Distribution of orphan metabolic activities

Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein "unknownme". This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteria Escherichia coli using the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.

Rv2231c, a unique histidinol phosphate aminotransferase from Mycobacterium tuberculosis, supports virulence by inhibiting host-directed defense

Nitrogen metabolism of M. tuberculosis is critical for its survival in infected host cells. M. tuberculosis has evolved sophisticated strategies to switch between de novo synthesis and uptake of various amino acids from host cells for metabolic demands. Pyridoxal phosphate-dependent histidinol phosphate aminotransferase-HspAT enzyme is critically required for histidine biosynthesis. HspAT is involved in metabolic synthesis of histidine, phenylalanine, tyrosine, tryptophan, and novobiocin. We showed that M. tuberculosis Rv2231c is a conserved enzyme with HspAT activity. Rv2231c is a monomeric globular protein that contains α-helices and β-sheets. It is a secretory and cell wall-localized protein that regulates critical pathogenic attributes. Rv2231c enhances the survival and virulence of recombinant M. smegmatis in infected RAW264.7 macrophage cells. Rv2231c is recognized by the TLR4 innate immune receptor and modulates the host immune response by suppressing the secretion of the antibacterial pro-inflammatory cytokines TNF, IL-12, and IL-6. It also inhibits the expression of co-stimulatory molecules CD80 and CD86 along with antigen presenting molecule MHC-I on macrophage and suppresses reactive nitrogen species formation, thereby promoting M2 macrophage polarization. Recombinant M. smegmatis expressing Rv2231c inhibited apoptosis in macrophages, promoting efficient bacterial survival and proliferation, thereby increasing virulence. Our results indicate that Rv2231c is a moonlighting protein that regulates multiple functions of M. tuberculosis pathophysiology to increase its virulence. These mechanistic insights can be used to better understand the pathogenesis of M. tuberculosis and to design strategies for tuberculosis mitigation.

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.

Metabolomics in infectious diseases and drug discovery

Metabolomics has emerged as an invaluable tool that can be used along with genomics, transcriptomics and proteomics to understand host–pathogen interactions at small-molecule levels. Metabolomics has been used to study a variety of infectious diseases and applications. The most common application of metabolomics is for prognostic and diagnostic purposes, specifically the screening of disease-specific biomarkers by either NMR-based or mass spectrometry-based metabolomics. In addition, metabolomics is of great significance for the discovery of druggable metabolic enzymes and/or metabolic regulators through the use of state-of-the-art flux analysis, for example, via the elucidation of metabolic mechanisms. This review discusses the application of metabolomics technologies to biomarker screening, the discovery of drug targets in infectious diseases such as viral, bacterial and parasite infections and immunometabolomics, highlights the challenges associated with accessing metabolite compartmentalization and discusses the available tools for determining local metabolite concentrations.

Metabolomics has emerged as an invaluable tool that can be used along with genomics, transcriptomics and proteomics to understand host–pathogen interactions at small-molecule levels.

Aspartate aminotransferase Rv3722c governs aspartate-dependent nitrogen metabolism in Mycobacterium tuberculosis

Gene rv3722c of Mycobacterium tuberculosis is essential for in vitro growth, and encodes a putative pyridoxal phosphate-binding protein of unknown function. Here we use metabolomic, genetic and structural approaches to show that Rv3722c is the primary aspartate aminotransferase of M. tuberculosis, and mediates an essential but underrecognized role in metabolism: nitrogen distribution. Rv3722c deficiency leads to virulence attenuation in macrophages and mice. Our results identify aspartate biosynthesis and nitrogen distribution as potential species-selective drug targets in M. tuberculosis.

Carboxylic Ester Hydrolases in Bacteria: Active Site, Structure, Function and Application

Carboxylic ester hydrolases (CEHs), which catalyze the hydrolysis of carboxylic esters to produce alcohol and acid, are identified in three domains of life. In the Protein Data Bank (PDB), 136 crystal structures of bacterial CEHs (424 PDB codes) from 52 genera and metagenome have been reported. In this review, we categorize these structures based on catalytic machinery, structure and substrate specificity to provide a comprehensive understanding of the bacterial CEHs. CEHs use Ser, Asp or water as a nucleophile to drive diverse catalytic machinery. The α/β/α sandwich architecture is most frequently found in CEHs, but 3-solenoid, β-barrel, up-down bundle, α/β/β/α 4-layer sandwich, 6 or 7 propeller and α/β barrel architectures are also found in these CEHs. Most are substrate-specific to various esters with types of head group and lengths of the acyl chain, but some CEHs exhibit peptidase or lactamase activities. CEHs are widely used in industrial applications, and are the objects of research in structure- or mutation-based protein engineering. Structural studies of CEHs are still necessary for understanding their biological roles, identifying their structure-based functions and structure-based engineering and their potential industrial applications.

Emerging Approaches to Tuberculosis Drug Development: At Home in the Metabolome

Once considered a crowning achievement of modern drug development, tuberculosis (TB) chemotherapy has proven increasingly unable to keep pace with the spread of the pandemic and rise of drug resistance. Efforts to revive the TB drug development pipeline have, in the meantime, faltered. Closer analysis reveals key experimental deficiencies that have hindered our ability to 'reverse engineer' knowledge of antibiotic mechanisms into rational drug development. Here, we discuss the emerging potential of metabolomics; the systems level study of small molecule metabolites, to help overcome these gaps and serve as a unique biochemical bridge between the phenotypic properties of chemical compounds and biological targets.

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.

Leveraging structure for enzyme function prediction: methods, opportunities, and challenges

The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, because only a small fraction (currently <1%) has been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently, there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to elucidate systematically protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism.

Profiling the orphan enzymes

The emergence of Next Generation Sequencing generates an incredible amount of sequence and great potential for new enzyme discovery. Despite this huge amount of data and the profusion of bioinformatic methods for function prediction, a large part of known enzyme activities is still lacking an associated protein sequence. These particular activities are called "orphan enzymes". The present review proposes an update of previous surveys on orphan enzymes by mining the current content of public databases. While the percentage of orphan enzyme activities has decreased from 38% to 22% in ten years, there are still more than 1,000 orphans among the 5,000 entries of the Enzyme Commission (EC) classification. Taking into account all the reactions present in metabolic databases, this proportion dramatically increases to reach nearly 50% of orphans and many of them are not associated to a known pathway. We extended our survey to "local orphan enzymes" that are activities which have no representative sequence in a given clade, but have at least one in organisms belonging to other clades. We observe an important bias in Archaea and find that in general more than 30% of the EC activities have incomplete sequence information in at least one superkingdom. To estimate if candidate proteins for local orphans could be retrieved by homology search, we applied a simple strategy based on the PRIAM software and noticed that candidates may be proposed for an important fraction of local orphan enzymes. Finally, by studying relation between protein domains and catalyzed activities, it appears that newly discovered enzymes are mostly associated with already known enzyme domains. Thus, the exploration of the promiscuity and the multifunctional aspect of known enzyme families may solve part of the orphan enzyme issue. We conclude this review with a presentation of recent initiatives in finding proteins for orphan enzymes and in extending the enzyme world by the discovery of new activities.

Metabolomic strategies for the identification of new enzyme functions and metabolic pathways

Recent technological advances in accurate mass spectrometry and data analysis have revolutionized metabolomics experimentation. Activity-based and global metabolomic profiling methods allow simultaneous and rapid screening of hundreds of metabolites from a variety of chemical classes, making them useful tools for the discovery of novel enzymatic activities and metabolic pathways. By using the metabolome of the relevant organism or close species, these methods capitalize on biological relevance, avoiding the assignment of artificial and non-physiological functions. This review discusses state-of-the-art metabolomic approaches and highlights recent examples of their use for enzyme annotation, discovery of new metabolic pathways, and gene assignment of orphan metabolic activities across diverse biological sources.

Finding sequences for over 270 orphan enzymes

Despite advances in sequencing technology, there are still significant numbers of well-characterized enzymatic activities for which there are no known associated sequences. These 'orphan enzymes' represent glaring holes in our biological understanding, and it is a top priority to reunite them with their coding sequences. Here we report a methodology for resolving orphan enzymes through a combination of database search and literature review. Using this method we were able to reconnect over 270 orphan enzymes with their corresponding sequence. This success points toward how we can systematically eliminate the remaining orphan enzymes and prevent the introduction of future orphan enzymes.

Reconstruction and validation of a genome-scale metabolic model for the filamentous fungus Neurospora crassa using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.

Few organisms have been as foundational to the development of modern genetics and cellular metabolism as Neurospora crassa. Given the wealth of knowledge available for this filamentous fungus, the effort required to manually curate a high-quality genome-scale metabolic reconstruction would be daunting. To aid the reconstruction process, we developed three optimization-based algorithms. The first algorithm predicts flux while linearly accounting for metabolite dilution; the second algorithm removes blocked reactions with one compact linear program; and the third algorithm reconciles differences between in silico predictions and experimental observations of mutant viability. We have used these algorithms to develop the first genome-scale metabolic model for Neurospora. We have validated the accuracy of our model against an independent test set of more than 300 growth/no-growth phenotypes, and our model displays 93% sensitivity and specificity. Simulating the biochemical genetics experiments originally performed on Neurospora, we comprehensively predicted essential genes, nutrient rescues of auxotroph mutants and synthetic lethal interactions. With these predictions, we provide potential mechanistic insight into known mutant phenotypes, and testable hypotheses for novel mutant phenotypes. The model, the algorithms and the testable hypotheses provide a computational foundation for the study of Neurospora crassa metabolism.

Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity

Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.

Fragment and conquer: from structure to complexes to function

In this issue of Structure, Shumilin and colleagues show the power of metabolite screening by means of X-ray crystallography to link an orphan protein domain with an orphan biochemical function. This result paves the way for large-scale functional annotation and sets new objectives for structural genomics.

Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation

The combination of high-resolution LC–MS-based untargeted metabolomics with stable isotope tracing provides a global overview of the cellular fate of precursor metabolites. This methodology enables detection of putative metabolites from biological samples and simultaneous quantification of the pattern and extent of isotope labeling. Labeling of Trypanosoma brucei cell cultures with 50% uniformly ¹³C-labeled glucose demonstrated incorporation of glucose-derived carbon into 187 of 588 putatively identified metabolites in diverse pathways including carbohydrate, nucleotide, lipid, and amino acid metabolism. Labeling patterns confirmed the metabolic pathways responsible for the biosynthesis of many detected metabolites, and labeling was detected in unexpected metabolites, including two higher sugar phosphates annotated as octulose phosphate and nonulose phosphate. This untargeted approach to stable isotope tracing facilitates the biochemical analysis of known pathways and yields rapid identification of previously unexplored areas of metabolism.

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 discovery of the various metabolic functions catalyzed by enzymes encoded by the genes from the exponentially increasing number of sequenced genomes is one of the main focuses of bioinformatics tools today. However, most of these tools rely on already identified enzyme-coding gene or protein sequence information to predict known enzymatic activities in new genomes. Therefore, they cannot be used to reveal metabolic activities without any corresponding sequenced genes, dubbed “sequence-orphan activities”. In such cases, the best approach is the bioanalysis of target genes by human expert curators, manually integrating so-called “context-based information” (such as gene co-localization on the genome, or the presence of incomplete metabolic pathways) to infer novel functions. Few bioinformatics tools exploit such information and render accessible results in an automated way. Here, we present “CanOE”, a strategy that uses contextual information to propose and rank Candidate genes for Orphan Enzymes in Bacteria and Archaea. Beyond the merit of extending our knowledge and comprehension of prokaryote metabolism, identifying coding genes for sequence-orphan activities opens new opportunities for functional annotation (homology-based transfer made accessible), drug design (new metabolic targets), synthetic biology (new building blocks) and biotechnology applications (new biocatalysts).

Mycobacterium tuberculosis metabolism and host interaction: mysteries and paradoxes

Metabolism is a widely recognized facet of all host-pathogen interactions. Knowledge of its roles in pathogenesis, however, remains comparatively incomplete. Existing studies have emphasized metabolism as a cell autonomous property of pathogens used to fuel replication in a quantitative, rather than qualitatively specific, manner. For Mycobacterium tuberculosis, however, matters could not be more different. M. tuberculosis is a chronic facultative intracellular pathogen that resides in humans as its only known host. Within humans, M. tuberculosis resides chiefly within the macrophage phagosome, the cell type, and compartment most committed to its eradication. M. tuberculosis has thus evolved its metabolic network to both maintain and propagate its survival as a species within a single host. The specific ways in which its metabolic network serves these distinct, through interdependent, functions, however, remain incompletely defined. Here, we review existing knowledge of the M. tuberculosis-host interaction, highlighting the distinct phases of its natural life cycle and the diverse microenvironments encountered therein.

Identification of nicotinamide mononucleotide deamidase of the bacterial pyridine nucleotide cycle reveals a novel broadly conserved amidohydrolase family

The pyridine nucleotide cycle is a network of salvage and recycling routes maintaining homeostasis of NAD(P) cofactor pool in the cell. Nicotinamide mononucleotide (NMN) deamidase (EC 3.5.1.42), one of the key enzymes of the bacterial pyridine nucleotide cycle, was originally described in Enterobacteria, but the corresponding gene eluded identification for over 30 years. A genomics-based reconstruction of NAD metabolism across hundreds of bacterial species suggested that NMN deamidase reaction is the only possible way of nicotinamide salvage in the marine bacterium Shewanella oneidensis. This prediction was verified via purification of native NMN deamidase from S. oneidensis followed by the identification of the respective gene, termed pncC. Enzymatic characterization of the PncC protein, as well as phenotype analysis of deletion mutants, confirmed its proposed biochemical and physiological function in S. oneidensis. Of the three PncC homologs present in Escherichia coli, NMN deamidase activity was confirmed only for the recombinant purified product of the ygaD gene. A comparative analysis at the level of sequence and three-dimensional structure, which is available for one of the PncC family member, shows no homology with any previously described amidohydrolases. Multiple alignment analysis of functional and nonfunctional PncC homologs, together with NMN docking experiments, allowed us to tentatively identify the active site area and conserved residues therein. An observed broad phylogenomic distribution of predicted functional PncCs in the bacterial kingdom is consistent with a possible role in detoxification of NMN, resulting from NAD utilization by DNA ligase.

The human metabolic reconstruction Recon 1 directs hypotheses of novel human metabolic functions

Background

Metabolic network reconstructions formalize our knowledge of metabolism. Gaps in these networks pinpoint regions of metabolism where biological components and functions are "missing." At the same time, a major challenge in the post genomic era involves characterisation of missing biological components to complete genome annotation.

Results

We used the human metabolic network reconstruction RECON 1 and established constraint-based modelling tools to uncover novel functions associated with human metabolism. Flux variability analysis identified 175 gaps in RECON 1 in the form of blocked reactions. These gaps were unevenly distributed within metabolic pathways but primarily found in the cytosol and often caused by compounds whose metabolic fate, rather than production, is unknown. Using a published algorithm, we computed gap-filling solutions comprised of non-organism specific metabolic reactions capable of bridging the identified gaps. These candidate solutions were found to be dependent upon the reaction environment of the blocked reaction. Importantly, we showed that automatically generated solutions could produce biologically realistic hypotheses of novel human metabolic reactions such as of the fate of iduronic acid following glycan degradation and of N-acetylglutamate in amino acid metabolism.

Conclusions

The results demonstrate how metabolic models can be utilised to direct hypotheses of novel metabolic functions in human metabolism; a process that we find is heavily reliant upon manual curation and biochemical insight. The effectiveness of a systems approach for novel biochemical pathway discovery in mammals is demonstrated and steps required to tailor future gap filling algorithms to mammalian metabolic networks are proposed.

Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

MOTIVATION

Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning.

RESULTS

We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment.

AVAILABILITY

The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.

Central carbon metabolism in Mycobacterium tuberculosis: an unexpected frontier

Recent advances in liquid chromatography and mass spectrometry have enabled the highly parallel, quantitative measurement of metabolites within a cell and the ability to trace their biochemical fates. In Mycobacterium tuberculosis (Mtb), these advances have highlighted major gaps in our understanding of central carbon metabolism (CCM) that have prompted fresh interpretations of the composition and structure of its metabolic pathways and the phenotypes of Mtb strains in which CCM genes have been deleted. High-throughput screens have demonstrated that small chemical compounds can selectively inhibit some enzymes of Mtb's CCM while sparing homologs in the host. Mtb's CCM has thus emerged as a frontier for both fundamental and translational research.

A systems biology perspective on plant-microbe interactions: biochemical and structural targets of pathogen effectors

Plants have biochemical defences against stresses from predators, parasites and pathogens. In this review we discuss the interaction of plant defences with microbial pathogens such as bacteria, fungi and oomycetes, and viruses. We examine principles of complex dynamic networks that allow identification of network components that are differentially and predictably sensitive to perturbation, thus making them likely effector targets. We relate these principles to recent developments in our understanding of known effector targets in plant-pathogen systems, and propose a systems-level framework for the interpretation and modelling of host-microbe interactions mediated by effectors. We describe this framework briefly, and conclude by discussing useful experimental approaches for populating this framework.

Systematizing the generation of missing metabolic knowledge

Genome-scale metabolic network reconstructions are built from all of the known metabolic reactions and genes in a target organism. However, since our knowledge of any organism is incomplete, these network reconstructions contain gaps. Reactions may be missing, resulting in dead-ends in pathways, while unknown gene products may catalyze known reactions. New computational methods that analyze data, such as growth phenotypes or gene essentiality, in the context of genome-scale metabolic networks, have been developed to predict these missing reactions or genes likely to fill these knowledge gaps. A growing number of experimental studies are appearing that address these computational predictions, leading to discovery of new metabolic capabilities in the target organism. Gap-filling methods can thus be used to improve metabolic network models while simultaneously leading to discovery of new metabolic gene functions.

A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1

BACKGROUND

Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms.

RESULTS

We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated.

CONCLUSIONS

We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening.

Activity-based metabolomic profiling of enzymatic function: identification of Rv1248c as a mycobacterial 2-hydroxy-3-oxoadipate synthase

Activity based metabolomic profiling (ABMP) allows unbiased discovery of enzymatic activities encoded by genes of unknown function, and applies liquid-chromatography mass spectrometry (LC-MS) to analyze the impact of a recombinant enzyme on the homologous cellular extract as a physiologic library of potential substrates and products. The Mycobacterium tuberculosis protein Rv1248c was incompletely characterized as a thiamine diphosphate-dependent alpha-ketoglutarate decarboxylase. Here, recombinant Rv1248c catalyzed consumption of alpha-ketoglutarate in a mycobacterial small molecule extract with matched production of 5-hydroxylevulinate (HLA) in a reaction predicted to require glyoxylate. As confirmed using pure substrates by LC-MS, (1)H-NMR, chemical trapping, and intracellular metabolite profiling, Rv1248c catalyzes C-C bond formation between the activated aldehyde of alpha-ketoglutarate and the carbonyl of glyoxylate to yield 2-hydroxy-3-oxoadipate (HOA), which decomposes to HLA. Thus, Rv1248c encodes an HOA synthase.

'Unknown' proteins and 'orphan' enzymes: the missing half of the engineering parts list--and how to find it

Like other forms of engineering, metabolic engineering requires knowledge of the components (the 'parts list') of the target system. Lack of such knowledge impairs both rational engineering design and diagnosis of the reasons for failures; it also poses problems for the related field of metabolic reconstruction, which uses a cell's parts list to recreate its metabolic activities in silico. Despite spectacular progress in genome sequencing, the parts lists for most organisms that we seek to manipulate remain highly incomplete, due to the dual problem of 'unknown' proteins and 'orphan' enzymes. The former are all the proteins deduced from genome sequence that have no known function, and the latter are all the enzymes described in the literature (and often catalogued in the EC database) for which no corresponding gene has been reported. Unknown proteins constitute up to about half of the proteins in prokaryotic genomes, and much more than this in higher plants and animals. Orphan enzymes make up more than a third of the EC database. Attacking the 'missing parts list' problem is accordingly one of the great challenges for post-genomic biology, and a tremendous opportunity to discover new facets of life's machinery. Success will require a co-ordinated community-wide attack, sustained over years. In this attack, comparative genomics is probably the single most effective strategy, for it can reliably predict functions for unknown proteins and genes for orphan enzymes. Furthermore, it is cost-efficient and increasingly straightforward to deploy owing to a proliferation of databases and associated tools.

Novel biological insights through metabolomics and 13C-flux analysis

Metabolomics and (13)C-flux analysis have become instrumental for analyzing cellular metabolism and its regulation. Driven primarily by technical advances in mass spectrometry-based analytics, they provide unmatched readouts on metabolic state and activity. Functional genomics leverages metabolomics for the discovery of novel enzymes and unexpected secondary activities of annotated enzymes. (13)C-flux analyses are frequently used for empirical elucidation of pathways in poorly characterized species and for network-wide analysis of mechanisms that realize energy and redox balancing. Integration of metabolomics, (13)C-flux analysis and other data enable the condition-dependent characterization of regulatory circuits that ultimately govern the metabolic phenotype.

Metabolite profiling reveals YihU as a novel hydroxybutyrate dehydrogenase for alternative succinic semialdehyde metabolism in Escherichia coli

The search for novel enzymes and enzymatic activities is important to map out all metabolic activities and reveal cellular metabolic processes in a more exhaustive manner. Here we present biochemical and physiological evidence for the function of the uncharacterized protein YihU in Escherichia coli using metabolite profiling by capillary electrophoresis time-of-flight mass spectrometry. To detect enzymatic activity and simultaneously identify possible substrates and products of the putative enzyme, we profiled a complex mixture of metabolites in the presence or absence of YihU. In this manner, succinic semialdehyde was identified as a substrate for YihU. The purified YihU protein catalyzed in vitro the NADH-dependent reduction of succinic semialdehyde to gamma-hydroxybutyrate. Moreover, a yihU deletion mutant displayed reduced tolerance to the cytotoxic effects of exogenous addition of succinic semialdehyde. Profiling of intracellular metabolites following treatment of E. coli with succinic semialdehyde supports the existence of a YihU-catalyzed reduction of succinic semialdehyde to gamma-hydroxybutyrate in addition to its known oxidation to succinate and through the tricarboxylic acid cycle. These findings suggest that YihU is a novel gamma-hydroxybutyrate dehydrogenase involved in the metabolism of succinic semialdehyde, and other potentially toxic intermediates that may accumulate under stress conditions in E. coli.

An introduction to metabolic networks and their structural analysis

There has been a renewed interest for metabolism in the computational biology community, leading to an avalanche of papers coming from methodological network analysis as well as experimental and theoretical biology. This paper is meant to serve as an initial guide for both the biologists interested in formal approaches and the mathematicians or computer scientists wishing to inject more realism into their models. The paper is focused on the structural aspects of metabolism only. The literature is vast enough already, and the thread through it difficult to follow even for the more experienced worker in the field. We explain methods for acquiring data and reconstructing metabolic networks, and review the various models that have been used for their structural analysis. Several concepts such as modularity are introduced, as are the controversies that have beset the field these past few years, for instance, on whether metabolic networks are small-world or scale-free, and on which model better explains the evolution of metabolism. Clarifying the work that has been done also helps in identifying open questions and in proposing relevant future directions in the field, which we do along the paper and in the conclusion.

New surveyor tools for charting microbial metabolic maps

The computational reconstruction and analysis of cellular models of microbial metabolism is one of the great success stories of systems biology. The extent and quality of metabolic network reconstructions is, however, limited by the current state of biochemical knowledge. Can experimental high-throughput data be used to improve and expand network reconstructions to include unexplored areas of metabolism? Recent advances in experimental technology and analytical methods bring this aim an important step closer to realization. Data integration will play a particularly important part in exploiting the new experimental opportunities.

Computational prediction and experimental verification of the gene encoding the NAD+/NADP+-dependent succinate semialdehyde dehydrogenase in Escherichia coli

Although NAD(+)-dependent succinate semialdehyde dehydrogenase activity was first described in Escherichia coli more than 25 years ago, the responsible gene has remained elusive so far. As an experimental proof of concept for a gap-filling algorithm for metabolic networks developed earlier, we demonstrate here that the E. coli gene yneI is responsible for this activity. Our biochemical results demonstrate that the yneI-encoded succinate semialdehyde dehydrogenase can use either NAD(+) or NADP(+) to oxidize succinate semialdehyde to succinate. The gene is induced by succinate semialdehyde, and expression data indicate that yneI plays a unique physiological role in the general nitrogen metabolism of E. coli. In particular, we demonstrate using mutant growth experiments that the yneI gene has an important, but not essential, role during growth on arginine and probably has an essential function during growth on putrescine as the nitrogen source. The NADP(+)-dependent succinate semialdehyde dehydrogenase activity encoded by the functional homolog gabD appears to be important for nitrogen metabolism under N limitation conditions. The yneI-encoded activity, in contrast, functions primarily as a valve to prevent toxic accumulation of succinate semialdehyde. Analysis of available genome sequences demonstrated that orthologs of both yneI and gabD are broadly distributed across phylogenetic space.