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. 2014 Jul;10(7):1668-78.
doi: 10.1039/c3mb70606k. Epub 2014 Feb 21.

Engineering Reduced Evolutionary Potential for Synthetic Biology

Free PMC article

Engineering Reduced Evolutionary Potential for Synthetic Biology

Brian A Renda et al. Mol Biosyst. .
Free PMC article


The field of synthetic biology seeks to engineer reliable and predictable behaviors in organisms from collections of standardized genetic parts. However, unlike other types of machines, genetically encoded biological systems are prone to changes in their designed sequences due to mutations in their DNA sequences after these devices are constructed and deployed. Thus, biological engineering efforts can be confounded by undesired evolution that rapidly breaks the functions of parts and systems, particularly when they are costly to the host cell to maintain. Here, we explain the fundamental properties that determine the evolvability of biological systems. Then, we use this framework to review current efforts to engineer the DNA sequences that encode synthetic biology devices and the genomes of their microbial hosts to reduce their ability to evolve and therefore increase their genetic reliability so that they maintain their intended functions over longer timescales.


Fig. 1
Fig. 1. What is evolutionary potential and how can it be engineered?
At its most basic level, an organism's evolutionary potential (or evolvability) is its capacity for producing viable offspring with genetic variation. Evolvability is often used to refer more specifically to the relative chances of accessing mutations or pathways consisting of multiple mutational steps that produce a beneficial change in the fitness of an organism, such that it can more rapidly reproduce or better survive to outcompete its progenitor. Different DNA sequences and host organism chassis can be used to engineer biological systems with similar or identical properties. Some of these choices may be more or less evolvable by two general mechanisms. First, there may be different rates of mutations in the genetic information used to encode two systems with the same function. Second, the same set of mutations may affect the properties of two systems with the same function in different ways. In the context of synthetic biology, it may be useful to reduce evolvability when mutations that inactivate an engineered biological device give the host organism a selective fitness advantage, such that malfunctioning systems rapidly take over a population of cells after they evolve.
Fig. 2
Fig. 2. Measuring the genetic reliability of biological machines
(A) Genetic reliability has been defined as the evolutionary half-life (E½) of a synthetic biology device, measured as the number of cell doublings over which 50% of the engineered function (e.g., output of a reporter gene) is maintained in a microbial culture., This value can be measured by monitoring device function while propagating cells through many cycles of serial transfer and regrowth or during continuous growth in a chemostat. Two different example decay curves are shown, for device 1 and for a more genetically reliable device 2. (B) Inactivation of many synthetic biology devices is dominated by a single mutational event that leads to a complete loss of the designed function. In this situation, all cells in a population have either 100% or 0% function at any given time. This distribution of activities present in the population at selected time points is shown on separate vertical axes for device 1 with each horizontal peak containing the cumulative number of cells with a given activity. This distribution would be obtained by measuring expression of a fluorescent protein reporter in each cell in the population by flow cytometry, for example. In this single-hit inactivation regime, competition within a microbial culture will lead to a relatively sharp decline in overall device function as one or more new mutant cells take over population when loss of the engineered function confers a fitness benefit. (C) The function of other synthetic biology designs like device 2 may degrade through accumulating multiple mutations that each partially reduce the activity (as shown) or as mutations that partially inactivate the engineered function activity compete with mutations that completely inactivate it. Competition within a microbial culture in this case may lead to a more gradual and complex decal curve for the level of function in the overall population over time.
Fig. 3
Fig. 3. Example of the molecular events causing evolutionary failure of a device
A classic study of the spectrum of spontaneous mutations that inactivate the lacI gene in E. coli serves as a fairly typical example of the relative rates of different types of mutations that may inactivate a DNA design used in synthetic biology. Each symbol indicates one observation of a given type of mutation. Of 140 total mutations, 94 (67%) were copy number changes in a sequence consisting of three tandem repeats of a four-base sequence: 78 additions of one unit (green) and 18 deletions of one unit (magenta). Of the 19 deletions (red), 7 were flanked by a repeat of at least five bases (circles). Near the beginning of the gene, 2 insertions of an IS1 transposable element (cyan) and 1 duplication of 88 base pairs that resulted in a frameshift (purple) were observed. The remaining 24 point mutations were base substitutions or insertions or deletions of a few bases. Thus, mutations are not randomly distributed within a DNA sequence, and repeat sequences can be a significant source of local mutational hotspots.
Fig. 4
Fig. 4. Engineering DNA sequences for reduced mutation rates
(A) Synthetic biology designs that rely on multiple copies of the same genetic part have inherent sequence homologies of tens to hundreds of bases that lead to instability due to the high rates of recombination events between these parts that will delete one copy of the repeat and any intervening sequences. This type of instability can be engineered against by designing systems to use alternative parts with fulfill the same function (e.g. different transcriptional terminators with the same strength) or by utilizing sequence re-coded variants of a part that function equivalently (e.g. by altering codon usage across an entire protein part). Eliminating the possibility of homologous recombination between copies of a part will lower the mutation rate in the designed DNA sequence, such that other types of mutations will now dominate the spectrum of inactivating mutations. (B) Simple sequence repeats experience slip-strand mutations that lead to locally elevated rates of base insertions and deletions that can cause frameshift mutations and inactivate a gene. Often these repeat sequences can be avoided by altering sequences locally to maintain the properties of the encoded protein (e.g. by substituting synonymous codons). (C) Certain DNA sequence sites result in DNA methylation by enzymes in common E. coli strains. In the case of Dcm methylation, the resultant 5-methyl cytosine can spontaneously deaminate directly to thymidine, leading to an increased mutation rate at these sites that cannot be repaired., Avoiding sites recognized by these methylases or that are prone to other types of chemical damage can decrease the overall rates of inactivating base substitution mutations.
Fig. 5
Fig. 5. Engineering clean genome hosts to reduce evolvability
(A) Deleting all copies of a self-replicating genomic element, such as a transposon, from a chassis genome can eliminate a major source of mutations that inactivate engineered biological systems. (B) E. coli encodes several error-prone DNA polymerases induced by stress or DNA damage. After a cell experiences DNA damage creating a genomic lesion, these error-prone mechanisms may compete with alternative pathways to repair the damage, or certain types of DNA damage may only be repairable by these mechanisms. By deleting the error-prone DNA polymerases, the rare cells that typically experience these types of damage under laboratory conditions either will have the damage repaired by high-fidelity mechanisms or will not survive. In either case, the overall mutation rate of the bacterial population is reduced.
Fig. 6
Fig. 6. Reducing the evolvability of DNA sequences through functional constraint
(A) Overlapping the sequence information required for expressing a protein such as GFP (green) with a selectable marker such as antibiotic resistance (cyan) is one strategy to limit a device's evolvability. In principle, since stable expression of the selectable marker is required for growth, the more sequence information that the two genes share (yellow), the smaller the target size for possible mutations that inactivate the synthetic construct and maintain organismal viability. This overlap can include sharing promoters, ribosome binding sites, terminators, reading frame, and coding space. (B) Bidirectional promoter design which enables constitutive expression of genes on both the forward and reverse strands of DNA. Since the −35 and −10 elements of the forward and reverse promoters require conservation of some of the same bases, a mutation in their overlap would alter the expression of both genes. If one of the genes is a selectable marker, this change can be made lethal to the organism. In this case selecting for kanamycin resistance conferred by the forward gene (cyan) prevented a high-frequency deletion between sequence repeats that eliminated the −35 element of the forward promoter and led to the more stable maintenance of GFP expression in the reverse direction (green).

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