A combined, detergent- and organic solvent-based proteomic method for the analysis of detergent-resistant membrane rafts (DRMR) is described. These specialized domains of the plasma membrane contain a distinctive and dynamic protein and/or lipid complement, which can be isolated from most mammalian cells. Lipid rafts are predominantly involved in signal transduction and adapted to mediate and produce different cellular responses. To facilitate a better understanding of their biology and role, DRMR were isolated from Vero cells as a Triton X-100 insoluble fraction. After detergent removal, sonication in 60% buffered methanol was used to extract, solubilize and tryptically digest the resulting protein complement. The peptide digestate was analyzed by microcapillary reversed-phase liquid chromatography-tandem mass spectrometry. Gas-phase fractionation in the mass-to-charge range was employed to broaden the selection of precursor ions and increase the number of identifications in an effort to detect less abundant proteins. A total of 380 proteins were identified including all known lipid raft markers. A total of 91 (24%) proteins were classified as integral alpha-helical membrane proteins, of which 51 (56%) were predicted to have multiple transmembrane domains.

In this study, we utilized a multidimensional peptide separation strategy combined with tandem mass spectrometry (MS/MS) for the identification of proteins in human serum. After enzymatically digesting serum with trypsin, the peptides were fractionated using liquid‐phase isoelectric focusing (IEF) in a novel ampholyte‐free format. Twenty IEF fractions were collected and analyzed by reversed‐phase microcapillary liquid chromatography (μLC)‐MS/MS. Bioinformatic analysis of the raw MS/MS spectra resulted in the identification of 844 unique peptides, corresponding to 437 proteins. This study demonstrates the efficacy of ampholyte‐free peptide autofocusing, which alleviates peptide losses in ampholyte removal strategies. The results show that the separation strategy is effective for high‐throughput characterization of proteins from complex proteomic mixtures.

Abstract: Network representations of biological pathways offer a functional view of molecular biology that is different from and complementary to sequence, expression, and structure databases. There is currently available a wide range of digital collections of pathway data, differing in organisms included, functional area covered (e.g., metabolism vs. signaling), detail of modeling, and support for dynamic pathway construction. While it is currently impossible for these databases to communicate with each other, there are several efforts at standardizing a data exchange language for pathway data. Databases that represent pathway data at the level of individual interactions make it possible to combine data from different predefined pathways and to query by network connectivity. Computable representations of pathways provide a basis for various analyses, including detection of broad network patterns, comparison with mRNA or protein abundance, and simulation.

Cleavable isotope-coded affinity tag (cICAT) reagents were utilized to identify and. quantitate protein expression differences in control and inorganic phosphate-treated murine MC3T3-E1 osteoblast cells. Proteins extracted from control and treated cells were labeled with the light and heavy isotopic versions of cICAT reagents, respectively. The cICAT-labeled samples were combined, proteolytically digested, and the cICAT-derivatized peptides isolated using immobilized avidin chromatography. The cICAT-labeled peptides were resolved into 96 fractions by strong cation-exchange (SCX) liquid chromatography (LC). Analysis of the SCX-LC cICAT peptide fractions by microcapillary reversed-phase LC-tandem mass spectrometry resulted in the identification and quantitation of 7227 unique peptides corresponding to 2501 proteins, or roughly 9% of the proteins currently predicted to be encoded by the mouse genome. A false positive analysis indicated a 98% confidence in the peptide identifications. To corroborate changes in abundance measured by cICAT with those detectable in traditionally prepared cell lysate, we chose to analyze cyclin D1. Cyclin D1 has been previously identified as a phosphate-responsive gene and was likewise identified as a phosphate-responsive protein in the current analysis. The 1.76-fold increase in abundance in cyclin D1 determined from cICAT corresponds well with the 2.41 -fold increase as determined by Western blotting. These results demonstrate that quantitative proteomics is capable of providing a quantitative view of thousands of proteins in mammalian cells within a defined set of experiments.

The National Institutes of Health Mammalian Gene Collection (MGC) Program is a multiinstitutional effort to identify and sequence a cDNA clone containing a complete ORF for each human and mouse gene. ESTs were generated from libraries enriched for full-length cDNAs and analyzed to identify candidate full-ORF clones, which then were sequenced to high accuracy. The MGC has currently sequenced and verified the full ORF for a nonredundant set of >9,000 human and >6,000 mouse genes. Candidate full-ORF clones for an additional 7,800 human and 3,500 mouse genes also have been identified. All MGC sequences and clones are available without restriction through public databases and clone distribution networks (see http:mgc.nci.nih.gov).

A gene's expression pattern provides clues to its role in normal physiology and disease. To provide quantitative expression levels on a genome-wide scale, the Cancer Genome Anatomy Project (CGAP) uses serial analysis of gene expression (SAGE). Over 5 million transcript tags from more than 100 human cell types have been assembled. To enhance the utility of this data, the CGAP SAGE project created SAGE Genie, a web site for the analysis and presentation of SAGE data ( http://cgap.nci.nih.gov/ SAGE). SAGE Genie provides an automatic link between gene names and SAGE transcript levels, accounting for alternative transcription and many potential errors. These informatics advances provide a rapid and intuitive view of transcript expression in the human body or brain, displayed on the SAGE Anatomic Viewer. We report here an easily accessible view of nearly any gene's expression in a wide variety of malignant and normal tissues.

Researchers working collaboratively in Brazil and the United States have assembled an International Database of Cancer Gene Expression. Several strategies have been employed to generate gene expression data including expressed sequence tags (ESTs), serial analysis of gene expression (SAGE), and open reading-frame expressed sequence tags (ORESTES). The database contains six million gene tags that reflect the gene expression profiles in a wide variety of cancerous tissues and their normal counterparts. All sequences are deposited in the public databases, GenBank and SAGEmap. A suite of informatics tools was designed to facilitate in silico analysis of the gene expression datasets and are available through the NCI Cancer Genome Anatomy Project web site (http://cgap.nci.nih.gov).

Four years ago, the National Cancer Institute implemented the Cancer Genome Anatomy Project (CGAP), which was designed to build an interface between genomics and cancer research. It was evident that new approaches to science, based on comprehensive molecular analysis, promised remarkable new opportunities to enhance our fundamental understanding of cancer. New technologies offered the potential to delineate specific types of genetic changes, including patterns of altered gene expression and function that could be used to define any cancer in the context of, but not strictly dependent upon, its site of origin. Therefore, we anticipated that these new technologies would elucidate the molecular features of an individual tumor, and profile progression and response to therapy. The molecular information generated could be used by basic and clinical researchers to define the molecular signatures that distinguish different cancers.Pivotal to this approach is the availability of a robust database and analysis tools. The goal of such an electronic database is to seamlessly integrate molecular and clinical data. Essential to the utility of such a database is the availability of analysis tools that allow researchers to perform in silico analysis to correlate alterations in genes and their expression products with clinical data about the tumor. The challenge would be to extract and integrate all of the relevant information from those dataset to enrich our understanding of cancer. Here, we describe the progress CGAP has made toward building such a database and designing analysis tools.

The Cancer Genome Anatomy Project (CGAP) was designed and implemented to provide public datasets, material resources and informatics tools to serve as a platform to support the elucidation of the molecular signatures of cancer. This overview of CGAP describes the status of this effort to develop resources based on gene expression, polymorphism identification and chromosome aberrations, and we describe a variety of analytical tools designed to facilitate in silico analysis of these datasets.