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Metagenomic mining of regulatory elements enables programmable species-selective gene expression

Nature Methods volume 15, pages 323–329 (2018)Cite this article

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Abstract

Robust and predictably performing synthetic circuits rely on the use of well-characterized regulatory parts across different genetic backgrounds and environmental contexts. Here we report the large-scale metagenomic mining of thousands of natural 5′ regulatory sequences from diverse bacteria, and their multiplexed gene expression characterization in industrially relevant microbes. We identified sequences with broad and host-specific expression properties that are robust in various growth conditions. We also observed substantial differences between species in terms of their capacity to utilize exogenous regulatory sequences. Finally, we demonstrate programmable species-selective gene expression that produces distinct and diverse output patterns in different microbes. Together, these findings provide a rich resource of characterized natural regulatory sequences and a framework that can be used to engineer synthetic gene circuits with unique and tunable cross-species functionality and properties, and also suggest the prospect of ultimately engineering complex behaviors at the community level.

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Figure 1: High-throughput characterization of regulatory sequences from 184 prokaryotic genomes.
Figure 2: Transcriptional activity of the regulatory library across three diverse species.
Figure 3: Assessing regulatory features that govern transcriptional activity.
Figure 4: FACS-seq of RS library.
Figure 5: Species-selective gene circuits.

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Acknowledgements

We thank members of the Wang lab for helpful discussions and feedback. H.H.W. acknowledges funding support from the NIH (1DP5OD009172-02, 1U01GM110714-01A1), NSF (MCB-1453219), Sloan Foundation (FR-2015-65795), DARPA (W911NF-15-2-0065), and ONR (N00014-15-1-2704). N.I.J. is supported by an NSF Graduate Research Fellowship (DGE-16-44869). S.S.Y. is supported by the National Research Foundation of Korea (NRF-2017R1A6A3A03003401). We also thank T. Seto for help with plasmid construction; A. Figueroa for assistance with cell sorting; H. Salis for helpful discussions regarding the RBS calculator; D.B. Goodman for discussions regarding FACS-seq; G.M. Church (Harvard Medical School, Boston, Massachusetts, USA) for access to OLS libraries; and D. Dubnau (Rutgers New Jersey Medical School, Newark, New Jersey, USA), S. Lory, and A. Rasouly (both at Harvard Medical School, Boston, Massachusetts, USA) for providing the BD3182 and PAO1 Δpsy2 strains.

Author information

Author notes

  1. Antonio L C Gomes

    Present address: Department of Immunology, Memorial Sloan Kettering Cancer Center, New York, New York, USA

  2. Nathan I Johns and Antonio L C Gomes: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Systems Biology, Columbia University Medical Center, New York, New York, USA

    Nathan I Johns, Antonio L C Gomes, Sung Sun Yim, Tomasz Blazejewski & Harris H Wang

  2. Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Medical Center, New York, New York, USA

    Nathan I Johns & Tomasz Blazejewski

  3. School of Engineering and Applied Sciences, Columbia University, New York, New York, USA

    Anthony Yang

  4. Broad Institute, Cambridge, Massachusetts, USA

    Christopher S Smillie & Eric J Alm

  5. Department of Biological Engineering, MIT, Cambridge, Massachusetts, USA

    Mark B Smith & Eric J Alm

  6. Computational and Systems Biology Initiative, MIT, Cambridge, Massachusetts, USA

    Eric J Alm

  7. The Center for Microbiome Informatics and Therapeutics, MIT, Cambridge, Massachusetts, USA

    Eric J Alm

  8. Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, USA

    Sriram Kosuri

  9. UCLA–DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, California, USA

    Sriram Kosuri

  10. Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, USA

    Sriram Kosuri

  11. Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA

    Harris H Wang

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Contributions

N.I.J., A.L.C.G., C.S.S., M.B.S., E.J.A., S.K., and H.H.W. designed the study. N.I.J., S.S.Y., and H.H.W. performed the experiments. N.I.J., A.L.C.G., A.Y., T.B., and H.H.W. analyzed the data. N.I.J., A.L.C.G., and H.H.W. wrote the manuscript, with input from all other authors.

Corresponding author

Correspondence to Harris H Wang.

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Integrated supplementary information

Supplementary Figure 1 Metadata of the 184 donor genomes used to derive the regulatory sequences used in this study.

(a) genome size, (b) genomic GC content, (c) gram staining, (d) lifestyle, (e) number of regulatory sequences mined per genome, (f) the number of genomes per phylum, and (g) the 16S phylogenetic tree.

Supplementary Figure 2 Vector designs.

Vector maps for pNJ1, pNJ2.1, and pNJ3.1 used for expression measurements of metagenomic regulatory sequence library (RS) in E. coli, B. subtilis, and P. aeruginosa respectively and pNJ6.0, pNJ3.1, pNJ7 and pNJ8, which were used for RS241 library measurements in E. coli, B. subtilis, P. aeruginosa, S. enterica, V. natriegens and C. glutamicum.

Supplementary Figure 3 Replication experiments to validate method performance.

(a) Correlation of transcriptional measurements of RS library across two independent replicate cultures (>10 DNA counts across both replicates, n = 18,845) in E. coli performed on different days. (b) Correlation of transcriptional measurements of identical RSs with two different barcodes in E. coli (>10 DNA counts across both constructs, n = 2,273). Pearson correlation (r) is listed in each panel.

Supplementary Figure 4 Validations of gene expression measurements.

(a) Correlation of pooled RNA-seq measurements with individual RT-PCR data from isolate strains containing RS library members for three host species. (b) GFP fluorescence distributions of post-FACS RS library populations displayed as violin plots (n = 10,000 cells, mean value shown as horizontal bar). (c) Correlation of pooled FACS-seq measurements with individual flow cytometry measurements of isolate strains. Pearson correlation coefficients and sample sizes are listed for (r and n) listed in each subplot.

Supplementary Figure 5 Alternative reporter gene experiments.

Correlation between transcription (a) and translation (b) data measured using sfGFP and an alternate reporter mCherry. Sample sizes (n) and Pearson correlation coefficients (r) are listed in the lower right of each plot.

Supplementary Figure 6 Transcription start sites in three species.

Distribution of transcription start sites (TSSs) for active regulatory sequences containing one primary TSS with >70% of reads starting within +/- 5 bp. Most TSSs occur between 20-50 bp upstream of the start codon for B. subtilis, E. coli, and P. aeruginosa.

Supplementary Figure 7 Alternative-growth-condition transcription data.

(a) Transcription activity for 18,205 members of the RS library across multiple growth conditions in E. coli is clustered and shown as a heatmap. Transcription levels are log2 (RNA/DNA) ratios normalized by the mean activity of control sequences (see Methods). (b) Ranked TSS locations of each RS measured in E. coli during LB exponential phase are shown, along with the TSS distribution (top panel) and the frequency of multiple TSSs (inset) of the RS library. (c) Frequency of matching TSS positions for RSs in LB and M9 growth media. Pearson correlation of 1 signifies perfectly matched TSS between conditions and -1 denoting no or anti-correlation. Intermediate values denote partial TSS matching. Example RSs with high, moderate, and no correlation in TSS positions in LB and M9 are shown in the inset (n = 18,205). (d) A subset of 100 robust RSs with condition-invariant transcription levels of different strengths (top panel) generated from a single TSS of different untranslated region (UTR) lengths (bottom panel) is provided as a useful community resource.

Supplementary Figure 8 Comparison of TSS data for regulatory sequences (RSs) across growth conditions in E. coli.

(a) A histogram of the distribution of all 10 pairwise comparisons of TSS position of regulatory sequences measured in 5 growth conditions (LB exponential growth phase, LB-exp; LB exponential with iron depletion, LB-Fe; LB exponential with high salt, LB-NaCl; LB stationary phase, LB-stat; M9 minimal media exponential phase, M9-exp) is shown (n = 18,205). Perfectly matched TSSs in two conditions have a Pearson correlation of 1, while an un-matched pair of TSSs has a correlation of -1. (b) A histogram of the mean TSS correlations (Pearson r) of all RSs across all pairwise conditions show almost half of RSs have the same TSS across all 5 conditions (n = 18,205).

Supplementary Figure 9 De novo motif search.

(a) Motif analysis of promoters binned by activity levels. The top two motifs identified by MEME for each recipient at the four activity bins (low, medium low, medium high, high) are shown. All motifs resembled the σ70 motif or its degenerate versions. Statistically non-significant motifs are displayed in gray color. Additional MEME motif outputs are not shown since none were significantly different from σ70-like motifs. (b) Transcriptional activity heatmap grouped by hierarchical clustering (n=395). Motif finding was performed to identify motifs across ten clusters. The corresponding motif for each cluster is indicated by colored circle. (c) Removal of regulatory sequences containing the σ70 motif from the dataset and repeating the analysis performed in a did not reveal additional non-σ70-like motifs (n=76). Statistically non-significant motifs (MEME E-value > 1e-2) are displayed in gray color in b and c.

Supplementary Figure 10 The σ70 motif is the dominant factor governing transcriptional activity of horizontally acquired regulatory sequences.

(a) Pearson correlation of transcriptional activity versus promoter GC content (%GC), RNA structural stability (ΔG RNA), best σ70 match score (max(σ70)) and number of σ70 matches (n(σ70)) are displayed per recipient species. (b) Partial correlation displays activity versus variable by controlling to the other variables. Sample sizes (n) are 4314, 14809, and 17787 regulatory sequences for B. subtilis, E. coli, and P. aeruginosa respectively.

Supplementary Figure 11 Regulatory sequence translation levels determined by FACS-seq in B. subtilis, E. coli, and P. aeruginosa.

(a) The distribution of GFP fluorescence values of the regulatory sequence library in each recipient. (b) Translational activity of 8,898 regulatory sequences with measurable GFP fluorescence data across all three recipients. (c) Analysis of ribosome binding site sequence motifs in highly translated constructs. Motif logos were constructed using WebLogo v3.5.0. The genomic GC content of each species was used for background nucleotide frequency models and are listed in each subplot.

Supplementary Figure 12 Protein expression from Firmicute and Proteobacterial regulatory sequences.

Heatmap panels show the fraction of RS library distributed across bins of transcription and translation levels in three recipients (colored columns). Donor RSs from Firmicutes genomes are shown in (a) and from Proteobacteria genomes in (b). The top row of each heatmap subpanels use values normalized by the total number of regulatory sequences. The middle row use values normalized by each column bin corresponding to transcription windows. The bottom row use values normalized by each row bin corresponding to translation windows. Grey colored rows indicate data points with fewer than 10 RSs in total and insufficient for analysis.

Supplementary Figure 13 Cross-species and in silico comparisons of gene expression levels.

(a) Correlation of regulatory sequence activity in terms of transcription level and translation efficiency (calculated as the ratio of GFP protein levels and transcription levels) between recipient species. Each point corresponds to a single regulatory sequence that has measurable transcription and translation data. Pearson correlation coefficient (r) and statistical significance values (p) are shown for each subplot (n=212 for all six panels). (b) Correlation between calculated translation (TL) efficiency based on the RBS calculator and our measured translation efficiency across highly transcribed regulatory sequences (top 15%) in each recipient species (n = 581, 2276, and 2198 for B. subtilis, E. coli, and P. aeruginosa respectively).

Supplementary Figure 14 Regulatory activity of RS241 library in six bacterial species.

Regulatory sequences are sorted by activity (from high to low) per species by (a) transcription or (b) translation levels. Regulatory sequences are re-sorted by mean transcription levels (from low to high) across all species and plotted for (c) transcription and (d) translation levels. Transcriptional values were normalized with the highest expression construct having a value of 106. Gray lines correspond to sequences where no data was available. Species names are abbreviated as: B. subtilis, B.s.; C. glutanicum, C.g.; P. aeruginosa, P.a.; V. natriegens, V.n.; S. enterica, S.e.; E. coli, E.c.

Supplementary Figure 15 Cross-species transcription and translation level correlations.

(a) Pairwise Pearson correlation of transcription (blue triangle) and translation (green triangle) activity profiles of the RS241 library across six host species. Species are arranged based their 16S phylogenetic similarity. Numbers in each box correspond to the Pearson correlation coefficients (n = 241). (b) Scatter plot showing each pairwise correlation described in (a).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15

Life Sciences Reporting Summary

Supplementary Table 1

Regulatory sequence library metadata

Supplementary Table 2

Library expression data for B. subtilis, E. coli, and P. aeruginosa

Supplementary Table 3

Library expression data for E. coli in five growth conditions

Supplementary Table 4

RS241 library expression data in six species

Supplementary Data Set 1

Strains and materials used in this study

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Johns, N., Gomes, A., Yim, S. et al. Metagenomic mining of regulatory elements enables programmable species-selective gene expression. Nat Methods 15, 323–329 (2018). https://doi.org/10.1038/nmeth.4633

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