Biomedical subjects

Ruedi Aebersold

Publications and source records attributed to Ruedi Aebersold.

At least 19 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2007

Comparison of pancreas juice proteins from cancer versus pancreatitis using quantitative proteomic analysis.

OBJECTIVES: Pancreatitis is an inflammatory condition of the pancreas. However, it often shares many molecular features with pancreatic cancer. Biomarkers present in pancreatic cancer frequently occur in the setting of pancreatitis. The efforts to develop diagnostic biomarkers for pancreatic cancer have thus been complicated by the false-positive involvement of pancreatitis. METHODS: In an attempt to develop protein biomarkers for pancreatic cancer, we previously use quantitative proteomics to identify and quantify the proteins from pancreatic cancer juice. Pancreatic juice is a rich source of proteins that are shed by the pancreatic ductal cells. In this study, we used a similar approach to identify and quantify proteins from pancreatitis juice. RESULTS: In total, 72 proteins were identified and quantified in the comparison of pancreatic juice from pancreatitis patients versus pooled normal control juice. Nineteen of the juice proteins were overexpressed, and 8 were underexpressed in pancreatitis juice by at least 2-fold compared with normal pancreatic juice. Of these 27 differentially expressed proteins in pancreatitis, 9 proteins were also differentially expressed in the pancreatic juice from pancreatic cancer patient. CONCLUSIONS: Identification of these differentially expressed proteins from pancreatitis juice provides useful information for future study of specific pancreatitis-associated proteins and to eliminate potential false-positive biomarkers for pancreatic cancer.

Biomarkers, Tumor↗

PubMed2007

Quantitative proteomics by stable isotope labeling and mass spectrometry.

The goal of quantitative proteomics is to systematically study static state or perturbation-induced changes in protein profile. Most of the recently developed mass spectrometry (MS)-based quantitative proteomic methods employ stable isotope labeling to introduce signature mass tags to peptides/proteins that can be used by a mass spectrometer to quantify each analyte and to determine the sample from which it originates. In this chapter, we discuss several methods for the introduction of mass tags to proteins and peptides for MS-based quantitative proteomic analysis, including isotope-coded affinity tags, stable isotope labeling by amino acids in cell culture, global internal standard technology, and mass-coded abundance tagging.

Isotope Labeling↗

PubMed2006

Computational prediction of proteotypic peptides for quantitative proteomics.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

Algorithms↗

PubMed2006

Identification of putative androgen receptor interaction protein modules: cytoskeleton and endosomes modulate androgen receptor signaling in prostate cancer cells.

We have developed a novel androgen receptor (AR) expression system in the 293 human embryonic kidney cell line that recapitulates AR biochemical activity as a steroid hormone receptor in prostate cancer cells. We used this system to identify putative AR-binding proteins in the cytosolic and nuclear compartments of mammalian cells using a large scale co-immunoprecipitation strategy coupled to quantitative mass spectrometry. For example, the heat shock 70 and 90 chaperones, which are known regulators of steroid hormone receptor, were identified as AR-binding proteins. AR purification enriched for proteins involved in RNA processing, protein transport, and cytoskeletal organization, suggesting a functional link between AR and these protein modules in mammalian cells. For example, AR purification in the nuclear compartment led to the specific enrichment of alpha-actinin-4, clathrin heavy chain, and serine-threonine protein kinase C delta. Short interfering RNA knockdown studies and co-transcriptional reporter assays revealed that clathrin heavy chain possessed co-activator activity during AR-mediated transcription, whereas alpha-actinin-4 and protein kinase C delta displayed both co-activator and co-repressor activity during AR-mediated transcription that was dependent upon their relative expression levels. Lastly immunohistochemical staining of prostate tissue showed that alpha-actinin-4 levels decreased in the nucleus of high grade cancerous prostate samples, suggesting its possible deregulation in advanced prostate cancers as previously observed in late stage metastatic breast cancers. Taken together, these findings suggest AR binds to specific protein modules in mammalian cells and that these protein modules may provide a molecular framework for interrogating AR function in normal and cancerous prostate epithelial cells.

Androgen-Binding Protein↗

PubMed2006

Mass spectrometric detection of tissue proteins in plasma.

It has long been thought that blood plasma could serve as a window into the state of one's organs in health and disease because tissue-derived proteins represent a significant fraction of the plasma proteome. Although substantial technical progress has been made toward the goal of comprehensively analyzing the blood plasma proteome, the basic assumption that proteins derived from a variety of tissues could indeed be detectable in plasma using current proteomics technologies has not been rigorously tested. Here we provide evidence that such tissue-derived proteins are both present and detectable in plasma via direct mass spectrometric analysis of captured glycopeptides and thus provide a conceptual basis for plasma protein biomarker discovery and analysis.

Antigens, CD↗

PubMed2006

An ent-kaurene that inhibits mitotic chromosome movement and binds the kinetochore protein ran-binding protein 2.

Using a chemical genetics screen, we have identified ent-15-oxokaurenoic acid (EKA) as a chemical that causes prolonged mitotic arrest at a stage resembling prometaphase. EKA inhibits the association of the mitotic motor protein centromeric protein E with kinetochores and inhibits chromosome movement. Unlike most antimitotic agents, EKA does not inhibit the polymerization or depolymerization of tubulin. To identify EKA-interacting proteins, we used a cell-permeable biotinylated form that retains biological activity to isolate binding proteins from living cells. Mass spectrometric analysis identified six EKA-binding proteins, including Ran-binding protein 2, a kinetochore protein whose depletion by small interfering RNA causes a similar mitotic arrest phenotype.

Biotinylation↗

PubMed2006

UniPep--a database for human N-linked glycosites: a resource for biomarker discovery.

There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep http://www.unipep.org--a database of human N-linked glycosites--as a resource for biomarker discovery.

Computational Biology↗

PubMed2006

Challenges and opportunities in proteomics data analysis.

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.

Databases, Protein↗

PubMed2006

A statistical method for chromatographic alignment of LC-MS data.

Integrated liquid-chromatography mass-spectrometry (LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples. The output of the LC-MS system measures the intensity of a peptide with a specific mass-charge ratio and retention time. In the last few years, this technology has been used to compare complex biological samples across multiple conditions. One challenge for comparative proteomic profiling with LC-MS is to match corresponding peptide features from different experiments. In this paper, we propose a new method--Peptide Element Alignment (PETAL) that uses raw spectrum data and detected peak to simultaneously align features from multiple LC-MS experiments. PETAL creates spectrum elements, each of which represents the mass spectrum of a single peptide in a single scan. Peptides detected in different LC-MS data are aligned if they can be represented by the same elements. By considering each peptide separately, PETAL enjoys greater flexibility than time warping methods. While most existing methods process multiple data sets by sequentially aligning each data set to an arbitrarily chosen template data set, PETAL treats all experiments symmetrically and can analyze all experiments simultaneously. We illustrate the performance of PETAL on example data sets.

Animals↗

PubMed2006

Quantitative proteomic analysis of B cell lipid rafts reveals that ezrin regulates antigen receptor-mediated lipid raft dynamics.

Ligation of the B cell antigen receptor (BCR) with antigen induces lipid raft coalescence, a process that occurs after crosslinking of a variety of signaling receptors and is thought to potentiate cellular activation. To investigate lipid raft dynamics during BCR signaling, we quantitatively analyzed the B cell lipid raft proteome. BCR engagement induced dissociation of the adaptor protein ezrin from lipid rafts as well as threonine dephosphorylation of ezrin and its concomitant detachment from actin, indicating a transient uncoupling of lipid rafts from the actin cytoskeleton. Expression of constitutively active ezrin chimeras inhibited the BCR-induced coalescence of lipid rafts. Our data demonstrate that the release of ezrin from lipid rafts acts as a critical trigger that regulates lipid raft dynamics during BCR signaling.

B-Lymphocytes↗

PubMed2006

Mass spectrometry and protein analysis.

Mass spectrometry is a central analytical technique for protein research and for the study of biomolecules in general. Driven by the need to identify, characterize, and quantify proteins at ever increasing sensitivity and in ever more complex samples, a wide range of new mass spectrometry-based analytical platforms and experimental strategies have emerged. Here we review recent advances in mass spectrometry instrumentation in the context of current and emerging research strategies in protein science.

Mass Spectrometry↗

PubMed2006

Biosynthesis of dermatan sulfate: chondroitin-glucuronate C5-epimerase is identical to SART2.

We identified the gene encoding chondroitin-glucuronate C5-epimerase (EC 5.1.3.19) that converts D-glucuronic acid to L-iduronic acid residues in dermatan sulfate biosynthesis. The enzyme was solubilized from bovine spleen, and an approximately 43,000-fold purified preparation containing a major 89-kDa candidate component was subjected to mass spectrometry analysis of tryptic peptides. SART2 (squamous cell carcinoma antigen recognized by T cell 2), a protein with unknown function highly expressed in cancer cells and tissues, was identified by 18 peptides covering 26% of the sequence. Transient expression of cDNA resulted in a 22-fold increase in epimerase activity in 293HEK cell lysate. Moreover, overexpressing cells produced dermatan sulfate chains with 20% of iduronic acid-containing disaccharide units, as compared with 5% for mock-transfected cells. The iduronic acid residues were preferentially clustered in blocks, as in naturally occurring dermatan sulfate. Given the discovered identity, we propose to rename SART2 (Nakao, M., Shichijo, S., Imaizumi, T., Inoue, Y., Matsunaga, K., Yamada, A., Kikuchi, M., Tsuda, N., Ohta, K., Takamori, S., Yamana, H., Fujita, H., and Itoh, K. (2000) J. Immunol. 164, 2565-2574) with a functional designation, chondroitin-glucuronate C5-epimerase (or DS epimerase). DS epimerase activity is ubiquitously present in normal tissues, although with marked quantitative differences. It is highly homologous to part of the NCAG1 protein, encoded by the C18orf4 gene, genetically linked to bipolar disorder. NCAG1 also contains a putative chondroitin sulfate sulfotransferase domain and thus may be involved in dermatan sulfate biosynthesis. The functional relation between dermatan sulfate and cancer is unknown but may involve known iduronic acid-dependent interactions with growth factors, selectins, cytokines, or coagulation inhibitors.

Amino Acid Sequence↗

PubMed2006

Proteome analysis of Halobacterium sp. NRC-1 facilitated by the biomodule analysis tool BMSorter.

To better understand the extremely halophilic archaeon Halobacterium species NRC-1, we analyzed its soluble proteome by two-dimensional liquid chromatography coupled to electrospray ionization tandem mass spectrometry. A total of 888 unique proteins were identified with a ProteinProphet probability (P) between 0.9 and 1.0. To evaluate the biochemical activities of the organism, the proteomic data were subjected to a biological network analysis using our BMSorter software. This allowed us to examine the proteins expressed in different biomodules and study the interactions between pertinent biomodules. Interestingly an integrated analysis of the enzymes in the amino acid metabolism and citrate cycle networks suggested that up to eight amino acids may be converted to oxaloacetate, fumarate, or oxoglutarate in the citrate cycle for energy production. In addition, glutamate and aspartate may be interconverted from other amino acids or synthesized from citrate cycle intermediates to meet the high demand for the acidic amino acids that are required to build the highly acidic proteome of the organism. Thus this study demonstrated that proteome analysis can provide useful information and help systems analyses of organisms.

Amino Acids↗

PubMed2006

The PeptideAtlas project.

The completion of the sequencing of the human genome and the concurrent, rapid development of high-throughput proteomic methods have resulted in an increasing need for automated approaches to archive proteomic data in a repository that enables the exchange of data among researchers and also accurate integration with genomic data. PeptideAtlas (http://www.peptideatlas.org/) addresses these needs by identifying peptides by tandem mass spectrometry (MS/MS), statistically validating those identifications and then mapping identified sequences to the genomes of eukaryotic organisms. A meaningful comparison of data across different experiments generated by different groups using different types of instruments is enabled by the implementation of a uniform analytic process. This uniform statistical validation ensures a consistent and high-quality set of peptide and protein identifications. The raw data from many diverse proteomic experiments are made available in the associated PeptideAtlas repository in several formats. Here we present a summary of our process and details about the Human, Drosophila and Yeast PeptideAtlas builds.

Animals↗

PubMed2006

Proteomic analysis of the binding partners to enteropathogenic Escherichia coli virulence proteins expressed in Saccharomyces cerevisiae.

Enteropathogenic Escherichia coli (EPEC) is an enteric human pathogen responsible for much worldwide morbidity and mortality. EPEC uses a type III secretion system to inject bacterial proteins into the cytosol of intestinal epithelial cells to cause diarrheal disease. We are interested in determining the host proteins to which EPEC translocator and effector proteins bind during infection. To facilitate protein enrichment, we created fusions between GST and EPEC virulence proteins, and expressed these fusions individually in Saccharomyces cerevisiae. The biology of S. cerevisiae is well understood and often employed as a model eukaryote to study the function of bacterial virulence factors. We isolated the yeast proteins that interact with individual EPEC proteins by affinity purifying against the GST tag. These complexes were subjected to ICAT combined with ESI-MS/MS. Database searching of sequenced peptides provided a list of proteins that bound specifically to each EPEC virulence protein. The dataset suggests several potential mammalian targets of these proteins that may guide future experimentation.

Affinity Labels↗

PubMed2006

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.

Amino Acid Sequence↗

PubMed2006

Autumn 2005 Workshop of the Human Proteome Organisation Proteomics Standards Initiative (HUPO-PSI) Geneva, September, 4-6, 2005.

The autumn workshop of the Proteomics Standards Initiative of the Human Proteomics Organisation met to further advance the development of the existing standards in the fields of molecular interactions and mass spectrometry. In addition, new areas were addressed, in particular developing standards for the description and exchange of data from gel electrophoresis experiments. The General Proteomics Standards group is now working closely with the FuGE (Functional Genomics Experiment) efforts to define a general standard in which to encode data that will enable a systems biology approach to data analysis. Common to all these efforts is the field of protein modifications, and work has been initiated to establish an ontology in this field that can be used by both workers in the field of proteomics and the wider scientific community.

Databases, Protein↗