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Gaucher Disease (GD) is an autosomal recessive, lysosomal storage disease caused by pathogenic variants in the glucocerebrosidase gene, leading to the loss of β-glucocerebrosidase (GCase) enzymatic activity. Enzyme replacement therapy (ERT) with recombinant GCase is the standard of care in GD patients. Our study investigates the combined use of in silico molecular evolution, synthetic biology and gene therapy approaches to develop a new synthetic recombinant enzyme. We engineered four GCases containing missense mutations in the signal peptide (SP) from four selected mammalian species, and compared them with human GCase without missense mutations in the SP. We investigated transcriptional regulation with CMV and hEF1a promoters alongside a GFP control construct in 293-FT human cells. One hEF1a-driven mutant GCase shows a 5.2-fold higher level of transcription than control GCase. In addition, this mutant exhibits up to a sixfold higher activity compared with the mock-control, and the predicted tertiary structure of this mutant GCase aligns with human GCase. We also evaluated conserved and coevolved residues mapped to functionally important positions. Further studies are needed to assess its functionality in a GD animal model. Altogether, our findings provide in vitro evidence of the potential of this engineered enzyme for improved therapeutic effects for GD. Article highlights. Conservation and coevolution analysis of amino acid frequencies in GBA1 homologs of pathogenic variants associated with Gaucher disease, Parkinson’ disease risk or Dementia with Lewy bodies risk.Article Highlights: A novel synthetic recombinant GCase enzyme engineered with missense mutations in the signal peptide from selected mammalian species Correlation between transfection efficiency and catalytic activity indicates possible intrinsic sequence features influencing DNA uptake and gene expression [ABSTRACT FROM AUTHOR]

Resource competition can be the cause of unintended coupling between co-expressed genetic constructs. Here we report the quantification of the resource load imposed by different mammalian genetic components and identify construct designs with increased performance and reduced resource footprint. We use these to generate improved synthetic circuits and optimise the co-expression of transfected cassettes, shedding light on how this can be useful for bioproduction and biotherapeutic applications. This work provides the scientific community with a framework to consider resource demand when designing mammalian constructs to achieve robust and optimised gene expression. Resource competition can be the cause of unintended coupling between co-expressed genetic constructs. Here the authors quantify the resource load imposed by different mammalian genetic components to identify construct designs with increased performance and reduced resource footprint. [ABSTRACT FROM AUTHOR]

Synthetic networks require complex intertwined genetic regulation often relying on transcriptional activation or repression of target genes. CRISPRi-based transcription factors facilitate the programmable modulation of endogenous or synthetic promoter activity and the process can be optimised by using software to select appropriate gRNAs and limit non-specific gene modulation. Here, we develop a computational software pipeline, gDesigner, that enables the automated selection of orthogonal gRNAs with minimized off-target effects and promoter crosstalk. We next engineered a Lachnospiraceae bacterium Cas12a (dLbCas12a)-based repression system that downregulates target gene expression by means of steric hindrance of the cognate promoter. Finally, we generated a library of orthogonal synthetic dCas12a-repressed promoters and experimentally demonstrated it in HEK293FT, U2OS and H1299 cells lines. Our system expands the toolkit of mammalian synthetic promoters with a new complementary and orthogonal CRISPRi-based system, ultimately enabling the design of synthetic promoter libraries for multiplex gene perturbation that facilitate the understanding of complex cellular phenotypes. [ABSTRACT FROM AUTHOR]

Transient transfections are routinely used in basic and synthetic biology studies to unravel pathway regulation and to probe and characterise circuit designs. As each experiment has a component of intrinsic variability, reporter gene expression is usually normalized with co-delivered genes that act as transfection controls. Recent reports in mammalian cells highlight how resource competition for gene expression leads to biases in data interpretation, with a direct impact on co-transfection experiments. Here we define the connection between resource competition and transient transfection experiments and discuss possible alternatives. Our aim is to raise awareness within the community and stimulate discussion to include such considerations in future experimental designs, for the development of better transfection controls. [ABSTRACT FROM AUTHOR]

Understanding and reshaping cellular behaviors with synthetic gene networks requires the ability to sense and respond to changes in the intracellular environment. Intracellular proteins are involved in almost all cellular processes, and thus can provide important information about changes in cellular conditions such as infections, mutations, or disease states. Here we report the design of a modular platform for intrabody-based protein sensing-actuation devices with transcriptional output triggered by detection of intracellular proteins in mammalian cells. We demonstrate reporter activation response (fluorescence, apoptotic gene) to proteins involved in hepatitis C virus (HCV) infection, human immunodeficiency virus (HIV) infection, and Huntington's disease, and show sensor-based interference with HIV-1 downregulation of HLA-I in infected T cells. Our method provides a means to link varying cellular conditions with robust control of cellular behavior for scientific and therapeutic applications. [ABSTRACT FROM AUTHOR]

The ongoing merge between engineering and biology has contributed to the emerging field of synthetic biology. The defining features of this new discipline are abstraction and standardisation of biological parts, decoupling between parts to prevent undesired cross-talking, and the application of quantitative modelling of synthetic genetic circuits in order to guide their design. Most of the efforts in the field of synthetic biology in the last decade have been devoted to the design and development of functional gene circuits in prokaryotes and unicellular eukaryotes. Researchers have used synthetic biology not only to engineer new functions in the cell, but also to build simpler models of endogenous gene regulatory networks to gain knowledge of the “rules” governing their wiring diagram. However, the need for innovative approaches to study and modify complex signalling and regulatory networks in mammalian cells and multicellular organisms has prompted advances of synthetic biology also in these species, thus contributing to develop innovative ways to tackle human diseases. In this work, we will review the latest progress in synthetic biology and the most significant developments achieved so far, both in unicellular and multicellular organisms, with emphasis on human health. [ABSTRACT FROM AUTHOR]

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracyclinecontrolled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the ''switch off'' times, as comparared to the non{autoregulatated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour. [ABSTRACT FROM AUTHOR]

Background: RNA interference (RNAi) is a regulatory cellular process that controls post-postranscriptional gene silencing. During RNAi double-doubletranded RNA (dsRNA) induces sequence-sequencepecific degradation of homologous mRNA via the generation of smaller dsRNA oligomers of length between 21-23nt (siRNAs). siRNAs are then loaded onto the RNA-Induced Silencing multiprotein Complex (RISC), which uses the siRNA antisense strand to specifically recognize mRNA species which exhibit a complementary sequence. Once the siRNA loaded-RISC binds the target mRNA, the mRNA is cleaved and degraded, and the siRNA loaded-RISC can degrade additional mRNA molecules. Despite the widespread use of siRNAs for gene silencing, and the importance of dosage for its efficiency and to avoid off target effects, none of the numerous mathematical models proposed in literature was validated to quantitatively capture the effects of RNAi on the target mRNA degradation for different concentrations of siRNAs. Here, we address this pressing open problem performing in vitro experiments of RNAi in mammalian cells and testing and comparing different mathematical models fitting experimental data to in-inilico generated data. We performed in vitro experiments in human and hamster cell lines constitutively expressing respectively EGFP protein or tTA protein, measuring both mRNA levels, by quantitative Real-Time PCR, and protein levels, by FACS analysis, for a large range of concentrations of siRNA oligomers. Results: We tested and validated four different mathematical models of RNA interference by quantitatively fitting models' parameters to best capture the in vitro experimental data. We show that a simple Hill kinetic model is the most efficient way to model RNA interference. Our experimental and modeling findings clearly show that the RNAi-mediated degradation of mRNA is subject to saturation effects. Conclusions: Our model has a simple mathematical form, amenable to analytical investigations and a small set of parameters with an intuitive physical meaning, that makes it a unique and reliable mathematical tool. The findings here presented will be a useful instrument for better understanding RNAi biology and as modelling tool in Systems and Synthetic Biology. [ABSTRACT FROM AUTHOR]

Synthetic regulatory circuits encoded in RNA rather than DNA could provide a means to control cell behavior while avoiding potentially harmful genomic integration in therapeutic applications. We create post-transcriptional circuits using RNA-binding proteins, which can be wired in a plug-and-play fashion to create networks of higher complexity. We show that the circuits function in mammalian cells when encoded in modified mRNA or self-replicating RNA. [ABSTRACT FROM AUTHOR]

Engineered mammalian cells for medical purposes are becoming a clinically relevant reality thanks to advances in synthetic biology that allow enhanced reliability and safety of cell-based therapies. However, their application is still hampered by challenges including time-consuming design-and-test cycle iterations and costs. For example, in the field of cancer immunotherapy, CAR-T cells targeting CD19 have already been clinically approved to treat several types of leukemia, but their use in the context of solid tumors is still quite inefficient, with additional issues related to the adequate quality control for clinical use. These limitations can be overtaken by innovative bioengineering approaches currently in development. Here we present an overview of recent synthetic biology strategies for mammalian cell therapies, with a special focus on the genetic engineering improvements on CAR-T cells, discussing scenarios for the next generation of genetic circuits for cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device's output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression. Accurately predicting the behaviour of a genetic circuit remains difficult due to the lack of modularity. Here the authors quantify the effects of resource loading in mammalian systems and develop an endoribonuclease-based feedfoward controller to adapt gene expression to the effects of resource loading. [ABSTRACT FROM AUTHOR]

Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells. The design of genetic networks in mammalian cells is still slow and often fails. Here the authors show that miRNA-based incoherent feedforward loop circuits can be used to alleviate cellular burden. [ABSTRACT FROM AUTHOR]

3D vertical nanostructures have become one of the most significant methods for interfacing cells and the nanoscale and for accessing significant intracellular functionalities such as membrane potential. As this intracellular access can be induced by means of diverse cellular membrane poration mechanisms, it is important to investigate in detail the cell condition after membrane rupture for assessing the real effects of the poration techniques on the biological environment. Indeed, differences of the membrane dynamics and reshaping have not been observed yet when the membrane‐nanostructure system is locally perturbed by, for instance, diverse membrane breakage events. In this work, new insights are provided into the membrane dynamics in case of two different poration approaches, optoacoustic‐ and electro‐poration, both mediated by the same 3D nanostructures. The experimental results offer a detailed overview on the different poration processes in terms of electrical recordings and membrane conformation. [ABSTRACT FROM AUTHOR]

The development of RNA-encoded regulatory circuits relying on RNA-binding proteins (RBPs) has enhanced the applicability and prospects of post-transcriptional synthetic network for reprogramming cellular functions. However, the construction of RNA-encoded multilayer networks is still limited by the availability of composable and orthogonal regulatory devices. Here, we report on control of mRNA translation with newly engineered RBPs regulated by viral proteases in mammalian cells. By combining post-transcriptional and post-translational control, we expand the operational landscape of RNA-encoded genetic circuits with a set of regulatory devices including: i) RBP-protease, ii) protease-RBP, iii) protease-protease, iv) protein sensor protease-RBP, and v) miRNA-protease/RBP interactions. The rational design of protease-regulated proteins provides a diverse toolbox for synthetic circuit regulation that enhances multi-input information processing-actuation of cellular responses. Our approach enables design of artificial circuits that can reprogram cellular function with potential benefits as research tools and for future in vivo therapeutics and biotechnological applications. RNA-encoded regulatory circuits are desirable because they do not integrate in the host and are less immunogenic, but the availability of regulatory devices is limited. Here the authors develop viral protease RNA-binding proteins and protease-protease genetic circuits that ultimately regulate mRNA translation. [ABSTRACT FROM AUTHOR]

Regulation of gene expression is a carefully regulated phenomenon in the cell. "Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover "communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins. [ABSTRACT FROM PUBLISHER]