iGEM 2019 Project
Background and Significance
Ever washed your laundry on cold? Ever wondered what research went into the enzymes that make your laundry come out feeling fresh and clean? The same technology used to develop these laundry enzymes, a technology called directed evolution, has been used across industries to produce everything from printer paper to modern processed oil (Gurung 2013). The 2018 Nobel Prize in medicine was even awarded to a directed evolution advance that led to the 21 billion dollar drug company Humira (Informa 2018).
So what is directed evolution? To better understand directed evolution (DE), let’s trace through the DE biological process of developing a protease typically used in laundry detergent.
The enzymes active in a Tide Pod, such as proteases, were isolated from living organisms, and thus were originally optimized to clean most efficiently at higher temperatures (STAT 2019). While these proteins have already evolved to operate in their biological environment of roughly 37ºC, these same natural enzymatic powerhouses are rendered almost inert when just 10ºC lower, the normal temperature for cold laundry cycles.
In order to develop mutant enzyme versions that were more thermostable and suitable for washing clothes at colder water temperatures, researchers tapped into the biological technique of directed evolution.
What this meant for our example of a protease, is that researchers took the starting sequence of the enzyme (“Natural Gene” in Figure 1), and began by mutagenizing the DNA to create a diverse gene library. The resulting protein library was then meticulously screened by researchers who looked for versions of the enzyme that could function slightly more effectively at lower temperatures. Following this, the gene that corresponded to the improved enzyme was input back into the start of the cycle to continue the process of gradual improvement over time.
This screening-based directed evolution is the current standard across industry, and is a massive expense that largely contributes to the multi-billion dollar R&D pipelines of large chemical companies (Pratap 2019). Though directed evolution aims to mimic the evolutionary process that is so powerful in nature, the continuity of the cycle is broken by the laborious screening stage that is generally done by hand.
More recently, self-selecting directed evolution techniques (self-selecting systems, or SSS) have been developed in an attempt to close the hole in the continuous loop caused by hand screening and fully actualize the immense power of the natural evolution cycle in a laboratory setting. One such example of this is PACE, or Phage-Assisted Continuous Evolution (Esvelt 2011). Developed in the Liu lab in 2011, PACE takes advantage of the error-prone DNA replication of bacteriophages and, most importantly, uses a synthetic biological circuit to couple the functionality of a mutant protein with its propogation. Thus, optimal library members are selectively amplified and time-consuming manual screening processes are bypassed.
Though PACE has been implemented by select labs, it still has significant barriers and limitations. At the start of the summer our team planned to apply an SSS DE method to evolve novel proteins, but we ended up pivoting from this focus due to the following:
- Challenging Set-Up
First and foremost, current self-selecting directed evolution systems generally rely on a complex custom-made bioreactor for continuous bacterial culture. This challenge has been documented by numerous researchers and has greatly decreased accessibility to SSS (Heidelberg 2017). Our team was excited by the advances of PADE and PREDCEL, which aim to mitigate this challenge by discretizing PACE into individual reaction steps. While these discretized SSS methods have been shown to be effective for some proteins, continuous growth DE methods have been consistently shown to confer the advantages of “shorter experiment duration, [as well as] greater constancy of selective pressure and population size throughout evolution,” thereby increasing the likelihood of success (Suzuki 2017).
- Phage Toxicity
An additional challenge is posed by the use of bacteriophages. While M13 does not kill E. coli, infection significantly reduces growth rate. Phage proteins can be toxic to the cell when overexpressed as our team found out the hard way over the summer when we tried to implement PREDCEL. To reduce toxicity, we had to troubleshoot driving expression of those proteins via implementation of a weak RBS and using alternate start codons. Other researchers such as the Liu Lab have aimed to bypass this toxicity by implementing more challenging
Figure 2: As acknowledged by Heidelberg 2017, although PACE can allow for effective self-selecting directed evolution, the continuous growth bioreactor that balances concentrations of both E. coli and phages is a significant barrier for most labs and iGEM teams.
These challenges led our team to shift focus from applying already developed directed evolution methods towards instead trying to foundationally improve the technique. We focused in on SSS directed evolution methods and did a deep dive into the literature. In addition to the above challenges that we wanted to address, we also came across the following aspects of SSS methods that had room for improvement:
- Organism Specific Constraints Self-selecting directed evolution techniques utilize chassis organism to amplify, mutate, and select for optimal gene/protein function. This reliance on in vivo protein development inherently limits the evolution to occur in organism-specific environmental constraints. For example, in the recent efforts to develop terminal deoxynucleotidyl transferase (TdT), researchers have struggled to apply directed evolution due to the fact that TdT is toxic for E. coli to produce (Perkel 2019). With our DiCE system, however, these organismal constraints would be lifted.
- Phage Washout A common problem with phage-based directed evolution technique is phage washout. This is when the selection pressure exceeds the proteins ability to evolve and therefore your protein fails to evolve.
- Lack of Standard Selection Schema For each directed evolution system, the user has to creatively design genetic circuits to link functionality to cell survival. This is a significant challenge and often limitation when trying to apply self-selecting directed evolution systems.
With these challenges and areas for improvement in mind, we converged on the 2019 Stanford iGEM project.
First and foremost, we developed the foundations for a novel self-selecting directed evolution method. This method, detailed in the next overview section, we termed Direct Chassis-agnostic Evolution, or DiCE. We worked to demonstrate that the fundamentals necessary for directed evolution could be achieved with our novel system. Furthermore, we worked to show that DiCE has the potential to be applied both within E. coli, as well as in cell-free extract. Hence both the chassis-free nomination for our new method as well as the division of our DiCE project sections into DiCE Cell-Free and DiCE In Vivo.
Additionally, we worked to advance SSS directed evolution methods by developing standard selection schema, termed PREDCEL-Plus, that could be applied both across prior directed evolution techniques such as PREDCEL and PACE, as well as for our novel DiCE system. Finally, we designed a DiCE self-selecting gene circuit to design a novel anti-CRISPR protein (AcrIIA4).
Results Overview
We developed Directed Chassis-agnostic Evolution, or DiCE, a novel, easy-to-implement selection-based directed evolution platform built off Qbeta replicase, an RNA-based RNA polymerase. We worked towards demonstrating DiCE’s ability to evolve proteins in both E. coli and cell-free environments. Check out our DiCE Cell-Free page to learn more about our primary work, and check out DiCE In Vivo to see additional foundational work demonstrating the viability of DiCE to be used a versatile range of chassis organisms.
Furthermore, we generated standardized Novel Selection Schema compatible with PREDCEL (Heidelberg 2017) to expand the range of synthetic biological parts that can be created by any SSS. Specifically, we were able to make first steps towards monitoring evolutionary progress with the infection reporter that was developed and created a new framework for conducting M13-based evolutions. Finally, we designed a selection schema to Implement DiCE and evolve a novel anti-CRISPR protein using our DiCE in vivo system.
Taken together, our work on SSS presents a foundational advance towards a future where part creation is easier, faster, and more accessible.