Difference between revisions of "Team:Vanderbilt/Project/Background"

Every system that is genetically engineered harbors a potentially fatal vulnerability. The source of life's great diversity - spontaneous mutation - is for the synthetic biologist the source of constant apprehension and risk. The relentlessness of genetic mutation has discouraged previous attempts at ever overcoming this perpetually looming menace. Whether in a multifaceted genetic circuit or a simple protein expression platform, mutation is inevitable, and once it disrupts function, the organism will no longer experience the burden of transgene expression, causing the mutant to outcompete whatever has the intended sequence. Evolution and mutation work hand in hand to select against the maintenance of synthetic DNA sequences. Indeed, the mantra has been that time in the form of mutation and evolution will always find a way to erode and ultimately destroy everything that an engineer builds, no matter how ingeniously it may designed.

This year, here at Vanderbilt iGEM, we are fighting back. We are proposing a novel approach based on rationally designed genomic architectures that promises to offer synthetic biologists unprecedented control over the evolutionary stability of their creations. At the heart of our strategy is an advanced computational algorithm that integrates decades worth of scientific data identify and correct the highly-mutation prone 'hotspots' that lurk in every gene. Our strategy has a strong foundation in a rich literature from the fields of cancer biology and others that have annotated and characterized mutation hotspots for almost every conceivable source of mutagen, from ultraviolet radiation to recombination to polymerase errors.

When combined with synthetic DNA technology, our process becomes a simple and reliable optimization that is universally applicable to any coding gene being expressed in any organism. Our project first demonstrates the power of this rational synthetic gene design strategy by employing several canonical as well as highly original protocols for assaying DNA damage and its effects on the stability of artificial genetic elements. From these techniques, we can quantify everything from the selective subset of mutation types occurring on an in vitro level, up to how mutational loss of function translates at the scale of populations of genetically modified organisms.

To complement our work, we have harnessed our algorithm for use in what is becoming one of the most important tools for engineering biological molecules: directed evolution experiments. Not only can our engineered changes increase evolutionary stability in applications such as transgenic bioreactors, but it can also construct gene sequences that are more prone to mutate, thus accelerating studies into how to use evolutionary selection to produce tailored functional modifications to proteins.

Finally, we have investigated ways to build new genetically modified organisms that exhibit greatly increased resistance to mutation. Combining our sequence-based strategies with the introduction of exogenous genes and removal of endogenous genes has enabled us to produce an expression platform for synthetic genes that not only has enhanced DNA repair mechanisms, but also has an entire artificial pathway introduced for the elimination of mutant strains from a population.

While any single engineered change to reduce mutation may still fail, when our innovative approaches to modulating evolutionary stability are taken in combination, they offer an unprecedented hope for taming evolutionary entropy. More than a victory for synthetic biology, we prove that through rational design principles- exactly what mutation most virulently tries to uproot- and with enough clever innovations, it is possible to defend against what seemed like an inevitability of nature. Score one for engineering.

In terms of production, bioreactors are especially vulnerable to gene mutation due to the metabolic stress exhibited by the cells in such an environment. The constant push for maximum production gives an evolutionary advantage to any cells that spontaneously mutated to produce less or no product. An entire incubator could be overtaken by these mutated cells leading to a loss in money, time, and resources. In a medical context where pharmaceutical agents are produced, a mutation can have even worse consequences of creating a deleterious drug that would be harmful, such as antibody-producing strain of cells that undergoes a spontaneous mutation leading to unintended interactions.

The advent of gene therapy which allows insertion of genes directly into living cells poses parallel difficulties in terms of ensuring continued function. The cell will not only try to get rid of the foreign DNA because of its associated metabolic load, the sequence will be prone to mutations that either cause a nonfunctional or dysfunctional protein product.

All of these issues are driven by the effect of metabolic load and selective pressures on genes which cause them to either be inactivated or deleted. It has been demonstrated that increasing the metabolic load of the cell decreases its rate of replication which is directly correlated to its Darwinian fitness. Cells that have to produce are less adapted to their environment and are more likely to be outcompeted in a heterogeneous population where other cells are not under the same load []. Moreover, the load of translating active proteins that create their own secondary metabolites compounds the stress on the cell leading to further decreased fitness.

The metabolic load itself can eliminate the cells ability to survive, but there is an even greater danger posed by increasing cellular load which is stress-induced mutagenesis. The stress of augmented rate of protein production strains the cell into initiating a global response system that changes gene expression and thereby cell metabolism. This includes pathways that increase mutation potential by up-regulating error prone DNA polymerases and down-regulating error correcting enzymes as well as encouraging the movement of insertion sequences. This mutator state under stressful conditions from increased metabolic load is important for adaptive evolution but is counterproductive to the goals of synthetic biologists attempting for stable expression [].

At the heart of our strategy is an advanced computational algorithm that integrates decade’s worth of scientific data in order to identify and correct the highly-mutation prone ‘hotspots’ that lurk in every gene. Our strategy has a strong foundation in a rich literature from the fields of cancer biology and others that have annotated and characterized mutation hotspots for almost every conceivable source of mutagen, from ultraviolet radiation to recombination to polymerase errors.

Our project first demonstrates the power of this rational synthetic gene design strategy by employing several canonical as well as highly original protocols for assaying DNA damage and its effects on the stability of artificial genetic elements. From these techniques, we can quantify everything from the selective subset of mutation types occurring on an in vitro level, up to how mutational loss of function translates at the scale of populations of genetically modified organisms.

To complement our work, we have harnessed our algorithm for use in what is becoming one of the most important tools for engineering biological molecules: directed evolution experiments. Not only can our engineered changes increase evolutionary stability in applications such as transgenic bioreactors, but it can also construct gene sequences that are more prone to mutate, thus accelerating studies into how to use evolutionary selection to produce tailored functional modifications to proteins.

Another way of decreasing the rate of mutation is to lessen the metabolic load on the cell. This can be easily done by incorporating weak promoters and ribosome binding sites throughout the circuit lessening the rate of protein expression. Ensuring rapid degradation of protein product also reduces metabolic load although the mechanism by which this is done (degradation tag) actually increases the load by using up cellular proteases. Additionally, plasmid copy-number can have a large influence on metabolic load meaning medium- to low-copy number plasmids are preferable for circuit stability. Studies show that reducing expression 4-fold can increase the evolutionary half-life of the circuit 17-fold [6].

Following the same reasoning as above, replacing constitutive promoters with inducible ones substantially decreases the stress on the cell. A constitutive promoter constantly engages the cell transcriptional and translational machinery leading to unsustainable protein production. And inducible promoter has the advantage of only increasing the metabolic load for a limited time that is tightly controlled. Empirical research demonstrated that after 60 generations, a circuit under the control of a constitutive promoter completely lost its function but the same circuit under an inducible promoter still had 25% of its original expression [6].

Visualizing Evolution in Real Time

Engineering organisms with recombinant DNA puts strain on the metabolism of the organism which can reduce the cell’s fitness. This can be quantified by using VERT (Visualizing Evolution in Real Time), a technique that uses three different fluorescent proteins to investigate the stability dynamics created by the transcriptional interactions of these three genes [6]. In order to create a competition differential, randomized promoters, ribosome binding sites (RBS), and terminators were included to create several unique circuits which would lead to certain proteins having transcriptional and translational advantages. Over time, each circuit has the potential to reach a particular equilibrium point of protein expression giving the cell population a unique color. See image below.