Predicting the Genetic Stability of Engineered DNA Sequences with the EFM Calculator

Optimizing multicopy chromosomal integration for stable high-performing strains

The copy number of genes in chromosomes can be modified by chromosomal integration to construct efficient microbial cell factories but the resulting genetic systems are prone to failure or instability from triggering homologous recombination in repetitive DNA sequences. Finding the optimal copy number of each gene in a pathway is also time and labor intensive. To overcome these challenges, we applied a multiple nonrepetitive coding sequence calculator that generates sets of coding DNA sequence (CDS) variants. A machine learning method was developed to calculate the optimal copy number combination of genes in a pathway. We obtained an engineered Yarrowia lipolytica strain for eicosapentaenoic acid biosynthesis in 6 months, producing the highest titer of 27.5 g l-1 in a 50-liter bioreactor. Moreover, the lycopene production in Escherichia coli was also greatly improved. Importantly, all engineered strains of Y. lipolytica, E. coli and Saccharomyces cerevisiae constructed with nonrepetitive CDSs maintained genetic stability.

Long-duration environmental biosensing by recording analyte detection in DNA using recombinase memory

Microbial biosensors that convert environmental information into real-time visual outputs are limited in their sensing abilities in complex environments, such as soil and wastewater, due to optical inaccessibility. Biosensors that could record transient exposure to analytes within a large time window for later retrieval represent a promising approach to solve the accessibility problem. Here, we test the performance of recombinase-memory biosensors that sense a sugar (arabinose) and a microbial communication molecule (3-oxo-C12-L-homoserine lactone) over 8 days (~70 generations) following analyte exposure. These biosensors sense the analyte and trigger the expression of a recombinase enzyme which flips a segment of DNA, creating a genetic memory, and initiates fluorescent protein expression. The initial designs failed over time due to unintended DNA flipping in the absence of the analyte and loss of the flipped state after exposure to the analyte. Biosensor performance was improved by decreasing recombinase expression, removing the fluorescent protein output, and using quantitative PCR to read out stored information. Application of memory biosensors in wastewater isolates achieved memory of analyte exposure in an uncharacterized Pseudomonas isolate. By returning these engineered isolates to their native environments, recombinase-memory systems are expected to enable longer duration and in situ investigation of microbial signaling, cross-feeding, community shifts, and gene transfer beyond the reach of traditional environmental biosensors.IMPORTANCEMicrobes mediate ecological processes over timescales that can far exceed the half-lives of transient metabolites and signals that drive their collective behaviors. We investigated strategies for engineering microbes to stably record their transient exposure to a chemical over many generations through DNA rearrangements. We identify genetic architectures that improve memory biosensor performance and characterize these in wastewater isolates. Memory biosensors are expected to be useful for monitoring cell-cell signals in biofilms, detecting transient exposure to chemical pollutants, and observing microbial cross-feeding through short-lived metabolites within cryptic methane, nitrogen, and sulfur cycling processes. They will also enable in situ studies of microbial responses to ephemeral environmental changes, or other ecological processes that are currently challenging to monitor non-destructively using real-time biosensors and analytical instruments.

"Metabolic burden" explained: stress symptoms and its related responses induced by (over)expression of (heterologous) proteins in Escherichia coli

BACKGROUND

Engineering bacterial strains to redirect the metabolism towards the production of a specific product has enabled the development of industrial biotechnology. However, rewiring the metabolism can have severe implications for a microorganism, rendering cells with stress symptoms such as a decreased growth rate, impaired protein synthesis, genetic instability and an aberrant cell size. On an industrial scale, this is reflected in processes that are not economically viable.

MAIN TEXT

In literature, most stress symptoms are attributed to "metabolic burden", however the actual triggers and stress mechanisms involved are poorly understood. Therefore, in this literature review, we aimed to get a better insight in how metabolic engineering affects Escherichia coli and link the observed stress symptoms to its cause. Understanding the possible implications that chosen engineering strategies have, will help to guide the reader towards optimising the envisioned process more efficiently.

CONCLUSION

This review addresses the gap in literature and discusses the triggers and effects of stress mechanisms that can be activated when (over)expressing (heterologous) proteins in Escherichia coli. It uncovers that the activation of the different stress mechanisms is complex and that many are interconnected. The reader is shown that care has to be taken when (over)expressing (heterologous) proteins as the cell's metabolism is tightly regulated.

Simultaneous utilization of glucose and xylose by metabolically engineered Pseudomonas putida for the production of 3-hydroxypropionic acid

Pseudomonas putida,a robust candidate for lignocellulosicbiomass-based biorefineries, encounters challenges in metabolizing xylose. In this study, Weimberg pathway was introduced intoP. putidaEM42 under a xylose-inducible promoter, resulting in slow cell growth (0.05 h-1) on xylose.Through adaptive laboratory evolution, an evolved strain exhibited highly enhanced growth on xylose (0.36 h-1), comparable to that on glucose (0.39 h-1). Whole genome sequencing identified four mutations, with two key mutations located inPP3380andPP2219. Reverse-engineered strain 8EM42_Xyl, harboring these two mutations, showed enhanced growth on xylose but co-utilizing glucose and xylose at a rate of 0.3 g/L/h. Furthermore, 8EM42_Xyl was employed for 3-hydroxypropionic acid (3HP) production from glucose and xylose by expressing malonyl-CoA reductase and acetyl-CoA carboxylase, yielding 29 g/L in fed-batch fermentation. Moreover, the engineered strain exhibited promising performance in 3HP production from empty palm fruit bunch hydrolysate, demonstrating its potential as a promising cell factory forbiorefineries.

Toolbox of Characterized Genetic Parts for Staphylococcus aureus

Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.

Universal and unique strategies for the production of polyunsaturated fatty acids in industrial oleaginous microorganisms

Polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA) and arachidonic acid (ARA), are beneficial for reducing blood cholesterol and enhancing memory. Traditional PUFA production relies on extraction from plants and animals, which is unsustainable. Thus, using microorganisms as lipid-producing factories holds promise as an alternative way for PUFA production. Several oleaginous microorganisms have been successfully industrialized to date. These can be divided into universal and specialized hosts according to the products range of biosynthesis. The Yarrowia lipolytica is universal oleaginous host that has been engineered to produce a variety of fatty acids, such as γ-linolenic acid (GLA), EPA, ARA and so on. By contrast, the specialized host are used to produce only certain fatty acids, such as ARA in Mortierella alpina, EPA in Nannochloropsis, and DHA in Thraustochytrids. The metabolic engineering and fermentation strategies for improving PUFA production in universal and specialized hosts are different, which is the subject of this review. In addition, the widely applicable strategies for microbial lipid production that are not specific to individual hosts were also reviewed.

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.

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.

Mutation bias and adaptation in bacteria

Genetic mutation, which provides the raw material for evolutionary adaptation, is largely a stochastic force. However, there is ample evidence showing that mutations can also exhibit strong biases, with some mutation types and certain genomic positions mutating more often than others. It is becoming increasingly clear that mutational bias can play a role in determining adaptive outcomes in bacteria in both the laboratory and the clinic. As such, understanding the causes and consequences of mutation bias can help microbiologists to anticipate and predict adaptive outcomes. In this review, we provide an overview of the mechanisms and features of the bacterial genome that cause mutational biases to occur. We then describe the environmental triggers that drive these mechanisms to be more potent and outline the adaptive scenarios where mutation bias can synergize with natural selection to define evolutionary outcomes. We conclude by describing how understanding mutagenic genomic features can help microbiologists predict areas sensitive to mutational bias, and finish by outlining future work that will help us achieve more accurate evolutionary forecasts.

Prolonging genetic circuit stability through adaptive evolution of overlapping genes

The development of synthetic biological circuits that maintain functionality over application-relevant time scales remains a significant challenge. Here, we employed synthetic overlapping sequences in which one gene is encoded or 'entangled' entirely within an alternative reading frame of another gene. In this design, the toxin-encoding relE was entangled within ilvA, which encodes threonine deaminase, an enzyme essential for isoleucine biosynthesis. A functional entanglement construct was obtained upon modification of the ribosome-binding site of the internal relE gene. Using this optimized design, we found that the selection pressure to maintain functional IlvA stabilized the production of burdensome RelE for >130 generations, which compares favorably with the most stable kill-switch circuits developed to date. This stabilizing effect was achieved through a complete alteration of the allowable landscape of mutations such that mutations inactivating the entangled genes were disfavored. Instead, the majority of lineages accumulated mutations within the regulatory region of ilvA. By reducing baseline relE expression, these more 'benign' mutations lowered circuit burden, which suppressed the accumulation of relE-inactivating mutations, thereby prolonging kill-switch function. Overall, this work demonstrates the utility of sequence entanglement paired with an adaptive laboratory evolution campaign to increase the evolutionary stability of burdensome synthetic 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.

Interpreting and de-noising genetically engineered barcodes in a DNA virus

The concept of a nucleic acid barcode applied to pathogen genomes is easy to grasp and the many possible uses are straightforward. But implementation may not be easy, especially when growing through multiple generations or assaying the pathogen long-term. The potential problems include: the barcode might alter fitness, the barcode may accumulate mutations, and construction of the marked pathogens may result in unintended barcodes that are not as designed. Here, we generate approximately 5,000 randomized barcodes in the genome of the prototypic small DNA virus murine polyomavirus. We describe the challenges faced with interpreting the barcode sequences obtained from the library. Our Illumina NextSeq sequencing recalled much greater variation in barcode sequencing reads than the expected 5,000 barcodes-necessarily stemming from the Illumina library processing and sequencing error. Using data from defined control virus genomes cloned into plasmid backbones we develop a vetted post-sequencing method to cluster the erroneous reads around the true virus genome barcodes. These findings may foreshadow problems with randomized barcodes in other microbial systems and provide a useful approach for future work utilizing nucleic acid barcoded pathogens.

Integrase-mediated differentiation circuits improve evolutionary stability of burdensome and toxic functions in E. coli

Advances in synthetic biology, bioengineering, and computation allow us to rapidly and reliably program cells with increasingly complex and useful functions. However, because the functions we engineer cells to perform are typically burdensome to cell growth, they can be rapidly lost due to the processes of mutation and natural selection. Here, we show that a strategy of terminal differentiation improves the evolutionary stability of burdensome functions in a general manner by realizing a reproductive and metabolic division of labor. To implement this strategy, we develop a genetic differentiation circuit in Escherichia coli using unidirectional integrase-recombination. With terminal differentiation, differentiated cells uniquely express burdensome functions driven by the orthogonal T7 RNA polymerase, but their capacity to proliferate is limited to prevent the propagation of advantageous loss-of-function mutations that inevitably occur. We demonstrate computationally and experimentally that terminal differentiation increases duration and yield of high-burden expression and that its evolutionary stability can be improved with strategic redundancy. Further, we show this strategy can even be applied to toxic functions. Overall, this study provides an effective, generalizable approach for protecting burdensome engineered functions from evolutionary degradation.

Inducible expression of large gRNA arrays for multiplexed CRISPRai applications

CRISPR gene activation and inhibition (CRISPRai) has become a powerful synthetic tool for influencing the expression of native genes for foundational studies, cellular reprograming, and metabolic engineering. Here we develop a method for near leak-free, inducible expression of a polycistronic array containing up to 24 gRNAs from two orthogonal CRISPR/Cas systems to increase CRISPRai multiplexing capacity and target gene flexibility. To achieve strong inducibility, we create a technology to silence gRNA expression within the array in the absence of the inducer, since we found that long gRNA arrays for CRISPRai can express themselves even without promoter. Using this method, we create a highly tuned and easy-to-use CRISPRai toolkit in the industrially relevant yeast, Saccharomyces cerevisiae, establishing the first system to combine simultaneous activation and repression, large multiplexing capacity, and inducibility. We demonstrate this toolkit by targeting 11 genes in central metabolism in a single transformation, achieving a 45-fold increase in succinic acid, which could be precisely controlled in an inducible manner. Our method offers a highly effective way to regulate genes and rewire metabolism in yeast, with principles of gRNA array construction and inducibility that should extend to other chassis organisms.

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/.

PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies

We present here a newly developed workflow—which we have called PASIV—designed to provide a solution to a practical problem with design of experiments (DoE) methodology: i.e., what can be done if the scoping phase of the DoE cycle is severely hampered by burden and toxicity issues (caused by either the metabolite or an intermediary), making it unreliable or impossible to proceed to the screening phase? PASIV—standing for pooled approach, screening, identification, and visualization—was designed so the (viable) region of interest can be made to appear through an interplay between biology and software. This was achieved by combining multiplex construction in a pooled approach (one-pot reaction) with a viability assay and with a range of bioinformatics tools (including a novel construct matching tool). PASIV was tested on the exemplar of the lycopene pathway—under stressful constitutive expression—yielding a region of interest with comparatively stronger producers.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

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.

Systematical Engineering of Synthetic Yeast for Enhanced Production of Lycopene

Synthetic biology allows the re-engineering of biological systems and promotes the development of bioengineering to a whole new level, showing great potential in biomanufacturing. Here, in order to make the heterologous lycopene biosynthesis pathway compatible with the host strain YSy 200, we evolved YSy200 using a unique Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) system that is built in the Sc2.0 synthetic yeast. By inducing SCRaMbLE, we successfully identified a host strain YSy201 that can be served as a suitable host to maintain the heterologous lycopene biosynthesis pathway. Then, we optimized the lycopene biosynthesis pathway and further integrated into the rDNA arrays of YSy201 to increase its copy number. In combination with culturing condition optimization, we successfully screened out the final yeast strain YSy222, which showed a 129.5-fold increase of lycopene yield in comparison with its parental strain. Our work shows that, the strategy of combining the engineering efforts on both the lycopene biosynthesis pathway and the host strain can improve the compatibility between the heterologous pathway and the host strain, which can further effectively increase the yield of the target product.

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.

Single-domain antibody C7b for address delivery of nanoparticles to HER2-positive cancers

Monoclonal antibodies (mAb) demonstrate great potential as immunotherapy agents for the treatment of diseases such as cancer as well as tagging for the targeted delivery of multicomponent therapeutic or diagnostic systems. Nevertheless, the large physical size, poor stability of mAbs and abnormal allergic reactions still remain the main issues affecting their generalised use. Single-domain antibodies (sdAb) are seen as the next generation of antibody derived therapeutics and diagnostics. This work presents the optimised production method for HER2-specific sdAb C7b, which led to an ∼11-fold increase in protein yield. In addition, the in vitro and in vivo efficiencies of the targeted delivery of a model nanoparticle cargo (50 nm silica particles doped with Mo6 phosphorescent clusters) conjugated to C7b against those conjugated to HER2-specific trastuzumab is benchmarked. Specifically, this paper demonstrates the significantly higher rate of accumulation in and excretion from xenograft cancer tissue of nanoparticles with C7b, which is of particular importance for diagnostics, i.e. delivery of imaging agents.

Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage

To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.

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.

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning

The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We developed a machine learning model, called the Synthesis Success Calculator, to predict whether a long DNA fragment can be readily synthesized with a short turnaround time. The model also identifies the sequence determinants associated with the synthesis outcome. We trained a random forest classifier using biophysical features and a compiled data set of 1076 DNA fragment sequences to achieve high predictive performance (F₁ score of 0.928 on 251 unseen sequences). Feature importance analysis revealed that repetitive DNA sequences were the most important contributor to synthesis failures. We then applied the Synthesis Success Calculator across large sequence data sets and found that 84.9% of the Escherichia coli MG1655 genome, but only 34.4% of sampled plasmids in NCBI, could be readily synthesized. Overall, the Synthesis Success Calculator can be applied on its own to prevent synthesis failures or embedded within optimization algorithms to design large genetic systems that can be rapidly synthesized and assembled.

Automated design of thousands of nonrepetitive parts for engineering stable genetic systems

Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters' transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.

Synthetic sequence entanglement augments stability and containment of genetic information in cells

In synthetic biology, methods for stabilizing genetically engineered functions and confining recombinant DNA to intended hosts are necessary to cope with natural mutation accumulation and pervasive lateral gene flow. We present a generalizable strategy to preserve and constrain genetic information through the computational design of overlapping genes. Overlapping a sequence with an essential gene altered its fitness landscape and produced a constrained evolutionary path, even for synonymous mutations. Embedding a toxin gene in a gene of interest restricted its horizontal propagation. We further demonstrated a multiplex and scalable approach to build and test >7500 overlapping sequence designs, yielding functional yet highly divergent variants from natural homologs. This work enables deeper exploration of natural and engineered overlapping genes and facilitates enhanced genetic stability and biocontainment in emerging applications.

Programming Escherichia coli to function as a digital display

Synthetic genetic circuits offer the potential to wield computational control over biology, but their complexity is limited by the accuracy of mathematical models. Here, we present advances that enable the complete encoding of an electronic chip in the DNA carried by Escherichia coli (E. coli). The chip is a binary-coded digit (BCD) to 7-segment decoder, associated with clocks and calculators, to turn on segments to visualize 0-9. Design automation is used to build seven strains, each of which contains a circuit with up to 12 repressors and two activators (totaling 63 regulators and 76,000 bp DNA). The inputs to each circuit represent the digit to be displayed (encoded in binary by four molecules), and output is the segment state, reported as fluorescence. Implementation requires an advanced gate model that captures dynamics, promoter interference, and a measure of total power usage (RNAP flux). This project is an exemplar of design automation pushing engineering beyond that achievable "by hand", essential for realizing the potential of biology.

Bioassay for Determining the Concentrations of Caffeine and Individual Methylxanthines in Complex Samples

Caffeine and other methylxanthines are stimulant molecules found in formulated beverages, including sodas and energy drinks, and in brewed beverages, such as coffee and teas. Previously, we developed a bioassay for caffeine that involves monitoring the growth of a ΔguaB mutant of Escherichia coli defective in de novo guanine biosynthesis. When supplemented with a plasmid expressing the genes for an N-demethylation pathway from Pseudomonas putida CBB5, these bacteria demethylate caffeine (1,3,7-trimethylxanthine) and other methylxanthines into xanthine, which is then converted into guanine to support cell growth. A major limitation of this bioassay was that it could only measure the total concentration of all methylxanthines in a mixture. Therefore, it could not be used to measure the caffeine content of beverages like teas, which contain substantial quantities of multiple methylxanthines. To overcome this limitation, we created seven new plasmids containing all subsets of the three demethylase genes (ndmA, ndmB, and ndmC). We show that strains of ΔguaB E. coli containing each plasmid are able to demethylate specific subsets of methylxanthines and that they can be used to determine the concentrations of individual methylxanthines in complex mixtures containing multiple methylxanthines, including coffee doped with an additional methylxanthine. While validating this assay, we also discovered an unexpected demethylation event at the 1-methyl position when NdmB and NdmC were expressed in the absence of NdmA. The improved cell-based bioassay is inexpensive, is easy to use, and gives results comparable to standard high-performance liquid chromatography methods for measuring methylxanthine concentrations.IMPORTANCE Caffeine (1,3,7-trimethylxanthine) is the dominant neurostimulant found in coffee, teas, sodas, and energy drinks. Measuring the amount of caffeine and other methylxanthines in these beverages is important for quality assurance and safety in food science. Methylxanthines are also used in medicine and as performance-enhancing drugs, two contexts in which accurately determining their concentrations in bodily fluids is important. Liquid chromatography is the standard method for measuring methylxanthine concentrations in a sample, but it requires specialized equipment and expertise. We improved a previous bioassay that links E. coli growth to methylxanthine demethylation so that it can now be used to determine the amounts of individual methylxanthines in complex mixtures or beverages, such as coffee.

Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays

Engineering cellular phenotypes often requires the regulation of many genes. When using CRISPR interference, coexpressing many single-guide RNAs (sgRNAs) triggers genetic instability and phenotype loss, due to the presence of repetitive DNA sequences. We stably coexpressed 22 sgRNAs within nonrepetitive extra-long sgRNA arrays (ELSAs) to simultaneously repress up to 13 genes by up to 3,500-fold. We applied biophysical modeling, biochemical characterization and machine learning to develop toolboxes of nonrepetitive genetic parts, including 28 sgRNA handles that bind Cas9. We designed ELSAs by combining nonrepetitive genetic parts according to algorithmic rules quantifying DNA synthesis complexity, sgRNA expression, sgRNA targeting and genetic stability. Using ELSAs, we created three highly selective phenotypes in Escherichia coli, including redirecting metabolism to increase succinic acid production by 150-fold, knocking down amino acid biosynthesis to create a multi-auxotrophic strain and repressing stress responses to reduce persister cell formation by 21-fold. ELSAs enable simultaneous and stable regulation of many genes for metabolic engineering and synthetic biology applications.

Recent advances in plasmid-based tools for establishing novel microbial chassis

A key challenge for domesticating alternative cultivable microorganisms with biotechnological potential lies in the development of innovative technologies. Within this framework, a myriad of genetic tools has flourished, allowing the design and manipulation of complex synthetic circuits and genomes to become the general rule in many laboratories rather than the exception. More recently, with the development of novel technologies such as DNA automated synthesis/sequencing and powerful computational tools, molecular biology has entered the synthetic biology era. In the beginning, most of these technologies were established in traditional microbial models (known as chassis in the synthetic biology framework) such as Escherichia coli and Saccharomyces cerevisiae, enabling fast advances in the field and the validation of fundamental proofs of concept. However, it soon became clear that these organisms, although extremely useful for prototyping many genetic tools, were not ideal for a wide range of biotechnological tasks due to intrinsic limitations in their molecular/physiological properties. Over the last decade, researchers have been facing the great challenge of shifting from these model systems to non-conventional chassis with endogenous capacities for dealing with specific tasks. The key to address these issues includes the generation of narrow and broad host plasmid-based molecular tools and the development of novel methods for engineering genomes through homologous recombination systems, CRISPR/Cas9 and other alternative methods. Here, we address the most recent advances in plasmid-based tools for the construction of novel cell factories, including a guide for helping with "build-your-own" microbial host.

High-resolution mapping of DNA polymerase fidelity using nucleotide imbalances and next-generation sequencing

DNA polymerase fidelity is affected by both intrinsic properties and environmental conditions. Current strategies for measuring DNA polymerase error rate in vitro are constrained by low error subtype sensitivity, poor scalability, and lack of flexibility in types of sequence contexts that can be tested. We have developed the Magnification via Nucleotide Imbalance Fidelity (MagNIFi) assay, a scalable next-generation sequencing assay that uses a biased deoxynucleotide pool to quantitatively shift error rates into a range where errors are frequent and hence measurement is robust, while still allowing for accurate mapping to error rates under typical conditions. This assay is compatible with a wide range of fidelity-modulating conditions, and enables high-throughput analysis of sequence context effects on base substitution and single nucleotide deletion fidelity using a built-in template library. We validate this assay by comparing to previously established fidelity metrics, and use it to investigate neighboring sequence-mediated effects on fidelity for several DNA polymerases. Through these demonstrations, we establish the MagNIFi assay for robust, high-throughput analysis of DNA polymerase fidelity.

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.

Predicting how evolution will beat us

In this Crystal Ball article, we look ahead to the possibility of model and tools that can predict the accumulation of mutations that inactivate engineered plasmids.

New bacitracin-resistant nisin-producing strain of Lactococcus lactis and its physiological characterization

Nisin A belonging to the class I bacteriocins and produced by Lactococcus lactis subsp. lactis is widely used in many countries as highly efficient and safe preservative preventing growth of undesirable bacteria in food products. Though this compound is efficient at very low concentrations, reduction of its manufacturing cost is still relevant problem. An increased nisin A production requires improved resistance of its producer to nisin. According to some studies, mechanisms of microbial resistance to nisin A and bacitracin have a similar basis, and the same transporters are used to export these antibiotics from cells. To obtain strains with improved growth rate and nisin A productivity, selection of spontaneous bacitracin-resistant L. lactis mutants followed by examination of their stability as well as physiological and fermentation characteristics was carried out. Spontaneous mutants were obtained by culturing of L. lactis VKPM B-2092 strain on selective bacitracin-containing agar medium. The obtained bacitracin-resistant strain FL-75 was characterized by accelerated growth rate, doubled biomass accumulation, and improved nisin A resistance. The nisin A productivity of FL-75 exceeded that of the parental strain by 25% reaching 8902 U/mL after 14-h cultivation. In addition, FL-75 was characterized by the improved resistance to oxidative stress that has never been reported earlier for bacitracin-resistant microorganisms. Based on the performed characterization of FL-75, we can consider it as a new independent strain promising for the industrial production of food and feed biopreservatives. Comparison of published data and the obtained results allowed us to suppose that the bacitracin resistance mutation in FL-75 is determined rather by an increased expression of a gene homologous to the bcrC gene of Bacillus sp. than by the activation of multidrug resistance mechanisms. The revealed resistance of FL-75 to bacitracin and oxidative stress can be regulated by a common transcription factor activating in response to various environmental stresses.

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.

Auxotrophy to Xeno-DNA: an exploration of combinatorial mechanisms for a high-fidelity biosafety system for synthetic biology applications

BACKGROUND

Biosafety is a key aspect in the international Genetically Engineered Machine (iGEM) competition, which offers student teams an amazing opportunity to pursue their own research projects in the field of Synthetic Biology. iGEM projects often involve the creation of genetically engineered bacterial strains. To minimize the risks associated with bacterial release, a variety of biosafety systems were constructed, either to prevent survival of bacteria outside the lab or to hinder horizontal or vertical gene transfer.

MAIN BODY

Physical containment methods such as bioreactors or microencapsulation are considered the first safety level. Additionally, various systems involving auxotrophies for both natural and synthetic compounds have been utilized by iGEM teams in recent years. Combinatorial systems comprising multiple auxotrophies have been shown to reduced escape frequencies below the detection limit. Furthermore, a number of natural toxin-antitoxin systems can be deployed to kill cells under certain conditions. Additionally, parts of naturally occurring toxin-antitoxin systems can be used for the construction of 'kill switches' controlled by synthetic regulatory modules, allowing control of cell survival. Kill switches prevent cell survival but do not completely degrade nucleic acids. To avoid horizontal gene transfer, multiple mechanisms to cleave nucleic acids can be employed, resulting in 'self-destruction' of cells. Changes in light or temperature conditions are powerful regulators of gene expression and could serve as triggers for kill switches or self-destruction systems. Xenobiology-based containment uses applications of Xeno-DNA, recoded codons and non-canonical amino acids to nullify the genetic information of constructed cells for wild type organisms. A 'minimal genome' approach brings the opportunity to reduce the genome of a cell to only genes necessary for survival under lab conditions. Such cells are unlikely to survive in the natural environment and are thus considered safe hosts. If suitable for the desired application, a shift to cell-free systems based on Xeno-DNA may represent the ultimate biosafety system.

CONCLUSION

Here we describe different containment approaches in synthetic biology, ranging from auxotrophies to minimal genomes, which can be combined to significantly improve reliability. Since the iGEM competition greatly increases the number of people involved in synthetic biology, we will focus especially on biosafety systems developed and applied in the context of the iGEM competition.

Binary addition in a living cell based on riboregulation

Synthetic biology aims at (re-)programming living cells like computers to perform new functions for a variety of applications. Initial work rested on transcription factors, but regulatory RNAs have recently gained much attention due to their high programmability. However, functional circuits mainly implemented with regulatory RNAs are quite limited. Here, we report the engineering of a fundamental arithmetic logic unit based on de novo riboregulation to sum two bits of information encoded in molecular concentrations. Our designer circuit robustly performs the intended computation in a living cell encoding the result as fluorescence amplitudes. The whole system exploits post-transcriptional control to switch on tightly silenced genes with small RNAs, together with allosteric transcription factors to sense the molecular signals. This important result demonstrates that regulatory RNAs can be key players in synthetic biology, and it paves the way for engineering more complex RNA-based biocomputers using this designer circuit as a building block.

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.

A novel whole-cell biosensor of Pseudomonas aeruginosa to monitor the expression of quorum sensing genes

A novel whole-cell biosensor was developed to noninvasively and simultaneously monitor the in situ genetic activities of the four quorum sensing (QS) networks in Pseudomonas aeruginosa PAO1, including the las, rhl, pqs, and iqs systems. P. aeruginosa PAO1 is a model bacterium for studies of biofilm and pathogenesis while both processes are closely controlled by the QS systems. This biosensor worked well by selectively monitoring the expression of one representative gene from each network. In the biosensor, the promoter regions of lasI, rhlI, pqsA, and ambB (QS genes) controlled the fluorescent reporter genes of Turbo YFP, mTag BFP2, mNEON Green, and E2-Orange, respectively. The biosensor was successful in monitoring the impact of an important environmental factor, salt stress, on the genetic regulation of QS networks. High salt concentrations (≥ 20 g·L^-1) significantly downregulated rhlI, pqsA, and ambB after the biosensor was incubated for 17 h to 18 h at 37 °C, resulting in slow bacterial growth.

Engineered promoters enable constant gene expression at any copy number in bacteria

The internal environment of growing cells is variable and dynamic, making it difficult to introduce reliable parts, such as promoters, for genetic engineering. Here, we applied control-theoretic ideas to design promoters that maintained constant levels of expression at any copy number. Theory predicts that independence to copy number can be achieved by using an incoherent feedforward loop (iFFL) if the negative regulation is perfectly non-cooperative. We engineered iFFLs into Escherichia coli promoters using transcription-activator-like effectors (TALEs). These promoters had near-identical expression in different genome locations and plasmids, even when their copy number was perturbed by genomic mutations or changes in growth medium composition. We applied the stabilized promoters to show that a three-gene metabolic pathway to produce deoxychromoviridans could retain function without re-tuning when the stabilized-promoter-driven genes were moved from a plasmid into the genome.

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.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Reduced Mutation Rate and Increased Transformability of Transposon-Free Acinetobacter baylyi ADP1-ISx

The genomes of most bacteria contain mobile DNA elements that can contribute to undesirable genetic instability in engineered cells. In particular, transposable insertion sequence (IS) elements can rapidly inactivate genes that are important for a designed function. We deleted all six copies of IS1236 from the genome of the naturally transformable bacterium Acinetobacter baylyi ADP1. The natural competence of ADP1 made it possible to rapidly repair deleterious point mutations that arose during strain construction. In the resulting ADP1-ISx strain, the rates of mutations inactivating a reporter gene were reduced by 7- to 21-fold. This reduction was higher than expected from the incidence of new IS1236 insertions found during a 300-day mutation accumulation experiment with wild-type ADP1 that was used to estimate spontaneous mutation rates in the strain. The extra improvement appears to be due in part to eliminating large deletions caused by IS1236 activity, as the point mutation rate was unchanged in ADP1-ISx. Deletion of an error-prone polymerase (dinP) and a DNA damage response regulator (umuD_Ab [the umuD gene of A. baylyi]) from the ADP1-ISx genome did not further reduce mutation rates. Surprisingly, ADP1-ISx exhibited increased transformability. This improvement may be due to less autolysis and aggregation of the engineered cells than of the wild type. Thus, deleting IS elements from the ADP1 genome led to a greater than expected increase in evolutionary reliability and unexpectedly enhanced other key strain properties, as has been observed for other clean-genome bacterial strains. ADP1-ISx is an improved chassis for metabolic engineering and other applications.IMPORTANCEAcinetobacter baylyi ADP1 has been proposed as a next-generation bacterial host for synthetic biology and genome engineering due to its ability to efficiently take up DNA from its environment during normal growth. We deleted transposable elements that are capable of copying themselves, inserting into other genes, and thereby inactivating them from the ADP1 genome. The resulting "clean-genome" ADP1-ISx strain exhibited larger reductions in the rates of inactivating mutations than expected from spontaneous mutation rates measured via whole-genome sequencing of lineages evolved under relaxed selection. Surprisingly, we also found that IS element activity reduces transformability and is a major cause of cell aggregation and death in wild-type ADP1 grown under normal laboratory conditions. More generally, our results demonstrate that domesticating a bacterial genome by removing mobile DNA elements that have accumulated during evolution in the wild can have unanticipated benefits.

Overcoming evolved resistance to population-suppressing homing-based gene drives

The recent development of a CRISPR-Cas9-based homing system for the suppression of Anopheles gambiae is encouraging; however, with current designs, the slow emergence of homing-resistant alleles is expected to result in suppressed populations rapidly rebounding, as homing-resistant alleles have a significant fitness advantage over functional, population-suppressing homing alleles. To explore this concern, we develop a mathematical model to estimate tolerable rates of homing-resistant allele generation to suppress a wild population of a given size. Our results suggest that, to achieve meaningful population suppression, tolerable rates of resistance allele generation are orders of magnitude smaller than those observed for current designs for CRISPR-Cas9-based homing systems. To remedy this, we theoretically explore a homing system architecture in which guide RNAs (gRNAs) are multiplexed, increasing the effective homing rate and decreasing the effective resistant allele generation rate. Modeling results suggest that the size of the population that can be suppressed increases exponentially with the number of multiplexed gRNAs and that, with four multiplexed gRNAs, a mosquito species could potentially be suppressed on a continental scale. We also demonstrate successful proof-of-principle use of multiplexed ribozyme flanked gRNAs to induce mutations in vivo in Drosophila melanogaster - a strategy that could readily be adapted to engineer stable, homing-based drives in relevant organisms.

Genome microsatellite diversity within the Apicomplexa phylum

The Apicomplexa phylum groups include unicellular and obligate intracellular protozoan parasites with an apical complex used for attachment and invasion to host cells. In this study, we analyze single sequence repeats (SSRs) in the whole genome of 20 apicomplexan organisms that represent four different lineages within the phylum. Only perfect SSRs with at least 12 nucleotides and composed of 2-6 mers were included. To better understand the association of SSR types with the genomic regions, the SSRs were classified accordingly with the genomic location into exon, intron and intergenic categories. Our results showed heterogeneous SSRs density within the studied genomes. However, the most frequent SSRs types were di- and tri-nucleotide repeats. The former was associated with intergenic regions, while the latter was associated with exon regions.

Microsatellites in Pursuit of Microbial Genome Evolution

Microsatellites or short sequence repeats are widespread genetic markers which are hypermutable 1-6 bp long short nucleotide motifs. Significantly, their applications in genetics are extensive due to their ceaseless mutational degree, widespread length variations and hypermutability skills. These features make them useful in determining the driving forces of evolution by using powerful molecular techniques. Consequently, revealing important questions, for example, what is the significance of these abundant sequences in DNA, what are their roles in genomic evolution? The answers of these important questions are hidden in the ways these short motifs contributed in altering the microbial genomes since the origin of life. Even though their size ranges from 1 -to- 6 bases, these repeats are becoming one of the most popular genetic probes in determining their associations and phylogenetic relationships in closely related genomes. Currently, they have been widely used in molecular genetics, biotechnology and evolutionary biology. However, due to limited knowledge; there is a significant gap in research and lack of information concerning hypermutational mechanisms. These mechanisms play a key role in microsatellite loci point mutations and phase variations. This review will extend the understandings of impacts and contributions of microsatellite in genomic evolution and their universal applications in microbiology.