Profiling the orphan enzymes

DeepES: deep learning-based enzyme screening to identify orphan enzyme genes

MOTIVATION

Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information is unavailable for many enzymes, known as orphan enzymes. These orphan enzymes hinder sequence similarity-based functional annotation, leading gaps in understanding the association between sequences and enzymatic reactions.

RESULTS

Therefore, we developed DeepES, a deep learning-based tool for enzyme screening to identify orphan enzyme genes, focusing on biosynthetic gene clusters and reaction class. DeepES uses protein sequences as inputs and evaluates whether the input genes contain biosynthetic gene clusters of interest by integrating the outputs of the binary classifier for each reaction class. The validation results suggested that DeepES can capture functional similarity between protein sequences, and it can be implemented to explore orphan enzyme genes. By applying DeepES to 4744 metagenome-assembled genomes, we identified candidate genes for 236 orphan enzymes, including those involved in short-chain fatty acid production as a characteristic pathway in human gut bacteria.

AVAILABILITY AND IMPLEMENTATION

DeepES is available at https://github.com/yamada-lab/DeepES. Model weights and the candidate genes are available at Zenodo (https://doi.org/10.5281/zenodo.11123900).

6-Phosphogluconolactonase is critical for the efficient functioning of the pentose phosphate pathway

The metabolic networks of microorganisms are remarkably robust to genetic and environmental perturbations. This robustness stems from redundancies such as gene duplications, isoenzymes, alternative metabolic pathways, and also from non-enzymatic reactions. In the oxidative branch of the pentose phosphate pathway (oxPPP), 6-phosphogluconolactone hydrolysis into 6-phosphogluconate is catalysed by 6-phosphogluconolactonase (Pgl) but in the absence of the latter, the oxPPP flux is thought to be maintained by spontaneous hydrolysis. However, in Δpgl Escherichia coli, an extracellular pathway can also contribute to pentose phosphate synthesis. This raises question as to whether the intracellular non-enzymatic reaction can compensate for the absence of 6-phosphogluconolactonase and, ultimately, on the role of 6-phosphogluconolactonase in central metabolism. Our results validate that the bypass pathway is active in the absence of Pgl, specifically involving the extracellular spontaneous hydrolysis of gluconolactones to gluconate. Under these conditions, metabolic flux analysis reveals that this bypass pathway accounts for the entire flux into the oxPPP. This alternative metabolic route-partially extracellular-sustains the flux through the oxPPP necessary for cell growth, albeit at a reduced rate in the absence of Pgl. Importantly, these findings imply that intracellular non-enzymatic hydrolysis of 6-phosphogluconolactone does not compensate for the absence of Pgl. This underscores the crucial role of Pgl in ensuring the efficient functioning of the oxPPP.

Cyanidin 3-glucoside targets a hepatic bilirubin transporter in rats

One of the organ-specific functions of the liver is the excretion of bilirubin into the bile. Membrane transport of bilirubin from the blood to the liver is not only an orphan function, because there is no link to the protein/gene units that perform this function, but also a poorly characterised function. The aim of this study was to investigate the pharmacology of bilirubin uptake in the liver of the female Wistar rat to improve basic knowledge in this neglected area of liver physiology. We treated isolated perfused livers of female rats with repeated single-pass, albumin-free bilirubin boli. We monitored both bilirubin and bilirubin glucuronide in perfusion effluent with a bio-fluorometric assay. We tested the ability of nine molecules known as substrates or inhibitors of sinusoidal membrane transporters to inhibit hepatic uptake of bilirubin. We found that cyanidin 3-glucoside and malvidin 3-glucoside were the only molecules that inhibited bilirubin uptake. These dietary anthocyanins resemble bromosulfophthalein (BSP), a substrate of several sinusoidal membrane transporters. The SLCO-specific substrates estradiol-17 beta-glucuronide, pravastatin, and taurocholate inhibited only bilirubin glucuronide uptake. Cyanidin 3-glucoside and taurocholate acted at physiological concentrations. The SLC22-specific substrates indomethacin and ketoprofen were inactive. We demonstrated the existence of a bilirubin-glucuronide transporter inhibited by bilirubin, a fact reported only once in the literature. The data suggest that bilirubin and bilirubin glucuronide are transported to the liver via pharmacologically distinct membrane transport pathways. Some dietary anthocyanins may physiologically modulate the uptake of bilirubin into the liver.

Activity-based annotation: the emergence of systems biochemistry

Current tools to annotate protein function have failed to keep pace with the speed of DNA sequencing and exponentially growing number of proteins of unknown function (PUFs). A major contributing factor to this mismatch is the historical lack of high-throughput methods to experimentally determine biochemical activity. Activity-based methods, such as activity-based metabolite and protein profiling, are emerging as new approaches for unbiased, global, biochemical annotation of protein function. In this review, we highlight recent experimental, activity-based approaches that offer new opportunities to determine protein function in a biologically agnostic and systems-level manner.

Implementation of Synthetic Pathways to Foster Microbe-Based Production of Non-Naturally Occurring Carboxylic Acids and Derivatives

Microbially produced carboxylic acids (CAs) are considered key players in the implementation of more sustainable industrial processes due to their potential to replace a set of oil-derived commodity chemicals. Most CAs are intermediates of microbial central carbon metabolism, and therefore, a biochemical production pathway is described and can be transferred to a host of choice to enable/improve production at an industrial scale. However, for some CAs, the implementation of this approach is difficult, either because they do not occur naturally (as is the case for levulinic acid) or because the described production pathway cannot be easily ported (as it is the case for adipic, muconic or glucaric acids). Synthetic biology has been reshaping the range of molecules that can be produced by microbial cells by setting new-to-nature pathways that leverage on enzyme arrangements not observed in vivo, often in association with the use of substrates that are not enzymes’ natural ones. In this review, we provide an overview of how the establishment of synthetic pathways, assisted by computational tools for metabolic retrobiosynthesis, has been applied to the field of CA production. The translation of these efforts in bridging the gap between the synthesis of CAs and of their more interesting derivatives, often themselves non-naturally occurring molecules, is also reviewed using as case studies the production of methacrylic, methylmethacrylic and poly-lactic acids.

Prokaryotic Genome Annotation

In the last decade, the high-throughput and relatively low cost of short-read sequencing technologies have revolutionized prokaryotic genomics. This has led to an exponential increase in the number of bacterial and archaeal genome sequences available, as well as corresponding increase of genome assembly and annotation tools developed. Together, these hardware and software technologies have given scientists unprecedented options to study their chosen microbial systems without the need for large teams of bioinformaticists or supercomputing facilities. While these analysis tools largely fall into only a few categories, each may have different requirements, caveats and file formats, and some may be rarely updated or even abandoned. And so, despite the apparent ease in sequencing and analyzing a prokaryotic genome, it is no wonder that the budding genomicist may quickly find oneself overwhelmed. Here, we aim to provide the reader with an overview of genome annotation and its most important considerations, as well as an easy-to-follow protocol to get started with annotating a prokaryotic genome.

Prospecting Biochemical Pathways to Implement Microbe-Based Production of the New-to-Nature Platform Chemical Levulinic Acid

Levulinic acid is a versatile platform molecule with potential to be used as an intermediate in the synthesis of many value-added products used across different industries, from cosmetics to fuels. Thus far, microbial biosynthetic pathways having levulinic acid as a product or an intermediate are not known, which restrains the development and optimization of a microbe-based process envisaging the sustainable bioproduction of this chemical. One of the doors opened by synthetic biology in the design of microbial systems is the implementation of new-to-nature pathways, that is, the assembly of combinations of enzymes not observed in vivo, where the enzymes can use not only their native substrates but also non-native ones, creating synthetic steps that enable the production of novel compounds. Resorting to a combined approach involving complementary computational tools and extensive manual curation, in this work, we provide a thorough prospect of candidate biosynthetic pathways that can be assembled for the production of levulinic acid in Escherichia coli or Saccharomyces cerevisiae. Out of the hundreds of combinations screened, five pathways were selected as best candidates on the basis of the availability of substrates and of candidate enzymes to catalyze the synthetic steps (that is, those steps that involve conversions not previously described). Genome-scale metabolic modeling was used to assess the performance of these pathways in the two selected hosts and to anticipate possible bottlenecks. Not only does the herein described approach offer a platform for the future implementation of the microbial production of levulinic acid but also it provides an organized research strategy that can be used as a framework for the implementation of other new-to-nature biosynthetic pathways for the production of value-added chemicals, thus fostering the emerging field of synthetic industrial microbiotechnology.

A computational workflow for the expansion of heterologous biosynthetic pathways to natural product derivatives

Plant natural products (PNPs) and their derivatives are important but underexplored sources of pharmaceutical molecules. To access this untapped potential, the reconstitution of heterologous PNP biosynthesis pathways in engineered microbes provides a valuable starting point to explore and produce novel PNP derivatives. Here, we introduce a computational workflow to systematically screen the biochemical vicinity of a biosynthetic pathway for pharmaceutical compounds that could be produced by derivatizing pathway intermediates. We apply our workflow to the biosynthetic pathway of noscapine, a benzylisoquinoline alkaloid (BIA) with a long history of medicinal use. Our workflow identifies pathways and enzyme candidates for the production of (S)-tetrahydropalmatine, a known analgesic and anxiolytic, and three additional derivatives. We then construct pathways for these compounds in yeast, resulting in platforms for de novo biosynthesis of BIA derivatives and demonstrating the value of cheminformatic tools to predict reactions, pathways, and enzymes in synthetic biology and metabolic engineering.

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

Optimization of protease production and sequence analysis of the purified enzyme from the cold adapted yeast Rhodotorula mucilaginosa CBMAI 1528

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A protease from a psychrotolerant yeast was characterized.

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Protease production was dependent on temperature and medium composition.

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Mass spectrometry analysis indicated that the protein belongs to the pepsin family.

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We propose that the enzyme reported here could be Rodothorulapepsin.

Enzymes from cold-adapted microorganisms are of high interest to industries due to their high activity at low and mild temperatures, which makes them suitable for their use in several processes that either require a supply of exogenous energy or involve the use of heat labile products. In this work, the protease production by the strain Rhodotorula mucilaginosa CBMAI 1528, previously isolated from the Antarctic continent, was optimized, and the purified enzyme analyzed. It was found that protease production was dependent on culture medium composition and growth temperature, being 20 °C and a culture medium containing both glucose and casein peptone (20 and 10 g/L, respectively) the optimal growing conditions in batch as well as in bioreactor. Moreover, mass spectrometry analysis revealed that the enzyme under study has a 100 % sequence identity with the deduced amino acid sequence of a putative aspartic protease from Rhodotorula sp. JG-1b (protein ID: KWU42276.1). This result was confirmed by the decrease of 95 % proteolytic activity by pepstatin A, a specific inhibitor of aspartic proteases. We propose that the enzyme reported here could be Rodothorulapepsin, a protein characterized in 1972 that did not have an associated sequence to date and has been classified as an orphan enzyme.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Systems Biochemistry Approaches to Defining Mitochondrial Protein Function

Defining functions for the full complement of proteins is a grand challenge in the post-genomic era and is essential for our understanding of basic biology and disease pathogenesis. In recent times, this endeavor has benefitted from a combination of modern large-scale and classical reductionist approaches-a process we refer to as "systems biochemistry"-that helps surmount traditional barriers to the characterization of poorly understood proteins. This strategy is proving to be particularly effective for mitochondria, whose well-defined proteome has enabled comprehensive analyses of the full mitochondrial system that can position understudied proteins for fruitful mechanistic investigations. Recent systems biochemistry approaches have accelerated the identification of new disease-related mitochondrial proteins and of long-sought "missing" proteins that fulfill key functions. Collectively, these studies are moving us toward a more complete understanding of mitochondrial activities and providing a molecular framework for the investigation of mitochondrial pathogenesis.

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

BACKGROUND

Metabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous-i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.

RESULTS

Our workflow utilizes PROXIMAL-a tool that uses reactant-product transformation patterns from the KEGG database-to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).

CONCLUSIONS

We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.

Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites

Recent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them “orphan.” A large number of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, the computational method BridgIT identified potential enzymes of orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. The BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze nonnatural transformations.

Thousands of biochemical reactions with characterized activities are “orphan,” meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.

The Complete Genome and Physiological Analysis of the Eurythermal Firmicute Exiguobacterium chiriqhucha Strain RW2 Isolated From a Freshwater Microbialite, Widely Adaptable to Broad Thermal, pH, and Salinity Ranges

Members of the genus Exiguobacterium are found in diverse environments from marine, freshwaters, permafrost to hot springs. Exiguobacterium can grow in a wide range of temperature, pH, salinity, and heavy-metal concentrations. We characterized Exiguobacterium chiriqhucha strain RW2 isolated from a permanently cold freshwater microbialite in Pavilion Lake, British Columbia using metabolic assays, genomics, comparative genomics, phylogenetics, and fatty acid composition. Strain RW2 has the most extensive growth range for temperature (4–50°C) and pH (5–11) of known Exiguobacterium isolates. Strain RW2 genome predicts pathways for wide differential thermal, cold and osmotic stress using cold and heat shock cascades (e.g., csp and dnaK), choline and betaine uptake/biosynthesis (e.g., opu and proU), antiporters (e.g., arcD and nhaC Na⁺/K⁺), membrane fatty acid unsaturation and saturation. Here, we provide the first complete genome from Exiguobacterium chiriqhucha strain RW2, which was isolated from a freshwater microbialite. Its genome consists of a single 3,019,018 bp circular chromosome encoding over 3,000 predicted proteins, with a GC% content of 52.1%, and no plasmids. In addition to growing at a wide range of temperatures and salinities, our findings indicate that RW2 is resistant to sulfisoxazole and has the genomic potential for detoxification of heavy metals (via mercuric reductases, arsenic resistance pumps, chromate transporters, and cadmium-cobalt-zinc resistance genes), which may contribute to the metabolic potential of Pavilion Lake microbialites. Strain RW2 could also contribute to microbialite formation, as it is a robust biofilm former and encodes genes involved in the deamination of amino acids to ammonia (i.e., L-asparaginase/urease), which could potentially boost carbonate precipitation by lowering the local pH and increasing alkalinity. We also used comparative genomic analysis to predict the pathway for orange pigmentation that is conserved across the entire Exiguobacterium genus, specifically, a C₃₀ carotenoid biosynthesis pathway is predicted to yield diaponeurosporene-4-oic acid as its final product. Carotenoids have been found to protect against ultraviolet radiation by quenching reactive oxygen, releasing excessive light energy, radical scavenging, and sunscreening. Together these results provide further insight into the potential of Exiguobacterium to exploit a wide range of environmental conditions, its potential roles in ecosystems (e.g., microbialites/microbial mats), and a blueprint model for diverse metabolic processes.

Respiratory chain Complex I of unparalleled divergence in diplonemids

Mitochondrial genes of Euglenozoa (Kinetoplastida, Diplonemea, and Euglenida) are notorious for being barely recognizable, raising the question of whether such divergent genes actually code for functional proteins. Here we demonstrate the translation and identify the function of five previously unassigned y genes encoded by mitochondrial DNA (mtDNA) of diplonemids. As is the rule in diplonemid mitochondria, y genes are fragmented, with gene pieces transcribed separately and then trans-spliced to form contiguous mRNAs. Further, y transcripts undergo massive RNA editing, including uridine insertions that generate up to 16-residue-long phenylalanine tracts, a feature otherwise absent from conserved mitochondrial proteins. By protein sequence analyses, MS, and enzymatic assays in Diplonema papillatum, we show that these y genes encode the subunits Nad2, -3, -4L, -6, and -9 of the respiratory chain Complex I (CI; NADH:ubiquinone oxidoreductase). The few conserved residues of these proteins are essentially those involved in proton pumping across the inner mitochondrial membrane and in coupling ubiquinone reduction to proton pumping (Nad2, -3, -4L, and -6) and in interactions with subunits containing electron-transporting Fe-S clusters (Nad9). Thus, in diplonemids, 10 CI subunits are mtDNA-encoded. Further, MS of D. papillatum CI allowed identification of 26 conventional and 15 putative diplonemid-specific nucleus-encoded components. Most conventional accessory subunits are well-conserved but unusually long, possibly compensating for the streamlined mtDNA-encoded components and for missing, otherwise widely distributed, conventional subunits. Finally, D. papillatum CI predominantly exists as a supercomplex I:III:IV that is exceptionally stable, making this protist an organism of choice for structural studies.

Discovery of enzymes for toluene synthesis from anoxic microbial communities

Microbial toluene biosynthesis was reported in anoxic lake sediments more than three decades ago, but the enzyme catalyzing this biochemically challenging reaction has never been identified. Here we report the toluene-producing enzyme PhdB, a glycyl radical enzyme of bacterial origin that catalyzes phenylacetate decarboxylation, and its cognate activating enzyme PhdA, a radical S-adenosylmethionine enzyme, discovered in two distinct anoxic microbial communities that produce toluene. The unconventional process of enzyme discovery from a complex microbial community (>300,000 genes), rather than from a microbial isolate, involved metagenomics- and metaproteomics-enabled biochemistry, as well as in vitro confirmation of activity with recombinant enzymes. This work expands the known catalytic range of glycyl radical enzymes (only seven reaction types had been characterized previously) and aromatic-hydrocarbon-producing enzymes, and will enable first-time biochemical synthesis of an aromatic fuel hydrocarbon from renewable resources, such as lignocellulosic biomass, rather than from petroleum.

Confronting the catalytic dark matter encoded by sequenced genomes

The post-genomic era has provided researchers with a deluge of protein sequences. However, a significant fraction of the proteins encoded by sequenced genomes remains without an identified function. Here, we aim at determining how many enzymes of uncertain or unknown function are still present in the Saccharomyces cerevisiae and human proteomes. Using information available in the Swiss-Prot, BRENDA and KEGG databases in combination with a Hidden Markov Model-based method, we estimate that >600 yeast and 2000 human proteins (>30% of their proteins of unknown function) are enzymes whose precise function(s) remain(s) to be determined. This illustrates the impressive scale of the ‘unknown enzyme problem’. We extensively review classical biochemical as well as more recent systematic experimental and computational approaches that can be used to support enzyme function discovery research. Finally, we discuss the possible roles of the elusive catalysts in light of recent developments in the fields of enzymology and metabolism as well as the significance of the unknown enzyme problem in the context of metabolic modeling, metabolic engineering and rare disease research.

Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas

Mono-ethylene glycol (MEG) is an important petrochemical with widespread use in numerous consumer products. The current industrial MEG-production process relies on non-renewable fossil fuel-based feedstocks, such as petroleum, natural gas, and naphtha; hence, it is useful to explore alternative routes of MEG-synthesis from gases as they might provide a greener and more sustainable alternative to the current production methods. Technologies of synthetic biology and metabolic engineering of microorganisms can be deployed for the expression of new biochemical pathways for MEG-synthesis from gases, provided that such promising alternative routes are first identified. We used the BNICE.ch algorithm to develop novel and previously unknown biological pathways to MEG from synthesis gas by leveraging the Wood-Ljungdahl pathway of carbon fixation of acetogenic bacteria. We developed a set of useful pathway pruning and analysis criteria to systematically assess thousands of pathways generated by BNICE.ch. Published genome-scale models of Moorella thermoacetica and Clostridium ljungdahlii were used to perform the pathway yield calculations and in-depth analyses of seven (7) newly developed biological MEG-producing pathways from gases, including CO₂, CO, and H₂. These analyses helped identify not only better candidate pathways, but also superior chassis organisms that can be used for metabolic engineering of the candidate pathways. The pathway generation, pruning, and detailed analysis procedures described in this study can also be used to develop biochemical pathways for other commodity chemicals from gaseous substrates.

Observing Biosynthetic Activity Utilizing Next Generation Sequencing and the DNA Linked Enzyme Coupled Assay

Currently, the identification of new genes drastically outpaces current experimental methods for determining their enzymatic function. This disparity necessitates the development of high-throughput techniques that operate with the same scalability as modern gene synthesis and sequencing technologies. In this paper, we demonstrate the versatility of the recently reported DNA-Linked Enzyme-Coupled Assay (DLEnCA) and its ability to support high-throughput data acquisition through next-generation sequencing (NGS). Utilizing methyltransferases, we highlight DLEnCA's ability to rapidly profile an enzyme's substrate specificity, determine relative enzyme kinetics, detect biosynthetic formation of a target molecule, and its potential to benefit from the scales and standardization afforded by NGS. This improved methodology minimizes the effort in acquiring biosynthetic knowledge by tying biochemical techniques to the rapidly evolving abilities in sequencing and synthesizing DNA.

Development and Application of Transcriptome-Derived Microsatellites in Actinidia eriantha (Actinidiaceae)

Actinidia eriantha Benth. is a diploid perennial woody vine native to China and is recognized as a valuable species for commercial kiwifruit improvement with high levels of ascorbic acid as well as having been used in traditional Chinese medicine. Due to the lack of genomic resources for the species, microsatellite markers for population genetics studies are scarce. In this study, RNASeq was conducted on fruit tissue of A. eriantha, yielding 5,678,129 reads with a total output of 3.41 Gb. De novo assembly yielded 69,783 non-redundant unigenes (41.3 Mb), of which 21,730 were annotated using protein databases. A total of 8,658 EST-SSR loci were identified in 7,495 unigene sequences, for which primer pairs were successfully designed for 3,842 loci (44.4%). Among these, 183 primer pairs were assayed for PCR amplification, yielding 69 with detectable polymorphism in A. eriantha. Additionally, 61 of the 69 polymorphic loci could be successfully amplified in at least one other Actinidia species. Of these, 14 polymorphic loci (mean N_A = 6.07 ± 2.30) were randomly selected for assessing levels of genetic diversity and population structure within A. eriantha. Finally, a neighbor-joining tree and Bayesian clustering analysis showed distinct clustering into two groups (K = 2), agreeing with the geographical distributions of these populations. Overall, our results will facilitate further studies of genetic diversity within A. eriantha and will aid in discriminating outlier loci involved in local adaptation.

Missing gene identification using functional coherence scores

Reconstructing metabolic and signaling pathways is an effective way of interpreting a genome sequence. A challenge in a pathway reconstruction is that often genes in a pathway cannot be easily found, reflecting current imperfect information of the target organism. In this work, we developed a new method for finding missing genes, which integrates multiple features, including gene expression, phylogenetic profile, and function association scores. Particularly, for considering function association between candidate genes and neighboring proteins to the target missing gene in the network, we used Co-occurrence Association Score (CAS) and PubMed Association Score (PAS), which are designed for capturing functional coherence of proteins. We showed that adding CAS and PAS substantially improve the accuracy of identifying missing genes in the yeast enzyme-enzyme network compared to the cases when only the conventional features, gene expression, phylogenetic profile, were used. Finally, it was also demonstrated that the accuracy improves by considering indirect neighbors to the target enzyme position in the network using a proper network-topology-based weighting scheme.

Biochemistry and physiology within the framework of the extended synthesis of evolutionary biology

Functional biologists, like Claude Bernard, ask "How?", meaning that they investigate the mechanisms underlying the emergence of biological functions (proximal causes), while evolutionary biologists, like Charles Darwin, asks "Why?", meaning that they search the causes of adaptation, survival and evolution (remote causes). Are these divergent views on what is life? The epistemological role of functional biology (molecular biology, but also biochemistry, physiology, cell biology and so forth) appears essential, for its capacity to identify several mechanisms of natural selection of new characters, individuals and populations. Nevertheless, several issues remain unsolved, such as orphan metabolic activities, i.e., adaptive functions still missing the identification of the underlying genes and proteins, and orphan genes, i.e., genes that bear no signature of evolutionary history, yet provide an organism with improved adaptation to environmental changes. In the framework of the Extended Synthesis, we suggest that the adaptive roles of any known function/structure are reappraised in terms of their capacity to warrant constancy of the internal environment (homeostasis), a concept that encompasses both proximal and remote causes.

Characterising Complex Enzyme Reaction Data

The relationship between enzyme-catalysed reactions and the Enzyme Commission (EC) number, the widely accepted classification scheme used to characterise enzyme activity, is complex and with the rapid increase in our knowledge of the reactions catalysed by enzymes needs revisiting. We present a manual and computational analysis to investigate this complexity and found that almost one-third of all known EC numbers are linked to more than one reaction in the secondary reaction databases (e.g., KEGG). Although this complexity is often resolved by defining generic, alternative and partial reactions, we have also found individual EC numbers with more than one reaction catalysing different types of bond changes. This analysis adds a new dimension to our understanding of enzyme function and might be useful for the accurate annotation of the function of enzymes and to study the changes in enzyme function during evolution.

De novo assembly and functional annotation of Myrciaria dubia fruit transcriptome reveals multiple metabolic pathways for L-ascorbic acid biosynthesis

Background

Myrciaria dubia is an Amazonian fruit shrub that produces numerous bioactive phytochemicals, but is best known by its high L-ascorbic acid (AsA) content in fruits. Pronounced variation in AsA content has been observed both within and among individuals, but the genetic factors responsible for this variation are largely unknown. The goals of this research, therefore, were to assemble, characterize, and annotate the fruit transcriptome of M. dubia in order to reconstruct metabolic pathways and determine if multiple pathways contribute to AsA biosynthesis.

Results

In total 24,551,882 high-quality sequence reads were de novo assembled into 70,048 unigenes (mean length = 1150 bp, N50 = 1775 bp). Assembled sequences were annotated using BLASTX against public databases such as TAIR, GR-protein, FB, MGI, RGD, ZFIN, SGN, WB, TIGR_CMR, and JCVI-CMR with 75.2 % of unigenes having annotations. Of the three core GO annotation categories, biological processes comprised 53.6 % of the total assigned annotations, whereas cellular components and molecular functions comprised 23.3 and 23.1 %, respectively. Based on the KEGG pathway assignment of the functionally annotated transcripts, five metabolic pathways for AsA biosynthesis were identified: animal-like pathway, myo-inositol pathway, L-gulose pathway, D-mannose/L-galactose pathway, and uronic acid pathway. All transcripts coding enzymes involved in the ascorbate-glutathione cycle were also identified. Finally, we used the assembly to identified 6314 genic microsatellites and 23,481 high quality SNPs.

Conclusions

This study describes the first next-generation sequencing effort and transcriptome annotation of a non-model Amazonian plant that is relevant for AsA production and other bioactive phytochemicals. Genes encoding key enzymes were successfully identified and metabolic pathways involved in biosynthesis of AsA, anthocyanins, and other metabolic pathways have been reconstructed. The identification of these genes and pathways is in agreement with the empirically observed capability of M. dubia to synthesize and accumulate AsA and other important molecules, and adds to our current knowledge of the molecular biology and biochemistry of their production in plants. By providing insights into the mechanisms underpinning these metabolic processes, these results can be used to direct efforts to genetically manipulate this organism in order to enhance the production of these bioactive phytochemicals.

The accumulation of AsA precursor and discovery of genes associated with their biosynthesis and metabolism in M. dubia is intriguing and worthy of further investigation. The sequences and pathways produced here present the genetic framework required for further studies. Quantitative transcriptomics in concert with studies of the genome, proteome, and metabolome under conditions that stimulate production and accumulation of AsA and their precursors are needed to provide a more comprehensive view of how these pathways for AsA metabolism are regulated and linked in this species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2225-6) contains supplementary material, which is available to authorized users.

A new network representation of the metabolism to detect chemical transformation modules

BACKGROUND

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS).

RESULTS

RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation.

CONCLUSIONS

This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

Functional Diversity of Haloacid Dehalogenase Superfamily Phosphatases from Saccharomyces cerevisiae: BIOCHEMICAL, STRUCTURAL, AND EVOLUTIONARY INSIGHTS

The haloacid dehalogenase (HAD)-like enzymes comprise a large superfamily of phosphohydrolases present in all organisms. The Saccharomyces cerevisiae genome encodes at least 19 soluble HADs, including 10 uncharacterized proteins. Here, we biochemically characterized 13 yeast phosphatases from the HAD superfamily, which includes both specific and promiscuous enzymes active against various phosphorylated metabolites and peptides with several HADs implicated in detoxification of phosphorylated compounds and pseudouridine. The crystal structures of four yeast HADs provided insight into their active sites, whereas the structure of the YKR070W dimer in complex with substrate revealed a composite substrate-binding site. Although the S. cerevisiae and Escherichia coli HADs share low sequence similarities, the comparison of their substrate profiles revealed seven phosphatases with common preferred substrates. The cluster of secondary substrates supporting significant activity of both S. cerevisiae and E. coli HADs includes 28 common metabolites that appear to represent the pool of potential activities for the evolution of novel HAD phosphatases. Evolution of novel substrate specificities of HAD phosphatases shows no strict correlation with sequence divergence. Thus, evolution of the HAD superfamily combines the conservation of the overall substrate pool and the substrate profiles of some enzymes with remarkable biochemical and structural flexibility of other superfamily members.

The widespread role of non-enzymatic reactions in cellular metabolism

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Non-enzymatic reactions are widespread and integral part of metabolism.

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Non-enzymatic metabolic reactions occur either spontaneously or small molecule catalyzed.

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They subdivide between broad/unspecific, and specific reactions that contribute to metabolism.

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Specific reactions occur both, exclusively non-enzymatically or parallel to enzymes.

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Non-enzymatic reactions affect drug design and network reconstruction.

Enzymes shape cellular metabolism, are regulated, fast, and for most cases specific. Enzymes do not however prevent the parallel occurrence of non-enzymatic reactions. Non-enzymatic reactions were important for the evolution of metabolic pathways, but are retained as part of the modern metabolic network. They divide into unspecific chemical reactivity and specific reactions that occur either exclusively non-enzymatically as part of the metabolic network, or in parallel to existing enzyme functions. Non-enzymatic reactions resemble catalytic mechanisms as found in all major enzyme classes and occur spontaneously, small molecule (e.g. metal-) catalyzed or light-induced. The frequent occurrence of non-enzymatic reactions impacts on stability and metabolic network structure, and has thus to be considered in the context of metabolic disease, network modeling, biotechnology and drug design.

The UniProt Consortium. UniProt: a hub for protein information

UniProt is an important collection of protein sequences and their annotations, which has doubled in size to 80 million sequences during the past year. This growth in sequences has prompted an extension of UniProt accession number space from 6 to 10 characters. An increasing fraction of new sequences are identical to a sequence that already exists in the database with the majority of sequences coming from genome sequencing projects. We have created a new proteome identifier that uniquely identifies a particular assembly of a species and strain or subspecies to help users track the provenance of sequences. We present a new website that has been designed using a user-experience design process. We have introduced an annotation score for all entries in UniProt to represent the relative amount of knowledge known about each protein. These scores will be helpful in identifying which proteins are the best characterized and most informative for comparative analysis. All UniProt data is provided freely and is available on the web at http://www.uniprot.org/.

Updates in Rhea--a manually curated resource of biochemical reactions

Rhea (http://www.ebi.ac.uk/rhea) is a comprehensive and non-redundant resource of expert-curated biochemical reactions described using species from the ChEBI (Chemical Entities of Biological Interest) ontology of small molecules. Rhea has been designed for the functional annotation of enzymes and the description of genome-scale metabolic networks, providing stoichiometrically balanced enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list and additional reactions), transport reactions and spontaneously occurring reactions. Rhea reactions are extensively curated with links to source literature and are mapped to other publicly available enzyme and pathway databases such as Reactome, BioCyc, KEGG and UniPathway, through manual curation and computational methods. Here we describe developments in Rhea since our last report in the 2012 database issue of Nucleic Acids Research. These include significant growth in the number of Rhea reactions and the inclusion of reactions involving complex macromolecules such as proteins, nucleic acids and other polymers that lie outside the scope of ChEBI. Together these developments will significantly increase the utility of Rhea as a tool for the description, analysis and reconciliation of genome-scale metabolic models.