General transcription factor specified global gene regulation in archaea.

Cells responding to dramatic environmental changes or undergoing a developmental switch typically change the expression of numerous genes. In bacteria, sigma factors regulate much of this process, whereas in eukaryotes, four RNA polymerases and a multiplicity of generalized transcription factors (GTFs) are required. Here, by using a systems approach, we provide experimental evidence (including protein-coimmunoprecipitation, ChIP-Chip, GTF perturbation and knockout, and measurement of transcriptional changes in these genetically perturbed strains) for how archaea likely accomplish similar large-scale transcriptional segregation and modulation of physiological functions. We are able to associate GTFs to nearly half of all putative promoters and show evidence for at least 7 of the possible 42 functional GTF pairs. This report represents a significant contribution toward closing the gap in our understanding of gene regulation by GTFs for all three domains of life and provides an example for how to use various experimental techniques to rapidly learn significant portions of a global gene regulatory network of organisms for which little has been previously known.

Mining the acute respiratory distress syndrome proteome: identification of the insulin-like growth factor (IGF)/IGF-binding protein-3 pathway in acute lung injury.

To obtain a more complete protein profile of the airspace milieu in acute respiratory distress syndrome (ARDS) and to identify new mediators, we analyzed bronchoalveolar lavage fluid (BALF) by shotgun proteomics. Using BALF from three patients, we identified a total of 870 different proteins, a nearly 10-fold increase from previous reports. Among the proteins identified were known markers of lung injury, such as surfactant, proteases, and serum proteins. We also identified several biologically interesting proteins not previously identified in patients with ARDS, including insulin-like growth factor-binding protein-3 (IGFBP-3). Because of the known role of IGFBP-3 in regulating cell survival, we measured IGFBP-3 levels by enzyme-linked immunosorbent assay in ARDS BALF. Normal controls had low levels of IGFBP-3, whereas patients with early ARDS had a significant increase in IGFBP-3. The IGF pathway, acting through the type 1 IGF-receptor, repressed apoptosis of lung fibroblasts but not lung epithelial cells. Furthermore, depletion of IGF in ARDS BALF led to enhanced fibroblast apoptosis. Our data suggest that the IGFBP-3/IGF pathway is involved in the pathogenesis of lung injury, illustrating the power of shotgun proteomics to catalog proteins present in complex biological fluids, such as BALF, from which hypotheses can be developed and tested.

Quantitative proteomic profiling of pancreatic cancer juice.

Pancreatic juice is an exceptionally rich source of cancer-specific proteins shed from cancerous ductal cells into the pancreatic juice. Quantitative proteomic analysis of the proteins specific to pancreatic cancer juice has not previously been reported. We used isotope-code affinity tag (ICAT) technology and MS/MS to perform quantitative protein profiling of pancreatic juice from pancreatic cancer patients and normal controls. ICAT technology coupled with MS/MS allows the systematic study of the proteome and measures the protein abundance in pancreatic juice with the potential for development of biomarkers. A total of 105 proteins were identified and quantified in the pancreatic juice from a pancreatic cancer patient, of which 30 proteins showed abundance changes of at least twofold in pancreatic cancer juice compared to normal controls. Many of these proteins have been externally validated. This is the first comprehensive study of the pancreatic juice proteome by quantitative global protein profiling, and the study reveals numerous proteins that are shown for the first time to be associated with pancreatic cancer, providing candidates for diagnostic biomarkers. One of the identified proteins, insulin-like growth factor binding protein-2 was further validated by Western blotting to be elevated in pancreatic cancer juice and overexpressed in pancreatic cancer tissue.

Pancreatic cancer proteome: the proteins that underlie invasion, metastasis, and immunologic escape.

BACKGROUND & AIMS: Pancreatic cancer is a highly lethal disease that has seen little headway in diagnosis and treatment for the past few decades. The effective treatment of pancreatic cancer is critically relying on the diagnosis of the disease at an early stage, which still remains challenging. New experimental approaches, such as quantitative proteomics, have shown great potential for the study of cancer and have opened new opportunities to investigate crucial events underlying pancreatic tumorigenesis and to exploit this knowledge for early detection and better intervention. METHODS: To systematically study protein expression in pancreatic cancer, we used isotope-coded affinity tag technology and tandem mass spectrometry to perform quantitative proteomic profiling of pancreatic cancer tissues and normal pancreas. RESULTS: A total of 656 proteins were identified and quantified in 2 pancreatic cancer samples, of which 151 were differentially expressed in cancer by at least 2-fold. This study revealed numerous proteins that are newly discovered to be associated with pancreatic cancer, providing candidates for future early diagnosis biomarkers and targets for therapy. Several differentially expressed proteins were further validated by tissue microarray immunohistochemistry. Many of the differentially expressed proteins identified are involved in protein-driven interactions between the ductal epithelium and the extracellular matrix that orchestrate tumor growth, migration, angiogenesis, invasion, metastasis, and immunologic escape. CONCLUSIONS: Our study is the first application of isotope-coded affinity tag technology for proteomic analysis of human cancer tissue and has shown the value of this technology in identifying differentially expressed proteins in cancer.

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry.

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information.

ICAT-MS-MS time course analysis of atrophying mouse skeletal muscle cytosolic subproteome.

Skeletal muscle atrophy is a process in which protein degradation exceeds protein synthesis, resulting in a decrease of the muscle's physiological cross-sectional area and mass, and is often a serious consequence of numerous health problems. We used the isotope-coded affinity tag (ICAT) labelling approach and MS-MS to protein profile cytosolic subcellular fractions from mouse tibialis anterior skeletal muscle undergoing 0, 4, 8, or 16 days of immobilisation-induced atrophy. For the validation of peptide and protein identifications statistical algorithms were applied to the sequence database search results in order to obtain consistent sensitivity/error rates for protein and peptide identifications at each immobilisation time point. In this study, we identified and quantified a large number of mouse skeletal muscle proteins. At a protein probability (P) of P> or = 0.9 (corresponding to a false positive error rate of less than 1%) 807 proteins were identified (231, 226, 217 for 4, 8, 16 days of immobilisation and 133 for the control sample, respectively), from which 51 displayed altered protein abundance with atrophy. Due to randomness of data acquisition, a full time course could be generated only for 62 proteins, most of which displayed unchanged protein abundance. In spite of this, useful information about dataset characteristics and underlying biological processes could be obtained through gene over-representation analysis. 20 gene categories-mainly but not exclusively encoded by the subset of overlapping proteins--were consistently found to be significantly (p < 0.05) over-represented in all 4 sub-datasets.

Lipid raft proteins and their identification in T lymphocytes.

This review focuses on how membrane lipid rafts have been detected and isolated, mostly from lymphocytes, and their associated proteins identified. These proteins include transmembrane antigens/receptors, GPI-anchored proteins, cytoskeletal proteins, Src-family protein kinases, G-proteins, and other proteins involved in signal transduction. To further understand the biology of lipid rafts, new methodological approaches are needed to help characterize the raft protein component, and changes that occur in this component as a result of cell perturbation. We describe the application of new proteomic approaches to the identification and quantification of raft proteins in T-lymphocytes. Similar approaches, applied to other model cell systems, will provide valuable new insights into both cellular signal transduction and lipid raft biology.

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: I. Statistically annotated datasets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins copurifying with T cell lipid rafts.

Lipid rafts were prepared according to standard protocols from Jurkat T cells stimulated via T cell receptor/CD28 cross-linking and from control (unstimulated) cells. Co-isolating proteins from the control and stimulated cell preparations were labeled with isotopically normal (d0) and heavy (d8) versions of the same isotope-coded affinity tag (ICAT) reagent, respectively. Samples were combined, proteolyzed, and resultant peptides fractionated via cation exchange chromatography. Cysteine-containing (ICAT-labeled) peptides were recovered via the biotin tag component of the ICAT reagents by avidin-affinity chromatography. On-line micro-capillary liquid chromatography tandem mass spectrometry was performed on both avidin-affinity (ICAT-labeled) and flow-through (unlabeled) fractions. Initial peptide sequence identification was by searching recorded tandem mass spectrometry spectra against a human sequence data base using SEQUEST software. New statistical data modeling algorithms were then applied to the SEQUEST search results. These allowed for discrimination between likely "correct" and "incorrect" peptide assignments, and from these the inferred proteins that they collectively represented, by calculating estimated probabilities that each peptide assignment and subsequent protein identification was a member of the "correct" population. For convenience, the resultant lists of peptide sequences assigned and the proteins to which they corresponded were filtered at an arbitrarily set cut-off of 0.5 (i.e. 50% likely to be "correct") and above and compiled into two separate datasets. In total, these data sets contained 7667 individual peptide identifications, which represented 2669 unique peptide sequences, corresponding to 685 proteins and related protein groups.

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis, and the application of statistical tools for data analysis and interpretation.

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.