B`5W0W@ @@@ @@@@PfJ=\W WP EN DB W`     & . 6Ms =  l_d I&B 4  }7U    4 Adam2001 Aebersold1999 Aebersold2000 Aebersold2001! Aebersold2002 Agarwal2002Anderson1997  Arava2003+ Arava2003 Ardekani20022' Baldi2001, Bayer2002 Beer200201 Bennetzen1982Berliner20022( Beynon200225 Bloomfield1999Botstein1999 Brown1999 Brown2003+ Brown2003. Bumgarner20030 Bussemaker20035 Caligiuri1999*Campbell1998 Celis2002 Chan2002g Chen20020 Chen2002 Cheung20022* Cho1998) Choe1999k Christians2002- Ciechanover20022 Coghlan20005 Coller19999 Conrads2002* Conway19989Corthals2000 Cramer20020 Currie20011 Davis1998* Davis1998 Davison2001 DeCamp2002 Deciu2002 Deciu2003Devenish20025 Downing1999 Drawid20022 Elias2003 Emmert-Buck2002 Eng2001! Eng2002Felschow2002 Fishman2002 Flaig2002 Fowler20020 Franza1999 Frishman2002 Fusaro20020# Futcher19995 Gaasenbeek1999* Gabrielian1998 Gaffney2002# Garrels1999( Gaskell2002,Gelbmann2002, Gerner2002$Gerstein (In Press)%Gerstein20011Gerstein20022 Gerstein20022 Gerstein20022 Gerstein200200Gerstein20030 Gharbi2002 Gharib2002 Gillespie2002Giordano2002-Glickman20025 Golub1999 Gong20020,Gotzmann2002% Greenbaum2001 Greenbaum2002  Greenbaum2002  Greenbaum2002! Griffin2002 Guldener2002 Gygi1999 Gygi2000! Gygi20020 Gygi20031 Hall1982 Han2001 Hanash20020)Hatzimanikatis1999 Hauptmann2002 Hawkins2001 Heidtman2002 Heller1998  Herschlag2003+ Herschlag2003 Heyman20022 Hitt20022! Hood20020 Hu2002i Huang20025 Huard1999 Iannettoni2002! Ideker20022 Issaq2002% Jansen20010 Jansen20020  Jansen2002 Jansen200220 Jansen2003 Jung20020 Kardia20023 Klose1975$Kluger (In Press) Kluger2002 Kohn20020 Koller20030 Konrad19989 Kristiansen2002  Kumar2002 Lachenmeier19985 Lander19991*Landsman19989# Latter19999. Law2003) Lee1999 Lein20020 Levine20020 Lewis2002/ Li19879 Li20020 Li2002 Lian2002 Lichtinghagen2002 Licklider2003 Liotta2002 Liotta20022 Liu2002 Liu2003+ Liu2003*Lockhart19989 Loening20025 Loh1999' Long20011%Luscombe20011 Macgregor2002 Macgregor2003. MacKay20030  Mannhaupt2002 Matson20022 Mayer2002 McGall2002# McLaughlin19995 Mesirov1999  Mewes2002 Middleton2001,Mikulits2002n Miller20022 Mills2002 Misek2002 Mokrejs2002# Monardo1999  Morgenstern2002. Morris20030  Munsterkotter2002 Musholt2002 Newburger20024 O'Farrell1975( Oliver20022 Orntoft2002Orringer20020 Oshiro20030" Pandey2002 Peng2003 Petricoin2002  Petricoin2002( Petty2002  Piccirillo2002 Plouffe2003 Pognan20010( Pratt2002$Qian (In Press)% Qian20010 Rayner20010( Riba-Garcia2002! Rist20020( Robertson2002 Rochon1999 Rochon20000 Roeder20020 Romer2002 Rosenzweig2002 Rowlinson2001 Rudd2002t Rudolph2002 Schena1998Schieltz2002 Schnorr2002,Schulte-Hermann2002 Seilhamer1997 Semmes2001.Serikawa2003/ Sharp1987 Shaw20011 Shu2002 Simone200205 Slonim1999 Snyder20020 Squire20020" Steen2002  Steinberg2002* Steinmetz1998 Stone2002 Storey2003+ Storey2003&Szallasi19995 Tamayo1999 Taylor20020 Taylor2002 Theriault1998 Thoreen2003Thykjaer2002 Timms2002 Tonge2001 Tuck2002 Ulaszek2002 Ulaszek2003 Umansky2002Veenstra2002, Vejda2002 Vlahou2001 Wait20022 Waldman2002 Wang2002g Wang20033+ Wang20033Washburn2001Washburn2001Washburn2002Washburn2003 Waterfield2002 Weil2002tWeissman20022*Winzeler1998Winzeler2003* Wodicka1998 Wolf200202 Wolfe2000* Wolfsberg1998 Wolters2001 Wolters2001 Wright2001. Xu2003a Yan2002 Yang20020 Yates2001 Yates2001 Yates2002 Yates2003 Young2001$Yu (In Press)e Zhang2000 Zhang2002 Zhao2002. Zhao20030 Zhou2001 Zhou2002. Zong20030Zvelebil2002!"'   Authors Journals  Keywords W                                 Adam, B. L. Aebersold, R. Agarwal, S. Anderson, L. Arava, Y.Ardekani, A. M. Baldi, P. Bayer, E. Beer, D. G.Bennetzen, J. L. Berliner, N. Beynon, R. J.Bloomfield, C. D. Botstein, D. Brown, P. O. Bumgarner, R.Bussemaker, H. J.Caligiuri, M. A.Campbell, M. J. Celis, J. E. Chan, D. W. Chen, G. Chen, S. Cheung, K. H. Cho, R. J. Choe, L. H.Christians, F. C.Ciechanover, A. Coghlan, A. Coller, H.Conrads, T. P. Conway, A.Corthals, G. L. Cramer, R. Currie, I. Davis, R. W. Davison, M. DeCamp, D. Deciu, C.Devenish, A. T.Downing, J. R. Drawid, A. Elias, J. E.Emmert-Buck, M. R. Eng, J. Felschow, D.Fishman, D. A. Flaig, M. Fowler, S. Franza, B. R. Frishman, D. Fusaro, V. A. Futcher, B.Gaasenbeek, M.Gabrielian, A. E. Gaffney, P.Garrels, J. I.Gaskell, S. J. Gelbmann, D. Gerner, C. Gerstein, M. Gharbi, S. Gharib, T. G.Gillespie, J. W.Giordano, T. J.Glickman, M. H. Golub, T. R. Gong, Y. Gotzmann, J. Greenbaum, D.Greenbaum, D. S.Griffin, T. J. Guldener, U. Gygi, S. P. Hall, B. D. Han, D. K. Hanash, S. M.Hatzimanikatis, V. Hauptmann, S. Hawkins, E. Heidtman, M. Heller, R. A. Herschlag, D. Heyman, J. A. Hitt, B. A. Hood, L. Hu, N. Huang, C. C. Huard, C.Iannettoni, M. D. Ideker, T. Issaq, H. J.("J Qian, Y Kluger, H Yu, M Gerstein Jansen, R. Jung, K. Kardia, S. L. Klose, J. Kluger, Y. Kohn, E. C. Koller, A. Konrad, K.Kristiansen, G. Kumar, A.Lachenmeier, E. Lander, E. S. Landsman, D. Latter, G. I. Law, G. L. Lee, K. H. Lein, M. Levine, P. J. Lewis, S. Li, H. Li, J. Li, W. H. Lian, Z.Lichtinghagen, R.Licklider, L. J. Liotta, L. A. Liu, C. L. Liu, Y.Lockhart, D. J.Loening, S. A. Loh, M. L. Long, A. D.Luscombe, N. M.Macgregor, P. F. MacKay, V. L. Mannhaupt, G. Matson, S. Mayer, K. McGall, G. H.McLaughlin, C. S.Mesirov, J. P. Mewes, H. W. Middleton, B. Mikulits, W. Miller, P. Mills, G. B. Misek, D. E. Mokrejs, M. Monardo, P.Morgenstern, B. Morris, D. R.Munsterkotter, M.Musholt, P. B.Newburger, P. E.O'Farrell, P. H. Oliver, S. G.Orntoft, T. F.Orringer, M. B. Oshiro, G. Pandey, A. Peng, J.Petricoin, E. F.Petricoin, E. F., 3rd Petty, J.Piccirillo, S. Plouffe, D. Pognan, F. Pratt, J. M.("Qian J, Kluger Y, Yu H, Gerstein M Qian, J. Rayner, S.Riba-Garcia, I. Rist, B.Robertson, D. H. Rochon, Y. Roeder, G. S. Romer, A.Rosenzweig, J. Rowlinson, R. Rudd, S. Rudolph, B. Schena, M.Schieltz, D. M. Schnorr, D.Schulte-Hermann, R. Seilhamer, J. Semmes, O. J.Serikawa, K. A. Sharp, P. M. Shaw, J. Shu, H. Simone, C. Slonim, D. K. Snyder, M. Squire, J. A. Steen, H.Steinberg, S. M. Steinmetz, L. Stone, T. Storey, J. D. Szallasi, Z. Tamayo, P. Taylor, J. M. Taylor, P. R.Theriault, T. P.Thoreen, C. C. Thykjaer, T. Timms, J. F. Tonge, R. Tuck, D. Ulaszek, R.Ulaszek, R. R. Umansky, L.Veenstra, T. D. Vejda, S. Vlahou, A. Wait, R.Waldman, F. M. Wang, Y. Wang, Y. Y.Washburn, M. P.Waterfield, M. D. Weil, B.Weissman, S. M. Winzeler, E.Winzeler, E. A. Wodicka, L. Wolf, H. Wolfe, K. H.Wolfsberg, T. G. Wolters, D.Wolters, D. A.Wright, G. L., Jr. Xu, X. L. Yan, J. X. Yang, A.Yates, J. R., 3rd Young, J. Yu, H. Zhang, Y. Zhang, Z. Zhao, L. P. Zhao, Y. Zhou, G. Zhou, H. Zong, Q.Zvelebil, M. J.   Adv Biochem Eng Biotechnol Anal Chem Biochem Biophys Res CommunBioinformatics BioTechniquesBiotechnol ProgBlood Clin ChemElectrophoresis Eur UrolExpert Rev Mol Diagnw Genes Dev Genome Res Humangenetik J Biol ChemJ Proteome Res Lancetw Mol Cell Mol Cell BiolMol Cell ProteomicswNat Biotechnol Nat GenetNucleic Acids ResPac Symp BiocomputIw Physiol RevProc Natl Acad Sci U S A Proteomics ScienceTrends BiotechnolYeast  *Bayes Theorem*Biotechnology *Codon$!*Data Interpretation, Statistical *Database Management Systems*Databases, Factualgy*Databases, Genetics*Databases, Nucleic Acidt*Databases, Protein A *DNA ProbesMa*DNA, Complementarygy($*Electrophoresis, Polyacrylamide Gel *Gene Dosage*Gene Expressionu *Gene Expression ProfilingZ<7*Gene Expression Regulation, Developmental/drug effects(#*Gene Expression Regulation, Fungal *Gene Library*Genes, Bacterial*Genes, Fungal*Genes, Structural*Genetic Techniques *Genomexp*Genome, Bacterialogy*Genome, Fungalol *Genomics*Isoelectric Focusing*Models, Genetic*Models, Statistical,(%*Molecular Probe Techniques/economics *Mutation *Protein Interaction Mapping *Proteome *Proteomicsal*RNA, Messenger*Transcription, GeneticAcetaminophen/toxicitynY Acute Disease Adenocarcinoma/*metabolismAdultAffinity LabelsAgedtAged, 80 and over$!Alcohol Oxidoreductases/*genetics AlgorithmsAmino Acid Sequence$ Amino Acids/chemistry/metabolism AnimalinoD>Antineoplastic Combined Chemotherapy Protocols/therapeutic useArabidopsis/genetics AutoanalysisAutoradiography,&Bacillus subtilis/*genetics/physiologyop\0,Bacterial Proteins/*isolation & purification Bacterial Proteins/genetics Base Sequence Biotechnology40Bladder Neoplasms/*genetics/metabolism/pathology Bladder Neoplasms/*metabolism Bladder/metabolism/pathology Blood Protein ElectrophoresisBlood Proteins/analysisBlotting, WesternBRCA1 Protein/bloodBRCA2 Protein/blood Breast Neoplasms/*geneticsZ0+Breast Neoplasms/blood/*diagnosis/pathologyCA-125 Antigen/blood$!Carcinoma, Squamous Cell/genetics@;Carcinoma, Transitional Cell/*genetics/metabolism/pathology CationsZ CattleCell Adhesion/genetics Cell CycleCell Cycle/geneticsCell Differentiation$!Cell Differentiation/drug effectsCell Fractionation$!Cell Line/drug effects/metabolismCell Membrane/metabolismCell Nucleus/metabolismY($Chromatography, High Pressure Liquid($Chromatography, Ion Exchange/methodsChromatography, Liquidsm$Chromatography, Liquid/*methodsthChromatography/*methodsYChromosome AberrationsChromosome Mapping Chromosomes, Fungal/*geneticsChromosomes/metabolismmYCluster AnalysisnCodonCodon/*geneticsCodon/geneticsuenComparative StudyComputational Biology Computational Biology/methodsComputer Simulation(#Cysteine Endopeptidases/*metabolismCytogenetic AnalysisCytoplasm/chemistryCytoplasm/metabolismsdatat$ Data Interpretation, Statistical DatabasesDatabases, FactualDisease Progression("Dissection/instrumentation/methodsDNA, ComplementaryDNA, Fungal/geneticsDNA, RecombinantDown-Regulation Ecosystem(%Electrophoresis, Gel, Two-Dimensional4.Electrophoresis, Gel, Two-Dimensional/*methodsHEElectrophoresis, Gel, Two-Dimensional/*methods/statistics & numerical0-Electrophoresis, Gel, Two-Dimensional/methods0+Electrophoresis, Polyacrylamide Gel/methodsEmbryo/analysis EpitopessEquipment Design Escherichia coli/*geneticsEscherichia coli/analysisEscherichia coli/genetics$Esophageal Neoplasms/*geneticsnsi EvolutionExpressed Sequence TagsYFalse Positive Reactionsm Femaler NFetus/analysis FluorescencesFluorescent Dyes Fluorescent Dyes/metabolismFungal Proteins/*analysis0-Fungal Proteins/*analysis/*chemistry/genetics,&Fungal Proteins/*chemistry/*metabolism,)Fungal Proteins/*isolation & purification Fungal Proteins/*metabolism(#Fungal Proteins/analysis/*chemistrysGene ExpressionteGene Expression Profiling83Gene Expression Profiling/*instrumentation/*methods("Gene Expression Profiling/*methods85Gene Expression Profiling/statistics & numerical data("Gene Expression Regulation, Fungal Gene Expression/drug effectsm !"' ' ' FractionationCytoplasm/chemistry120961121,4o 2002 AprnD>Discordant protein and mRNA expression in lung adenocarcinomas 304-13F?The relationship between gene expression measured at the mRNA level and the corresponding protein level is not well characterized in human cancer. In this study, we compared mRNA and protein expression for a cohort of genes in the same lung adenocarcinomas. The abundance of 165 protein spots representing 98 individual genes was analyzed in 76 lung adenocarcinomas and nine non-neoplastic lung tissues using two-dimensional polyacrylamide gel electrophoresis. Specific polypeptides were identified using matrix-assisted laser desorption/ionization mass spectrometry. For the same 85 samples, mRNA levels were determined using oligonucleotide microarrays, allowing a comparative analysis of mRNA and protein expression among the 165 protein spots. Twenty-eight of the 165 protein spots (17%) or 21 of 98 genes (21.4%) had a statistically significant correlation between protein and mRNA expression (r > 0.2445; p < 0.05); however, among all 165 proteins the correlation coefficient values (r) ranged from -0.467 to 0.442. Correlation coefficient values were not related to protein abundance. Further, no significant correlation between mRNA and protein expression was found (r = -0.025) if the average levels of mRNA or protein among all samples were applied across the 165 protein spots (98 genes). The mRNA/protein correlation coefficient also varied among proteins with multiple isoforms, indicating potentially separate isoform-specific mechanisms for the regulation of protein abundance. Among the 21 genes with a significant correlation between mRNA and protein, five genes differed significantly between stage I and stage III lung adenocarcinomas. Using a quantitative analysis of mRNA and protein expression within the same lung adenocarcinomas, we showed that only a subset of the proteins exhibited a significant correlation with mRNA abundance.'TNDepartment of Surgery, University of Michigan, Ann Arbor, Michigan 48109, USA.Chen, G. Gharib, T. G. Huang, C. C. Taylor, J. M. Misek, D. E. Kardia, S. L. Giordano, T. J. Iannettoni, M. D. Orringer, M. B. Hanash, S. M. Beer, D. G. 1535-9476 Journal ArticleeMol Cell Proteomics & Adenocarcinoma/*metabolism Blotting, Western Electrophoresis, Gel, Two-Dimensional Human Lung/metabolism Lung Neoplasms/*metabolism Oligonucleotide Array Sequence Analysis Protein Isoforms RNA, Messenger/*metabolism Spectrum Analysis, Mass Support, U.S. Gov't, P.H.S. Translation, Geneticlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12096112aGygi, S. P. Corthals, G. L. Zhang, Y. Rochon, Y. Aebersold, R.ZTEvaluation of two-dimensional gel electrophoresis-based proteome analysis technologyAmino Acid Sequence Codon/genetics Electrophoresis, Gel, Two-Dimensional Fungal Proteins/*analysis/*chemistry/genetics Hydrogen-Ion Concentration Isoelectric Focusing Molecular Sequence Data Molecular Weight Peptide Mapping/*methods *Proteome Saccharomyces cerevisiae/*chemistry/genetics Sample Size Sensitivity and Specificity Silver Staining Spectrum Analysis, Mass Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S.lProteome analysis is most commonly accomplished by a combination of two- dimensional gel electrophoresis (2DE) to separate and visualize proteins and mass spectrometry (MS) for protein identification. Although this technique is powerful, mature, and sensitive, questions remain concerning its ability to characterize all of the elements of a proteome. In the current study, more than 1,500 features were visualized by silver staining a narrow pH range (4.9-5. 7) 2D gel in which 0.5 mg of total soluble yeast protein was separated. Fifty spots migrating to a region of 4 cm(2) were subjected to MS protein identification. Despite the high sample load and extended electrophoretic separation, proteins from genes with codon bias values of <0.1 (lower abundance proteins) were not found, even though fully one-half of all yeast genes fall into that range. Proteins from genes with codon bias values of <0.1 were found, however, if protein amounts exceeding the capacity of 2DE were fractionated and analyzed. We conclude that the large range of protein expression levels limits the ability of the 2DE-MS approach to analyze proteins of medium to low abundance, and thus the potential of this technique for proteome analysis is likewise limited.'d]Department of Molecular Biotechnology, University of Washington, Seattle, WA 98195-7730, USA. 10920198Proc Natl Acad Sci U S A 200097179390-5.P1j7037777 2576 1982 Mar 25Codon selection in yeast3026-31f_Extreme codon bias is seen for the Saccharomyces cerevisiae genes for the fermentative alcohol dehydrogenase isozyme I (ADH-I) and glyceraldehyde-3-phosphate dehydrogenase. Over 98% of the 1004 amino acid residues analyzed by DNA sequencing are coded for by a select 25 of the 61 possible coding triplets. These preferred codons tend to be highly homologous to the anticodons of the major yeast isoacceptor tRNA species. Codons which necessitate site by side GC base pairs between the codons and the tRNA anticodons are always avoided whenever possible. Codons containing 100% G, C, A, U, GC, or AU are also avoided. This provides for approximately equivalent codon-anticodon binding energies for all preferred triplets. All sequenced yeast genes show a distinct preference for these same 25 codons. The degree of preference varies from greater than 90% for glyceraldehyde-3-phosphate dehydrogenase and ADH-I to less than 20% for iso-2 cytochrome c. The degree of bias for these 25 preferred triplets in each gene is correlated with the level of its mRNA in the cytoplasm. Genes which are strongly expressed are more biased than genes with a lower level of expression. A similar phenomenon is observed in the codon preferences of highly expressed genes in Escherichia coli. High levels of gene expression are well correlated with high levels of codon bias toward 22 of the 61 coding triplets. As in yeast, these preferred codons are highly complementary to the major cellular isoacceptor tRNA species. In at least four cases (Ala, Arg, Leu, and Val), these preferred E. coli codons are incompatible with the preferred yeast codons."Bennetzen, J. L. Hall, B. D. 0021-9258 Journal Article J Biol ChemAlcohol Oxidoreductases/*genetics Amino Acid Sequence Base Sequence Codon/*genetics Escherichia coli/genetics *Genes, Structural Isoenzymes/genetics RNA, Messenger/*genetics Saccharomyces cerevisiae/enzymology/*geneticsjdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7037777XRhttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=9915498 Brown, P. O. Botstein, D.l@:Exploring the new world of the genome with DNA microarraysAnimal Chromosome Mapping *DNA Probes Databases, Factual Gene Expression *Genome Human *Molecular Probe Techniques/economics Oligonucleotide Array Sequence Analysis/economics/*methods Sequence Analysis, DNA Thousands of genes are being discovered for the first time by sequencing the genomes of model organisms, an exhilarating reminder that much of the natural world remains to be explored at the molecular level. DNA microarrays provide a natural vehicle for this exploration. The model organisms are the first for which comprehensive genome-wide surveys of gene expression patterns or function are possible. The results can be viewed as maps that reflect the order and logic of the genetic program, rather than the physical order of genes on chromosomes. Exploration of the genome using DNA microarrays and other genome-scale technologies should narrow the gap in our knowledge of gene function and molecular biology between the currently-favoured model organisms and other species.'Department of Biochemistry, Howard Hughes Medical Institute, Stanford University School of Medicine, California 94305, USA. pbrown@cmgm.stanford.edu9915498 Nat Genet 1999211 Suppl 33-7.J. X. ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12626741.piWashburn, M. P. Koller, A. Oshiro, G. Ulaszek, R. R. Plouffe, D. Deciu, C. Winzeler, E. Yates, J. R., 3rdh|uProtein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiaeoAmino Acid Sequence Cluster Analysis Data Interpretation, Statistical Gene Expression Profiling/statistics & numerical data Genes, Fungal Molecular Sequence Data Oligonucleotide Array Sequence Analysis/statistics & numerical data Peptides/genetics/metabolism Protein Array Analysis/statistics & numerical data RNA, Fungal/*genetics/*metabolism RNA, Messenger/*genetics/*metabolism Saccharomyces cerevisiae/*genetics/growth & development/*metabolism Saccharomyces cerevisiae Proteins/*genetics/*metabolismThe mRNA and protein expression in Saccharomyces cerevisiae cultured in rich or minimal media was analyzed by oligonucleotide arrays and quantitative multidimensional protein identification technology. The overall correlation between mRNA and protein expression was weakly positive with a Spearman rank correlation coefficient of 0.45 for 678 loci. To place the data sets in a proper biological context, a clustering approach based on protein pathways and protein complexes was implemented. Protein expression levels were transcriptionally controlled for not only single loci but for entire protein pathways (e.g., Met, Arg, and Leu biosynthetic pathways). In contrast, the protein expression of loci in several protein complexes (e.g., SPT, COPI, and ribosome) was posttranscriptionally controlled. The coupling of the methods described provided insight into the biology of S. cerevisiae and a clustering strategy by which future studies should be based. '~xProteomics, Torrey Mesa Research Institute, 3115 Merryfield Row, San Diego, CA 92121, USA. michael.washburn@syngenta.com12626741Proc Natl Acad Sci U S A 2003 100a6.3107-12. &ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=10380181i Szallasi, Z.ZSGenetic network analysis in light of massively parallel biological data acquisitioniComputational Biology/methods *DNA, Complementary *Databases, Factual *Gene Expression *Gene Library Genetic Engineering Models, Genetic Stochastic ProcessesiPJComplementary DNA microarray and high density oligonucleotide arrays opened the opportunity for massively parallel biological data acquisition. Application of these technologies will shift the emphasis in biological research from primary data generation to complex quantitative data analysis. Reverse engineering of time-dependent gene- expression matrices is amongst the first complex tools to be developed. The success of reverse engineering will depend on the quantitative features of the genetic networks and the quality of information we can obtain from biological systems. This paper reviews how the (1) stochastic nature, (2) the effective size, and (3) the compartmentalization of genetic networks as well as (4) the information content of gene expression matrices will influence our ability to perform successful reverse engineering.'Department of Pharmacology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA. zszallas@mxc.usuhs.mil10380181Pac Symp Biocomput 1999 5-16.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11680884nxqTonge, R. Shaw, J. Middleton, B. Rowlinson, R. Rayner, S. Young, J. Pognan, F. Hawkins, E. Currie, I. Davison, M.mxqValidation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technologyt Acetaminophen/toxicity Animal Biotechnology Electrophoresis, Gel, Two-Dimensional/*methods Fluorescence Fluorescent Dyes Liver/chemistry/drug effects Male Mice Proteins/isolation & purification Proteome/*isolation & purification Spectrum Analysis, Mass/methodsFluorescence two-dimensional differential gel electrophoresis (2-D DIGE*) is a new development in protein detection for two-dimensional gels. Using mouse liver homogenates (control and paracetamol (N-acetyl- p-aminophenol, APAP)-treated), we have determined the quantitative variation in the 2-D DIGE process and established statistically valid thresholds for assigning quantitative changes between samples. Thresholds were dependent on normalised spot volume, ranged from approximately 1.2 fold for large volume spots to 3.5 fold for small volume spots and were not markedly affected by the particular cyanine dye combination or by multiple operators carrying out the dye labelling reaction. To minimise the thresholds, substantial user editing was required when using ImageMaster 2D-Elite software. The difference thresholds were applied to the test system and quantitative protein differences were determined using replicate gels of pool samples and single gels from multiple individual animals (control vs treated in each gel). Throughout, the differences revealed with a particular cyanine dye combination were mirrored almost without exception when the dye combination was reversed. Both pool and individual sample analyses provided unique data to the study. The inter-animal response variability in inbred mice was approximately nine times that contributed by the 2-D DIGE process. A number of the most frequently observed protein changes resulting from APAP-treatment were identified by mass spectrometry. Several of these can be rationalised based on available data on the mechanism of APAP hepatotoxicity but others cannot, indicating that proteomics can provide further insights into the biochemical basis of APAP toxicity.'Proteomics Group, Enabling Science and Technology (Biology), CTL, AstraZeneca Pharmaceuticals, Alderley Park, Macclesfield, Cheshire, SK10 7TG, UK. robert.tonge@astrazeneca.com11680884 Proteomics 200113377-96. 0 X12682375318r 2003 Apr 15Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of modelss2242-51e*$Highly expressed genes in many bacteria and small eukaryotes often have a strong compositional bias, in terms of codon usage. Two widely used numerical indices, the codon adaptation index (CAI) and the codon usage, use this bias to predict the expression level of genes. When these indices were first introduced, they were based on fairly simple assumptions about which genes are most highly expressed: the CAI was originally based on the codon composition of a set of only 24 highly expressed genes, and the codon usage on assumptions about which functional classes of genes are highly expressed in fast-growing bacteria. Given the recent advent of genome-wide expression data, we should be able to improve on these assumptions. Here, we measure, in yeast, the degree to which consideration of the current genome-wide expression data sets improves the performance of both numerical indices. Indeed, we find that by changing the parameterization of each model its correlation with actual expression levels can be somewhat improved, although both indices are fairly insensitive to the exact way they are parameterized. This insensitivity indicates a consistent codon bias amongst highly expressed genes. We also attempt direct linear regression of codon composition against genome-wide expression levels (and protein abundance data). This has some similarity with the CAI formalism and yields an alternative model for the prediction of expression levels based on the coding sequences of genes. More information is available at http://bioinfo.mbb.yale.edu/expression/codons.'Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.0)Jansen, R. Bussemaker, H. J. Gerstein, M. 1362-4962 Journal ArticleNucleic Acids ResCodon/*genetics Computational Biology/methods Gene Expression Profiling Gene Expression Regulation, Fungal *Genome Genome, Fungal *Models, Genetic Saccharomyces cerevisiae/genetics Support, U.S. Gov't, P.H.S.lehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12682375ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11914276,Kumar, A. Agarwal, S. Heyman, J. A. Matson, S. Heidtman, M. Piccirillo, S. Umansky, L. Drawid, A. Jansen, R. Liu, Y. Cheung, K. H. Miller, P. Gerstein, M. Roeder, G. S. Snyder, M.m4.Subcellular localization of the yeast proteome0*Algorithms Cell Nucleus/metabolism Chromosomes/metabolism Cytoplasm/metabolism Databases Epitopes *Genome, Fungal Microscopy, Fluorescence Mitochondria/metabolism Models, Genetic Mutagenesis Phenotype Saccharomyces cerevisiae/*metabolism Software Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S.Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome- wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.'xqDepartment of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06520, USA.11914276 Genes Dev 2002166707-19. ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11779829h,%Jansen, R. Greenbaum, D. Gerstein, M.pNGRelating whole-genome expression data with protein-protein interactionseComparative Study *Gene Expression Profiling *Genome Models, Genetic Normal Distribution *Protein Interaction Mapping Proteome RNA, Messenger/metabolism Support, Non-U.S. Gov't~wWe investigate the relationship of protein-protein interactions with mRNA expression levels, by integrating a variety of data sources for yeast. We focus on known protein complexes that have clearly defined interactions between their subunits. We find that subunits of the same protein complex show significant coexpression, both in terms of similarities of absolute mRNA levels and expression profiles, e.g., we can often see subunits of a complex having correlated patterns of expression over a time course. We classify the yeast protein complexes as either permanent or transient, with permanent ones being maintained through most cellular conditions. We find that, generally, permanent complexes, such as the ribosome and proteasome, have a particularly strong relationship with expression, while transient ones do not. However, we note that several transient complexes, such as the RNA polymerase II holoenzyme and the replication complex, can be subdivided into smaller permanent ones, which do have a strong relationship to gene expression. We also investigated the interactions in aggregated, genome-wide data sets, such as the comprehensive yeast two-hybrid experiments, and found them to have only a weak relationship with gene expression, similar to that of transient complexes. (Further details on genecensus.org/expression/interactions and bioinfo.mbb.yale.edu/expression/interactions.)e'^WDepartment of Molecular Biophysics, Yale University, New Haven, Connecticut 06520, USA. 11779829 Genome Res 2002121a 37-46.   Genes, erbB-2 Genes, Fungal Genetic CodeGenetic Engineeringgy GenomexprGenome, Fungalenc Genome, Human Genome, Plant Genotyper Germany PHeat$!Heat-Shock Proteins 70/metabolism0-Hematopoietic Stem Cells/cytology/*metabolism HL-60 Cellsen Homeodomain Proteins/geneticsHuman Hydrogen-Ion Concentration*ch(#Image Processing, Computer-Assisted,)Information Storage and Retrieval/methodsInterleukin-6/metabolism InternetPIonsaIsoelectric FocusingrIsoenzymes/geneticsIsotope Labeling/*methods Lasers, HHCLeukemia, Lymphocytic, Acute/*classification/drug therapy/*genetics<8Leukemia, Myeloid/*classification/drug therapy/*geneticsLiver/*chemistryLiver/analysis Liver/chemistry/drug effectsmLoss of Heterozygosity Luminescent Proteins/genetics Lung Neoplasms/*metabolismLung/metabolismMalerMass Screening/methodsnY Mathematics40Matrix Metalloproteinases/*biosynthesis/genetics,&Membrane Proteins/chemistry/metabolismMicerMice, Inbred StrainsMicrosatellite RepeatsMicroscopy, FluorescenceMicrosomes/*chemistry Middle AgenalMitochondria/metabolisme$Mitochondrial Proteins/geneticsMitosis/*geneticsModels, BiologicalthoModels, ChemicalPModels, Geneticn Models, Molecular Molecular Probe TechniquesagnMolecular Sequence DataioMolecular Weightc$!Multienzyme Complexes/*metabolism Mutagenesiset Mutagens$!Myeloid Cells/cytology/metabolismMyelopoiesis/*genetics/meNanotechnologys/aNeoplasm Invasiveness85Neoplasm Proteins/*genetics/*isolation & purification,&Neoplasm Proteins/*genetics/metabolism Neoplasm Proteins/*metabolism Neoplasm Proteins/genetics83Neoplasm Proteins/genetics/isolation & purificationisNeoplasm StagingT$Neoplasms/*genetics/metabolismNeoplasms/*metabolism$!Neoplasms/classification/geneticsNeuregulin-1/pharmacology Neurospora crassa/geneticsetiNeutrophils/metabolism/meNormal Distributionof Nucleic Acid Hybridization/me,'Oligonucleotide Array Sequence AnalysisboLHOligonucleotide Array Sequence Analysis/*instrumentation/*methods/trends40Oligonucleotide Array Sequence Analysis/*methodsHDOligonucleotide Array Sequence Analysis/*statistics & numerical data@:Oligonucleotide Array Sequence Analysis/economics/*methodsD@Oligonucleotide Array Sequence Analysis/instrumentation/*methodsHCOligonucleotide Array Sequence Analysis/statistics & numerical data OncogenesOpen Reading Frames Open Reading Frames/geneticsi("Ovarian Neoplasms/*blood/diagnosisPeptide MappingarPeptide Mapping/*methodsoPeptides/chemistryaly Peptides/genetics/metabolismc Phenotype($Photography/instrumentation/*methodss$!Polyribosomes/genetics/metabolismPredictive Value of TestsProgramming Languages,)Prostatic Neoplasms/*enzymology/pathology$Prostatic Neoplasms/*metabolism82Protein Array Analysis/statistics & numerical data & Protein Isoforms Protein Structure, Tertiary/mProteins/*analysis Da Proteins/*analysis/*genetics$Proteins/*chemistry/metabolismProteins/*genetics Proteins/analysis/geneticsenc<7Proteins/chemistry/*classification/*genetics/metabolism$!Proteins/isolation & purificationProteins/metabolism Proteome Proteome/*analysisthoProteome/*chemistryui0,Proteome/*genetics/*isolation & purificationi("Proteome/*isolation & purificationnalProteome/*metabolism*,'Proteome/genetics/*physiology/secretionp\ ProteomicsotiQuality ControltrRadioisotopes/metabolism0,Receptors, Retinoic Acid/deficiency/geneticsm Recombinant Proteins/geneticsReference Valuesr Reproducibility of Results/isRetrospective Studies$!Ribosomes/genetics/ultrastructureRNA, Fungal/*analysis("RNA, Fungal/*biosynthesis/genetics$!RNA, Fungal/*genetics/*metabolism(%RNA, Fungal/*isolation & purificationRNA, Fungal/geneticsRNA, Messenger/*analysisi(%RNA, Messenger/*biosynthesis/genetics,'RNA, Messenger/*biosynthesis/metabolismpVZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11231557h4-Washburn, M. P. Wolters, D. Yates, J. R., 3rdrf`Large-scale analysis of the yeast proteome by multidimensional protein identification technologyAlgorithms Cell Membrane/metabolism Chromatography, Liquid Codon Databases Fungal Proteins/*analysis Membrane Proteins/chemistry/metabolism Models, Molecular Peptide Mapping Protein Structure, Tertiary *Proteome Saccharomyces cerevisiae/chemistry/growth & development/*metabolism Solubility Spectrum Analysis, Mass Subcellular Fractions/chemistry/metabolism Support, U.S. Gov't, P.H.S.*& We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.'jdSyngenta Agricultural Discovery Institute, 3115 Merryfield Row, Suite 100, San Diego, CA 92121, USA.11231557Nat Biotechnol 2001193  242-7.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12043600UNGWashburn, M. P. Ulaszek, R. Deciu, C. Schieltz, D. M. Yates, J. R., 3rdlnhAnalysis of quantitative proteomic data generated via multidimensional protein identification technologyWe describe the analysis of quantitative proteomic samples via multidimensional protein identification technology (MudPIT). Ratio amounts of the soluble portion of the S. cerevisiae proteome from cultures of S. cerevisiae strain S288C grown in either 14N minimal media or 15N-enriched minimal media were mixed and digested into a complex peptide mixture. A 1 x 14N/1 x 15N complex peptide mixture was analyzed by single-dimensional reversed-phase chromatography and electrospray ionization quadrapole time-of-flight mass spectrometry in order to demonstrate the replacement of 14N by 15N under the growth conditions used. After conformation of the incorporation of 15N into the labeled sample, three separate samples consisting of a 1 x 14N/1 x 15N complex peptide mixture, a 5 x 14N/1 x 15N complex peptide mixture, and a 10 x 14N/1 x 15N complex peptide mixture were analyzed via MudPIT. We demonstrate the dynamic range of the system by analyzing a 1:1, 5:1, and 10:1 data set using the soluble portion from S. cerevisiae grown in either 14N or 15N-enriched minimal media. The method described provides an accurate way to undertake a large-scale quantitative proteomic study.'rkProteomics, Torrey Mesa Research Institute, San Diego, California 92121, USA. michael.washburn@syngenta.coms12043600 Anal Chem  2002747r1650-7.n, 1223928117 2002 JulConcomitant determination of absolute values of cellular protein amounts, synthesis rates, and turnover rates by quantitative proteome profiling 528-37Two-dimensional gel electrophoresis of protein fractions isolated from (35)S-radiolabeled cells provides qualitative information on intracellular amounts, (35)S incorporation rates, protein modifications, and subcellular localizations of up to thousands of individual proteins. In this study we extended proteome profiling to provide quantitative data on synthesis rates of individual proteins. We combined fluorescence detection of radiolabeled proteins with SYPRO ruby(TM) staining and subsequent autoradiography of the same gels, thereby quantifying protein amounts and (35)S incorporation. To calibrate calculation of absolute synthesis rates, we determined the amount and autoradiograph intensity of radiolabeled haptoglobin secreted by interleukin-6 pretreated HepG2 cells. This allowed us to obtain a standard calibration value for (35)S incorporation per autoradiograph intensity unit. This value was used to measure protein synthesis rates during time course experiments of heat-shocked U937 cells. We measured the increasing amounts of hsp70 and calculated it by integration of the determined hsp70 synthesis rates over time. Similar results were obtained by both methods, validating our standardization procedure. Based on the assumption that the synthesis rate of proteins in a steady state of cell metabolism would essentially compensate protein degradation, we calculated biological half-lives of proteins from protein amounts and synthesis rates determined from two-dimensional gels. Calculated protein half-lives were found close to those determined by pulse-chase experiments, thus validating this new method. In conclusion, we devised a method to assess quantitative proteome profiles covering determination of individual amounts, synthesis, and turnover rates of proteins.'piInstitute of Cancer Research, University of Vienna, 1090 Vienna, Austria. Christopher.Gerner@univie.ac.at`YGerner, C. Vejda, S. Gelbmann, D. Bayer, E. Gotzmann, J. Schulte-Hermann, R. Mikulits, W. 1535-9476 Journal ArticleMol Cell ProteomicshaCell Fractionation Cytoplasm/chemistry Electrophoresis, Gel, Two-Dimensional/methods Fluorescent Dyes/metabolism Heat Heat-Shock Proteins 70/metabolism Human Interleukin-6/metabolism Proteins/*chemistry/metabolism Proteome/*analysis *Proteomics Statistics Sulfur Radioisotopes/metabolism Transcription, Genetic Translation, Genetic Tumor Cells, Culturedlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12239281ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12096126 `YGharbi, S. Gaffney, P. Yang, A. Zvelebil, M. J. Cramer, R. Waterfield, M. D. Timms, J. F.1Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system Breast Neoplasms/*genetics Electrophoresis, Gel, Two-Dimensional/*methods/statistics & numerical data Fluorescent Dyes Gene Expression/drug effects Genes, erbB-2 Neoplasm Proteins/genetics/isolation & purification Neuregulin-1/pharmacology *Proteome Reproducibility of Results Sensitivity and Specificity Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Staining and Labeling Support, Non-U.S. Gov't Tumor Cells, Cultured60The technique of fluorescent two-dimensional (2D) difference gel electrophoresis for differential protein expression analysis has been evaluated using a model breast cancer cell system of ErbB-2 overexpression. Labeling of paired cell lysate samples with N-hydroxy succinimidyl ester-derivatives of fluorescent Cy3 and Cy5 dyes for separation on the same 2D gel enabled quantitative, sensitive, and reproducible differential expression analysis of the cell lines. SyproRuby staining was shown to be a highly sensitive and 2D difference gel electrophoresis-compatible method for post-electrophoretic visualization of proteins, which could then be picked and identified by matrix-assisted laser-desorption ionization mass spectroscopy. Indeed, from these experiments, we have identified multiple proteins that are likely to be involved in ErbB-2-mediated transformation. A triple dye labeling methodology was used to identify proteins differentially expressed in the cell system over a time course of growth factor stimulation. A Cy2-labeled pool of samples was used as a standard with all Cy3- and Cy5-labeled sample pairs to facilitate cross-gel quantitative analysis. DeCyder (Amersham Biosciences, Inc.) software was used to distinguish clear statistical differences in protein expression over time and between the cell lines.'d]Ludwig Institute for Cancer Research, 91 Riding House Street, London W1W 7BS, United Kingdom.12096126Mol Cell Proteomics 200212 91-8.me, Fungal9150937 18 3-4 1997Mar-AprJCA comparison of selected mRNA and protein abundances in human liver 533-7}In order to obtain an estimate of the overall level of correlation between mRNA and protein abundances for a well-characterized pharmaceutically relevant biological system, we have analyzed human liver by quantitative two-dimensional electrophoresis (for protein abundances) and by Transcript Image methodology (for mRNA abundances). Incyte's LifeSeq database was searched for expressed sequence tag (EST) sequences corresponding to a series of 23 proteins identified on 2-D maps in the Large Scale Biology (LSB) Molecular Anatomy database, resulting in estimated abundances for 19 messages (4 were undetected) among 7926 liver clones sequenced. A correlation coefficient of 0.48 was obtained between the mRNA and protein abundances determined by the two approaches, suggesting that post-transcriptional regulation of gene expression is a frequent phenomenon in higher organisms. A comparison with published data (Kawamoto, S., et al., Gene 1996, 174, 151-158) on the abundances of liver mRNAs for plasma proteins (secreted by the liver) suggests that higher abundance messages are strongly enriched in secreted sequences. Our data confirms this: of the 50 most abundant liver mRNAs, 29 coded for secreted proteins, while none of the 50 most abundant proteins appeared to be secreted products (although four plasma and red blood cell proteins were present in this group as contaminants from tissue blood).'TNLarge Scale Biology Corporation, Rockville, MD 20850-3338, USA. leigh@lsbc.com Anderson, L. Seilhamer, J. 0173-0835 Journal ArticleElectrophoresisComparative Study DNA, Complementary Databases, Factual Electrophoresis, Gel, Two-Dimensional Gene Expression Human Liver/*chemistry Proteins/*analysis/*genetics RNA, Messenger/*analysisjdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9150937lZnYZ 8iYuZNat GenetPW@NW kBkBPW7Chromosome MappingnYZ ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11721637i>7Adam, B. L. Vlahou, A. Semmes, O. J. Wright, G. L., Jr.sRKProteomic approaches to biomarker discovery in prostate and bladder cancerseBladder Neoplasms/*metabolism Female Human Male Neoplasm Proteins/*metabolism Prostatic Neoplasms/*metabolism Proteome/*metabolism Tumor Markers, Biological/analysis/*metabolismeRLProteomic technologies, including high resolution two-dimensional electrophoresis (2-DE), antibody/protein arrays, and advances in mass spectrometry (MS), are providing the tools needed to discover and identify disease associated biomarkers. Although application of these technologies to search for potential diagnostic/prognostic biomarkers associated with prostate and bladder cancer have been somewhat limited to date, proteins either overexpressed or underexpressed have been detected in both these urological cancers. Recent advances in mass spectrometry, especially platforms that permit rapid "fingerprint" profiling of multiple biomarkers, and tandem mass spectrometers for protein identification, will most assuredly enhance the discovery, identification, and characterization of potential cancer associated biomarkers. Furthermore, application of laser capture microdissection microscopes has provided a rapid and reproducible approach to procure pure populations of cells. This technology coupled to 2-DE and MS has significantly aided the elucidation of the differential expression profiles between disease, benign and normal prostate and bladder cell populations. Finally, development and application of learning algorithms and bioinformatics to the data generated by these proteomic technologies will be essential in determining the clinical potential of a protein biomarker. The purpose of this review is to provide the reader with an overview of the application of these technologies in the search and identification of potential diagnostic/prognostic biomarkers for prostate and bladder cancers.'Department of Microbiology and Molecular Cell Biology, Virginia Prostate Center, Eastern Virginia Medical School, Norfolk, Virginia, USA.11721637 Proteomics 20011101264-70. :ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11774908c60Wolters, D. A. Washburn, M. P. Yates, J. R., 3rd\VAn automated multidimensional protein identification technology for shotgun proteomicsAutoanalysis Chromatography, Liquid Proteins/*analysis Proteome/*chemistry Reproducibility of Results Saccharomyces cerevisiae/chemistry Spectrometry, Mass, Electrospray Ionization Support, U.S. Gov't, P.H.S.We describe an automated method for shotgun proteomics named multidimensional protein identification technology (MudPIT), which combines multidimensional liquid chromatography with electrospray ionization tandem mass spectrometry. The multidimensional liquid chromatography method integrates a strong cation-exchange (SCX) resin and reversed-phase resin in a biphasic column. We detail the improvements over a system described by Link et al. (Link, A. J.; Eng, J.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., III. Nat. Biotechnol. 1999, 17, 676-682) that separates and acquires tandem mass spectra for thousands of peptides. Peptides elute off the SCX phase by increasing pI, and elution off the SCX material is evenly distributed across an analysis. In addition, we describe the chromatographic benchmarks of MudPIT. MudPIT was reproducible within 0.5% between two analyses. Furthermore, a dynamic range of 10000 to 1 between the most abundant and least abundant proteins/peptides in a complex peptide mixture has been demonstrated. By improving sample preparation along with separations, the method improves the overall analysis of proteomes by identifying proteins of all functional and physical classes.'HATorrey Mesa Research Institute, San Diego, California 92121, USA.I11774908 Anal Chemh 200173235683-90.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12469338lHBYan, J. X. Devenish, A. T. Wait, R. Stone, T. Lewis, S. Fowler, S.~Fluorescence two-dimensional difference gel electrophoresis and mass spectrometry based proteomic analysis of Escherichia coli\VSeparation and relative quantitation of complex protein mixtures remain two of the most challenging aspects of proteomics. Here an advanced technique called fluorescence difference 2-D gel electrophoresis technology (2D-DIGE) has been applied to a model system study of the Escherichia coli proteome after benzoic acid treatment. The molecular weight and charge matched cyanine dyes enable pre-electrophoretic labelling of control and treated samples which are then mixed and run in the same gel. Pooled control and treated samples labelled with Cy trade mark 3 were used as an internal standard for both Cy5 labelled control and treated E. coli samples. Together with DeCyder trade mark imaging analysis software, more accurate quantitative analysis than conventional two-dimensional polyacrylamide gel electrophoresis was achieved. Using matrix-assisted laser desorption/ionization-time of flight and quadrupole-time of flight mass spectrometry a total of 179 differentially expressed protein spots were identified. These included enzymes, stress related and substrate (e.g. amino acids, maltose, ribose and TRP repressor) binding proteins. Of the spots analysed, 77% contained only one protein species per spot, hence the change in protein expression measured was solely attributed to the identified protein. Many membrane proteins and protein isoforms were identified indicating both adequate solubilization of E. coli samples and potential post-translational modification. The results indicate that the regulatory mechanisms following benzoic acid treatment of E. coli are far more complicated than hitherto expected.'0*Amersham Biosciences, Little Chalfont, UK.12469338 Proteomics 20022121682-98.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12096129tZhou, G. Li, H. DeCamp, D. Chen, S. Shu, H. Gong, Y. Flaig, M. Gillespie, J. W. Hu, N. Taylor, P. R. Emmert-Buck, M. R. Liotta, L. A. Petricoin, E. F., 3rd Zhao, Y.|v2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markersngCarcinoma, Squamous Cell/genetics Chromatography, High Pressure Liquid Electrophoresis, Gel, Two-Dimensional/*methods Esophageal Neoplasms/*genetics Gene Expression Profiling Neoplasm Proteins/*genetics/*isolation & purification Proteome/*genetics/*isolation & purification Spectrum Analysis, Mass Tumor Markers, Biological/*genetics/*isolation & purificationnThe reproducibility of conventional two-dimensional (2D) gel electrophoresis can be improved using differential in-gel electrophoresis (DIGE), a new emerging technology for proteomic analysis. In DIGE, two pools of proteins are labeled with 1-(5- carboxypentyl)-1'-propylindocarbocyanine halide (Cy3) N-hydroxy- succinimidyl ester and 1-(5-carboxypentyl)-1'-methylindodi-carbocyanine halide (Cy5) N-hydroxysuccinimidyl ester fluorescent dyes, respectively. The labeled proteins are mixed and separated in the same 2D gel. 2D DIGE was applied to quantify the differences in protein expression between laser capture microdissection-procured esophageal carcinoma cells and normal epithelial cells and to define cancer- specific and normal-specific protein markers. Analysis of the 2D images from protein lysates of approximately 250,000 cancer cells and normal cells identified 1038 protein spots in cancer cell lysates and 1088 protein spots in normal cell lysates. Of the detected proteins, 58 spots were up-regulated by >3-fold and 107 were down-regulated by >3- fold in cancer cells. In addition to previously identified down- regulated protein annexin I, tumor rejection antigen (gp96) was found up-regulated in esophageal squamous cell cancer. Global quantification of protein expression between laser capture-microdissected patient- matched cancer cells and normal cells using 2D DIGE in combination with mass spectrometry is a powerful tool for the molecular characterization of cancer progression and identification of cancer-specific protein markers.'pjDepartment of Biochemistry University of Texas Southwestern Medical Center, Dallas, Texas 75390-9038, USA.12096129Mol Cell Proteomics 200212117-24. ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12142369C$Macgregor, P. F. Squire, J. A.NGApplication of microarrays to the analysis of gene expression in cancerfvp*Gene Expression Profiling Human Neoplasms/*genetics/metabolism Oligonucleotide Array Sequence Analysis/*methodsMolecular diagnostics is a rapidly advancing field in which insights into disease mechanisms are being elucidated by use of new gene-based biomarkers. Until recently, diagnostic and prognostic assessment of diseased tissues and tumors relied heavily on indirect indicators that permitted only general classifications into broad histologic or morphologic subtypes and did not take into account the alterations in individual gene expression. Global expression analysis using microarrays now allows for simultaneous interrogation of the expression of thousands of genes in a high-throughput fashion and offers unprecedented opportunities to obtain molecular signatures of the state of activity of diseased cells and patient samples. Microarray analysis may provide invaluable information on disease pathology, progression, resistance to treatment, and response to cellular microenvironments and ultimately may lead to improved early diagnosis and innovative therapeutic approaches for cancer.'piMicroarray Centre, Clinical Genomics Center, University Health Network, Toronto, Ontario, M5G 1L7 Canada.12142369 Clin Chem 20024881170-7.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12647995,Macgregor, P. F.@9Gene expression in cancer: the application of microarrayseCytogenetic Analysis Dissection/instrumentation/methods Gene Expression Gene Expression Profiling Genome, Human Human Lasers Neoplasms/*metabolism Nucleic Acid Hybridization Oligonucleotide Array Sequence Analysis/instrumentation/*methods Proteomics Reproducibility of Results4.Genome-wide monitoring of gene expression using DNA microarrays represents one of the latest breakthroughs in experimental molecular biology and provides unprecedented opportunity to explore the biological processes underlying human diseases by providing a comprehensive survey of a cell's transcriptional landscape. In the cancer field, this revolutionary technology allows the simultaneous assessment of the transcription of tens of thousands of genes, and of their relative expression between normal cells and malignant cells. As microarray analysis emerges from its infancy, there is widespread hope that microarrays will significantly impact on our ability to explore the genetic changes associated with cancer etiology and development, and ultimately lead to the discovery of new biomarkers for disease diagnosis and prognosis prediction, and of new therapeutic tools. This review provides an overview of microarray technology, specifically in the context of cancer research and describes some of its recent applications to the study of cancer. In addition, the challenges of translating microarray findings into molecular cancer diagnosis and prognosis tools, with the potential of altering clinical practice through individualized cancer care and ultimately of contributing to the battle against cancer, are discussed.'~xMicroarray Centre, Clinical Genomics Centre, University Health Network, Toronto, ON, Canada. macgrego@uhnres.utoronto.ca12647995Expert Rev Mol Diagn 20033c2 185-200.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12227735n&McGall, G. H. Christians, F. C.h82High-density genechip oligonucleotide probe arrays`YBase Sequence Equipment Design Gene Expression Gene Expression Profiling/*instrumentation/*methods Genotype Human Models, Chemical Models, Molecular Molecular Sequence Data Oligonucleotide Array Sequence Analysis/*instrumentation/*methods/trends Photography/instrumentation/*methods Quality Control Sequence Analysis, DNA/instrumentation/methodsPHigh-density DNA probe arrays provide a highly parallel approach to nucleic acid sequence analysis that is transforming gene-based biomedical research. Photolithographic DNA synthesis has enabled the large-scale production of GeneChip probe arrays containing hundreds of thousands of oligonucleotide sequences on a glass "chip" about 1.5 cm2 in size. The manufacturing process integrates solid-phase photochemical oligonucleotide synthesis with lithographic techniques similar to those used in the microelectronics industry. Due to their very high information content, GeneChip probe arrays are finding widespread use in the hybridization-based detection and analysis of mutations and polymorphisms ("genotyping"), and in a wide range of gene expression studies.'NHAffymetrix, Inc, Santa Clara, CA 95051, USA. glenn_mcgall@affymetrix.com12227735 2002 Adv Biochem Eng Biotechnol77 21-42 Using Smart Source ParsingZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11752246e~Mewes, H. W. Frishman, D. Guldener, U. Mannhaupt, G. Mayer, K. Mokrejs, M. Morgenstern, B. Munsterkotter, M. Rudd, S. Weil, B.82MIPS: a database for genomes and protein sequences*#Amino Acid Sequence Arabidopsis/genetics Base Sequence *Databases, Genetic *Databases, Protein Expressed Sequence Tags *Genome Genome, Fungal Genome, Human Genome, Plant Germany Human Internet Mitochondrial Proteins/genetics Neurospora crassa/genetics Support, Non-U.S. Gov't Yeasts/geneticspThe Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz- Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91-93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155-158; Barker et al. (2001) Nucleic Acids Res., 29, 29-32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de).'Institute for Bioinformatics (MIPS), GSF National Research Center for Environment and Health, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany. w.mewes@gsf.de11752246Nucleic Acids Res  2002301d 31-4.  @ 1209613911 2002 JanGenome-wide study of gene copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human transitional cell carcinomas 37-45Gain and loss of chromosomal material is characteristic of bladder cancer, as well as malignant transformation in general. The consequences of these changes at both the transcription and translation levels is at present unknown partly because of technical limitations. Here we have attempted to address this question in pairs of non-invasive and invasive human bladder tumors using a combination of technology that included comparative genomic hybridization, high density oligonucleotide array-based monitoring of transcript levels (5600 genes), and high resolution two-dimensional gel electrophoresis. The results showed that there is a gene dosage effect that in some cases superimposes on other regulatory mechanisms. This effect depended (p < 0.015) on the magnitude of the comparative genomic hybridization change. In general (18 of 23 cases), chromosomal areas with more than 2-fold gain of DNA showed a corresponding increase in mRNA transcripts. Areas with loss of DNA, on the other hand, showed either reduced or unaltered transcript levels. Because most proteins resolved by two-dimensional gels are unknown it was only possible to compare mRNA and protein alterations in relatively few cases of well focused abundant proteins. With few exceptions we found a good correlation (p < 0.005) between transcript alterations and protein levels. The implications, as well as limitations, of the approach are discussed.d'Department of Clinical Biochemistry, Molecular Diagnostic Laboratory, Aarhus University Hospital, Skejby, DK-8200 Aarhus N, Denmark. orntoft@kba.sks.au.dkF@Orntoft, T. F. Thykjaer, T. Waldman, F. M. Wolf, H. Celis, J. E. 1535-9476 Journal ArticlecMol Cell Proteomicsr Bladder/metabolism/pathology Bladder Neoplasms/*genetics/metabolism/pathology Carcinoma, Transitional Cell/*genetics/metabolism/pathology Chromosome Aberrations Comparative Study Disease Progression Down-Regulation *Gene Dosage Gene Expression Profiling Human Loss of Heterozygosity Microsatellite Repeats Neoplasm Invasiveness Neoplasm Proteins/*genetics/metabolism Nucleic Acid Hybridization Phenotype RNA, Messenger/metabolism Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Transcription, Genetic Up-Regulationlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12096139 ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12643542.HAPeng, J. Elias, J. E. Thoreen, C. C. Licklider, L. J. Gygi, S. P.oEvaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteomezCations Chromatography, High Pressure Liquid Chromatography, Ion Exchange/methods Chromatography, Liquid/*methods Databases False Positive Reactions Fungal Proteins/analysis/*chemistry Ions Nanotechnology Peptides/chemistry *Proteome Saccharomyces cerevisiae/*chemistry/metabolism Spectrum Analysis, Mass/*methods Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Time FactorsHighly complex protein mixtures can be directly analyzed after proteolysis by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). In this paper, we have utilized the combination of strong cation exchange (SCX) and reversed-phase (RP) chromatography to achieve two-dimensional separation prior to MS/MS. One milligram of whole yeast protein was proteolyzed and separated by SCX chromatography (2.1 mm i.d.) with fraction collection every minute during an 80-min elution. Eighty fractions were reduced in volume and then re-injected via an autosampler in an automated fashion using a vented-column (100 microm i.d.) approach for RP-LC-MS/MS analysis. More than 162,000 MS/MS spectra were collected with 26,815 matched to yeast peptides (7,537 unique peptides). A total of 1,504 yeast proteins were unambiguously identified in this single analysis. We present a comparison of this experiment with a previously published yeast proteome analysis by Yates and colleagues (Washburn, M. P.; Wolters, D.; Yates, J. R., III. Nat. Biotechnol. 2001, 19, 242-7). In addition, we report an in-depth analysis of the false-positive rates associated with peptide identification using the Sequest algorithm and a reversed yeast protein database. New criteria are proposed to decrease false- positives to less than 1% and to greatly reduce the need for manual interpretation while permitting more proteins to be identified.'Department of Cell Biology, and Taplin Biological Mass Spectrometry Facility, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, USA.12643542J Proteome Res 20032 1r 43-50. T ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12142387n>7Li, J. Zhang, Z. Rosenzweig, J. Wang, Y. Y. Chan, D. W.engProteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancerfrlAdult Aged Aged, 80 and over BRCA1 Protein/blood BRCA2 Protein/blood Breast Neoplasms/blood/*diagnosis/pathology Computational Biology Female Human Middle Age Molecular Probe Techniques Neoplasm Staging Proteome/*analysis Retrospective Studies Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Support, Non-U.S. Gov't Tumor Markers, Biological/*bloodBACKGROUND: Surface-enhanced laser desorption/ionization (SELDI) is an affinity-based mass spectrometric method in which proteins of interest are selectively adsorbed to a chemically modified surface on a biochip, whereas impurities are removed by washing with buffer. This technology allows sensitive and high-throughput protein profiling of complex biological specimens. METHODS: We screened for potential tumor biomarkers in 169 serum samples, including samples from a cancer group of 103 breast cancer patients at different clinical stages [stage 0 (n = 4), stage I (n = 38), stage II (n = 37), and stage III (n = 24)], from a control group of 41 healthy women, and from 25 patients with benign breast diseases. Diluted serum samples were applied to immobilized metal affinity capture Ciphergen ProteinChip Arrays previously activated with Ni2+. Proteins bound to the chelated metal were analyzed on a ProteinChip Reader Model PBS II. Complex protein profiles of different diagnostic groups were compared and analyzed using the ProPeak software package. RESULTS: A panel of three biomarkers was selected based on their collective contribution to the optimal separation between stage 0-I breast cancer patients and noncancer controls. The same separation was observed using independent test data from stage II-III breast cancer patients. Bootstrap cross- validation demonstrated that a sensitivity of 93% for all cancer patients and a specificity of 91% for all controls were achieved by a composite index derived by multivariate logistic regression using the three selected biomarkers. CONCLUSIONS: Proteomics approaches such as SELDI mass spectrometry, in conjunction with bioinformatics tools, could greatly facilitate the discovery of new and better biomarkers. The high sensitivity and specificity achieved by the combined use of the selected biomarkers show great potential for the early detection of breast cancer.'\VDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA.12142387 Clin Chem 2002488 1296-304.ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12384419?nhLian, Z. Kluger, Y. Greenbaum, D. S. Tuck, D. Gerstein, M. Berliner, N. Weissman, S. M. Newburger, P. E.Genomic and proteomic analysis of the myeloid differentiation program: global analysis of gene expression during induced differentiation in the MPRO cell lineAnimal Cell Differentiation/drug effects Cell Line/drug effects/metabolism Comparative Study Electrophoresis, Gel, Two-Dimensional Gene Expression Profiling *Gene Expression Regulation, Developmental/drug effects *Genomics Hematopoietic Stem Cells/cytology/*metabolism Mice Myeloid Cells/cytology/metabolism Myelopoiesis/*genetics Neutrophils/metabolism Oligonucleotide Array Sequence Analysis Proteins/analysis/genetics *Proteomics RNA, Messenger/analysis/genetics Receptors, Retinoic Acid/deficiency/genetics Reproducibility of Results Spectrum Analysis, Mass Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Transcription Factors/genetics Tretinoin/pharmacologyWe have used an approach using 2-dimensional gel electrophoresis with mass spectrometry analysis combined with oligonucleotide chip hybridization for a comprehensive and quantitative study of the temporal patterns of protein and mRNA expression during myeloid development in the MPRO murine cell line. This global analysis detected 123 known proteins and 29 "new" proteins out of 220 protein spots identified by tandem mass spectroscopy, including proteins in 12 functional categories such as transcription factors and cytokines. Bioinformatic analysis of these proteins revealed clusters with functional importance to myeloid differentiation. Previous analyses have found that for a substantial number of genes the absolute amount of protein in the cell is not strongly correlated to the amount of mRNA. These conclusions were based on simultaneous measurement of mRNA and protein at just a single time point. Here, however, we are able to investigate the relationship between mRNA and protein in terms of simultaneous changes in their levels over multiple time points. This is the first time such a relationship has been studied, and we find that it gives a much stronger correlation, consistent with the hypothesis that a substantial proportion of protein change is a consequence of changed mRNA levels, rather than posttranscriptional effects. Cycloheximide inhibition also showed that most of the proteins detected by gel electrophoresis were relatively stable. Specific investigation of transcription factor mRNA representation showed considerable similarity to those of mature human neutrophils and highlighted several transcription factors and other functional nuclear proteins whose mRNA levels change prominently during MPRO differentiation but which have not been investigated previously in the context of myeloid development. Data are available online at http://bioinfo.mbb.yale.edu/expression/myelopoiesis.o'ztDepartment of Genetics, Boyer Center for Molecular Medicine, Yale University School of Medicine, New Haven, CT, USA.12384419 BloodC 2002 100r9i3209-20.123619074249 2002 Oct2Different mRNA and protein expression of matrix metalloproteinases 2 and 9 and tissue inhibitor of metalloproteinases 1 in benign and malignant prostate tissue398-406f60OBJECTIVE: The aim of this study was to assess the behavior of the matrix metalloproteinases (MMPs) 2 and 9 and the tissue inhibitor of metalloproteinases 1 (TIMP-1) in human prostate cancer. METHODS: mRNA and protein expression patterns of MMP-2, MMP-9, and TIMP-1 were studied in cancerous and noncancerous parts of 17 prostates removed by radical prostatectomy. Competitive RT-PCR, gelatin-substrate zymography, and ELISA techniques were used for quantification. RESULTS: On the mRNA level, MMP-2 expression was decreased and MMP-9, TIMP-1, the ratios of MMP-2 and MMP-9 to TIMP-1 were unchanged in cancerous tissue compared to the normal counterparts. On the protein level, expression of MMP-9 was significantly higher and TIMP-1 expression was significantly lower, MMP-2 was unchanged and the ratios of MMP-2 and MMP-9 to TIMP-1 were increased in tumor tissue. CONCLUSIONS: The higher concentration of MMP-9 as well as the increased ratios of MMP-2 and MMP-9 to TIMP-1 in malignant tissue prove the proteolytic dysbalance in prostate cancer, which does not seem to be associated with the stage and grade of the tumor. Comparison of mRNA and protein expression of MMP-2, MMP-9 and TIMP-1, respectively, did not show any significant relationships illustrating the necessity to study these components at both molecular levels.'VODepartment of Clinical Chemistry, University Medical School, Hannover, Germany.iLichtinghagen, R. Musholt, P. B. Lein, M. Romer, A. Rudolph, B. Kristiansen, G. Hauptmann, S. Schnorr, D. Loening, S. A. Jung, K.C 0302-2838 Journal Article2Eur UrolAged Cluster Analysis Human Male Matrix Metalloproteinases/*biosynthesis/genetics Middle Age Neoplasm Staging Prostatic Neoplasms/*enzymology/pathology RNA, Messenger/*biosynthesis/metabolism Support, Non-U.S. Gov't Tissue Inhibitor of Metalloproteinases/*biosynthesis/geneticshlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12361907u<! ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12016056t,%Greenbaum, D. Jansen, R. Gerstein, M..Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcriptsoAlgorithms Comparative Study *Database Management Systems *Databases, Nucleic Acid *Databases, Protein Gene Expression Gene Expression Profiling/*methods Genome Information Storage and Retrieval/methods Open Reading Frames/genetics Proteins/chemistry/*classification/*genetics/metabolism Reference Values Sensitivity and Specificity Support, Non-U.S. Gov't Transcription, Genetic/genetics Yeasts/genetics/metabolism   MOTIVATION: Protein abundance is related to mRNA expression through many different cellular processes. Up to now, there have been conflicting results on how correlated the levels of these two quantities are. Given that expression and abundance data are significantly more complex and noisy than the underlying genomic sequence information, it is reasonable to simplify and average them in terms of broad proteomic categories and features (e.g. functions or secondary structures), for understanding their relationship. Furthermore, it will be essential to integrate, within a common framework, the results of many varied experiments by different investigators. This will allow one to survey the characteristics of highly expressed genes and proteins. RESULTS: To this end, we outline a formalism for merging and scaling many different gene expression and protein abundance data sets into a comprehensive reference set, and we develop an approach for analyzing this in terms of broad categories, such as composition, function, structure and localization. As the various experiments are not always done using the same set of genes, sampling bias becomes a central issue, and our formalism is designed to explicitly show this and correct for it. We apply our formalism to the currently available gene expression and protein abundance data for yeast. Overall, we found substantial agreement between gene expression and protein abundance, in terms of the enrichment of structural and functional categories. This agreement, which was considerably greater than the simple correlation between these quantities for individual genes, reflects the way broad categories collect many individual measurements into simple, robust averages. In particular, we found that in comparison to the population of genes in the yeast genome, the cellular populations of transcripts and proteins (weighted by their respective abundances, the transcriptome and what we dub the translatome) were both enriched in: (i) the small amino acids Val, Gly, and Ala; (ii) low molecular weight proteins; (iii) helices and sheets relative to coils; (iv) cytoplasmic proteins relative to nuclear ones; and (v) proteins involved in 'protein synthesis,' 'cell structure,' and 'energy production.' SUPPLEMENTARY INFORMATION: http://genecensus.org/expression/translatome 'hbDepartment Genetics, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.12016056Bioinformatics 2002184u585-96.o1209611414 2002 Apr.voComplementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae 323-33Using an integrated genomic and proteomic approach, we have investigated the effects of carbon source perturbation on steady-state gene expression in the yeast Saccharomyces cerevisiae growing on either galactose or ethanol. For many genes, significant differences between the abundance ratio of the messenger RNA transcript and the corresponding protein product were observed. Insights into the perturbative effects on genes involved in respiration, energy generation, and protein synthesis were obtained that would not have been apparent from measurements made at either the messenger RNA or protein level alone, illustrating the power of integrating different types of data obtained from the same sample for the comprehensive characterization of biological systems and processes.'D>Institute for Systems Biology, Seattle, Washington 98103, USA.TMGriffin, T. J. Gygi, S. P. Ideker, T. Rist, B. Eng, J. Hood, L. Aebersold, R. 1535-9476 Journal ArticleMol Cell ProteomicsDatabases Mitochondria/metabolism Models, Biological Oligonucleotide Array Sequence Analysis/*methods Proteins/*analysis RNA, Messenger/*metabolism Saccharomyces cerevisiae/*genetics/*metabolism Spectrum Analysis, Mass Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S.lehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12096114tZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=10022859s82Gygi, S. P. Rochon, Y. Franza, B. R. Aebersold, R.>7Correlation between protein and mRNA abundance in yeastCodon Fungal Proteins/*analysis *Gene Expression Regulation, Fungal RNA, Fungal/*analysis RNA, Messenger/*analysis Saccharomyces cerevisiae/*genetics/metabolism Support, U.S. Gov't, Non-P.H.S. Support, U.S. Gov't, P.H.S.We have determined the relationship between mRNA and protein expression levels for selected genes expressed in the yeast Saccharomyces cerevisiae growing at mid-log phase. The proteins contained in total yeast cell lysate were separated by high-resolution two-dimensional (2D) gel electrophoresis. Over 150 protein spots were excised and identified by capillary liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein spots were quantified by metabolic labeling and scintillation counting. Corresponding mRNA levels were calculated from serial analysis of gene expression (SAGE) frequency tables (V. E. Velculescu, L. Zhang, W. Zhou, J. Vogelstein, M. A. Basrai, D. E. Bassett, Jr., P. Hieter, B. Vogelstein, and K. W. Kinzler, Cell 88:243- 251, 1997). We found that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data. Indeed, for some genes, while the mRNA levels were of the same value the protein levels varied by more than 20-fold. Conversely, invariant steady-state levels of certain proteins were observed with respective mRNA transcript levels that varied by as much as 30-fold. Another interesting observation is that codon bias is not a predictor of either protein or mRNA levels. Our results clearly delineate the technical boundaries of current approaches for quantitative analysis of protein expression and reveal that simple deduction from mRNA transcript analysis is insufficient.'leDepartment of Molecular Biotechnology, University of Washington, Seattle, Washington 98195-7730, USA.10022859 Mol Cell Biol 19991931720-30.   ?RNA, Messenger/*genetics($RNA, Messenger/*genetics/*metabolismn,(RNA, Messenger/*isolation & purification RNA, Messenger/*metabolism$ RNA, Messenger/analysis/geneticsnRNA, Messenger/metabolism<7Saccharomyces cerevisiae Proteins/*genetics/*metabolism/*0,Saccharomyces cerevisiae/*chemistry/geneticss4.Saccharomyces cerevisiae/*chemistry/metabolism("Saccharomyces cerevisiae/*genetics4.Saccharomyces cerevisiae/*genetics/*metabolismHCSaccharomyces cerevisiae/*genetics/growth & development/*metabolism0-Saccharomyces cerevisiae/*genetics/metabolism($Saccharomyces cerevisiae/*metabolism("Saccharomyces cerevisiae/chemistry0,Saccharomyces cerevisiae/chemistry/*geneticsHCSaccharomyces cerevisiae/chemistry/growth & development/*metabolism<6Saccharomyces cerevisiae/cytology/*genetics/metabolism0-Saccharomyces cerevisiae/enzymology/*genetics$!Saccharomyces cerevisiae/genetics Sample Sizees Sensitivity and SpecificityheSequence Analysis, DNASeq4.Sequence Analysis, DNA/instrumentation/methodsume0*Sequence Analysis, Protein/*methods/trendsSilver Staining SSodium Dodecyl Sulfate Softwarem Solubilityces0+Spectrometry, Mass, Electrospray IonizationD?Spectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationerSpectrum Analysis, Massci$ Spectrum Analysis, Mass/*methods,'Spectrum Analysis, Mass/*methods/trends$Spectrum Analysis, Mass/methodsioStaining and Labeling StatisticsStochastic Processesy0*Subcellular Fractions/chemistry/metabolism& d$Sulfur Radioisotopes/metabolismSupport, Non-U.S. Gov'tci$Support, U.S. Gov't, Non-P.H.S.ic Support, U.S. Gov't, P.H.S.H. Teratogens,)Tetradecanoylphorbol Acetate/pharmacology Time Factors.@=Tissue Inhibitor of Metalloproteinases/*biosynthesis/genetics$Transcription Factors/geneticsienTranscription, Genetic$Transcription, Genetic/geneticsioTranslation, GeneticTreatment OutcomeTretinoin/pharmacologygenTumor Cells, Cultured(#Tumor Markers, Biological/*analysis$ Tumor Markers, Biological/*bloodt@=Tumor Markers, Biological/*genetics/*isolation & purification4.Tumor Markers, Biological/analysis/*metabolismUbiquitin/*metabolism Up-Regulation Yeasts/chemistry/metabolismYeasts/geneticsS. Yeasts/genetics/metabolismeti'+ 12660367 100e7M 2003 Apr 1_TMGenome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiaes3889-94lWe have analyzed the translational status of each mRNA in rapidly growing Saccharomyces cerevisiae. mRNAs were separated by velocity sedimentation on a sucrose gradient, and 14 fractions across the gradient were analyzed by quantitative microarray analysis, providing a profile of ribosome association with mRNAs for thousands of genes. For most genes, the majority of mRNA molecules were associated with ribosomes and presumably engaged in translation. This systematic approach enabled us to recognize genes with unusual behavior. For 43 genes, most mRNA molecules were not associated with ribosomes, suggesting that they may be translationally controlled. For 53 genes, including GCN4, CPA1, and ICY2, three genes for which translational control is known to play a key role in regulation, most mRNA molecules were associated with a single ribosome. The number of ribosomes associated with mRNAs increased with increasing length of the putative protein-coding sequence, consistent with longer transit times for ribosomes translating longer coding sequences. The density at which ribosomes were distributed on each mRNA (i.e., the number of ribosomes per unit ORF length) was well below the maximum packing density for nearly all mRNAs, consistent with initiation as the rate-limiting step in translation. Global analysis revealed an unexpected correlation: Ribosome density decreases with increasing ORF length. Models to account for this surprising observation are discussed.t'TNDepartment of Biochemistry, Stanford University, Stanford, CA 94305-5307, USA.LFArava, Y. Wang, Y. Storey, J. D. Liu, C. L. Brown, P. O. Herschlag, D. 0027-8424 Journal ArticletProc Natl Acad Sci U S A*Genome, Fungal Oligonucleotide Array Sequence Analysis Polyribosomes/genetics/metabolism RNA, Fungal/genetics RNA, Messenger/*genetics Ribosomes/genetics/ultrastructure Saccharomyces cerevisiae/*genetics Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Translation, Genetic-lehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12660367s12660367 1007 2003 Apr 1TMGenome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae3889-94We have analyzed the translational status of each mRNA in rapidly growing Saccharomyces cerevisiae. mRNAs were separated by velocity sedimentation on a sucrose gradient, and 14 fractions across the gradient were analyzed by quantitative microarray analysis, providing a profile of ribosome association with mRNAs for thousands of genes. For most genes, the majority of mRNA molecules were associated with ribosomes and presumably engaged in translation. This systematic approach enabled us to recognize genes with unusual behavior. For 43 genes, most mRNA molecules were not associated with ribosomes, suggesting that they may be translationally controlled. For 53 genes, including GCN4, CPA1, and ICY2, three genes for which translational control is known to play a key role in regulation, most mRNA molecules were associated with a single ribosome. The number of ribosomes associated with mRNAs increased with increasing length of the putative protein-coding sequence, consistent with longer transit times for ribosomes translating longer coding sequences. The density at which ribosomes were distributed on each mRNA (i.e., the number of ribosomes per unit ORF length) was well below the maximum packing density for nearly all mRNAs, consistent with initiation as the rate-limiting step in translation. Global analysis revealed an unexpected correlation: Ribosome density decreases with increasing ORF length. Models to account for this surprising observation are discussed.t'TNDepartment of Biochemistry, Stanford University, Stanford, CA 94305-5307, USA.LFArava, Y. Wang, Y. Storey, J. D. Liu, C. L. Brown, P. O. Herschlag, D. 0027-8424 Journal ArticletProc Natl Acad Sci U S A*Genome, Fungal Oligonucleotide Array Sequence Analysis Polyribosomes/genetics/metabolism RNA, Fungal/genetics RNA, Messenger/*genetics Ribosomes/genetics/ultrastructure Saccharomyces cerevisiae/*genetics Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Translation, Geneticlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12660367ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11395427hBaldi, P. Long, A. D.aA Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changeso*Bayes Theorem Computer Simulation *Data Interpretation, Statistical Models, Genetic *Models, Statistical Oligonucleotide Array Sequence Analysis/*statistics & numerical data Programming Languages Software Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S.MOTIVATION: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data. RESULTS: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t -test, provide a systematic inference approach that compares favorably with simple t -test or fold methods, and partly compensate for the lack of replication.'Department of Information and Computer Science, University of California at Irvine, Irvine, CA 92697-3425, USA. pfbaldi@ics.uci.eduh11395427Bioinformatics 2001176o509-19.,$( ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=118671128Petricoin, E. F. Ardekani, A. M. Hitt, B. A. Levine, P. J. Fusaro, V. A. Steinberg, S. M. Mills, G. B. Simone, C. Fishman, D. A. Kohn, E. C. Liotta, L. A.D=Use of proteomic patterns in serum to identify ovarian cancercCA-125 Antigen/blood Female Human Mass Screening/methods Ovarian Neoplasms/*blood/diagnosis Predictive Value of Tests Proteome/*isolation & purification Support, U.S. Gov't, Non-P.H.S.HBBACKGROUND: New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. METHODS: Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. FINDINGS: The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99). INTERPRETATION: These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.'Food and Drug Administration/National Institutes of Health Clinical Proteomics Program, Department of Therapeutic Proteins/Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD, USA. petricoin@cber.fda.gov11867112 Lancet 2002 359 9306 572-7.1237657318 2002 AugF?Dynamics of protein turnover, a missing dimension in proteomics 579-91Functional genomic experiments frequently involve a comparison of the levels of gene expression between two or more genetic, developmental, or physiological states. Such comparisons can be carried out at either the RNA (transcriptome) or protein (proteome) level, but there is often a lack of congruence between parallel analyses using these two approaches. To fully interpret protein abundance data from proteomic experiments, it is necessary to understand the contributions made by the opposing processes of synthesis and degradation to the transition between the states compared. Thus, there is a need for reliable methods to determine the rates of turnover of individual proteins at amounts comparable to those obtained in proteomic experiments. Here, we show that stable isotope-labeled amino acids can be used to define the rate of breakdown of individual proteins by inspection of mass shifts in tryptic fragments. The approach has been applied to an analysis of abundant proteins in glucose-limited yeast cells grown in aerobic chemostat culture at steady state. The average rate of degradation of 50 proteins was 2.2%/h, although some proteins were turned over at imperceptible rates, and others had degradation rates of almost 10%/h. This range of values suggests that protein turnover is a significant missing dimension in proteomic experiments and needs to be considered when assessing protein abundance data and comparing it to the relative abundance of cognate mRNA species.'~wDepartment of Veterinary Preclinical Sciences, University of Liverpool, Crown Street, Liverpool L69 7ZJ, United Kingom.hbPratt, J. M. Petty, J. Riba-Garcia, I. Robertson, D. H. Gaskell, S. J. Oliver, S. G. Beynon, R. J. 1535-9476 Journal ArticleMol Cell ProteomicsAmino Acids/chemistry/metabolism Fungal Proteins/*chemistry/*metabolism Peptide Mapping *Proteomics Radioisotopes/metabolism Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Statistics Support, Non-U.S. Gov't Yeasts/chemistry/metabolismlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12376573.'Qian, J. Kluger, Y. Yu, H. Gerstein, M. (In Press)XQIdentification and correction of spurious spatial correlations in microarray data BioTechniques)ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11581660e0)Han, D. K. Eng, J. Zhou, H. Aebersold, R.c}Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometryn Affinity Labels Amino Acid Sequence Cell Differentiation HL-60 Cells Human Microsomes/*chemistry Molecular Sequence Data Proteins/*analysis Software Spectrum Analysis, Mass Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Tetradecanoylphorbol Acetate/pharmacologyAn approach to the systematic identification and quantification of the proteins contained in the microsomal fraction of cells is described. It consists of three steps: (1) preparation of microsomal fractions from cells or tissues representing different states; (2) covalent tagging of the proteins with isotope-coded affinity tag (ICAT) reagents followed by proteolysis of the combined labeled protein samples; and (3) isolation, identification, and quantification of the tagged peptides by multidimensional chromatography, automated tandem mass spectrometry, and computational analysis of the obtained data. The method was used to identify and determine the ratios of abundance of each of 491 proteins contained in the microsomal fractions of naive and in vitro- differentiated human myeloid leukemia (HL-60) cells. The method and the new software tools to support it are well suited to the large-scale, quantitative analysis of membrane proteins and other classes of proteins that have been refractory to standard proteomics technology.'d]University of Connecticut Health Center, 263 Farmington Ave., Farmington, CT 06030-0002, USA.t11581660Nat Biotechnol 20011910946-51.c103562481539 1999May-Jun7>7Proteomics: theoretical and experimental considerationsn 312-8 "Cellular engineering relies on the ability to decipher the genetic basis of various phenotypes. Emerging technologies for analyzing the biological function of the information encoded in the genome of particular organisms and/or tissues focus on the monitoring of transcription (mRNA) and translation (protein) processes. Elementary theoretical considerations presented in this article strongly suggest that a combination of mRNA and protein expression patterns should be simultaneously considered to fully develop a conceptual understanding of the functional architecture of genomes and gene networks. We propose a framework of experimental and mathematical methods for acquiring and analyzing quantitative proteomic information and discuss recent developments in proteome analytical technology.o'School of Chemical Engineering, Cornell University, Ithaca, New York 14853-5201, and Biosciences Division, Wayzata, Minnesota 55391-2397, USA.0)Hatzimanikatis, V. Choe, L. H. Lee, K. H.m 8756-7938 Journal ArticleBiotechnol ProgBacterial Proteins/genetics Biotechnology Escherichia coli/genetics *Gene Expression Luminescent Proteins/genetics Proteins/*genetics Recombinant Proteins/genetics Support, Non-U.S. Gov'tlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10356248ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11922607d>8Issaq, H. J. Veenstra, T. D. Conrads, T. P. Felschow, D.^WThe SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification Chromatography/*methods Human Models, Biological Proteome/*analysis Spectrum Analysis, Mass/*methods Support, U.S. Gov't, P.H.S. Tumor Markers, Biological/*analysisThe need for methods to identify disease biomarkers is underscored by the survival-rate of patients diagnosed at early stages of cancer progression. Surface enhanced laser desorption/ionization time-of- flight mass spectrometry (SELDI-TOF MS) is a novel approach to biomarker discovery that combines two powerful techniques: chromatography and mass spectrometry. One of the key features of SELDI- TOF MS is its ability to provide a rapid protein expression profile from a variety of biological and clinical samples. It has been used for biomarker identification as well as the study of protein-protein, and protein-DNA interaction. The versatility of SELDI-TOF MS has allowed its use in projects ranging from the identification of potential diagnostic markers for prostate, bladder, breast, and ovarian cancers and Alzheimer's disease, to the study of biomolecular interactions and the characterization of posttranslational modifications. In this minireview we discuss the application of SELDI-TOF MS to protein biomarker discovery and profiling.'SAIC-Frederick, Inc., National Cancer Institute at Frederick, National Institutes of Health, Frederick, Maryland, USA. issaqh@mail.ncifcrf.gov21702d11922607 Biochem Biophys Res Commun 2002 292t3t587-92.e%- 11917093822l 2002 Apru\VThe ubiquitin-proteasome proteolytic pathway: destruction for the sake of construction373-428ZTBetween the 1960s and 1980s, most life scientists focused their attention on studies of nucleic acids and the translation of the coded information. Protein degradation was a neglected area, considered to be a nonspecific, dead-end process. Although it was known that proteins do turn over, the large extent and high specificity of the process, whereby distinct proteins have half-lives that range from a few minutes to several days, was not appreciated. The discovery of the lysosome by Christian de Duve did not significantly change this view, because it became clear that this organelle is involved mostly in the degradation of extracellular proteins, and their proteases cannot be substrate specific. The discovery of the complex cascade of the ubiquitin pathway revolutionized the field. It is clear now that degradation of cellular proteins is a highly complex, temporally controlled, and tightly regulated process that plays major roles in a variety of basic pathways during cell life and death as well as in health and disease. With the multitude of substrates targeted and the myriad processes involved, it is not surprising that aberrations in the pathway are implicated in the pathogenesis of many diseases, certain malignancies, and neurodegeneration among them. Degradation of a protein via the ubiquitin/proteasome pathway involves two successive steps: 1) conjugation of multiple ubiquitin moieties to the substrate and 2) degradation of the tagged protein by the downstream 26S proteasome complex. Despite intensive research, the unknown still exceeds what we currently know on intracellular protein degradation, and major key questions have remained unsolved. Among these are the modes of specific and timed recognition for the degradation of the many substrates and the mechanisms that underlie aberrations in the system that lead to pathogenesis of diseases.'ztFaculty of Biology and the Institute for Catalysis Science and Technology, Haifa, Israel. glickman@tx.technion.ac.il&Glickman, M. H. Ciechanover, A.810031-9333 Journal Article Review Review, Academic Physiol RevAnimal Cysteine Endopeptidases/*metabolism Human Multienzyme Complexes/*metabolism Proteins/metabolism Support, Non-U.S. Gov't Support, U.S. Gov't, Non-P.H.S. Ubiquitin/*metabolismlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11917093ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11544189eD>Greenbaum, D. Luscombe, N. M. Jansen, R. Qian, J. Gerstein, M.f`Interrelating different types of genomic data, from proteome to secretome: 'oming in on functionBacillus subtilis/*genetics/physiology Comparative Study Computational Biology *Genome, Bacterial Proteome/genetics/*physiology/secretion Support, Non-U.S. Gov'tcWith the completion of genome sequences, the current challenge for biology is to determine the functions of all gene products and to understand how they contribute in making an organism viable. For the first time, biological systems can be viewed as being finite, with a limited set of molecular parts. However, the full range of biological processes controlled by these parts is extremely complex. Thus, a key approach in genomic research is to divide the cellular contents into distinct sub-populations, which are often given an "-omic" term. For example, the proteome is the full complement of proteins encoded by the genome, and the secretome is the part of it secreted from the cell. Carrying this further, we suggest the term "translatome" to describe the members of the proteome weighted by their abundance, and the "functome" to describe all the functions carried out by these. Once the individual sub-populations are defined and analyzed, we can then try to reconstruct the full organism by interrelating them, eventually allowing for a full and dynamic view of the cell. All this is, of course, made possible because of the increasing amount of large-scale data resulting from functional genomics experiments. However, there are still many difficulties resulting from the noisiness and complexity of the information. To some degree, these can be overcome through averaging with broad proteomic categories such as those implicit in functional and structural classifications. For illustration, we discuss one example in detail, interrelating transcript and cellular protein populations (transcriptome and translatome). Further information is available at http://bioinfo.mbb.yale.edu/what-is-it.'XQDepartment of Genetics, Yale University, New Haven, Connecticut 06520- 8114, USA.11544189 Genome Res 20011191463-8."/Z. xXRhttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=9675914XQSchena, M. Heller, R. A. Theriault, T. P. Konrad, K. Lachenmeier, E. Davis, R. W.NGMicroarrays: biotechnology's discovery platform for functional genomicse@9*Biotechnology DNA, Recombinant Ecosystem Gene ExpressionlAdvances in microarray technology enable massive parallel mining of biological data, with biological chips providing hybridization-based expression monitoring, polymorphism detection and genotyping on a genomic scale. Microarrays containing sequences representative of all human genes may soon permit the expression analysis of the entire human genome in a single reaction. These 'genome chips' will provide unprecedented access to key areas of human health, including disease prognosis and diagnosis, drug discovery, toxicology, aging, and mental illness. Microarray technology is rapidly becoming a central platform for functional genomics.'d^Department of Biochemistry, Beckman Center, Stanford University Medical Center, CA 94305, USA.9675914dTrends Biotechnole 1998167p 301-6.126845412p3g 2003 MarsnhThe Transcriptome and Its Translation during Recovery from Cell Cycle Arrest in Saccharomyces cerevisiae191-204aComplete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.'VPDepartment of Biochemistry, University of Washington, Seattle, Washington 98195.jcSerikawa, K. A. Xu, X. L. MacKay, V. L. Law, G. L. Zong, Q. Zhao, L. P. Bumgarner, R. Morris, D. R. 1535-9476 Journal ArticleMol Cell Proteomicslehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1268454135473351153b 1987 Feb 11vpThe codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications1281-95sA simple, effective measure of synonymous codon usage bias, the Codon Adaptation Index, is detailed. The index uses a reference set of highly expressed genes from a species to assess the relative merits of each codon, and a score for a gene is calculated from the frequency of use of all codons in that gene. The index assesses the extent to which selection has been effective in moulding the pattern of codon usage. In that respect it is useful for predicting the level of expression of a gene, for assessing the adaptation of viral genes to their hosts, and for making comparisons of codon usage in different organisms. The index may also give an approximate indication of the likely success of heterologous gene expression.Sharp, P. M. Li, W. H. 0305-1048 Journal ArticleNucleic Acids ResAnimal Base Sequence Cattle *Codon Comparative Study Escherichia coli/*genetics Evolution *Genes, Bacterial *Genes, Fungal *Genes, Structural Human Mathematics *Models, Genetic *RNA, Messenger Saccharomyces cerevisiae/*genetics Support, U.S. Gov't, P.H.S.jdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=354733512175758209e 2002 Sepy@9Proteomics goes quantitative: measuring protein abundancen 361-4tProtein-based methodologies are catching up with established DNA-based methods at an astonishing speed. Recent developments in mass spectrometry enable high-throughput automated identification of proteins as is already the case with DNA sequencing methods. Furthermore, methods for the quantitation of relative protein abundance at the protein level are getting more advanced, which should complement gene expression monitoring at the mRNA level.'NGDepartment Cell Biology, Harvard Medical School, Boston, MA 02115, USA.Steen, H. Pandey, A. 0167-7799 Journal ArticleTrends BiotechnolIsotope Labeling/*methods Proteins/*analysis/*genetics *Proteomics Sequence Analysis, Protein/*methods/trends Spectrum Analysis, Mass/*methods/trendsMlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12175758#22*"970219221 1998 JulF@A genome-wide transcriptional analysis of the mitotic cell cycle 65-73Progression through the eukaryotic cell cycle is known to be both regulated and accompanied by periodic fluctuation in the expression levels of numerous genes. We report here the genome-wide characterization of mRNA transcript levels during the cell cycle of the budding yeast S. cerevisiae. Cell cycle-dependent periodicity was found for 416 of the 6220 monitored transcripts. More than 25% of the 416 genes were found directly adjacent to other genes in the genome that displayed induction in the same cell cycle phase, suggesting a mechanism for local chromosomal organization in global mRNA regulation. More than 60% of the characterized genes that displayed mRNA fluctuation have already been implicated in cell cycle period-specific biological roles. Because more than 20% of human proteins display significant homology to yeast proteins, these results also link a range of human genes to cell cycle period-specific biological functions.'\VDepartment of Genetics, Stanford University School of Medicine, California 94305, USA.Cho, R. J. Campbell, M. J. Winzeler, E. A. Steinmetz, L. Conway, A. Wodicka, L. Wolfsberg, T. G. Gabrielian, A. E. Landsman, D. Lockhart, D. J. Davis, R. W. 1097-2765 Journal ArticleMol CellPJCell Cycle Chromosome Mapping Chromosomes, Fungal/*genetics DNA, Fungal/genetics *Gene Expression Regulation, Fungal *Genome, Fungal Mitosis/*genetics RNA, Fungal/*biosynthesis/genetics RNA, Messenger/*biosynthesis/genetics Saccharomyces cerevisiae/cytology/*genetics/metabolism Support, U.S. Gov't, P.H.S. *Transcription, Geneticjdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9702192109530851612 2000 Sep 15f_Relationship of codon bias to mRNA concentration and protein length in Saccharomyces cerevisiae1131-45In 1982, Ikemura reported a strikingly unequal usage of different synonymous codons, in five Saccharomyces cerevisiae nuclear genes having high protein levels. To study this trend in detail, we examined data from three independent studies that used oligonucleotide arrays or SAGE to estimate mRNA concentrations for nearly all genes in the genome. Correlation coefficients were calculated for the relationship of mRNA concentration to four commonly used measures of synonymous codon usage bias: the codon adaptation index (CAI), the codon bias index (CBI), the frequency of optimal codons (F(op)), and the effective number of codons (N(c)). mRNA concentration was best approximated as an exponential function of each of these four measures. Of the four, the CAI was the most strongly correlated with mRNA concentration (r(s)=0.62+/-0.01, n=2525, p<10(-17)). When we controlled for CAI, mRNA concentration and protein length were negatively correlated (partial r(s)=-0.23+/-0.01, n=4765, p<10(-17)). This may result from selection to reduce the size of abundant proteins to minimize transcriptional and translational costs. When we controlled for mRNA concentration, protein length and CAI were positively correlated (partial r(s)=0.16+/-0.01, n=4765, p<10(-17)). This may reflect more effective selection in longer genes against missense errors during translation. The correlation coefficients between the mRNA levels of individual genes, as measured by different investigators and methods, were low, in the range r(s)=0.39-0.68.g'jdDepartment of Genetics, Smurfit Institute, University of Dublin, Trinity College, Dublin 2, Ireland.Coghlan, A. Wolfe, K. H. 0749-503x Journal Article  YeastnCodon Fungal Proteins/*metabolism Gene Expression Profiling *Genome, Fungal Oligonucleotide Array Sequence Analysis Open Reading Frames RNA, Messenger/*metabolism Saccharomyces cerevisiae/*genetics/metabolismlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10953085u105236241911 1999 Nov& A sampling of the yeast proteome7357-68In this study, we examined yeast proteins by two-dimensional (2D) gel electrophoresis and gathered quantitative information from about 1,400 spots. We found that there is an enormous range of protein abundance and, for identified spots, a good correlation between protein abundance, mRNA abundance, and codon bias. For each molecule of well-translated mRNA, there were about 4,000 molecules of protein. The relative abundance of proteins was measured in glucose and ethanol media. Protein turnover was examined and found to be insignificant for abundant proteins. Some phosphoproteins were identified. The behavior of proteins in differential centrifugation experiments was examined. Such experiments with 2D gels can give a global view of the yeast proteome.'^XCold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA. futcher@cshl.orgLFFutcher, B. Latter, G. I. Monardo, P. McLaughlin, C. S. Garrels, J. I. 0270-7306 Journal Article Mol Cell Biol|Codon Electrophoresis, Gel, Two-Dimensional Fungal Proteins/*isolation & purification *Gene Expression Profiling Genes, Fungal Genetic Code Image Processing, Computer-Assisted RNA, Fungal/*isolation & purification RNA, Messenger/*isolation & purification Saccharomyces cerevisiae/chemistry/*genetics Support, U.S. Gov't, Non-P.H.S. Support, U.S. Gov't, P.H.S. Translation, Geneticlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10523624  30 X12682375318r 2003 Apr 15Revisiting the codon adaptation index from a whole-genome perspective: analyzing the relationship between gene expression and codon occurrence in yeast using a variety of modelss2242-51e*$Highly expressed genes in many bacteria and small eukaryotes often have a strong compositional bias, in terms of codon usage. Two widely used numerical indices, the codon adaptation index (CAI) and the codon usage, use this bias to predict the expression level of genes. When these indices were first introduced, they were based on fairly simple assumptions about which genes are most highly expressed: the CAI was originally based on the codon composition of a set of only 24 highly expressed genes, and the codon usage on assumptions about which functional classes of genes are highly expressed in fast-growing bacteria. Given the recent advent of genome-wide expression data, we should be able to improve on these assumptions. Here, we measure, in yeast, the degree to which consideration of the current genome-wide expression data sets improves the performance of both numerical indices. Indeed, we find that by changing the parameterization of each model its correlation with actual expression levels can be somewhat improved, although both indices are fairly insensitive to the exact way they are parameterized. This insensitivity indicates a consistent codon bias amongst highly expressed genes. We also attempt direct linear regression of codon composition against genome-wide expression levels (and protein abundance data). This has some similarity with the CAI formalism and yields an alternative model for the prediction of expression levels based on the coding sequences of genes. More information is available at http://bioinfo.mbb.yale.edu/expression/codons.'Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, CT 06520, USA.0)Jansen, R. Bussemaker, H. J. Gerstein, M. 1362-4962 Journal ArticleNucleic Acids ResCodon/*genetics Computational Biology/methods Gene Expression Profiling Gene Expression Regulation, Fungal *Genome Genome, Fungal *Models, Genetic Saccharomyces cerevisiae/genetics Support, U.S. Gov't, P.H.S.lehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=126823751093965:263m 1975Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. A novel approach to testing for induced point mutations in mammals 231-43$The protein-mapping method which combines isoelectric focusing in acrylamide gel and gel electrophoresis was previously used mainly for the separation of plant proteins and human serum proteins. We investigated with this technique soluble proteins of mouse tissues (whole embryos, the liver of fetal and adult mice, kidneys) and the proteins of mouse serum. The technique was tested under a number of different conditions to find those best for our purpose; they may represent some general improvements in the method. The protein patterns show high resolution and excellent reproducibility. About 275 spots were found for fetal liver, about 230 for whole embryos (day 14 p.c.) and about 100 for serum. The fact that a high number of protein spots can be evaluated by a single and comparatively simple experiment suggests that this method may be useful as an assay system for induced point mutations. The protein patterns demonstrated are compared and discgs of dominant lethal examinations after acute and subacute application of these three substances. Klose, J. 0018-7348 Journal Article Humangenetik Animal Blood Protein Electrophoresis Blood Proteins/analysis *Electrophoresis, Polyacrylamide Gel Embryo/analysis Fetus/analysis *Genetic Techniques *Isoelectric Focusing Liver/analysis Mice Mice, Inbred Strains Mutagens *Mutation Proteins/*analysis Teratogensjdhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1093965ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11914276,Kumar, A. Agarwal, S. Heyman, J. A. Matson, S. Heidtman, M. Piccirillo, S. Umansky, L. Drawid, A. Jansen, R. Liu, Y. Cheung, K. H. Miller, P. Gerstein, M. Roeder, G. S. Snyder, M.m4.Subcellular localization of the yeast proteome0*Algorithms Cell Nucleus/metabolism Chromosomes/metabolism Cytoplasm/metabolism Databases Epitopes *Genome, Fungal Microscopy, Fluorescence Mitochondria/metabolism Models, Genetic Mutagenesis Phenotype Saccharomyces cerevisiae/*metabolism Software Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S.Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome- wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.'xqDepartment of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06520, USA.11914276 Genes Dev 2002166707-19. @ 4 236308 25010 1975 May 25B;High resolution two-dimensional electrophoresis of proteinst4007-21mVOA technique has been developed for the separation of proteins by two-dimensional polyacrylamide gel electrophoresis. Due to its resolution and sensitivity, this technique is a powerful tool for the analysis and detection of proteins from complex biological sources. Proteins are separated according to isoelectric point by isoelectric focusing in the first dimension, and according to molecular weight by sodium dodecyl sulfate electrophoresis in the second dimension. Since these two parameters are unrelated, it is possible to obtain an almost uniform distribution of protein spots across a two-diminsional gel. This technique has resolved 1100 different components from Escherichia coli and should be capable of resolving a maximum of 5000 proteins. A protein containing as little as one disintegration per min of either 14C or 35S can be detected by autoradiography. A protein which constitutes 10 minus 4 to 10 minus 5% of the total protein can be detected and quantified by autoradiography. The reproducibility of the separation is sufficient to permit each spot on one separation to be matched with a spot on a different separation. This technique provides a method for estimation (at the described sensitivities) of the number of proteins made by any biological system. This system can resolve proteins differing in a single charge and consequently can be used in the analysis of in vivo modifications resulting in a change in charge. Proteins whose charge is changed by missense mutations can be identified. A detailed description of the methods as well as the characteristics of this system are presented.O'Farrell, P. H. 0021-9258 Journal Article J Biol ChemAutoradiography Bacterial Proteins/*isolation & purification Electrophoresis, Polyacrylamide Gel/methods Escherichia coli/analysis Hydrogen-Ion Concentration Isoelectric Focusing Molecular Weight Sodium Dodecyl Sulfate Support, U.S. Gov't, Non-P.H.S.jchttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=2363081209613911 2002 JanGenome-wide study of gene copy numbers, transcripts, and protein levels in pairs of non-invasive and invasive human transitional cell carcinomas 37-45Gain and loss of chromosomal material is characteristic of bladder cancer, as well as malignant transformation in general. The consequences of these changes at both the transcription and translation levels is at present unknown partly because of technical limitations. Here we have attempted to address this question in pairs of non-invasive and invasive human bladder tumors using a combination of technology that included comparative genomic hybridization, high density oligonucleotide array-based monitoring of transcript levels (5600 genes), and high resolution two-dimensional gel electrophoresis. The results showed that there is a gene dosage effect that in some cases superimposes on other regulatory mechanisms. This effect depended (p < 0.015) on the magnitude of the comparative genomic hybridization change. In general (18 of 23 cases), chromosomal areas with more than 2-fold gain of DNA showed a corresponding increase in mRNA transcripts. Areas with loss of DNA, on the other hand, showed either reduced or unaltered transcript levels. Because most proteins resolved by two-dimensional gels are unknown it was only possible to compare mRNA and protein alterations in relatively few cases of well focused abundant proteins. With few exceptions we found a good correlation (p < 0.005) between transcript alterations and protein levels. The implications, as well as limitations, of the approach are discussed.d'Department of Clinical Biochemistry, Molecular Diagnostic Laboratory, Aarhus University Hospital, Skejby, DK-8200 Aarhus N, Denmark. orntoft@kba.sks.au.dkF@Orntoft, T. F. Thykjaer, T. Waldman, F. M. Wolf, H. Celis, J. E. 1535-9476 Journal ArticlecMol Cell Proteomicsr Bladder/metabolism/pathology Bladder Neoplasms/*genetics/metabolism/pathology Carcinoma, Transitional Cell/*genetics/metabolism/pathology Chromosome Aberrations Comparative Study Disease Progression Down-Regulation *Gene Dosage Gene Expression Profiling Human Loss of Heterozygosity Microsatellite Repeats Neoplasm Invasiveness Neoplasm Proteins/*genetics/metabolism Nucleic Acid Hybridization Phenotype RNA, Messenger/metabolism Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Transcription, Genetic Up-Regulationlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12096139 ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=12643542.HAPeng, J. Elias, J. E. Thoreen, C. C. Licklider, L. J. Gygi, S. P.oEvaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteomezCations Chromatography, High Pressure Liquid Chromatography, Ion Exchange/methods Chromatography, Liquid/*methods Databases False Positive Reactions Fungal Proteins/analysis/*chemistry Ions Nanotechnology Peptides/chemistry *Proteome Saccharomyces cerevisiae/*chemistry/metabolism Spectrum Analysis, Mass/*methods Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Time FactorsHighly complex protein mixtures can be directly analyzed after proteolysis by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). In this paper, we have utilized the combination of strong cation exchange (SCX) and reversed-phase (RP) chromatography to achieve two-dimensional separation prior to MS/MS. One milligram of whole yeast protein was proteolyzed and separated by SCX chromatography (2.1 mm i.d.) with fraction collection every minute during an 80-min elution. Eighty fractions were reduced in volume and then re-injected via an autosampler in an automated fashion using a vented-column (100 microm i.d.) approach for RP-LC-MS/MS analysis. More than 162,000 MS/MS spectra were collected with 26,815 matched to yeast peptides (7,537 unique peptides). A total of 1,504 yeast proteins were unambiguously identified in this single analysis. We present a comparison of this experiment with a previously published yeast proteome analysis by Yates and colleagues (Washburn, M. P.; Wolters, D.; Yates, J. R., III. Nat. Biotechnol. 2001, 19, 242-7). In addition, we report an in-depth analysis of the false-positive rates associated with peptide identification using the Sequest algorithm and a reversed yeast protein database. New criteria are proposed to decrease false- positives to less than 1% and to greatly reduce the need for manual interpretation while permitting more proteins to be identified.'Department of Cell Biology, and Taplin Biological Mass Spectrometry Facility, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, USA.12643542J Proteome Res 20032 1r 43-50.%5R- 11917093822l 2002 Apru\VThe ubiquitin-proteasome proteolytic pathway: destruction for the sake of construction373-428ZTBetween the 1960s and 1980s, most life scientists focused their attention on studies of nucleic acids and the translation of the coded information. Protein degradation was a neglected area, considered to be a nonspecific, dead-end process. Although it was known that proteins do turn over, the large extent and high specificity of the process, whereby distinct proteins have half-lives that range from a few minutes to several days, was not appreciated. The discovery of the lysosome by Christian de Duve did not significantly change this view, because it became clear that this organelle is involved mostly in the degradation of extracellular proteins, and their proteases cannot be substrate specific. The discovery of the complex cascade of the ubiquitin pathway revolutionized the field. It is clear now that degradation of cellular proteins is a highly complex, temporally controlled, and tightly regulated process that plays major roles in a variety of basic pathways during cell life and death as well as in health and disease. With the multitude of substrates targeted and the myriad processes involved, it is not surprising that aberrations in the pathway are implicated in the pathogenesis of many diseases, certain malignancies, and neurodegeneration among them. Degradation of a protein via the ubiquitin/proteasome pathway involves two successive steps: 1) conjugation of multiple ubiquitin moieties to the substrate and 2) degradation of the tagged protein by the downstream 26S proteasome complex. Despite intensive research, the unknown still exceeds what we currently know on intracellular protein degradation, and major key questions have remained unsolved. Among these are the modes of specific and timed recognition for the degradation of the many substrates and the mechanisms that underlie aberrations in the system that lead to pathogenesis of diseases.'ztFaculty of Biology and the Institute for Catalysis Science and Technology, Haifa, Israel. glickman@tx.technion.ac.il&Glickman, M. H. Ciechanover, A.810031-9333 Journal Article Review Review, Academic Physiol RevAnimal Cysteine Endopeptidases/*metabolism Human Multienzyme Complexes/*metabolism Proteins/metabolism Support, Non-U.S. Gov't Support, U.S. Gov't, Non-P.H.S. Ubiquitin/*metabolismlehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1191709310521349 286 5439 1999 Oct 15lfMolecular classification of cancer: class discovery and class prediction by gene expression monitoring 531-7Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.'Whitehead Institute/Massachusetts Institute of Technology Center for Genome Research, Cambridge, MA 02139, USA. golub@genome.wi.mit.eduGolub, T. R. Slonim, D. K. Tamayo, P. Huard, C. Gaasenbeek, M. Mesirov, J. P. Coller, H. Loh, M. L. Downing, J. R. Caligiuri, M. A. Bloomfield, C. D. Lander, E. S. 0036-8075 Journal ArticleScience$Acute Disease Antineoplastic Combined Chemotherapy Protocols/therapeutic use Cell Adhesion/genetics Cell Cycle/genetics *Gene Expression Profiling Homeodomain Proteins/genetics Human Leukemia, Lymphocytic, Acute/*classification/drug therapy/*genetics Leukemia, Myeloid/*classification/drug therapy/*genetics Neoplasm Proteins/genetics Neoplasms/classification/genetics Oligonucleotide Array Sequence Analysis Oncogenes Predictive Value of Tests Reproducibility of Results Support, Non-U.S. Gov't Support, U.S. Gov't, P.H.S. Treatment Outcomelehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10521349ZShttp://www.ncbi.nlm.nih.gov/htbin-post/Entrez/query?db=m&form=6&dopt=r&uid=11544189eD>Greenbaum, D. Luscombe, N. M. Jansen, R. Qian, J. Gerstein, M.f`Interrelating different types of genomic data, from proteome to secretome: 'oming in on functionBacillus subtilis/*genetics/physiology Comparative Study Computational Biology *Genome, Bacterial Proteome/genetics/*physiology/secretion Support, Non-U.S. Gov'tcWith the completion of genome sequences, the current challenge for biology is to determine the functions of all gene products and to understand how they contribute in making an organism viable. For the first time, biological systems can be viewed as being finite, with a limited set of molecular parts. However, the full range of biological processes controlled by these parts is extremely complex. Thus, a key approach in genomic research is to divide the cellular contents into distinct sub-populations, which are often given an "-omic" term. For example, the proteome is the full complement of proteins encoded by the genome, and the secretome is the part of it secreted from the cell. Carrying this further, we suggest the term "translatome" to describe the members of the proteome weighted by their abundance, and the "functome" to describe all the functions carried out by these. Once the individual sub-populations are defined and analyzed, we can then try to reconstruct the full organism by interrelating them, eventually allowing for a full and dynamic view of the cell. All this is, of course, made possible because of the increasing amount of large-scale data resulting from functional genomics experiments. However, there are still many difficulties resulting from the noisiness and complexity of the information. To some degree, these can be overcome through averaging with broad proteomic categories such as those implicit in functional and structural classifications. For illustration, we discuss one example in detail, interrelating transcript and cellular protein populations (transcriptome and translatome). Further information is available at http://bioinfo.mbb.yale.edu/what-is-it.'XQDepartment of Genetics, Yale University, New Haven, Connecticut 06520- 8114, USA.11544189 Genome Res 20011191463-8.