Using RNA-binding proteins to build genetic circuits enables RNA-only synthetic biology. 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.

MiRNAs are small non-coding RNAs able to modulate target gene expression. It has been postulated that miRNAs confer robustness to biological processes, but clear experimental evidence is still missing. Here, using a synthetic biological approach, we demonstrate that microRNAs provide phenotypic robustness to transcriptional regulatory networks by buffering fluctuations in protein levels. We construct a network motif in mammalian cells exhibiting a ‘toggle-switch’ phenotype in which two alternative protein expression levels define its ON and OFF states. The motif consists of an inducible transcription factor that self-regulates its own transcription and that of a miRNA against the transcription factor itself. We confirm, using mathematical modelling and experimental approaches, that the microRNA confers robustness to the toggle-switch by enabling the cell to maintain and transmit its state. When absent, a dramatic increase in protein noise level occurs, causing the cell to randomly switch between the two states.

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.

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.

We collected a massive and heterogeneous dataset of 20 255 gene expression profiles (GEPs) from a variety of human samples and experimental conditions, as well as 8895 GEPs from mouse samples. We developed a mutual information (MI) reverse-engineering approach to quantify the extent to which the mRNA levels of two genes are related to each other across the dataset. The resulting networks consist of 4 817 629 connections among 20 255 transcripts in human and 14 461 095 connections among 45 101 transcripts in mouse, with a inter-species conservation of 12%. The inferred connections were compared against known interactions to assess their biological significance. We experimentally validated a subset of not previously described protein–protein interactions. We discovered co-expressed modules within the networks, consisting of genes strongly connected to each other, which carry out specific biological functions, and tend to be in physical proximity at the chromatin level in the nucleus. We show that the network can be used to predict the biological function and subcellular localization of a protein, and to elucidate the function of a disease gene. We experimentally verified that granulin precursor (GRN) gene, whose mutations cause frontotemporal lobar degeneration, is involved in lysosome function. We have developed an online tool to explore the human and mouse gene networks.

Prokaryotic regulatory proteins respond to diverse signals and represent a rich resource for building synthetic sensors and circuits. The TetR family contains >10⁵ members that use a simple mechanism to respond to stimuli and bind distinct DNA operators. We present a platform that enables the transfer of these regulators to mammalian cells, which is demonstrated using human embryonic kidney (HEK293) and Chinese hamster ovary (CHO) cells. The repressors are modified to include nuclear localization signals (NLS) and responsive promoters are built by incorporating multiple operators. Activators are also constructed by modifying the protein to include a VP16 domain. Together, this approach yields 15 new regulators that demonstrate 19- to 551-fold induction and retain both the low levels of crosstalk in DNA binding specificity observed between the parent regulators in Escherichia coli, as well as their dynamic range of activity. By taking advantage of the DAPG small molecule sensing mediated by the PhlF repressor, we introduce a new inducible system with 50-fold induction and a threshold of 0.9 μM DAPG, which is comparable to the classic Dox-induced TetR system. A set of NOT gates is constructed from the new repressors and their response function quantified. Finally, the Dox- and DAPG- inducible systems and two new activators are used to build a synthetic enhancer (fuzzy AND gate), requiring the coordination of 5 transcription factors organized into two layers. This work introduces a generic approach for the development of mammalian genetic sensors and circuits to populate a toolbox that can be applied to diverse applications from biomanufacturing to living therapeutics.