The Problem: Genetic Instability
In industrial production, technical personnel use genetically engineered microorganisms (GEMs) as cell factories to synthesize drugs[1]or produce food [2]. However, genetic instability including random mutations, horizontal gene transfer, genome rearrangement and the activities of mobile DNA elements [3]will lead to the appearance of cheater cells which get rid of the metabolic burden of synthesizing exogenous products. Given the liberation from synthesis, cheaters may gain remarkable survival advantages over common workers. Such phenomenon will therefore cause a great loss in both quality and quantity of the products. If no further step is taken, it will lead to irreversible strain degeneration in the end.
The problem also arises in other applications of GEMs, such as scientific research, medical treatment and more practical fields like bioremediation [4] and biodecomposition [5]. With the innate genetic instability, GEMs evolve everlastingly towards the optimal metabolic condition until the loss of extra synthetic burden, hence the sustainability and safety of GEMs products cannot be guaranteed.
Previous iGEM teams had also paid attention to the problem of genetic instability. Austin UTexas 2015 endeavored to identify and better characterize the types of gene sequences that are prone to be affected by genetic instability; Austin UTexas 2019 focused on the measurement of metabolic burden of an exogenous gene and used a model to predict how big of a metabolic burden would cause genetic instability; TAU_Israel 2020 did an excellent work in managing genetic instability by linking a target gene to the N-terminus of a necessary gene in the genome of the engineered microorganism. All these projects provided profound insights of genetic instability and helped us identify the problem we faced. However, the two projects of Austin UTexas didn’t put forward practical approaches to solve the problem and TAU_Israel’s project didn’t consider the case of single-base substitution of target protein. We were dedicated to figure out a more comprehensive and universal solution.
Our Inspiration: A Natural Model of Policing
In the natural microorganism's colonies, cooperation is a common strategy that microbes use for survival. The workers secrete public substances into the environment for all to use, including enzymes to digest nutrients [6] and biosurfactants to promote group motility [7]. However, some cunning cheaters emerge in the course of evolution. Those mutated cheaters gain the benefit of public goods without making any efforts to production, therefore acquire superior survival fitness. But surprisingly, instead of being overthrown by the cheaters, the mixed population reaches a stable equilibrium after multiple generations.
According to recent research [8,9,10], the cause for the population balance lies in the policing mechanism of the colony. The key role of the mechanism is guards who are transformed from workers through the activation of quorum-sensing (QS) system. After the transformation, guards secrete a toxin to selectively target cheaters. The production of public substances is associated to the toxin endurance mechanism, which protects workers from the cytotoxin. In short, the guard-cheater policing mechanism protects workers’ collaboration from exploitation of cheaters [9].
The cheating-policing model provides profound insights about how to prevent infiltration by cheaters [10]. We consider it as natural mechanism to tackle genetic instability.
Our Solution: Guards Control Cheaters
Enlighted by the natural model, we design worker and guard cells. Workers are responsible for the synthesis of aimed products, while guards are responsible to detect and kill cheaters that evolve from workers. They interact with each other through two sensing systems, Pmr and Agr.
Since different protein tends to occur different types of mutation [11], the platform provides two options of mutation-reporting mechanism in workers for users to choose according to the characteristic of their protein. Despite the differences, these two ways achieve the same goal, which is to detect the mutation that converts workers to cheaters and then present the message to guard through Agr system.
Guards are designed to synthesize aimed products when there is no cheaters’ signal. Once the guards receive the specific signal produced by cheaters, they will switch themselves into killing mode and secrete mcherry fluorescent proteins as lethal weapons to exterminate cheaters through Pmr system.
Our Goal: A Cheater-proof System
Our goal is to construct a universal platform for protein production to kill cheaters and manage genetic instability. Moreover, the platform utilizes mCherry as an indicator for the quantity of cheaters, allowing users to quantitatively control the population growth by altering the number of guards they put in. These unique advantages enable further applications of our platform, such as strain screening, directed evolution and therapeutic GEMs.
References
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