Multilevel Regulation and Translational Switches in Synthetic Biology

In contrast to the versatility of regulatory mechanisms in natural systems, synthetic genetic circuits have been so far predominantly composed of transcriptionally regulated modules.Thisisabouttochangeastherepertoireoffoundational tools for post-transcriptional regulation is quickly expanding. We provide an overview of the different types of translational regulators: protein, small molecule and ribonucleic acid (RNA) responsive and we describe the new emerging circuit designs utilizing these tools. There are several advantages of achieving multilevel regulation via translational switches and it is likely that such designs will have the greatest and earliest impact in mammalian synthetic biology for regenerative medicine and gene therapy applications

The plant hormone ethylene restricts Arabidopsis growth via the epidermis

The gaseous hormone ethylene plays a key role in plant growth and development, and it is a major regulator of stress responses. It inhibits vegetative growth by restricting cell elongation, mainly through cross-talk with auxins. However, it remains unknown whether ethylene controls growth throughout all plant tissues or whether its signaling is confined to specific cell types. We employed a targeted expression approach to map the tissue site(s) of ethylene growth regulation. The ubiquitin E3 ligase complex containing Skp1, Cullin1, and the F-box protein EBF1 or EBF2 (SCFEBF1/2) target the degradation of EIN3, the master transcription factor in ethylene signaling. We coupled EBF1 and EBF2 to a number of cell type-specific promoters. Using phenotypic assays for ethylene response and mutant complementation, we revealed that the epidermis is the main site of ethylene action controlling plant growth in both roots and shoots. Suppression of ethylene signaling in the epidermis of the constitutive ethylene signaling mutant ctr1-1 was sufficient to rescue the mutant phenotype, pointing to the epidermis as a key cell type required for ethylene-mediated growth inhibition

OptCircuit: An optimization based method for computational design of genetic circuits

<p>Abstract</p> <p>Background</p> <p>Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. It has been widely acknowledged that that task of building circuits to meet multiple inducer-specific requirements is a challenging one. This is because of the incomplete description of component interactions compounded by the fact that the number of ways in which one can chose and interconnect components, increases exponentially with the number of components.</p> <p>Results</p> <p>In this paper we introduce OptCircuit, an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. Multiple literature sources are used to compile a comprehensive compilation of kinetic descriptions of promoter-protein pairs. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ordinary differential equations but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. Computational results for a toggle switch example demonstrate the ability of the framework to generate the complete list of circuit designs of varying complexity that exhibit the desired response. Designs identified for a genetic decoder highlight the ability of OptCircuit to suggest circuit configurations that go beyond the ones compatible with digital logic-based design principles. Finally, the results obtained from the concentration band detector example demonstrate the ability of OptCircuit to design circuits whose responses are contingent on the level of external inducer as well as pinpoint parameters for modification to rectify an existing (non-functional) biological circuit and restore functionality.</p> <p>Conclusion</p> <p>Our results demonstrate that OptCircuit framework can serve as a design platform to aid in the construction and finetuning of integrated biological circuits.</p

Patient and Disease-Specific Induced Pluripotent Stem Cells for Discovery of Personalized Cardiovascular Drugs and Therapeutics.

Human induced pluripotent stem cells (iPSCs) have emerged as an effective platform for regenerative therapy, disease modeling, and drug discovery. iPSCs allow for the production of limitless supply of patient-specific somatic cells that enable advancement in cardiovascular precision medicine. Over the past decade, researchers have developed protocols to differentiate iPSCs to multiple cardiovascular lineages, as well as to enhance the maturity and functionality of these cells. Despite significant advances, drug therapy and discovery for cardiovascular disease have lagged behind other fields such as oncology. We speculate that this paucity of drug discovery is due to a previous lack of efficient, reproducible, and translational model systems. Notably, existing drug discovery and testing platforms rely on animal studies and clinical trials, but investigations in animal models have inherent limitations due to interspecies differences. Moreover, clinical trials are inherently flawed by assuming that all individuals with a disease will respond identically to a therapy, ignoring the genetic and epigenomic variations that define our individuality. With ever-improving differentiation and phenotyping methods, patient-specific iPSC-derived cardiovascular cells allow unprecedented opportunities to discover new drug targets and screen compounds for cardiovascular disease. Imbued with the genetic information of an individual, iPSCs will vastly improve our ability to test drugs efficiently, as well as tailor and titrate drug therapy for each patient

MazF activation promotes translational heterogeneity of the grcA mRNA in Escherichia coli populations

Bacteria adapt to adverse environmental conditions by altering gene expression patterns. Recently, a novel stress adaptation mechanism has been described that allows Escherichia coli to alter gene expression at the post-transcriptional level. The key player in this regulatory pathway is the endoribonuclease MazF, the toxin component of the toxin-antitoxin module mazEF that is triggered by various stressful conditions. In general, MazF degrades the majority of transcripts by cleaving at ACA sites, which results in the retardation of bacterial growth. Furthermore, MazF can process a small subset of mRNAs and render them leaderless by removing their ribosome binding site. MazF concomitantly modifies ribosomes, making them selective for the translation of leaderless mRNAs. In this study, we employed fluorescent reporter-systems to investigate mazEF expression during stressful conditions, and to infer consequences of the mRNA processing mediated by MazF on gene expression at the single-cell level. Our results suggest that mazEF transcription is maintained at low levels in single cells encountering adverse conditions, such as antibiotic stress or amino acid starvation. Moreover, using the grcA mRNA as a model for MazF-mediated mRNA processing, we found that MazF activation promotes heterogeneity in the grcA reporter expression, resulting in a subpopulation of cells with increased levels of GrcA reporter protein

Exploring Heterogeneous Phenotypes in Response to Stress

This work combines traditional microbiology with bioinformatic and synthetic biology approaches to study antibiotic tolerance. Antibiotic tolerance is a widespread phenomenon that facilitates antibiotic resistance and decreases the effectiveness of antibiotic treatment. Tolerance is distinct from antibiotic resistance, because tolerance is short term survival and typically results from phenotypic variations rather than genetic variation. The molecular mechanisms underlying tolerance are varied and debated in the literature. I have explored two intracellular processes related to tolerance, toxin-antitoxin (TA) systems (Chapter 2) and proteases (Chapter 4). Specifically, I focus on the ratio of antitoxin-to-toxin in type II TA systems, because type II TA systems must be regulated in such a way that antitoxins are more prevalent than their toxins. Our analysis of RNA-sequencing and ribosome profiling data demonstrates that most type II TA systems in E. coli are regulated at the translational level, while others rely on various combinations of transcriptional and post-transcriptional regulation. Before publishing this article, researchers often cited transcriptional regulation as the primary method of regulating TA systems. Studying antibiotic tolerance and other subpopulations necessitates the ability to study single-cell dynamics in the context of the whole population. To facilitate single-cell analysis, we have developed single-cell tracking software that leverages machine learning to identify cells. The software then tracks the cell based on this classification and returns data on cell size, location, division and fluorescence. The software provides the means of quantifying cell behavior before and after antibiotic treatment. One such system we would like to apply this software to is our work on proteolytic queueing and antibiotic tolerance. Proteases are responsible for protein degradation and, as such, regulate many cellular functions. To better identify the role proteases play in persistence, we used proteolytic queueing to interfere with proteolytic activity. We found that interfering with degradation at the protease ClpXP increases antibiotic tolerance ~80 and ~60 fold in an E. coli population treated with ampicillin and ciprofloxacin, respectively. I used stochastic modeling to support our results, and we have experimentally determined that altering the expression of the synthetic system affects the level of tolerance in the population. I am currently using next-generation sequencing to identify the systems being affected by the queue