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Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli

Nature Biotechnology volume 36, pages 10051015 (2018) | Download Citation

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Abstract

Comparative analyses of natural and mutated sequences have been used to probe mechanisms of gene expression, but small sample sizes may produce biased outcomes. We applied an unbiased design-of-experiments approach to disentangle factors suspected to affect translation efficiency in E. coli. We precisely designed 244,000 DNA sequences implementing 56 replicates of a full factorial design to evaluate nucleotide, secondary structure, codon and amino acid properties in combination. For each sequence, we measured reporter transcript abundance and decay, polysome profiles, protein production and growth rates. Associations between designed sequences properties and these consequent phenotypes were dominated by secondary structures and their interactions within transcripts. We confirmed that transcript structure generally limits translation initiation and demonstrated its physiological cost using an epigenetic assay. Codon composition has a sizable impact on translatability, but only in comparatively rare elongation-limited transcripts. We propose a set of design principles to improve translation efficiency that would benefit from more accurate prediction of secondary structures in vivo.

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Change history

  • 12 November 2018

    In the supplementary information originally posted for this article, the outer file extension for Supplementary Data 1, 2, 4–6, 9, 15, 22, 25 and 28 should have been zip instead of csv. Supplementary Data 16–21, 23, 24, 26, 27, 29–32, 34 and 35 should have had inner and outer file extensions of gz.zip instead of just zip. In addition, the wrong version of Supplementary Code 28 was posted. These file have been reposted.

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Referenced accessions

NCBI Reference Sequence

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Acknowledgements

We thank V. Mutalik, C. Liu, L. Jacob, M. Price, A. Deutschbauer, M. Samoilov, P. Shah, J. Plotkin, J. Savitskaya and L. Ciandrini for discussions. We are grateful to the Agilent Laboratories and the Synthetic Biology Institute (SBI) for providing the OLS array. We thank J. Sampson, P. Anderson and S. Laderman from Agilent Laboratories for discussing OLS setup and processing. G.C. was funded by the Human Frontier Science Program (LT000873/2011-l), J.C.G. by the Portuguese Fundação para a Ciência e Tecnologia (SFRH/BD/47819/2008). We acknowledge financial support by the Synthetic Biology Engineering Research Center (SynBERC under National Science Foundation grant 04-570/0540879). This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley (NIH S10 Instrumentation Grants S10RR029668 and S10RR027303).

Author information

Affiliations

  1. California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, California, USA.

    • Guillaume Cambray
    •  & Joao C Guimaraes
  2. DGIMI, Univ. Montpellier, INRA, Montpellier, France.

    • Guillaume Cambray
  3. Department of Bioengineering, University of California, Berkeley, Berkeley, California, USA.

    • Joao C Guimaraes
    •  & Adam Paul Arkin
  4. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

    • Adam Paul Arkin

Authors

  1. Search for Guillaume Cambray in:

  2. Search for Joao C Guimaraes in:

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Contributions

G.C. and A.P.A. conceived the work; G.C. and J.C.G. designed sequences; G.C. performed experiments and processed data; G.C. and A.P.A. analyzed the data and J.C.G. contributed post hoc secondary structure analyses; G.C. and A.P.A. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Guillaume Cambray or Adam Paul Arkin.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–14

  2. 2.

    Life Sciences Reporting Summary

  3. 3.

    Supplementary Tables

    Supplementary Tables 1–3

  4. 4.

    Supplementary Notes

    Supplementary Note 1

Text files

  1. 1.

    Supplementary Code 1

    Parameter file. Used to parameterize python scripts involved with processing of sequencing data (Supplementary Code 2–5).

  2. 2.

    Supplementary Code 2

    De-multiplex fastq. Python script to identify and trim custom sequencing barcodes. Support parallelization. Outputs a separate fastq file for eachbarcode.

  3. 3.

    Supplementary Code 3

    Python wrapper for BWA and samtools. Produce mapping and quality check of the reads by calling BWA and samtools. Support parallelization.

  4. 4.

    Supplementary Code 4

    Read counter. Python script to summarize the number of read mapping to each target sequence from the bam files generated by SupplementaryCode 3. Support parallelization.

  5. 5.

    Supplementary Code 5

    Count aggregator. Python script to aggregate count tables generated by Supplementary Code 4.

  6. 6.

    Supplementary Code 6

    Processing of protein production under regular and facilitated initiation from FACS-seq data. R script to normalize, rescale and aggregate readcount data from multiple FACS-seq replicate experiments. Convert digital read distribution into a continuous linear measure of protein productionranging between 1 and 100 (PNI and PFI).

  7. 7.

    Supplementary Code 7

    Computation of ANOVA's sum of squares for PNI. R script to run an ANOVA on PNI data and extract the sum of squares accounted by designproperties and their first-order interactions.

  8. 8.

    Supplementary Code 8

    Computation of sum of squares for multiple linear regression of PNI on design properties. R script to run a multiple linear regression on PNI dataand extract the ANOVA-like sum of squares accounted by design properties and their first-order interactions.

  9. 9.

    Supplementary Code 9

    Regression tree analysis for PNI. R script to run a CART analysis on PNI.

  10. 10.

    Supplementary Code 10

    Computation of ANOVA's sum of squares for PFI. R script to run an ANOVA on PFI data and extract the sum of squares accounted for by designproperties and their first-order interactions.

  11. 11.

    Supplementary Code 11

    Computation of sum of squares for multiple linear regression of PFI on design properties. R script to run a multiple linear regression on PFI dataand extract the ANOVA-like sum of squares accounted for by design properties and their first-order interactions.

  12. 12.

    Supplementary Code 12

    Regression tree analysis for PFI. R script to run a CART analysis on PFI.

  13. 13.

    Supplementary Code 13

    Effect of structure strength predicted across sliding windows of different sizes. R script to run linear regression of PNI and PFI against minimal freeenergies computed over sliding windows of different length. Report the ANOVA-like sum of squares.

  14. 14.

    Supplementary Code 14

    Multiple linear regression of PNI and PFI on predicted nucleotide accessibilities. R script to run a multiple linear regression of protein production data on predicted nucleotide availabilities. Report the ANOVA-like sum of squares accounted by every position.

  15. 15.

    Supplementary Code 15

    Call to the RBS calculator web service. Python script to remotely run the RBS calculator on designed sequences.

  16. 16.

    Supplementary Code 16

    Effect of predictions from the RBS calculator. R script to run linear regression of PNI and PFI against RBS calculator outputs. Report theANOVA-like sum of squares.

  17. 17.

    Supplementary Code 17

    Partial correlation between PFI and various codon metrics, given PNI. R script to compute various alternative codon metric for the codon sequence and determine their partial correlations with PFI accounting for PNI.

  18. 18.

    Supplementary Code 18

    Processing of growth measurements from FIT-seq data collected under various conditions. R script to convert differential enrichment of read count data over time into an integrated measure of cell growth. Process read count data from multiple replicate experiments. Convert read count ratios into aggregated measures of relative growth in a given environment(WNI, WFI, WUTX, WM).

  19. 19.

    Supplementary Code 19

    Computation of sum of squares for multiple linear regression of WNI on PNI and design properties. R script to run a multiple linear regression on of WNI against PNI, PNI2 and design properties. Report ANOVA-like sum of squares

  20. 20.

    Supplementary Code 20

    Computation of sum of squares for multiple linear regression of WFI on PFI and design properties. R script to run a multiple linear regression on of WFI against PFI, PFI2 and design properties. Report ANOVA-like sum of squares.

  21. 21.

    Supplementary Code 21

    Processing of RNA abundance and decay measurements from serial RNA-seq. R script to compute RNA decay after transcription arrest. Sampleread counts are corrected using coefficients derived from ratioing counts of spiked-in RNA standards over time. Performs a nonlinear decay fit to the corrected count frequencies to estimate RNA abundance at steady state (RNASS), RNA half-life (RNAHL) and RNA protection (WPTX).

  22. 22.

    Supplementary Code 22

    Compute 3D animation of the data. R scripts to produce the images necessary for Supplementary Video 1.

  23. 23.

    Supplementary Code 23

    Processing of polysome profiles from DNA-seq of separate polysome fractions. R script to compute the distribution of polysome (up to fifthfraction) for each design sequence from read counts.

  24. 24.

    Supplementary Code 24

    Definition of sequence archetypes. R script to categorize sequences into the most relevant combinations of sequence properties. Calculate the series-wise means of various phenotypes for sequences belonging to these archetypes.

  25. 25.

    Supplementary Code 25

    GenBank parser. A script to parse coding sequence from GenBank file using BioPython.

  26. 26.

    Supplementary Code 26

    D-Tailor module. Links to specific D-Tailor modules used in this work.

  27. 27.

    Supplementary Code 28

    Seed generator for D-Tailor. Python script to generate a random input sequence for D-Tailor that maximizes the distance to other input sequences.

Zip files

  1. 1.

    Supplementary Code 27

    Genome randomization. Perl modules to produce random genome variants that retain codon usage and protein's amino acid composition.

  2. 2.

    Supplementary Data 1

    E. coli's features and measurements. Dataset aggregating various measures of sequence property for every gene in a reference E. coli and corresponding expression data for a subset (Taniguchi, 2009).

  3. 3.

    Supplementary Data 2

    Mean hydropathy index over sliding windows. Calculation of the MHI over sliding windows for every gene in the reference E. coli genome.

  4. 4.

    Supplementary Data 4

    Accessible bottleneck strengths. Calculation of bottleneck strength for random sequence cloned in the translation reporter.

  5. 5.

    Supplementary Data 5

    E. coli's features and levels. Calculation of property scores and discrete categorisation for every gene in the E. coli genome, based on the properties and thresholds set for the Design of Experiments.

  6. 6.

    Supplementary Data 6

    Random solutions. Calculation of property scores and categorization for random sequences cloned in the translation reporter context, based on the properties and thresholds set for the Design of Experiments.

  7. 7.

    Supplementary Data 8

    Series logo. Position-wise nucleotide and amino acid frequency matrices for each series.

  8. 8.

    Supplementary Data 9

    Sequencing count summary. A table reporting the number of counts associated with each design sequence for every sequencing library in this work.

  9. 9.

    Supplementary Data 15

    Integrated phenotypic measurements. Consolidated dataset comprising design information, intermediates and fully processed phenotypic measurements for all 244,000 synthetic sequences.

  10. 10.

    Supplementary Data 16

    ANOVA on PNI. An R object containing the sum of squares computed by running ANOVAs on the full dataset and independent series (Supplementary Code 7).

  11. 11.

    Supplementary Data 17

    MLR on PNI. An R object containing the sum of squares computed by running multiple linear regressions on the full dataset and independent series (Supplementary Code 8).

  12. 12.

    Supplementary Data 18

    CART on PNI. An R object containing the result of CART analysis (Supplementary Code 9).

  13. 13.

    Supplementary Data 19

    ANOVA on PFI. An R object containing the sum of squares computed by running ANOVAs on the full dataset and independent series (output of Supplementary Code 7).

  14. 14.

    Supplementary Data 20

    MLR on PFI. An R object containing the sum of squares computed by running multiple linear regressions on the full dataset and independent series (output of Supplementary Code 8).

  15. 15.

    Supplementary Data 21

    CART on PFI. An R object containing the result of CART analysis (output of Supplementary Code 9).

  16. 16.

    Supplementary Data 22

    Effect of minimum free energy over sliding windows. MFE predicted for sliding windows of different length on each designed sequence.

  17. 17.

    Supplementary Data 23

    Sum of squares corresponding to regression of PNI on MFE over sliding windows (output of Supplementary Code 13).

  18. 18.

    Supplementary Data 24

    Sum of squares corresponding to regression of PFI to the residuals of PNI's regression on MFEs (output of Supplementary Code 13).

  19. 19.

    Supplementary Data 25

    Single nucleotide accessibilities. Predicted accessibilities at every position of each designed sequences.

  20. 20.

    Supplementary Data 26

    Sum of squares for multiple linear regression of PNI on accessibilities (output of Supplementary Code 14).

  21. 21.

    Supplementary Data 27

    Sum of squares for multiple linear regression of PFI on accessibilities (output of Supplementary Code 14).

  22. 22.

    Supplementary Data 28

    RBS calculator predictions. Aggregation of outputs obtained by running each designed sequence in reporter context in the RBS calculator (output of Supplementary Code 15).

  23. 23.

    Supplementary Data 29

    Sum of squares corresponding to the regression of PNI on RBS calculator's predictions (output of Supplementary Code 16).

  24. 24.

    Supplementary Data 30

    Partial correlation of various codon-based metrics with PFI, given PNI (output of Supplementary Code 17).

  25. 25.

    Supplementary Data 31

    Sum of squares for multiple linear regression of WNI on design properties and PNI (output of Supplementary Code 19).

  26. 26.

    Supplementary Data 32

    Sum of squares for multiple linear regression of WFI on design properties and PFI (output of Supplementary Code 20).

  27. 27.

    Supplementary Data 34

    Nonlinear decay fit. An R object containing fit data (output of Supplementary Code 21).

  28. 28.

    Supplementary Data 35

    Phenotypic archetypes. Quartiles of series-wise mean for various phenotypes (output of Supplementary Code 24).

  29. 29.

    Supplementary Data 36

    Random E. coli genomes. Result of constrained genome randomization (output of Supplementary Code 27).

CSV files

  1. 1.

    Supplementary Data 3

    tAI profiles for sfGFP and a designed variant. Calculates tAI over a sliding window.

  2. 2.

    Supplementary Data 10

    Illumina lane description. Mapping of the different sequencing libraries on Illumina sequencing lane.

  3. 3.

    Supplementary Data 11

    TAG coupling upon activation by unnatural amino acids. Table reporting the mean fluorescence observed upon induction by increasing concentration of the unnatural amino acid pAcF.

  4. 4.

    Supplementary Data 12

    TAG coupling mutants. Table reporting the mean fluorescence observed in various mutants of the TAG position.

  5. 5.

    Supplementary Data 13

    Growth of TAG mutants. Density of cell culture (OD600) over time for various mutants at the TAG position.

  6. 6.

    Supplementary Data 14

    Number of cells sorted during FACS-seq. Report the number of cells sorted in each bin during the FACS-seq experiments. Used to normalize read counts upon sequencing.

  7. 7.

    Supplementary Data 33

    RNA standards. Counts of reads mapping to RNA standard sequences in RNA decay libraries.

Excel files

  1. 1.

    Supplementary Data 7

    Intra-series distance. Collection of tables reporting Hamming distances between every pair of sequences within the same series.

Videos

  1. 1.

    3D animation of the data in RNA–Protein–Fitness space.

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DOI

https://doi.org/10.1038/nbt.4238