Biomedical subjects

Ilan Beer

Publications and source records attributed to Ilan Beer.

7 recordsLinked to original sources

PubMed2005

The turnover kinetics of major histocompatibility complex peptides of human cancer cells.

Peptides presented by the major histocompatibility complex (MHC) are derived from the degradation of cellular proteins. Thus, the repertoire of these peptides (the MHC peptidome) should correlate better with the cellular protein degradation scheme (the degradome) than with the cellular proteome. To test the validity of this statement and to determine whether the majority of MHC peptides are derived from short lived proteins, from defective ribosome products, or from regular long lived cellular proteins we analyzed in parallel the turnover kinetics of both MHC peptides and cellular proteins in the same cancer cells. The analysis was performed by pulse-chase experiments based on stable isotope labeling in tissue culture followed by capillary chromatography and tandem mass spectrometry. Indeed only a limited correlation was observed between the proteome and the MHC peptidome observed in the same cells. Moreover a detailed analysis of the turnover kinetics of the MHC peptides helped to assign their origin to normal, to short lived or long lived proteins, or to the defective ribosome products. Furthermore the analysis of the MHC peptides turnover kinetics helped to direct attention to abnormalities in the degradation schemes of their source proteins. These observations can be extended to search for cancer-related abnormalities in protein degradation, including those that lead to loss of tumor suppressors and cell cycle regulatory proteins.

Amino Acid Sequence↗

PubMed2005

Evaluation of prefractionation methods as a preparatory step for multidimensional based chromatography of serum proteins.

Prefractionations of proteins prior to their proteolysis, chromatography, and MS/MS analyses help reduce complexity and increase the yield of protein identifications. A number of methods were evaluated here for prefractionating serum samples distributed to the participating laboratories as part of the human Plasma Proteome Project. These methods include strong cation exchange (SCX) chromatography, slicing of SDS-PAGE gel bands, and liquid-phase IEF of the proteins. The fractionated proteins were trypsinized and the resulting peptides were resolved and analyzed by multidimensional protein identification technology coupled to IT MS/MS. The MS/MS spectra were clustered, combined, and searched against the IPI protein databank using Pep-Miner. The identification results were evaluated for the efficacy of the different prefractionation methodologies to identify larger numbers of proteins at higher confidence and to achieve the best coverage of the proteins with the identified peptides. Prefractionation based on SCX resulted in the largest number of identified proteins, followed by gel slices and then the liquid-phase IEF. An important observation was that each of the methods revealed a set of unique proteins, some identified with high confidence. Therefore, for comprehensive identification of the serum proteins, several different prefractionation approaches should be used in parallel.

Blood Proteins↗

PubMed2005

Centralized data analysis of a large interlaboratory proteomics project: a feasibility study.

The human Plasma Proteome Project (PPP) is a large-scale collaboration between many laboratories. One of the most demanding tasks in the PPP involved the analysis of very large amounts of raw MS/MS data produced by the participants. The main approach for managing this task was letting the participants analyze their own data and submit the results to the central PPP repository as lists of identified proteins and peptides. To complement this distributed approach, we also performed centralized analysis of the raw MS/MS data provided by the participants. Due to the data redundancy inherent in such a project, centralized analysis has the potential to reduce the computational effort by reducing redundancy before the analysis. Centralized analysis can also unify the process and take advantage of data sharing among laboratories to improve protein identification and validation. The process we employed included removing low-quality spectra, clustering spectra by mutual similarity, and applying uniform peptide and protein identification procedures. To demonstrate the process, we analyzed 5.28 million MS/MS spectra derived by eight laboratories from tryptic peptides of serum and plasma proteins.

Blood Proteins↗

PubMed2005

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

Algorithms↗

PubMed2004

Improving large-scale proteomics by clustering of mass spectrometry data.

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.

Algorithms↗

PubMed2003

Large-scale analysis of HLA peptides presented by HLA-Cw4.

A large number of HLA-Cw4 (Cw *0402) peptides were purified, sequenced, and identified from breast and ovarian carcinoma cell lines. HLA-Cw4 molecules were expressed in these cells as soluble, secreted HLA (sHLA) and recovered from the growth medium. The peptides were separated by capillary reversed-phase HPLC and analyzed by tandem mass-spectrometry. The resulting peptides fit to some extent, but not completely, the known consensus of the Cw4 peptide-binding motif. Among the identified peptides, there are a few that originate from proteins of possible interest for cancer immunotherapy or diagnostics, including mucin-5B, ART-1, fatty acid synthase, putative prostate cancer tumor suppressor, DNA topoisomerase-1, and Rac1. This work demonstrates that large-scale identification of HLA peptides recovered from sHLA is an advantageous approach for establishing the HLA peptide consensus of different haplotypes and the identification of useful peptides for treatment of diseases such as cancer, viral, and autoimmune diseases.

HLA-C Antigens↗

PubMed2002

Analysis of endogenous peptides bound by soluble MHC class I molecules: a novel approach for identifying tumor-specific antigens.

The Human MHC Project aims at comprehensive cataloging of peptides presented within the context of different human leukocyte antigens (HLA) expressed by cells of various tissue origins, both in health and in disease. Of major interest are peptides presented on cancer cells, which include peptides derived from tumor antigens that are of interest for immunotherapy. Here, HLA-restricted tumor-specific antigens were identified by transfecting human breast, ovarian and prostate tumor cell lines with truncated genes of HLA-A2 and HLA-B7. Soluble HLA secreted by these cell lines were purified by affinity chromatography and analyzed by nano-capillary electrospray ionization-tandem mass spectrometry. Typically, a large peptide pool was recovered and sequenced including peptides derived from MAGE-B2 and mucin and other new tumor-derived antigens that may serve as potential candidates for immunotherapy.

Animals↗