Metabolic Pathway analysis

Metabolic pathway analysis is a key method to study a metabolism in its steady state, and the concept of elementary fluxes (EFs) plays a major role in the analysis of a network in terms of non-decomposable pathways. The supports of the EFs contain in particular those of the elementary flux modes (EFMs), which are the support-minimal pathways, and EFs coincide with EFMs when the only flux constraints are given by the irreversibility of certain reactions. Practical use of both EFMs and EFs has been hampered by the combinatorial explosion of their number in large, genomescale systems. The EFs give the possible pathways in a steady state but the real pathways are limited by biological constraints, such as thermodynamic or, more generally, kinetic constraints and regulatory constraints from the genetic network. We provide results on the mathematical structure and geometrical characterization of the solution space in the presence of such biological constraints (which is no longer a convex polyhedral cone or a convex polyhedron) and revisit the concept of EFMs and EFs in this framework. We show that most of the results depend only on very general properties of compatibility of constraints with vector signs: either sign-invariance, satisfied by regulatory constraints, or sign-monotonicity (a stronger property), satisfied by thermodynamic and kinetic constraints. We show in particular that the solution space for sign-monotone constraints is a union of particular faces of the original polyhedral cone or polyhedron and that EFs still coincide with EFMs and are just those of the original EFs that satisfy the constraint, and we show how to integrate their computation efficiently in the double description method, the most widely used method in the tools dedicated to EFs computation. We show that, for sign-invariant constraints, the situation is more complex: the solution space is a disjoint union of particular semi-open faces (i.e., without some of their own faces of lesser dimension) of the original polyhedral cone or polyhedron and, if EFs are still those of the original EFs that satisfy the constraint, their computation cannot be incrementally integrated into the double description method, and the result is not true for EFMs, that are in general strictly more numerous than those of the original EFMs that satisfy the constraint.

Background/Aims: Clinical manifestations of Niemann Pick type-C1 (NP-C1) disease include neonatal hepatosplenomegaly, and in some patients progressive liver dysfunction and failure. This study involved a H-NMR linked metabolomics analysis of liver samples collected from a NP-C1 disease mutant mouse model in order to explore time-dependent imbalances in metabolic pathways associated with NP-C1 liver dysfunction, including fibrosis. Methods: NP-C1 mutant (Npc1-/-; NP-C1), control (Npc1 +/+ ; WT) and NP-C1 heterozygous mice (Npc1 +/-; HET) were generated from heterozygote matings. Aqueous extracts of these liver samples collected at time-points of 3, 6, 9 and 11 weeks were subjected to highresolution NMR analysis, and multivariate (MV) metabolomics analyses of datasets acquired were performed. Results: A MV random forests (RFs) model effectively discriminated between the NP-C1 and a combined HC/HET hepatic NMR profiles with very high predictive accuracy and reliability. Key distinguishing features included significant upregulations in the hepatic concentrations of phenylalanine, tyrosine, glutamate, lysine/ornithine, valine, threonine, and hypotaurine/methionine, and diminished levels of nicotinate/niacinamide, inosine, phosphoenolpyruvate and 3-hydroxyphenylacetate. Quantitative pathway topological analysis confirmed that imbalances in tyrosine biosynthesis, hepatic phenylalanine, tyrosine, glutamate/glutamine and nicotinate/niacinamide metabolism were involved in the pathogenesis of NP-C1 disease-associated liver dysfunction/damage. Conclusions: 1 H NMR-linked metabolomics analysis provides valuable biomarker information regarding hepatic dysfunction or damage in NP-C1 disease.

Clinical manifestations of Niemann Pick type-C1 (NP-C1) disease include neonatal hepatosplenomegaly, and in some patients progressive liver dysfunction and failure. This study involved a 1H-NMR linked metabolomics analysis of liver samples collected from a NP-C1 disease mutant mouse model in order to explore time-dependent imbalances in metabolic pathways associated with NP-C1 liver dysfunction, including fibrosis. NP-C1 mutant (Npc1-/-; NP-C1), control (Npc1+/+; WT) and NP-C1 heterozygous mice (Npc1+/-; HET) were generated from heterozygote matings. Aqueous extracts of these liver samples collected at time-points of 3, 6, 9 and 11 weeks were subjected to high-resolution NMR analysis, and multivariate (MV) metabolomics analyses of datasets acquired were performed. A MV random forests (RFs) model effectively discriminated between the NP-C1 and a combined HC/HET hepatic NMR profiles with very high predictive accuracy and reliability. Key distinguishing features included significant up...

OptFlux is an open-source and extensible workbench for Metabolic Engineering (ME) tasks. Since 2012, OptFlux adoption has been steadily increasing among users, making it one of the reference go-to platforms among the ME community. The workbench supports common ME tasks such as phenotype prediction for both wild-type and mutant genotypes, metabolic control analysis and pathway analysis as well as strain optimization procedures. Moreover, a visualization plug-in is included, allowing the navigation and edition of biochemical network layouts in a multitude of standard formats. This plug-in also allows the overlap of specific phenotypic conditions in the network layouts, providing an intuitive mechanism to explore and understand the associated flux distributions. Navigation between multiple layouts is also included. However, for more specialized applications, such as the inclusion of experimental data, this framework was still lagging behind. In this work, the current visualization plat...

Clinical manifestations of Niemann Pick type-C1 (NP-C1) disease include neonatal hepatosplenomegaly, and in some patients progressive liver dysfunction and failure. This study involved a 1H-NMR linked metabolomics analysis of liver samples collected from a NP-C1 disease mutant mouse model in order to explore time-dependent imbalances in metabolic pathways associated with NP-C1 liver dysfunction, including fibrosis. NP-C1 mutant (Npc1-/-; NP-C1), control (Npc1+/+; WT) and NP-C1 heterozygous mice (Npc1+/-; HET) were generated from heterozygote matings. Aqueous extracts of these liver samples collected at time-points of 3, 6, 9 and 11 weeks were subjected to high-resolution NMR analysis, and multivariate (MV) metabolomics analyses of datasets acquired were performed. A MV random forests (RFs) model effectively discriminated between the NP-C1 and a combined HC/HET hepatic NMR profiles with very high predictive accuracy and reliability. Key distinguishing features included significant up...

Genome-scale stoichiometric models (GSMs) have been widely utilized to predict and understand cellular metabolism. GSMs and the flux predictions resulting from them have proven indispensable to fields ranging from metabolic engineering to human disease. Nonetheless, it is challenging to parse these flux predictions due to the inherent size and complexity of the GSMs. Several previous approaches have reduced this complexity by identifying key pathways contained within the genome-scale flux predictions. However, a reduction method that overlays carbon atom transitions on stoichiometry and flux predictions is lacking. To fill this gap, we developed NetFlow, an algorithm that leverages genome-scale carbon mapping to extract and quantitatively distinguish biologically relevant metabolic pathways from a given genome-scale flux prediction. NetFlow extends prior approaches by utilizing both full carbon mapping and context-specific flux predictions. Thus, NetFlow is uniquely able to quantitatively distinguish between biologically relevant pathways of carbon flow within the given flux map. NetFlow simulates 13 C isotope labeling experiments to calculate the extent of carbon exchange, or carbon yield, between every metabolite in the given GSM. Based on the carbon yield, the carbon flow to or from any metabolite or between any pair of metabolites of interest can be isolated and readily visualized. The resulting pathways are much easier to interpret, which enables an in-depth mechanistic understanding of the metabolic phenotype of interest. Here, we first demonstrate NetFlow with a simple network. We then depict the utility of NetFlow on a model of central carbon metabolism in E. coli. Specifically, we isolated the production pathway for succinate synthesis in this model and the metabolic mechanism driving the predicted increase in succinate yield in a double knockout of E. coli. Finally, we describe the application of NetFlow to a GSM of lycopene-producing E. coli, which enabled the rapid identification of the mechanisms behind the measured increases in lycopene production following single, double, and triple knockouts.

Clinical manifestations of Niemann Pick type-C1 (NP-C1) disease include neonatal hepatosplenomegaly, and in some patients progressive liver dysfunction and failure. This study involved a 1H-NMR linked metabolomics analysis of liver samples collected from a NP-C1 disease mutant mouse model in order to explore time-dependent imbalances in metabolic pathways associated with NP-C1 liver dysfunction, including fibrosis. NP-C1 mutant (Npc1-/-; NP-C1), control (Npc1+/+; WT) and NP-C1 heterozygous mice (Npc1+/-; HET) were generated from heterozygote matings. Aqueous extracts of these liver samples collected at time-points of 3, 6, 9 and 11 weeks were subjected to high-resolution NMR analysis, and multivariate (MV) metabolomics analyses of datasets acquired were performed. A MV random forests (RFs) model effectively discriminated between the NP-C1 and a combined HC/HET hepatic NMR profiles with very high predictive accuracy and reliability. Key distinguishing features included significant up...

Background Heterotrophic fermentation using simple sugars such as glucose is an established and cost-effective method for synthesizing bioproducts from bacteria, yeast and algae. Organisms incapable of metabolizing glucose have limited applications as cell factories, often despite many other advantageous characteristics. Therefore, there is a clear need to investigate glucose metabolism in potential cell factories. One such organism, with a unique metabolic network and a propensity to synthesize highly reduced compounds as a large fraction of its biomass, is the marine diatom Phaeodactylum tricornutum (Pt). Although Pt has been engineered to metabolize glucose, conflicting lines of evidence leave it unresolved whether Pt can natively consume glucose. Results Isotope labeling experiments in which Pt was mixotrophically grown under light on 100% U-13C glucose and naturally abundant (~99% 12C) dissolved inorganic carbon resulted in proteinogenic amino acids with an average 13C-enrichme...