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[Preprint]. 2024 Apr 8:2024.04.08.588465.
doi: 10.1101/2024.04.08.588465.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Noor Radde  1 , Genevieve A Mortensen  1 , Diya Bhat  1 , Shireen Shah  1 , Joseph J Clements  1 , Sean P Leonard  1 , Matthew J McGuffie  1 , Dennis M Mishler  1   2 , Jeffrey E Barrick  1
Affiliations

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Noor Radde et al. bioRxiv. .

Update in

Abstract

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Populations of engineered cells can rapidly become dominated by "escape mutants" that evolve to alleviate this burden by inactivating the intended function. Synthetic biologists working with bacteria rely on genetic parts and devices encoded on plasmids, but the burden of different engineered DNA sequences is rarely characterized. We measured how 301 BioBricks on high-copy plasmids affected the growth rate of Escherichia coli. Of these, 59 (19.6%) negatively impacted growth. The burden imposed by engineered DNA is commonly associated with diverting ribosomes or other gene expression factors away from producing endogenous genes that are essential for cellular replication. In line with this expectation, BioBricks exhibiting burden were more likely to contain highly active constitutive promoters and strong ribosome binding sites. By monitoring how much each BioBrick reduced expression of a chromosomal GFP reporter, we found that the burden of most, but not all, BioBricks could be wholly explained by diversion of gene expression resources. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be "unclonable" because escape mutants will take over during growth of a bacterial colony or small laboratory culture from a transformed cell. We made this model available as an interactive web tool for synthetic biology education and added our burden measurements to the iGEM Registry descriptions of each BioBrick.

Keywords: International Genetically Engineered Machines (iGEM) competition; Registry of Standard Biological Parts; evolutionary failure; genetic stability; metabolic burden.

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Figures

Fig. 1.
Fig. 1.. Evolutionary failure of a population of engineered cells.
(A) Graphical representation of a differential equation model with one class of failure mutations that completely alleviates the fitness burden of an engineered DNA construct on a host cell. (B) Population dynamics expected from this model. Subpopulations of failed cells with mutated constructs evolve and outcompete the original engineered cells with functional constructs. Complete failure happens rapidly once the mutant cells reach a detectable frequency in the population. (C) Approximate numbers of cell divisions required for scale-up from a single engineered cell to laboratory and industrial processes requiring different culture sizes. (See the text and Table S1 for details.)
Fig. 2.
Fig. 2.. Simulations of evolutionary failure times for populations of engineered cells.
In each panel, the results for deterministic (black) and stochastic (red) simulations of the failure model are shown for one combination of burden (b) and failure mutation rate (μ) parameters. Vertical blue lines represent the culture scales shown in Figure 1C. Curves for stochastic simulations are partially transparent so that one appears pink and overlapping trajectories from multiple simulations appear red. Twenty stochastic simulations are displayed in each panel.
Fig. 3.
Fig. 3.. Cumulative distributions of times to 50% failure in stochastic simulations.
Curves represent the output from 10,000 replicate simulations for each parameter combination.
Fig. 4.
Fig. 4.. Measurements of BioBrick burden.
(A) Maps of the two plasmid backbones that housed most of the 301 BioBricks that were tested and the five BFP controls that were included in every assay. The prefix (pre) and suffix (suf) multiple cloning sites used in BioBrick assembly are shown. (B) Burden of each BioBrick tested. Burden is the percentage reduction in the growth rate of E. coli cells transformed with a BioBrick plasmid. Gray points are individual measurements. Bars are the means for all measurements of a BioBrick. For BioBricks with orange bars, the measured burden was significantly greater than zero (adjusted p < 0.05, one-tailed t-tests with Benjamini-Hochberg correction for multiple testing). Data used to create this figure are provided in Table S2 and Table S3.
Fig. 5.
Fig. 5.. Strong promoter and ribosome binding sites are more likely to be found in BioBricks exhibiting significant burden.
(A, B) Relative strengths of common promoters and ribosome binding site (RBS) BioBrick parts, as reported in the iGEM Registry. The numbers of examples of each promoter or RBS in the 301 BioBricks examined in this study are indicated above the bars (n). Some of these BioBricks contain multiple instances of these promoter and RBS parts. Dashed lines in A are the thresholds used to classify promoters as weak, medium, or strong. (C, D) Fraction of BioBricks tested that exhibited significant burden when grouped by the strongest gene expression element of each type that they contain. The total numbers of parts in each category are shown above the bars (n).
Fig. 6.
Fig. 6.. Expression of recombinant proteins from a plasmid reduces the growth rate of E. coli because it diverts some of its capacity for gene expression.
(A) E. coli DH10B-GEM host strain with the gene expression capacity monitoring device that constitutively expresses GFP integrated into its chromosome. (B) Maps for the BFP and RFP plasmid series. (C) Growth rates and fluorescent protein production rates for different BFP and RFP plasmids in E. coli DH10B-GEM. Dashed lines are Deming regressions showing that the reduction in growth rate is proportional to the reduction in the capacity of the host cell for protein expression within each set of strains. The rate of GFP production from the monitoring device is used as a readout of gene expression capacity. Rates of BFP and RFP production in cells with each type of plasmid are indicated by shading in the respective color. Error bars are 95% confidence limits. Two independent transformants of each BFP plasmid that were tested separately are displayed as points with different shapes. GFP and BFP production rates were measured on different relative scales and each series uses a different vector backbone and was measured under different growth conditions, so results should only be compared within each series. Data used to create this figure are provided in Table S5 and Table S6.
Fig. 7.
Fig. 7.. Some BioBricks exhibit burden from sources other than gene expression.
(A) Examples of expected results for two BioBricks that exhibit burden (b) that is wholly due to utilizing the gene expression capacity of the host cell. The reduction in growth rate is proportional to the reduction in GFP production according to a linear relationship (dashed line) that is established from measurements of control strains. (B) Examples of expected results for two BioBricks that exhibit burden from sources other than gene expression. (C) Results of measuring growth rates and GFP production rates for 259 BioBricks that do not contain fluorescent proteins that are expected to interfere with measuring GFP fluorescence in the E. coli host strain containing the gene expression capacity monitor. Points for each BioBrick are colored based on whether there was significant burden (reduction in growth rate). Symbols indicate whether the null hypothesis that all burden was due to utilizing the gene expression capacity of the host cell could be rejected. BioBricks with significant burden from sources other than gene expression are labeled with their accession numbers. Estimates of bO/b for these BioBricks are shown in Table 1.
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