Ultrashort-Peptide-Responsive Gene Switches for Regulation of Therapeutic Protein Expression in Mammalian Cells.

Despite the array of mammalian transgene switches available for regulating therapeutic protein expression in response to small molecules or physical stimuli, issues remain, including cytotoxicity of chemical inducers and limited biocompatibility of physical cues. This study introduces gene switches driven by short peptides comprising eight or fewer amino acid residues. Utilizing a competence regulator (ComR) and sigma factor X-inducing peptide (XIP) from Streptococcus vestibularis as the receptor and inducer, respectively, this study develops two strategies for a peptide-activated transgene control system. The first strategy involves fusing ComR with a transactivation domain and utilizes ComR-dependent synthetic promoters to drive expression of the gene-of-interest, activated by XIP, thereby confirming its membrane penetrability and intracellular functionality. The second strategy features an orthogonal synthetic receptor exposing ComR extracellularly (ComREXTRA), greatly increasing sensitivity with exceptional responsiveness to short peptides. In a proof-of-concept study, peptides are administered to type-1 diabetic mice with microencapsulated engineered human cells expressing ComREXTRA for control of insulin expression, restoring normoglycemia. It is envisioned that this system will encourage the development of short peptide drugs and promote the introduction of non-toxic, orthogonal, and highly biocompatible personalized biopharmaceuticals for gene- and cell-based therapies.

Intelligent computation in cancer gene therapy.

In recent years, the use of gene therapy for the treatment of disease has gained substantial interest, both in academic research and in the biomedical industry. Initial experimentation in gene therapy has generated positive results, as well as questions regarding safety. However, lessons have been learned from these first investigations, among them a realization that such treatments require a method to fine-tune the expression of therapeutic genes in real-time. A logical solution to this problem arose through the field of synthetic biology in the form of synthetic gene circuits. Thus, the synthetic biology community today aims to create "smart cells" for a variety of gene therapy applications, in an attempt to precisely target malignant cells while avoiding harming healthy ones. To generate safer and more effective gene therapies, new approaches with emerging computational abilities are necessary. In this review, we present several computational approaches which allow demonstrating artificial intelligence in living cells. Specifically, we will focus on implementing artificial neural networks using synthetic gene regulatory networks for cancer therapy and discuss the state-of-the-art computational developments.

dCas12a:Pre-crRNA: A New Tool to Induce mRNA Degradation in Saccharomyces cerevisiae Synthetic Gene Circuits.

We describe a new way to trigger mRNA degradation in Saccharomyces cerevisiae synthetic gene circuits. Our method demands to modify either the 5'- or the 3'-UTR that flanks a target gene with elements from the pre-crRNA of type V Cas12a proteins and expresses a DNase-deficient Cas12a (dCas12a). dCas12a recognizes and cleaves the pre-crRNA motifs on mRNA sequences. Our tool does not require complex engineering operations and permits an efficient control of protein expression via mRNA degradation.

In Vitro Generation of Megakaryocytes from Engineered Mouse Embryonic Stem Cells.

The in vitro differentiation of pluripotent stem cells into desired lineages enables mechanistic studies of cell transitions into more mature states that can provide insights into the design principles governing cell fate control. We are interested in reprogramming pluripotent stem cells with synthetic gene circuits to drive mouse embryonic stem cells (mESCs) down the hematopoietic lineage for the production of megakaryocytes, the progenitor cells for platelets. Here, we describe the methodology for growing and differentiating mESCs, in addition to inserting a transgene to observe its expression throughout differentiation. This entails four key methods: (1) growing and preparing mouse embryonic fibroblasts for supporting mESC growth and expansion, (2) growing and preparing OP9 feeder cells to support the differentiation of mESCs, (3) the differentiation of mESCs into megakaryocytes, and (4) utilizing an integrase-mediated docking site to insert transgenes for their stable integration and expression throughout differentiation. Altogether, this approach demonstrates a streamline differentiation protocol that emphasizes the reprogramming potential of mESCs that can be used for future mechanistic and therapeutic studies of controlling cell fate outcomes.

Recombinant protein expression in proteome-reduced cells under aerobic and oxygen-limited regimes.

Industrial cultures are hindered by the physiological complexity of the host and the limited mass transfer capacity of conventional bioreactors. In this study, a minimal cell approach was combined with genetic devices to overcome such issues. A flavin mononucleotide-based fluorescent protein (FbFP) was expressed in a proteome-reduced Escherichia coli (PR). When FbFP was expressed from a constitutive protein generator (CPG), the PR strain produced 47% and 35% more FbFP than its wild type (WT), in aerobic or oxygen-limited regimes, respectively. Metabolic and expression models predicted more efficient biomass formation at higher fluxes to FbFP, in agreement with these results. A microaerobic protein generator (MPG) and a microaerobic transcriptional cascade (MTC) were designed to induce FbFP expression upon oxygen depletion. The FbFP fluorescence using the MTC in the PR strain was 9% higher than that of the WT bearing the CPG under oxygen limitation. To further improve the PR strain, the pyruvate dehydrogenase complex regulator gene was deleted, and the Vitreoscilla hemoglobin was expressed. Compared to oxygen-limited cultures of the WT, the engineered strains increased the FbFP expression more than 50% using the MTC. Therefore, the designed expression systems can be a valuable alternative for industrial cultivations.

Synthetic Gene Circuits Combining CRISPR Interference and CRISPR Activation in E. coli: Importance of Equal Guide RNA Binding Affinities to Avoid Context-Dependent Effects.

Gene expression control based on clustered regularly interspaced short palindromic repeats (CRISPR) has emerged as a powerful approach for constructing synthetic gene circuits. While the use of CRISPR interference (CRISPRi) is already well-established in prokaryotic circuits, CRISPR activation (CRISPRa) is less mature, and a combination of the two in the same circuits is only just emerging. Here, we report that combining CRISPRi with SoxS-based CRISPRa in Escherichia coli can lead to context-dependent effects due to different affinities in the formation of CRISPRa and CRISPRi complexes, resulting in loss of predictable behavior. We show that this effect can be avoided by using the same scaffold guide RNA structure for both complexes.

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability.

A ubiquitous feature of eukaryotic transcriptional regulation is cooperative self-assembly between transcription factors (TFs) and DNA cis-regulatory motifs. It is thought that this strategy enables specific regulatory connections to be formed in gene networks between otherwise weakly interacting, low-specificity molecular components. Here, using synthetic gene circuits constructed in yeast, we find that high regulatory specificity can emerge from cooperative, multivalent interactions among artificial zinc-finger-based TFs. We show that circuits "wired" using the strategy of cooperative TF assembly are effectively insulated from aberrant misregulation of the host cell genome. As we demonstrate in experiments and mathematical models, this mechanism is sufficient to rescue circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally inspired approach offers a simple, generalizable means for building high-fidelity, evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

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.

Synthetic biology, genetic circuits and machine learning: a new age of cancer therapy.

Synthetic biology has made it possible to rewire natural cellular responses to treat disease, notably demonstrated by chimeric antigen receptor (CAR) T cells as cancer immunotherapy. Building on the success of T-cell activation using synthetic receptors, the field is now investigating how induction of noncanonical signalling pathways and sophisticated synthetic gene circuitry can enhance the antitumour phenotype of engineered T cells. This commentary explores two recently published studies that provide proof of concept for how new technologies achieve this. The first demonstrated that non-naturally occurring combinations of signalling motifs derived from various immune receptors and arranged as a CAR drove unique signal transduction pathways in T cells and improved their tumour killing ability. Here, machine learning complemented the screening process and successfully predicted CAR T-cell phenotype dependent on signalling motif choice. The second explored how synthetic zinc fingers can be engineered into controllable transcriptional regulators, where their activity was dependent on the presence or absence of FDA-approved small-molecule drugs. These studies are pivotal in expanding the design choices available for gene circuits of the future and highlight how a single cellular therapy could respond to multiple environmental cues including target cell antigen expression, the tumour microenvironment composition and small molecule drugs.

Performance Prediction of Fundamental Transcriptional Programs.

Transcriptional programming leverages systems of engineered transcription factors to impart decision-making (e.g., Boolean logic) in chassis cells. The number of components used to construct said decision-making systems is rapidly increasing, making an exhaustive experimental evaluation of iterations of biological circuits impractical. Accordingly, we posited that a predictive tool is needed to guide and accelerate the design of transcriptional programs. The work described here involves the development and experimental characterization of a large collection of network-capable single-INPUT logical operations─i.e., engineered BUFFER (repressor) and engineered NOT (antirepressor) logical operations. Using this single-INPUT data and developed metrology, we were able to model and predict the performances of all fundamental two-INPUT compressed logical operations (i.e., compressed AND gates and compressed NOR gates). In addition, we were able to model and predict the performance of compressed mixed phenotype logical operations (A NIMPLY B gates and complementary B NIMPLY A gates). These results demonstrate that single-INPUT data is sufficient to accurately predict both the qualitative and quantitative performance of a complex circuit. Accordingly, this work has set the stage for the predictive design of transcriptional programs of greater complexity.

Recent advances in bacterial therapeutics based on sense and response.

Intelligent drug delivery is a promising strategy for cancer therapies. In recent years, with the rapid development of synthetic biology, some properties of bacteria, such as gene operability, excellent tumor colonization ability, and host-independent structure, make them ideal intelligent drug carriers and have attracted extensive attention. By implanting condition-responsive elements or gene circuits into bacteria, they can synthesize or release drugs by sensing stimuli. Therefore, compared with traditional drug delivery, the usage of bacteria for drug loading has better targeting ability and controllability, and can cope with the complex delivery environment of the body to achieve the intelligent delivery of drugs. This review mainly introduces the development of bacterial-based drug delivery carriers, including mechanisms of bacterial targeting to tumor colonization, gene deletions or mutations, environment-responsive elements, and gene circuits. Meanwhile, we summarize the challenges and prospects faced by bacteria in clinical research, and hope to provide ideas for clinical translation.

Avoiding the All-or-None Response in Gene Expression During E. coli Continuous Cultivation Based on the On-Line Monitoring of Cell Phenotypic Switching Dynamics.

Different expression vectors are available for the effective production of recombinant proteins by bacterial populations. However, the productivity of such systems is limited by the inherent noise of the gene circuits used for the synthesis of recombinant products. An extreme case of cell-to-cell heterogeneity that has been previously reported for the ara- and lac-based expression systems in E. coli is the all-or-none response. According to this mode of response, two subpopulations of cells are generated, i.e., a "low-" subpopulation exhibiting a shallow expression level and a "high-" subpopulation exhibiting a high-expression level. The "low-" subpopulation can be considered as a cluster of non-producing cells contributing to the loss of productivity. Here we describe the setup, design, and operation of a continuous culture where inducer addition is operated based on microbial population dynamics. The determination of population dynamics is done based on an automated flow cytometry (FC) procedure previously denoted as segregostat. We illustrate how this setup can be used to control the activation of an ara-based expression system and avoid phenotypic diversification leading to an all-or-none response. Upon the determination of the natural frequency of the gene circuit used as an expression system, our current protocol can be set up without the requirement of a feedback controller.

Recent advances in machine learning applications in metabolic engineering.

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Synthetic developmental biology: New tools to deconstruct and rebuild developmental systems.

Technological advances have driven many recent advances in developmental biology. Light sheet imaging can reveal single-cell dynamics in living three-dimensional tissues, whereas single-cell genomic methods open the door to a complete catalogue of cell types and gene expression states. An equally powerful but complementary set of approaches are also becoming available to define development processes from the bottom up. These synthetic approaches aim to reconstruct the minimal developmental patterns, signaling processes, and gene networks that produce the basic set of developmental operations: spatial polarization, morphogen interpretation, tissue movement, and cellular memory. In this review we discuss recent approaches at the intersection of synthetic biology and development, including synthetic circuits to deliver and record signaling stimuli and synthetic reconstitution of pattern formation on multicellular scales.

Recent advances in bacterial therapeutics based on sense and response

Intelligent drug delivery is a promising strategy for cancer therapies. In recent years, with the rapid development of synthetic biology, some properties of bacteria, such as gene operability, excellent tumor colonization ability, and host-independent structure, make them ideal intelligent drug carriers and have attracted extensive attention. By implanting condition-responsive elements or gene circuits into bacteria, they can synthesize or release drugs by sensing stimuli. Therefore, compared with traditional drug delivery, the usage of bacteria for drug loading has better targeting ability and controllability, and can cope with the complex delivery environment of the body to achieve the intelligent delivery of drugs. This review mainly introduces the development of bacterial-based drug delivery carriers, including mechanisms of bacterial targeting to tumor colonization, gene deletions or mutations, environment-responsive elements, and gene circuits. Meanwhile, we summarize the challenges and prospects faced by bacteria in clinical research, and hope to provide ideas for clinical translation.

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.

Recent progress of gene circuit designs in immune cell therapies.

The success of chimeric antigen receptor (CAR) T cell therapy against hematological cancers has convincingly demonstrated the potential of using genetically engineered cells as therapeutic agents. Although much progress has been achieved in cell therapy, more beneficial capabilities have yet to be fully explored. One of the unique advantages afforded by cell therapies is the possibility to implement genetic control circuits, which enables diverse signal sensing and logical processing for optimal response in the complex tumor microenvironment. In this perspective, we will first outline design considerations for cell therapy control circuits that address clinical demands. We will compare and contrast key design features in some of the latest control circuits developments and conclude by discussing potential future directions.

Supercoiling-mediated feedback rapidly couples and tunes transcription.

Transcription induces a wave of DNA supercoiling, altering the binding affinity of RNA polymerases and reshaping the biochemical landscape of gene regulation. As supercoiling rapidly diffuses, transcription dynamically reshapes the regulation of proximal genes, forming a complex feedback loop. However, a theoretical framework is needed to integrate biophysical regulation with biochemical transcriptional regulation. To investigate the role of supercoiling-mediated feedback within multi-gene systems, we model transcriptional regulation under the influence of supercoiling-mediated polymerase dynamics, allowing us to identify patterns of expression that result from physical inter-gene coupling. We find that gene syntax-the relative ordering and orientation of genes-defines the expression profiles, variance, burst dynamics, and inter-gene correlation of two-gene systems. Furthermore, supercoiling can enhance or weaken biochemical regulation. Our results suggest that supercoiling couples behavior between neighboring genes, providing a regulatory mechanism that tunes transcriptional variance in engineered gene networks and explains the behavior of co-localized native circuits.

Synthetic biology approaches for dynamic CHO cell engineering.

Fed-batch culture of Chinese hamster ovary (CHO) cells remains the most commonly used method for producing biopharmaceuticals. Static CHO cell-line engineering approaches have incrementally improved productivity, growth and product quality through permanent knockout of genes with a negative impact on production, or constitutive overexpression of genes with a positive impact. However, during fed-batch culture, conditions (such as nutrient availability) are continually changing. Therefore, traits that are most beneficial during early-phase culture (such as high growth rate) may be less desirable in late phase. Unlike with static approaches, dynamic cell line engineering strategies can optimise such traits by implementing synthetic sense-and-respond programmes. Here, we review emerging synthetic biology tools that can be used to build dynamic, self-regulating CHO cells, capable of detecting intra-/extracellular cues and generating user-defined responses tailored to the stage-specific needs of the production process.

Two-Layered Microfluidic Devices for High-Throughput Dynamic Analysis of Synthetic Gene Circuits in E. coli.

Escherichia coli is a common chassis for synthetic gene circuit studies. In addition to the dose-response of synthetic gene circuits, the analysis of dynamic responses is also an important part of the future design of more complicated synthetic systems. Recently, microfluidic-based methods have been widely used for the analysis of gene expression dynamics. Here, we established a two-layered microfluidic platform for the systematic characterization of synthetic gene circuits (eight strains in eight different culture environments could be observed simultaneously with a 5 min time resolution). With this platform, both dose responses and dynamic responses with a high temporal resolution could be easily derived for further analysis. A controlled environment ensures the stability of the bacterial growth rate, excluding changes in gene expression dynamics caused by changes of the growth dilution rate. The precise environmental switch and automatic micrograph shooting ensured that there was nearly no time lag between the inducer addition and the data recording. We studied four four-node incoherent-feedforward-loop (IFFL) networks with different operators using this device. The experimental results showed that as the effect of inhibition increased, two of the IFFL networks generated pulselike dynamic gene expressions in the range of the inducer concentrations, which was different from the dynamics of the two other circuits with only a simple pattern of rising to the platform. Through fitting the dose-response curves and the dynamic response curves, corresponding parameters were derived and introduced to a simple model that could qualitatively explain the generation of pulse dynamics.