Metabolic reconstruction and analysis for parasite genomes

Metabolomic Profile of BALB/c Macrophages Infected with Leishmania amazonensis: Deciphering L-Arginine Metabolism

Background: Leishmaniases are neglected tropical diseases that are caused by Leishmania, being endemic worldwide. L-arginine is an essential amino acid that is required for polyamines production on mammal cells. During Leishmania infection of macrophages, L-arginine is used by host and parasite arginase to produce polyamines, leading to parasite survival; or, by nitric oxide synthase 2 to produce nitric oxide leading to parasite killing. Here, we determined the metabolomic profile of BALB/c macrophages that were infected with L. amazonensis wild type or with L. amazonensis arginase knockout, correlating the regulation of L-arginine metabolism from both host and parasite. Methods: The metabolites of infected macrophages were analyzed by capillary electrophoresis coupled with mass spectrometry (CE-MS). The metabolic fingerprints analysis provided the dual profile from the host and parasite. Results: We observed increased levels of proline, glutamic acid, glutamine, L-arginine, ornithine, and putrescine in infected-L. amazonensis wild type macrophages, which indicated that this infection induces the polyamine production. Despite this, we observed reduced levels of ornithine, proline, and trypanothione in infected-L. amazonensis arginase knockout macrophages, indicating that this infection reduces the polyamine production. Conclusions: The metabolome fingerprint indicated that Leishmania infection alters the L-arginine/polyamines/trypanothione metabolism inside the host cell and the parasite arginase impacts on L-arginine metabolism and polyamine production, defining the infection fate.

Plasmodium Genomics and Genetics: New Insights into Malaria Pathogenesis, Drug Resistance, Epidemiology, and Evolution

Protozoan Plasmodium parasites are the causative agents of malaria, a deadly disease that continues to afflict hundreds of millions of people every year. Infections with malaria parasites can be asymptomatic, with mild or severe symptoms, or fatal, depending on many factors such as parasite virulence and host immune status. Malaria can be treated with various drugs, with artemisinin-based combination therapies (ACTs) being the first-line choice. Recent advances in genetics and genomics of malaria parasites have contributed greatly to our understanding of parasite population dynamics, transmission, drug responses, and pathogenesis. However, knowledge gaps in parasite biology and host-parasite interactions still remain. Parasites resistant to multiple antimalarial drugs have emerged, while advanced clinical trials have shown partial efficacy for one available vaccine. Here we discuss genetic and genomic studies of Plasmodium biology, host-parasite interactions, population structures, mosquito infectivity, antigenic variation, and targets for treatment and immunization. Knowledge from these studies will advance our understanding of malaria pathogenesis, epidemiology, and evolution and will support work to discover and develop new medicines and vaccines.

Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum

Human macrophage phagolysosome and sandfly midgut provide antagonistic ecological niches for Leishmania parasites to survive and proliferate. Parasites optimize their metabolism to utilize the available inadequate resources by adapting to those environments. Lately, a number of metabolomics studies have revived the interest to understand metabolic strategies utilized by the Leishmania parasite for optimal survival within its hosts. For the first time, we propose a reconstructed genome-scale metabolic model for Leishmania infantum JPCM5, the analyses of which not only captures observations reported by metabolomics studies in other Leishmania species but also divulges novel features of the L. infantum metabolome. Our results indicate that Leishmania metabolism is organized in such a way that the parasite can select appropriate alternatives to compensate for limited external substrates. A dynamic non-essential amino acid motif exists within the network that promotes a restricted redistribution of resources to yield required essential metabolites. Further, subcellular compartments regulate this metabolic re-routing by reinforcing the physiological coupling of specific reactions. This unique metabolic organization is robust against accidental errors and provides a wide array of choices for the parasite to achieve optimal survival.

Genomics of apicomplexan parasites

The increasing prevalence of infections involving intracellular apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium (the causative agents of malaria, toxoplasmosis, and cryptosporidiosis, respectively) represent a significant global healthcare burden. Despite their significance, few treatments are available; a situation that is likely to deteriorate with the emergence of new resistant strains of parasites. To lay the foundation for programs of drug discovery and vaccine development, genome sequences for many of these organisms have been generated, together with large-scale expression and proteomic datasets. Comparative analyses of these datasets are beginning to identify the molecular innovations supporting both conserved processes mediating fundamental roles in parasite survival and persistence, as well as lineage-specific adaptations associated with divergent life-cycle strategies. The challenge is how best to exploit these data to derive insights into parasite virulence and identify those genes representing the most amenable targets. In this review, we outline genomic datasets currently available for apicomplexans and discuss biological insights that have emerged as a consequence of their analysis. Of particular interest are systems-based resources, focusing on areas of metabolism and host invasion that are opening up opportunities for discovering new therapeutic targets.

Insights into the pyrimidine biosynthetic pathway of human malaria parasite Plasmodium falciparum as chemotherapeutic target

Malaria is a major cause of morbidity and mortality in humans. Artemisinins remain as the first-line treatment for Plasmodium falciparum (P. falciparum) malaria although drug resistance has already emerged and spread in Southeast Asia. Thus, to fight this disease, there is an urgent need to develop new antimalarial drugs for malaria chemotherapy. Unlike human host cells, P. falciparum cannot salvage preformed pyrimidine bases or nucleosides from the extracellular environment and relies solely on nucleotides synthesized through the de novo biosynthetic pathway. This review presents significant progress on understanding the de novo pyrimidine pathway and the functional enzymes in the human parasite P. falciparum. Current knowledge in genomics and metabolomics are described, particularly focusing on the parasite purine and pyrimidine nucleotide metabolism. These include gene annotation, characterization and molecular mechanism of the enzymes that are different from the human host pathway. Recent elucidation of the three-dimensional crystal structures and the catalytic reactions of three enzymes: dihydroorotate dehydrogenase, orotate phosphoribosyltransferase, and orotidine 5'-monophosphate decarboxylase, as well as their inhibitors are reviewed in the context of their therapeutic potential against malaria.

PSAMM: A Portable System for the Analysis of Metabolic Models

The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

The broad application of genome-scale metabolic modeling has made it a useful technique for tackling fundamental questions in biological research and engineering. Today over 100 models have been constructed for organisms that carry out a diverse array of metabolic activities spanning all three kingdoms of life. These models, however, have been curated independently following different conventions. The maintenance of model consistency has been challenging due to the lack of consensus in model representation and the absence of integrated modeling software for associating mathematical simulations with the annotation and biological interpretation of metabolic models. To solve this problem, we developed a new software package, PSAMM, and a new model format that incorporates heterogeneous, model-specific annotation information into modular representations of model definitions and simulation settings. PSAMM provides significant advances in standardizing the workflow of model annotation and consistency checking. Compared to existing tools, PSAMM supports more flexible configurations and is more efficient in running constraint-based simulations. All functions of PSAMM are freely available for academic users and can be downloaded from a public Git repository (https://zhanglab.github.io/psamm/) under the GNU General Public License.

Dissecting Leishmania infantum Energy Metabolism - A Systems Perspective

Leishmania infantum, causative agent of visceral leishmaniasis in humans, illustrates a complex lifecycle pertaining to two extreme environments, namely, the gut of the sandfly vector and human macrophages. Leishmania is capable of dynamically adapting and tactically switching between these critically hostile situations. The possible metabolic routes ventured by the parasite to achieve this exceptional adaptation to its varying environments are still poorly understood. In this study, we present an extensively reconstructed energy metabolism network of Leishmania infantum as an attempt to identify certain strategic metabolic routes preferred by the parasite to optimize its survival in such dynamic environments. The reconstructed network consists of 142 genes encoding for enzymes performing 237 reactions distributed across five distinct model compartments. We annotated the subcellular locations of different enzymes and their reactions on the basis of strong literature evidence and sequence-based detection of cellular localization signal within a protein sequence. To explore the diverse features of parasite metabolism the metabolic network was implemented and analyzed as a constraint-based model. Using a systems-based approach, we also put forth an extensive set of lethal reaction knockouts; some of which were validated using published data on Leishmania species. Performing a robustness analysis, the model was rigorously validated and tested for the secretion of overflow metabolites specific to Leishmania under varying extracellular oxygen uptake rate. Further, the fate of important non-essential amino acids in L. infantum metabolism was investigated. Stage-specific scenarios of L. infantum energy metabolism were incorporated in the model and key metabolic differences were outlined. Analysis of the model revealed the essentiality of glucose uptake, succinate fermentation, glutamate biosynthesis and an active TCA cycle as driving forces for parasite energy metabolism and its optimal growth. Finally, through our in silico knockout analysis, we could identify possible therapeutic targets that provide experimentally testable hypotheses.

Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks

MOTIVATION

Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals.

RESULTS

We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genome-scale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.

AVAILABILITY AND IMPLEMENTATION

The MATLAB implementation of the algorithm, compatible with COBRA toolbox v2.0, is available at https://github.com/RamanLab/FastSL CONTACT: kraman@iitm.ac.in

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.

Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.

Flux balance analysis (FBA) is a powerful approach for genome-scale metabolic modeling. It provides metabolic engineers with a tool for manipulating, predicting, and optimizing metabolism for biotechnological and biomedical purposes. However, we posit that it can also be used as tool for fundamental research in understanding and curating metabolic networks. Specifically, by using a genetic algorithm integrated with FBA, we developed a curation approach to identify missing reactions, incomplete reactions, and erroneous reactions. Additionally, it was possible to take advantage of the ensemble information from the genetic algorithm to identify the most critical reactions for curation. We tested our strategy using Mycoplasma gallisepticum as our model organism. Using the genome annotation as the basis, the preliminary genome-scale metabolic model consisted of 446 metabolites involved in 380 reactions. Carrying out our analysis, we found over 80 incorrect reactions and 16 missing reactions. Based upon the guidance of the algorithm, we were able to curate and resolve all discrepancies. The model predicted an average bacterial growth rate of 0.358±0.12 h⁻¹ compared to the experimentally observed 0.244±0.03 h⁻¹. Thus, our approach facilitated the curation of a genome-scale metabolic network and generated a high quality metabolic model.

Functional genomics of Plasmodium falciparum using metabolic modelling and analysis

Plasmodium falciparum is an obligate intracellular parasite and the leading cause of severe malaria responsible for tremendous morbidity and mortality particularly in sub-Saharan Africa. Successful completion of the P. falciparum genome sequencing project in 2002 provided a comprehensive foundation for functional genomic studies on this pathogen in the following decade. Over this period, a large spectrum of experimental approaches has been deployed to improve and expand the scope of functionally annotated genes. Meanwhile, rapidly evolving methods of systems biology have also begun to contribute to a more global understanding of various aspects of the biology and pathogenesis of malaria. Herein we provide an overview on metabolic modelling, which has the capability to integrate information from functional genomics studies in P. falciparum and guide future malaria research efforts towards the identification of novel candidate drug targets.

Library of Apicomplexan Metabolic Pathways: a manually curated database for metabolic pathways of apicomplexan parasites

The Library of Apicomplexan Metabolic Pathways (LAMP, http://www.llamp.net) is a web database that provides near complete mapping from genes to the central metabolic functions for some of the prominent intracellular parasites of the phylum Apicomplexa. This phylum includes the causative agents of malaria, toxoplasmosis and theileriosis-diseases with a huge economic and social impact. A number of apicomplexan genomes have been sequenced, but the accurate annotation of gene function remains challenging. We have adopted an approach called metabolic reconstruction, in which genes are systematically assigned to functions within pathways/networks for Toxoplasma gondii, Neospora caninum, Cryptosporidium and Theileria species, and Babesia bovis. Several functions missing from pathways have been identified, where the corresponding gene for an essential process appears to be absent from the current genome annotation. For each species, LAMP contains interactive diagrams of each pathway, hyperlinked to external resources and annotated with detailed information, including the sources of evidence used. We have also developed a section to highlight the overall metabolic capabilities of each species, such as the ability to synthesize or the dependence on the host for a particular metabolite. We expect this new database will become a valuable resource for fundamental and applied research on the Apicomplexa.

Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans

BACKGROUND

Methanogens are ancient organisms that are key players in the carbon cycle accounting for about one billion tones of biological methane produced annually. Methanosarcina acetivorans, with a genome size of ~5.7 mb, is the largest sequenced archaeon methanogen and unique amongst the methanogens in its biochemical characteristics. By following a systematic workflow we reconstruct a genome-scale metabolic model for M. acetivorans. This process relies on previously developed computational tools developed in our group to correct growth prediction inconsistencies with in vivo data sets and rectify topological inconsistencies in the model.

RESULTS

The generated model iVS941 accounts for 941 genes, 705 reactions and 708 metabolites. The model achieves 93.3% prediction agreement with in vivo growth data across different substrates and multiple gene deletions. The model also correctly recapitulates metabolic pathway usage patterns of M. acetivorans such as the indispensability of flux through methanogenesis for growth on acetate and methanol and the unique biochemical characteristics under growth on carbon monoxide.

CONCLUSIONS

Based on the size of the genome-scale metabolic reconstruction and extent of validated predictions this model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. The reconstructed model is available in spreadsheet and SBML formats to enable dissemination.

Analysis of ultra low genome conservation in Clostridium difficile

Microarray-based comparative genome hybridisations (CGH) and genome sequencing of Clostridium difficile isolates have shown that the genomes of this species are highly variable. To further characterize their genome variation, we employed integration of data from CGH, genome sequencing and putative cellular pathways. Transcontinental strain comparison using CGH data confirmed the emergence of a human-specific hypervirulent cluster. However, there was no correlation between total toxin production and hypervirulent phenotype, indicating the possibility of involvement of additional factors towards hypervirulence. Calculation of C. difficile core and pan genome size using CGH and sequence data estimated that the core genome is composed of 947 to 1,033 genes and a pan genome comprised of 9,640 genes. The reconstruction, annotation and analysis of cellular pathways revealed highly conserved pathways despite large genome variation. However, few pathways such as tetrahydrofolate biosynthesis were found to be variable and could be contributing to adaptation towards virulence such as antibiotic resistance.

A genome-scale metabolic model of Cryptosporidium hominis

The apicomplexan Cryptosporidium is a protozoan parasite of humans and other mammals. Cryptosporidium species cause acute gastroenteritis and diarrheal disease in healthy humans and animals, and cause life-threatening infection in immunocompromised individuals such as people with AIDS. The parasite has a one-host life cycle and commonly invades intestinal epithelial cells. The current genome annotation of C. hominis, the most serious human pathogen, predicts 3884 genes of which ca. 1581 have predicted functional annotations. Using a combination of bioinformatics analysis, biochemical evidence, and high-throughput data, we have constructed a genome-scale metabolic model of C. hominis. The model is comprised of 213 gene-associated enzymes involved in 540 reactions among the major metabolic pathways and provides a link between the genotype and the phenotype of the organism, making it possible to study and predict behavior based upon genome content. This model was also used to analyze the two life stages of the parasite by integrating the stage-specific proteomic data for oocyst and sporozoite stages. Overall, this model provides a computational framework to systematically study and analyze various functional behaviors of C. hominis with respect to its life cycle and pathogenicity.

Graph methods for the investigation of metabolic networks in parasitology

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

Comparative genomics of metabolic networks of free-living and parasitic eukaryotes

Background

Obligate endoparasites often lack particular metabolic pathways as compared to free-living organisms. This phenomenon comprises anabolic as well as catabolic reactions. Presumably, the corresponding enzymes were lost in adaptation to parasitism. Here we compare the predicted core metabolic graphs of obligate endoparasites and non-parasites (free living organisms and facultative parasites) in order to analyze how the parasites' metabolic networks shrunk in the course of evolution.

Results

Core metabolic graphs comprising biochemical reactions present in the presumed ancestor of parasites and non-parasites were reconstructed from the Kyoto Encyclopedia of Genes and Genomes. While the parasites' networks had fewer nodes (metabolites) and edges (reactions), other parameters such as average connectivity, network diameter and number of isolated edges were similar in parasites and non-parasites. The parasites' networks contained a higher percentage of ATP-consuming reactions and a lower percentage of NAD-requiring reactions. Control networks, shrunk to the size of the parasites' by random deletion of edges, were scale-free but exhibited smaller diameters and more isolated edges.

Conclusions

The parasites' networks were smaller than those of the non-parasites regarding number of nodes or edges, but not regarding network diameters. Network integrity but not scale-freeness has acted as a selective principle during the evolutionary reduction of parasite metabolism. ATP-requiring reactions in particular have been retained in the parasites' core metabolism while NADH- or NADPH-requiring reactions were lost preferentially.

Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut?

The comprehension of living organisms in all their complexity poses a major challenge to the biological sciences. Recently, systems biology has been proposed as a new candidate in the development of such a comprehension. The main objective of this paper is to address what systems biology is and how it is practised. To this end, the basic tools of a systems biological approach are explored and illustrated. In addition, it is questioned whether systems biology 'revolutionizes' molecular biology and 'transcends' its assumed reductionism. The strength of this claim appears to depend on how molecular and systems biology are characterised and on how reductionism is interpreted. Doing credit to molecular biology and to methodological reductionism, it is argued that the distinction between molecular and systems biology is gradual rather than sharp. As such, the classical challenge in biology to manage, interpret and integrate biological data into functional wholes is further intensified by systems biology's use of modelling and bioinformatics, and by its scale enlargement.

Eating at the table of another: metabolomics of host-parasite interactions

The application of metabolomics, the global analysis of metabolite levels, to the study of protozoan parasites has become an important tool for understanding the host-parasite relationship and holds promise for the development of direly needed therapeutics and improved diagnostics. Research advances over the past decade have opened the door for a systems biology approach to protozoan parasites with metabolomics, providing a crucial readout of metabolic activity. In this review, we highlight recent metabolomic approaches to protozoan parasites, including metabolite profiling, integration with genomics, transcription, and proteomic analysis, and the use of metabolic fingerprints for the diagnosis of parasitic infections.

Exploring metabolomic approaches to analyse phospholipid biosynthetic pathways in Plasmodium

SUMMARYPlasmodium falciparum, the agent responsible for malaria, is an obligate intracellular protozoan parasite. For proliferation, differentiation and survival, it relies on its own protein-encoding genes, as well as its host cells for nutrient sources. Nutrients and subsequent metabolites are required by the parasites to support their high rate of growth and replication, particularly in the intra-erythrocytic stages of the parasite that are responsible for the clinical symptoms of the disease. Advances in mass spectrometry have improved the analysis of endogenous metabolites and enabled a global approach to identify the parasite's metabolites by the so-called metabolomic analyses. This level of analysis complements the genomic, transcriptomic and proteomic data already available and should allow the identification of novel metabolites, original pathways and networks of regulatory interactions within the parasite, and between the parasite and its hosts. The field of metabolomics is just in its infancy in P. falciparum, hence in this review, we concentrate on the available methodologies and their potential applications for deciphering important biochemical processes of the parasite, such as the astonishingly diverse phospholipid biosynthesis pathways. Elucidating the regulation of the biosynthesis of these crucial metabolites could help design of future anti-malarial drugs.

'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.

EFICAz2: enzyme function inference by a combined approach enhanced by machine learning

BACKGROUND

We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment.

RESULTS

We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz2, exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz2 and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz2 generates considerably more unique assignments than KEGG.

CONCLUSION

Performance benchmarks and the comparison with KEGG demonstrate that EFICAz2 is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz2 web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.html.

A unique dual activity amino acid hydroxylase in Toxoplasma gondii

The genome of the protozoan parasite Toxoplasma gondii was found to contain two genes encoding tyrosine hydroxylase; that produces l-DOPA. The encoded enzymes metabolize phenylalanine as well as tyrosine with substrate preference for tyrosine. Thus the enzymes catabolize phenylalanine to tyrosine and tyrosine to l-DOPA. The catalytic domain descriptive of this class of enzymes is conserved with the parasite enzyme and exhibits similar kinetic properties to metazoan tyrosine hydroxylases, but contains a unique N-terminal extension with a signal sequence motif. One of the genes, TgAaaH1, is constitutively expressed while the other gene, TgAaaH2, is induced during formation of the bradyzoites of the cyst stages of the life cycle. This is the first description of an aromatic amino acid hydroxylase in an apicomplexan parasite. Extensive searching of apicomplexan genome sequences revealed an ortholog in Neospora caninum but not in Eimeria, Cryptosporidium, Theileria, or Plasmodium. Possible role(s) of these bi-functional enzymes during host infection are discussed.

Reconstruction of biochemical networks in microorganisms

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

Caveat emptor: limitations of the automated reconstruction of metabolic pathways in Plasmodium

The functional reconstruction of metabolic pathways from an annotated genome is a tedious and demanding enterprise. Automation of this endeavor using bioinformatics algorithms could cope with the ever-increasing number of sequenced genomes and accelerate the process. Here, the manual reconstruction of metabolic pathways in the functional genomic database of Plasmodium falciparum--Malaria Parasite Metabolic Pathways--is described and compared with pathways generated automatically as they appear in PlasmoCyc, metaSHARK and the Kyoto Encyclopedia for Genes and Genomes. A critical evaluation of this comparison discloses that the automatic reconstruction of pathways generates manifold paths that need an expert manual verification to accept some and reject most others based on manually curated gene annotation.