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. 2019 Aug;15(8):e8875.
doi: 10.15252/msb.20198875.

Multiplex Transcriptional Characterizations Across Diverse Bacterial Species Using Cell-Free Systems

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Free PMC article

Multiplex Transcriptional Characterizations Across Diverse Bacterial Species Using Cell-Free Systems

Sung Sun Yim et al. Mol Syst Biol. .
Free PMC article

Abstract

Cell-free expression systems enable rapid prototyping of genetic programs in vitro. However, current throughput of cell-free measurements is limited by the use of channel-limited fluorescent readouts. Here, we describe DNA Regulatory element Analysis by cell-Free Transcription and Sequencing (DRAFTS), a rapid and robust in vitro approach for multiplexed measurement of transcriptional activities from thousands of regulatory sequences in a single reaction. We employ this method in active cell lysates developed from ten diverse bacterial species. Interspecies analysis of transcriptional profiles from > 1,000 diverse regulatory sequences reveals functional differences in promoter activity that can be quantitatively modeled, providing a rich resource for tuning gene expression in diverse bacterial species. Finally, we examine the transcriptional capacities of dual-species hybrid lysates that can simultaneously harness gene expression properties of multiple organisms. We expect that this cell-free multiplex transcriptional measurement approach will improve genetic part prototyping in new bacterial chassis for synthetic biology.

Keywords: cell-free expression systems; gene expression; massively parallel reporter assay; synthetic biology; transcription.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1. DNA Regulatory element Analysis by Cell‐Free Transcription and Sequencing (DRAFTS)

DRAFTS uses crude cell lysate‐based cell‐free expression systems to harness source host cell's endogenous gene expression machineries. Compared to conventional single‐channel reporters with color or fluorescence readouts, multiplexed sequencing readouts from pooled reactions in DRAFTS scale up the throughput of measurement.

Biological replicates of transcriptional profiles (Tx) measured in separately prepared cell‐free lysate batches of E. coli.

Comparison of abundances of each library constructs in in vivo and in vitro measurements.

Correlation between transcriptional profiles (Tx) for regulatory sequence libraries from in vitro and in vivo measurements in E. coli. The color scale indicates the density of data points in a given area of the plot.

Correlation between primary TSS calls (in bp from ATG start codon) of regulatory sequences from in vitro and in vivo measurements in E. coli.

Data information: Dashed lines represent y = x in (B), (D), and (E). Sample sizes (n) and Pearson correlation coefficients (r) are shown in each plot. For normalization, transcription levels in log10 scale were transformed to Z‐scores. All measurements except (C) are based on two biological replicates.Source data are available online for this figure.
Figure 2
Figure 2. Development of cell‐free expression systems of diverse bacterial species

Schematic diagram of experimental pipeline for preparation and optimization of cell‐free expression systems.

Optimization of transcriptional output using different concentrations of Mg‐glutamate and K‐glutamate in 10 bacterial cell‐free expression systems with Broccoli as a reporter (solid lines, optimal buffer composition shown in red). No DNA template controls are shown as gray dashed lines. 12.5 nM of DNA template was used in all systems, except L. lactis (50 nM used).

Source data are available online for this figure.
Figure 3
Figure 3. Comparative functional analysis of regulatory sequences across 10 bacterial species through DRAFTS

Unrooted maximum‐likelihood phylogenetic tree of 10 bacterial species used in this study based on multiple sequence alignment of 16S rRNA genes (distance scale of 0.1) using Clustal Omega.

Transcriptional activities (Tx) of 421 regulatory sequences that were active in all 10 bacterial cell‐free expression systems.

Pairwise comparison of transcriptional profiles of 421 universally active regulatory sequences between bacterial species. Example scatter plots with relatively high and low Pearson correlation in the heat map (marked i and ii) are shown on left.

Correlation between evolutionary divergences (16S rRNA percent identity) and pairwise Pearson correlation of transcriptional profiles. Dashed line represents linear regression.

Activity profiles (Tx) of regulatory sequences from donor phyla Proteobacteria and Firmicutes in 10 bacterial species. GC contents of regulatory sequences from the phylogenetic groups are shown on right. Box plots are displaying the interquartile range (IQR) with median values (black line) and whiskers extending to the highest and lowest points within 1.5× the IQR.

Data information: For normalization, transcription levels in log10 scale were transformed to Z‐score. All measurements are based on two biological replicates.Source data are available online for this figure.
Figure 4
Figure 4. Linear regression modeling of transcriptional activation in 10 bacterial species through DRAFTS

Correlation between sequence features of regulatory sequences and transcriptional activities in each bacterial species.

Correlation between predicted and measured transcription levels for each bacterial species with various proportions of data for model training. Data were randomly split for the training and test sets, respectively, and Pearson correlation between predicted and observed transcription levels was computed for 10 times for each proportion. Box plots are displaying the interquartile range (IQR) with median values (black line) and whiskers extending to the highest and lowest points within 1.5× of the IQR.

Example linear regression models for transcriptional activation (Tx) in 10 bacterial species using data generated through DRAFTS. Data were randomly split in 10 and 90% for the training and test sets, respectively. Dashed lines represent linear regression. Sample sizes (n) and Pearson correlation coefficients (r) are shown in each plot.

Data information: For normalization, transcription levels in log10 scale were transformed to Z‐score. All measurements are based on two biological replicates.Source data are available online for this figure.
Figure 5
Figure 5. Transcriptional characterization of dual‐species hybrid cell‐free systems

Construction of hybrid systems through cell lysate mixing.

Principal component analysis of RS7003 transcriptional profile similarity for individual and hybrid lysates with different mixing ratios for (B) E. coli + B. subtilis and (C) E. coli + C. glutamicum hybrids.

Pairwise correlation of RS7003 transcriptional profiles between hybrid lysate and single constituent species lysates for (D) E. coli + B. subtilis and (E) E. coli + C. glutamicum hybrids with different mixing ratios.

Data information: For normalization, transcription levels in log10 scale were transformed to Z‐score. All measurements are based on two biological replicates.Source data are available online for this figure.

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