Deconvolution of Luminescence Cross-Talk in High-Throughput Gene Expression Profiling

Accelerated Engineering of Optimized Functional Composite Hydrogels via High-Throughput Experimentation

The Materials Genome Initiative (MGI) seeks to accelerate the discovery and engineering of advanced materials via high-throughput experimentation (HTE), which is a challenging task, given the common trade-off between design for optimal processability vs performance. Here, we report a HTE method based on automated formulation, synthesis, and multiproperty characterization of bulk soft materials in well plate formats that enables accelerated engineering of functional composite hydrogels with optimized properties for processability and performance. The method facilitates rapid high-throughput screening of hydrogel composition-property relations for multiple properties in well plate formats. The feasibility and utility of the method were demonstrated by application to several functional composite hydrogel systems, including alginate/poly(N-isopropylacrylamide) (PNIPAM) and poly(ethylene glycol) dimethacrylate (PEGDMA)/poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) hydrogels. The HTE method was leveraged to identify formulations of conductive PEGDMA/PEDOT:PSS composite hydrogels for optimized performance and processability in three-dimensional (3D) printing. This work provides an advance in experimental methods based on automated dispensing, mixing, and sensing for the accelerated engineering of soft functional materials.

Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multimodule circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.

Engineering a Non-Natural Photoenzyme for Improved Photon Efficiency*

Photoenzymes are biological catalysts that use light to convert starting materials into products. These catalysts require photon absorption for each turnover, making quantum efficiency an important optimization parameter. Flavin-dependent "ene"-reductases (EREDs) display latent photoenzymatic activity for synthetically valuable hydroalkylations; however, protein engineering has not been used to optimize this non-natural function. We describe a protein engineering platform for the high throughput optimization of photoenzymes. A single round of engineering results in improved catalytic function toward the synthesis of γ, δ, ϵ-lactams, and acyclic amides. Mechanistic studies show that key mutations can alter the enzyme's excited state dynamics, enhance its photon efficiency, and ultimately increase catalyst performance. Transient absorption spectroscopy reveals that engineered variants display dramatically decreased radical lifetimes, indicating an evolution toward a concerted mechanism.

Large-scale data analysis for robotic yeast one-hybrid platforms and multi-disciplinary studies using GateMultiplex

BACKGROUND

Yeast one-hybrid (Y1H) is a common technique for identifying DNA-protein interactions, and robotic platforms have been developed for high-throughput analyses to unravel the gene regulatory networks in many organisms. Use of these high-throughput techniques has led to the generation of increasingly large datasets, and several software packages have been developed to analyze such data. We previously established the currently most efficient Y1H system, meiosis-directed Y1H; however, the available software tools were not designed for processing the additional parameters suggested by meiosis-directed Y1H to avoid false positives and required programming skills for operation.

RESULTS

We developed a new tool named GateMultiplex with high computing performance using C++. GateMultiplex incorporated a graphical user interface (GUI), which allows the operation without any programming skills. Flexible parameter options were designed for multiple experimental purposes to enable the application of GateMultiplex even beyond Y1H platforms. We further demonstrated the data analysis from other three fields using GateMultiplex, the identification of lead compounds in preclinical cancer drug discovery, the crop line selection in precision agriculture, and the ocean pollution detection from deep-sea fishery.

CONCLUSIONS

The user-friendly GUI, fast C++ computing speed, flexible parameter setting, and applicability of GateMultiplex facilitate the feasibility of large-scale data analysis in life science fields.

High-Throughput Screening Technology in Industrial Biotechnology

Based on the development of automatic devices and rapid assay methods, various high-throughput screening (HTS) strategies have been established for improving the performance of industrial microorganisms. We discuss the most significant factors that can improve HTS efficiency, including the construction of screening libraries with high diversity and the use of new detection methods to expand the search range and highlight target compounds. We also summarize applications of HTS for enhancing the performance of industrial microorganisms. Current challenges and potential improvements to HTS in industrial biotechnology are discussed in the context of rapid developments in synthetic biology, nanotechnology, and artificial intelligence. Rational integration will be an important driving force for constructing more efficient industrial microorganisms with wider applications in biotechnology.

CRIMoClo plasmids for modular assembly and orthogonal chromosomal integration of synthetic circuits in Escherichia coli

Background

Synthetic biology heavily depends on rapid and simple techniques for DNA engineering, such as Ligase Cycling Reaction (LCR), Gibson assembly and Golden Gate assembly, all of which allow for fast, multi-fragment DNA assembly. A major enhancement of Golden Gate assembly is represented by the Modular Cloning (MoClo) system that allows for simple library propagation and combinatorial construction of genetic circuits from reusable parts. Yet, one limitation of the MoClo system is that all circuits are assembled in low- and medium copy plasmids, while a rapid route to chromosomal integration is lacking. To overcome this bottleneck, here we took advantage of the conditional-replication, integration, and modular (CRIM) plasmids, which can be integrated in single copies into the chromosome of Escherichia coli and related bacteria by site-specific recombination at different phage attachment (att) sites.

Results

By combining the modularity of the MoClo system with the CRIM plasmids features we created a set of 32 novel CRIMoClo plasmids and benchmarked their suitability for synthetic biology applications. Using CRIMoClo plasmids we assembled and integrated a given genetic circuit into four selected phage attachment sites. Analyzing the behavior of these circuits we found essentially identical expression levels, indicating orthogonality of the loci. Using CRIMoClo plasmids and four different reporter systems, we illustrated a framework that allows for a fast and reliable sequential integration at the four selected att sites. Taking advantage of four resistance cassettes the procedure did not require recombination events between each round of integration. Finally, we assembled and genomically integrated synthetic ECF σ factor/anti-σ switches with high efficiency, showing that the growth defects observed for circuits encoded on medium-copy plasmids were alleviated.

Conclusions

The CRIMoClo system enables the generation of genetic circuits from reusable, MoClo-compatible parts and their integration into 4 orthogonal att sites into the genome of E. coli. Utilizing four different resistance modules the CRIMoClo system allows for easy, fast, and reliable multiple integrations. Moreover, utilizing CRIMoClo plasmids and MoClo reusable parts, we efficiently integrated and alleviated the toxicity of plasmid-borne circuits. Finally, since CRIMoClo framework allows for high flexibility, it is possible to utilize plasmid-borne and chromosomally integrated circuits simultaneously. This increases our ability to permute multiple genetic modules and allows for an easier design of complex synthetic metabolic pathways in E. coli.

WellInverter: a web application for the analysis of fluorescent reporter gene data

Background

Fluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion.

Results

We present a new version of WellInverter that considerably improves the performance and usability of the original application. In particular, we have put in place a parallel computing architecture with a load balancer to distribute analysis queries over several back-end servers, we have completely redesigned the graphical user interface to better support the different analysis steps, and we have developed a plug-in system for the parsing of data files produced by microplate readers from different manufacturers. We illustrate the functioning of WellInverter by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations. We show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene.

Conclusions

The new version of WellInverter is a robust, easy-to-use and scalable web application, which has been deployed on two publicly accessible web servers and which can also be installed locally. A demo version of the application with two sample datasets is available on-line.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2920-4) contains supplementary material, which is available to authorized users.