Controlled Oil/Water Partitioning of Hydrophobic Substrates Extending the Bioanalytical Applications of Droplet-Based Microfluidics.

Functional annotation of novel proteins lags behind the number of sequences discovered by the next-generation sequencing. The throughput of conventional testing methods is far too low compared to sequencing; thus, experimental alternatives are needed. Microfluidics offer high throughput and reduced sample consumption as a tool to keep up with a sequence-based exploration of protein diversity. The most promising droplet-based systems have a significant limitation: leakage of hydrophobic compounds from water compartments to the carrier prevents their use with hydrophilic reagents. Here, we present a novel approach of substrate delivery into microfluidic droplets and apply it to high-throughput functional characterization of enzymes that convert hydrophobic substrates. Substrate delivery is based on the partitioning of hydrophobic chemicals between the oil and water phases. We applied a controlled distribution of 27 hydrophobic haloalkanes from oil to reaction water droplets to perform substrate specificity screening of eight model enzymes from the haloalkane dehalogenase family. This droplet-on-demand microfluidic system reduces the reaction volume 65 000-times and increases the analysis speed almost 100-fold compared to the classical test tube assay. Additionally, the microfluidic setup enables a convenient analysis of dependences of activity on the temperature in a range of 5 to 90 °C for a set of mesophilic and hyperstable enzyme variants. A high correlation between the microfluidic and test tube data supports the approach robustness. The precision is coupled to a considerable throughput of >20 000 reactions per day and will be especially useful for extending the scope of microfluidic applications for high-throughput analysis of reactions including compounds with limited water solubility.ERC Advanced Investigator grant no. 69566

Tuning DNA–nanoparticle conjugate properties allows modulation of nuclease activity

Enzyme–nanoparticle interactions can give rise to a range of new phenomena, most notably significant enzymatic rate enhancement. Accordingly, the careful study and optimization of such systems is likely to give rise to advanced biosensing applications. Herein, we report a systematic study of the interactions between nuclease enzymes and oligonucleotide-coated gold nanoparticles (spherical nucleic acids, SNAs), with the aim of revealing phenomena worthy of evolution into functional nanosystems. Specifically, we study two nucleases, an exonuclease (ExoIII) and an endonuclease (Nt.BspQI), via fluorescence-based kinetic experiments, varying parameters including enzyme and substrate concentrations, and nanoparticle size and surface coverage in non-recycling and a recycling formats. We demonstrate the tuning of nuclease activity by SNA characteristics and show that the modular units of SNAs can be leveraged to either accelerate or suppress nuclease kinetics. Additionally, we observe that the enzymes are capable of cleaving restriction sites buried deep in the oligonucleotide surface layer and that enzymatic rate enhancement occurs in the target recycling format but not in the non-recycling format. Furthermore, we demonstrate a new SNA phenomenon, we term ‘target stacking’, whereby nucleic acid hybridization efficiency increases as enzyme cleavage proceeds during the beginning of a reaction. This investigation provides important data to guide the design of novel SNAs in biosensing and in vitro diagnostic applications.ISSN:2040-3364ISSN:2040-337

Towards an active droplet-based microfluidic platform for programmable fluid handling

Droplet-based microfluidic systems have emerged as powerful alternatives to conventional high throughput screening platforms, due to their operational flexibility, high-throughput nature and ability to efficiently process small fluid volumes. However, the challenges associated with performing bespoke operations on user-defined droplets often limit their utility in screening applications that involve complex workflows. To this end, the marriage of droplet- and valve-based microfluidic technologies offers the prospect of balancing the controllability of droplet manipulations and analytical throughput. In this spirit, we present a microfluidic platform that combines the capabilities of integrated microvalve technology with droplet-based sample compartmentalization to realize a highly adaptable programmable fluid handling functionality. The microfluidic device consists of a programmable formulator linked to an automated droplet generation device and storage array. The formulator leverages multiple inputs coupled to a mixing ring to produce combinatorial solution mixtures, with a peristaltic pump enabling titration of reagents into the ring with picoliter resolution. The platform allows for the execution of user-defined reaction protocols within an array of storage chambers by consecutively merging programmable sequences of pL-volume droplets containing specified reagents. The precision in formulating solutions with small differences in concentration is perfectly suited for the accurate estimation of kinetic parameters. The utility of our platform is showcased through the performance of enzymatic kinetic measurements of beta-galactosidase and horseradish peroxidase with fluorogenic substrates. The presented platform provides for a range of automated manipulations and paves the way for a more diverse range of droplet-based biological experiments.ISSN:1473-0197ISSN:1473-018

Mechanism-Based Discovery of Novel Substrates of Haloalkane Dehalogenases Using <i>in Silico</i> Screening

Substrate specificity is a key feature of enzymes determining their applicability in biomaterials and biotechnologies. Experimental testing of activities with novel substrates is a time-consuming and inefficient process, typically resulting in many failures. Here, we present an experimentally validated <i>in silico</i> method for the discovery of novel substrates of enzymes with a known reaction mechanism. The method was developed for a model system of biotechnologically relevant enzymes, haloalkane dehalogenases. On the basis of the parametrization of six different haloalkane dehalogenases with 30 halogenated substrates, mechanism-based geometric criteria for reactivity approximation were defined. These criteria were subsequently applied to the previously experimentally uncharacterized haloalkane dehalogenase DmmA. The enzyme was computationally screened against 41,366 compounds, yielding 548 structurally unique compounds as potential substrates. Eight out of 16 experimentally tested top-ranking compounds were active with DmmA, indicating a 50% success rate for the prediction of substrates. The remaining eight compounds were able to bind to the active site and inhibit enzymatic activity. These results confirmed good applicability of the method for prioritizing active compoundstrue substrates and bindersfor experimental testing. All validated substrates were large compounds often containing polyaromatic moieties, which have never before been considered as potential substrates for this enzyme family. Whereas four of these novel substrates were specific to DmmA, two substrates showed activity with three other tested haloalkane dehalogenases, i.e., DhaA, DbjA, and LinB. Additional validation of the developed screening strategy with the data set of over 200 known substrates of Candida antarctica lipase B confirmed its applicability for the identification of novel substrates of other biotechnologically relevant enzymes with an available tertiary structure and known reaction mechanism

Development of a Universal NADH Detection Assay for High Throughput Enzyme Evolution Using Fluorescence Activated Droplet Sorting

Directed evolution is an enzyme engineering approach based on the generation and screening of large mutagenesis libraries, with a view to discovering enzymes with improved properties such as activity, specificity or stability. Recently, droplet-based microfluidics has emerged as a powerful technology enabling ultra-high throughput screening of enzyme libraries and the effective identification and isolation of novel, improved enzyme variants, outperforming conventional enzyme screening platforms by several orders of magnitude in terms of speed and chemical consumption. When using droplet-based platforms fluorescence remains the predominant choice for detection of enzymatic activity due to its high sensitivity and low limits of detection. However, this approach often requires the use of labeled, non-natural substrates, which are typically not commercially available. In addition, fluorescence detection is only suitable for a few enzyme classes such as hydrolases or oxidases, whose reactions can often lead to a fluorescent signal. Herein, we describe an assay that enables fluorescence detection of enzymatic activity through a reaction cascade for the industrially important enzyme subclass of dehydrogenases. By applying a hydrogen peroxide-forming NADH oxidase coupled with peroxidase-catalyzed fluorescence generation, quantification of NADH and dehydrogenase activity becomes possible. We explored the utility of this assay in the evolution of a low performing alcohol dehydrogenase from Sphingomonas species A1 (SpsADH). A fluorescence-activated droplet sorting (FADS) platform was utilized for the screening of a 50,000 variant SpsADH library towards the non-native substrate L-guluronate, a primary component of macroalgae, with the potential to serve as raw material for the bio-based production of chemicals. Significantly, we found an enzyme variant with a 2.6-fold improvement in catalytic efficiency kcat/Km towards the non-native substrate, with only a single round of mutagenesis. The screening of SpsADH libraries confirms the ability of the developed method to enrich active enzyme variants

Development of a Universal NADH Detection Assay for High Throughput Enzyme Evolution Using Fluorescence Activated Droplet Sorting

Directed evolution is an enzyme engineering approach based on the generation and screening of large mutagenesis libraries, with a view to discovering enzymes with improved properties such as activity, specificity or stability. Recently, droplet-based microfluidics has emerged as a powerful technology enabling ultra-high throughput screening of enzyme libraries and the effective identification and isolation of novel, improved enzyme variants, outperforming conventional enzyme screening platforms by several orders of magnitude in terms of speed and chemical consumption. When using droplet-based platforms fluorescence remains the predominant choice for detection of enzymatic activity due to its high sensitivity and low limits of detection. However, this approach often requires the use of labeled, non-natural substrates, which are typically not commercially available. In addition, fluorescence detection is only suitable for a few enzyme classes such as hydrolases or oxidases, whose reactions can often lead to a fluorescent signal. Herein, we describe an assay that enables fluorescence detection of enzymatic activity through a reaction cascade for the industrially important enzyme subclass of dehydrogenases. By applying a hydrogen peroxide-forming NADH oxidase coupled with peroxidase-catalyzed fluorescence generation, quantification of NADH and dehydrogenase activity becomes possible. We explored the utility of this assay in the evolution of a low performing alcohol dehydrogenase from Sphingomonas species A1 (SpsADH). A fluorescence-activated droplet sorting (FADS) platform was utilized for the screening of a 50,000 variant SpsADH library towards the non-native substrate L-guluronate, a primary component of macroalgae, with the potential to serve as raw material for the bio-based production of chemicals. Significantly, we found an enzyme variant with a 2.6-fold improvement in catalytic efficiency kcat/Km towards the non-native substrate, with only a single round of mutagenesis. The screening of SpsADH libraries confirms the ability of the developed method to enrich active enzyme variants.ISSN:2573-229

Interfacing Microwells with Nanoliter Compartments: A Sampler Generating High-Resolution Concentration Gradients for Quantitative Biochemical Analyses in Droplets

Analysis of concentration dependencies is key to the quantitative understanding of biological and chemical systems. In experimental tests involving concentration gradients such as inhibitor library screening, the number of data points and the ratio between the stock volume and the volume required in each test determine the quality and efficiency of the information gained. Titerplate assays are currently the most widely used format, even though they require microlitre volumes. Compartmentalization of reactions in pico- to nanoliter water-in-oil droplets in microfluidic devices provides a solution for massive volume reduction. This work addresses the challenge of producing microfluidic-based concentration gradients in a way that every droplet represents one unique reagent combination. We present a simple microcapillary technique able to generate such series of monodisperse water-in-oil droplets (with a frequency of up to 10 Hz) from a sample presented in an open well (e.g., a titerplate). Time-dependent variation of the well content results in microdroplets that represent time capsules of the composition of the source well. By preserving the spatial encoding of the droplets in tubing, each reactor is assigned an accurate concentration value. We used this approach to record kinetic time courses of the haloalkane dehalogenase DbjA and analyzed 150 combinations of enzyme/substrate/inhibitor in less than 5 min, resulting in conclusive Michaelis–Menten and inhibition curves. Avoiding chips and merely requiring two pumps, a magnetic plate with a stirrer, tubing, and a pipet tip, this easy-to-use device rivals the output of much more expensive liquid handling systems using a fraction (∼100-fold less) of the reagents consumed in microwell format

Advanced Database Mining of Efficient Biocatalysts by Sequence and Structure Bioinformatics and Microfluidics

Next-generation sequencing doubles genomic databases every 2.5 years. The accumulation of sequence data provides a unique opportunity to identify interesting biocatalysts directly in the databases without tedious and time-consuming engineering. Herein, we present a pipeline integrating sequence and structural bioinformatics with microfluidic enzymology for bioprospecting of efficient and robust haloalkane dehalogenases. The bioinformatic part identified 2,905 putative dehalogenases and prioritized a “small-but-smart” set of 45 genes, yielding 40 active enzymes, 24 of which were biochemically characterized by microfluidic enzymology techniques. Combining microfluidics with modern global data analysis provided precious mechanistic insights related to the high catalytic efficiency of selected enzymes. Overall, we have doubled the dehalogenation “toolbox” characterized over three decades, yielding biocatalysts that surpass the efficiency of currently available wild-type and engineered enzymes. This pipeline is generally applicable to other enzyme families and can accelerate the identification of efficient biocatalysts for industrial use