@article {49578, title = {The effects of telomere shortening on cancer cells: a network model of proteomic and microRNA analysis.}, volume = {105}, year = {2015}, month = {2015 Jan}, pages = {5-16}, abstract = {

Previously, we have shown that shortening of telomeres by telomerase inhibition sensitized cancer cells to cisplatinum, slowed their migration, increased DNA damage and impaired DNA repair. The mechanism behind these effects is not fully characterized. Its clarification could facilitate novel therapeutics development and may obviate the time consuming process of telomere shortening achieved by telomerase inhibition. Here we aimed to decipher the microRNA and proteomic profiling of cancer cells with shortened telomeres and identify the key mediators in telomere shortening-induced damage to those cells. Of 870 identified proteins, 98 were differentially expressed in shortened-telomere cells. 47 microRNAs were differentially expressed in these cells; some are implicated in growth arrest or act as oncogene repressors. The obtained data was used for a network construction, which provided us with nodal candidates that may mediate the shortened-telomere dependent features. These proteins{\textquoteright} expression was experimentally validated, supporting their potential central role in this system.

}, keywords = {Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, HUMANS, MicroRNAs, Neoplasms, Oligonucleotides, Proteome, proteomics, Telomere Shortening, Tumor Cells, Cultured}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2014.10.013}, author = {Uziel, O and Yosef, N and Sharan, R and Ruppin, E and Kupiec, M and Kushnir, M and Beery, E and Cohen-Diker, T and Nordenberg, J and Lahav, M} } @article {49737, title = {Stable isotope labeling of phosphoproteins for large-scale phosphorylation rate determination.}, journal = {Mol Cell Proteomics}, volume = {13}, year = {2014}, month = {2014 Apr}, pages = {1106-18}, abstract = {

Signals that control responses to stimuli and cellular function are transmitted through the dynamic phosphorylation of thousands of proteins by protein kinases. Many techniques have been developed to study phosphorylation dynamics, including several mass spectrometry (MS)-based methods. Over the past few decades, substantial developments have been made in MS techniques for the large-scale identification of proteins and their post-translational modifications. Nevertheless, all of the current MS-based techniques for quantifying protein phosphorylation dynamics rely on the measurement of changes in peptide abundance levels, and many methods suffer from low confidence in phosphopeptide identification due to poor fragmentation. Here we have optimized an approach for the stable isotope labeling of amino acids by phosphate using [γ-{\textonesuperior}$^{8}$O$_{4}$]ATP in nucleo to determine global site-specific phosphorylation rates. The advantages of this metabolic labeling technique are increased confidence in phosphorylated peptide identification, direct labeling of phosphorylation sites, measurement phosphorylation rates, and the identification of actively phosphorylated sites in a cell-like environment. In this study we calculated approximate rate constants for over 1,000 phosphorylation sites based on labeling progress curves. We measured a wide range of phosphorylation rate constants from 0.34 min$^{-}${\textonesuperior} to 0.001 min$^{-}${\textonesuperior}. Finally, we applied stable isotope labeling of amino acids by phosphate to identify sites that have different phosphorylation kinetics during G1/S and M phase. We found that most sites had very similar phosphorylation rates under both conditions; however, a small subset of sites on proteins involved in the mitotic spindle were more actively phosphorylated during M phase, whereas proteins involved in DNA replication and transcription were more actively phosphorylated during G1/S phase. The data have been deposited to the ProteomeXchange with the identifier PXD000680.

}, keywords = {cell cycle, HEK293 Cells, HeLa Cells, HUMANS, Isotope Labeling, Kinetics, Peptide Mapping, Phosphoproteins, Phosphorylation, proteomics, Tandem Mass Spectrometry}, issn = {1535-9484}, doi = {10.1074/mcp.O113.036145}, author = {Molden, Rosalynn C and Goya, Jonathan and Khan, Zia and Garcia, Benjamin A} } @article {49740, title = {Global secretome analysis identifies novel mediators of bone metastasis.}, journal = {Cell Res}, volume = {22}, year = {2012}, month = {2012 Sep}, pages = {1339-55}, abstract = {

Bone is the one of the most common sites of distant metastasis of solid tumors. Secreted proteins are known to influence pathological interactions between metastatic cancer cells and the bone stroma. To comprehensively profile secreted proteins associated with bone metastasis, we used quantitative and non-quantitative mass spectrometry to globally analyze the secretomes of nine cell lines of varying bone metastatic ability from multiple species and cancer types. By comparing the secretomes of parental cells and their bone metastatic derivatives, we identified the secreted proteins that were uniquely associated with bone metastasis in these cell lines. We then incorporated bioinformatic analyses of large clinical metastasis datasets to obtain a list of candidate novel bone metastasis proteins of several functional classes that were strongly associated with both clinical and experimental bone metastasis. Functional validation of selected proteins indicated that in vivo bone metastasis can be promoted by high expression of (1) the salivary cystatins CST1, CST2, and CST4; (2) the plasminogen activators PLAT and PLAU; or (3) the collagen functionality proteins PLOD2 and COL6A1. Overall, our study has uncovered several new secreted mediators of bone metastasis and therefore demonstrated that secretome analysis is a powerful method for identification of novel biomarkers and candidate therapeutic targets.

}, keywords = {Animals, Biomarkers, Tumor, Bone Neoplasms, Cell Line, Tumor, Collagen Type VI, Computational Biology, HUMANS, Mass Spectrometry, Mice, Neoplasms, Plasminogen Activators, Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase, Proteome, proteomics, Salivary Cystatins}, issn = {1748-7838}, doi = {10.1038/cr.2012.89}, author = {Blanco, Mario Andres and LeRoy, Gary and Khan, Zia and Ale{\v c}kovi{\'c}, Ma{\v s}a and Zee, Barry M and Garcia, Benjamin A and Kang, Yibin} } @article {49548, title = {Quantitative measurement of allele-specific protein expression in a diploid yeast hybrid by LC-MS.}, volume = {8}, year = {2012}, month = {2012}, pages = {602}, abstract = {

Understanding the genetic basis of gene regulatory variation is a key goal of evolutionary and medical genetics. Regulatory variation can act in an allele-specific manner (cis-acting) or it can affect both alleles of a gene (trans-acting). Differential allele-specific expression (ASE), in which the expression of one allele differs from another in a diploid, implies the presence of cis-acting regulatory variation. While microarrays and high-throughput sequencing have enabled genome-wide measurements of transcriptional ASE, methods for measurement of protein ASE (pASE) have lagged far behind. We describe a flexible, accurate, and scalable strategy for measurement of pASE by liquid chromatography-coupled mass spectrometry (LC-MS). We apply this approach to a hybrid between the yeast species Saccharomyces cerevisiae and Saccharomyces bayanus. Our results provide the first analysis of the relative contribution of cis-acting and trans-acting regulatory differences to protein expression divergence between yeast species.

}, keywords = {Alleles, Chromatography, Liquid, Fungal Proteins, Gene Expression Profiling, Gene Expression Regulation, Fungal, HUMANS, Mass Spectrometry, proteomics, Regression Analysis, Saccharomyces, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins, Species Specificity}, issn = {1744-4292}, doi = {10.1038/msb.2012.34}, author = {Khan, Zia and Bloom, Joshua S and Amini, Sasan and Singh, Mona and Perlman, David H and Caudy, Amy A and Kruglyak, Leonid} } @article {49744, title = {Accurate proteome-wide protein quantification from high-resolution 15N mass spectra.}, journal = {Genome Biol}, volume = {12}, year = {2011}, month = {2011}, pages = {R122}, abstract = {

In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods.

}, keywords = {algorithms, Amino Acid Sequence, Bacterial Proteins, Escherichia coli, Isotope Labeling, Mass Spectrometry, Molecular Sequence Data, Nitrogen Isotopes, Proteome, proteomics, Sensitivity and Specificity, software}, issn = {1474-760X}, doi = {10.1186/gb-2011-12-12-r122}, author = {Khan, Zia and Amini, Sasan and Bloom, Joshua S and Ruse, Cristian and Caudy, Amy A and Kruglyak, Leonid and Singh, Mona and Perlman, David H and Tavazoie, Saeed} } @proceedings {38179, title = {A cooperative combinatorial Particle Swarm Optimization algorithm for side-chain packing}, year = {2009}, month = {2009}, publisher = {IEEE}, type = {10.1109/SIS.2009.4937840}, abstract = {Particle Swarm Optimization (PSO) is a well-known, competitive technique for numerical optimization with real-parameter representation. This paper introduces CCPSO, a new Cooperative Particle Swarm Optimization algorithm for combinatorial problems. The cooperative strategy is achieved by splitting the candidate solution vector into components, where each component is optimized by a particle. Particles move throughout a continuous space, their movements based on the influences exerted by static particles that then get feedback based on the fitness of the candidate solution. Here, the application of this technique to side-chain packing (a proteomics optimization problem) is investigated. To verify the efficiency of the proposed CCPSO algorithm, we test our algorithm on three side-chain packing problems and compare our results with the provably optimal result. Computational results show that the proposed algorithm is very competitive, obtaining a conformation with an energy value within 1\% of the provably optimal solution in many proteins.}, keywords = {Algorithm design and analysis, Amino acids, combinatorial mathematics, cooperative combinatorial particle swarm optimization algorithm, Design optimization, Encoding, Feedback, numerical optimization, Optimization methods, particle swarm optimisation, Particle swarm optimization, Partitioning algorithms, Proteins, proteomics, proteomics optimization, Robustness, side-chain packing}, isbn = {978-1-4244-2762-8}, author = {Lapizco-Encinas, G. and Kingsford, Carl and Reggia, James A.} } @article {38379, title = {Microbial oceanography in a sea of opportunity}, journal = {NatureNature}, volume = {459}, year = {2009}, type = {10.1038/nature08056}, abstract = {Plankton use solar energy to drive the nutrient cycles that make the planet habitable for larger organisms. We can now explore the diversity and functions of plankton using genomics, revealing the gene repertoires associated with survival in the oceans. Such studies will help us to appreciate the sensitivity of ocean systems and of the ocean{\textquoteright}s response to climate change, improving the predictive power of climate models.}, keywords = {Astronomy, astrophysics, Biochemistry, Bioinformatics, Biology, biotechnology, cancer, cell cycle, cell signalling, climate change, Computational Biology, development, developmental biology, DNA, drug discovery, earth science, ecology, environmental science, Evolution, evolutionary biology, functional genomics, Genetics, Genomics, geophysics, immunology, interdisciplinary science, life, marine biology, materials science, medical research, medicine, metabolomics, molecular biology, molecular interactions, nanotechnology, Nature, neurobiology, neuroscience, palaeobiology, pharmacology, Physics, proteomics, quantum physics, RNA, Science, science news, science policy, signal transduction, structural biology, systems biology, transcriptomics}, isbn = {0028-0836}, author = {Bowler, Chris and Karl, David M. and Rita R. Colwell} } @article {49747, title = {Protein quantification across hundreds of experimental conditions.}, journal = {Proc Natl Acad Sci U S A}, volume = {106}, year = {2009}, month = {2009 Sep 15}, pages = {15544-8}, abstract = {

Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use space-partitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graph-theoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.

}, keywords = {algorithms, Animals, Automatic Data Processing, Chromatography, Liquid, Databases, Factual, Fungal Proteins, HUMANS, Isotopes, Mice, Proteins, proteomics, Tandem Mass Spectrometry}, issn = {1091-6490}, doi = {10.1073/pnas.0904100106}, author = {Khan, Zia and Bloom, Joshua S and Garcia, Benjamin A and Singh, Mona and Kruglyak, Leonid} }