Project description:Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal, steep input-output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.
Project description:Mechanobiologic signals play critical roles in regulating cellular responses under both physiologic and pathologic conditions. Using a combination of synthetic biology and tissue engineering, we developed a mechanically-responsive bioartificial tissue that responds to mechanical loading to produce a pre-programmed therapeutic biologic drug. By deconstructing the signaling networks induced by activation the mechanically-sensitive ion channel transient receptor potential vanilloid 4 (TRPV4), we synthesized synthetic TRPV4-responsive genetic circuits in chondrocytes. These cells were then engineered into living tissues that respond to mechanical compression to drive the production of the anti-inflammatory drug interleukin-1 receptor antagonist. Mechanical loading of these tissues in the presence of the cytokine interleukin-1 protected constructs from inflammatory degradation. This “mechanogenetic” approach enables long-term autonomous delivery of therapeutic compounds that is driven by physiologically-relevant mechanical loading with cell-scale mechanical force resolution. The development of synthetic mechanogenetic gene circuits provides a novel approach for the autonomous regulation of cell-based drug delivery systems.
Project description:Cells respond heterogeneously to DNA damage. We engineered genetic circuits to detect differential responses in a population that persist for many days post-stimulus. We used microarrays to compare memory and non-memory subpopulations 3 days after DNA damage or doxycycline exposure. MD12/p53R2-RE and MD10/TetOx2 cells were either exposed to UV (10uJ/m^2) or doxycycline (1 ug/mL, 24 hours) and allowed to recover 3 days before sortng of memory and non-memory cells and RNA extraction. Two replicates were submitted for each condition (UV memory, UV non-memory, dox memory, dox non-memory)
Project description:Cells respond heterogeneously to DNA damage. We engineered genetic circuits to detect differential responses in a population that persist for many days post-stimulus. We used microarrays to compare memory and non-memory subpopulations 3 days after DNA damage or doxycycline exposure.
Project description:Synthetic biology has focused on engineering genetic modules that operate orthogonally from the host cells. A synthetic circuit, however, can be designed to reprogram the host proteome, which in turn enhances the function of the synthetic circuit. Here, we apply this holistic synthetic biology concept by exploiting the crosstalk between metabolic networks in cells, leading to a protein environment more favorable for protein synthesis. Specifically, we show that a local module expressing translation machinery can reprogram the bacterial proteome, changing the expression levels of more than 780 proteins. The integration of the proteins synthesized by the local modules and the reprogramed proteome generate a cell-free system that can synthesize a diverse set of proteins in different reaction formats, with up to 5-fold higher expression level than classical cell-free systems. Our work demonstrates a holistic approach that integrates synthetic and systems biology concepts. This approach has the potential to achieve outcomes not possible by only local, orthogonal circuits.
Project description:Engineered transactivation domains (TADs) combined with programmable DNA binding platforms have revolutionized synthetic transcriptional control. Despite recent progress in programmable CRISPR/Cas-based transactivation (CRISPRa) technologies, the TADs used in these systems often contain poorly tolerated elements and/or are prohibitively large for many applications. Here we defined and optimized minimal TADs built from human mechanosensitive transcription factors (MTFs). We used these components to construct potent and compact multipartite transactivation modules (MSN, NMS, and eN3×9) and to build the CRISPR-dCas9 recruited enhanced activation module (CRISPR-DREAM) platform. We found that CRISPR-DREAM was specific, robust across mammalian cell types, and efficiently stimulated transcription from diverse regulatory loci. We also showed that MSN and NMS were portable across Type I, II, and V CRISPR systems, TALEs, and ZF proteins. Further, as proofs of concepts, we used dCas9-NMS to efficiently reprogram human fibroblasts into iPSCs and demonstrated that MTF TADs are efficacious and well tolerated in therapeutically important primary human cell types. Finally, we leveraged the compact and potent features of these engineered TADs to build new dual and all-in-one CRISPRa AAV systems. Altogether, these compact human TADs, fusion modules, and new delivery architectures should be valuable for synthetic transcriptional control in biomedical applications.
Project description:Micro-RNAs (miRNAs) play a crucial role in post-transcriptional gene regulation by pairing with target mRNAs to repress protein production. It has been shown that over one-third of human genes are targeted by miRNA. Although hundreds of miRNAs have been identified in mammalian genomes, the function of miRNA-based repression in the context of gene regulation networks still remains unclear. In this study, we explore the functional roles of feedback regulation by miRNAs. In a model where repression of translation occurs by sequestration of mRNA by miRNA, we find that miRNA and mRNA levels are anti-correlated, resulting in larger fluctuation in protein levels than theoretically expected assuming no correlation between miRNA and mRNA levels. If miRNA repression is due to a catalytic suppression of translation rates, we analytically show that the protein fluctuations can be strongly repressed with miRNA regulation. We also discuss how either of these modes may be relevant for cell function.
Project description:Positive feedback driven by transcriptional regulation has long been considered a key mechanism underlying cell lineage segregation during embryogenesis. Using the developing spinal cord as a paradigm, we found that canonical, transcription-driven feedback cannot explain robust lineage segregation of motor neuron subtypes marked by two cardinal factors, Hoxa5 and Hoxc8. We propose a feedback mechanism involving elementary microRNA-mRNA reaction circuits that differ from known feedback loop-like structures. Strikingly, we show that a wide range of biologically-plausible post-transcriptional regulatory parameters are sufficient to generate bistable switches, a hallmark of positive feedback. Through mathematical analysis, we explain intuitively the hidden source of this feedback. Using embryonic stem cell differentiation and mouse genetics, we corroborate that microRNA-mRNA circuits govern tissue boundaries and hysteresis upon motor neuron differentiation with respect to transient morphogen signals. Our findings reveal a previously underappreciated feedback mechanism that may have widespread functions in cell fate decisions and tissue patterning.
Project description:We developed genetically engineered HepG2/8F_HS cells, in which eight liver-enriched transcription factor (LETF) genes—hepatocyte nuclear factor (HNF)-1α, HNF-1β, HNF-3β, HNF-4α, HNF-6, CCAAT/enhancer binding protein (C/EBP)-α, C/EBP-β and C/EBP-γ— under the control of TRE/PCMVmin promoter were introduced into a previously developed human hepatoma cell line (HepG2-HSP). The heat-inducible synthetic promoter system was introduced into HepG2 cells and tetracycline-responsive transactivator (tTA) and enhanced green fluorescent protein (EGFP) were expressed via positive feedback of tTA transcription in response to heat treatment. HepG2/8F_HS cells can induce high liver functions by heat treatment via overexpression of LETF genes.
Project description:Stochastic models of reaction networks are widely used to depict gene expression dynamics. However, stochastic does not necessarily imply accurate, as subtle assumptions can yield erroneous results, masking key discrete effects. For instance, transcription and translation are not instantaneous processes-explicit delays separate their initiation from the appearance of their functional products. However, delays are often ignored in stochastic, single-gene expression models. By consequence, effects such as delay-induced stochastic oscillations at the single-cell level have remained relatively unexplored. Here, we present a systematic study of periodicity and multimodality in a simple gene circuit with negative feedback, analyzing the influence of negative feedback strength and transcriptional/translational delays on expression dynamics. We demonstrate that an oscillatory regime emerges through a Hopf bifurcation in both deterministic and stochastic frameworks. Of importance, a shift in the stochastic Hopf bifurcation evidences inaccuracies of the deterministic bifurcation analysis. Furthermore, noise fluctuations within stochastic oscillations decrease alongside increasing values of transcriptional delays and within a specific range of negative feedback strengths, whereas a strong feedback is associated with oscillations triggered by bursts. Finally, we demonstrate that explicitly accounting for delays increases the number of accessible states in the multimodal regime, and also introduces features typical of excitable systems.