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Table of Contents
Vol. 1, No. 1, 2003
Issue release date: 2003
Section title: Original Research Paper
Complexus 2003;1:19–28
(DOI:10.1159/000067640)

Prediction of Protein Essentiality Based on Genomic Data

Jeong H.a, c · Oltvai Z.N.b · Barabási A.-L.a
aDepartment of Physics, University of Notre Dame, Notre Dame, Ind.; bDepartment of Pathology, Northwestern University Medical School, Chicago, Ill., USA; cDepartment of Physics, Korea Advanced Institute of Science and Technology, Taejon, Korea
email Corresponding Author

Abstract

A major goal of pharmaceutical bioinformatics is to develop computational tools for systematic in silico molecular target identification. Here we demonstrate that in the yeast Saccharomyces cerevisiae the phenotypic effect of single gene deletions simultaneously correlates with fluctuations in mRNA expression profiles, the functional categorization of the gene products, and their connectivity in the yeasts protein-protein interaction network. Building on these quantitative correlations, we developed a computational method for predicting the phenotypic effect of a given genes functional disabling or removal. Our subsequent analyses were in good agreement with the results of systematic gene deletion experiments, allowing us to predict the deletion phenotype of a number of untested yeast genes. The results underscore the utility of large genomic databases for in silico systematic drug target identification in the postgenomic era.

© 2003 S. Karger AG, Basel


  

Key Words

  • Scale-free networks
  • Lethality
  • Microarray
  • Two-hybrid protein interaction
  • Drug target identification

References

  1. Rosamond J, Allsop A: Harnessing the power of the genome in the search for new antibiotics. Science 2000;287:1973–1976.
  2. Gibbs JB: Mechanism-based target identification and drug discovery in cancer research. Science 2000;287: 1969–1973.
  3. Bailey D, Zanders E, Dean P: The end of the beginning for genomic medicine. Nat Biotechnol 2001;19: 207–209.
  4. Eisenberg D, Marcotte EM, Xenarios I, Yeates TO: Protein function in the post-genomic era. Nature 2000; 405:823–826.
  5. Jeong H, Mason SP, Barabsi A-L, Oltvai ZN: Lethality and centrality in protein networks. Nature 2001;411: 41–42.
  6. Costanzo MC, et al: The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): Comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res 2000;28:73–76.
  7. Winzeler EA, et al: Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 1999;285:901–906.
  8. Staley JP, Guthrie C: Mechanical devices of the spliceosome: Motors, clocks, springs, and things. Cell 1998;92:315–326.
  9. Hartwell LH, Hopfield JJ, Leibler S, Murray AW: From molecular to modular cell biology. Nature 1999;402 (suppl):C47–C52.
  10. Hughes TR, et al: Functional discovery via a compendium of expression profiles. Cell 2000;102:109–126.

    External Resources

  11. Uetz P, et al: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000;403:623–627.
  12. Ito T, et al: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001;98:4569–4574.
  13. Xenarios I, et al: DIP: The database of interacting proteins. Nucleic Acids Res 2000;28:289–291.
  14. Mewes HW, et al: MIPS: A database for genomes and protein sequences. Nucleic Acids Res 2000;28:37–40.
  15. von Mering G, et al: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002;417:399–403.
  16. Barabsi A-L, Albert R: Emergence of scaling in random networks. Science 1999;286:509–512.
  17. Albert R, Jeong H, Barabsi A-L: Error and attack tolerance of complex networks. Nature 2000;406:378–382.
  18. Camilli A, Mekalanos JJ: Use of recombinase gene fusions to identify Vibrio cholerae genes induced during infection. Mol Microbiol 1995;18:671–683.
  19. Heithoff DM, Conner CP, Mahan MJ: Dissecting the biology of a pathogen during infection. Trends Microbiol 1997;5:509–513.
  20. Brown MP, et al: Knowledge-based analysis of micro-array gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000;97:262–267.
  21. Pavlidis P, Cai J, Weston J, Grundy WN: Gene classification from heterogenous data. Recomb 2001 http://www.cs.columbia.edu/compbio/exp-phylo/.
  22. Wu LF, et al: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat Genet 2002;31: 255–265.
  23. De Backer MD, et al: An antisense-based functional genomics approach for identification of genes critical for growth of Candida albicans. Nat Biotechnol 2001;19:235–241.
  24. Elbashir SM, et al: Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 2001;411:494–498.
  25. Holter NS, et al: Fundamental patterns underlying gene expression profiles: Simplicity from complexity. Proc Natl Acad Sci USA 2000;97:8409–8414.
  26. Alter O, Brown PO, Botstein D: Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 2000;97: 10101–10106.
  27. McAdams HH, Arkin A: Stochastic mechanisms in gene expression. Proc Natl Acad Sci USA 1997;94:814–819.
  28. Hasty J, Pradines J, Dolnik M, Collins JJ: Noise-based switches and amplifiers for gene expression. Proc Natl Acad Sci USA 2000;97:2075–2080.
  29. Hughes TR, et al: Widespread aneuploidy revealed by DNA microarray expression profiling. Nat Genet 2000;25:333–337.

  

Author Contacts

Prof. Albert-Lszl Barabsi
Department of Physics, University of Notre Dame
213 Nieuwland Science, Notre Dame, IN 46556 (USA)
Tel. +1 219 631 5767, Fax +1 219 631 5952
E-Mail alb@nd.edu

  

Article Information

Received: May 7, 2002
Accepted after revision: August 5, 2002
Number of Print Pages : 10
Number of Figures : 4, Number of Tables : 1, Number of References : 29

  

Publication Details

Complexus

Vol. 1, No. 1, Year 2003 (Cover Date: 2003)

Journal Editor: Henri Atlan, Paris/Jerusalem
ISSN: 1424–8492 (print), 1424–8506 (Online)

For additional information: http://www.karger.com/cpu


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

References

  1. Rosamond J, Allsop A: Harnessing the power of the genome in the search for new antibiotics. Science 2000;287:1973–1976.
  2. Gibbs JB: Mechanism-based target identification and drug discovery in cancer research. Science 2000;287: 1969–1973.
  3. Bailey D, Zanders E, Dean P: The end of the beginning for genomic medicine. Nat Biotechnol 2001;19: 207–209.
  4. Eisenberg D, Marcotte EM, Xenarios I, Yeates TO: Protein function in the post-genomic era. Nature 2000; 405:823–826.
  5. Jeong H, Mason SP, Barabsi A-L, Oltvai ZN: Lethality and centrality in protein networks. Nature 2001;411: 41–42.
  6. Costanzo MC, et al: The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): Comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res 2000;28:73–76.
  7. Winzeler EA, et al: Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 1999;285:901–906.
  8. Staley JP, Guthrie C: Mechanical devices of the spliceosome: Motors, clocks, springs, and things. Cell 1998;92:315–326.
  9. Hartwell LH, Hopfield JJ, Leibler S, Murray AW: From molecular to modular cell biology. Nature 1999;402 (suppl):C47–C52.
  10. Hughes TR, et al: Functional discovery via a compendium of expression profiles. Cell 2000;102:109–126.

    External Resources

  11. Uetz P, et al: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000;403:623–627.
  12. Ito T, et al: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001;98:4569–4574.
  13. Xenarios I, et al: DIP: The database of interacting proteins. Nucleic Acids Res 2000;28:289–291.
  14. Mewes HW, et al: MIPS: A database for genomes and protein sequences. Nucleic Acids Res 2000;28:37–40.
  15. von Mering G, et al: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002;417:399–403.
  16. Barabsi A-L, Albert R: Emergence of scaling in random networks. Science 1999;286:509–512.
  17. Albert R, Jeong H, Barabsi A-L: Error and attack tolerance of complex networks. Nature 2000;406:378–382.
  18. Camilli A, Mekalanos JJ: Use of recombinase gene fusions to identify Vibrio cholerae genes induced during infection. Mol Microbiol 1995;18:671–683.
  19. Heithoff DM, Conner CP, Mahan MJ: Dissecting the biology of a pathogen during infection. Trends Microbiol 1997;5:509–513.
  20. Brown MP, et al: Knowledge-based analysis of micro-array gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000;97:262–267.
  21. Pavlidis P, Cai J, Weston J, Grundy WN: Gene classification from heterogenous data. Recomb 2001 http://www.cs.columbia.edu/compbio/exp-phylo/.
  22. Wu LF, et al: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat Genet 2002;31: 255–265.
  23. De Backer MD, et al: An antisense-based functional genomics approach for identification of genes critical for growth of Candida albicans. Nat Biotechnol 2001;19:235–241.
  24. Elbashir SM, et al: Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 2001;411:494–498.
  25. Holter NS, et al: Fundamental patterns underlying gene expression profiles: Simplicity from complexity. Proc Natl Acad Sci USA 2000;97:8409–8414.
  26. Alter O, Brown PO, Botstein D: Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 2000;97: 10101–10106.
  27. McAdams HH, Arkin A: Stochastic mechanisms in gene expression. Proc Natl Acad Sci USA 1997;94:814–819.
  28. Hasty J, Pradines J, Dolnik M, Collins JJ: Noise-based switches and amplifiers for gene expression. Proc Natl Acad Sci USA 2000;97:2075–2080.
  29. Hughes TR, et al: Widespread aneuploidy revealed by DNA microarray expression profiling. Nat Genet 2000;25:333–337.