Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.

Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.

Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

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The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

Mass spectrometry-based proteomics

We describe an approach for the accurate quantification and concurrent sequence identification of the individual proteins within complex mixtures. The method is based on a class of new chemical reagents termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry. Using this strategy, we com- pared protein expression in the yeast Saccharomyces cerevisiae, using either ethanol or galactose as a carbon source. The measured differences in protein expression correlated with known yeast metabolic function under glucose-repressed conditions. The method is redundant if multiple cysteinyl residues are present, and the relative quantification is highly accurate because it is based on stable isotope dilution techniques. The ICAT approach should provide a widely applicable means to compare quantitatively glob- al protein expression in cells and tissues.

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Quantitative analysis of complex protein mixtures using isotope-coded affinity tags

We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

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Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents

The description of the state of a biological system by the quantitative measurement of the system constituents is an essential but largely unexplored area of biology. With recent technical advances including the development of differential display-PCR (21), of cDNA microarray and DNA chip technology (20, 27), and of serial analysis of gene expression (SAGE) (34, 35), it is now feasible to establish global and quantitative mRNA expression profiles of cells and tissues in species for which the sequence of all the genes is known. However, there is emerging evidence which suggests that mRNA expression patterns are necessary but are by themselves insufficient for the quantitative description of biological systems. This evidence includes discoveries of posttranscriptional mechanisms controlling the protein translation rate (15), the half-lives of specific proteins or mRNAs (33), and the intracellular location and molecular association of the protein products of expressed genes (32).

Proteome analysis, defined as the analysis of the protein complement expressed by a genome (26), has been suggested as an approach to the quantitative description of the state of a biological system by the quantitative analysis of protein expression profiles (36). Proteome analysis is conceptually attractive because of its potential to determine properties of biological systems that are not apparent by DNA or mRNA sequence analysis alone. Such properties include the quantity of protein expression, the subcellular location, the state of modification, and the association with ligands, as well as the rate of change with time of such properties. In contrast to the genomes of a number of microorganisms (for a review, see reference 11) and the transcriptome of Saccharomyces cerevisiae (35), which have been entirely determined, no proteome map has been completed to date.

The most common implementation of proteome analysis is the combination of two-dimensional gel electrophoresis (2DE) (isoelectric focusing-sodium dodecyl sulfate [SDS]-polyacrylamide gel electrophoresis) for the separation and quantitation of proteins with analytical methods for their identification. 2DE permits the separation, visualization, and quantitation of thousands of proteins reproducibly on a single gel (18, 24). By itself, 2DE is strictly a descriptive technique. The combination of 2DE with protein analytical techniques has added the possibility of establishing the identities of separated proteins (1, 2) and thus, in combination with quantitative mRNA analysis, of correlating quantitative protein and mRNA expression measurements of selected genes.

The recent introduction of mass spectrometric protein analysis techniques has dramatically enhanced the throughput and sensitivity of protein identification to a level which now permits the large-scale analysis of proteins separated by 2DE. The techniques have reached a level of sensitivity that permits the identification of essentially any protein that is detectable in the gels by conventional protein staining (9, 29). Current protein analytical technology is based on the mass spectrometric generation of peptide fragment patterns that are idiotypic for the sequence of a protein. Protein identity is established by correlating such fragment patterns with sequence databases (10, 22, 37). Sophisticated computer software (8) has automated the entire process such that proteins are routinely identified with no human interpretation of peptide fragment patterns.

In this study, we have analyzed the mRNA and protein levels of a group of genes expressed in exponentially growing cells of the yeast S. cerevisiae. Protein expression levels were quantified by metabolic labeling of the yeast proteins to a steady state, followed by 2DE and liquid scintillation counting of the selected, separated protein species. Separated proteins were identified by in-gel tryptic digestion of spots with subsequent analysis by microspray liquid chromatography-tandem mass spectrometry (LC-MS/MS) and sequence database searching. The corresponding mRNA transcript levels were calculated from SAGE frequency tables (35).

This study, for the first time, explores a quantitative comparison of mRNA transcript and protein expression levels for a relatively large number of genes expressed in the same metabolic state. The resultant correlation is insufficient for prediction of protein levels from mRNA transcript levels. We have also compared the relative amounts of protein and mRNA with the respective codon bias values for the corresponding genes. This comparison indicates that codon bias by itself is insufficient to accurately predict either the mRNA or the protein expression levels of a gene. In addition, the results demonstrate that only highly expressed proteins are detectable by 2DE separation of total cell lysates and that therefore the construction of complete proteome maps with current technology will be very challenging, irrespective of the type of organism.

Correlation between Protein and mRNA Abundance in Yeast

he genomics revolution has changed the paradigm for the comprehensive analysis of biological processes and systems. It is now hypothesized that biological processes and systems can be described based on the comparison of global, quantitative gene expression patterns from cells or tissues representing different states. To test this hypothesis, it is essential that methods for the precise measurement of gene expression be developed and applied. Several methods, including serial analysis of gene expression, oligonucleotide and cDNA microarrays, and large-scale se- quencing of expressed sequence tags have been developed to globally and quantitatively measure gene expression at the mRNA level (1, 2). The discovery of posttranscriptional mech- anisms that control rate of synthesis and half-life of proteins (3) and the ensuing nonpredictive correlation between mRNA and protein levels expressed by a particular gene (4, 5) indicate that direct measurement of protein expression also is essential for the analysis of biological processes and systems. Global analysis of gene expression at the protein level is now also termed proteomics. The standard method for quantitative proteome analysis combines protein separation by high- resolution (isoelectric focusingySDS-PAGE) two-dimensional gel electrophoresis (2DE) with mass spectrometric (MS) or tandem MS (MSyMS) identification of selected protein spots. Important technical advances related to 2DE and protein MS have increased sensitivity, reproducibility, and throughput of proteome analysis while creating an integrated technology. By using 2DE with extended pH range and high-sensitivity protein identification by electrospray ionization and MSyMS, we have evaluated the potential of the 2DE-MS strategy to serve as the technology base for comprehensive and quantitative pro- teome analysis.

https://www.pnas.org/content/pnas/97/17/9390.full.pdf

Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology

Immunized mice after inhalation of specific antigen have the following characteristic features of human asthma: airway eosinophilia, mucus and Th2 cytokine release, and hyperresponsiveness to methacholine. A model of late-phase allergic pulmonary inflammation in ovalbumin-sensitized mice was used to address the role of the alpha4 integrin (CD49d) in mediating the airway inflammation and hyperresponsiveness. Local, intrapulmonary blockade of CD49d by intranasal administration of CD49d mAb inhibited all signs of lung inflammation, IL-4 and IL-5 release, and hyperresponsiveness to methacholine. In contrast, CD49d blockade on circulating leukocytes by intraperitoneal CD49d mAb treatment only prevented the airway eosinophilia. In this asthma model, a CD49d-positive intrapulmonary leukocyte distinct from the eosinophil is the key effector cell of allergen-induced pulmonary inflammation and hyperresponsiveness.

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Blockade of CD49d (alpha4 integrin) on intrapulmonary but not circulating leukocytes inhibits airway inflammation and hyperresponsiveness in a mouse model of asthma.

Several methods have been developed for the comprehensive analysis of gene expression in complex biological systems Generally these procedures assess either a portion of the cellular transcriptome or a portion of the cellular proteome Each approach has distinct conceptual and methodological advantages and disadvantages We have investigated the application of both methods to characterize the gene expression pathway mediated by androgens and the androgen receptor in prostate cancer cells This pathway is of critical importance for the development and progression of prostate cancer Of clinical importance, modulation of androgens remains the mainstay of treatment for patients with advanced disease To facilitate global gene expression studies we have first sought to define the prostate transcriptome by assembling and annotating prostate-derived expressed sequence tags (ESTs) A total of 55 000 prostate ESTs were assembled into a set of 15 953 clusters putatively representing 15 953 distinct transcripts These clusters were used to construct cDNA microarrays suitable for examining the androgen-response pathway at the level of transcription The expression of 20 genes was found to be induced by androgens This cohort included known androgen-regulated genes such as prostate-specific antigen (PSA) and several novel complementary DNAs (cDNAs) Protein expression profiles of androgen-stimulated prostate cancer cells were generated by two-dimensional electrophoresis (2-DE) Mass spectrometric analysis of androgen-regulated proteins in these cells identified the metastasis-suppressor gene NDKA/nm23, a finding that may explain a marked reduction in metastatic potential when these cells express a functional androgen receptor pathway

Comprehensive analyses of prostate gene expression: convergence of expressed sequence tag databases, transcript profiling and proteomics.

Methods for the determination of the rate of synthesis of biopolymer synthesis and degradation in cells, tissues, or cell-free systems using monomer which has been labeled with a stable isotope are provided. Further, the present invention provides methods for the determination or identification of an unknown biopolymer and for the identification of an unknown cell type, a physiological state of a cell or tissue. Also, the present invention provides a database of descriptors which can be used to define an organism, tissue type, cell type, and the like, and which database can be used in conjunction with other public and private databases to identify or characterize an organism, tissue type, cell type, state of differentiation, or physiologic state of an organism, or tissue or cell sample.

Yvan Rochon

Papers

Correlation between Protein and mRNA Abundance in Yeast

Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology

Blockade of CD49d (alpha4 integrin) on intrapulmonary but not circulating leukocytes inhibits airway inflammation and hyperresponsiveness in a mouse model of asthma.

Comprehensive analyses of prostate gene expression: convergence of expressed sequence tag databases, transcript profiling and proteomics.

Stable isotope metabolic labeling for analysis of biopolymers