MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks

BackgroundConstraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment.ResultsHere, we present Acorn, an open source (GNU GPL) grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users) increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a web server allowing constraint based simulations of the genome scale metabolic reaction networks of E. coli, S. cerevisiae and M. tuberculosis.ConclusionsAcorn is a free software package, which can be installed by research groups to create a web based environment for computer simulations of genome scale metabolic reaction networks. It facilitates shared access to models and creation of publicly available constraint based modelling resources.

Metabolic pathways in the post-genome era

Along with genes and proteins, metabolites play important roles in sustaining life. Their functions include "primary" functions in metabolism and energy storage, as well as "secondary" functions in cell-to-cell signaling, metal acquisition, and virulence. There remains much to be learned about the in vivo roles of metabolites. Approaches that accelerate measurement of metabolite levels directly from cells and tissues should increase our understanding of the diverse roles of metabolites and potentially lead to discovery of novel metabolites and metabolic pathways. Metabolomics is an important comparative tool to study global metabolite levels in samples under various conditions. In this unit, the steps needed to perform a mass spectrometry (MS)--based untargeted metabolomics experiment using bacterial supernatants are detailed. In contrast to a targeted metabolomics experiment, which measures ions from known metabolites, an untargeted metabolomics experiment registers all ions within a certain mass range, including ions belonging to structurally novel metabolites. The protocols in this unit describe the conditions necessary for analyzing hydrophobic metabolites and provide an example of how to structurally characterize a novel metabolite.

Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.

MotivationThe integration of genome-scale metabolic and regulatory networks has received significant interest in cancer systems biology. However, the identification of lethal genetic interventions in these integrated models remains challenging due to the combinatorial explosion of potential solutions. To address this, we developed the genetic Minimal Cut Set (gMCS) framework, which computes synthetic lethal interactions—minimal sets of gene knockouts that are lethal for cellular proliferation- in genome-scale metabolic networks with signed directed acyclic regulatory pathways. Here, we present a novel formulation to calculate genetic Minimal Intervention Sets, gMISs, which incorporate both gene knockouts and knock-ins.ResultsWith our gMIS approach, we assessed the landscape of lethal genetic interactions in human cells, capturing interventions beyond synthetic lethality, including synthetic dosage lethality and tumor suppressor gene complexes. We applied the concept of synthetic dosage lethality to predict essential genes in cancer and demonstrated a significant increase in sensitivity when compared to large-scale gene knockout screen data. We also analyzed tumor suppressors in cancer cell lines and identified lethal gene knock-in strategies. Finally, we demonstrate how gMISs can help uncover potential therapeutic targets, providing examples in hematological malignancies.Availability and implementationThe gMCSpy Python package now includes gMIS functionalities. Access: https://github.com/PlanesLab/gMCSpy.

BackgroundThe genomic information of a species allows for the genome-scale reconstruction of its metabolic capacity. Such a metabolic reconstruction gives support to metabolic engineering, but also to integrative bioinformatics and visualization. Sequence-based automatic reconstructions require extensive manual curation, which can be very time-consuming. Therefore, we present a method to accelerate the time-consuming process of network reconstruction for a query species. The method exploits the availability of well-curated metabolic networks and uses high-resolution predictions of gene equivalency between species, allowing the transfer of gene-reaction associations from curated networks.ResultsWe have evaluated the method using Lactococcus lactis IL1403, for which a genome-scale metabolic network was published recently. We recovered most of the gene-reaction associations (i.e. 74 – 85%) which are incorporated in the published network. Moreover, we predicted over 200 additional genes to be associated to reactions, including genes with unknown function, genes for transporters and genes with specific metabolic reactions, which are good candidates for an extension to the previously published network. In a comparison of our developed method with the well-established approach Pathologic, we predicted 186 additional genes to be associated to reactions. We also predicted a relatively high number of complete conserved protein complexes, which are derived from curated metabolic networks, illustrating the potential predictive power of our method for protein complexes.ConclusionWe show that our methodology can be applied to accelerate the reconstruction of genome-scale metabolic networks by taking optimal advantage of existing, manually curated networks. As orthology detection is the first step in the method, only the translated open reading frames (ORFs) of a newly sequenced genome are necessary to reconstruct a metabolic network. When more manually curated metabolic networks will become available in the near future, the usefulness of our method in network prediction is likely to increase.

Detecting breakdown points in metabolic networks

Background Candidate markers for Crohn’s Disease (CD) may be identified via gene expression-based construction of metabolic networks (MN). These can computationally describe gene-protein-reaction associations for entire tissues and also predict the flux of reactions (rate of turnover of specific molecules via a metabolic pathway). Recon3D is the most comprehensive human MN to date. We used publicly available CD transcriptomic data along with Recon3D to identify metabolites as potential diagnostic and prognostic biomarkers. Methods Terminal ileal gene expression profiles (36,372 genes; 218 CD. 42 controls) from the RISK cohort (Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn’s Disease) and their transcriptomic abundances were used. Recon3D was pruned to only include RISK dataset transcripts which determined metabolic reaction linkage with transcriptionally active genes. Flux balance analysis (FBA) was then run using RiPTiDe with context specific transcriptomic data to further constrain genes (Figure 1). RiPTiDe was independently run on transcriptomic data from both CD and controls. From the pruned and constricted MN obtained, reactions were extracted for further analysis. Results After applying the necessary constraints to modify Recon3D, 527 CD and 537 control reactions were obtained. Reaction comparison with a publicly available list of healthy small intestinal epithelial reactions (n=1282) showed an overlap of 80 CD and 84 control reactions. These were then further grouped based on their metabolic pathways. RiPTiDe identified context specific metabolic pathway activity without supervision and the percentage of forward, backward, and balanced reactions for each metabolic pathway (Figure 2). The metabolite concentrations in the small intestine was altered among CD patients. Notably, the citric acid cycle and malate-aspartate shuttle were affected, highlighting changes in mitochondrial metabolic pathways. This is illustrated by changes in the number of reactions at equilibrium between CD and control. Conclusions The results are relevant as cytosolic acetyl-CoA is needed for fatty acid synthesis and is obtained by removing citrate from the citric acid cycle. An intermediate removal from the cycle has significant cataplerotic effects. The malate-aspartate shuttle also allows electrons to move across the impermeable membrane in the mitochondria (fatty acid synthesis location). These findings are reported by previously published studies where gene expression for fatty acid synthesis is altered in CD patients along with mitochondrial metabolic pathway changes, resulting in altered cell homeostasis. In-depth analysis is currently underway with our work supporting the utility of potential metabolic biomarkers for CD diagnosis, management and improved care.

BackgroundInfections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction.ResultsWe have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection.ConclusionOur analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains.

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/β-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.

The human extended mitochondrial metabolic network: New hubs from lipids

Complex enzymes with multiple catalytic activities are hypothesized to have evolved from more primitive precursors. Global analysis of the Phytophthora sojae genome using conservative criteria for evaluation of complex proteins identified 273 novel multifunctional proteins that were also conserved in P. ramorum. Each of these proteins contains combinations of protein motifs that are not present in bacterial, plant, animal, or fungal genomes. A subset of these proteins were also identified in the two diatom genomes, but the majority of these proteins have formed after the split between diatoms and oomycetes. Documentation of multiple cases of domain fusions that are common to both oomycetes and diatom genomes lends additional support for the hypothesis that oomycetes and diatoms are monophyletic. Bifunctional proteins that catalyze two steps in a metabolic pathway can be used to infer the interaction of orthologous proteins that exist as separate entities in other genomes. We postulated that the novel multifunctional proteins of oomycetes could function as potential Rosetta Stones to identify interacting proteins of conserved metabolic and regulatory networks in other eukaryotic genomes. However ortholog analysis of each domain within our set of 273 multifunctional proteins against 39 sequenced bacterial and eukaryotic genomes, identified only 18 candidate Rosetta Stone proteins. Thus the majority of multifunctional proteins are not Rosetta Stones, but they may nonetheless be useful in identifying novel metabolic and regulatory networks in oomycetes. Phylogenetic analysis of all the enzymes in three pathways with one or more novel multifunctional proteins was conducted to determine the probable origins of individual enzymes. These analyses revealed multiple examples of horizontal transfer from both bacterial genomes and the photosynthetic endosymbiont in the ancestral genome of Stramenopiles. The complexity of the phylogenetic origins of these metabolic pathways and the paucity of Rosetta Stones relative to the total number of multifunctional proteins suggests that the proteome of oomycetes has few features in common with other Kingdoms.

PhDSeeker: Pheromone-Directed Seeker for metabolic pathways