Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks.
ABSTRACT: The metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterization is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organism's annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible until now. We explain and extend the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell.
Project description:In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.
Project description:BACKGROUND: Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties. RESULTS: We reconstructed a genome-scale metabolic network of S. aureus strain N315. This reconstruction, termed iSB619, consists of 619 genes that catalyze 640 metabolic reactions. For 91% of the reactions, open reading frames are explicitly linked to proteins and to the reaction. All but three of the metabolic reactions are both charge and elementally balanced. The reaction list is the most complete to date for this pathogen. When the capabilities of the reconstructed network were analyzed in the context of maximal growth, we formed hypotheses regarding growth requirements, the efficiency of growth on different carbon sources, and potential drug targets. These hypotheses can be tested experimentally and the data gathered can be used to improve subsequent versions of the reconstruction. CONCLUSION: iSB619 represents comprehensive biochemically and genetically structured information about the metabolism of S. aureus to date. The reconstructed metabolic network can be used to predict cellular phenotypes and thus advance our understanding of a troublesome pathogen.
Project description:Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models (iCyt773 and iSyn731) for two phylogenetically related cyanobacterial species, namely Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. All reactions are elementally and charge balanced and localized into four different intracellular compartments (i.e., periplasm, cytosol, carboxysome and thylakoid lumen) and biomass descriptions are derived based on experimental measurements. Newly added reactions absent in earlier models (266 and 322, respectively) span most metabolic pathways with an emphasis on lipid biosynthesis. All thermodynamically infeasible loops are identified and eliminated from both models. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bio-production potentials are reflected in both models.
Project description:Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions.An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth.E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.
Project description:The rapid development of high-throughput techniques and expansion of bacterial databases have accelerated efforts to bring plant microbiomes into cultivation. We introduced plant-only-based culture media as a successful candidate to mimic the nutritional matrices of plant roots. We herein employed a G3 PhyloChip microarray to meticulously characterize the culture-dependent and -independent bacterial communities of the maize root compartments, the endo- and ecto-rhizospheres. An emphasis was placed on the preference of the growth of unculturable candidate divisions/phyla on plant-only-based culture media over standard culture media (nutrient agar). A total of 1,818 different operational taxonomic units (OTUs) were resolved representing 67 bacterial phyla. Plant-only-based culture media displayed particular affinity towards recovering endophytic over ectophytic rhizobacteria. This was shown by the slightly higher recovery of CFUs for endophytes on plant-only-based culture media (26%) than on standard culture media (10%) as well as the higher taxa richness and numbers of exclusive families of unculturable divisions/phyla. Out of 30 bacterial phyla (comprising >95% of the whole population), 13 were of a significantly higher incidence on plant-only-based culture media, 6 phyla of which were not-yet-cultured (Atribacteria, OP9; Dependentiae, TM6; Latescibacteria, WS3; Marinimicrobia, SAR406; Omnitrophica, OP3; BRC1). Furthermore, plant-only-based culture media significantly enriched less abundant and/or hard-to-culture bacterial phyla (Acidobacteria, Gemmatimonadetes, and Tenericutes). These results present conclusive evidence of the ability of plant-only-based culture media to bring the plant-fed in situ microbiome into the status of plant-fed in vitro cultures, and to widen the scope of cultivation of heretofore-unculturable bacterial divisions/phyla.
Project description:Extracting fungal mRNA from ectomycorrhizas (ECMs) and forest soil samples for monitoring in situ metabolic activities is a significant challenge when studying the role of ECMs in biogeochemical cycles. A robust, simple, rapid, and effective method was developed for extracting RNA from rhizospheric soil and ECMs by adapting previous grinding and lysis methods. The quality and yield of the extracted RNA were sufficient to be used for reverse transcription. RNA extracted from ECMs of Lactarius quietus in a 100-year-old oak stand was used to construct a cDNA library and sequence expressed sequence tags. The transcripts of many genes involved in primary metabolism and in the degradation of organic matter were found. The transcription levels of four targeted fungal genes (glutamine synthase, a general amino acid transporter, a tyrosinase, and N-acetylhexosaminidase) were measured by quantitative reverse transcription-PCR in ECMs and in the ectomycorrhizospheric soil (the soil surrounding the ECMs containing the extraradical mycelium) in forest samples. On average, levels of gene expression for the L. quietus ECM root tips were similar to those for the extraradical mycelium, although gene expression varied up to 10-fold among the samples. This study demonstrates that gene expression from ECMs and soil can be analyzed. These results provide new perspectives for investigating the role of ectomycorrhizal fungi in the functioning of forest ecosystems.
Project description:Many cells are small and rounded on soft extracellular matrices (ECM), elongated on stiffer ECMs, and flattened on hard ECMs. Cells also migrate up stiffness gradients (durotaxis). Using a hybrid cellular Potts and finite-element model extended with ODE-based models of focal adhesion (FA) turnover, we show that the full range of cell shape and durotaxis can be explained in unison from dynamics of FAs, in contrast to previous mathematical models. In our 2D cell-shape model, FAs grow due to cell traction forces. Forces develop faster on stiff ECMs, causing FAs to stabilize and, consequently, cells to spread on stiff ECMs. If ECM stress further stabilizes FAs, cells elongate on substrates of intermediate stiffness. We show that durotaxis follows from the same set of assumptions. Our model contributes to the understanding of the basic responses of cells to ECM stiffness, paving the way for future modeling of more complex cell-ECM interactions.
Project description:The existence and function of unculturable microorganisms are necessary to explain patterns of microbial diversity and investigate the assembly and succession of the complex microbial community. Chinese traditional alcoholic fermentation starter contains a complex microbial community harboring unculturable species that control the microbial diversity and have distinct functions. In this study, we revealed the presence, functions, and interactions of these unculturable species. Results of microbial diversity revealed by culture-dependent and metagenomic sequencing methods identified unculturable species and the potential functional species. Unculturable Saccharomyces cerevisiae and Lactobacillus sp. had a strong ability to form biofilms and co-existed as a mixed-species biofilm in the starter community. Using a hydrolase activity assay and fortified fermentation, we determined that the function of S. cerevisiae and Lactobacillus sp. to produce ethanol and flavor compounds. Widespread microbial interactions were identified among the biofilm isolates. S. cerevisiae was the main component of the biofilm and dominated the metabolic activities in the mixed-species biofilm. The environmental adaptability and biomass of Lactobacillus sp. were increased through its interaction with S. cerevisiae. The mixed biofilm of S. cerevisiae and Lactobacillus sp. also provides a tool for correlating microbial diversity patterns with their function in the alcoholic fermentation starter, and may provide a new understanding of fermentation mechanisms. Formation of a mixed-species biofilm represents a strategy for unculturable species to survive in competition with other microbes in a complex community.
Project description:Cell-free systems present a significant opportunity to harness the metabolic potential of diverse organisms. Removing the cellular context provides the ability to produce biological products without the need to maintain cell viability and enables metabolic engineers to explore novel chemical transformation systems. Crude extracts maintain much of a cell's capabilities. However, only limited tools are available for engineering the contents of the extracts used for cell-free systems. Thus, our ability to take full advantage of the potential of crude extracts for cell-free metabolic engineering is constrained. Here, we employ Multiplex Automated Genomic Engineering (MAGE) to tag proteins for selective depletion from crude extracts so as to specifically direct chemical production. Specific edits to central metabolism are possible without significantly impacting cell growth. Selective removal of pyruvate degrading enzymes resulted in engineered crude lysates that are capable of up to 40-fold increases in pyruvate production when compared to the non-engineered extract. The described approach melds the tools of systems and synthetic biology to showcase the effectiveness of cell-free metabolic engineering for applications like bioprototyping and bioproduction.
Project description:The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.