Genome-scale mapping models and algorithms for stationary and instationary MFA-based metabolic flux elucidation

Metabolic models used in 13C metabolic flux analysis (13C-MFA) generally include a limited number of reactions primarily from central metabolism, neglecting degradation pathways and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up core bacterial and cyanobacterial mapping models to a genome-scale carbon mapping (GSCM) models, imEco726 (668 reaction and 566 metabolites) and imSyn711 (731 reactions, 679 metabolites) for E. coli and Synechocystis PCC 6803, respectively, representing a ten-fold increase in model size. The GSCM models are constructed using the CLCA algorithm following reduction of the corresponding metabolic models, iAF1260 and iSyn731, using experimentally measured biomass and product yield during growth on glucose and CO2, respectively. The mapping models are then deployed for flux elucidation using isotopic steady-state MFA for E. coli to recapitulate experimentally observed labeling distributions of 12 measured amino acids, and isotopic instationary MFA for Synechocystis, to recapitulate labeling dynamics of 15 central metabolites. In both models, 80% of all fluxes varies less than onetenth of the basis carbon substrate uptake rate primarily due to the flux coupling with biomass production. Overall, we find that both the topology and estimated values of the metabolic fluxes remain largely consistent between the core and GSMM models for E. coli. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Significant flux range shifts were observed using a GSCM model compared to a core model in Synechocystis arising from the inclusion of 18 novel carbon paths in the GSCM model. In particular, no flux is channeled through the oxidative pentose phosphate pathway, resulting in a reduced carbon fixation flux. In addition, a higher flux is seen through the Transaldolase reaction, serving as a bypass route to Fructose bisphosphatase. Serine and glycine are found to be synthesized from 3-phosphoglycerate and the photorespiratory pathway, respectively. Pyruvate is synthesized exclusively via the malate bypass with trace contributions from pyruvate kinase. Furthermore, trace flux is predicted through the lower TCA cycle indicating either pathway incompleteness or dispensability during photoautotrophic growth. Despite these differences, 80% of all reactions in both genome-scale models are resolved to within 10% of the respective substrate uptake rate due to the presence of 411 and 407 growth-coupled reactions in E. coli and Synechocystis, respectively. Flux ranges obtained with GSCM models are compared with those obtained upon projecting core model ranges on to a genome-scale metabolic model to elucidate the loss of information and erroneous biological inferences about pathway usage arising from assumptions contained within core models, reaffirming the importance of using mapping models with global carbon path coverage in 13C metabolic flux analysis

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted

Redistribution of metabolic fluxes in Chlorella protothecoides by variation of media nitrogen concentration

In this study, the Elementary Metabolite Unit (EMU) algorithm was employed to calculate intracellular fluxes for Chlorella protothecoides using previously generated growth and mass spec data. While the flux through glycolysis remained relatively constant, the pentose phosphate pathway (PPP) flux increased from 3% to 20% of the glucose uptake during nitrogen-limited growth. The TCA cycle flux decreased from 94% to 38% during nitrogen-limited growth while the flux of acetyl-CoA into lipids increased from 58% to 109% of the glucose uptake, increasing total lipid accumulation. Phosphoenolpyruvate carboxylase (PEPCase) activity was higher during nitrogen-sufficient growth. The glyoxylate shunt was found to be partially active in both cases, indicating the nutrient nature has an impact on flux distribution. It was found that the total NADPH supply within the cell remained almost constant under both conditions. In summary, algal cells substantially reorganize their metabolism during the switch from carbon-limited (nitrogen-sufficient) to nitrogen-limited (carbon-sufficient) growth. Keywords: Microalgae, Biofuels, Chlorella, MFA, EMU algorith

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline.

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Genome-scale metabolic models comprehensively describe an organisms metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organisms phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models

MOESM2 of Assessing methanotrophy and carbon fixation for biofuel production by Methanosarcina acetivorans

Additional file 2. M. acetivorans gene re-annotation list

MOESM1 of Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis

Additional file 1.  Deciphering cyanobacterial phenotypes for fast photoautotrophic growth via isotopically nonstationary metabolic flux analysis: Supporting Information. Supporting information contains biomass measurements, model development, Supporting Tables 1–5, Supporting Figures 1–7, and a Supporting photo

MOESM1 of Reversing methanogenesis to capture methane for liquid biofuel precursors

Additional file 1. This file consists of four supplemental tables and six supplemental figures. Table S1 lists the components in the HS medium used to grow ANME-1 Mcr-producing M. acetivorans on methane and 0.1 mM or 10 mM FeCl3. Table S2 shows the fold changes of differentially expressed genes in M. acetivorans/pES1-MATmcr3 grown on methane and 0.1 mM FeCl3, in comparison to the same strain grown on methanol. Table S3 lists the strains and plasmids, and Table S4 lists the oligonucleotides, used in this study. Figure S1 shows the three promoters used to express ANME-1 mcrBGA genes. Figure S2 shows the detected McrA-FLAG in M. acetivorans/pES1-MATmcr3-flag grown on methane after five days. Figure S3 shows the detection of ANME-1 mcrA after 30 days of growth on methane. Figure S4 shows the GC/MS spectra of culture supernatants used to identify acetate from H13CO. Figure S5 shows the flux through the various reactions in the methanogenesis pathway of M. acetivorans estimated by 13C-metabolic flux analysis using 13C-labeled bicarbonate as the input tracer. Figure S6 shows a simplified methanogenesis pathway from CO2 and CH3OH of M. acetivorans