Intragenomic enzyme complements

Researchers in applied biocatalysis are now reaping the rewards of intensive effort and technological developments in the sequencing of the genomes of microbial and plant species. The genomic resource contains the sequences of millions of new genes with potential application in industrial biotechnology and includes families of enzymes within discrete genomes that potentially catalyze equivalent chemical reactions. One of the key emerging characteristics of these intragenomic complements of enzymes is the impressive breadth of catalytic diversity that is observed within them. This diversity may have been acquired either in order to combat the spectrum of metabolic challenges with which the organism may be presented in its natural environment or as part of the biosynthetic machinery evolved to produce a spectrum of secondary metabolites that will prove to be advantageous in establishing a niche. Attempts have been made to functionally characterize the intragenomic complements of enzyme families catalyzing diverse reactions including carbonyl reduction, ester hydrolysis and Baeyer–Villiger oxidation, in Gram-positive bacteria, yeasts, filamentous fungi and the plant Arabidopsis thaliana. These studies are beginning to describe in detail for the first time the impressive range of catalytic potential within single organisms for attributes such as substrate range, enantioselectivity or thermostability, each of which is of interest from an enzyme discovery perspective.     

Prediction of missing enzyme genes in a bacterial metabolic network. Reconstruction of the lysine-degradation pathway of Pseudomonas aeruginosa

The metabolic network is an important biological network which consists of enzymes and chemical compounds. However, a large number of metabolic pathways remains unknown, and most organism-specific metabolic pathways contain many missing enzymes. We present a novel method to identify the genes coding for missing enzymes using available genomic and chemical information from bacterial genomes. The proposed method consists of two steps: (a) estimation of the functional association between the genes with respect to chromosomal proximity and evolutionary association, using supervised network inference; and (b) selection of gene candidates for missing enzymes based on the original candidate score and the chemical reaction information encoded in the EC number. We applied the proposed methods to infer the metabolic network for the bacteria Pseudomonas aeruginosa from two genomic datasets: gene position and phylogenetic profiles. Next, we predicted several missing enzyme genes to reconstruct the lysine-degradation pathway in P. aeruginosa using EC number information. As a result, we identified PA0266 as a putative 5-aminovalerate aminotransferase (EC 2.6.1.48) and PA0265 as a putative glutarate semialdehyde dehydrogenase (EC 1.2.1.20). To verify our prediction, we conducted biochemical assays and examined the activity of the products of the predicted genes, PA0265 and PA0266, in a coupled reaction. We observed that the predicted gene products catalyzed the expected reactions; no activity was seen when both gene products were omitted from the reaction.     

Structural Phylogenomics Reveals Gradual Evolutionary Replacement of Abiotic Chemistries by Protein Enzymes in Purine Metabolism

The origin of metabolism has been linked to abiotic chemistries that existed in our planet at the beginning of life. While plausible chemical pathways have been proposed, including the synthesis of nucleobases, ribose and ribonucleotides, the cooption of these reactions by modern enzymes remains shrouded in mystery. Here we study the emergence of purine metabolism. The ages of protein domains derived from a census of fold family structure in hundreds of genomes were mapped onto enzymes in metabolic diagrams. We find that the origin of the nucleotide interconversion pathway benefited most parsimoniously from the prebiotic formation of adenine nucleosides. In turn, pathways of nucleotide biosynthesis, catabolism and salvage originated ∼300 million years later by concerted enzymatic recruitments and gradual replacement of abiotic chemistries. Remarkably, this process led to the emergence of the fully enzymatic biosynthetic pathway ∼3 billion years ago, concurrently with the appearance of a functional ribosome. The simultaneous appearance of purine biosynthesis and the ribosome probably fulfilled the expanding matter-energy and processing needs of genomic information.     

The enzymatic and metabolic capabilities of early life

We introduce the concept of metaconsensus and employ it to make high confidence predictions of early enzyme functions and the metabolic properties that they may have produced. Several independent studies have used comparative bioinformatics methods to identify taxonomically broad features of genomic sequence data, protein structure data, and metabolic pathway data in order to predict physiological features that were present in early, ancestral life forms. But all such methods carry with them some level of technical bias. Here, we cross-reference the results of these previous studies to determine enzyme functions predicted to be ancient by multiple methods. We survey modern metabolic pathways to identify those that maintain the highest frequency of metaconsensus enzymes. Using the full set of modern reactions catalyzed by these metaconsensus enzyme functions, we reconstruct a representative metabolic network that may reflect the core metabolism of early life forms. Our results show that ten enzyme functions, four hydrolases, three transferases, one oxidoreductase, one lyase, and one ligase, are determined by metaconsensus to be present at least as late as the last universal common ancestor. Subnetworks within central metabolic processes related to sugar and starch metabolism, amino acid biosynthesis, phospholipid metabolism, and CoA biosynthesis, have high frequencies of these enzyme functions. We demonstrate that a large metabolic network can be generated from this small number of enzyme functions.     

Elucidation of new structures in lignins of CAD- and COMT-deficient plants by NMR.

Studying lignin-biosynthetic-pathway mutants and transgenics provides insights into plant responses to perturbations of the lignification system, and enhances our understanding of normal lignification. When enzymes late in the pathway are downregulated, significant changes in the composition and structure of lignin may result. NMR spectroscopy provides powerful diagnostic tools for elucidating structures in the difficult lignin polymer, hinting at the chemical and biochemical changes that have occurred. COMT (caffeic acid O-methyl transferase) downregulation in poplar results in the incorporation of 5-hydroxyconiferyl alcohol into lignins via typical radical coupling reactions, but post-coupling quinone methide internal trapping reactions produce novel benzodioxane units in the lignin. CAD (cinnamyl alcohol dehydrogenase) downregulation results in the incorporation of the hydroxycinnamyl aldehyde monolignol precursors intimately into the polymer. Sinapyl aldehyde cross-couples 8-O-4 with both guaiacyl and syringyl units in the growing polymer, whereas coniferyl aldehyde cross-couples 8-O-4 only with syringyl units, reflecting simple chemical cross-coupling propensities. The incorporation of hydroxycinnamyl aldehyde and 5-hydroxyconiferyl alcohol monomers indicates that these monolignol intermediates are secreted to the cell wall for lignification. The recognition that novel units can incorporate into lignins portends significantly expanded opportunities for engineering the composition and consequent properties of lignin for improved utilization of valuable plant resources.     

Metabolic potential of the organic‐solvent tolerant Pseudomonas putida DOT‐T1E deduced from its annotated genome

Pseudomonas putida DOT‐T1E is an organic solvent tolerant strain capable of degrading aromatic hydrocarbons. Here we report the DOT‐T1E genomic sequence (6 394 153 bp) and its metabolic atlas based on the classification of enzyme activities. The genome encodes for at least 1751 enzymatic reactions that account for the known pattern of C, N, P and S utilization by this strain. Based on the potential of this strain to thrive in the presence of organic solvents and the subclasses of enzymes encoded in the genome, its metabolic map can be drawn and a number of potential biotransformation reactions can be deduced. This information may prove useful for adapting desired reactions to create value‐added products. This bioengineering potential may be realized via direct transformation of substrates, or may require genetic engineering to block an existing pathway, or to re‐organize operons and genes, as well as possibly requiring the recruitment of enzymes from other sources to achieve the desired transformation.     

Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

Despite the current wealth of sequencing data, one‐third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome‐scale metabolic models with these new sequence–function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction.     

Origins of specificity and promiscuity in metabolic networks

How enzymes have evolved to their present form is linked to the question of how pathways emerged and evolved into extant metabolic networks. To investigate this mechanism, we have explored the chemical diversity present in a largely unbiased data set of catalytic reactions processed by modern enzymes across the tree of life. In order to get a quantitative estimate of enzyme chemical diversity, we measure enzyme multispecificity or promiscuity using the reaction molecular signatures. Our main finding is that reactions that are catalyzed by a highly specific enzyme are shared by poorly divergent species, suggesting a later emergence of this function during evolution. In contrast, reactions that are catalyzed by highly promiscuous enzymes are more likely to appear uniformly distributed across species in the tree of life. From a functional point of view, promiscuous enzymes are mainly involved in amino acid and lipid metabolisms, which might be associated with the earliest form of biochemical reactions. In this way, results presented in this paper might assist us with the identification of primeval promiscuous catalytic functions contributing to life's minimal metabolism.     

Analysis of metabolic network based on conservation of molecular structure

Recent work has revealed much about chemical reactions inside hundreds of organisms as well as universal characteristics of metabolic networks, which shed light on the evolution of the networks. However, characteristics of individual metabolites have been neglected. For example, some carbohydrates have structures that are decomposed into small molecules by metabolic reactions, but coenzymes such as ATP are mostly preserved. Such differences in metabolite characteristics are important for understanding the universal characteristics of metabolic networks. To quantify the structure conservation of metabolites, we defined the "structure conservation index" (SCI) for each metabolite as the fraction of metabolite atoms restored to their original positions through metabolic reactions. As expected, coenzymes and coenzyme-like metabolites that have reaction loops in the network show a higher SCI. Using the index, we found that the sum of metabolic fluxes is negatively correlated with the structure preservation of metabolite. Also, we found that each reaction path around high SCI metabolites changes independently, while changes in reaction paths involving low SCI metabolites coincide through evolution processes. These correlations may provide a clue to universal properties of metabolic networks.     

Enzymes of triacylglycerol synthesis and their regulation

Since the pathways of glycerolipid biosynthesis were elucidated in the 1950's, considerable knowledge has been gained about the enzymes that catalyze the lipid biosynthetic reactions and the factors that regulate triacylglycerol biosynthesis. In the last few decades, in part due to advances in technology and the wide availability of nucleotide and amino acid sequences, we have made enormous strides in our understanding of these enzymes at the molecular level. In many cases, sequence information obtained from lipid biosynthetic enzymes of prokaryotes and yeast has provided the means to search the genomic and expressed sequence tag databases for mammalian homologs and most of the genes have now been identified. Surprisingly, multiple isoforms appear to catalyze the same chemical reactions, suggesting that each isoform may play a distinct functional role in the pathway of triacylglycerol and phospholipid biosynthesis. This review focuses on the de novo biosynthesis of triacylglycerol in eukaryotic cells, the isoenzymes that are involved, their subcellular locations, how they are regulated, and their putative individual roles in glycerolipid biosynthesis.     

Environmental sensing by chromatin: an epigenetic contribution to evolutionary change

Chromatin structure and function are regulated by families of protein-modifying enzymes that are sensitive to a variety of metabolic and environmental agents. These enzymes, and proteins that read the modifications they maintain, constitute a system by which environmental agents, such as chemical toxins and dietary components, can directly regulate patterns of gene expression. This review describes this environmental sensing system from an evolutionary perspective. It is proposed that persistent environmentally-induced changes in gene expression patterns can cause changes in phenotype that are acted upon by natural selection, and that epigenetic processes can potentially play central roles in evolution.     

A computational analysis of protein interactions in metabolic networks reveals novel enzyme pairs potentially involved in metabolic channeling

Protein-protein interactions are operative at almost every level of cell structure and function as, for example, formation of sub-cellular organelles, packaging of chromatin, muscle contraction, signal transduction, and regulation of gene expression. Public databases of reported protein-protein interactions comprise hundreds of thousands interactions, and this number is steadily growing. Elucidating the implications of protein-protein interactions for the regulation of the underlying cellular or extra-cellular reaction network remains a great challenge for computational biochemistry. In this work, we have undertaken a systematic and comprehensive computational analysis of reported enzyme-enzyme interactions in the metabolic networks of the model organisms Escherichia coli and Saccharomyces cerevisiae. We grouped all enzyme pairs according to the topological distance that the catalyzed reactions have in the metabolic network and performed a statistical analysis of reported enzyme-enzyme interactions within these groups. We found a higher frequency of reported enzyme-enzyme interactions within the group of enzymes catalyzing reactions that are adjacent in the network, i.e. sharing at least one metabolite. As some of these interacting enzymes have already been implicated in metabolic channeling our analysis may provide a useful screening for candidates of this phenomenon. To check for a possible regulatory role of interactions between enzymes catalyzing non-neighboring reactions, we determined potentially regulatory enzymes using connectivity in the network and absolute change of Gibbs free energy. Indeed a higher portion of reported interactions pertain to such potentially regulatory enzymes.     

The CanOE strategy: integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.     

The metabolic core and catalytic switches are fundamental elements in the self-regulation of the systemic metabolic structure of cells

Background

Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms.

Methodology/Principal Findings

In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson''s correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows.

Conclusions/Significance

We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub –with a high degree of effective connectivity- exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.     

Phenolic metabolism in petunia tissues. I. Characteristic responses of enzymes involved in different steps of polyphenol synthesis to different hormonal influences.

Pronounced changes in enzymatic patterns occur in petunia tissues when calluses are subcultured on media containing different growth substances. As judged by variations of enzymes related to primary metabolism (6-phosphogluconate and malate dehydrogenases) there are individual responses for each metabolic pathway. Concerning the enzymes of aromatic metabolism: (a) Phenylalanine ammonia-lyase, cinnamate and p-coumarate hydroxylases and the enzyme(s) activating phenylpropanoid units vary in the same manner. (b) Chalcone-flavanone isomerase, a key enzyme in the synthesis of flavonoids, and coniferyl alcohol dehydrogenase, which leads to the monomers of lignins, have, on the other hand, an independent behaviour. These responses show that the enzymes involved in the synthesis and activation of phenylpropanoid units seem to act coordinately in plants. Moreover, the data suggest that the common pathway leading to the activated cinnamic acids and the specific metabolic steps of lignin and flavonoid synthesis are regulated in a different way.     

Reconstructing genome-scale metabolic models with merlin

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin.     

Efficient Reconstruction of Metabolic Pathways by Bidirectional Chemical Search

One of the main challenges in systems biology is the establishment of the metabolome: a catalogue of the metabolites and biochemical reactions present in a specific organism. Current knowledge of biochemical pathways as stored in public databases such as KEGG, is based on carefully curated genomic evidence for the presence of specific metabolites and enzymes that activate particular biochemical reactions. In this paper, we present an efficient method to build a substantial portion of the artificial chemistry defined by the metabolites and biochemical reactions in a given metabolic pathway, which is based on bidirectional chemical search. Computational results on the pathways stored in KEGG reveal novel biochemical pathways. This work has been partially supported by the Spanish CICYT project TIN2004-07925-C03-01 GRAMMARS, by Spanish DGI projects MTM2006-07773 COMGRIO and MTM2006-15038-C02-01, and by EU project INTAS IT 04-77-7178.     

Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing ‘big’ biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.     

Molecular evolution of enzymes involved in the arachidonic acid cascade

The metabolic pathway called the arachidonic acid cascade produces a wide range of eicosanoids, such as prostaglandins, thromboxanes and leukotrienes with potent biological activities. Recombinant DNA techniques have made it possible to determine the nucleotide sequences of cDNAs and/or genomic structures for the enzymes involved in the pathway. Sequence comparison analyses of the accumulated sequence data have brought great insights into the structure, function and molecular evolution of the enzymes. This paper reviews the sequence comparison analyses of the enzymes involved in the arachidonic acid cascade.