"Omics" technologies are rapidly generating high amounts of data at varying levels of biological detail. In addition, there is a rapidly growing literature and accompanying databases that compile this information. This has provided the basis for the assembly of genome-scale metabolic networks for various microbial and eukaryotic organisms [1
]. These network reconstructions serve as manually curated knowledge bases of biological information as well as mathematical representations of biochemical components and interactions specific to each organism.
A genome-scale network reconstruction is a structured collection of genes, proteins, biochemical reactions, and metabolites determined to exist and operate within a particular organism. This network can be converted into a predictive model that enables in silico
simulations of allowable network states based on governing physico-chemical and genetic constraints [12
]. A wide range of constraint-based methods have been developed and applied in order to analyze network metabolic capabilities under different environmental and genetic conditions [13
]. These methods have been extensively used to study genome-scale metabolic networks and have successfully predicted, for example, optimal metabolic states, gene deletion lethality, and adaptive evolutionary endpoints [14
]. Most of these applications utilize optimization-based methods such as flux balance analysis (FBA) to explore the metabolic flux space. However, the behavior of genome-scale metabolic networks can also be studied using unbiased approaches such as uniform random sampling of steady-state flux distributions [17
]. Instead of identifying a single optimal flux distribution based on a given optimization criterion (e.g. biomass production), these methods allow statistical analysis of a large range of possible alternative flux solutions determined by constraints imposed on the network. Sampling methods have been previously used to study global organization of E. coli
] as well as to identify candidate disease states in the cardiomyocyte mitochondria [19
Network reconstructions provide a structured framework to systematically integrate and analyze disparate datasets including transcriptomic, proteomic, metabolomic, and fluxomic data. Metabolomic data is one of the more relevant data types for this type of analysis as network reconstructions define the biochemical links between metabolites, and recent advancements in analytical technologies have allowed increasingly comprehensive intracellular and extracellular metabolite level measurements [20
]. The metabolome is the set of metabolites present under a given physiological condition at a particular time and is the culminating phenotype resulting from various "upstream" control mechanisms of metabolic processes. Of particular interest to this present study are the quantitative profiles of metabolites that are secreted into the extracellular environment by cells under different conditions. Recent advances in profiling the extracellular metabolome (EM) have allowed obtaining insightful biological information on cellular metabolism without disrupting the cell itself. This information can be obtained through various analytical detection, identification, and quantization techniques for a variety of systems ranging from unicellular model organisms to human biofluids [20
Metabolite secretion by a cell reflects its internal metabolic state, and its composition varies in response to genetic or experimental perturbations due to changes in intracellular pathway activities involved in the production and utilization of extracellular metabolites [21
]. Variations in metabolic fluxes can be reflected in EM changes which can, in turn, provide insight into the intracellular pathway activities related to metabolite secretion. The extracellular metabolomic approach has already shown promise in a variety of applications, including capturing detailed metabolite biomarker variations related to disease and drug-induced states and characterizing gene functions in yeast [24
]. However, interpreting changes in the extracellular metabolome can be challenging due to the indirect relationship between the proximal cause of the change (e.g. a mutation) and metabolite secretion.
Since metabolic networks describe mechanistic, biochemical links between metabolites, integration of such data can allow a systematic approach to identifying altered pathways linked to observed quantitative changes in secretion profiles. Measured secretion rates of major byproduct metabolites can be applied as additional exchange flux constraints that define observed metabolic behavior. For example, a recent study integrating small-scale EM data with a genome-scale yeast model correctly predicted oxygen consumption and ethanol production capacities in mutant strains with respiratory deficiencies [28
]. The respiratory deficient mutant study used high accuracy measurements for a small number of major byproduct secretion rates together with an optimization-based method that are well suited for such data. Here, we expand the application range of the model-based method used in [28
] to extracellular metabolome profiles, which represent a temporal snapshot of the relative abundance for a larger number of secreted metabolites. Our approach is complementary to statistical (i.e. "top-down") approaches to metabolome analysis [29
] and can potentially be used in applications such as biofluid-based diagnostics or large-scale characterization of mutants strains using metabolite profiles.
In this study, we implemented a constraint-based sampling approach on an updated genome-scale network of yeast metabolism to systematically determine how EM level variations are linked to global changes in intracellular metabolic flux states. By using a sampling-based network approach and statistical methods (Figure ), EM changes were linked to systemic intracellular flux perturbations in an unbiased manner without relying on defining single optimal flux distributions as was used in the previously mentioned study [28
]. The inferred perturbations in intracellular reaction fluxes were further analyzed using reporter metabolite and subsystem (i.e., metabolic pathway) approaches [30
] in order to identify dominant metabolic features that are collectively perturbed (Figure ). The sampling-based approach also has the additional benefit of being less sensitive to inaccuracies in metabolite secretion profiles than optimization-based methods and thus can more readily be used in settings such as biofluid metabolome analysis.
Figure 1 Schematic illustrating the integration of exometabolomic (EM) data with the constraint-based framework. (A) Cells are subjected to genetic and/or environmental perturbations to secrete metabolite patterns unique to that condition. (B) EM is detected, (more ...)
Figure 2 Schematic of sampling and scoring analysis to determine intracellular flux changes. (A) Reaction fluxes are sampled for two conditions. (B & C) Sample of flux differences is calculated by selecting random flux values from each condition to obtain (more ...)
This study was divided into two parts and describes: (i) the reconstruction and validation of an expanded S. cerevisiae
metabolic network, i
MM904; and (ii) the systematic inference of intracellular metabolic states from two yeast EM data sets using a constraint-based sampling approach. The first EM data set compares wild type yeast to the gdh1/GDH2
(glutamate dehydrogenase) strain [31
], which indicated good agreement between predicted metabolic changes of intracellular metabolite levels and fluxes [31
]. The second EM data set focused on secreted amino acid measurements from a separate study of yeast cultured in different ammonium and potassium concentrations [33
]. We analyzed the EM data to gain further insight into perturbed ammonium assimilation processes as well as metabolic states relating potassium limitation and ammonium excess conditions to one another. The model-based analysis of both separately published extracellular metabolome datasets suggests a relationship between glutamate, threonine and folate metabolism, which are collectively perturbed when ammonium assimilation processes are broadly disrupted either by environmental (excess ammonia) or genetic (gene deletion/overexpression) perturbations. The methods herein present an approach to interpreting extracellular metabolome data and associating these measured secreted metabolite variations to changes in intracellular metabolic network states.