Kluyveromyces marxianus, in glucose and xylose media using RNA-seq. The data were explored in a number of innovative ways using a variety of networks types, pathway maps, enrichment statistics, reporter metabolites and a flux simulation model, revealing different aspects of the genome-scale response in an integrative systems biology manner. The importance of the subcellular localisation in the transcriptomic response is emphasised here, revealing new insights. As was previously reported by others using a rich medium, we show that peroxisomal fatty acid catabolism was dramatically up-regulated in a defined xylose mineral medium without fatty acids, along with mechanisms to activate fatty acids and transfer products of β-oxidation to the mitochondria. Notably, we observed a strong up-regulation of the 2-methylcitrate pathway, supporting capacity for odd-chain fatty acid catabolism. Next we asked which pathways would respond to the additional requirement for NADPH for xylose utilisation, and rationalised the unexpected results using simulations with Flux Balance Analysis. On a fundamental level, we investigated the contribution of the hierarchical and metabolic regulation levels to the regulation of metabolic fluxes. Metabolic regulation analysis suggested that genetic level regulation plays a major role in regulating metabolic fluxes in adaptation to xylose, even for the high capacity reactions, which is unexpected. In addition, isozyme switching may play an important role in re-routing of metabolic fluxes in subcellular compartments in K. marxianus.We investigated the transcriptomic response of a new strain of the yeast  Kluyveromyces marxianus is emerging as a host for metabolic engineering and recombinant protein production, having a number of advantages over Saccharomyces cerevisiae. These characteristics include thermotolerance and the ability to utilise a wide variety of sugars, including the pentose xylose, that are abundant in lignocellulosic biomass. Moreover, it is probably the fastest growing eukaryote on Earth [K. marxianus strains, in terms of their proneness to fermentation and ability to utilize various substrates, suggesting genetic diversity within the species [Agave americana. To enable metabolic engineering of K. marxianus and other yeasts, it is important to understand genetic responses to environmental factors and different substrates. Such an understanding requires both advanced high-throughput omics methods as well as various integrative computational methods. Thus far, only two studies of genome-scale transcript levels in K. marxianus have been published. Work on strain DMKU 3–1042 suggested that the yeast up-regulated β-oxidation in the absence of glucose repression in a complex xylose medium due to the use of lipids as additional carbon source in the absence of glucose, implying possible utilization of the small amounts of lipids present in the rich medium [The yeast on Earth . Previou species . Our strNext-generation sequencing (NGS) has become a popular method not only for sequencing genomes but also for various other experiments ,6. RNA-sAnalysis of metabolic pathways could be a sensible additional approach as it gives a sense of directionality to the response, and the scientist often associates a certain endpoint metabolite with a pathway, providing a means to simplify the understanding of the dataset. Painted renderings could be made of individual pathways, but often the number of pathways excludes a concise representation, and the integrated nature of metabolism is lost. Further, in metabolic pathways the interactions (reactions) consist of hypergraphs containing more than one substrate or product, complicating the rendering. Cellular overviews, as is available with software such as Pathway Tools, summarises metabolism into a single image . HoweverAn approach that is complementary to the pathway-based understanding of metabolism is that of the study of metabolite levels. Metabolomics, which entails the characterisation and quantification of small compounds in the cell, is still technologically demanding and labour intensive, however . A nK. lactis [13C-Metabolic Flux Analysis, estimation of fluxes at different physiological states can be very informative. Metabolic Regulation Analysis (MRA) combines differential expression levels with differential flux estimates to reveal for each flux the contribution of the metabolic and hierarchical levels of regulation [Models of biochemical pathways have much potential to reveal new insights that are not obvious from the exploration of datasets. The understanding of complex biochemical datasets using computational models is called systems biology. Some flux modelling approaches such as Flux Balance Analysis (FBA) simulations could be done even at the genome scale ,16, as w. lactis . A long-gulation .K. marxianus UFS-2791 to glucose and xylose in a chemically defined medium under aerobic conditions, including a number of different systemic analyses. Although a major potential future application of K. marxianus may be ethanol production from lignocellulosic biomass, which is an anaerobic or oxygen limited process where both glucose and xylose may be present, we chose to perform aerobic cultivations with glucose or xylose separately to remove any confounding effects. The strain also utilizes these sugars sequentially. The high cultivation temperature of 35°C was also chosen as this strain was determined to have a growth optimum close to 35°C. Our samples were also taken during mid-exponential phase to eliminate the effect of any possible ethanol stress that may occur later during the fermentation. To address the need for effective integrative exploration of omics data, including RNA-seq, and to combine these data with modelling and simulation, we developed the Reactomica software. We combined gene set enrichment of Gene Ontology (GO), reporter metabolites, metabolic pathway maps with a strong focus on subcellular compartmentalisation, and two new approaches of representation, namely pathway-to-pathway networks and reporter metabolite-enzyme networks.Here we report on a detailed RNA-seq transcriptomics study to explore the response of + dependent xylitol dehydrogenase is present and that the co-factor independent isomerase reaction is absent, as in K. marxianus [The aims were to first effectively explore the key features of the differential response to xylose in a defined medium under aerobic conditions and determine whether the peroxisomal lipid catabolic response previously observed  was limiarxianus . Under aK. marxianus UFS-Y-2791 was sequenced on the Illumina HiSeq platform to 100-fold coverage at the Onderstepoort Biotechnology Platform, Pretoria, South Africa. Assembly was performed de novo using Abyss. Open reading frames were found by Augustus. Putative protein sequences were annotated using one of two methods. For annotation of enzymes (778 genes), creation of a pathway genome database (PGDB) and all subsequent analysis involving metabolic pathways, protein sequences were annotated against the Kegg-Kaas server with default settings on the server, resulting in a list of EC number annotations. Output was subsequently parsed and converted to input for the PathoLogic algorithm of Pathway Tools. For gene set enrichment using Gene Ontology (GO), sequences were additionally annotated against the UniProtKB database on a high-performance computing cluster, using BLASTP. An E-value cut-off of 1E-10 was used for gene set enrichment as was done by others [S. cerevisiae, 17.4% from K. lactis, 3.2% and 2.8% from two strains of K. marxianus, and the rest from other species. The draft genome, predicted open reading frames, predicted protein sequences and functional annotations are made available in the Supplementary materials : glucose or xylose, 5; (NH4)2SO4, 2.5; MgSO4·7H2O, 0.5; CaCl2·H2O, 0.03; NaCl, 0.1; citric acid, 0.25 and KH2PO4, 10. Filter-sterilised vitamins were added to the autoclaved medium at the following concentrations (mg l-1): biotin, 0.025; calcium pantothenate, 0.5; nicotinic acid, 0.5; p-aminobenzoic acid, 0.1; pyridoxine HCl, 0.5; thiamine HCl, 0.5 and myo-inositol, 12.5. Trace elements were added according to du Preez and van der Walt [600 of 0.8 was reached.All chemicals and fermentation media used in cultivations were obtained from Sigma Aldrich, Seelze, Germany. der Walt . The pH 690 of 0.8 by centrifugation at 1 000 × g at 4°C for 5 min. RNA extraction was carried out according to the RNeasy yeast RNA kit (Qiagen) protocol, including a DNAase step. Ribosomal RNA was removed by the Ribo-Zero rRNA Removal Kit (Illumina) and remaining RNA was sequenced at the Onderstepoort Biotechnology platform in Pretoria, South Africa. Paired-end reads were generated using Illumina HiSeq Next Generation Sequencing. For each of duplicate samples, 3.75 million trimmed paired-end reads were mapped to the UFS-Y2791 genome. Processing of sequencing data was carried out in Galaxy. Reads were trimmed using Trimmomatic [Cells from duplicate cultivations were harvested in mid-exponential phase at an ODmmomatic  and mappmmomatic  was usedmmomatic . Genes wFor gene set enrichment and all other network constructions and renderings, programs were developed as part of a new software suite for integrative systems biology that we call Reactomica, implemented in the Wolfram Mathematica language. Gene set enrichment scores for GO terms and pathways were calculated similar to that described by Ideker  as folloFor pathway gene set analysis, MetaCyc pathways were used from the BioCyc pathway genome database constructed for this strain using Pathway Tools, which is based on the MetaCyc database. Reporter metabolite enrichment scores were calculated in the same manner as described above and according to Patil and Nielsen . For rep2(fold change) colouring scheme, in the case of more than one enzyme that could perform the same function, the largest fold change in expression was used for the colour rendering. Additional gene-reaction linkages were made from a UniProt BLASTP annotation. Subsequent compartmentalisation made use of the GO ‘cellular_component’ ontology terms. For purpose, a mapping was built into Reactomica that maps GO ‘cellular_component’ ontology terms to subscellular compartments.To explore the metabolic response, metabolic pathway maps were created from MetaCyc pathways and RNA-seq data were mapped using various colouring schemes with Reactomica, harnessing automated hypergraph map layout and manual override. The initial linkage between genes and reactions were made by the Pathway Tools algorithm PathoLogic, with genomic annotations from the Kegg-Kaas annotation server. For the LogPathways were clustered by the number of metabolites in common to generate a scoring matrix. The number of metabolites in common was normalised by the smaller of the two metabolite sets of a pathway and a threshold was selected for including a mapping that resulted in optimal rendering. Orphaned pathways were clustered together.Molecules were clustered by the simple similarity criterion of string matching of SMILES strings of each compound in the PGDB using the edit distance. Only the closest match was included as a mapping. The method is similar to that done by Barupal et al. .The metabolic network was converted from a hypergraph into a graph in which only the interactions between differentially expressed enzyme genes and enriched reported metabolites were retained.K. marxianus UFS-Y2791. Some additional reactions were defined such as the combined electron transport and oxidative phosphorylation reaction. The biomass reaction was adapted from Fischer et al. [The aim was to approximate the fluxes on glucose and xylose, sufficient for approximation of MRA values. FBA was used which included optimisation of biomass formation, and measured specific sugar consumption rate and specific ethanol, acetate and were included to constrain the flux solution. The model was constructed in Reactomica using reactions from MetaCyc for which representative genes were found in the genome annotation of r et al. . The flur et al. . FBA useThe metabolic regulation of a flux can be separated into a metabolic component ρm and a hierarchical component ρh. The two levels of regulation are combined in the relation below .J is the flux though that enzyme, modelled by the rate equation v, and affected by changes in the concentration of metabolite X. The hierarchical component ρh models the effect of changes in maximal activity of the enzyme, which is usually linearly dependent on the enzyme concentration e and described by:The metabolic component ρm models the contribution to regulation that changes in metabolite concentrations make and is described byρh, ρm could also be calculated. Though maximal activity is affected not only by the protein concentration and post-translational modifications, it is reasonable and practical to use changes in transcript levels obtained from RNA-seq instead of maximal activities or protein levels for an estimated MRA. For MRA, gene expression changes were considered significant if at least one gene  was considered significant from q-values. Transcript fold changes were calculated from total transcript abundances for all genes mapping to a reaction, in case of isozymes resulting from paralogs or multi-functional proteins. In this manner, the method is robust to potential errors in annotation of paralogs since central metabolic genes are known to have on average higher transcript abundance in comparison with the vast majority of other genes. The fold changes of individual minor isozymes with very low expression levels also cannot dominate the analysis.The hierarchical component could thus be experimentally determined by measuring a difference in maximal enzyme activity. By using 600 values, with ethanol, glycerol and acetate as fermentation products. In xylose medium, fermentation products were absent, with an apparently purely respiratory metabolism. The maximum specific growth rate in glucose medium was approximately 0.8 h-1, while the maximum specific growth rate in xylose medium was approximately 0.35 h-1. All graphs and other renderings were generated using a new software suite for bioinformatics and integrated systems biology, termed Reactomica, which was developed in-house. Interactive datasets are provided in Computable Document Format (.CDF) as supplementary materials and are viewable using the free Wolfram CDF player, which can be downloaded from the Wolfram website [In glucose medium, respiro-fermentative growth was observed, even though fully aerobic conditions were ensured by the small working volume in a large flask, vigorous shaking, and sampling at low OD website , or by uWe compared results from reference , which rThirteen genes were uncorrelated with the reference data (constitutive in UFS-2791)  and are thus specific to either the strain or the rich medium. Most of these are uncharacterised proteins.S. cerevisiae [Finally, two genes were anti-correlated with the reference data (down-regulated in UFS-2791). These are the ammonia transport outward protein 3 (ATO3) and dihydroxyacetone kinase 1 (DAK1). The exact functions of ATO1, ATO2 and ATO3 are not currently known, apart from their possible involvement in mitochondrial retrograde signalling and ammonia production during starvation. It seems that ATO3 requires rich medium containing amino acids to be up-regulated, as was also observed in revisiae . In summA total of 1 611 GO terms were included in the UniProt annotations of all proteins in the genome see . To obtaK. marxianus since it does not possess a xylose isomerase.In the ‘molecular_function’ ontology, only three terms are regarded as significant. ‘Oxidoreductase activity’, ‘kinase activity’ and ‘peptidase activity’ . ‘OxidorThe ‘biological_process’ ontology is rich in providing a framework for exploring the regulation of cellular processes in response to different carbon sources. The ‘carbohydrate transport’ gene set was the most significantly altered, with 21 out of 65 transporter genes up-regulated and 7 down-regulated . The ‘trHGT1 homologs of K. lactis, were up-regulated as high as 1242-fold , only peroxiredoxin-5  and catalase T  were moderately differentially regulated, resulting in an enrichment score of 1.19, which was insignificant against the background enrichment.K. marxianus UFS-Y2791 also does not possess typical secreted xylanases, proteases, peptidases or lipases, which would allow a microorganism to thrive on plant matter or even attack a live plant. Instead, the strain possesses the inulinase gene INU1 which hydrolyses the fructan inulin or the disaccharide sucrose. The INU1 gene was dramatically up-regulated 91-fold and abundantly expressed in xylose medium.The data indicated several interesting aspects regarding sugar utilisation, including up-regulation of pathways for utilisation of galactose, xylose and arabinose (see S5). Several enzymes with β-glucosidase activity were found in the annotation see , whereasThe pheromone signalling system involved in sexual reproduction, as well as several other genes involved in the conjugation process as well as in invasive growth, were also up-regulated  or down-regulated .Most metabolic pathways for amino acid synthesis were down-regulated, as was expected at the lower growth rate; μTo capture the global metabolic response to growth on xylose in a single view, an innovative pathway-to-pathway network was constructed by clustering pathways together by their common metabolites . The widGUT2 gene 4.4-fold up-regulated. Note that the up-regulated glycerol-3-phosphate shuttle functions independently from down-regulated glycerol production.Fatty acid oxidation, peroxisomal ethanol degradation and mixed acid fermentation, which cluster together, were mostly up-regulated. Close by in the network is glycolysis, which was down-regulated. A moderately increased capacity in the glycerol-3-phosphate shuttle was evident here, with the mitochondrial glycerol-3-phosphate dehydrogenase (a) a marked change in capacity for their utilisation or production between conditions, (b) to have required a significant degree of regulation by their neighbouring enzymes to establish homeostasis in the different environment, or (c) to have a different concentration predicted between conditions where it may be a candidate as a signalling molecule. The top eight reporter metabolites of highest enrichment were 3-phosphoglycerate, acetaldehyde, NADH, glyceraldehyde-3-phosphate, β-alanine, NAD+, glycerate and ethanol, in this order . It was interesting to note that the enrichment score of ATP was among the lowest against the background and considered insignificant. Also, glutathione and its reduced form, which are known to be involved in redox metabolism, were not significantly enriched, suggesting no severe oxidative stress. Oxidative stress in K. marxianus is seemingly more important under oxygen limiting conditions, as imposed by static and high-temperature conditions [Reporter metabolite enrichment was performed to reveal those metabolites around which significant differential expression of enzymes took place. These compounds could be interpreted as those from which there was is order . NADH, NTo determine whether the reporter metabolites represented any distinct molecular structural groups, a molecular network of the reporter metabolites was reconstructed using a simple molecular structure similarity matching protocol. This approach could identify some co-regulated groups of structurally related molecules that were not evident from pathways-based analyses. Groupings of CoA-conjugates, long-chain and short-chain fatty acids are representative of increased catabolic activity of β-oxidation and activation steps. Up-regulated sugar clusters corresponded to those found in reporter metabolite-enzyme networks, but were represented in a clearer fashion. Noteworthy is that effectors of trehalose containing sugars and sugar lipids were down-regulated. The three-carbon molecules in central carbon metabolism, which are close together in the network including 3-phosphoglycerate, 2-phosphoglycerate, glyceraldehyde-3-phosphate and glyceraldehyde, were strongly enriched and their effectors down-regulated. This mapping is useful as a concise representation of potentially all metabolites in a cell, grouped by their structures. The method would especially be useful for visualising metabolomics datasets of which the link to reactions and pathways is not yet clear. A better separation of clusters is obtained in mapping larger metabolites as found in secondary metabolism.Combining both the reporter metabolites and the enzymes by which they are regulated into an enzyme-reporter metabolite network, effectively reveals the hotspots of metabolic regulation as well as the key players in regulation. Including all interactions with enriched compounds  was not feasible for a detailed investigation (1352 interactions). Instead, we extracted the interactions containing nodes with enrichment scores above 3.0 .+/NADH, oxygen and the aldehyde dehydrogenase have a strong involvement. A second subnetwork involves sugar metabolism and glycosylation. A third revolves around one-carbon metabolism. The subnetwork of NAD+ (enrichment score = 4.2) was extracted . The enzymes directly affecting NADPH are explored in IDP1  was only moderately up-regulated (2.2-fold). A more significantly up-regulated enzyme, mitochondrial quinone oxidoreductase  reduces 1,4-benzoquinone, as was shown by cloning this gene from K. marxianus in E. coli [YPR127W, a putative pyridoxal reductase that functions to degrade vitamin B6 and involved with multidrug resistance and not central metabolism [GND1 (6-phosphogluconate dehydrogenase) and SOL1 (6-phosphogluconolactonase) were, however, constitutively expressed. Another enzyme, SPS19 , was strongly up-regulated at 269-fold. It functions to reduce double bonds to facilitate β-oxidation of unsaturated fatty acids in the peroxisome [Since  E. coli . It is ntabolism . In the roxisome , anotherTo explore the metabolic response of the central carbon metabolism, metabolic pathway maps were created from MetaCyc pathways and RNA-seq data were mapped using various colouring schemes. Figs XYL1 was drastically up-regulated on xylose (48.8-fold) with very high gene expression levels on xylose. Xylitol dehydrogenase was absent from the annotation. However, is has been demonstrated that sorbitol dehydrogenase SOR1 can act as a xylitol dehydrogenase in S. cerevisiae [SOR1 by 208-fold supports this function. Xylulokinase XKS1 was also 11.3-fold up-regulated. Transaldolase TAL1 of the non-oxidative pentose phosphate pathway (PPP) and ribose-5-phosphate isomerase RKI1 were moderately up-regulated (4.8 and 2.6-fold). We did, however, not observe up-regulation of any enzymes in the oxidative branch to support additional NAPDH production for xylose utilisation, or to combat oxidative stress by charging of the glutathione system. We found a remarkably clear down-regulation of glycolytic genes on xylose with a moderate fold change. The gluconeogenesis-associated gene fructose-1,6-bisphosphatase FBP1 was also sharply up-regulated by 27-fold, which was also observed in a microarray experiment of a xylose utilising recombinant S. cerevisiae strain [An interesting differential expression pattern is evident in revisiae . Signifie strain . β-OxidaFurther, there was also a strong apparent down-regulatory effect on alcohol dehydrogenases and aldehyde dehydrogenases, and both the citrate synthase of the TCA cycle and the isocitrate lyase of the glyoxylate cycle were seemingly dramatically up-regulated. The latter two observations in this analysis, however, are misguided by the lack of compartmentalisation of the response. It should be noted that many metabolic reactions could be catalysed by more than one enzyme, where for some reactions like those catalysed by alcohol dehydrogenase (ADH), there are at least five gene products that could potentially catalyse the same reaction. +-specific acetaldehyde dehydrogenase (ALD) and glycerol production were down-regulated. Further, a number of reactions usually associated with the TCA cycle were also present in the cytosol and were constitutively expressed. More than one type of ADH with opposite regulatory direction is present in the cytosol. In the peroxisomal compartment  and ADHs were still isozyme switching reactions. Both ALD4 (up-regulated 83-fold) and ALD5 (down-regulated 167-fold) were strongly differentially expressed. Several ADH genes were found in K. marxianus. Two of these were annotated as ADH3 and ADH4, both mitochondrial, whereas five  were taken to be cytoplasmic. As previously reported [S. cerevisiae and K. lactis [S. cerevisiae and K. lactis where glucose stimulated ADH1 expression. Transcriptional rewiring of ADH isozymes is thus present in K. marxianus compared to its relatives.Subsequently, the GO ‘cellular_component’ terms from the UniProt_SwissProt_fungi were used to render compartmentalised maps reconstructed from MetaCyc pathways. ment see , β-oxidaoxisomes , is cleareported , ADH2 wa. lactis . Convers. lactis , which dICL1) was down-regulated. Upon further investigation, the isozyme present in the mitochondrion, which was mapped to the TCA cycle by the PathoLogic algorithm using Kegg-Kaas annotations, was in fact ICL2, the 2-methylisocitrate lyase, perhaps confusingly termed ICL2. Although ICL1 and ICL2 share a high sequence similarity, the latter does not use isocitrate as a substrate and does not produce glyoxylate but uses 2-methylisocitrate instead to produce succinate and pyruvate [ICL2 is part of the propionyl-coezyme A pathway, otherwise known as the 2-methylcitrate pathway [CIT3 (YNR001C), the mitochondrial enzyme that condenses oxaloacetate with propionyl coenzyme A to form 2-methylcitrate. Indeed, the citrate synthase that was up-regulated (CIT1 gene (YPR001W) that is both cytosolic and mitochondrial and associated with the TCA cycle, but instead the mitochondrial CIT3. Further inspection of the full GO ontology enrichment set revealed that all three key genes in the 2-methylcitrate pathway were significantly up-regulated. The strength of the response in the 2-methylcitrate pathway is given in CIT3, PDH1 and ICL2 were up-regulated 294, 32 and 28-fold, respectively, and GLY1). Another gene was also annotated as GLY1 and down-regulated. Thus, there is no conclusive evidence that threonine catabolism was up-regulated. The up-regulation of the 2-methylcitrate pathway is, therefore, more likely for the catabolism of short-chain fatty acids and not threonine.In the mitochondrial map, the NADPH specific isocitrate dehydrogenase gene was up-regulated, whereas citrate synthase of the TCA cycle and isocitrate lyase were seemingly up-regulated. Surprisingly, the glyoxysome map revealed that the glyoxylate cycle specific isocitrate lyase  scheme). Although transcript levels do not generally correspond well to protein levels and hence to fluxes according to reports using model organisms [Differential metabolic flux analysis can be combined with differential gene expression data in the framework of MRA. From Figs rganisms , the oveAlthough there is not always a linear correlation between transcript level, protein concentrations and maximal activities of an enzyme, we assume differential transcript expression to provide a sufficient approximation to hierarchical regulation, as was done by others . Fig 17 Modelling of bioprocesses has been attempted for a long time, in many different forms. It was realised in the second half of the twentieth century that several confounding factors had to be eliminated in the study of metabolism and its gene regulation, arguably with the growth rate as the most important confounding factor, since the expression level of many genes are correlated with the growth rate. Chemostat cultivation is a useful tool, since the growth rate can be easily controlled. However, for the vast majority of industrial applications, chemostats are unrealistic as industrial applications typically involve batch cultivation, or fed-batch cultivation to avoid catabolite repression, improve volumetric productivity and allow sufficient aeration by controlling the growth rate. Moreover, systems biology has become increasingly data-driven with the availability of high-throughput methods to measure large numbers of intracellular metabolites (metabolomics), proteins (proteomics) and especially RNAs (RNA-seq and microarray) from a single experiment. It is proposed here that much more could be learned in terms of genetic regulation in microorganisms by testing a variety of different cultivation conditions, especially substrates, in small batch experiments as compared to the same amount of work in a more sophisticated, labour intensive chemostat setup, or at least, complementary information. We showed that in a simple and cost-effective batch setup, a large amount of rich RNA-seq data could be generated and fruitfully explored. This small working volume also makes expensive isotope labelling studies feasible. In batch experiments, however, special care needs to be taken in terms of the timing of sampling, as, for instance, fermentation products would accumulate over time and a transcriptome sample late in the fermentation may be more reflective of chemical stress than of the metabolic mode. This may especially be a confounding factor when comparing the effects of the concentration of substrate. Our experimental design alleviated this effect and allowed us to focus on the effects of alternative substrates only.Previously, exploration of omics datasets mostly focused on only the lists of the most significantly up-regulated or down-regulated genes separately, or on one or two more advanced types of analyses such as GO enrichment, and occasionally on the extraction of active networks. Here we presented one of the first examples that explore high-quality RNA-seq data from various perspectives, using different types of enrichments and networks, and rationalisation of the response with FBA and MRA as theoretical frameworks. Furthermore, combining information from databases such as GO and MetaCyc in the same problem and with subcellular compartmentalisation revealed several important features that would have been missed using either on its own.A. americana [A. americana sample.The vastness of the xylose response under aerobic conditions indicates a different, opportunistic lifestyle that this yeast apparently adapts to when cultivated on xylose, which may be reminiscent of its natural environment where a variety of plant-derived substrates may be utilised. Down-regulation of many biosynthetic pathways is concordant with a lower growth rate, although there is a notable absence of growth rate specific gene sets in the enrichment statistics. Although many mitochondrial genes were differentially expressed, these are not the major enzymes in energy production in mitochondria. Oxidative stress also seems minimal in the aerobic xylose medium. Instead, a strong response is seen in the up-regulation of alternative sugar utilisation machinery, including inulinase, sugar transporters and catabolic routes for alternative sugars. Inulin is a fructan stored in large amounts in some plants, including mericana . Our strK. marxianus lacks enzymes such as secreted proteases, lipases and carbohydrate hydrolases. This yeast may thus be dependent on other fungi in the environment for these functions, or its natural habitat may be some commensal niche where these monomers are supplied by the plant. The transcriptional regulatory basis for this response is likely to be glucose repression by transcription factors such as MIG1 binding to carbon source response elements, as was suggested to be the case for the inulinase gene KmINU1 [e KmINU1 , 41.It is interesting to find that the majority of genes for a complete organelle like the peroxisome, which is dedicated to lipid oxidation, was dramatically up-regulated in the xylose medium, yet without apparent function in the experimental setting. Thus, glucose de-repression is likely sufficient to activate most of the response to enable lipid catabolism, and stimulation by lipids may play a smaller role. The mitochondrial 2-methylcitrate pathway for the degradation of three-carbon molecules was also strongly up-regulated, suggesting that a variety of odd-chain fatty acids originating from peroxisomes may be oxidised.FBA simulations predicted no significant up-regulation of the oxidative PPP flux, but a more intense up-regulation of the non-oxidative PPP flux and a down-regulation of glycolysis, which were consistent with RNA-seq data. Since biomass formation, the major sink for NADPH, is down-regulated in xylose medium, and another NADPH sink, xylose reductase, is up-regulated, it thus makes sense that the major source flux of NADPH (oxidative PPP) may be similar in both conditions . Normalising fluxes to the uptake rate of a carbon source or to the biomass formation rate could thus be misleading when interpreting expression data. FBA simulations here thus shed light on what could be expected in terms of gene regulation.S. cerevisiae classified these high-capacity reactions as well as the non-oxidative PPP reactions as pseudo-equilibrium or near-equilibrium reactions, suggesting that they can be sufficiently described by simple equations making use of thermodynamics and empirical studies at various dilution rates [K. marxianus showed that the transcript levels in lower glycolysis were substantially lower on xylose, and MRA showed a dominating hierarchical (genetic) level regulation. Flux through transaldolase was also dominated by hierarchical regulation. The contribution of hierarchical and metabolic level regulation of a reaction would differ between substrates utilised, and may also differ between species.MRA was used to separate metabolic regulation into hierarchical and metabolic levels. Reactions in lower glycolysis are known to a have high flux capacity due to high enzyme concentrations, and our transcript data also indicated this feature on glucose. Chemostat studies of on rates ; hence bReporter metabolite networking suggested that NAD(H), acetaldehyde, ethanol, glyceraldehyde-3-phosphate, 3-phosphoglycerate and hydrogen peroxide formed a strongly interconnected redox active system, with aldehyde dehydrogenases and alcohol dehydrogenases being the main players. This system may work across membranes, since acetaldehyde, ethanol and hydrogen peroxide can cross membranes, shuttles connect NAD(H) across compartments, and glyceraldehyde-3-phosphate and 3-phosphoglycerate are closely connected to NADH. At this stage it is not possible to resolve the fluxes and MRA through the acetaldehyde-dependent pyruvate dehydrogenase bypass. It was, however, evident that dramatic changes occurred in the transcript levels of the alcohol dehydrogenases and aldehyde dehydrogenases, including compartment-specific isozyme switching. As also suggested by Lertwattanasakul et al. [K. marxianus seemingly lacks the secreted enzymes required for depolymerisation of biopolymers, the species is probably dependent on other species for supply of monomers such as sugars, amino acids and free fatty acids, whereas inulinase is a specialist feature enabling this species to utilise this plant storage oligosaccharide. It would be interesting to see whether xylose may have a stimulatory role as suggested recently for Saccharomyces cerevisiae [We believe to have captured in a unique manner, and from a number of perspectives, a complex transcriptional pattern telling an interesting story about how the cell ‘explores its options’ when the nutrient availability changes under aerobic conditions. Strong up-regulation of transporters and pathways for utilisation of alternative carbohydrates was evident. In addition, the more opportunistic lifestyle was supported by invasive growth, and sexual reproduction was activated as a long-term survival strategy. The strong peroxisomal fatty acid catabolic response accompanied by the mitochondrial 2-methylcitrate pathway is likely explained by glucose de-repression, similar to that seen for carbohydrate utilisation. As revisiae  and throMRA was demonstrated here as an informative method to dissect the regulation of fluxes into the metabolic and hierarchical levels. It is evident that the genetic level plays a dominating role in the regulation of fluxes in central carbon metabolism, not only in the early enabling steps of utilisation of xylose as the carbon source, but also in the high capacity reactions of lower glycolysis. In kinetic modelling of metabolism, emphasis should thus be placed on genetic regulation, which is currently very challenging. Multiple omics would need to be combined to predict such regulatory networks and build realistic models. However, in order to resolve fluxes originating from pyruvate, isotopic tracer studies would be required, which currently is under investigation. In addition, the isozyme switching observed with alcohol dehydrogenase, aldehyde dehydrogenase and acetate-CoA ligase calls for a detailed investigation into the compartmentalisation and kinetics of these enzymes, and detailed bottom-up kinetic modelling to understand the role of this interesting behaviour.S1 Draft Genome(FA)Click here for additional data file.S1 Network(PDF)Click here for additional data file.S2 Network(CDF)Click here for additional data file.S3 Network(CDF)Click here for additional data file.S4 Network(CDF)Click here for additional data file.S5 Network(CDF)Click here for additional data file.S1 ORF(GFF)Click here for additional data file.S1 Pathway(PPTX)Click here for additional data file.S2 Pathway(PDF)Click here for additional data file.S3 Pathway(PPTX)Click here for additional data file.S1 Proteins(AA)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


Syntrophobacter fumaroxidans and the methanogenic archaeon Methanospirillum hungatei. The models were used to verify proposed mechanisms of ATP production within each species. We then identified additional constraints and the cellular objective function required to match experimental observations. The thermodynamic S. fumaroxidans model could not explain why S. fumaroxidans does not produce H2 in monoculture, indicating that current methods might not adequately estimate the thermodynamics, or that other cellular processes  play a role. We also developed a thermodynamic coculture model of the association between the organisms. The coculture model correctly predicted the exchange of both H2 and formate between the two species and suggested conditions under which H2 and formate produced by S. fumaroxidans would be fully consumed by M. hungatei.Microorganisms in nature do not exist in isolation but rather interact with other species in their environment. Some microbes interact via syntrophic associations, in which the metabolic by-products of one species serve as nutrients for another. These associations sustain a variety of natural communities, including those involved in methanogenesis. In anaerobic syntrophic communities, energy is transferred from one species to another, either through direct contact and exchange of electrons, or through small molecule diffusion. Thermodynamics plays an important role in governing these interactions, as the oxidation reactions carried out by the first community member are only possible because degradation products are consumed by the second community member. This work presents the development and analysis of genome-scale network reconstructions of the bacterium  Syntrophobacter fumaroxidans and the methanogenic archaeon Methanospirillum hungatei. These models were used to analyze energy conservation mechanisms within each species, as well as small molecule exchange between the two organisms in coculture. The coculture model correctly predicted the exchange of both H2 and formate between the two species and suggested conditions under which these molecules would be fully metabolized within the community.Natural and engineered microbial communities can contain up to hundreds of interacting microbes. These interactions may be positive, negative, or neutral, as well as obligate or facultative. Syntrophy is an obligate, positive interaction, in which one species lives off the metabolic by-products of another. Syntrophic associations play an important role in sustaining a variety of natural communities, including those involved in the breakdown and conversion of short-chain fatty acids  to methane. In many syntrophic communities, electrons are transferred from one species to the other through small molecule diffusion. In this work, we expand the study of a two-member syntrophic, methanogenic community through the development and analysis of computational models for both species: the bacterium  Microorganisms in nature engage in a variety of interactions with other species in their environment. Syntrophy is one such type of inter-species interaction in which one species lives off the metabolic by-products of another –3. SynthIn anaerobic syntrophic communities, electrons are transferred from one partner to the other through direct contact or small molecule diffusion . Traditi2 exchange between the sulfate-reducing bacterium Desulfovibrio vulgaris and the methanogenic archaeon Methanococcus maripaludis [One of the earliest microbial community models used flux balance analysis  to inveipaludis . In thisipaludis –19, as wipaludis –24. Thesipaludis –24 have ipaludis ,26 insteipaludis  extends ipaludis –24 to spSyntrophobacter fumaroxidans and the methanogenic archaeon Methanospirillum hungatei. In association with M. hungatei, S. fumaroxidans converts propionate to acetate, CO2, and H2 [2 and H2 can be interconverted to formate [2 and formate serving as the electron carriers between the two species. H2 and formate production are only observed during syntrophic growth. Using a thermodynamic, constraint-based model, we set out to test the proposed hypothesis that this behavior is governed by thermodynamics [Genome-scale models can also be used to study the relationship between thermodynamics and metabolism, by ensuring that network predictions are consistent with thermodynamic principles –34. In t, and H2 –37. CO2  formate –40, withdynamics ,6,8,9.S. fumaroxidans does not produce H2 in monoculture.We developed genome-scale metabolic reconstructions of both microorganisms, and verified proposed mechanisms of ATP production within each individual species. Additional constraints and a cellular objective function were identified to predict the proper flux through experimentally characterized carbon and electron transport pathways during monoculture and syntrophic  growth. Our analysis revealed that thermodynamic constraints alone are insufficient to explain why We also extended TMFA to model the syntrophic association between the two microorganisms. The association is modeled as a continuous coculture system with constraint-based models for each microbe and a mass balance around the reactor. Similar to cFBA , the cocS. fumaroxidans (iSfu648) and M. hungatei (iMhu428) were tested and parameterized using experimental data from growth on single substrates in monoculture and coculture. The iSfu648 and iMhu428 models were used to verify proposed mechanisms of energy conservation within each species. A coculture model was then developed to identify conditions where H2 and formate, produced by S. fumaroxidans, is fully metabolized by M. hungatei.Metabolic reconstructions of iMhu428 reconstruction of M. hungatei was built from the iMB745 reconstruction of M. acetivorans [iMB745 using the RAVEN Toolbox [M. hungatei orthologs were found for only 428 of the 745 genes in iMB745. To avoid extensive gapfilling, reactions from the iMB745 were copied into the M. hungatei reconstruction, with modifications to reflect key metabolic features of M. hungatei  had to be allowed to vary within a 99% confidence interval of its estimated standard transformed Gibbs free energy of reaction  and a Na+/H+ antiporter (NAT3_1) were selected to give an ATP yield matching the experimental estimates: two Na+ ions exported by MTSPCMMT_CM5HBCMT, and a one Na+ per H+ transported by NAT3_1. However, different stoichiometries for these reactions are also thermodynamically possible  and non-growth-associated (NGAM) ATP maintenance requirements for M. hungatei, as described in iMhu428 model, the NGAM was estimated to be 0.6 mmol ATP/gDW/day, GAM was estimated to be 47 mmol ATP/gDW, SURCO2 was estimated to be 75.7 mmol/gDW/day, and SURformate was estimated to be 955 mmol/gDW/day.Experimental measurements of growth rates, yields, and maintenance costs were also used to identify substrate uptake rates (SUR) for COiSfu648 reconstruction of S. fumaroxidans was built from the KEGG database using the RAVEN Toolbox [The  Toolbox . The res Toolbox ,48–51, wS. fumaroxidans: four in monoculture and one in coculture with M. hungatei  [FRD) consumes reducing equivalents (menaquinol) [RNF), the ferredoxin-oxidizing hydrogenase (frH2ase), the cytosolic hydrogenase (cytH2ase) and formate hydrogen lyase (FHL). The reduction of fumarate to succinate also generates the proton motive force (PMF) responsible for driving the RNF reaction and producing ATP.The oxidation of one fumarate to COredoxin) ,52, whilaquinol) . Electro2. Two additional moles of fumarate get reduced to succinate [During monoculture growth on fumarate plus propionate , one moluccinate ,48:prop2 produces one NADH and one pair of reduced ferredoxin, while the reduction of fumarate to succinate by FRD consumes menaquinol. Electrons are transferred from NADH and reduced ferredoxin to menaquinone through the combined action of the confurcating hydrogenase (cH2ase) and cytH2ase. Oxidation of propionate to succinate produces one ATP, while FRD generates the PMF necessary for additional ATP production.The oxidation of fumarate to acetate and CO2 via the methylmalonyl-CoA pathway [During coculture growth on propionate , propion pathway ,52:SDH), producing menaquinone. cytH2ase then transfers electrons from menaquinol to two protons, generating H2. The oxidation of fumarate to acetate and CO2 produces one NADH and one pair of reduced ferredoxin, and cH2ase couples NADH and ferredoxin re-oxidation with H2 production. Unlike in the monoculture growth modes, the H2 is not consumed intracellularly and must diffuse outside the cell. It has been proposed that the net production of H2 by S. fumaroxidans is only thermodynamically favorable at the low H2 concentrations maintained by methanogens, thereby explaining why S. fumaroxidans only produces H2 during coculture growth.ATP is generated during the oxidation of propionate to succinate, and this ATP establishes the PMF necessary to drive the endergonic oxidation of succinate to fumarate ,48,51S. owth ([2  yieldingcytFDH) substitutes for the activity of cytH2ase. In a second mechanism (cFDH) substitutes for cH2ase. Here, cFDH couples NADH and ferredoxin re-oxidation with the conversion of CO2 (from propionate oxidation) to formate.In one mechanism , activitechanism , the conS. fumaroxidans energy metabolism suggest that the carbon and electron transfer pathways shown in S. fumaroxidans, either by substrate-level phosphorylation or through establishment of a proton gradient used by ATP synthase [Experimental evidence and conceptual models of synthase . To testiSfu648 model correctly predicted the flux distributions shown in S. fumaroxidans has adapted to maximize its energy yield.When flux was restricted to a reduced network containing all the reactions shown in S. fumaroxidans have shown that H2 is not produced during growth in monoculture [2 production is only thermodynamically favorable at low partial pressures [2 production by consuming H2 and keeping its partial pressure low [2 production was observed in monoculture, H2 production ceased at a partial pressure of approximately 10 Pa [Experimental studies of oculture ,52,53, aressures ,8,9. In sure low ,8,9. Ind2 production under monoculture conditions, simulations reveal that H2 production remains thermodynamically feasible. For example, during monoculture growth on fumarate, the iSfu648 model predicts that H2 can be produced via the following mechanism:However, when maximizing H2 molecules produced by the ferredoxin-oxidizing hydrogenase are exported outside the cell, instead of serving as substrates for the cytosolic hydrogenase. As a result, no PMF is generated by fumarate reductase, and the net ATP yield is zero. Thus, while H2 production remains thermodynamically possible, H2 production is only associated with sub-optimal mechanisms of ATP generation. This suggests that thermodynamic considerations alone may not explain the absence of H2 production during monoculture growth, but that the observed flux distribution may instead be driven by demands for energy generation.In this scenario, H2 production was not predicted for monoculture conditions when ATP production was maximized, H2 production was initially predicted when growth was instead maximized. To eliminate monoculture H2 production in the TMFA model, we first sought to constrain ratios of metabolite concentrations with an approach similar to that used to correct TMFA growth predictions [2 production mechanisms, H2 production during monoculture growth could not be completely eliminated. If thermodynamics prevents H2 production in monoculture, then the current thermodynamic model may contain too much uncertainty in its Gibbs free energy estimates . Experimental measurements of growth rates, yields, and maintenance costs were used to identify the SURs, GAM, and NGAM parameters for S. fumaroxidans. These parameters were estimated using data from monoculture growth on fumarate alone and coculture growth on propionate alone . This discrepancy could be caused by differences in uptake rates or maintenance costs in the fumarate plus propionate condition compared to the conditions with propionate alone or fumarate alone.Using these parameter values, the redicted . Not suriSfu648 model predicted a wide range of product secretion rates. When the enzyme cost  was minimized at the maximum growth  and two pairs of electrons (used to reduce two fumarate). While this coupling arises naturally on the reduced network, the full metabolic network enables alternative coupling mechanisms (not shown) that permit other fumarate to propionate ratios. Since propionate oxidation generates carbon precursors and ATP for biomass, maximizing biomass production results in the model under-predicting the fumarate to propionate uptake ratio. Increasing the ratio of fumarate to propionate uptake (to 3:1) decreases the predicted propionate SUR and growth rate , the pTMFA-predicted intracellular flux distribution during monoculture growth on fumarate and propionate substantially deviated from that shown in S. fumaroxidans converts propionate to acetate, H2, and CO2 or formate [M. hungatei consumes acetate, CO2, H2, and formate and produces CH4 [M. hungatei can also optionally interconvert excess CO2 and H2 to formate via a formate dehydrogenase [During growth in coculture,  formate ,52, whiluces CH4 . M. hungrogenase .M. hungatei and S. fumaroxidans have been grown in both batch and continuous (chemostat) systems. During batch growth, H2 pressure rose during the lag phase and became constant during exponential growth [2 partial pressure [M. hungatei to S. fumaroxidans at constant H2 pressure  have not been reported. Instead, an overall reaction for the coculture ofM. hungatei to four S. fumaroxidans. Since initial conditions for batch experiments were not reported, a continuous culture model was constructed and first evaluated using a 3:4 relative biomass ratio (M. hungatei: S. fumaroxidans).Cocultures of l growth . Continupressure . Howeveriscussed , which cM. hungatei were produced by S. fumaroxidans.The continuous coculture model included constraint-based models for each microbe and mass balances around the reactor . As the reactor dilution rate increased, the predicted species yields of H2 and CO2/formate (S. fumaroxidans) and CH4 (M. hungatei) decreased slightly , M. hungatei did not fully utilize all of the H2 and CO2/formate produced by S. fumaroxidans, even at high dilution rates , M. hungatei growing at faster rates than S. fumaroxidans , or a combination of the two.Species’ uptake rates, secretion rates, and relative biomass ratio affects overall bioreactor yields, and these bioreactor yields were subsequently investigated . At a 3:on rates . The netly 1.6:1 . These sS. fumaroxidans passes electrons to M. hungatei via formate, as well as H2 [2  and formate , which must be maintained in order for methanogenesis to occur. All of these predictions were insensitive to both the dilution rate and biomass ratio (M. hungatei: S. fumaroxidans).Finally, steady-state metabolite concentrations in the coculture were predicted using thermodynamic variability analysis ,34 S1 T. SimilariMhu428 and iSfu648 thermodynamic models were successfully used to verify proposed carbon and ATP production pathways in M. hungatei and S. fumaroxidans; however, some more efficient pathways (based on ATP or biomass yields) were found in some cases. The computational results highlighted topics that necessitate further discussion relating to (1) the stoichiometry of transport reactions, (2) the thermodynamics of H2 production, and (3) the carbon and electron shuttles between species in coculture.The iMhu428 model content comes from the iMB745 reconstruction of M. acetivorans and still needs to be verified. Changes to model content could affect the conclusions drawn about M. hungatei behavior. Despite containing a complete methanogenesis pathway, the iMhu428 model was unable to identify the H+/Na+ transport stoichiometry of the energy-converting (Eha- or Ehb-type) hydrogenase , which is thought to pump H+/Na+ while reducing ferredoxin [HDR, 1.8.98.1) can also reduce ferredoxin, and the iMhu428 model predicted HDR to be the only ferredoxin-reducing reaction required for methanogenesis. This observation is consistent with the observation that the expression of Eha/Ehb is considerably lower than that of HDR [The majority of the t of HDR .MTSPCMMT_CM5HBCMT, E.C. 2.1.1.86) could drive transport of up to 4 Na+ ions under standard conditions, instead of the 2 Na+ ions used in the iMhu428 reconstruction. Furthermore, some studies suggest the archaeal A1A0 ATP synthase is coupled to Na+ instead of H+ translocation [iMhu428 model was modified to reflect this coupling, the model predicted the Na+/H+ antiporter was no longer active, as Na+ ions from MTSPCMMT_CM5HBCMT were directly used for ATP synthesis. Thus, while the modeled methanogenesis pathway is consistent with available data, it is not the only possibility.Additionally, different stoichiometries for other ion transport reactions important to methanogenesis remain thermodynamically possible. For example, the group contribution method predicted that tetrahydromethanopterin S-methyltransferase  of ferredoxin, resulting in no ΔrG'0 estimates for these reactions. Fortunately, new quantum chemical approaches for estimating the thermodynamics of metabolism [fG'0 estimates.Analysis of the tabolism  may poteG'0 of groups directly when modeling thermodynamics [E. coli made the model computationally difficult to solve. In this work, using either the ΔG'0 of molecules or groups to model thermodynamics proved computationally difficult (results not shown). Instead, only using ΔrG'0 as the basis for thermodynamic calculations enabled uncertainties in free energy estimates to be handled without any computational difficulties. However, using ΔrG'0 as a basis for thermodynamic calculations leads to larger uncertainties in ΔrG'0 and greater network flexibility, as it does not account for thermodynamic interconnectivity between reactions with shared metabolites. As a result, the model-predicted feasible ΔrG'0 range through a linear combination of reactions considerably exceeds the group-contribution predicted ΔrG'0 range of the overall reaction. In the future, thermodynamic interconnectivity should be captured using the ΔG'0 of molecules or groups, and optimization techniques are needed to improve the runtime performance of the resulting thermodynamic models.This work also raises important questions about the appropriate mathematical basis for representing thermodynamic constraints. Previous studies used the Δdynamics , and fourG'0 as a basis for thermodynamic calculations is insufficient to explain why the iSfu648 model could still produce H2 under monoculture growth conditions. As described in fG'0 was used as a basis for thermodynamic calculations when attempting to find additional constraints which would prevent H2 production. The failure to find such constraints indicates either that thermodynamics does not explain the absence of H2 production in monoculture, or that current thermodynamic models cannot capture this phenomenon. If it is the latter, more accurate group contribution methods with smaller error estimates may eventually be able to explain the role of thermodynamics in syntrophic associations.However, the use of Δ2 in electron transfer in the community, and led us to hypothesize that total H2 consumption by the community indicates that M. hungatei cells are more abundant and/or faster growing than the S. fumaroxidans cells. The coculture model correctly predicted that both H2 and formate could shuttle electrons between members of this community. Formate may be preferred over H2 for electron transfer for thermodynamic reasons, as the ΔrG'0 of 2 and H2, S. fumaxoridans could sustain propionate oxidation at higher extracellular concentrations of formate than of H2. Formate exchange could also be preferred due to differences in kinetics, diffusion, and/or volatility. These scenarios could stabilize the syntrophic association by enabling faster shuttling of electrons to M. hungatei.The thermodynamic coculture model of the syntrophic association between these species confirmed the role of formate and HiSfu648 model to predict the observed behavior of S. fumaroxidans. Additional constraints on reaction directions and flux ratios were required before iSfu648 could be combined with iMhu428 model to simulate the coculture. In addition, the iSfu648 model relied on data from gene expression and 13C NMR experiments, suggesting that constraint-based approaches will complement traditional top down (‘omics’) approaches [This work highlights some important obstacles to successful modeling of microbial consortia. In order for computational models of microbial communities to make meaningful predictions, individual species models must be integrated into a community model in a biologically relevant manner. Such integration will require an understanding of the objectives and constraints governing the behavior of each community member, and this work demonstrates that identifying the proper constraints and objectives requires extensive experimental characterization of the community. For example, neither maximization of growth rate nor maximization of ATP yield were sufficient for the proaches  by enablproaches .iMhu428 reconstruction of M. hungatei was built from the iMB745 reconstruction of M. acetivorans [iMB745 M. acetivorans reconstruction were copied into the M. hungatei reconstruction, with modifications to reflect key metabolic features of M. hungatei , and 639 metabolites. Of the 428 genes, 351 were added based on sequence homology, and 77 were added manually. The reconstruction is available in The final iSfu648 reconstruction of S. fumaroxidans was built from KEGG [iSfu648 reconstruction contains 874 reactions, 648 genes (associated with 770 reactions), and 893 metabolites. The reconstruction is available in The rom KEGG   and its flux (v) have opposite signs. Estimates   is a conn method  via the n method . TMFA wan method , with adpFBA  is a conFor the coculture simulations, a community model of growth in a continuous stirred-tank reactor was developed that accounts for the biomass concentrations of each species. The model minimizes the species-weighted total flux through the metabolic networks subject to TMFA constraints for each species and mass balances around the entire reactor. Details on the specific implementation used in this work can be found in The uptake fluxes for carbon and other nutrients used in the simulations are given in S1 Table in S1 DatasetThis file contains Supporting Tables S1 to S7 as described in the text.(XLS)Click here for additional data file.S2 Dataset(XLS)Click here for additional data file.S3 Dataset(XML)Click here for additional data file.S4 Dataset(XLS)Click here for additional data file.S5 Dataset(XML)Click here for additional data file.S6 Dataset(XLS)Click here for additional data file.S7 Dataset(XML)Click here for additional data file.S1 File(ZIP)Click here for additional data file.S2 File(ZIP)Click here for additional data file.S1 TextThis file contains Supporting Methods, Results, and Figs to complement the main text.(PDF)Click here for additional data file.


These analyses include concertedly linked experimental and computational stages: (i) assuming the metabolic model and optimizing the experimental design; (ii) feeding the investigated organism using a chosen 13C-labeled substrate (tracer); (iii) measuring the extracellular effluxes and detecting the 13C-patterns of intracellular metabolites; and (iv) computing flux parameters that minimize the differences between observed and simulated measurements, followed by evaluating flux statistics. In its early stages, 13C-MFA was performed on the basis of data obtained in a single labeling experiment (SLE) followed by exploiting the developed high-performance computational software. Recently, the advantages of parallel labeling experiments (PLEs), where several LEs are conducted under the conditions differing only by the tracer(s) choice, were demonstrated, particularly with regard to improving flux precision due to the synergy of complementary information. The availability of an open-source software adjusted for PLE-based 13C-MFA is an important factor for PLE implementation.Steady-state in silico simulation confirmed that flux precision is improved when 13C-MFA is implemented by fitting PLE data to the common model compared with SLE-based analysis. Efficient flux resolution could be achieved in the PLE-mediated analysis when the choice of tracer was based on an experimental design computed to minimize the flux variances from different parts of the metabolic network. The analysis provided by OpenFLUX2 mainly includes (i) the optimization of the experimental design, (ii) the computation of the flux parameters from LEs data, (iii) goodness-of-fit testing of the model’s adequacy, (iv) drawing conclusions concerning the identifiability of fluxes and construction of a contribution matrix reflecting the relative contribution of the measurement variances to the flux variances, and (v) precise determination of flux confidence intervals using a fine-tunable and convergence-controlled Monte Carlo-based method.The open-source software OpenFLUX, initially developed for the analysis of SLEs, was extended for the computation of PLE data. Using the OpenFLUX2, open-source OpenFLUX2 provides a friendly software environment that facilitates beginners and existing OpenFLUX users to implement LEs for steady-state 13C-MFA including experimental design, quantitative evaluation of flux parameters and statistics.The developed The online version of this article (doi:10.1186/s12934-014-0152-x) contains supplementary material, which is available to authorized users. The corresponding calculations were provided for 5 independent SLE with earlier simulated MIDs and the measured effluxes with the η -function. The summarized values of the η -function for all fluxes, (Ση(0.95)([1 − 13C]SLE))k, which were calculated for each flux from k = 1, 2, …, 5 SLEs, varied in a rather narrow range (between 37.9 and 38.5). Considering the total number of fluxes  for the assumed metabolic model and the detected measurement-dependent sensitivity of the η -function, if the difference between the Ση(0.95) values is no greater than 1, then the LEs can be considered to be provided with an essentially equivalent flux precision. Certainly, the value of Ση(0.95) could be considered only as a general, conditional measure of flux resolutions in the investigated network: some metabolic branches could be resolved better, and other branches – worse in different experiments with, perhaps, equal values of summing normalized flux precision functions. So, only the value of the ηγ(ui) -function could be considered as the absolute measure of the ui-flux resolution efficiency that could be compared with other LEs performed with the same metabolic model.Concerning the tracer-specific values of entclass1pt{minima13C-MFA included the set of estimated fluxes, the goodness-of-fit of the model’s adequacy, and the confidence intervals of fluxes, in accord with the recently recommended publishing guidelines -glucose as a tracer (see Table (Exp_3.1)). The “contribution matrix” , u(θtrue), as an important component of the flux statistics ij elements of this matrix indicate the relative importance of the variance of the j-th measurement to the local variance of the i-th flux. As can be observed in Additional file F P5P + CO2; PYR + CO2 →F OAA; OAA →F PYR + CO2; and CO2_EX →R CO2, respectively. The new set of “experimental data” was generated in the following fashion: the measured effluxes and all MIDs, except for “SER” MIDs, were considered as in the previously analyzed example. The “SER” MIDs were modified as in the case of “poor” resolution of the SER-390 (m + 0) MID, which exhibited an unknown by-product that increased the value of the corresponding SER peak to +4.5%. Due to the necessity of normalizing all SER-isotopomers, the applied modification resulted in a proportional decrease in the other SER MID portions; therefore, the SER-390 MIDs were modified from (m + 0)/(m + 1)/(m + 2)/(m + 3) = 0.443/0.357/0.140/0.042 (in the previously described example) to a ratio of 0.463/0.344/0.135/0.040.In this report, an example is presented in which the mistaken flux parameters could be assumed when the statistical analysis of the obtained solution was partially provided but to an insufficient extent. Let us analyze the first from five earlier described with 99% -13C-glucθ) objective function with the empty null space of the Jf(θ) matrix evaluated at the new point of convergence, χ2 -statistically acceptable interval (see Table (Exp_4.1)). Simultaneously, the parameters obtained for several fluxes did not coincide with the true flux values even in the range of their confidence intervals. Among them there were fluxes θ8 and θ31 ij elements in the Additional file It is obvious that measured MIDs and effluxes’ parameters are completely separate categories of experimental data that could not be trivially compared. It is well established  that lab13C-labeled isotopomer of glucose, [U-13C]-glucose, is often used as a tracer in 13C-MFA. In the present study, new sets of “experimental data” were generated for the same metabolic model when different relative amounts of [U-13C]-glucose  mixed with non-labeled ([U-12C]) glucose were used as the sole carbon source  and Additional file 13C-MFA resulted in values between 35.4 and 40.4 for the Ση(0.95)([U − 13/12C]SLE) function , e.g., θ22: PYR + CO2 →F OAA; v23: OAA →F PYR + CO2; θ31: CO2_EX →R CO2. The PLE-based 13C-MFA of experiments using [U-13/12C]-glucose as the carbon source actually improved the resolution of all fluxes estimated in the corresponding SLEs, Ση(0.95)([U − 13/12C]PLE) = 42.4. Moreover, the tracer-specific behavior described in relation to SLEs was reproduced in the PLE. The colored scheme presented in Figure η -function for all fluxes obtained in different experiments, illustrating this fact and demonstrating that optimization of the experimental design is necessary to increase the precision of the targeted fluxes -glucose as the tracer) using the value of the To achieve the best flux resolution in an LE employing a mixture 78 %/[U − 13C]22 % glucose isotopomers in the SLE  when some modifications differed in the metabolic model of the l-lysine-producing C. glutamicum strain applied in the present study, and plans were made to obtain another set of measurements , [U − 13C] and [U − 12C] glucose isotopomers (“randomly mixed”) as the tracers. As shown in the presented results ([1 − 13C]78 %[U − 13C]22 %SLE) = 38.4, with “partially optimized” conditions – Ση(0.95) = 38.4 was detected here, and among described earlier . Simultaneously, the free fluxes, θi, that were used as the targets for the partial optimization generally showed the best resolutions ,  function ). On the other hand, the real difference between summarized values of the η -function for all fluxes determined for these PLEs was very small  in the individual LEs of the PLE, was recently developed to evaluate metabolic fluxes for the metabolic model of wild-type Escherichia coli with a high precision [13C]-glucose, and separate [3-13C]-glucose in the medium provided the most precise fluxes for the E. coli model among all possible paired combinations of singly labeled glucose added as the sole tracer.It has been repeatedly shown ,59,72 threcision . Additio13C-glucose isotopomers, which were separately added to the medium as tracers for SLEs and PLEs, was investigated. The solutions of the corresponding constrained NLLSPs are summarized in Additional file D-factor (see (S − 1.5.14)) for the six singly labeled tracers, the volume of the confidence ellipsoid for free fluxes had to be increased in the following order for the applied tracers:It was of interest to evaluate the advantages of this approach in the case of another metabolic model used in the present study. Initially, the possible exploitation of all six singly labeled 13C]-glucose as a singly labeled glucose tracer. Simultaneously, a different order of the tracers could be formed by increasing the summarized values of the normalized flux precision functions, Ση(0.95)([i − 13C]SLE) ≡ Ση(i), i = 1, 2, …, 6:13C]-glucose was the best tracer according to this Ση -based criterion; namely, [4-13C]-glucose was the best tracer for determining 13 fluxes, and the corresponding values of the η -function for this tracer for 28 other fluxes were among the maximal values in the range of no more than 0.02, which is a typical value for measurement-specific errors. Notably, the applicability of [4-13C]-glucose as the tracer for the efficient resolution of primary branch points and reversibilities was previously demonstrated for a similar C. glutamicum metabolic model -glucose and [1-13C]-glucose provided the most precise fluxes. In contrast, the use of [2-13C]-glucose and [5-13C]-glucose as the tracers in two independent LEs provided the least precise fluxes:The flux resolution obtained in each of the six independent LEs could clearly be improved via PLE-based flux analysis if two or more LEs were combined in the PLE. The corresponding calculations for the possible pair combinations are provided  that previously resulted in the best flux resolution according to the Ση -based criterion . The obtained results are presented in Additional file θ4, θ14, θ22, see Additional file θ8, θ10, θ26, see Additional file 13C] labeled glucose. The second PLE consisted of six LEs, where, in addition to the three LEs from the previous PLE, three LEs used the optimized mixed paired combinations of glucose isotopomers. The corresponding PLEs were simulated, and 13C-MFA was performed, resulting in solutions of NLLSPs with highly resolved fluxes PLE = 44.6 Additional file , Figure To test this possibility, partial optimization was conducted by searching the minimal linear approximation of free flux variances in the triangle composed of all possible mixed tracers, which consisted of the three singly labeled glucose isotopomers  > 0; i.e., the length of the 95% confidence interval did not exceed the value of the corresponding flux corrected by the scaling factor.Notably, an exceptionally high flux resolution was obtained in both PLEs including 6 LEs. For all fluxes, 13C-MFA. These extensions included (i) fitting the obtained experimental data, not only in SLEs but also in PLEs, to the assumed metabolic model; (ii) computing the flux parameters and providing the goodness-of-fit of the model’s adequacy, followed by an estimation of the model’s viability and its probable improvement; (iii) fine-tunable and convergence-controlled Monte Carlo-based approach to obtain distribution of optimized flux estimations followed by precise computing of flux confidence intervals; and (iv) conducting general and/or partial experimental design through searching for the minimal value to characterize the average confidence interval length for all free fluxes and/or the minimal linear approximation of the targeted free flux variances, respectively. The considered examples demonstrated the specific features of these steps and their concerted essentiality for obtaining statistically verified results of the described 13C-MFA provided with the help of OpenFLUX2.The main aim of the present study was to extend the possibilities of the previously developed open-access OpenFLUX software for comprehensive ηγ, allowed for the quantitative characterization of the efficiency of the flux resolution at a confidence level of γ, with values of η close to “1” or “0” being obtained for efficiently or poorly resolved fluxes, respectively, depending on β as the scaling parameter, in particular. Moreover, the sum of η for all of the evaluated fluxes in the model, Ση, could be a rather convenient parameter for conditional comparison of the flux precision achieved in different experiments, i.e., depending on the tracer(s) used.Introducing the normalized flux precision function, χ2 -statistically acceptable value of the Ξ objective function but also confirmation of the expectation concerning the The goodness-of-fit test of the assumed metabolic model’s adequacy is an essential and extremely important part of the statistically verified solution of the NLLSP. This test involved not only obtaining the 13C-labeled glucose isotopomers or combinations of mixed labeled tracer(s), chosen according to the partial optimization of the targeted fluxes, usually resulted in better flux precision compared with the resolution of fluxes computed from SLE data, which were provided according to a sophisticated general experimental design strategy. Only one simplified metabolic model, consisting of an l-lysine-producing C. glutamicum strain, was used for the simulation of experimental data and for comprehensive 13C-MFA in the present study; however, the obtained general conclusions coincided well with the data reported by other groups working with other models  using as e.g., ,58)..13C-labe13C-MFA of SLE and PLE data will help to broaden investigations aimed at quantitatively estimating the cellular metabolic state with high precision.We hope that the developed OpenFLUX2 open-access software adjusted for comprehensive OpenFLUX2 is being released as open-source software. Regarding the citation of the OpenFLUX2 application, it is highly recommended that the present paper be cited, adding that this software is an extended version of the OpenFLUX open-source software described in .C. glutamicum metabolic model used as an example included catabolic reactions of the central metabolism, such as the Embden-Meyerhof-Parnas (EMP) and pentose phosphate (PP) pathways, the tricarboxylic acid (TCA) cycle, anaplerotic carboxylation, and the decarboxylation reaction of oxaloacetate and malate. Moreover, the pathways for the biosynthesis and transport of l-lysine and different extracellular co-products  were included. For glycine synthesis, two possible pathways, starting from serine and threonine [2-associated carbon transfer, the reactions, accompanied by CO2 production or consumption, were expressed in an explicit manner, including an anabolic reaction and a reaction involving CO2 exchange with environment. Additionally, the linear consequence of the irreversible reactions was represented as a single reaction. The forward and reverse components of a bi-directional reaction were considered as two non-negative fluxes. The biosynthetic pathways for the following amino acids were expressed explicitly: (i) the amino acids whose mass isotopomer distribution (MID) was assumed to be measured and (ii) the amino acids whose synthesis was accomplished by CO2 release. In Figure C. glutamicum [The hreonine ,76, werehreonine . Pools outamicum  as the sutamicum .S, dim(S) = (36 × 51), with 36 balanced metabolites, 51 unknown fluxes, and r = rank(S) = 36, was finally generated. As a result, 15 fluxes should be assigned as free. Seven fluxes were experimentally determined effluxes, which included biomass biosynthesis (1 flux), the effluxes of secreted products (5 fluxes), and the glucose uptake rate (1 flux). Unless otherwise stated, these fluxes were considered free fluxes constrained according to Eq. (S − 1.2.2) with the experimentally determined parameters S − 1.3.7), which was subjected to a least-squares minimization procedure. Five free fluxes were automatically assigned due to their accordance to the reverse reactions: (i) in the non-oxidative branch of the PP pathway (3 fluxes), (ii) catalyzed by glucose-6-phosphate isomerase (1 flux), and (iii) for the carbon dioxide intracellular exchange (1 flux). The three remaining free fluxes were previously automatically assigned using OpenFLUX software [The resulting metabolic model contained 54 reactions and 36 balanced metabolites. The reactions, which only map the labeled distribution of the metabolite onto the corresponding amino acid , did not participate in the stoichiometric balance. Thus, the stoichiometric matrix software . There w2 (13C isotope abundance of 1.07%). In total, 17 sets of matrix equations of EMU balances were used to calculate the 107 unknown EMUs from 15 known EMUs of input substrates. The application of the EMU approach reduced the number of unknown variables from 9,138 unknown scalar variables in the full isotopomer model to 360 scalar MID variables, corresponding to 107 unknown EMUs.An isotopomer model was automatically built based on the EMU approach that simulated the MIDs of target compounds from the known MIDs of input substrates. The input substrates were specifically labeled glucose and naturally labeled COn carbon backbone atoms was represented by a mass distribution column vector (MDV), whose elements corresponded to the fractional abundances , with m0 is the molecular weight of an unlabeled compound. As the published MIDs were uncorrected in accordance with the natural mass isotopomer abundance, all the simulated EMU variables were modified for mass interference from non-carbon backbone isotopes in dependence on the chemical structure of the derivatized substance according to the method developed in [n + 1) simulated mass isotopomers  were normalized to unit followed by truncation of the isotopomers number to the dimension of the m0, m1, m2) were presented for derivatized mass isotopomers fragments of Ala-260, Val-288, Thr-404, Asp-418, Glu-432, Ser-390, Phe-336, Tyr-466, Tre -361 isotopomers, and MIDs m0, m1) of Gly-246. At the same time, an extremely small MID variance of 0.15% for mass isotopomer fractional abundances was indicated in [SF-1.3.). These MID variances were assumed to be significantly underestimated. Indeed, the minimal published GC-MS measurement errors are usually approximately 0.2-0.4 mol% [13C-MFA has been applied [Initially, the calculation of “true” flux values for the assumed metabolic model was performed using experimental data on effluxes and MIDs, together with their variances, available from the literature ,68. The loped in . Then fomentclass2pt{minim0.4 mol% , and val applied ,58,79. Tin silico LEs, the OpenFLUX(2) forward simulation option was used, which calculated the MIDs of selected EMU variables from the assumed metabolic and isotopomer networks as well as the known label states of input substrates (13C-tracer(s)) and the assumed values of the fluxes. Unless otherwise stated, the true flux values, utrue(θtrue), were used for direct “experimental” data simulation in the present study. An isotopic purity of 99% was assumed for all of the specifically labeled carbons in the input substrates, and natural enrichment (1.07%) was assumed for other carbons. Again, the simulated MIDs of a substance with n carbon backbone atoms were represented by MDV with (n + 1) elements, n + 1)  .Flux estimation, identifiability, and goodness-of-fit analyses were performed using OpenFLUX2 software. To determine the global minimum of the constrained NLLSP, as a rule, 100 independent iterative trials, starting from randomly selected points from a feasible constrained domain in the free flux variation space, ℜSF-1.7. for details).Monte Carlo-based approach included previously in OpenFLUX, but significantly modified in the OpenFLUX2 software for convenience of the computation process tuning and control of convergence, was used for estimation of the flux confidence intervals. Usually the most reliable results could be obtained if the variant when “multi runs per trials” approach was chosen for estimation of the optimized flux parameter distributions followed by determination of flux confidence interval borders according to “discarding” strategy after confirmation of the borders convergence  requires Java and MATLAB, including the Optimization and Statistics Toolboxes. The current version of the OpenFLUX2 software was tested using Java 6  and MATLAB 7.12.0  software, together with the Optimization Toolbox (version 6.0) and the Statistics Toolbox (version 7.5), on the Microsoft Windows 7 Professional  platform on a PC equipped with a 3.2 GHz CPU and 4 Gb of RAM. Using these computation facilities, the calculation of 51 fluxes for the applied metabolic model (see OpenFLUX2 (odel see  requiresodel see  takes ap