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Global analysis of protein expression in yeast

Author

Listed:
  • Sina Ghaemmaghami

    (University of California–San Francisco
    University of California–San Francisco)

  • Won-Ki Huh

    (University of California–San Francisco
    Biochemistry & Biophysics, University of California–San Francisco)

  • Kiowa Bower

    (University of California–San Francisco
    University of California–San Francisco)

  • Russell W. Howson

    (University of California–San Francisco
    Biochemistry & Biophysics, University of California–San Francisco)

  • Archana Belle

    (University of California–San Francisco
    Biochemistry & Biophysics, University of California–San Francisco)

  • Noah Dephoure

    (University of California–San Francisco
    Biochemistry & Biophysics, University of California–San Francisco)

  • Erin K. O'Shea

    (University of California–San Francisco
    Biochemistry & Biophysics, University of California–San Francisco)

  • Jonathan S. Weissman

    (University of California–San Francisco
    University of California–San Francisco)

Abstract

The availability of complete genomic sequences and technologies that allow comprehensive analysis of global expression profiles of messenger RNA1,2,3 have greatly expanded our ability to monitor the internal state of a cell. Yet biological systems ultimately need to be explained in terms of the activity, regulation and modification of proteins—and the ubiquitous occurrence of post-transcriptional regulation makes mRNA an imperfect proxy for such information. To facilitate global protein analyses, we have created a Saccharomyces cerevisiae fusion library where each open reading frame is tagged with a high-affinity epitope and expressed from its natural chromosomal location. Through immunodetection of the common tag, we obtain a census of proteins expressed during log-phase growth and measurements of their absolute levels. We find that about 80% of the proteome is expressed during normal growth conditions, and, using additional sequence information, we systematically identify misannotated genes. The abundance of proteins ranges from fewer than 50 to more than 106 molecules per cell. Many of these molecules, including essential proteins and most transcription factors, are present at levels that are not readily detectable by other proteomic techniques nor predictable by mRNA levels or codon bias measurements.

Suggested Citation

  • Sina Ghaemmaghami & Won-Ki Huh & Kiowa Bower & Russell W. Howson & Archana Belle & Noah Dephoure & Erin K. O'Shea & Jonathan S. Weissman, 2003. "Global analysis of protein expression in yeast," Nature, Nature, vol. 425(6959), pages 737-741, October.
    • Handle: RePEc:nat:nature:v:425:y:2003:i:6959:d:10.1038_nature02046
    DOI: 10.1038/nature02046
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    1. Xinkai Wu & Mengze Xu & Jian-Rong Yang & Jian Lu, 2024. "Genome-wide impact of codon usage bias on translation optimization in Drosophila melanogaster," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
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    13. Alexander M. Franks & Gábor Csárdi & D. Allan Drummond & Edoardo M. Airoldi, 2015. "Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 27-44, March.
    14. Alexey A Gritsenko & Marc Hulsman & Marcel J T Reinders & Dick de Ridder, 2015. "Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-26, August.
    15. Keisuke Obara & Taku Yoshikawa & Ryu Yamaguchi & Keiko Kuwata & Kunio Nakatsukasa & Kohei Nishimura & Takumi Kamura, 2022. "Proteolysis of adaptor protein Mmr1 during budding is necessary for mitochondrial homeostasis in Saccharomyces cerevisiae," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    16. Maximilian Seidel & Natalie Romanov & Agnieszka Obarska-Kosinska & Anja Becker & Nayara Trevisan Doimo de Azevedo & Jan Provaznik & Sankarshana R. Nagaraja & Jonathan J. M. Landry & Vladimir Benes & M, 2023. "Co-translational binding of importins to nascent proteins," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    17. Zhdanov, Vladimir P., 2011. "Periodic perturbation of the bistable kinetics of gene expression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 57-64.
    18. Emma Pierson & the GTEx Consortium & Daphne Koller & Alexis Battle & Sara Mostafavi, 2015. "Sharing and Specificity of Co-expression Networks across 35 Human Tissues," PLOS Computational Biology, Public Library of Science, vol. 11(5), pages 1-19, May.
    19. Ulises H. Guzman & Henriette Aksnes & Rasmus Ree & Nicolai Krogh & Magnus E. Jakobsson & Lars J. Jensen & Thomas Arnesen & Jesper V. Olsen, 2023. "Loss of N-terminal acetyltransferase A activity induces thermally unstable ribosomal proteins and increases their turnover in Saccharomyces cerevisiae," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    20. Paola Fabrizio & Shawn Hoon & Mehrnaz Shamalnasab & Abdulaye Galbani & Min Wei & Guri Giaever & Corey Nislow & Valter D Longo, 2010. "Genome-Wide Screen in Saccharomyces cerevisiae Identifies Vacuolar Protein Sorting, Autophagy, Biosynthetic, and tRNA Methylation Genes Involved in Life Span Regulation," PLOS Genetics, Public Library of Science, vol. 6(7), pages 1-14, July.
    21. Joke J F A van Vugt & Martijn de Jager & Magdalena Murawska & Alexander Brehm & John van Noort & Colin Logie, 2009. "Multiple Aspects of ATP-Dependent Nucleosome Translocation by RSC and Mi-2 Are Directed by the Underlying DNA Sequence," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-14, July.
    22. Shinsuke Ohnuki & Yoshikazu Ohya, 2018. "High-dimensional single-cell phenotyping reveals extensive haploinsufficiency," PLOS Biology, Public Library of Science, vol. 16(5), pages 1-23, May.
    23. Ying Xiong & Weijing Han & Chunhua Xu & Jing Shi & Lisha Wang & Taoli Jin & Qi Jia & Ying Lu & Shuxin Hu & Shuo-Xing Dou & Wei Lin & Terence R. Strick & Shuang Wang & Ming Li, 2024. "Single-molecule reconstruction of eukaryotic factor-dependent transcription termination," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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