Synthetic Genomes.

DNA synthesis technology has progressed to the point that it is now practical to synthesize entire genomes. Quite a variety of methods have been developed, first to synthesize single genes but ultimately to massively edit or write from scratch entire genomes. Synthetic genomes can essentially be clones of native sequences, but this approach does not teach us much new biology. The ability to endow genomes with novel properties offers special promise for addressing questions not easily approachable with conventional gene-at-a-time methods. These include questions about evolution and about how genomes are fundamentally wired informationally, metabolically, and genetically. The techniques and technologies relating to how to design, build, and deliver big DNA at the genome scale are reviewed here. A fuller understanding of these principles may someday lead to the ability to truly design genomes from scratch.

Designing cell function: assembly of synthetic gene circuits for cell biology applications.

Synthetic biology is the discipline of engineering application-driven biological functionalities that were not evolved by nature. Early breakthroughs of cell engineering, which were based on ectopic (over)expression of single sets of transgenes, have already had a revolutionary impact on the biotechnology industry, regenerative medicine and blood transfusion therapies. Now, we require larger-scale, rationally assembled genetic circuits engineered to programme and control various human cell functions with high spatiotemporal precision in order to solve more complex problems in applied life sciences, biomedicine and environmental sciences. This will open new possibilities for employing synthetic biology to advance personalized medicine by converting cells into living therapeutics to combat hitherto intractable diseases.

Synthetic reversed sequences reveal default genomic states.

Pervasive transcriptional activity is observed across diverse species. The genomes of extant organisms have undergone billions of years of evolution, making it unclear whether these genomic activities represent effects of selection or 'noise'1-4. Characterizing default genome states could help understand whether pervasive transcriptional activity has biological meaning. Here we addressed this question by introducing a synthetic 101-kb locus into the genomes of Saccharomyces cerevisiae and Mus musculus and characterizing genomic activity. The locus was designed by reversing but not complementing human HPRT1, including its flanking regions, thus retaining basic features of the natural sequence but ablating evolved coding or regulatory information. We observed widespread activity of both reversed and native HPRT1 loci in yeast, despite the lack of evolved yeast promoters. By contrast, the reversed locus displayed no activity at all in mouse embryonic stem cells, and instead exhibited repressive chromatin signatures. The repressive signature was alleviated in a locus variant lacking CpG dinucleotides; nevertheless, this variant was also transcriptionally inactive. These results show that synthetic genomic sequences that lack coding information are active in yeast, but inactive in mouse embryonic stem cells, consistent with a major difference in 'default genomic states' between these two divergent eukaryotic cell types, with implications for understanding pervasive transcription, horizontal transfer of genetic information and the birth of new genes.

Synthetic gene drives as an anthropogenic evolutionary force.

Genetic drive represents a fundamental evolutionary force that can exact profound change to the genetic composition of populations by biasing allele transmission. Herein I propose that the use of synthetic homing gene drives, the human-mediated analog of endogenous genetic drives, warrants the designation of 'genetic welding' as an anthropogenic evolutionary force. Conceptually, this distinction parallels that of artificial and natural selection. Genetic welding is capable of imposing complex and rapid heritable phenotypic change on entire populations, whether motivated by biodiversity conservation or public health. Unanticipated possible long-term evolutionary outcomes, however, demand further investigation and bioethical consideration. The emerging importance of genetic welding also compels our explicit recognition of genetic drive as an addition to the other four fundamental forces of evolution.

Computational design of transmembrane pores.

Transmembrane channels and pores have key roles in fundamental biological processes1 and in biotechnological applications such as DNA nanopore sequencing2-4, resulting in considerable interest in the design of pore-containing proteins. Synthetic amphiphilic peptides have been found to form ion channels5,6, and there have been recent advances in de novo membrane protein design7,8 and in redesigning naturally occurring channel-containing proteins9,10. However, the de novo design of stable, well-defined transmembrane protein pores that are capable of conducting ions selectively or are large enough to enable the passage of small-molecule fluorophores remains an outstanding challenge11,12. Here we report the computational design of protein pores formed by two concentric rings of α-helices that are stable and monodisperse in both their water-soluble and their transmembrane forms. Crystal structures of the water-soluble forms of a 12-helical pore and a 16-helical pore closely match the computational design models. Patch-clamp electrophysiology experiments show that, when expressed in insect cells, the transmembrane form of the 12-helix pore enables the passage of ions across the membrane with high selectivity for potassium over sodium; ion passage is blocked by specific chemical modification at the pore entrance. When incorporated into liposomes using in vitro protein synthesis, the transmembrane form of the 16-helix pore-but not the 12-helix pore-enables the passage of biotinylated Alexa Fluor 488. A cryo-electron microscopy structure of the 16-helix transmembrane pore closely matches the design model. The ability to produce structurally and functionally well-defined transmembrane pores opens the door to the creation of designer channels and pores for a wide variety of applications.

Dynamic context-dependent regulation of auxin feedback signaling in synthetic gene circuits.

Phytohormone auxin plays a key role in regulating plant organogenesis. However, understanding the complex feedback signaling network that involves at least 29 proteins in Arabidopsis in the dynamic context remains a significant challenge. To address this, we transplanted an auxin-responsive feedback circuit responsible for plant organogenesis into yeast. By generating dynamic microfluidic conditions controlling gene expression, protein degradation, and binding affinity of auxin response factors to DNA, we illuminate feedback signal processing principles in hormone-driven gene expression. In particular, we recorded the regulatory mode shift between stimuli counting and rapid signal integration that is context-dependent. Overall, our study offers mechanistic insights into dynamic auxin response interplay trackable by synthetic gene circuits, thereby offering instructions for engineering plant architecture.

Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance.

Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.

Synthetic Genomics: Rewriting the Genome Chromosome by Chromosome.

The Synthetic Yeast Genome Project (Sc2.0) consortium has recently published a collection of seven reports on the design, assembly, and in vivo characterization of five entirely synthetic yeast chromosomes that set the stage for de novo construction of synthetic custom-engineered eukaryotes.

Establishing artificial gene connections through RNA displacement-assembly-controlled CRISPR/Cas9 function.

Construction of synthetic circuits that can reprogram genetic networks and signal pathways is a long-term goal for manipulation of biosystems. However, it is still highly challenging to build artificial genetic communications among endogenous RNA species due to their sequence independence and structural diversities. Here we report an RNA-based synthetic circuit that can establish regulatory linkages between expression of endogenous genes in both Escherichiacoli and mammalian cells. This design employs a displacement-assembly approach to modulate the activity of guide RNA for function control of CRISPR/Cas9. Our experiments demonstrate the great effectiveness of this RNA circuit for building artificial connections between expression of originally unrelated genes. Both exogenous and naturally occurring RNAs, including small/microRNAs and long mRNAs, are capable of controlling expression of another endogenous gene through this approach. Moreover, an artificial signal pathway inside mammalian cells is also successfully established to control cell apoptosis through our designed synthetic circuit. This study provides a general strategy for constructing synthetic RNA circuits, which can introduce artificial connections into the genetic networks of mammalian cells and alter the cellular phenotypes.

CRISPR-associated type V proteins as a tool for controlling mRNA stability in S. cerevisiae synthetic gene circuits.

Type V-A CRISPR-(d)Cas system has been used in multiplex genome editing and transcription regulation in both eukaryotes and prokaryotes. However, mRNA degradation through the endonuclease activity of Cas12a has never been studied. In this work, we present an efficient and powerful tool to induce mRNA degradation in the yeast Saccharomyces cerevisiae via the catalytic activity of (d)Cas12a on pre-crRNA structure. Our results point out that dFnCas12a, (d)LbCas12a, denAsCas12a and two variants (which carry either NLSs or NESs) perform significant mRNA degradation upon insertion of pre-crRNA fragments into the 5'- or 3' UTR of the target mRNA. The tool worked well with two more Cas12 proteins-(d)MbCas12a and Casϕ2-whereas failed by using type VI LwaCas13a, which further highlights the great potential of type V-A Cas proteins in yeast. We applied our tool to the construction of Boolean NOT, NAND, and IMPLY gates, whose logic operations are fully based on the control of the degradation of the mRNA encoding for a reporter protein. Compared to other methods for the regulation of mRNA stability in yeast synthetic gene circuits (such as RNAi and riboswitches/ribozymes), our system is far easier to engineer and ensure very high performance.

MIRELLA: a mathematical model explains the effect of microRNA-mediated synthetic genes regulation on intracellular resource allocation.

Competition for intracellular resources, also known as gene expression burden, induces coupling between independently co-expressed genes, a detrimental effect on predictability and reliability of gene circuits in mammalian cells. We recently showed that microRNA (miRNA)-mediated target downregulation correlates with the upregulation of a co-expressed gene, and by exploiting miRNAs-based incoherent-feed-forward loops (iFFLs) we stabilise a gene of interest against burden. Considering these findings, we speculate that miRNA-mediated gene downregulation causes cellular resource redistribution. Despite the extensive use of miRNA in synthetic circuits regulation, this indirect effect was never reported before. Here we developed a synthetic genetic system that embeds miRNA regulation, and a mathematical model, MIRELLA, to unravel the miRNA (MI) RolE on intracellular resource aLLocAtion. We report that the link between miRNA-gene downregulation and independent genes upregulation is a result of the concerted action of ribosome redistribution and 'queueing-effect' on the RNA degradation pathway. Taken together, our results provide for the first time insights into the hidden regulatory interaction of miRNA-based synthetic networks, potentially relevant also in endogenous gene regulation. Our observations allow to define rules for complexity- and context-aware design of genetic circuits, in which transgenes co-expression can be modulated by tuning resource availability via number and location of miRNA target sites.

A genetic mammalian proportional-integral feedback control circuit for robust and precise gene regulation.

The processes that keep a cell alive are constantly challenged by unpredictable changes in its environment. Cells manage to counteract these changes by employing sophisticated regulatory strategies that maintain a steady internal milieu. Recently, the antithetic integral feedback motif has been demonstrated to be a minimal and universal biological regulatory strategy that can guarantee robust perfect adaptation for noisy gene regulatory networks in Escherichia coli. Here, we present a realization of the antithetic integral feedback motif in a synthetic gene circuit in mammalian cells. We show that the motif robustly maintains the expression of a synthetic transcription factor at tunable levels even when it is perturbed by increased degradation or its interaction network structure is perturbed by a negative feedback loop with an RNA-binding protein. We further demonstrate an improved regulatory strategy by augmenting the antithetic integral motif with additional negative feedback to realize antithetic proportional-integral control. We show that this motif produces robust perfect adaptation while also reducing the variance of the regulated synthetic transcription factor. We demonstrate that the integral and proportional-integral feedback motifs can mitigate the impact of gene expression burden, and we computationally explore their use in cell therapy. We believe that the engineering of precise and robust perfect adaptation will enable substantial advances in industrial biotechnology and cell-based therapeutics.

Unbiased identification of signal-activated transcription factors by barcoded synthetic tandem repeat promoter screening (BC-STAR-PROM).

The discovery of transcription factors (TFs) controlling pathways in health and disease is of paramount interest. We designed a widely applicable method, dubbed barcorded synthetic tandem repeat promoter screening (BC-STAR-PROM), to identify signal-activated TFs without any a priori knowledge about their properties. The BC-STAR-PROM library consists of ∼3000 luciferase expression vectors, each harboring a promoter (composed of six tandem repeats of synthetic random DNA) and an associated barcode of 20 base pairs (bp) within the 3' untranslated mRNA region. Together, the promoter sequences encompass >400,000 bp of random DNA, a sequence complexity sufficient to capture most TFs. Cells transfected with the library are exposed to a signal, and the mRNAs that it encodes are counted by next-generation sequencing of the barcodes. This allows the simultaneous activity tracking of each of the ∼3000 synthetic promoters in a single experiment. Here we establish proof of concept for BC-STAR-PROM by applying it to the identification of TFs induced by drugs affecting actin and tubulin cytoskeleton dynamics. BC-STAR-PROM revealed that serum response factor (SRF) is the only immediate early TF induced by both actin polymerization and microtubule depolymerization. Such changes in cytoskeleton dynamics are known to occur during the cell division cycle, and real-time bioluminescence microscopy indeed revealed cell-autonomous SRF-myocardin-related TF (MRTF) activity bouts in proliferating cells.

Synthetic Genes For Dynamic Regulation Of DNA-Based Receptors.

We present a strategy to control dynamically the loading and release of molecular ligands from synthetic nucleic acid receptors using in vitro transcription. We demonstrate this by engineering three model synthetic DNA-based receptors: a triplex-forming DNA complex, an ATP-binding aptamer, and a hairpin strand, whose ability to bind their specific ligands can be cotranscriptionally regulated (activated or inhibited) through specific RNA molecules produced by rationally designed synthetic genes. The kinetics of our DNA sensors and their genetically generated inputs can be captured using differential equation models, corroborating the predictability of the approach used. This approach shows that highly programmable nucleic acid receptors can be controlled with molecular instructions provided by dynamic transcriptional systems, illustrating their promise in the context of coupling DNA nanotechnology with biological signaling.

Efficient design of synthetic gene circuits under cell-to-cell variability.

BACKGROUND: Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. RESULTS: Here, we present a computational framework and an efficient algorithm to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a Non-linear Mixed-Effects (NLME) framework into a Markov Chain Monte-Carlo (MCMC) algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. CONCLUSION: We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.

Isolating live cells after high-throughput, long-term, time-lapse microscopy.

Single-cell genetic screens can be incredibly powerful, but current high-throughput platforms do not track dynamic processes, and even for non-dynamic properties they struggle to separate mutants of interest from phenotypic outliers of the wild-type population. Here we introduce SIFT, single-cell isolation following time-lapse imaging, to address these limitations. After imaging and tracking individual bacteria for tens of consecutive generations under tightly controlled growth conditions, cells of interest are isolated and propagated for downstream analysis, free of contamination and without genetic or physiological perturbations. This platform can characterize tens of thousands of cell lineages per day, making it possible to accurately screen complex phenotypes without the need for barcoding or genetic modifications. We applied SIFT to identify a set of ultraprecise synthetic gene oscillators, with circuit variants spanning a 30-fold range of average periods. This revealed novel design principles in synthetic biology and demonstrated the power of SIFT to reliably screen diverse dynamic phenotypes.

Overcoming selection bias in synthetic lethality prediction.

MOTIVATION: Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data. RESULTS: We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples. AVAILABILITY AND IMPLEMENTATION: https://github.com/joanagoncalveslab/sbsl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Synthetic gene circuits for cell state detection and protein tuning in human pluripotent stem cells.

During development, cell state transitions are coordinated through changes in the identity of molecular regulators in a cell type- and dose-specific manner. The ability to rationally engineer such transitions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein, we report the generation of synthetic gene circuits that can detect a desired cell state using AND-like logic integration of endogenous miRNAs (classifiers) and, upon detection, produce fine-tuned levels of output proteins using an miRNA-mediated output fine-tuning technology (miSFITs). Specifically, we created an "hPSC ON" circuit using a model-guided miRNA selection and circuit optimization approach. The circuit demonstrates robust PSC-specific detection and graded output protein production. Next, we used an empirical approach to create an "hPSC-Off" circuit. This circuit was applied to regulate the secretion of endogenous BMP4 in a state-specific and fine-tuned manner to control the composition of differentiating hPSCs. Our work provides a platform for customized cell state-specific control of desired physiological factors in hPSC, laying the foundation for programming cell compositions in hPSC-derived tissues and beyond.

The design of synthetic gene circuits in plants: new components, old challenges.

The fascination produced by the possibility of engineering plants with augmented capabilities has accompanied plant biotechnology since its origins. This prospect has become even more relevant in present times under the pressure imposed by climate change and population growth. Today's plant biotechnologists approach this challenge with the tools of synthetic biology, which facilitate the assembly of synthetic gene circuits (SGCs) from their modular components. Transcriptional SGCs take environmental or endogenous inputs and operate them using transcriptional signals in ways that do not necessarily occur in nature, generating new physiological outputs. Many genetic components have been developed over the years that can be employed in the design and construction of plant SGCs. This review aims to provide an updated view of the components available, proposing a general scheme that facilitates the classification of circuit components in sensor, processor, and actuator modules. Following this analogy, we review the latest advances in the design of SGCs and discuss the main challenges ahead.

Codon Bias as a Means to Fine-Tune Gene Expression.

The redundancy of the genetic code implies that most amino acids are encoded by multiple synonymous codons. In all domains of life, a biased frequency of synonymous codons is observed at the genome level, in functionally related genes (e.g., in operons), and within single genes. Other codon bias variants include biased codon pairs and codon co-occurrence. Although translation initiation is the key step in protein synthesis, it is generally accepted that codon bias contributes to translation efficiency by tuning the elongation rate of the process. Moreover, codon bias plays an important role in controlling a multitude of cellular processes, ranging from differential protein production to protein folding. Here we review currently known types of codon bias and how they may influence translation. We discuss how understanding the principles of codon bias and translation can contribute to improved protein production and developments in synthetic biology.