A Sense of Balance: Experimental Investigation and Modeling of a Malonyl-CoA Sensor in Escherichia coli

Branch point control at malonyl-CoA node: A computational framework to uncover the design principles of an ideal genetic-metabolic switch

Living organism is an intelligent system coded by hierarchically-organized information to perform precisely-controlled biological functions. Biophysical models are important tools to uncover the design rules underlying complex genetic-metabolic circuit interactions. Based on a previously engineered synthetic malonyl-CoA switch (Xu et al., PNAS, 2014), we have formulated nine differential equations to unravel the design principles underlying an ideal metabolic switch to improve fatty acids production in E. coli. By interrogating the physiologically accessible parameter space, we have determined the optimal controller architecture to configure both the metabolic source pathway and metabolic sink pathway. We determined that low protein degradation rate, medium strength of metabolic inhibitory constant, high metabolic source pathway induction rate, strong binding affinity of the transcriptional activator toward the metabolic source pathway, weak binding affinity of the transcriptional repressor toward the metabolic sink pathway, and a strong cooperative interaction of transcriptional repressor toward metabolic sink pathway benefit the accumulation of the target molecule (fatty acids). The target molecule (fatty acid) production is increased from 50% to 10-folds upon application of the autonomous metabolic switch. With strong metabolic inhibitory constant, the system displays multiple steady states. Stable oscillation of metabolic intermediate is the driving force to allow the system deviate from its equilibrium state and permits bidirectional ON-OFF gene expression control, which autonomously compensates enzyme level for both the metabolic source and metabolic sink pathways. The computational framework may facilitate us to design and engineer predictable genetic-metabolic switches, quest for the optimal controller architecture of the metabolic source/sink pathways, as well as leverage autonomous oscillation as a powerful tool to engineer cell function.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Modularization and Response Curve Engineering of a Naringenin-Responsive Transcriptional Biosensor

To monitor the intra- and extracellular environment of micro-organisms and to adapt their metabolic processes accordingly, scientists are reprogramming nature's myriad of transcriptional regulatory systems into transcriptional biosensors, which are able to detect small molecules and, in response, express specific output signals of choice. However, the naturally occurring response curve, the key characteristic of biosensor circuits, is typically not in line with the requirements for real-life biosensor applications. In this contribution, a natural LysR-type naringenin-responsive biosensor circuit is developed and characterized with Escherichia coli as host organism. Subsequently, this biosensor is dissected into a clearly defined detector and effector module without loss of functionality, and the influence of the expression levels of both modules on the biosensor response characteristics is investigated. Two collections of ten unique synthetic biosensors each are generated. Each collection demonstrates a unique diversity of response curve characteristics spanning a 128-fold change in dynamic and 2.5-fold change in operational ranges and 3-fold change in levels of Noise, fit for a wide range of applications, such as adaptive laboratory evolution, dynamic pathway control and high-throughput screening methods. The established biosensor engineering concepts, and the developed biosensor collections themselves, are of use for the future development and customization of biosensors in general, for the multitude of biosensor applications and as a compelling alternative for the commonly used LacI-, TetR- and AraC-based inducible circuits.

Complete alanine scanning of the Escherichia coli RbsB ribose binding protein reveals residues important for chemoreceptor signaling and periplasmic abundance

The Escherichia coli RbsB ribose binding protein has been used as a scaffold for predicting new ligand binding functions through in silico modeling, yet with limited success and reproducibility. In order to possibly improve the success of predictive modeling on RbsB, we study here the influence of individual residues on RbsB-mediated signaling in a near complete library of alanine-substituted RbsB mutants. Among a total of 232 tested mutants, we found 10 which no longer activated GFPmut2 reporter expression in E. coli from a ribose-RbsB hybrid receptor signaling chain, and 13 with significantly lower GFPmut2 induction than wild-type. Quantitative mass spectrometry abundance measurements of 25 mutants and wild-type RbsB in periplasmic space showed four categories of effects. Some (such as D89A) seem correctly produced and translocated but fail to be induced with ribose. Others (such as N190A) show lower induction probably as a result of less efficient production, folding and translocation. The third (such as N41A or K29A) have defects in both induction and abundance. The fourth category consists of semi-constitutive mutants with increased periplasmic abundance but maintenance of ribose induction. Our data show how RbsB modeling should include ligand-binding as well as folding, translocation and receptor binding.

An automated microreactor for semi-continuous biosensor measurements

Living bacteria or yeast cells are frequently used as bioreporters for the detection of specific chemical analytes or conditions of sample toxicity. In particular, bacteria or yeast equipped with synthetic gene circuitry that allows the production of a reliable non-cognate signal (e.g., fluorescent protein or bioluminescence) in response to a defined target make robust and flexible analytical platforms. We report here how bacterial cells expressing a fluorescence reporter ("bactosensors"), which are mostly used for batch sample analysis, can be deployed for automated semi-continuous target analysis in a single concise biochip. Escherichia coli-based bactosensor cells were continuously grown in a 13 or 50 nanoliter-volume reactor on a two-layered polydimethylsiloxane-on-glass microfluidic chip. Physiologically active cells were directed from the nl-reactor to a dedicated sample exposure area, where they were concentrated and reacted in 40 minutes with the target chemical by localized emission of the fluorescent reporter signal. We demonstrate the functioning of the bactosensor-chip by the automated detection of 50 μgarsenite-As l(-1) in water on consecutive days and after a one-week constant operation. Best induction of the bactosensors of 6-9-fold to 50 μg l(-1) was found at an apparent dilution rate of 0.12 h(-1) in the 50 nl microreactor. The bactosensor chip principle could be widely applicable to construct automated monitoring devices for a variety of targets in different environments.

Synthetic biology for pharmaceutical drug discovery

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.