Synthetic gene circuits as tools for drug discovery

The diversification of methods for studying cell-cell interactions and communication

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

Engineering pluripotent stem cells with synthetic biology for regenerative medicine

Pluripotent stem cells (PSCs), characterized by self-renewal and capacity of differentiating into three germ layers, are the programmable building blocks of life. PSC-derived cells and multicellular systems, particularly organoids, exhibit great potential for regenerative medicine. However, this field is still in its infancy, partly due to limited strategies to robustly and precisely control stem cell behaviors, which are tightly regulated by inner gene regulatory networks in response to stimuli from the extracellular environment. Synthetic receptors and genetic circuits are powerful tools to customize the cellular sense-and-response process, suggesting their underlying roles in precise control of cell fate decision and function reconstruction. Herein, we review the progress and challenges needed to be overcome in the fields of PSC-based cell therapy and multicellular system generation, respectively. Furthermore, we summarize several well-established synthetic biology tools and their applications in PSC engineering. Finally, we highlight the challenges and perspectives of harnessing synthetic biology to PSC engineering for regenerative medicine.

Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system

Context-dependency of mammalian transcriptional elements has hindered the quantitative investigation of multigene expression stoichiometry and its biological functions. Here, we describe a host- and local DNA context-independent transcription system to gradually fine-tune single and multiple gene expression with predictable stoichiometries. The mammalian transcription system is composed of a library of modular and programmable promoters from bacteriophage and its cognate RNA polymerase (RNAP) fused to a capping enzyme. The relative expression of single genes is quantitatively determined by the relative binding affinity of the RNAP to the promoters, while multigene expression stoichiometry is predicted by a simple biochemical model with resource competition. We use these programmable and modular promoters to predictably tune the expression of three components of an influenza A virus-like particle (VLP). Optimized stoichiometry leads to a 2-fold yield of intact VLP complexes. The host-independent orthogonal transcription system provides a platform for dose-dependent control of multiple protein expression which may be applied for advanced vaccine engineering, cell-fate programming and other therapeutic applications.

Ultra-high throughput mapping of genetic design space

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 ⁵ drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

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.

Supercoiling-mediated feedback couples the transcription of proximal genes. Here, Johnstone and Galloway provide a framework for integrating biochemical gene regulation with the biophysical effects of DNA supercoiling. This unified model provides design principles for improving the performance of gene networks, developing novel regulatory functions, and accessing previously inaccessible regulatory dynamics.

Overstatements and Understatements in the Debate on Synthetic Biology, Bioterrorism and Ethics

Synthetic biology has many valuable applications, but it also gives rise to certain risks. In this paper I discuss the risk of bioterrorism, which often attracts attention in both the mass media and scientific debate, as well as in government reports. While some authors argue that there is a significant risk of bioterrorism connected to synthetic biology, other scholars claim that the risk is exaggerated and that actors often have motives for overstating the risk. In this paper, I argue that some estimates of the risk may be overstated but that certain risks of bioterrorism, such as the creation and spread of known pathogenic viruses, need to be taken seriously. Actors may also have scientific and financial motives for understating the risk. Such understatements are sometimes based on a principle of hope, which says that technological progress is important for the future welfare of humanity and that too much precaution would have bad consequences. I argue that this principle is problematic as the burdens and benefits of synthetic biology may not be equally divided between different social groups. Instead, I claim that the principle of precaution is more justified as a point of departure for assessing advancements within synthetic biology. It tells us that we need strong evidence that such advancements are safe, because there is a potential risk that they may make it easier for terrorist groups to create and spread known pathogenic viruses.