This week’s BEACON Researchers at Work post is by University of Washington postdoc Sean Sleight.
Synthetic biology is a relatively new field that uses engineering principles to design and build novel biological functions and systems. In 2000, Michael Elowitz and colleagues constructed the first synthetic gene network called the repressilator. This genetic circuit consisted of three repressor genes connected in a feedback loop, such that each gene represses the next gene in the loop, like a genetic version of the paper-rock-scissors game. The output of the circuit was Green Fluorescent Protein (GFP) to read out the oscillatory behavior of the network using fluorescence microscopy.
Fast forward to 2011. Now we have several synthetic biology labs worldwide and hundreds of undergraduate teams that compete every year in the International Genetically Engineered Machine competition (iGEM) to engineer novel organisms. At the beginning of the summer, the students are given a kit of plasmids that encode standardized biological parts such as promoters, ribosome binding sites, coding sequences, and transcriptional terminators. These parts, called BioBricks, have been used to engineer bacteria to function as a black and white photographic film, generate colored pigments, smell like bananas, and develop a designer vaccine for Helicobacter pylori (the bacterium that causes ulcers). Successes in synthetic biology groups include the overproduction of an anti-malaria compound, multicellular pattern formation, Craig Venter‘s synthetic cell, and the development of various biofuels.
Besides its numerous applications, synthetic biology is also a powerful system for studying evolution. After finishing my graduate work doing experimental evolution in Richard Lenski‘s lab, I was intrigued by the possibility of being able to assemble large numbers of BioBricks together on plasmids and watching how these modular DNA sequences change over time. I decided that my project should tackle one of the biggest problems in synthetic biology, evolutionary stability of genetic circuits, while at the same time involve the study of evolution. So my research deals with understanding the evolutionary stability dynamics and loss-of-function mutations in genetic circuits, then using this information to engineer mutationally robust circuits.
Genetic circuits are destined to fail unless they impart some beneficial function to the cell or there is a selective environment designed for maintaining circuit function over evolutionary time. Due to the metabolic load of having to express foreign genes, as cells divide, one with a mutant plasmid may grow slighly faster than cells having all functional plasmids. As plasmids segregate to daughter cells, a new cell may have multiple copies of the mutant plasmid and grow even faster. Eventually a cell emerges with no copies of the original plasmid and this cell can outcompete the functional cells in the population.
For my project (referenced below), I measured the stability of several BioBrick-assembled genetic circuits propagated in Escherichia coli over multiple generations and found the mutations that caused their loss-of-function. In this post, I will focus on discussing the results of one circuit called T9002. T9002 works by expressing an activator protein called LuxR. When the inducer molecule AHL is added to the media, it binds to LuxR and activates GFP expression downstream from the luxR promoter. This circuit loses function in less than 20 generations and the mutation that causes its loss-of-function is a deletion between two repeated transcriptional terminators. To measure the effect between transcriptional terminator sequence similarity and evolutionary stability, six versions of T9002 were re-engineered with a different transcriptional terminator at the end of the circuit. The figure below shows the BioBrick ID numbers/names with promoters (arrows), ribosome binding sites (ovals), coding sequences (arrows), and double transcriptional terminators (octagons) for the original circuit (top) and re-engineered circuit (bottom).
Can mutational robustness be increased by removing the sequence similarity between the first and second terminators? How predictable are mutations in genetic circuits?
The figure below shows the evolutionary stability dynamics of the original T9002 circuit vs. three of the re-engineered circuits. The circuit expression level (shown in fluorescence/OD ) is plotted vs. generations. It turns out that changing the terminator of the circuit also changes its expression level since terminator strength can change RNA degradation. The table in the corner shows the relationship of each circuit to expression level and sequence similarity between terminators. The T9002 circuit, with high sequence similarity between terminators and high expression, loses function in less than 20 generations. The T9002-E circuit has an increased evolutionary half-life of over 2-fold on average and this is likely due to having no terminator sequence similarity since its expression level is similar to T9002. The T9002-F circuit is the most robust, with a 17-fold increase in evolutionary half-life, due to having both a low expression level and low sequence similarity. The T9002-D circuit, with medium expression level and sequence similarity, has a half-life in between the T9002 and T9002-F circuits. After noticing the pattern between expression level and evolutionary half-life, I tested this relationship with several circuits and found that on average half-life decreases exponentially with expression level.
To understand the predictability of mutations in these circuits, the loss-of-function mutations in one clone from nine populations were discovered and shown in the table below. When there are repeated terminators, as in the T9002 circuit, the same exact deletion is found between terminators in all nine populations. However, when sequence similarity is decreased, as in the T9002-D circuit, there are sometimes deletions between repeated sequences, but also less predictable mutations. The T9002-E and F circuits, with no sequence similarity between terminators, have mutations of various types that involve inactivating either the luxR gene or luxR promoter.
Overall, the T9002 circuit can be re-engineered to be more mutationally robust by decreasing its expression level and sequence similarity between terminators. Presumably deletions between repeated terminators occur at a high rate and therefore this circuit can be re-engineered to have a lower mutation rate by removing a certain class of mutations from occurring. Although decreasing mutation rate effectively increases mutational robustness (T9002 vs. T9002-E), decreasing expression level has a stronger relative effect on evolutionary half-life (T9002 and T9002-E vs. T9002-F).
My current BEACON project extends this work by understanding mutational robustness in metabolic pathways. For this project, instead of rationally re-engineering pathways, I will be using a directed evolution approach to identify designs that are the most robust.
References: Sleight S.C., B.A. Bartley, J.A. Lieviant, and H.M. Sauro. Designing and engineering evolutionary robust genetic circuits. 2010. Journal of Biological Engineering, 4:12.
For more information about Sean’s work, contact him at sleight at u dot washington dot edu.