84491 692.74

letters to nature 9. Gatz, C. Chemical control of gene expression. Annu. Rev. Plant Physiol. Plant Mol. Biol. 48, 89–108 high-affinity epitope tag so that the resulting fusion proteins are expressed under the control of their natural promoters. The fusion 10. Gatz, C., Frohberg, C. & Wendenburg, R. Stringent repression and homogeneous de-repression by tetracycline of a modified CaMV 35S promoter in intact transgenic tobacco plants. Plant J. 2, 397–404 library allows the immunodetection and immunopurification of the entire yeast proteome using a single antibody, enabling the 11. Raschke, K. Stomatal action. Annu. Rev. Plant Physiol. Plant Mol. Biol. 26, 309–340 (1975).
development of a range of high-throughput functional assays. To 12. Mansfield, T. A., Hetherington, A. M. & Atkinson, C. J. Some current aspects of stomatal physiology.
allow for the facile construction of epitope-tagged yeast fusion Annu. Rev. Plant Physiol. Plant Mol. Biol. 41, 55–75 (1990).
13. Hedrich, R. et al. Changes in apoplastic pH and membrane potential in leaves in relation to stomatal libraries, we synthesized 6,234 pairs of ORF-specific oligonucleotide responses to CO2, malate, abscisic acid or interruption of water supply. Planta 213, 594–601 (2001).
primers. Each of the oligonucleotide pairs have shared 3 0 ends that 14. Hedrich, R. et al. Malate-sensitive anion channels enable guard-cells to sense changes in the ambient allow for polymerase chain reaction (PCR) amplification of a CO2 concentration. Plant J. 6, 741–748 (1994).
common insertion cassette, as well as gene-specific 5 0 ends that 15. Otto, B. & Kaldenhoff, R. Cell-specific expression of the mercury-insensitive plasma-membrane aquaporin NtAQP1 from Nicotiana tabacum. Planta 211, 167–172 (2000).
allow for the precise introduction, through homologous recombi- 16. Raschke, K. Saturation kinetics of velocity of stomatal closing in response to CO2. Plant Physiol. 49, nation, of the amplified insertion cassettes as a perfect in-frame 229–234 (1972).
fusion at the carboxy-terminal end of the coding region of each 17. Gallois, P. & Marinho, P. Leaf disk transformation using Agrobacterium tumefaciens—expression of heterologous genes in tobacco. Methods Mol. Biol. 49, 39–48 (1995).
gene4 (Fig. 1a). The insertion cassettes contained the coding region 18. Kaiser, W. M. Correlation between changes in photosynthetic activity and changes in total protoplast volume in leaf tissue from hygro-, meso- and xerophytes under osmotic stress. Planta 154, 538–545(1982).
Acknowledgements We thank W. M. Kaiser, M. Eckert and A. Schubert for discussion and help inexperimental design.
Competing interests statement The authors declare that they have no competing financialinterests.
Correspondence and requests for materials should be addressed to R.K.
([email protected]).
Global analysis of proteinexpression in yeast Sina Ghaemmaghami1,2, Won-Ki Huh1,3, Kiowa Bower1,2,Russell W. Howson1,3, Archana Belle1,3, Noah Dephoure1,3,Erin K. O'Shea1,3 & Jonathan S. Weissman1,2 1Howard Hughes Medical Institute, 2Departments of Cellular & MolecularPharmacology and 3Biochemistry & Biophysics, University of California–SanFrancisco, San Francisco, California 94143-2240, USA.
The availability of complete genomic sequences and technologiesthat allow comprehensive analysis of global expression profiles ofmessenger RNA1–3 have greatly expanded our ability to monitorthe internal state of a cell. Yet biological systems ultimately needto be explained in terms of the activity, regulation and modifi-cation of proteins—and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy forsuch information. To facilitate global protein analyses, we havecreated a Saccharomyces cerevisiae fusion library where eachopen reading frame is tagged with a high-affinity epitope andexpressed from its natural chromosomal location. Throughimmunodetection of the common tag, we obtain a census ofproteins expressed during log-phase growth and measurementsof their absolute levels. We find that about 80% of the proteome is Figure 1 Tagging and detection of the yeast proteome. a, Schematic diagram of tagging expressed during normal growth conditions, and, using strategy. b, Detection of tagged proteins. Extracts containing TAP-fusion proteins were additional sequence information, we systematically identify mis- prepared and analysed by western blots using an anti-CBP antibody (see Supplementary annotated genes. The abundance of proteins ranges from fewer Information). Immunodetection of an endogenous protein (hexokinase) provided a loading than 50 to more than 106 molecules per cell. Many of these control. Serial dilutions of TAP-tagged proteins provided an internal abundance molecules, including essential proteins and most transcription standard (right). c, Monitoring dynamic protein levels for two cell-cycle regulated proteins.
factors, are present at levels that are not readily detectable by Strains expressing Clb2– and Sic1–TAP fusions were grown to log-phase and arrested in other proteomic techniques nor predictable by mRNA levels or G1 by a-factor treatment. The cell cycle was resumed by a-factor removal, aliquots were codon bias measurements.
taken at 7-min intervals and levels of the tagged proteins were quantified using The diverse chemical nature of proteins makes the development western blot analysis (filled circles). For comparison, we include mRNA levels of the two of globally applicable proteomic assays very challenging. We have proteins obtained by an earlier microarray analysis29 (open circles) as well as changes in overcome this obstacle in the yeast S. cerevisiae by individually untagged Clb2 protein levels (open squares) obtained using an antibody against the tagging each of its annotated open reading frames (ORFs) with a endogenous protein in an untagged strain.
NATURE VOL 425 16 OCTOBER 2003 www.nature.com/nature 2003 Nature Publishing Group
letters to nature for a modified version of the tandem affinity purification (TAP) short ORFs11,12. For the original annotation of the yeast genome, an tag5,6, which consists of a calmodulin binding peptide, a TEV arbitrary cut-off of 100 codons was used to qualify ORFs as cleavage site and two IgG binding domains of Staphylococcus aureus potential genes, leading to an anomalous peak centred between protein A, as well as a selectable marker (see Supplementary 100 and 150 amino acids in the sequence length distribution (Fig. 2c, Information). In total, we obtained successful integrants for 98% black)13 of the genome that is not present in the length distribution of all ORFs annotated in the Saccharomyces genome database (as of of the subset of named genes (Fig. 2c, green). Importantly, although we tagged and analysed all potential ORFs, the length distribution of including 93% of all essential ORFs7 in haploid yeast.
the subset of observed proteins did not contain the above artefactual Western blot analysis, using an antibody that specifically recog- peak (Fig. 2c, red), indicating that our analysis of expressed nizes the TAP tag, demonstrated that the large majority (.95%) of genes has a very low false-positive rate (see also Supplementary detected fusion proteins migrate predominantly as a single band of the approximate expected molecular mass (Fig. 1b). Furthermore, A number of bioinformatics approaches, including recent ana- analysis of two known cell-cycle-regulated proteins, Clb2 and lyses of the genomic sequences of a number of related yeast species, Sic18,9, indicated that the tagging does not hinder their regulated have been used to distinguish between the real and misannoted proteolysis by the ubiquitin/proteasome degradation system and ORFs14–17, although the true number and identity of the spurious that the TAP tag itself is rapidly destroyed during the targeted ORFs remain unclear. Our results offer experimental verification for degradation of the fusion protein (Fig. 1c). These and other data6 a large number of hypothetical genes (we observed 1,018 protein suggest that the function, regulation and stability of most, but not products belonging to functionally uncharacterized ORFs), and all (see Supplementary Information), of the proteome is uncom- yields a large, experimentally validated set to evaluate the success of promised by the fused tag.
computational methods for identifying falsely annotated genes. By We observed a protein product for 4,251 of the TAP-tagged ORFs combining a novel metric—termed the codon enrichment corre- by comprehensive western blot analysis. This set of proteins shows lation (CEC), which evaluates the patterns of codon usage in excellent overlap (.90%) with the set of green fluorescent protein potential ORFs—with our protein expression data, we identified a (GFP) fusion proteins detected by fluorescence microscopy10(Fig. 2a), and together indicate that at least 4,517 proteins areexpressed during log-phase growth in rich media. We detect 79% ofall essential proteins and 83% of gene products corresponding toORFs with assigned gene names. By contrast, only 73% of allannotated ORFs expressed a detectable protein product (Fig. 2b).
This discrepancy largely results from the presence of spurious ORFsin the annotated yeast genome database stemming from well-knowndifficulties in distinguishing actual coding regions from fortuitous Figure 2 Analysis of proteins expressed during log-phase growth. a, Venn diagramcomparing sets of proteins detected by western blot of TAP-tagged strains (red),fluorescence microscopy of GFP-tagged strains10 (green) and both (yellow). b, Fraction of Figure 3 Functional categorization of proteins expressed during log-phase growth in rich the indicated set of ORFs observed in either the TAP-tagged or GFP-tagged libraries.
medium. 33 modules of co-expressed, functionally related genes were identified by global c, Size distribution of ORFs, binned by length using 50-codon intervals. The number of analysis of ,1,000 microarray data sets18,19. Plotted is the fraction of the ORFs in each ORFs per bin is plotted for the indicated sets of ORFs. d, Codon enrichment correlation module that produced a detectable protein product by TAP western analysis or GFP (CEC) distribution of small ORFs. CECs were calculated for ORFs with lengths from 100 to microscopy10 alone (grey), or both methods (black). Where possible, modules are 150 codons. ORFs were binned according to CEC values using intervals of 0.05 units. The annotated by function. The gene composition of the modules can be obtained at number of ORFs in each bin is plotted for the indicated sets of ORFs. Note, observed http://barkai-serv.weizmann.ac.il/modules/ using a cut-off threshold of 4.0. ‘Not proteins have a positive CEC value characteristic of named genes, whereas unobserved annotated1–6' correspond to modules containing YHR025W, YER103W, YPL016W, ORFs show a major peak centred near a zero value expected for random sequences.
YPL180W, YER039C-A and YCL076W, respectively.
2003 Nature Publishing Group
NATURE VOL 425 16 OCTOBER 2003 www.nature.com/nature letters to nature set of 525 potentially spurious ORFs (listed in Supplementary majority of the protein products (Fig. 3). By contrast, modules Information) that have codon compositions not characteristic composed of genes involved in functions required only under of genuine genes and did not yield detectable protein products specialized conditions (for example, meiosis/sporulation and (Fig. 2d, Methods). On the basis of the CEC distribution of genuine alternative nitrogen utilization) generally produced few detectable ORFs, we estimate that this list is contaminated by ,20 genuine coding sequences. Our proteomics-based approach complements We took advantage of the fact that all gene products were detected the comparative genomics strategy for identifying spurious ORFs16.
using the same epitope/antibody interaction to measure the abso- The large majority (all but seven) of the 496 spurious ORFs lute abundance of each of the tagged proteins using quantitative suggested by Kellis et al.16 were not observed in our TAP and GFP western blot analyses. This effort was facilitated by the inclusion of studies. The set of spurious ORFs that we identified overlaps well internal standards in each gel (Fig. 1b). We find that the levels of with those detected by this cross-species genome study (381 genes different proteins show an enormous dynamic range, varying from were identified as spurious by both studies), and expands the set by fewer than 50 to more than 106 molecules per cell (Fig. 4a, b). The 144 ORFs. Among these 144 ORFs are a large number of sequences results show that previous efforts to quantify protein levels using that overlap with real genes on the opposite strand, and therefore are two-dimensional gel electrophoresis or mass spectrometry were difficult to distinguish through homology analysis.
strongly biased towards the detection of abundant proteins (Fig. 4a, After discounting the spurious ORFs, there remain ,1,000 see also Supplementary Fig. S3)20–23. For example, a recent study genuine coding regions that did not produce a detectable protein using mass spectrometry and isotope labelling succeeded in quan- product. To determine if the unobserved proteins belong to classes titatively monitoring changes in the abundance of 688 yeast pro- of genes that are not transcribed during normal log-phase growth teins22. For the most abundant proteins (.50,000 molecules per conditions, we compared our results with global transcriptional cell) the coverage was excellent (,60%), whereas for the 75% of the array data. A recent analysis of mRNA expression profiles from proteome that is present at fewer than 5,000 molecules per cell, only ,1,000 published microarray experiments allowed for the identi- 8% of the proteins were observed. Another mass-spectrometry fication of 33 ‘modules' of transcriptionally co-regulated genes18,19.
effort that focused on detecting, without directly quantifying, the For modules that are expressed in log phase (for example, those complement of proteins in log-phase yeast23 observed a larger coding for housekeeping functions, such as ergosterol and amino- number (1,484) of proteins, although it was also biased towards acid biosynthesis and cell cycle), we were able to detect the large abundant proteins (90% of the proteome present at .50,000 Figure 4 Abundance distribution of the yeast proteome. a, Distribution of yeast proteins ORFs are sorted according to mRNA levels, and binned into successive groups with observed by TAP/western-blot (red), liquid chromatography/mass spectrometry cut-offs of 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 10, 20, 50 and 100 molecules per multidimensional protein identification technology (LC/MS MudPIT) analysis focusing on cell. For each bin, the mean protein abundance is plotted against the mean mRNA level.
comprehensive detection23 (purple) and quantitative analysis22 (green), and combined Bottom plot, protein versus mRNA relationship for a subset of essential soluble proteins results from 2 two-dimensional (2D) gel analyses20,21 (blue). The bins are log2 increments (see Supplementary Information). Errors represent the standard deviation of three with upper boundaries indicated. b, Normalized abundance distribution of observed measurements. d, Relationship between codon adaptation index (CAI) and protein levels.
proteins (red), essential proteins (purple) and transcription factors (dashed line). c, The Individual and averaged protein values are plotted against CAI27. In the middle plot, the relationship between steady-state mRNA and protein levels. Top plot, abundance of each values are binned using CAI cut-offs of 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.50, protein is plotted against its mRNA level determined by microarray analysis25. Middle plot, 0.60, 0.70, 0.80 and 1.0.
NATURE VOL 425 16 OCTOBER 2003 www.nature.com/nature 2003 Nature Publishing Group
letters to nature molecules per cell was detected, whereas only 19% of the proteome present at fewer than 5,000 molecules per cell was observed). Our Quantification of protein levels validated list of expressed proteins will help evaluate future Cultures (1.7 ml) of tagged strains were grown in 96-well format to log phase, and total cell advances in mass spectrometry approaches24.
extracts were examined by SDS–polyacrylamide gel electrophoresis (PAGE)/western blot Overall, we observe a significant relationship between mRNA analysis as described in Supplementary Information. The bands corresponding to thetagged proteins were detected using chemiluminescence and a CCD camera (FluorChem levels, as measured by an earlier microarray analysis of log-phase 8800, Alpha Innotech). To control for variation in extraction and loading, each blot was yeast25, and protein levels (Spearman rank correlation coefficient probed with an antibody against endogenous hexokinase in addition to the TAP-specific 0.57). Very abundant mRNAs generally encode for abundant anti-CBP antibody. Extracts whose hexokinase signals varied by greater than a factor of ,2 proteins, and the average protein per mRNA ratio remains remark- from the expected value were re-grown and re-analysed. A standard containing a mixture ably constant throughout the full range of mRNA abundances of three TAP-tagged proteins (Pgk1, Cdc19, Rpl1A) were included in each gel at one-, ten-and 100-fold dilutions. Proteins whose chemiluminescence signals were approaching (Fig. 4c, middle, and Supplementary Fig. S4). The average protein saturation were re-examined by performing the western blot analysis using a tenfold per mRNA ratio is 4,800 using this measure of mRNA levels, and is dilution of the extract and/or lower exposure times during detection. Before the 4,200 using an alternative mRNA abundance measurement based quantitative SDS–PAGE/western blot analysis, strains were ordered on the basis of on a microarray analysis comparing mRNA to genomic DNA estimates of TAP abundance from a preliminary dot-blot analysis. In order to provide astandard for the conversion of western signals to absolute protein levels, a TAP-tagged levels26 (Supplementary Fig. S4). However, individual genes with protein (Escherichia coli initiation factor A, INFA) was overexpressed in E. coli and purified equivalent mRNA levels can result in large differences in protein to homogeneity. Yeast extracts containing serial dilutions of INFA ranging from abundances (Fig. 4c, top). To assess if this variability was primarily 500 attomoles (which was the limit of detection, see Supplementary Fig. S1) to caused by protein measurement error and/or disruption of protein 25 picomoles were run on a gel along with extracts from 25 different yeast TAP-taggedstrains representing the full range of observed protein signals (a second TAP-tagged function by the TAP tag, we performed further triplicate measure- protein (initiation factor B) was also analysed to ensure that the observed TAP signal was ments of protein abundances on a subset of 206 essential, soluble not influenced by the fusion protein). Comparison of the signals generated by these 25 proteins (See Supplementary Information); the selected strains proteins to the known standards allowed the creation of a conversion factor between the grew robustly, showing that the tagged proteins were functional.
observed western blot signals and absolute protein levels. Based on the number of cells(,1 £ 107) used for the SDS–PAGE/western blot analysis, the protein levels were then This subset also shows a high degree of protein to mRNA variability converted to measurements of protein molecules per cell.
relative to our measurement error, indicating that the large differ- In order to assess the error in our quantification, a set of 33 proteins with a range of ences in individual protein to mRNA ratios are not due primarily to abundances were grown in duplicate cultures, separately extracted and analysed on noise in the protein abundance measurements or disruption of the different gels. The replicate signals showed a linear correlation coefficient of R ¼ 0.94,with the pairs of proteins having a median variation of a factor of 2.0. This error analysis protein by the tag (Fig. 4c, bottom). However, the correlation does not account for potential alterations in the endogenous levels of the proteins caused between mRNA and protein levels is somewhat greater by the fused tag, which may be particularly disruptive for small proteins (Supplementary 0.66), suggesting that the disruption of protein by the TAP Information) or difficulty in analysing some polytopic membrane proteins by SDS–PAGE.
tag or difficulty in analysing membrane proteins may have con- For dynamic measurements of protein levels (for example, the cell-cycle dependence of tributed to some of the variation. We also observed a significant Clb2 and Sic1 levels shown in Fig. 1c or triplicate measurements in Fig. 4c, d) muchsmaller errors can be obtained by running the samples being compared side-by-side on a relationship (r ¼ 0.55) between protein abundance and codon single gel. For quantification in the triplicate measurements shown at the bottom of usage as measured by the codon adaptation index (CAI)27. Protein Fig. 4c, d, serial dilutions of extracts containing purified TAP-tagged INFA were run on abundances drop rapidly for genes with CAI values ,0.2, explaining the difficulty that previous proteomic approaches have typically hadin detecting these proteins22. But on an individual gene basis, there CEC and identification of spurious ORFs is great variability that is also present in the subset of more carefully Codon usage in genuine protein-coding regions deviates systematically from randomlygenerated ORFs, owing to both preferences in amino-acid composition and biases in the measured essential, soluble proteins (Fig. 4d).
usage of synonymous codons28, and the codon enrichment correlation (CEC) provides a A number of observations support the argument that the full measure of this deviation. To calculate CEC values, we first determined the relative range of abundances detected in this study, including the very low prevalence of the 61 amino acids specifying codons in the 3,753 named ORFs expression levels, represent functionally significant amounts of the (Supplementary Table S1). The codon usage expected in random sequences was thencalculated based on the approximate prevalence of 30% T, 30% A, 20% C and 20% G proteins. First, the analysis of transcription modules (Fig. 3) nucleotides in the yeast genomes. The enrichment of each codon for the positive set is indicates that within groups of genes that are turned off during given by dividing its prevalence among the named ORFs by its expected prevalence in log-phase growth the corresponding proteins are not observed, even random sequences (Supplementary Table S1). Codon enrichments were similarly at residual levels. Second, the abundance distribution profile of the calculated for each test ORF. The CEC is the linear correlation coefficient (r) between the entire yeast proteome (Fig. 4b, red) is similar to the profile of the codon enrichments of the test ORF and the positive set (for examples, see SupplementaryFig. S2). ORFs were designated as spurious if they failed to be detected by both the TAP and portion of the proteome whose function is required for survival GFP analyses, and they had CEC values below a cut-off of 0.25, 0.16, 0.07 or 0.06 for ORFs under standard growth conditions (Fig. 4b, purple). This suggests of size 0–150, 151–200, 201–250 and 251–300 codons, respectively. For ORFs .150 amino that, in general, functional proteins are not under-represented acids, these values were chosen so that ,4.5% of the ORFs falling below these cut-offs that amongst low-abundance proteins. Third, there are entire classes are not detected by the GFP or TAP analyses are genuine coding sequences. The number ofgenuine coding sequences contaminating our list of spurious ORFs was estimated for each of functionally important proteins, such as transcription factors size range and CEC cut-off by the following equation: N NobsR, where Nobs is the (Fig. 4b, line) and cell-cycle proteins (Supplementary Fig. S5), that number of detected ORFs that have a CEC value below the cut-off, and R is the ratio of are present at very low expression levels. Thus the low-abundance unobserved to observed ORFs, as determined by the probability of detecting named ORFs proteins detected and quantified in the present study represent a for the given size range.
large and functionally important portion of the yeast proteome that Received 28 July; accepted 28 August 2003; doi:10.1038/nature02046.
is almost entirely invisible to systematic quantitative analysis by 1. Lashkari, D. A. et al. Yeast microarrays for genome wide parallel genetic and gene expression analysis.
other proteomic methods.
Proc. Natl Acad. Sci. USA 94, 13057–13062 (1997).
The TAP-tagged library now makes it feasible to monitor dyna- 2. Collins, F. S., Green, E. D., Guttmacher, A. E. & Guyer, M. S. A vision for the future of genomics research. Nature 422, 835–847 (2003).
mically the abundance of the yeast proteome through basic cellular 3. Lockhart, D. J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays.
events such as the cell cycle and meiosis, and will allow the Nature Biotechnol. 14, 1675–1680 (1996).
determination of protein lifetimes. In addition, important subsets 4. Longtine, M. S. et al. Additional modules for versatile and economical PCR-based gene deletion and modification in Saccharomyces cerevisiae. Yeast 14, 953–961 (1998).
of proteins, such as transcription factors, can be readily studied 5. Rigaut, G. et al. A generic protein purification method for protein complex characterization and under a more comprehensive set of conditions. This protein-based proteome exploration. Nature Biotechnol. 17, 1030–1032 (1999).
data will provide critical information for efforts to understand the 6. Gavin, A. C. et al. Functional organization of the yeast proteome by systematic analysis of protein logic of cellular regulatory circuits, and, by comparison to mRNA complexes. Nature 415, 141–147 (2002).
7. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and levels, the data will give insight into the nature and extent of post- parallel analysis. Science 285, 901–906 (1999).
8. Schwob, E., Bohm, T., Mendenhall, M. D. & Nasmyth, K. The B-type cyclin kinase inhibitor p40SIC1 2003 Nature Publishing Group
NATURE VOL 425 16 OCTOBER 2003 www.nature.com/nature letters to nature controls the G1 to S transition in S. cerevisiae. Cell 79, 233–244 (1994).
Acknowledgements We thank A. Carroll and F. Sanchez for technical assistance; J. Falvo, L. Gerke, 9. Grandin, N. & Reed, S. I. Differential function and expression of Saccharomyces cerevisiae B-type J. Newman and members of the Weissman and O'Shea laboratories for discussions; and N. Barkai cyclins in mitosis and meiosis. Mol. Cell. Biol. 13, 2113–2125 (1993).
for providing data before publication. This work was supported by the Howard Hughes Medical 10. Huh, W.-K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).
Institute and the David and Lucile Packard Foundation. S.G. is a recipient of the Ruth 11. Harrison, P. M., Kumar, A., Lang, N., Snyder, M. & Gerstein, M. A question of size: The eukaryotic L. Kirschstein National Research Service Award.
proteome and the problems in defining it. Nucleic Acids Res. 30, 1083–1090 (2002).
12. Goffeau, A. Four years of post-genomic life with 6,000 yeast genes. FEBS Lett. 480, 37–41 (2000).
Competing interests statement The authors declare that they have no competing financialinterests.
13. Das, S. et al. Biology's new Rosetta stone. Nature 385, 29–30 (1997).
14. Kowalczuk, M., Mackiewicz, P., Gierlik, A., Dudek, M. R. & Cebrat, S. Total number of coding open Correspondence and requests for materials should be addressed to J.S.W. ([email protected]).
reading frames in the yeast genome. Yeast 15, 1031–1034 (1999).
15. Zhang, C. T. & Wang, J. Recognition of protein coding genes in the yeast genome at better than 95% accuracy based on the Z curve. Nucleic Acids Res. 28, 2804–2814 (2000).
16. Kellis, M., Patterson, N., Endrizzi, M., Birren, B. & Lander, E. S. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 423, 241–254 (2003).
17. Cliften, P. et al. Finding functional features in Saccharomyces genomes by phylogenetic footprinting.
Science 301, 71–76 (2003).
18. Ihmels, J. et al. Revealing modular organization in the yeast transcriptional network. Nature Genet. 31, 370–377 (2002).
19. Bergmann, S., Ihmels, J. & Barkai, N. Iterative signature algorithm for the analysis of large-scale gene expression data. Phys. Rev. E 67, 031902 (2003).
Invariant scaling relations across 20. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999).
21. Futcher, B., Latter, G. I., Monardo, P., McLaughlin, C. S. & Garrels, J. I. A sampling of the yeast proteome. Mol. Cell. Biol. 19, 7357–7368 (1999).
22. Washburn, M. P. et al. Protein pathway and complex clustering of correlated mRNA and protein Brian J. Enquist & Karl J. Niklas expression analyses in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 100, 3107–3112 (2003).
23. Washburn, M. P., Wolters, D. & Yates, J. R. III Large-scale analysis of the yeast proteome by Nature 410, 655–660 (2001).
multidimensional protein identification technology. Nature Biotechnol. 19, 242–247 (2001).
Equation (1) of this Article was incorrect as printed. The total 24. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).
25. Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 Tot; per unit area is the summation, or integral, across the size distribution of the number of individuals per unit area, 26. Wang, Y. et al. Precision and functional specificity in mRNA decay. Proc. Natl Acad. Sci. USA 99, multiplied by their body mass. Thus MTot ¼ MNðMÞdM: Because 5860–5865 (2002).
the number of individuals in a given area is an allometric function 27. Sharp, P. M. & Li, W. H. The codon Adaptation Index—a measure of directional synonymous codon of their size, M, we can substitute the observed relationship N ¼ usage bias, and its potential applications. Nucleic Acids Res. 15, 1281–1295 (1987).
CmM23=4 to yield the community biomass equation: 28. Grantham, R., Gautier, C. & Gouy, M. Codon frequencies in 119 individual genes confirm consistent choices of degenerate bases according to genome type. Nucleic Acids Res. 8, 1893–1912 (1980).
29. Spellman, P. T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).
This change does not affect any of the reported conclusions or Supplementary Information accompanies the paper on www.nature.com/nature.
empirical patterns.
NATURE VOL 425 16 OCTOBER 2003 www.nature.com/nature 2003 Nature Publishing Group

Source: http://www.ccmm.ma/documents/cours%20de%20genome/References/Global%20analysis%20of%20protein%20expression%20in%20yeast.pdf

Microsoft word - document

Dynamic Renal Radionuclide Studies – British Nuclear Medicine Society Dynamic Renal Radionuclide Studies This guideline must be read in conjunction with the BNMS Generic Guidelines. The purpose of this guideline is to assist specialists in Nuclear Medicine and Radionuclide Radiology in recommending, performing, interpreting and reporting the results of dynamic renal radionuclide studies. This guideline will assist


NIH Public AccessAuthor ManuscriptEur Neuropsychopharmacol. Author manuscript; available in PMC 2010 March 3. NIH-PA Author Manuscript Published in final edited form as: Eur Neuropsychopharmacol. 2008 November ; 18(11): 773–786. doi:10.1016/j.euroneuro.2008.06.005. Glutamatergic Dysfunction in Schizophrenia: from basic