SCALEs: multiscale analysis of library enrichment

Abstract

We report a genome-wide, multiscale approach to simultaneously measure the effect that the increased copy of each gene and/or operon has on a desired trait or phenotype. The method involves (i) growth selections on a mixture of several different plasmid-based genomic libraries of defined insert sizes or SCALEs, (ii) microarray studies of enriched plasmid DNA, and a (iii) mathematical multiscale analysis that precisely identifies the relevant genetic elements. This approach allows for identification of all single open reading frames and larger multigene fragments within a genomic library that alter the expression of a given phenotype. We have demonstrated this method in Escherichia coli by monitoring, in parallel, a population of >106 genomic library clones of different insert sizes, throughout continuous selections over a period of 100 generations.

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Figure 1: Overview of SCALEs.
Figure 2: Multiscale analysis.
Figure 3: Genome-wide multiscale analysis.
Figure 4: Relative fitness distributions for the different clones remaining (W > 10−6) in the culture during selection after 36, 48 and 60 h.
Figure 5: Overlap of enriched regions in replicate cultures.

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References

  1. 1

    DeRisi, J.L., Iyer, V.R. & Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997).

    CAS  Article  Google Scholar 

  2. 2

    Fodor, S.P. et al. Light-directed, spatially addressable parallel chemical synthesis. Science 251, 767–773 (1991).

    CAS  Article  Google Scholar 

  3. 3

    Schena, M., Shalon, D., Davis, R.W. & Brown, P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    CAS  Article  Google Scholar 

  4. 4

    Badarinarayana, V. et al. Selection analyses of insertional mutants using subgenic resolution arrays. Nat. Biotechnol. 19, 1060–1064 (2001).

    CAS  Article  Google Scholar 

  5. 5

    Cho, R.J. et al. Parallel analysis of genetic selections using whole genome oligonucleotide arrays. Proc. Natl. Acad. Sci. USA 95, 3752–3757 (1998).

    CAS  Article  Google Scholar 

  6. 6

    Gill, R.T., Wildt, S., Yang, Y.T., Ziesman, S. & Stephanopoulos, G. Genome-wide screening for trait conferring genes using DNA microarrays. Proc. Natl. Acad. Sci. USA 99, 7033–7038 (2002).

    CAS  Article  Google Scholar 

  7. 7

    Winzeler, E.A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    CAS  Article  Google Scholar 

  8. 8

    Shoemaker, D.D., Lashkari, D., Morris, D., Mittmann, M. & Davis, R. Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nat. Genet. 14, 450–456 (1996).

    CAS  Article  Google Scholar 

  9. 9

    Karlyshev, A.V. et al. Application of high-density array-based signature-tagged mutagenesis to discover novel Yersinia virulence-associated genes. Infect. Immun. 69, 7810–7819 (2001).

    CAS  Article  Google Scholar 

  10. 10

    Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

    CAS  Article  Google Scholar 

  11. 11

    Elena, S.F. & Lenski, R. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).

    CAS  Article  Google Scholar 

  12. 12

    Garcia, B. et al. Role of the GGDEF protein family in Salmonella cellulose biosynthesis and biofilm formation. Mol. Microbiol. 54, 264–277 (2004).

    CAS  Article  Google Scholar 

  13. 13

    Kirillina, O., Fetherston, J.D., Bobrov, A.G., Abney, J. & Perry, R.D. HmsP, a putative phosphodiesterase, and HmsT, a putative diguanylate cyclase, control Hms-dependent biofilm formation in Yersinia pestis. Mol. Microbiol. 54, 75–88 (2004).

    CAS  Article  Google Scholar 

  14. 14

    Simm, R., Fetherston, J., Kader, A., Romling, U. & Perry, R. Phenotypic convergence mediated by GGDEF-domain-containing proteins. J. Bacteriol. 187, 6816–6823 (2005).

    CAS  Article  Google Scholar 

  15. 15

    Simm, R., Morr, M., Kader, A., Nimtz, M. & Romling, U. GGDEF and EAL domains inversely regulate cyclic di-GMP levels and transition from sessility to motility. Mol. Microbiol. 53, 1123–1134 (2004).

    CAS  Article  Google Scholar 

  16. 16

    Brown, P.K. et al. MlrA, a novel regulator of curli (AgF) and extracellular matrix synthesis by Escherichia coli and Salmonella enterica serovar Typhimurium. Mol. Microbiol. 41, 349–363 (2001).

    CAS  Article  Google Scholar 

  17. 17

    Brombacher, E., Dorel, C., Zehnder, A. & Landini, P. The curli biosynthesis regulator CsgD co-ordinates the expression of both positive and negative determinants for biofilm formation in Escherichia coli. Microbiology 149, 2847–2857 (2003).

    CAS  Article  Google Scholar 

  18. 18

    Hickman, J.W., Tifrea, D. & Harwood, C. A chemosensory system the regulates biofilm formation through modulatiaon of cyclic diguanylate levels. Proc. Natl. Acad. Sci. USA 102, 14422–14427 (2005).

    CAS  Article  Google Scholar 

  19. 19

    Jenal, U. Cyclic di-guanosine-monophosphate comes of age: a novel secondary messenger involved in modulating cell surface structures in bacteria? Curr. Opin. Microbiol. 7, 185–191 (2004).

    CAS  Article  Google Scholar 

  20. 20

    Covert, M.W., Knight, E.M., Reed, J.L., Herrgard, M.J. & Palsson, B.O. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429, 92–96 (2004).

    CAS  Article  Google Scholar 

  21. 21

    Edwards, J.S. & Palsson, B.O. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc. Natl. Acad. Sci. USA 97, 5528–5533 (2000).

    CAS  Article  Google Scholar 

  22. 22

    Ibarra, R.U., Edwards, J.S. & Palsson, B.O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420, 186–189 (2002).

    CAS  Article  Google Scholar 

  23. 23

    Godiska, R., Patterson, M., Schoenfeld, T. & Mead, D. Beyond pUC: vectors for cloning unstable DNA. In DNA Sequencing: Optimizing the Process and Analysis (ed., Kieleczawa, J.) 55–75 (Jones and Bartlett, Boston, 2004).

    Google Scholar 

  24. 24

    Lynch, M.D. & Gill, R.T. Broad host range vectors for stable genomic library construction. Biotechnol. Bioeng. 94, 151–158 (2006).

    CAS  Article  Google Scholar 

  25. 25

    Sambrook, J., Fritsch, E.F. & Maniatis, T. Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, Cold Spring Harbor, 1989).

    Google Scholar 

  26. 26

    Naef, F. & Magnasco, M.O. Solving the riddle of the bright mismatches: labeling and effective binding in oligonucleotide arrays. Phys. Rev. E 68, 011906 (2003).

    Article  Google Scholar 

  27. 27

    Hoaglin, D.C., Mosteller, F. & Tukey, J.W. Understanding robust and exploratory data analysis (John Wiley & Sons Inc., New York, 1983).

    Google Scholar 

  28. 28

    Bangham, J., Chardaire, P., Pye, J. & Ling, P. Multiscale nonlinear decomposition: the sieve decomposition theorem. IEEE Trans. Pattern Anal. Mach. Intell. 18, 529–539 (1996).

    Article  Google Scholar 

  29. 29

    Bangham, J., Ling, P. & Harvey, R. Scale-space from nonlinear filters. IEEE Trans. Pattern Anal. Mach. Intell. 18, 520–529 (1996).

    Article  Google Scholar 

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Acknowledgements

This work was supported by US National Institutes of Health grants R21 AI055773-01 and K25 AI064338 and National Science Foundation grant BES0228584. M.D.L. was supported by a National Institutes of Health F31 award A1056687. T.W. was supported by a US Department of Education Graduate Assistantship in Areas of National Need fellowship. We thank H. Marshall at the University of Colorado Microarray Facility, and P.D. Bevins for his help with this work.

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Correspondence to Ryan T Gill.

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University of Colorado has filed a patent on this method. M.D.L. and R.T.G. have started a company that is seeking a license on this technology. M.D.L. may be a future employee of this company.

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Lynch, M., Warnecke, T. & Gill, R. SCALEs: multiscale analysis of library enrichment. Nat Methods 4, 87–93 (2007). https://doi.org/10.1038/nmeth946

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