Haseong Kim

Toward a Generalized and High-throughput Enzyme Screening System Based on Artificial Genetic Circuits

Large-scale screening of enzyme libraries is essential for the development of cost-effective biological processes, which will be indispensable for the production of sustainable biobased chemicals. Here, we introduce a genetic circuit termed the Genetic Enzyme Screening System that is highly useful for high-throughput enzyme screening from diverse microbial metagenomes. The circuit consists of two AND logics. The first AND logic, the two inputs of which are the target enzyme and its substrate, is responsible for the accumulation of a phenol compound in cell. Then, the phenol compound and its inducible transcription factor, whose activation turns on the expression of a reporter gene, interact in the other logic gate. We confirmed that an individual cell harboring this genetic circuit can present approximately a 100-fold higher cellular fluorescence than the negative control and can be easily quantified by flow cytometry depending on the amounts of phenolic derivatives. The high sensitivity of the genetic circuit enables the rapid discovery of novel enzymes from metagenomic libraries, even for genes that show marginal activities in a host system. The crucial feature of this approach is that this single system can be used to screen a variety of enzymes that produce a phenol compound from respective synthetic phenyl-substrates, including cellulase, lipase, alkaline phosphatase, tyrosine phenol-lyase, and methyl parathion hydrolase. Consequently, the highly sensitive and quantitative nature of this genetic circuit along with flow cytometry techniques could provide a widely applicable toolkit for discovering and engineering novel enzymes at a single cell level.

A method to identify differential expression profiles of time-course gene data with Fourier transformation

BackgroundTime course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis.ResultsThis work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles.ConclusionsUsing this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.

Enzyme-linked assay of cellulose-binding domain functions from Cellulomonas fimi on multi-well microtiter plate

Cellulose-binding domain (CBD) enriches cellulolytic enzymes on cellulosic surfaces and contributes to the catalytic efficiency by increasing enzyme-substrate complex formations. Thus, high affinity CBDs are essential for the development of efficient cellulose-degrading enzymes. Here, we present a microtiter plate-based assay system to measure the binding affinity of CBDs to cellulose. The assay uses a periplasmic alkaline phosphatase (AP) as a fusion reporter and its activity is detected using a fluorogenic substrate, 4-methylumbelliferyl phosphate. Lignocellulose discs of 6 mm in diameter were used as substrates in 96-well plate. As a result, the enzyme-linked assay detected the binding of CBDs on the cellulosic discs in a highly sensitive manner, detecting from 0.05 to 1.0 μg/mL of APCBD proteins, which is several hundred times more sensitive than conventional protein measurements. The proposed method was applied to compare the binding affinity of different CBDs from Cellulomonas fimi to lignocellulose discs.

Modelling and analysis of gene regulatory networks based on the G-network

G-networks are a class of stochastic models that have had a broad range of applications ranging from the performance analysis of computer systems and networks to the modelling of gene regulatory networks. Gene regulatory networks consist of thousands of genes and proteins which are dynamically interacting with each other. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviours since pathway activities could be changed by their given conditions. This review mainly focuses on a stochastic GRN modelling techniques based on G-networks which provide the analytical steady-state solution of a system for efficient GRN dynamics modelling. Three applications of the G-network model to GRNs show that this novel approach can serve to detect abnormalities from protein expression data, and that they can help to explicit the behaviour of complicated GRN models by dividing the gene regulatory processes into DNA and protein layers.

Controlled Aggregation and Increased Stability of β-Glucuronidase by Cellulose Binding Domain Fusion

Cellulose-binding domains (CBDs) are protein domains with cellulose-binding activity, and some act as leaders in the localization of cellulosomal scaffoldin proteins to the hydrophobic surface of crystalline cellulose. In this study, we found that a CBD fusion enhanced and improved soluble β-glucuronidase (GusA) enzyme properties through the formation of an artificially oligomeric state. First, a soluble CBD fused to the C-terminus of GusA (GusA-CBD) was obtained and characterized. Interestingly, the soluble GusA-CBD showed maximum activity at higher temperatures (65°C) and more acidic pH values (pH 6.0) than free GusA did (60°C and pH 7.5). Moreover, the GusA-CBD enzyme showed higher thermal and pH stabilities than the free GusA enzyme did. Additionally, GusA-CBD showed higher enzymatic activity in the presence of methanol than free GusA did. Evaluation of the protease accessibility of both enzymes revealed that GusA-CBD retained 100% of its activity after 1 h incubation in 0.5 mg/ml protease K, while free GusA completely lost its activity. Simple fusion of CBD as a single domain may be useful for tunable enzyme states to improve enzyme stability in industrial applications.

Partitioning of functional gene expression data using principal points

BackgroundDNA microarrays offer motivation and hope for the simultaneous study of variations in multiple genes. Gene expression is a temporal process that allows variations in expression levels with a characterized gene function over a period of time. Temporal gene expression curves can be treated as functional data since they are considered as independent realizations of a stochastic process. This process requires appropriate models to identify patterns of gene functions. The partitioning of the functional data can find homogeneous subgroups of entities for the massive genes within the inherent biological networks. Therefor it can be a useful technique for the analysis of time-course gene expression data. We propose a new self-consistent partitioning method of functional coefficients for individual expression profiles based on the orthonormal basis system.ResultsA principal points based functional partitioning method is proposed for time-course gene expression data. The method explores the relationship between genes using Legendre coefficients as principal points to extract the features of gene functions. Our proposed method provides high connectivity in connectedness after clustering for simulated data and finds a significant subsets of genes with the increased connectivity. Our approach has comparative advantages that fewer coefficients are used from the functional data and self-consistency of principal points for partitioning. As real data applications, we are able to find partitioned genes through the gene expressions found in budding yeast data and Escherichia coli data.ConclusionsThe proposed method benefitted from the use of principal points, dimension reduction, and choice of orthogonal basis system as well as provides appropriately connected genes in the resulting subsets. We illustrate our method by applying with each set of cell-cycle-regulated time-course yeast genes and E. coli genes. The proposed method is able to identify highly connected genes and to explore the complex dynamics of biological systems in functional genomics.

A molecular nanodevice for targeted degradation of mRNA during protein synthesis.

RNase H is an endonuclease that catalyzes the cleavage of RNA. Because it only acts on RNA in RNA:DNA hybrids, RNase H can be used for targeted degradation of RNA when used in combination with antisense oligodeoxyribonucleotides (ASODNs) designed against a specific sequence of the target RNA. In this study, ASODN and RNase H were co-conjugated on magnetic nanoparticles. The resulting nanoparticles, having integrated functions of probing and processing target RNA, were able to remove target mRNA sequences more effectively than free ASODNs. The paramagnetic property of the nanoparticles also enabled timed engagement and disengagement of the RNA-degrading components in a given system, and these nanoparticles were able to be used for ON/OFF control of gene expression during cell-free protein synthesis reactions.

Ratiometric analyses at critical temperatures can magnify the signal intensity of FRET-based sugar sensors with periplasmic binding proteins

Fluorescence resonance energy transfer (FRET)-based sensors transduce ligand recognition into a change in the fluorophore spectrum, as ligand binding alters the distance between and orientation of two fluorescent proteins. Here, we report a dramatic increase in the signal intensity of FRET-based sugar sensors with bacterial periplasmic binding proteins (PBPs) in the binding moiety, by increasing the analysis temperature, usually higher than 50°C. The increased signal intensity results from a sudden decrease in background signal at critical temperatures, while recovering the maximum FRET ratios in the presence of ligands. When tested with a maltose sensor using a maltose-binding protein as the binding moiety, the FRET ratio at the critical temperature, 55°C, was 17-fold higher than at ambient temperatures. Similar effects were observed using analogous sensors for allose, arabinose, and glucose, providing highly dynamic and quantitative ratio changes at the critical temperatures. The proposed mechanism underlying the signal improvement is thermal relaxation of the binding proteins at the critical temperature; this hypothesis was supported by the results of intrinsic tryptophan fluorescence and circular dichroism experiments. In summary, this study shows that the conformational relaxation of proteins under specific conditions can be leveraged for highly sensitive and rapid measurements of ligands using FRET-based sensors.

Long-Term Stable and Tightly Controlled Expression of Recombinant Proteins in Antibiotics-Free Conditions

Plasmid-based gene expression is a fundamental tool in the field of biotechnology. However, overexpression of genes of interest with multi-copy plasmids often causes detrimental effects on host cells. To overcome this problem, chromosomal integration of target genes has been used for decades; however, insufficient protein expression occurred with this method. In this study, we developed a novel cloning and expression system named the chromosomal vector (ChroV) system, that has features of stable and high expression of target genes on the F′ plasmid in the Escherichia coli JM109(DE3) strain. We used an RMT cluster (KCTC 11994BP) containing a silent cat gene from a previous study to clone a gene into the F′ plasmid. The ChroV system was applied to clone two model targets, GFPuv and carotenoids gene clusters (4 kb), and successfully used to prove the inducible tightly regulated protein expression in the F′ plasmid. In addition, we verified that the expression of heterologous genes in ChroV system maintained stably in the absence of antibiotics for 1 week, indicating ChroV system is applicable to antibiotics-free production of valuable proteins. This protocol can be widely applied to recombinant protein expression for antibiotics-free, stable, and genome-based expression, providing a new platform for recombinant protein synthesis in E. coli. Overall, our approach can be widely used for the economical and industrial production of proteins in E. coli.

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System.

The recent development of a high-throughput single-cell assay technique enables the screening of novel enzymes based on functional activities from a large-scale metagenomic library(1). We previously proposed a genetic enzyme screening system (GESS) that uses dimethylphenol regulator activated by phenol or p-nitrophenol. Since a vast amount of natural enzymatic reactions produce these phenolic compounds from phenol deriving substrates, this single genetic screening system can be theoretically applied to screen over 200 different enzymes in the BRENDA database. Despite the general applicability of GESS, applying the screening process requires a specific procedure to reach the maximum flow cytometry signals. Here, we detail the developed screening process, which includes metagenome preprocessing with GESS and the operation of a flow cytometry sorter. Three different phenolic substrates (p-nitrophenyl acetate, p-nitrophenyl-β-D-cellobioside, and phenyl phosphate) with GESS were used to screen and to identify three different enzymes (lipase, cellulase, and alkaline phosphatase), respectively. The selected metagenomic enzyme activities were confirmed only with the flow cytometry but DNA sequencing and diverse in vitro analysis can be used for further gene identification.