SILAC compatible strain of Pichia pastoris for expression of isotopically labeled protein standards and quantitative proteomics

The methylotrophic yeast Pichia pastoris is a powerful eukaryotic platform for the production of heterologous protein. Recent publication of the P. pastoris genome has facilitated strain development toward biopharmaceutical and environmental science applications and has advanced the organism as a model system for the study of peroxisome biogenesis and methanol metabolism. Here we report the development of a P. pastoris arg-/lys- auxotrophic strain compatible with SILAC (stable isotope labeling by amino acids in cell culture) proteomic studies, which is capable of generating large quantities of isotopically labeled protein for mass spectrometry-based biomarker measurements. We demonstrate the utility of this strain to produce high purity human serum albumin uniformly labeled with isotopically heavy arginine and lysine. In addition, we demonstrate the first quantitative proteomic analysis of methanol metabolism in P. pastoris, reporting new evidence for a malate-aspartate NADH shuttle mechanism in the organism. This strain will be a useful model organism for the study of metabolism and peroxisome generation.     

CONSeQuence: prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches

Mass spectrometric based methods for absolute quantification of proteins, such as QconCAT, rely on internal standards of stable-isotope labeled reference peptides, or "Q-peptides," to act as surrogates. Key to the success of this and related methods for absolute protein quantification (such as AQUA) is selection of the Q-peptide. Here we describe a novel method, CONSeQuence (consensus predictor for Q-peptide sequence), based on four different machine learning approaches for Q-peptide selection. CONSeQuence demonstrates improved performance over existing methods for optimal Q-peptide selection in the absence of prior experimental information, as validated using two independent test sets derived from yeast. Furthermore, we examine the physicochemical parameters associated with good peptide surrogates, and demonstrate that in addition to charge and hydrophobicity, peptide secondary structure plays a significant role in determining peptide "detectability" in liquid chromatography-electrospray ionization experiments. We relate peptide properties to protein tertiary structure, demonstrating a counterintuitive preference for buried status for frequently detected peptides. Finally, we demonstrate the improved efficacy of the general approach by applying a predictor trained on yeast data to sets of proteotypic peptides from two additional species taken from an existing peptide identification repository.     

Isobaric tag for relative and absolute quantitation based quantitative proteomics reveals unique urinary protein profiles in patients with preeclampsia

Preeclampsia (PE) is one of the most significant pregnancy‐related hypertensive disorders. Currently, there are no useful markers to predict the onset of the condition in pregnant women. To provide further insights into the pathogenesis of PE and identify biomarkers of the condition, we used isobaric tags for relative and absolute quantitation (iTRAQ) proteomics coupled with 2‐D LC‐MS/MS, to analyze urinary protein profiles from 7 PE patients and 7 normotensive pregnant women. A total of 294 proteins were abnormally expressed in PE patients. Of these, 233 were significantly down‐regulated and 61 proteins were significantly up‐regulated. Bioinformatics analysis using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, found that the most differentially expressed proteins (DEPs) were involved in coagulation and complement pathways, the renin‐angiotensin system and cell adhesion molecules (CAMs) pathways. We further validated three of the DEPs, including serotransferrin (TF) and complement factor B (CFB) by immunoblottingand serum paraoxonase/arylesterase 1 (PON1) by ELISA using 14 pairs of urine samples from PE patients and normal pregnant women. Taken together, our results provide the basis for further understanding the pathogenesis of PE and identifying predictive biomarkers.     

The proteomics standards initiative

The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and verification. Progress has been made in the development of common standards for data exchange in the fields of both mass spectrometry and protein-protein interaction. A proteomics-specific extension is being created for the emerging American Society for Tests and Measurements mass spectrometry standard, which will be supported by manufacturers of both hardware and software. A data model for proteomics experimentation is under development and discussions on a public repository for published proteomics data are underway. The Protein-Protein Interactions group expects to publish the Level 1 PSI data exchange format for protein-protein interactions soon and discussions as to the content of Level 2 have been initiated.     

A novel strategy for quantitative proteomics using isotope-coded protein labels

Stable isotope labelling in combination with mass spectrometry has emerged as a powerful tool to identify and relatively quantify thousands of proteins within complex protein mixtures. Here we describe a novel method, termed isotope-coded protein label (ICPL), which is capable of high-throughput quantitative proteome profiling on a global scale. Since ICPL is based on stable isotope tagging at the frequent free amino groups of isolated intact proteins, it is applicable to any protein sample, including extracts from tissues or body fluids, and compatible to all separation methods currently employed in proteome studies. The method showed highly accurate and reproducible quantification of proteins and yielded high sequence coverage, indispensable for the detection of post-translational modifications and protein isoforms. The efficiency (e.g. accuracy, dynamic range, sensitivity, speed) of the approach is demonstrated by comparative analysis of two differentially spiked proteomes.     

Experimental standards for high-throughput proteomics

Proteome analysis, utilizing high-throughput proteomics approaches, involves studying proteins that a whole organism (or specific tissue or cellular compartment) expresses under certain conditions. Intrinsic difficulties of these studies, as well as the enormous volumes of data they typically produce, make the proteome analysis and interpretation very difficult. As with any high-throughput approach, proteomics experiments should be carefully designed, analyzed, and verified. In addition to computational standards,experimental standards--simple and complex mixtures of known proteins--for high-throughput proteomics have to be developed and utilized. This article discusses such experimental standards and their implementations.     

Population proteomics: quantitative variation within and among populations in cardiac protein expression

Population analysis of gene expression is typically achieved by quantifying levels of mRNA; however, gene expression is also a function of protein translation and turnover. Therefore, a complete understanding of population variation in gene expression requires quantitative knowledge of protein expression within and among natural populations. We used two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) to quantitatively compare expression of heart ventricle proteins among 18 individuals in three populations of the teleost fish Fundulus. Among populations, expressions between orthologous proteins and mRNAs were generally positively correlated. Additionally, similar to the pattern of cardiac mRNA expression for the same populations, we found considerable variation in protein expression both within and among populations: Of 408 protein features in 2D gels, 34% are significantly different (P < 0.01) among individuals within a population, 9% differ between populations, and 12% have a pattern of expression that suggests they have evolved by natural selection. Although similar to mRNA expression, the frequency of significant differences among populations is larger for proteins. Similar to mRNA expressions, expressions of most proteins are correlated to the expressions of many other proteins. However, the correlations among proteins are more extensive than the correlation for similar RNAs. These correlations suggest a greater coordinate regulation of protein than mRNA expression. The larger frequency of significant differences among populations and the greater frequency of correlated expression among proteins versus among RNAs suggest that the molecular mechanisms affecting protein expression enhance the differences among populations, and these regulatory steps could be a source of variation for adaptation.     

A QconCAT informatics pipeline for the analysis, visualization and sharing of absolute quantitative proteomics data

Absolute protein concentration determination is becoming increasingly important in a number of fields including diagnostics, biomarker discovery and systems biology modeling. The recently introduced quantification concatamer methodology provides a novel approach to performing such determinations, and it has been applied to both microbial and mammalian systems. While a number of software tools exist for performing analyses of quantitative data generated by related methodologies such as SILAC, there is currently no analysis package dedicated to the quantification concatamer approach. Furthermore, most tools that are currently available in the field of quantitative proteomics do not manage storage and dissemination of such data sets.     

Mass spectrometry-based quantitative proteomics

A major aim of present-day proteomics is to study changes in protein expression levels at a global level, ideally monitoring all proteins present in cells or tissue. Mass spectrometry is a well-respected technology in proteomics that is widely used for the identification of proteins. More recently, methodologies have been introduced showing that mass spectrometry can also be used for protein quantification. This article reviews various mass spectrometry-based technologies in quantitative proteomics, highlighting several interesting applications in areas ranging from cell biology to clinical applications.     

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.     

Mass spectrometry-based quantitative proteomics

A major aim of present-day proteomics is to study changes in protein expression levels at a global level, ideally monitoring all proteins present in cells or tissue. Mass spectrometry is a well-respected technology in proteomics that is widely used for the identification of proteins. More recently, methodologies have been introduced showing that mass spectrometry can also be used for protein quantification. This article reviews various mass spectrometry-based technologies in quantitative proteomics, highlighting several interesting applications in areas ranging from cell biology to clinical applications.     

The HUPO proteomics standards initiative--overcoming the fragmentation of proteomics data

Proteomics is a key field of modern biomolecular research, with many small and large scale efforts producing a wealth of proteomics data. However, the vast majority of this data is never exploited to its full potential. Even in publicly funded projects, often the raw data generated in a specific context is analysed, conclusions are drawn and published, but little attention is paid to systematic documentation, archiving, and public access to the data supporting the scientific results. It is often difficult to validate the results stated in a particular publication, and even simple global questions like "In which cellular contexts has my protein of interest been observed?" can currently not be answered with realistic effort, due to a lack of standardised reporting and collection of proteomics data. The Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organisation (HUPO), defines community standards for data representation in proteomics to facilitate systematic data capture, comparison, exchange and verification. In this article we provide an overview of PSI organisational structure, activities, and current results, as well as ways to get involved in the broad-based, open PSI process.     

Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics

Inferring protein abundances from peptide intensities is the key step in quantitative proteomics. The inference is necessarily more accurate when many peptides are taken into account for a given protein. Yet, the information brought by the peptides shared by different proteins is commonly discarded. We propose a statistical framework based on a hierarchical modeling to include that information. Our methodology, based on a simultaneous analysis of all the quantified peptides, handles the biological and technical errors as well as the peptide effect. In addition, we propose a practical implementation suitable for analyzing large data sets. Compared to a method based on the analysis of one protein at a time (that does not include shared peptides), our methodology proved to be far more reliable for estimating protein abundances and testing abundance changes. The source codes are available at http://pappso.inra.fr/bioinfo/all_p/.