Engineering reduced evolutionary potential for synthetic biology

Improving the genetic stability of bacterial growth control for long-term bioproduction

Using microorganisms for bioproduction requires the reorientation of metabolic fluxes from biomass synthesis to the production of compounds of interest. We previously engineered a synthetic growth switch in Escherichia coli based on inducible expression of the β- and β'-subunits of RNA polymerase. Depending on the level of induction, the cells stop growing or grow at a rate close to that of the wild-type strain. This strategy has been successful in transforming growth-arrested bacteria into biofactories with a high production yield, releasing cellular resources from growth towards biosynthesis. However, high selection pressure is placed on a growth-arrested population, favoring mutations that allow cells to escape from growth control. Accordingly, we made the design of the growth switch more robust by building in genetic redundancy. More specifically, we added the rpoA gene, encoding for the α-subunit of RNA polymerase, under the control of a copy of the same inducible promoter used for expression control of ββ'. The improved growth switch is much more stable (escape frequency <10-9), while preserving the capacity to improve production yields. Moreover, after a long period of growth inhibition the population can be regenerated within a few generations. This opens up the possibility to alternate biomass accumulation and product synthesis over a longer period of time and is an additional step towards the dynamical control of bioproduction.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Escape mutants that alleviate this burden can rapidly evolve and take over cell populations, making genetic engineering less reliable and predictable. Synthetic biologists often use genetic parts encoded on plasmids, but their burden is rarely characterized. We measured how 301 BioBrick plasmids affected Escherichia coli growth and found that 59 (19.6%) were burdensome, primarily because they depleted the limited gene expression resources of host cells. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be unclonable. We made this model available online for education ( https://barricklab.org/burden-model ) and added our burden measurements to the iGEM Registry. Our results establish a fundamental limit on what DNA constructs and genetic modifications can be successfully engineered into cells.

Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences

Engineered plasmids have been workhorses of recombinant DNA technology for nearly half a century. Plasmids are used to clone DNA sequences encoding new genetic parts and to reprogram cells by combining these parts in new ways. Historically, many genetic parts on plasmids were copied and reused without routinely checking their DNA sequences. With the widespread use of high-throughput DNA sequencing technologies, we now know that plasmids often contain variants of common genetic parts that differ slightly from their canonical sequences. Because the exact provenance of a genetic part on a particular plasmid is usually unknown, it is difficult to determine whether these differences arose due to mutations during plasmid construction and propagation or due to intentional editing by researchers. In either case, it is important to understand how the sequence changes alter the properties of the genetic part. We analyzed the sequences of over 50,000 engineered plasmids using depositor metadata and a metric inspired by the natural language processing field. We detected 217 uncatalogued genetic part variants that were especially widespread or were likely the result of convergent evolution or engineering. Several of these uncatalogued variants are known mutants of plasmid origins of replication or antibiotic resistance genes that are missing from current annotation databases. However, most are uncharacterized, and 3/5 of the plasmids we analyzed contained at least one of the uncatalogued variants. Our results include a list of genetic parts to prioritize for refining engineered plasmid annotation pipelines, highlight widespread variants of parts that warrant further investigation to see whether they have altered characteristics, and suggest cases where unintentional evolution of plasmid parts may be affecting the reliability and reproducibility of science.

Beneficial applications of biofilms

Many microorganisms live in the form of a biofilm. Although they are feared in the medical sector, biofilms that are composed of non-pathogenic organisms can be highly beneficial in many applications, including the production of bulk and fine chemicals. Biofilm systems are natural retentostats in which the biocatalysts can adapt and optimize their metabolism to different conditions over time. The adherent nature of biofilms allows them to be used in continuous systems in which the hydraulic retention time is much shorter than the doubling time of the biocatalysts. Moreover, the resilience of organisms growing in biofilms, together with the potential of uncoupling growth from catalytic activity, offers a wide range of opportunities. The ability to work with continuous systems using a potentially self-advancing whole-cell biocatalyst is attracting interest from a range of disciplines, from applied microbiology to materials science and from bioengineering to process engineering. The field of beneficial biofilms is rapidly evolving, with an increasing number of applications being explored, and the surge in demand for sustainable and biobased solutions and processes is accelerating advances in the field. This Review provides an overview of the research topics, challenges, applications and future directions in beneficial and applied biofilm research.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Populations of engineered cells can rapidly become dominated by "escape mutants" that evolve to alleviate this burden by inactivating the intended function. Synthetic biologists working with bacteria rely on genetic parts and devices encoded on plasmids, but the burden of different engineered DNA sequences is rarely characterized. We measured how 301 BioBricks on high-copy plasmids affected the growth rate of Escherichia coli. Of these, 59 (19.6%) negatively impacted growth. The burden imposed by engineered DNA is commonly associated with diverting ribosomes or other gene expression factors away from producing endogenous genes that are essential for cellular replication. In line with this expectation, BioBricks exhibiting burden were more likely to contain highly active constitutive promoters and strong ribosome binding sites. By monitoring how much each BioBrick reduced expression of a chromosomal GFP reporter, we found that the burden of most, but not all, BioBricks could be wholly explained by diversion of gene expression resources. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be "unclonable" because escape mutants will take over during growth of a bacterial colony or small laboratory culture from a transformed cell. We made this model available as an interactive web tool for synthetic biology education and added our burden measurements to the iGEM Registry descriptions of each BioBrick.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

The Causes for Genomic Instability and How to Try and Reduce Them Through Rational Design of Synthetic DNA

Genetic engineering has revolutionized our ability to manipulate DNA and engineer organisms for various applications. However, this approach can lead to genomic instability, which can result in unwanted effects such as toxicity, mutagenesis, and reduced productivity. To overcome these challenges, smart design of synthetic DNA has emerged as a promising solution. By taking into consideration the intricate relationships between gene expression and cellular metabolism, researchers can design synthetic constructs that minimize metabolic stress on the host cell, reduce mutagenesis, and increase protein yield. In this chapter, we summarize the main challenges of genomic instability in genetic engineering and address the dangers of unknowingly incorporating genomically unstable sequences in synthetic DNA. We also demonstrate the instability of those sequences by the fact that they are selected against conserved sequences in nature. We highlight the benefits of using ESO, a tool for the rational design of DNA for avoiding genetically unstable sequences, and also summarize the main principles and working parameters of the software that allow maximizing its benefits and impact.

Competition and evolutionary selection among core regulatory motifs in gene expression control

Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory schemes that carry their own bioenergetic cost. By ensuring that regulators are only expressed when needed, we demonstrate that autoregulation generally offers an advantage in an environment combining mutation and time-varying selection. Whether positive or negative feedback emerges as dominant depends primarily on the demand for the target gene product, typically to ensure that the detrimental impact of inevitable mutations is minimized. While self-repression of the regulator curbs the spread of these loss-of-function mutations, self-activation instead facilitates their propagation. By analyzing the transcription network of multiple model organisms, we reveal that reduced bioenergetic cost may contribute to the preferential selection of autoregulation among transcription factors. Our results not only uncover how seemingly equivalent regulatory motifs have fundamentally different impact on population structure, growth dynamics, and evolutionary outcomes, but they can also be leveraged to promote the design of evolutionarily robust synthetic gene circuits.

Modelling genetic stability in engineered cell populations

Predicting the evolution of engineered cell populations is a highly sought-after goal in biotechnology. While models of evolutionary dynamics are far from new, their application to synthetic systems is scarce where the vast combination of genetic parts and regulatory elements creates a unique challenge. To address this gap, we here-in present a framework that allows one to connect the DNA design of varied genetic devices with mutation spread in a growing cell population. Users can specify the functional parts of their system and the degree of mutation heterogeneity to explore, after which our model generates host-aware transition dynamics between different mutation phenotypes over time. We show how our framework can be used to generate insightful hypotheses across broad applications, from how a device's components can be tweaked to optimise long-term protein yield and genetic shelf life, to generating new design paradigms for gene regulatory networks that improve their functionality.

The Pathfinder plasmid toolkit for genetically engineering newly isolated bacteria enables the study of Drosophila-colonizing Orbaceae

Toolkits of plasmids and genetic parts streamline the process of assembling DNA constructs and engineering microbes. Many of these kits were designed with specific industrial or laboratory microbes in mind. For researchers interested in non-model microbial systems, it is often unclear which tools and techniques will function in newly isolated strains. To address this challenge, we designed the Pathfinder toolkit for quickly determining the compatibility of a bacterium with different plasmid components. Pathfinder plasmids combine three different broad-host-range origins of replication with multiple antibiotic resistance cassettes and reporters, so that sets of parts can be rapidly screened through multiplex conjugation. We first tested these plasmids in Escherichia coli, a strain of Sodalis praecaptivus that colonizes insects, and a Rosenbergiella isolate from leafhoppers. Then, we used the Pathfinder plasmids to engineer previously unstudied bacteria from the family Orbaceae that were isolated from several fly species. Engineered Orbaceae strains were able to colonize Drosophila melanogaster and could be visualized in fly guts. Orbaceae are common and abundant in the guts of wild-caught flies but have not been included in laboratory studies of how the Drosophila microbiome affects fly health. Thus, this work provides foundational genetic tools for studying microbial ecology and host-associated microbes, including bacteria that are a key constituent of the gut microbiome of a model insect species.

Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences

Engineered plasmids have been workhorses of recombinant DNA technology for nearly half a century. Plasmids are used to clone DNA sequences encoding new genetic parts and to reprogram cells by combining these parts in new ways. Historically, many genetic parts on plasmids were copied and reused without routinely checking their DNA sequences. With the widespread use of high-throughput DNA sequencing technologies, we now know that plasmids often contain variants of common genetic parts that differ slightly from their canonical sequences. Because the exact provenance of a genetic part on a particular plasmid is usually unknown, it is difficult to determine whether these differences arose due to mutations during plasmid construction and propagation or due to intentional editing by researchers. In either case, it is important to understand how the sequence changes alter the properties of the genetic part. We analyzed the sequences of over 50,000 engineered plasmids using depositor metadata and a metric inspired by the natural language processing field. We detected 217 uncatalogued genetic part variants that were especially widespread or were likely the result of convergent evolution or engineering. Several of these uncatalogued variants are known mutants of plasmid origins of replication or antibiotic resistance genes that are missing from current annotation databases. However, most are uncharacterized, and 3/5 of the plasmids we analyzed contained at least one of the uncatalogued variants. Our results include a list of genetic parts to prioritize for refining engineered plasmid annotation pipelines, highlight widespread variants of parts that warrant further investigation to see whether they have altered characteristics, and suggest cases where unintentional evolution of plasmid parts may be affecting the reliability and reproducibility of science.

The Pathfinder plasmid toolkit for genetically engineering newly isolated bacteria enables the study of Drosophila -colonizing Orbaceae

Toolkits of plasmids and genetic parts streamline the process of assembling DNA constructs and engineering microbes. Many of these kits were designed with specific industrial or laboratory microbes in mind. For researchers interested in non-model microbial systems, it is often unclear which tools and techniques will function in newly isolated strains. To address this challenge, we designed the Pathfinder toolkit for quickly determining the compatibility of a bacterium with different plasmid components. Pathfinder plasmids combine three different broad-host-range origins of replication with multiple antibiotic resistance cassettes and reporters, so that sets of parts can be rapidly screened through multiplex conjugation. We first tested these plasmids in Escherichia coli , a strain of Sodalis praecaptivus that colonizes insects, and a Rosenbergiella isolate from leafhoppers. Then, we used the Pathfinder plasmids to engineer previously unstudied bacteria from the family Orbaceae that were isolated from several fly species. Engineered Orbaceae strains were able to colonize Drosophila melanogaster and could be visualized in fly guts. Orbaceae are common and abundant in the guts of wild-caught flies but have not been included in laboratory studies of how the Drosophila microbiome affects fly health. Thus, this work provides foundational genetic tools for studying new host-associated microbes, including bacteria that are a key constituent of the gut microbiome of a model insect species.

IMPORTANCE

To fully understand how microbes have evolved to interact with their environments, one must be able to modify their genomes. However, it can be difficult and laborious to discover which genetic tools and approaches work for a new isolate. Bacteria from the recently described Orbaceae family are common in the microbiomes of insects. We developed the Pathfinder plasmid toolkit for testing the compatibility of different genetic parts with newly cultured bacteria. We demonstrate its utility by engineering Orbaceae strains isolated from flies to express fluorescent proteins and characterizing how they colonize the Drosophila melanogaster gut. Orbaceae are widespread in Drosophila in the wild but have not been included in laboratory studies examining how the gut microbiome affects fly nutrition, health, and longevity. Our work establishes a path for genetic studies aimed at understanding and altering interactions between these and other newly isolated bacteria and their hosts.

In vitro assembly of plasmid DNA for direct cloning in Lactiplantibacillus plantarum WCSF1

Lactobacilli are gram-positive bacteria that are growing in importance for the healthcare industry and genetically engineering them as living therapeutics is highly sought after. However, progress in this field is hindered since most strains are difficult to genetically manipulate, partly due to their complex and thick cell walls limiting our capability to transform them with exogenous DNA. To overcome this, large amounts of DNA (>1 μg) are normally required to successfully transform these bacteria. An intermediate host, like E. coli, is often used to amplify recombinant DNA to such amounts although this approach poses unwanted drawbacks such as an increase in plasmid size, different methylation patterns and the limitation of introducing only genes compatible with the intermediate host. In this work, we have developed a direct cloning method based on in-vitro assembly and PCR amplification to yield recombinant DNA in significant quantities for successful transformation in L. plantarum WCFS1. The advantage of this method is demonstrated in terms of shorter experimental duration and the possibility to introduce a gene incompatible with E. coli into L. plantarum WCFS1.

Addressing the challenges of symbiont-mediated RNAi in aphids

Because aphids are global agricultural pests and models for bacterial endosymbiosis, there is a need for reliable methods to study and control their gene function. However, current methods available for aphid gene knockout and knockdown of gene expression are often unreliable and time consuming. Techniques like CRISPR-Cas genome editing can take several months to achieve a single gene knockout because they rely on aphids going through a cycle of sexual reproduction, and aphids often lack strong, consistent levels of knockdown when fed or injected with molecules that induce an RNA interference (RNAi) response. In the hopes of addressing these challenges, we attempted to adapt a new method called symbiont-mediated RNAi (smRNAi) for use in aphids. smRNAi involves engineering a bacterial symbiont of the insect to continuously supply double-stranded RNA (dsRNA) inside the insect body. This approach has been successful in thrips, kissing bugs, and honeybees. We engineered the laboratory Escherichia coli strain HT115 and the native aphid symbiont Serratia symbiotica CWBI-2.3^T to produce dsRNA inside the gut of the pea aphid (Acyrthosiphon pisum) targeting salivary effector protein (C002) or ecdysone receptor genes. For C002 assays, we also tested co-knockdown with an aphid nuclease (Nuc1) to reduce RNA degradation. However, we found that smRNAi was not a reliable method for aphid gene knockdown under our conditions. We were unable to consistently achieve the expected phenotypic changes with either target. However, we did see indications that elements of the RNAi pathway were modestly upregulated, and expression of some targeted genes appeared to be somewhat reduced in some trials. We conclude with a discussion of the possible avenues through which smRNAi, and aphid RNAi in general, could be improved in the future.

Comparison of Kill Switch Toxins in Plant-Beneficial Pseudomonas fluorescens Reveals Drivers of Lethality, Stability, and Escape

Kill switches provide a biocontainment strategy in which unwanted growth of an engineered microorganism is prevented by expression of a toxin gene. A major challenge in kill switch engineering is balancing evolutionary stability with robust cell killing activity in application relevant host strains. Understanding host-specific containment dynamics and modes of failure helps to develop potent yet stable kill switches. To guide the design of robust kill switches in the agriculturally relevant strain Pseudomonas fluorescens SBW25, we present a comparison of lethality, stability, and genetic escape of eight different toxic effectors in the presence of their cognate inactivators (i.e., toxin-antitoxin modules, polymorphic exotoxin-immunity systems, restriction endonuclease-methyltransferase pair). We find that cell killing capacity and evolutionary stability are inversely correlated and dependent on the level of protection provided by the inactivator gene. Decreasing the proteolytic stability of the inactivator protein can increase cell killing capacity, but at the cost of long-term circuit stability. By comparing toxins within the same genetic context, we determine that modes of genetic escape increase with circuit complexity and are driven by toxin activity, the protective capacity of the inactivator, and the presence of mutation-prone sequences within the circuit. Collectively, the results of our study reveal that circuit complexity, toxin choice, inactivator stability, and DNA sequence design are powerful drivers of kill switch stability and valuable targets for optimization of biocontainment systems.

Refactored genetic codes enable bidirectional genetic isolation

The near-universal genetic code defines the correspondence between codons in genes and amino acids in proteins. We refactored the structure of the genetic code in Escherichia coli and created orthogonal genetic codes that restrict the escape of synthetic genetic information into natural life. We developed orthogonal and mutually orthogonal horizontal gene transfer systems, which permit the transfer of genetic information between organisms that use the same genetic code but restrict the transfer of genetic information between organisms that use different genetic codes. Moreover, we showed that locking refactored codes into synthetic organisms completely blocks invasion by mobile genetic elements, including viruses, which carry their own translation factors and successfully invade organisms with canonical and compressed genetic codes.

Evolutionary Stability Optimizer (ESO): A Novel Approach to Identify and Avoid Mutational Hotspots in DNA Sequences While Maintaining High Expression Levels

Modern synthetic biology procedures rely on the ability to generate stable genetic constructs that keep their functionality over long periods of time. However, maintenance of these constructs requires energy from the cell and thus reduces the host’s fitness. Natural selection results in loss-of-functionality mutations that negate the expression of the construct in the population. Current approaches for the prevention of this phenomenon focus on either small-scale, manual design of evolutionary stable constructs or the detection of mutational sites with unstable tendencies. We designed the Evolutionary Stability Optimizer (ESO), a software tool that enables the large-scale automatic design of evolutionarily stable constructs with respect to both mutational and epigenetic hotspots and allows users to define custom hotspots to avoid. Furthermore, our tool takes the expression of the input constructs into account by considering the guanine-cytosine (GC) content and codon usage of the host organism, balancing the trade-off between stability and gene expression, allowing to increase evolutionary stability while maintaining the high expression. In this study, we present the many features of the ESO and show that it accurately predicts the evolutionary stability of endogenous genes. The ESO was created as an easy-to-use, flexible platform based on the notion that directed genetic stability research will continue to evolve and revolutionize current applications of synthetic biology. The ESO is available at the following link: https://www.cs.tau.ac.il/~tamirtul/ESO/.

Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware

In recent years, intracellular biophysical simulations have been used with increasing frequency not only for answering basic scientific questions but also in the field of synthetic biology. However, since these models include networks of interaction between millions of components, they are extremely time-consuming and cannot run easily on parallel computers. In this study, we demonstrate for the first time a novel approach addressing this challenge by using a dedicated hardware designed specifically to simulate such processes. As a proof of concept, we specifically focus on mRNA translation, which is the process consuming most of the energy in the cell. We design a hardware that simulates translation in Escherichia coli and Saccharomyces cerevisiae for thousands of mRNAs and ribosomes, which is in orders of magnitude faster than a similar software solution. With the sharp increase in the amount of genomic data available today and the complexity of the corresponding models inferred from them, we believe that the strategy suggested here will become common and can be used among others for simulating entire cells with all gene expression steps.

Construction of an IS-Free Corynebacterium glutamicum ATCC 13 032 Chassis Strain and Random Mutagenesis Using the Endogenous ISCg1 Transposase

Mobile genetic elements (MGEs) contribute to instability of the host genome and plasmids. Previously, removal of the prophages in the industrial amino acid producer Corynebacterium glutamicum ATCC 13 032 resulted in strain MB001 which showed better survival under stress conditions and increased transformability. Still, eight families of Insertion Sequence (IS) elements with 27 potentially active members remain in MB001, two of which were demonstrated to be detrimental in biotechnological processes. In this study, systematical deletion of all complete IS elements in MB001 resulted in the MGE-free strain CR101. CR101 shows growth characteristics identical to the wildtype and the increased transformability of MB001. Due to its improved genome stability, we consider this strain to be an optimal host for basic research and biotechnology. As a “zero-background” host, it is also an ideal basis to study C. glutamicum IS elements. Re-sequencing of CR101 revealed that only five spontaneous point mutations had occurred during the construction process, highlighting the low mutation rate of C. glutamicum on the nucleotide level. In a second step, we developed an easily applicable ISCg1-based transposon mutagenesis system to randomly transpose a selectable marker. For optimal plasmid stability during cloning in Escherichia coli, the system utilizes a genetic switch based on the phage integrase Bxb1. Use of this integrase revealed the presence of a functional attB site in the C. glutamicum genome. To avoid cross-talk with our system and increase ease-of-use, we removed the attB site and also inserted the Bxb1 encoding gene into the chromosome of CR101. Successful insertion of single markers was verified by sequencing randomly selected mutants. Sequencing pooled mutant libraries revealed only a weak target site specificity, seemingly random distribution of insertion sites and no general strand bias. The resulting strain, ML103, together with plasmid pML10 provides a easily customizable system for random mutagenesis in an otherwise genomically stable C. glutamicum. Taken together, the MGE-free C. glutamicum strain CR101, the derivative ML103, and the plasmid pML10 provide a useful set of tools to study C. glutamicum in the future.

Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application

We investigated the scalability of a previously developed growth switch based on external control of RNA polymerase expression. Our results indicate that, in liter-scale bioreactors operating in fed-batch mode, growth-arrested Escherichia coli cells are able to convert glucose to glycerol at an increased yield. A multiomics quantification of the physiology of the cells shows that, apart from acetate production, few metabolic side effects occur. However, a number of specific responses to growth slow-down and growth arrest are launched at the transcriptional level. These notably include the downregulation of genes involved in growth-associated processes, such as amino acid and nucleotide metabolism and translation. Interestingly, the transcriptional responses are buffered at the proteome level, probably due to the strong decrease of the total mRNA concentration after the diminution of transcriptional activity and the absence of growth dilution of proteins. Growth arrest thus reduces the opportunities for dynamically adjusting the proteome composition, which poses constraints on the design of biotechnological production processes but may also avoid the initiation of deleterious stress responses.

Engineering gene overlaps to sustain genetic constructs in vivo

Evolution is often an obstacle to the engineering of stable biological systems due to the selection of mutations inactivating costly gene circuits. Gene overlaps induce important constraints on sequences and their evolution. We show that these constraints can be harnessed to increase the stability of costly genes by purging loss-of-function mutations. We combine computational and synthetic biology approaches to rationally design an overlapping reading frame expressing an essential gene within an existing gene to protect. Our algorithm succeeded in creating overlapping reading frames in 80% of E. coli genes. Experimentally, scoring mutations in both genes of such overlapping construct, we found that a significant fraction of mutations impacting the gene to protect have a deleterious effect on the essential gene. Such an overlap thus protects a costly gene from removal by natural selection by associating the benefit of this removal with a larger or even lethal cost. In our synthetic constructs, the overlap converts many of the possible mutants into evolutionary dead-ends, reducing the evolutionary potential of the system and thus increasing its stability over time.

Genomes are translated by triplets of nucleotides on two different strands, allowing for six different reading frames. This permits the existence of gene overlaps, often observed in microbial genomes, where two different proteins are encoded on the same piece of DNA, but in different reading frames. Gene overlaps are classically considered an obstacle for both evolution and genetic engineering, as mutations in overlapping regions likely have pleitrotropic effects on several genes. In 2013, we identified specific evolutionary scenarios where the decrease in evolutionary potential caused by gene overlaps could instead be advantageous and selected for. In this work, we demonstrate the use of gene overlaps in another context where reducing evolutionary potential can be useful: preventing evolution from inactivating synthetic circuits. We show that gene overlaps can be engineered to increase the evolutionary stability of genes that are costly to their hosts, by entangling these costly genes with essential genes.

High-throughput characterization of mutations in genes that drive clonal evolution using multiplex adaptome capture sequencing

Understanding how cells are likely to evolve can guide medical interventions and bioengineering efforts that must contend with unwanted mutations. The adaptome of a cell-the neighborhood of genetic changes that are most likely to drive adaptation in a given environment-can be mapped by tracking rare beneficial variants during the early stages of clonal evolution. We used multiplex adaptome capture sequencing (mAdCap-seq), a procedure that combines unique molecular identifiers and hybridization-based enrichment, to characterize mutations in eight Escherichia coli genes known to be under selection in a laboratory environment. We tracked 301 mutations at frequencies as low as 0.01% and inferred the fitness effects of 240 of these mutations. There were distinct molecular signatures of selection on protein structure and function for the three genes with the most beneficial mutations. Our results demonstrate how mAdCap-seq can be used to deeply profile a targeted portion of a cell's adaptome.

Layman N, Redwood AJ, Chan B, Bull JJ. Methods for measuring the evolutionary stability of engineered genomes to improve their longevity

Diverse applications rely on engineering microbes to carry and express foreign transgenes. This engineered baggage rarely benefits the microbe and is thus prone to rapid evolutionary loss when the microbe is propagated. For applications where a transgene must be maintained for extended periods of growth, slowing the rate of transgene evolution is critical and can be achieved by reducing either the rate of mutation or the strength of selection. Because the benefits realized by changing these quantities will not usually be equal, it is important to know which will yield the greatest improvement to the evolutionary half-life of the engineering. Here, we provide a method for jointly estimating the mutation rate of transgene loss and the strength of selection favoring these transgene-free, revertant individuals. The method requires data from serial transfer experiments in which the frequency of engineered genomes is monitored periodically. Simple mathematical models are developed that use these estimates to predict the half-life of the engineered transgene and provide quantitative predictions for how alterations to mutation and selection will influence longevity. The estimation method and predictive tools have been implemented as an interactive web application, MuSe.

Towards an engineering theory of evolution

Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution's potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology.

Engineering insects from the endosymbiont out

Insects are an incredibly diverse group of animals with species that benefit and harm natural ecosystems, agriculture, and human health. Many insects have consequential associations with microbes: bacterial symbionts may be embedded in different insect tissues and cell types, inherited across insect generations, and required for insect survival and reproduction. Genetically engineering insect symbionts is key to understanding and harnessing these associations. We summarize different types of insect-bacteria relationships and review methods used to genetically modify endosymbiont and gut symbiont species. Finally, we discuss recent studies that use this approach to study symbioses, manipulate insect-microbe interactions, and influence insect biology. Further progress in insect symbiont engineering promises to solve societal challenges, ranging from controlling pests to protecting pollinator health.

A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs

Simple Summary

In synthetic biology, it is commonplace to design and insert gene expression constructs into cells for the production of useful proteins. In order to maximise production yield, it is useful to predict the performance of these “engineered cells” in advance of conducting experiments. This is typically a complex task, which in recent years has motivated the use of “whole-cell models” (WCMs) that act as computational tools for predicting different aspects of cell growth. Many useful WCMs exist, however a common problem is their over-simplification of ribosome movement on mRNA transcripts during translation. WCMs typically don’t consider that, for constructs with inefficient (“slow”) codons, ribosomes can stall and form “traffic jams”, thereby becoming unavailable for translation of other proteins. To more accurately address these scenarios, we have built a computational framework that combines whole-cell modelling with a detailed account of ribosome movement on mRNA. We show how our framework can be used to link the modular design of a gene expression construct (via its promoter, ribosome binding site and codon composition) to protein yield during continuous cell culture, with a particular focus on how the optimal design can change over time in the presence or absence of “slow” codons.

Abstract

The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation in sufficient detail to consider the impact of ribosomal queue formation on mRNA transcripts. To address this, we have built a “stochastic cell calculator” (StoCellAtor) that combines a modified TASEP with a stochastic implementation of an existing WCM. We show how our framework can be used to link a synthetic construct’s modular design (promoter, ribosome binding site (RBS) and codon composition) to protein yield during continuous culture, with a particular focus on the effects of low-efficiency codons and their impact on ribosomal queues. Through our analysis, we recover design principles previously established in our work on burden-sensing strategies, namely that changing promoter strength is often a more efficient way to increase protein yield than RBS strength. Importantly, however, we show how these design implications can change depending on both the duration of protein expression, and on the presence of ribosomal queues.

Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate

mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.

Rapid and assured genetic engineering methods applied to Acinetobacter baylyi ADP1 genome streamlining

One goal of synthetic biology is to improve the efficiency and predictability of living cells by removing extraneous genes from their genomes. We demonstrate improved methods for engineering the genome of the metabolically versatile and naturally transformable bacterium Acinetobacter baylyi ADP1 and apply them to a genome streamlining project. In Golden Transformation, linear DNA fragments constructed by Golden Gate Assembly are directly added to cells to create targeted deletions, edits, or additions to the chromosome. We tested the dispensability of 55 regions of the ADP1 chromosome using Golden Transformation. The 18 successful multiple-gene deletions ranged in size from 21 to 183 kb and collectively accounted for 23.4% of its genome. The success of each multiple-gene deletion attempt could only be partially predicted on the basis of an existing collection of viable ADP1 single-gene deletion strains and a new transposon insertion sequencing (Tn-Seq) dataset that we generated. We further show that ADP1’s native CRISPR/Cas locus is active and can be retargeted using Golden Transformation. We reprogrammed it to create a CRISPR-Lock, which validates that a gene has been successfully removed from the chromosome and prevents it from being reacquired. These methods can be used together to implement combinatorial routes to further genome streamlining and for more rapid and assured metabolic engineering of this versatile chassis organism.

Mapping Protein-Protein Interaction Interface Peptides with Jun-Fos Assisted Phage Display and Deep Sequencing

Protein-protein interactions govern many cellular processes, and identifying binding interaction sites on proteins can facilitate the discovery of inhibitors to block such interactions. Here we identify peptides from a randomly fragmented plasmid encoding the β-lactamase inhibitory protein (BLIP) and the Lac repressor (LacI) that represent regions of protein-protein interactions. We utilized a Jun-Fos-assisted phage display system that has previously been used to screen cDNA and genomic libraries to identify antibody antigens. Affinity selection with polyclonal antibodies against LacI or BLIP resulted in the rapid enrichment of in-frame peptides from various regions of the proteins. Further, affinity selection with β-lactamase enriched peptides that encompass regions of BLIP previously shown to contribute strongly to the binding energy of the BLIP/β-lactamase interaction, i.e., hotspot residues. Further, one of the regions enriched by affinity selection encompassed a disulfide-constrained region of BLIP that forms part of the BLIP interaction surface in the native complex that we show also binds to β-lactamase as a disulfide-constrained macrocycle peptide with a K_D of ∼1 μM. Fragmented open reading frame (ORF) libraries may efficiently identify such naturally constrained peptides at protein-protein interaction interfaces. With sufficiently deep coverage of ORFs by peptide-coding inserts, phage display and deep sequencing can provide detailed information on the domains or peptides that contribute to an interaction. Such information should enable the design of potentially therapeutic macrocycles or peptidomimetics that block the interaction.

Statistical noise from recombinant plasmids can be abated via complementation of a ribosomal protein gene deletion

The phenotypes conferred by recombinant plasmids upon host cells often exhibit variability between replicate populations. This statistical noise is mostly a consequence of adaptive evolution in response to fitness burdens imposed by the plasmids themselves. We developed a novel strategy, 'ribosome pegging', to exclude common unwanted mutations that benefit host cells at the expense of heterologous gene expression. Plasmids that constitutively co-expressed the fluorescent reporter tagRFP and ribosomal protein L23 (rplW) were used to transform Escherichia coli cells that lacked the essential chromosomal rplW gene. Cells within the population that expressed too little L23, or too much, were evidently inviable. Ribosome pegging obviates the need for antibiotics, thus facilitating the deployment of recombinant bacteria in uncontrolled environments. We show that ribosome-pegged E. coli carrying a plasmid that constitutively expresses L23 and an artificially evolved enzyme protects fruit flies from otherwise toxic doses of the insecticide malathion.

Fail-safe genetic codes designed to intrinsically contain engineered organisms

One challenge in engineering organisms is taking responsibility for their behavior over many generations. Spontaneous mutations arising before or during use can impact heterologous genetic functions, disrupt system integration, or change organism phenotype. Here, we propose restructuring the genetic code itself such that point mutations in protein-coding sequences are selected against. Synthetic genetic systems so-encoded should fail more safely in response to most spontaneous mutations. We designed fail-safe codes and simulated their expected effects on the evolution of so-encoded proteins. We predict fail-safe codes supporting expression of 20 or 15 amino acids could slow protein evolution to ∼30% or 0% the rate of standard-encoded proteins, respectively. We also designed quadruplet-codon codes that should ensure all single point mutations in protein-coding sequences are selected against while maintaining expression of 20 or more amino acids. We demonstrate experimentally that a reduced set of 21 tRNAs is capable of expressing a protein encoded by only 20 sense codons, whereas a standard 64-codon encoding is not expressed. Our work suggests that biological systems using rationally depleted but otherwise natural translation systems should evolve more slowly and that such hypoevolvable organisms may be less likely to invade new niches or outcompete native populations.

A critical comparison of cellular and cell-free bioproduction systems

Conversion of biological feedstocks into value-added chemicals is mostly performed via microbial fermentation. An emerging alternative approach is the use of cell-free systems, consisting of purified enzymes and cofactors. Unfortunately, the in vivo and in vitro research communities rarely interact, which leads to oversimplifications and exaggerations that do not permit fair comparison of the two strategies and impede synergistic interactions. Here, we provide a comprehensive account for the advantages and drawbacks associated with each strategy, and further discuss recent research efforts that aim to breach the limits of cellular and cell-free production. We also explore emerging hybrid solutions that integrate the benefits of both worlds and could expand the boundaries of biosynthesis.

Synthetic Genome Defenses against Selfish DNA Elements Stabilize Engineered Bacteria against Evolutionary Failure

Mobile genetic elements drive evolution by disrupting genes and rearranging genomes. Eukaryotes have evolved epigenetic mechanisms, including DNA methylation and RNA interference, that silence mobile elements and thereby preserve the integrity of their genomes. We created an artificial reprogrammable epigenetic system based on CRISPR interference to give engineered bacteria a similar line of defense against transposons and other selfish elements in their genomes. We demonstrate that this CRISPR interference against mobile elements (CRISPRi-ME) approach can be used to simultaneously repress two different transposon families in Escherichia coli, thereby increasing the evolutionary stability of costly protein expression. We further show that silencing a transposon in Acinetobacter baylyi ADP1 reduces mutation rates by a factor of 5, nearly as much as deleting all copies of this element from its genome. By deploying CRISPRi-ME on a broad-host-range vector, we have created a generalizable platform for stabilizing the genomes of engineered bacterial cells for applications in metabolic engineering and synthetic biology.

CRISPR-interference-based modulation of mobile genetic elements in bacteria

Spontaneous mutagenesis of synthetic genetic constructs by mobile genetic elements frequently results in the rapid loss of engineered functions. Previous efforts to minimize such mutations required the exceedingly time-consuming manipulation of bacterial chromosomes and the complete removal of insertional sequences (ISes). To this aim, we developed a single plasmid-based system (pCRIS) that applies CRISPR-interference to inhibit the transposition of bacterial ISes. pCRIS expresses multiple guide RNAs to direct inactivated Cas9 (dCas9) to simultaneously silence IS1, IS3, IS5 and IS150 at up to 38 chromosomal loci in Escherichia coli, in vivo. As a result, the transposition rate of all four targeted ISes dropped to negligible levels at both chromosomal and episomal targets. Most notably, pCRIS, while requiring only a single plasmid delivery performed within a single day, provided a reduction of IS-mobility comparable to that seen in genome-scale chromosome engineering projects. The fitness cost of multiple IS-knockdown, detectable in flask-and-shaker systems was readily outweighed by the less frequent inactivation of the transgene, as observed in green fluorescent protein (GFP)-overexpression experiments. In addition, global transcriptomics analysis revealed only minute alterations in the expression of untargeted genes. Finally, the transposition-silencing effect of pCRIS was easily transferable across multiple E. coli strains. The plasticity and robustness of our IS-silencing system make it a promising tool to stabilize bacterial genomes for synthetic biology and industrial biotechnology applications.

Directed evolution of Escherichia coli with lower-than-natural plasmid mutation rates

Unwanted evolution of designed DNA sequences limits metabolic and genome engineering efforts. Engineered functions that are burdensome to host cells and slow their replication are rapidly inactivated by mutations, and unplanned mutations with unpredictable effects often accumulate alongside designed changes in large-scale genome editing projects. We developed a directed evolution strategy, Periodic Reselection for Evolutionarily Reliable Variants (PResERV), to discover mutations that prolong the function of a burdensome DNA sequence in an engineered organism. Here, we used PResERV to isolate Escherichia coli cells that replicate ColE1-type plasmids with higher fidelity. We found mutations in DNA polymerase I and in RNase E that reduce plasmid mutation rates by 6- to 30-fold. The PResERV method implicitly selects to maintain the growth rate of host cells, and high plasmid copy numbers and gene expression levels are maintained in some of the evolved E. coli strains, indicating that it is possible to improve the genetic stability of cellular chassis without encountering trade-offs in other desirable performance characteristics. Utilizing these new antimutator E. coli and applying PResERV to other organisms in the future promises to prevent evolutionary failures and unpredictability to provide a more stable genetic foundation for synthetic biology.

Population dynamics of synthetic terraformation motifs

Ecosystems are complex systems, currently experiencing several threats associated with global warming, intensive exploitation and human-driven habitat degradation. Because of a general presence of multiple stable states, including states involving population extinction, and due to the intrinsic nonlinearities associated with feedback loops, collapse in ecosystems could occur in a catastrophic manner. It has been recently suggested that a potential path to prevent or modify the outcome of these transitions would involve designing synthetic organisms and synthetic ecological interactions that could push these endangered systems out of the critical boundaries. In this paper, we investigate the dynamics of the simplest mathematical models associated with four classes of ecological engineering designs, named Terraformation motifs (TMs). These TMs put in a nutshell different ecological strategies. In this context, some fundamental types of bifurcations pervade the systems' dynamics. Mutualistic interactions can enhance persistence of the systems by means of saddle-node bifurcations. The models without cooperative interactions show that ecosystems achieve restoration through transcritical bifurcations. Thus, our analysis of the models allows us to define the stability conditions and parameter domains where these TMs must work.

A Dynamic Model of Resource Allocation in Response to the Presence of a Synthetic Construct

Introducing synthetic constructs into bacteria often carries a burden that leads to reduced fitness and selective pressure for organisms to mutate their constructs and hence to a reduced functional lifetime. Understanding burden requires suitable methods for accurate measurement and quantification. We develop a dynamic growth model from physiologically relevant first-principles that allows parameters relevant to burden to be extracted from standard growth curves. We test several possibilities for the response of a bacterium to a new environment in terms of resource allocation. We find that burden manifests in the time taken to respond to new conditions as well as the rate of growth in exponential phase. Furthermore, we see that the presence of a synthetic construct hastens the reduction of ribosomes when approaching stationary phase, altering memory effects from previous periods of growth.

Rational engineering of synthetic microbial systems: from single cells to consortia

One promise of synthetic biology is to provide solutions for biomedical and industrial problems by rational design of added functionality in living systems. Microbes are at the forefront of this biological engineering endeavor due to their general ease of handling and their relevance in many potential applications from fermentation to therapeutics. In recent years, the field has witnessed an explosion of novel regulatory tools, from synthetic orthogonal transcription factors to posttranslational mechanisms for increased control over the behavior of synthetic circuits. Tool development has been paralleled by the discovery of principles that enable increased modularity and the management of host-circuit interactions. Engineered cell-to-cell communication bridges the scales from intracellular to population-level coordination. These developments facilitate the translation of more than a decade of circuit design into applications.

Alignment of microbial fitness with engineered product formation: obligatory coupling between acetate production and photoautotrophic growth

BACKGROUND

Microbial bioengineering has the potential to become a key contributor to the future development of human society by providing sustainable, novel, and cost-effective production pipelines. However, the sustained productivity of genetically engineered strains is often a challenge, as spontaneous non-producing mutants tend to grow faster and take over the population. Novel strategies to prevent this issue of strain instability are urgently needed.

RESULTS

In this study, we propose a novel strategy applicable to all microbial production systems for which a genome-scale metabolic model is available that aligns the production of native metabolites to the formation of biomass. Based on well-established constraint-based analysis techniques such as OptKnock and FVA, we developed an in silico pipeline-FRUITS-that specifically 'Finds Reactions Usable in Tapping Side-products'. It analyses a metabolic network to identify compounds produced in anabolism that are suitable to be coupled to growth by deletion of their re-utilization pathway(s), and computes their respective biomass and product formation rates. When applied to Synechocystis sp. PCC6803, a model cyanobacterium explored for sustainable bioproduction, a total of nine target metabolites were identified. We tested our approach for one of these compounds, acetate, which is used in a wide range of industrial applications. The model-guided engineered strain shows an obligatory coupling between acetate production and photoautotrophic growth as predicted. Furthermore, the stability of acetate productivity in this strain was confirmed by performing prolonged turbidostat cultivations.

CONCLUSIONS

This work demonstrates a novel approach to stabilize the production of target compounds in cyanobacteria that culminated in the first report of a photoautotrophic growth-coupled cell factory. The method developed is generic and can easily be extended to any other modeled microbial production system.

Challenges in the Application of Synthetic Biology Toward Synthesis of Commodity Products by Cyanobacteria via "Direct Conversion"

Cyanobacterial direct conversion of CO₂ to several commodity chemicals has been recognized as a potential contributor to support the much-needed sustainable development of human societies. However, the feasibility of this "green conversion" hinders on our ability to overcome the hurdles presented by the natural evolvability of microbes. The latter may result in the genetic instability of engineered cyanobacterial strains leading to impaired productivity. This challenge is general to any "cell factory" approach in which the cells grow for multiple generations, and based on several studies carried out in different microbial hosts, we could identify that three distinct strategies have been proposed to tackle it. These are (1) to reduce microbial evolvability by decreasing the native mutation rate, (2) to align product formation with cell growth/fitness, and, paradoxically, (3) to efficiently reallocate cellular resources to product formation by uncoupling it from growth. The implementation of either of these strategies requires an advanced synthetic biology toolkit. Here, we review the existing methods available for cyanobacteria and identify areas of focus in which specific developments are still needed. Furthermore, we discuss how potentially stabilizing strategies may be used in combination leading to further increases of productivity while ensuring the stability of the cyanobacterial-based direct conversion process.

Enabling Graded and Large-Scale Multiplex of Desired Genes Using a Dual-Mode dCas9 Activator in Saccharomyces cerevisiae

Standard approaches for dCas9-based modification of gene expression are limited in the ability to multiplex targets, establish streamlined cassettes, and utilize commonly studied Pol II promoters. In this work, we repurpose the dCas9-VPR activator to act as a dual-mode activator/repressor that can be programmed solely on the basis of target position at gene loci. Furthermore, we implement this approach using a streamlined Pol II-ribozyme system that allows expression of many sgRNAs from a single transcript. By "stepping" dCas9-VPR within the promoter region and ORF we create graded activation and repression (respectively) of target genes, allowing precise control over multiplexed gene modulation. Expression from the Pol II system increased the net amount of sgRNA production in cells by 3.88-fold relative to the Pol III SNR52 promoter, leading to a significant improvement in dCas9-VPR repression strength. Finally, we utilize our Pol II system to create galactose-inducible switching of gene expression states and multiplex constructs capable of modulating up to 4 native genes from a single vector. Our approach represents a significant step toward minimizing DNA required to assemble CRISPR systems in eukaryotes while enhancing the efficacy (greater repression strength), scale (more sgRNAs), and scope (inducibility) of dCas9-mediated gene regulation.

Genome-Wide Abolishment of Mobile Genetic Elements Using Genome Shuffling and CRISPR/Cas-Assisted MAGE Allows the Efficient Stabilization of a Bacterial Chassis

The ideal bacterial chassis provides a simplified, stable and predictable host environment for synthetic biological circuits. Mutability and evolution can, however, compromise stability, leading to deterioration of artificial genetic constructs. By eliminating certain sources of instability, these undesired genetic changes can be mitigated. Specifically, deletion of prophages and insertion sequences, nonessential constituents of bacterial genomes, has been shown to be beneficial in cellular and genetic stabilization. Here, we sought to establish a rapid methodology to improve the stability of microbial hosts. The novel workflow involves genome shuffling between a mobile genetic element-free strain and the target cell, and subsequent rounds of CRISPR/Cas-assisted MAGE on multiplex targets. The power and speed of the procedure was demonstrated on E. coli BL21(DE3), a host routinely used for plasmid-based heterologous protein expression. All 9 prophages and 50 insertion elements were efficiently deleted or inactivated. Together with additional targeted manipulations (e.g., inactivation of error-prone DNA-polymerases), the changes resulted in an improved bacterial host with a hybrid (harboring segments of K-12 DNA), 9%-downsized and clean genome. The combined capacity of phage-mediated generalized transduction and CRISPR/Cas-selected MAGE offers a way for rapid, large scale editing of bacterial genomes.

Synthetic Biology: Engineering Living Systems from Biophysical Principles

Synthetic biology was founded as a biophysical discipline that sought explanations for the origins of life from chemical and physical first principles. Modern synthetic biology has been reinvented as an engineering discipline to design new organisms as well as to better understand fundamental biological mechanisms. However, success is still largely limited to the laboratory and transformative applications of synthetic biology are still in their infancy. Here, we review six principles of living systems and how they compare and contrast with engineered systems. We cite specific examples from the synthetic biology literature that illustrate these principles and speculate on their implications for further study. To fully realize the promise of synthetic biology, we must be aware of life's unique properties.

Synthetic Microbial Ecology: Engineering Habitats for Modular Consortia

The metabolic diversity present in microbial communities enables cooperation toward accomplishing more complex tasks than possible by a single organism. Members of a consortium communicate by exchanging metabolites or signals that allow them to coordinate their activity through division of labor. In contrast with monocultures, evidence suggests that microbial consortia self-organize to form spatial patterns, such as observed in biofilms or in soil aggregates, that enable them to respond to gradient, to improve resource interception and to exchange metabolites more effectively. Current biotechnological applications of microorganisms remain rudimentary, often relying on genetically engineered monocultures (e.g., pharmaceuticals) or mixed-cultures of partially known composition (e.g., wastewater treatment), yet the vast potential of “microbial ecological power” observed in most natural environments, remains largely underused. In line with the Unified Microbiome Initiative (UMI) which aims to “discover and advance tools to understand and harness the capabilities of Earth's microbial ecosystems,” we propose in this concept paper to capitalize on ecological insights into the spatial and modular design of interlinked microbial consortia that would overcome limitations of natural systems and attempt to optimize the functionality of the members and the performance of the engineered consortium. The topology of the spatial connections linking the various members and the regulated fluxes of media between those modules, while representing a major engineering challenge, would allow the microbial species to interact. The modularity of such spatially linked microbial consortia (SLMC) could facilitate the design of scalable bioprocesses that can be incorporated as parts of a larger biochemical network. By reducing the need for a compatible growth environment for all species simultaneously, SLMC will dramatically expand the range of possible combinations of microorganisms and their potential applications. We briefly review existing tools to engineer such assemblies and optimize potential benefits resulting from the collective activity of their members. Prospective microbial consortia and proposed spatial configurations will be illustrated and preliminary calculations highlighting the advantages of SLMC over co-cultures will be presented, followed by a discussion of challenges and opportunities for moving forward with some designs.

A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis

Microbial ecologists are increasingly turning to small, synthesized ecosystems1-5 as a reductionist tool to probe the complexity of native microbiomes6,7. Concurrently, synthetic biologists have gone from single-cell gene circuits8-11 to controlling whole populations using intercellular signalling12-16. The intersection of these fields is giving rise to new approaches in waste recycling17, industrial fermentation18, bioremediation19 and human health16,20. These applications share a common challenge7 well-known in classical ecology21,22-stability of an ecosystem cannot arise without mechanisms that prohibit the faster-growing species from eliminating the slower. Here, we combine orthogonal quorum-sensing systems and a population control circuit with diverse self-limiting growth dynamics to engineer two 'ortholysis' circuits capable of maintaining a stable co-culture of metabolically competitive Salmonella typhimurium strains in microfluidic devices. Although no successful co-cultures are observed in a two-strain ecology without synthetic population control, the 'ortholysis' design dramatically increases the co-culture rate from 0% to approximately 80%. Agent-based and deterministic modelling reveal that our system can be adjusted to yield different dynamics, including phase-shifted, antiphase or synchronized oscillations, as well as stable steady-state population densities. The 'ortholysis' approach establishes a paradigm for constructing synthetic ecologies by developing stable communities of competitive microorganisms without the need for engineered co-dependency.

Experimental Evolution Reveals Favored Adaptive Routes to Cell Aggregation in Yeast

Yeast flocculation is a community-building cell aggregation trait that is an important mechanism of stress resistance and a useful phenotype for brewers; however, it is also a nuisance in many industrial processes, in clinical settings, and in the laboratory. Chemostat-based evolution experiments are impaired by inadvertent selection for aggregation, which we observe in 35% of populations. These populations provide a testing ground for understanding the breadth of genetic mechanisms Saccharomyces cerevisiae uses to flocculate, and which of those mechanisms provide the biggest adaptive advantages. In this study, we employed experimental evolution as a tool to ask whether one or many routes to flocculation are favored, and to engineer a strain with reduced flocculation potential. Using a combination of whole genome sequencing and bulk segregant analysis, we identified causal mutations in 23 independent clones that had evolved cell aggregation during hundreds of generations of chemostat growth. In 12 of those clones, we identified a transposable element insertion in the promoter region of known flocculation gene FLO1, and, in an additional five clones, we recovered loss-of-function mutations in transcriptional repressor TUP1, which regulates FLO1 and other related genes. Other causal mutations were found in genes that have not been previously connected to flocculation. Evolving a flo1 deletion strain revealed that this single deletion reduces flocculation occurrences to 3%, and demonstrated the efficacy of using experimental evolution as a tool to identify and eliminate the primary adaptive routes for undesirable traits.

Why Quorum Sensing Controls Private Goods

Cell-cell communication, also termed quorum sensing (QS), is a widespread process that coordinates gene expression in bacterial populations. The generally accepted view is that QS optimizes the cell density-dependent benefit attained from cooperative behaviors, often in the form of secreted products referred to as "public goods." This view is challenged by an increasing number of cell-associated products or "private goods" reported to be under QS-control for which a collective benefit is not apparent. A prominent example is nucleoside hydrolase from Pseudomonas aeruginosa, a periplasmic enzyme that catabolizes adenosine. Several recent studies have shown that private goods can function to stabilize cooperation by co-regulated public goods, seemingly explaining their control by QS. Here we argue that this property is a by-product of selection for other benefits rather than an adaptation. Emphasizing ecophysiological context, we propose alternative explanations for the QS control of private goods. We suggest that the benefit attained from private goods is associated with high cell density, either because a relevant ecological condition correlates with density, or because the private good is, directly or indirectly, involved in cooperative behavior. Our analysis helps guide a systems approach to QS, with implications for antivirulence drug design and synthetic biology.

The genotype-phenotype map of an evolving digital organism

To understand how evolving systems bring forth novel and useful phenotypes, it is essential to understand the relationship between genotypic and phenotypic change. Artificial evolving systems can help us understand whether the genotype-phenotype maps of natural evolving systems are highly unusual, and it may help create evolvable artificial systems. Here we characterize the genotype-phenotype map of digital organisms in Avida, a platform for digital evolution. We consider digital organisms from a vast space of 10¹⁴¹ genotypes (instruction sequences), which can form 512 different phenotypes. These phenotypes are distinguished by different Boolean logic functions they can compute, as well as by the complexity of these functions. We observe several properties with parallels in natural systems, such as connected genotype networks and asymmetric phenotypic transitions. The likely common cause is robustness to genotypic change. We describe an intriguing tension between phenotypic complexity and evolvability that may have implications for biological evolution. On the one hand, genotypic change is more likely to yield novel phenotypes in more complex organisms. On the other hand, the total number of novel phenotypes reachable through genotypic change is highest for organisms with simple phenotypes. Artificial evolving systems can help us study aspects of biological evolvability that are not accessible in vastly more complex natural systems. They can also help identify properties, such as robustness, that are required for both human-designed artificial systems and synthetic biological systems to be evolvable.

The phenotype of an organism comprises the set of morphological and functional traits encoded by its genome. In natural evolving systems, phenotypes are organized into mutationally connected networks of genotypes, which increase the likelihood for an evolving population to encounter novel adaptive phenotypes (i.e., its evolvability). We do not know whether artificial systems, such as self-replicating and evolving computer programs—digital organisms—are more or less evolvable than natural systems. By studying how genotypes map onto phenotypes in digital organisms, we characterize many commonalities between natural and artificial evolving systems. In addition, we show that phenotypic complexity can both facilitate and constrain evolution, which harbors lessons not only for designing evolvable artificial systems, but also for synthetic biology.