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Composability of regulatory sequences controlling transcription and translation in Escherichia coli

  1. George M. Churcha,b,2
  1. aWyss Institute for Biologically Inspired Engineering, Boston, MA 02115;
  2. bDepartment of Genetics, Harvard Medical School, Boston, MA 02115;
  3. cHarvard-MIT Health Sciences and Technology, Cambridge, MA 02139;
  4. dBIOFAB: International Open Facility Advancing Biotechnology, Emeryville, CA 94608;
  5. ePhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720;
  6. fDepartment of Bioengineering, University of California, Berkeley, CA 94720;
  7. gDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205;
  8. hNeuroregeneration and Stem Cell Biology Program, Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD 21205;
  9. iLieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205; and
  10. jDepartment of Bioengineering, Stanford University, Stanford, CA 94305

  1. Edited by Charles R. Cantor, Sequenom, Inc., San Diego, CA, and approved July 2, 2013 (received for review February 11, 2013)

Abstract

The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.

Footnotes

  • ↵1S.K. and D.B.G. contributed equally to this work.

  • ↵2To whom correspondence should be addressed. E-mail: gchurch{at}genetics.med.harvard.edu.

  • Author contributions: S.K., D.B.G., and G.M.C. designed research; S.K., D.B.G., and Y.G. performed research; S.K., D.B.G., G.C., V.K.M., A.P.A., and D.E. contributed new reagents/analytic tools; S.K. and D.B.G. analyzed data; and S.K., D.B.G., and G.M.C. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • Data deposition: The sequence reported in this paper has been deposited in the AddGene database (accession no. 47441).

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1301301110/-/DCSupplemental.

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