes for a protein to acclimate to its new state correlates with the magnitude of the excess mRNA induction. Thus, the cell imparts an urgency to protein induction by transiently producing excess transcript.
The most surprising result, however, involves transcripts that decrease in abundance. In response to osmotic shock, the cell transiently reduces over 600 transcripts, many of which are among the most highly expressed in unstressed cells. But protein levels for these genes remain, for the most part, almost completely unchanged. The stark absence of protein repression is independent of basal protein abundance, independent of reported protein half-lives, reproducible across biological replicates, and validated by quantitative western blots. Furthermore, since we do detect a handful of proteins whose abundance is significantly reduced, our technology is capable of identifying protein loss. Thus, we conclude that transcript reduction serves another purpose besides reducing protein levels.
To explore alternate interpretations of the consequence of transcriptional repression, we devised a mass-action kinetic model, which describes protein changes based on mRNA dynamics in the context of transient changes in the rates of cell division. The model successfully recapitulated the observed data, allowing us to alter modeling parameters to test various hypotheses.
In response to osmotic shock, overall rates of translation temporarily decrease and cell growth transiently arrests before resuming at a slower rate. We reasoned that mRNA reduction might lower the rate of new protein synthesis, but that retarded production is balanced by reduced cell division. We explored both aspects of this logic with our model.
As expected, removing cell division from our model led to a calculated decrease of protein levels, indicating that reduced growth is necessary for maintaining protein levels. However, when we computationally held mRNA levels stable and calculated protein levels in the absence of mRNA repression, we did not find the expected increase in protein abundance.
We then considered the possibility that one function of the regulated repression of these highly abundant transcripts was to liberate proteins essential for translation, such as ribosomes or translation initiation factors. To explore this, we examined a mutant lacking the Dot6p/Tod6p transcriptional repressors, which fails to properly repress ∼250 genes in response to osmotic shock. In the wild type, the mRNA for a Dot6p/Tod6p target (ARX1) decreased seven-fold, and the remaining transcript was generally unassociated with poly-ribosomes. In the mutant, however, the mRNA levels were reduced only two-fold, while the remaining transcript continued to bind ribosomes. Therefore, failure to reduce transcript levels led to a persistent association with poly-ribosomes, thereby consuming translational machinery.
Our hypothesis is, therefore, that widespread changes in the transcriptome promote efficient translation of new proteins. Transcript increase serves to increase abundance of the encoded proteins, while reduction of some of the most abundant and highly translated mRNAs supports this project by liberating translational capacity. While it is not clear what factors are the limiting elements, it is clear that a full picture of cellular biology requires exploring the dynamics of the cellular response.
The transcriptome and proteome change dynamically as cells respond to environmental stress; however, prior proteomic studies reported poor correlation between mRNA and protein, rendering their relationships unclear. To address this, we combined high mass accuracy mass spectrometry with isobaric tagging to quantify dynamic changes in ∼2500 Saccharomyces cerevisiae proteins, in biological triplicate and with paired mRNA samples, as cells acclimated to high osmolarity. Surprisingly, while transcript induction correlated extremely well with protein increase, transcript reduction produced little to no change in the corresponding proteins. We constructed a mathematical model of dynamic protein changes and propose that the lack of protein reduction is explained by cell-division arrest, while transcript reduction supports redistribution of translational machinery. Furthermore, the transient ‘burst' of mRNA induction after stress serves to accelerate change in the corresponding protein levels. We identified several classes of post-transcriptional regulation, but show that most of the variance in protein changes is explained by mRNA. Our results present a picture of the coordinated physiological responses at the levels of mRNA, protein, protein-synthetic capacity, and cellular growth.
PMCID: PMC3159980  PMID: 21772262
dynamics; modeling; proteomics; stress; transcriptomics
20.  Mapping Intact Protein Isoforms in Discovery Mode Using Top Down Proteomics 
Nature  2011;480(7376):254-258.
A full description of the human proteome relies on the challenging task of detecting mature and changing forms of protein molecules in the body. Large scale proteome analysis1 has routinely involved digesting intact proteins followed by inferred protein identification using mass spectrometry (MS)2. This “bottom up” process affords a high number of identifications (not always unique to a single gene). However, complications arise from incomplete or ambiguous2 characterization of alternative splice forms, diverse modifications (e.g., acetylation and methylation), and endogenous protein cleavages, especially when combinations of these create complex patterns of intact protein isoforms and species3. “Top down” interrogation of whole proteins can overcome these problems for individual proteins4,5, but has not been achieved on a proteome scale due to the lack of intact protein fractionation methods that are well integrated with tandem MS. Here we show, using a new four dimensional (4D) separation system, identification of 1,043 gene products from human cells that are dispersed into >3,000 protein species created by post-translational modification, RNA splicing, and proteolysis. The overall system produced >20-fold increases in both separation power and proteome coverage, enabling the identification of proteins up to 105 kilodaltons and those with up to 11 transmembrane helices. Many previously undetected isoforms of endogenous human proteins were mapped, including changes in multiply-modified species in response to accelerated cellular aging (senescence) induced by DNA damage. Integrated with the latest version of the Swiss-Prot database6, the data provide precise correlations to individual genes and proof-of-concept for large scale interrogation of whole protein molecules. The technology promises to improve the link between proteomics data and complex phenotypes in basic biology and disease research7.
PMCID: PMC3237778  PMID: 22037311
21.  Proteome Dataset of Human Gingival Crevicular Fluid from Healthy Periodontium Sites by Multi-Dimensional Protein Separation and Mass Spectrometry 
Journal of Periodontal Research  2011;47(2):248-262.
Background and Objective
Gingival crevicular fluid (GCF) has been of major interest for many decades as valuable body fluid that may serve as a source of biomarkers for both periodontal and systemic diseases. Because of its very small sample size, sub-μl level, identification of its protein composition by classical biochemical methods has been limited. The advent of highly sensitive mass spectrometric technology has permitted large-scale identification of protein components of many biological samples. This technology has been employed to identify protein composition of GCF from inflamed and periodontal sites. In this report we present a proteome dataset of GCF from healthy periodontium sites.
A combination of periopaper collection method with application of multidimensional protein separation and mass spectrometric (MS) technology led to a large-scale documentation of the proteome of GCF from healthy periodontium sites.
The approaches utilized have culminated in identification of 199 proteins in GCF of periodontally healthy sites. The current GCF proteome from healthy sites was compared and contrasted with those proteomes of GCF from inflamed and periodontal sites as well as serum. The cross-correlation of the GCF and plasma proteomes permitted dissociation of the 199 identified GCF proteins into, 105 proteins (57%) that can be identified in plasma and 94 proteins (43%) which are distinct and unique to GCF microenvironment. Such analysis also revealed distinctions in protein functional categories between serum proteins and those specific to GCF microenvironment.
Firstly, the data presented herein provide the proteome of GCF from periodontally healthy sites through establishment of innovative analytical approaches for effective analysis of GCF from periopapers both at the level of complete elusion and removal of abundant albumin which restricts identification of low abundant proteins. Secondly, it adds significantly to the knowledge of GCF composition and highlights new groups of proteins specific to GCF microenvironment.
PMCID: PMC3272151  PMID: 22029670
Gingival crevicular fluid; mass spectrometry; proteomics; saliva; oral; biomarkers; diagnostics
22.  Methods for visual mining of genomic and proteomic data atlases 
BMC Bioinformatics  2012;13:58.
As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.
This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.
The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.
PMCID: PMC3352268  PMID: 22524279
23.  Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT 
Journal of proteome research  2011;10(8):3690-3700.
Ideally shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors which facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.
PMCID: PMC3161494  PMID: 21702434
shotgun proteomics; peptide identification; proteome coverage; protein abundance dynamic range
24.  Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets 
PLoS ONE  2009;4(9):e7162.
The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center ( has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification.
PMCID: PMC2745575  PMID: 19779614
25.  In-depth analysis of the chicken egg white proteome using an LTQ Orbitrap Velos 
Proteome Science  2011;9:7.
Hen's egg white has been the subject of intensive chemical, biochemical and food technological research for many decades, because of its importance in human nutrition, its importance as a source of easily accessible model proteins, and its potential use in biotechnological processes. Recently the arsenal of tools used to study the protein components of egg white has been complemented by mass spectrometry-based proteomic technologies. Application of these fast and sensitive methods has already enabled the identification of a large number of new egg white proteins. Recent technological advances may be expected to further expand the egg white protein inventory.
Using a dual pressure linear ion trap Orbitrap instrument, the LTQ Orbitrap Velos, in conjunction with data analysis in the MaxQuant software package, we identified 158 proteins in chicken egg white with two or more sequence unique peptides. This group of proteins identified with very high confidence included 79 proteins identified in egg white for the first time. In addition, 44 proteins were identified tentatively.
Our results, apart from identifying many new egg white components, indicate that current mass spectrometry technology is sufficiently advanced to permit direct identification of minor components of proteomes dominated by a few major proteins without resorting to indirect techniques, such as chromatographic depletion or peptide library binding, which change the composition of the proteome.
PMCID: PMC3041730  PMID: 21299891

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