Characterization of three 2-hydroxy-acid dehydrogenases in the context of a biotechnological approach to short-circuit photorespiration

Photorespiration results from the incorporation of oxygen into ribulose-1,5-bisphosphate due to the failure of RuBisCO to properly discriminate between oxygen and carbon dioxide. This process lowers photosynthetic efficiency in that CO2 and ammonia should be re-assimilated with the concomitant consumption of both ATP and reducing power. Two recent approaches, aimed at decreasing the detrimental effects of photorespiration by introducing novel metabolic pathways into plant chloroplasts, show great promise. The goal of this work was to identify and biochemically characterize a single-gene glycolate dehydrogenase for use in further improving the synthetic pathways. Forward and reverse genetics were used to identify three candidate genes in Arabidopsis thaliana; At5g06580, At4g36400 and At4g18360. The proteins encoded by these genes were expressed in Escherichia coli, purified and characterized. Moreover, in silico analysis and the analysis of loss-of-function mutants yielded insights into the significance of these novel enzymatic activities in plant metabolism. AtD-LDH, encoded by At5g06580, is a homodimeric FAD-binding flavoprotein that catalyzes the cytochrome c- dependent oxidation of substrates. The enzyme has high activity with D- and L-lactate, D-2-hydroxybutyrate and D-glycerate, but of these only D-lactate and D-2-hydroxybutyrate are bound with high affinity. Knock-out mutants show impaired growth on medium containing methylglyoxal and D-lactate. Together, the data indicates a role for AtD-LDH in the mitochondrial intermembrane space where it oxidizes D-lactate to pyruvate in the final step of methylglyoxal detoxification. AtD-2HGDH, encoded by At4g36400, is a homodimeric FAD-binding flavoprotein. The enzyme only has activity with D-2-hydroxyglutarate and uses a synthetic electron acceptor in vitro. Metabolic analysis of knock-out mutants reveals high accumulation of D-2-hydroxyglutarate in plants exposed to long periods of extended darkness, confirming that this is the in vivo substrate for the enzyme. Co-expression analysis reveals that AtD-2HGDH is co-expressed with enzymes and transporters participating in the breakdown of lipids, branched-chain amino acids and chlorophyll, all pathways that converge in the production of propionyl-CoA. Together, the data suggest a role for AtD-2HGDH in the mitochondrial matrix where it oxidizes D-2-hydroxyglutarate, most probably originating from propionyl-CoA metabolism, to 2-oxoglutarate, using an electron transfer flavoprotein as an electron acceptor. Finally, AtGOX3, encoded by At4g18360, is a peroxisomal (S)-2-hydroxy-acid oxidase with specificity towards glycolate, L-lactate and L-2-hydroxybutyrate. AtGOX3 is almost exclusively expressed in roots where it might participate in either the metabolism of L-lactate produced during hypoxia, or glycolate produced from glycolaldehyde. In this work, the identification and thorough characterization of three novel enzymatic activities in the model plant A. thaliana are described. Moreover, novel plant metabolic pathways in which these enzymes participate were discovered. The biochemical characterization of these enzymes indicated that they are not suited for use in pathways aimed at decreasing photorespiration and thus, the search for a single-gene glycolate dehydrogenase should continue

Highlighting the Need for Systems-Level Experimental Characterization of Plant Metabolic Enzymes

The biology of living organisms is determined by the action and interaction of a large number of individual gene products, each with specific functions. Discovering and annotating the function of gene products is key to our understanding of these organisms. Controlled experiments and bioinformatic predictions both contribute to functional gene annotation. For most species it is difficult to gain an overview of what portion of gene annotations are based on experiments and what portion represent predictions. Here, I survey the current state of experimental knowledge of enzymes and metabolism in Arabidopsis thaliana as well as eleven economically important crops and forestry trees – with a particular focus on reactions involving organic acids in central metabolism. I illustrate the limited availability of experimental data for functional annotation of enzymes in most of these species. Many enzymes involved in metabolism of citrate, malate, fumarate, lactate, and glycolate in crops and forestry trees have not been characterized. Furthermore, enzymes involved in key biosynthetic pathways which shape important traits in crops and forestry trees have not been characterized. I argue for the development of novel high-throughput platforms with which limited functional characterization of gene products can be performed quickly and relatively cheaply. I refer to this approach as systems-level experimental characterization. The data collected from such platforms would form a layer intermediate between bioinformatic gene function predictions and in-depth experimental studies of these functions. Such a data layer would greatly aid in the pursuit of understanding a multiplicity of biological processes in living organisms

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation. Copyright

Adaptation of a Microfluidic qPCR System for Enzyme Kinetic Studies

Microfluidic platforms offer a drastic increase in throughput while minimizing sample usage and hands-on time, which make them important tools for large-scale biological studies. A range of such systems have been developed for enzyme activity studies, although their complexity largely hinders their application to the wider scientific community. Here, we present adaptation of an easy-to-use commercial microfluidic qPCR system for performing enzyme kinetic studies. We demonstrate the functionality of the Fluidigm Biomark HD system (the Fluidigm system) by determining the kinetic properties of three oxidases in a resorufin-based fluorescence assay. The results obtained in the microfluidic system proved reproducible and comparable to the ones obtained in a standard microplate-based assay. With a wide range of easy-to-use, off-the-shelf components, the microfluidic system presents itself as a simple and customizable platform for high-throughput enzyme activity studies

Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures

Background: The ambient temperature of all habitats is a key physical property that shapes the biology of microbes inhabiting them. The optimal growth temperature (OGT) of a microbe, is therefore a key piece of data needed to understand evolutionary adaptations manifested in their genome sequence. Unfortunately there is no growth temperature database or easily downloadable dataset encompassing the majority of cultured microorganisms. We are thus limited in interpreting genomic data to identify temperature adaptations in microbes. Results: In this work I significantly contribute to closing this gap by mining data from major culture collection centres to obtain growth temperature data for a nonredundant set of 21,498 microbes. The dataset (https://doi.org/10.5281/zenodo.1175608) contains mainly bacteria and archaea and spans psychrophiles, mesophiles, thermophiles and hyperthermophiles. Using this data a full 43% of all protein entries in the UniProt database can be annotated with the growth temperature of the species from which they originate. I validate the dataset by showing a Pearson correlation of up to 0.89 between growth temperature and mean enzyme optima, a physiological property directly influenced by the growth temperature. Using the temperature dataset I correlate the genomic occurance of enzyme functional annotations with growth temperature. I identify 319 enzyme functions that either increase or decrease in occurrence with temperature. Eight metabolic pathways were statistically enriched for these enzyme functions. Furthermore, I establish a correlation between 33 domains of unknown function (DUFs) with growth temperature in microbes, four of which (DUF438, DUF1524, DUF1957 and DUF3458-C) were significant in both archaea and bacteria. Conclusions: The growth temperature dataset enables large-scale correlation analysis with enzyme function- and domain-level annotations. Growth-temperature dependent changes in their occurrence highlight potential evolutionary adaptations. A few of the identified changes are previously known, such as the preference for menaquinone biosynthesis through the futalosine pathway in bacteria growing at high temperatures. Others represent important starting points for future studies, such as DUFs where their occurrence change with temperature. The growth temperature dataset should become a valuable community resource and will find additional, important, uses in correlating genomic, transcriptomic, proteomic, metabolomic, phenotypic or taxonomic properties with temperature in future studies

A general model to predict small molecule substrates of enzymes based on machine and deep learning

For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science

Deep learning allows genome-scale prediction of Michaelis constants from structural features

AU The:Michaelis Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly constant KM describes the affinity of an enzyme : for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism

CAZyme prediction in ascomycetous yeast genomes guides discovery of novel xylanolytic species with diverse capacities for hemicellulose hydrolysis

Background: Ascomycetous yeasts from the kingdom fungi inhabit every biome in nature. While filamentous fungi have been studied extensively regarding their enzymatic degradation of the complex polymers comprising lignocellulose, yeasts have been largely overlooked. As yeasts are key organisms used in industry, understanding their enzymatic strategies for biomass conversion is an important factor in developing new and more efficient cell factories. The aim of this study was to identify polysaccharide-degrading yeasts by mining CAZymes in 332 yeast genomes from the phylum Ascomycota. Selected CAZyme-rich yeasts were then characterized in more detail through growth and enzymatic activity assays. Results: The CAZyme analysis revealed a large spread in the number of CAZyme-encoding genes in the ascomycetous yeast genomes. We identified a total of 217 predicted CAZyme families, including several CAZymes likely involved in degradation of plant polysaccharides. Growth characterization of 40 CAZyme-rich yeasts revealed no cellulolytic yeasts, but several species from the Trichomonascaceae and CUG-Ser1 clades were able to grow on xylan, mixed-linkage\ua0β-glucan and xyloglucan. Blastobotrys mokoenaii, Sugiyamaella lignohabitans, Spencermartinsiella europaea and several Scheffersomyces species displayed superior growth on xylan and well as high enzymatic activities. These species possess genes for\ua0several putative xylanolytic enzymes, including ones from\ua0the well-studied xylanase-containing glycoside hydrolase families GH10 and GH30, which appear\ua0to be attached to the cell surface. B. mokoenaii was the only species containing a GH11 xylanase, which was shown to be secreted. Surprisingly, no known xylanases were predicted in the xylanolytic species Wickerhamomyces canadensis, suggesting that this yeast possesses novel xylanases. In addition, by examining non-sequenced yeasts closely related to the xylanolytic yeasts, we were able to identify novel species with high xylanolytic capacities. Conclusions: Our approach of combining high-throughput bioinformatic CAZyme-prediction with growth and enzyme characterization proved to be a powerful pipeline for discovery of novel xylan-degrading yeasts and enzymes. The identified yeasts display diverse profiles in terms of growth, enzymatic activities and xylan substrate preferences, pointing towards different strategies for degradation and utilization of xylan. Together, the results provide novel insights into how yeast degrade xylan, which can be used to improve cell factory design and industrial bioconversion processes

Discovery of Two Novel Oxidases Using a High-Throughput Activity Screen

Discovery of novel enzymes is a challenging task, yet a crucial one, due to their increasing relevance as chemical catalysts and biotechnological tools. In our work we present a high-throughput screening approach to discovering novel activities. A screen of 96 putative oxidases with 23 substrates led to the discovery of two new enzymes. The first enzyme, N-acetyl-D-hexosamine oxidase (EC 1.1.3.29) from Ralstonia solanacearum, is a vanillyl alcohol oxidase-like flavoprotein displaying the highest activity with N-acetylglucosamine and N-acetylgalactosamine. Before our discovery of the enzyme, its activity was an orphan one - experimentally characterized but lacking the link to amino acid sequence. The second enzyme, from an uncultured marine euryarchaeota, is a long-chain alcohol oxidase (LCAO, EC 1.1.3.20) active with a range of fatty alcohols, with 1-dodecanol being the preferred substrate. The enzyme displays no sequence similarity to previously characterised LCAOs, and thus is a completely novel representative of a protein with such activity

Modeling-Assisted Design of Thermostable Benzaldehyde Lyases from Rhodococcus erythropolis for Continuous Production of α-Hydroxy Ketones

Enantiopure α-hydroxy ketones are important building blocks of active pharmaceutical ingredients (APIs), which can be produced by thiamine-diphosphate-dependent lyases, such as benzaldehyde lyase. Here we report the discovery of a novel thermostable benzaldehyde lyase from Rhodococcus erythropolis R138 (ReBAL). While the overall sequence identity to the only experimentally confirmed benzaldehyde lyase from Pseudomonas fluorescens Biovar I (PfBAL) was only 65 %, comparison of a structural model of ReBAL with the crystal structure of PfBAL revealed only four divergent amino acids in the substrate binding cavity. Based on rational design, we generated two ReBAL variants, which were characterized along with the wild-type enzyme in terms of their substrate spectrum, thermostability and biocatalytic performance in the presence of different co-solvents. We found that the new enzyme variants have a significantly higher thermostability (up to 22 \ub0C increase in T50) and a different co-solvent-dependent activity. Using the most stable variant immobilized in packed-bed reactors via the SpyCatcher/SpyTag system, (R)-benzoin was synthesized from benzaldehyde over a period of seven days with a stable space-time-yield of 9.3 mmol ⋅ L-1 ⋅ d−1. Our work expands the important class of benzaldehyde lyases and therefore contributes to the development of continuous biocatalytic processes for the production of α-hydroxy ketones and APIs