Prolonging genetic circuit stability through adaptive evolution of overlapping genes

Methane to bioproducts: unraveling the potential of methanotrophs for biomanufacturing

With the continuous increase in the world population, anthropogenic activities will generate more waste and create greenhouse gases such as methane, amplifying global warming. The biological conversion of methane into biochemicals is a sustainable solution to sequester and convert this greenhouse gas. Methanotrophic bacteria fulfill this role by utilizing methane as a feedstock while manufacturing various bioproducts. Recently, methanotrophs have made their mark in industrial biomanufacturing. However, unlike glucose-utilizing model organisms such as Escherichia coli and Saccharomyces cerevisiae, methanotrophs do not have established transformation methods and genetic tools, making these organisms challenging to engineer. Despite these challenges, recent advancements in methanotroph engineering demonstrate great promise, showcasing these C1-carbon-utilizing microbes as prospective hosts for bioproduction. This review discusses the recent developments and challenges in strain engineering, biomolecule production, and process development methodologies in the methanotroph field.

Toxin-Antitoxin Systems Reflect Community Interactions Through Horizontal Gene Transfer

Bacterial evolution through horizontal gene transfer (HGT) reflects their community interactions. In this way, HGT networks do well at mapping community interactions, but offer little toward controlling them-an important step in the translation of synthetic strains into natural contexts. Toxin-antitoxin (TA) systems serve as ubiquitous and diverse agents of selection; however, their utility is limited by their erratic distribution in hosts. Here we examine the heterogeneous distribution of TAs as a consequence of their mobility. By systematically mapping TA systems across a 10,000 plasmid network, we find HGT communities have unique and predictable TA signatures. We propose these TA signatures arise from plasmid competition and have further potential to signal the degree to which plasmids, hosts, and phage interact. To emphasize these relationships, we construct an HGT network based solely on TA similarity, framing specific selection markers in the broader context of bacterial communities. This work both clarifies the evolution of TA systems and unlocks a common framework for manipulating community interactions through TA compatibility.

Degradation of polyethylene terephthalate (PET) plastics by wastewater bacteria engineered via conjugation

Wastewater treatment plants are one of the major pathways for microplastics to enter the environment. In general, microplastics are contaminants of global concern that pose risks to ecosystems and human health. Here, we present a proof-of-concept for reduction of microplastic pollution emitted from wastewater treatment plants: delivery of recombinant DNA to bacteria in wastewater to enable degradation of polyethylene terephthalate (PET). Using a broad-host-range conjugative plasmid, we enabled various bacterial species from a municipal wastewater sample to express FAST-PETase, which was released into the extracellular environment. We found that FAST-PETase purified from some transconjugant isolates could degrade about 40% of a 0.25 mm thick commercial PET film within 4 days at 50°C. We then demonstrated partial degradation of a post-consumer PET product over 5-7 days by exposure to conditioned media from isolates. These results have broad implications for addressing the global plastic pollution problem by enabling environmental bacteria to degrade PET.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

GENTANGLE: integrated computational design of gene entanglements

SUMMARY

The design of two overlapping genes in a microbial genome is an emerging technique for adding more reliable control mechanisms in engineered organisms for increased stability. The design of functional overlapping gene pairs is a challenging procedure, and computational design tools are used to improve the efficiency to deploy successful designs in genetically engineered systems. GENTANGLE (Gene Tuples ArraNGed in overLapping Elements) is a high-performance containerized pipeline for the computational design of two overlapping genes translated in different reading frames of the genome. This new software package can be used to design and test gene entanglements for microbial engineering projects using arbitrary sets of user-specified gene pairs.

AVAILABILITY AND IMPLEMENTATION

The GENTANGLE source code and its submodules are freely available on GitHub at https://github.com/BiosecSFA/gentangle. The DATANGLE (DATA for genTANGLE) repository contains related data and results and is freely available on GitHub at https://github.com/BiosecSFA/datangle. The GENTANGLE container is freely available on Singularity Cloud Library at https://cloud.sylabs.io/library/khyox/gentangle/gentangle.sif. The GENTANGLE repository wiki (https://github.com/BiosecSFA/gentangle/wiki), website (https://biosecsfa.github.io/gentangle/), and user manual contain detailed instructions on how to use the different components of software and data, including examples and reproducing the results. The code is licensed under the GNU Affero General Public License version 3 (https://www.gnu.org/licenses/agpl.html).

A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover

Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.

Fighting the battle against evolution: designing genetically modified organisms for evolutionary stability

Synthetic biology has made significant progress in many areas, but a major challenge that has received limited attention is the evolutionary stability of synthetic constructs made of heterologous genes. The expression of these constructs in microorganisms, that is, production of proteins that are not necessary for the organism, is a metabolic burden, leading to a decrease in relative fitness and make the synthetic constructs unstable over time. This is a significant concern for the synthetic biology community, particularly when it comes to bringing this technology out of the laboratory. In this review, we discuss the issue of evolutionary stability in synthetic biology and review the available tools to address this challenge.