Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network. Metabolism constitutes a fundamental component of cell physiology. It allows for the processing of nutrients through chemical reaction networks, resulting in the production of energy and biosynthetic components and regulation of signal transduction processes by affecting the levels of metabolites that control the activity of proteins. Its function is essential for human health and its aberrant status is a hallmark of many diseases,2. A quaOne major limitation in this systems level understanding stems from the lack of quantitative measurements of protein abundance. The absPrevious studies have carried out extensive analyses of the transcriptional abundance of metabolic genes. These sAdvances in mass spectrometry based proteomics have allowed for in depth identification and quantitation of mammalian proteomes from biological samples–17. Theshttp://wzw.tum.de/proteomics/nci60. We began by filtering out proteins in the CPC dataset that were detected in only one sample or tissue type. These sample proteins also contained a far lower average CPC than all other samples  indicating that they might not be relevant for making global predictions on metabolism. Each protein in this list was mapped to 86 known metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes  mapping of genes to pathways. Any protein that was included in at least one pathway was included as part of the metabolic proteome. The average CPC values across all cell lines for each metabolic and non-metabolic protein were used to calculate the metabolic percentage. The pathway percentages were calculated in a similar manner, though, one protein could be involved in multiple pathways, and thus the sum of all percentages does not necessarily equal 100%. In order to calculate the various probability distribution functions of different protein classes, all of the CPC measurements across cell lines were inputted into PRISM (version 6.03), with a standardized analysis computing bin centers.To begin our analysis we considered the NCI-60 cell line panel which is a set of cell lines developed and maintained by the National Cancer Institute that has been used extensively for integrated molecular analyses and drug sensitivity profiling. The proFor each protein involved in the Glycolysis/Gluconeogenesis pathway from the KEGG database, the distribution was classified as the CPC values across all cell lines. The core glycolytic pathway was defined as the progressive steps through which glucose entering glycolysis is converted to lactate or can be diverted to multiple different biological pathways. In order to visualize the differences in protein counts throughout core glycolysis, a pathway was created in CytoScape (version 3.1.1) with the size of nodes corresponding to the average CPC of the respective proteins,21.In order to isolate metabolic pathways containing branching steps, we utilized the Recon 2.2 stoichiometry matrix to separate pathways where a single metabolite acted as a reactant to multiple different reactions—these were defined as our branches. Since sWe then separated the branches into 2 additional categories—one way leaning pathways where branching scores produced a score above. 8, and equally distributed pathways where the top 2 BD score was less than. 2 or the top 3 BD score was less than 0. Branches were filtered to make sure no reactions were repeated in each set. For both sets of branches, either the top 2 or top 3 involved enzymes—dependent of which type of score was used to differentiate them—were separated and mapped to their respective KEGG pathways. For each set, the number of enzymes falling into each pathway was summed and a Fischer’s Exact Test was performed to see if there were any pathways that differed significantly across the two branching sets. Ratios of counts were computed by dividing the number of involved One Sided enzymes by the number of Equally Distributed Enzymes for each of the KEGG pathways.th 2013) identifiers [We defined oxidoreductases as proteins that either had NADP+/NADPH or NAD+/NADH as the reactants or products. Both reactants and products were included so as to allow for proteins with reversible reactions. EC Numbers of appropriate reactions were extracted using the BRaunschweig ENzyme DAtabase  database and then mapped to their appropriate Uniprot (Release 2013_11) or Entrez  interface was used to obtain kinetic properties for all metabolic proteins. We excluded all values not referenced as a wild-type experiment  and further refined the list by excluding any outlier measurements. We then used a previously published list of available ΔG° values[10(CPC), log10(KM), ΔG° values for proteins for which we obtained all three of these variables  and producers  of NAD(P)H are subject of major research efforts as drug targets against cancer[We first attempted to analyze the relative usage of nitrogen, or which aminotransferases were most prevalent within a cell. Using the BRENDA database we isolated all aminotransferases that were detected within the dataset, and found that among the most abundant enzymes were GOT2, GLUD1, and IDH2 . Togetheize NADPH. A majorst cancer,33–36.M) and standard Gibbs Free Energies (ΔG°) of the reactions  and were therefore responsible for the overall lack of correlation between these three variables. This finding is in contrast to a previously held assumption that larger protein concentrations are required for reactions close to equilibrium[M and ΔG° for each reaction involving a given protein concentration appears uncorrelated. There were however a few exceptions to this general rule. First, the proteins in group 2  contained enzymes with either very large KM values or very low ΔG°. The extreme KM values indicate that these enzymes need a substantial buildup of substrates in order to result in an appreciable forward flux while reactions with very low ΔG° are highly irreversible reactions. Indeed, three enzymes with large KM values  directly drain glycolytic intermediates into biosynthetic pathways while three enzymes with very low ΔG°  utilize phosphoribosyl diphosphate (PRRP) derived from the pentose phosphate pathway for NAD(P)H and nucleotide synthesis. Also several enzymes in group 3  are involved in arginine, aspartate, glutamine metabolism, for which the metabolites have some of the highest intracellular concentrations and/or fluxes.Ultimately, we note that that the reaction rate or flux through a point in metabolism involves not only the enzyme concentration, but kinetic parameters and the thermodynamics of the chemical compounds involved in the reaction as well. These parameters include the Michaelis constant  derived from the label-free quantification (LFQ) output generated using the MaxQuant  software(XLSX)Click here for additional data file.S2 TableM values were retrieved from BRENDA and ΔG° from Henry et al.[Kry et al..(XLSX)Click here for additional data file.


Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways  was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. This limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of  Gaining an understanding of metabolism is a central goal of biology, as it defines the growth and development of an organism. In the ‘omics era, cell processes can be described at the level of the transcriptome, the proteome or the metabolome, or possibly combinations thereof. Although none of these approaches actually measure metabolic activity, the data obtained can be used to make indirect inferences. The direct determination of metabolic flux would be highly desirable, as it represents a highly informative aspect of the metabolic phenotype , an important temperate oilseed crop, and a close relative of the model plant Arabidopsis thaliana. Our data demonstrated a disconnect between transcript abundance and the corresponding metabolic flux. The seemingly low contribution of transcriptional regulation over metabolic flux control and the biological significance of this finding are discussed.The aim of the current study was to attempt to relate transcript abundance with metabolic flux on a genome-wide scale, with a view to assessing its relevance for understanding the central metabolism of a crop seed. The plant model employed was the developing embryo of oilseed rape (−2 s−1) and 60% relative humidity. Intact embryos were isolated 20 days post flowering and maintained for 10 days under 50 μmol quanta m−2 s−1 at 20°C in a liquid culture containing a source of organic carbon and nitrogen, as described by Schwender et al.  were grown under conditions of 18°C, a 16 h photoperiod  and total experimental time 5 h 40 min. To calculate the volumes of the different seed organs, the MRI-dataset was imported into the software AMIRA (Mercury Inc.) and image segmentation was conducted. The average signal in each of embryo organs was calculated as previously described  using an absolute quantification method , we designated a reaction “T”  if the flux magnitude is higher in accession 3170, “S” (starch responsive) if the flux magnitude is higher in 3231, and “N” if a flux is unchanged. For a difference to be judged as a change the numerical tolerance was 10−7 μmol/h. bna572+ is referenced to the Arabidopsis genome, i.e., reactions have associations with Arabidopsis gene identifiers (Gene-Protein-Reaction associations). As the B. napus transcriptome was annotated by sequence homology to Arabidopsis, differences in transcript abundance between the two accessions could be compared to differences in flux based on the common Arabidopsis reference.Experimental details and the analysis of data related to MFA and flux variability analysis are detailed in the accompanying paper by Hay et al. . In shorB. napus embryos was isolated using the Spectrum Plant Total RNA kit (Sigma) according to the manufacturer's protocol. The samples for transcriptome analysis were prepared using an Illumina kit following the manufacturer's instructions. Briefly, beads with Oligo(dT) were used to isolate poly(A) mRNA from the total RNA. Fragmentation buffer was added in order to convert mRNA into short fragments. Taking these short fragments as templates, random hexamer-priming was used to synthesize the first-strand cDNA. The second-strand cDNA was synthesized using buffer, dNTPs, RNaseH, and DNA polymerase I. Short fragments were purified with the QiaQuick PCR extraction kit and resolved with EB buffer for end repair and for addition of poly(A). Subsequently, the short fragments were connected with sequencing adapters. Following agarose gel electrophoresis, the suitable fragment fraction was selected for PCR amplification. Finally, the library was sequenced using Illumina HiSeq™ 2000 .The total RNA from the Brassica rapa and Brassica oleracea using the splice-aware aligner TopHat (v2.0.8b)  putative cDNA sequences by aligning RNA-seq reads to the genome references of diploid progenitors www.bioconductor.org) using default parameters were run for the normalization of the RNA-seq read count data. The negative binomial test implemented in DESeq was used to assess differential expression between the two genotypes. Variance stabilizing transformation  using the org.At.tair.db v2.9.0 package (www.bioconductor.org). A Fisher exact test, as implemented in topGO v2.12.0 (www.bioconductor.org), was employed to test for the significance of category enrichment  to a particular functional category, GO annotations were retrieved from TAIR (http://primer3.ut.ee) to generate an amplicon size of 150–200 bp, targeted mainly the 3′-UTR of the selected genes (three pairs of primers per gene). The specificity and amplification efficiency of each primer pair was checked by means of running standard curves with melting curves. Each assay comprised three biological replicates, each of which was repeated three times. Relative transcript abundances were estimated using the 2-ΔΔCt method . A 100 ng template of this cDNA provided the template for a qPCR based on the SYBR® Green PCR Master Mix . The necessary primers, designed with Primer3 software (RNASeq data derived from this study (accession SRP047515), including all project and biosample data were made publicly available under the following links:http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA262144http://www.ncbi.nlm.nih.gov/biosample/?term=SRS709415http://www.ncbi.nlm.nih.gov/biosample/?term=SRS717382.http://mapman.gabipd.org) was used for the functional characterization of DTGs. The threshold for significance was set to 0.01 with a minimum log2 (fold change) of 2. Differentially expressed transcripts that exhibited similarity to annotated A. thaliana genes, but showed a different trend in expression level were sorted out for further analysis with MapMan software.The MapMan visualization tool  as the major class of storage lipid. The flux data consist of flux variability bounds, calculated using the extended bna572+ stoichiometric model  and flux-change-value are positive in accession 3170 vs. 3231 or vice versa. VANTED was used to create a simple hierarchical map of the bna572+ model , we selected two accessions: 3170 accumulated more lipid but slightly less dry matter in its mature seed than 3231  and genome wide transcript profiling (RNA-sequencing). As detailed in the Hay et al. , followed by organic acids and free amino acids  between the accessions were accompanied by obvious shifts in the corresponding intermediates. The outcome of the combined analysis is presented in Figure In order to compare metabolite profiles with net flux rates  to the two rapeseed accessions ; the dataset is listed in Supplementary Table Based on the definition of differential gene expression , the RNA-Seq data suggested that 2156 transcripts were more abundant in accession 3170 than in 3231 (up-regulated DTGs), while 1958 were less abundant (down-regulated DTGs) . Supplementary Table Arabidopsis associated gene identifiers. Arabidopsis related MapMan ontology is also shown  and starch (higher in 3231) content and in analogy to the flux responsiveness types “T”  and “S” , we designated significantly higher transcript levels as “T” and “S,” respectively. For further interpretation the genome of the closely related model plant species ls Table . For 847ls Table .Arabidopsis genes  while others were down-regulated  in high-oil accession.The presence of DTGs within the MAPMAN category “regulation” provided some leads to explain the contrasting metabolic phenotypes of the accessions Figure . There wArabidopsis genes map to 545 reactions. 860 of these genes where found in the B. napus transcript annotations. Supplementary Table In the bna572+ metabolic model, 966 The generic photon uptake flux in reaction #6  is found TAG responsive. Note that the embryo culture experiment of the two accessions took place in parallel under the same light conditions. However, this observation might be explainable considering the indication of up-regulation of photosynthetic genes in the high oil accession Table . This suThe metabolic network derived from the bna572+ model comprised 515 nodes, with some model reactions for metabolite uptake and secondary pathways having been excluded. This set of reactions is shown as Figure Overall, there is little evidence for a scenario where a positive fold change of one data domain (transcript) is leading to a corresponding fold change in the related data domain (flux). The inevitable conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes.http://sourceforge.net/projects/vanted/), the ViewData-Addon can be readily installed, using the addon manager implemented in VANTED—this feature will enable the user to browse details of the data contained within each node. For example, it allows to easily explore multiple mappings by listing all of the Brassica transcript IDs that are related to one model reaction ID  were quite distinct Figure . The com2 fixation, to provide additional ATP/NADH and eventually to contribute to the light-dependent regulation of fatty acid synthesis  fatty acid synthesis pathway Figure . This fiB. napus has biological significance. Similar lack of correlation has been found for prokaryotic and eukaryotic microorganisms .In conclusion, this genome-wide study has revealed a remarkably low level of connectivity between transcript abundance and metabolic fluxes. With few exceptions , Hardy Rolletschek, Ljudmilla Borisjuk and Jörg Schwender designed the research. Ljudmilla Borisjuk analyzed data. H. Roll. (seed composition) and Jörg Schwender (13C-MFA) performed research, analyzed data, and wrote the paper. Jordan O. Hay analyzed data. Christina König performed research (qPCR), analyzed data and wrote the paper. Inga Hebbelmann performed research . Nicolas Heinzel performed research  and analyzed data. Matthias Klapperstück analyzed data  and wrote the paper. Eberhard Munz, Peter M. Jakob and Ljudmilla Borisjuk performed research (MRI) and analyzed data. Peter Denolf, Stefanie De Bodt, Henning Redestig and Evelyne Caestecker performed research  and analyzed data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mycobacterium tuberculosis after exposure to antibiotics using proteomics data and flux balance analysis (FBA). The use of FBA to study prokaryotic organisms is well-established and allows insights into the metabolic pathways chosen by the organisms under different environmental conditions. To apply FBA a specific objective function must be selected that represents the metabolic goal of the organism. FBA estimates the metabolism of the cell by linear programming constrained by the stoichiometry of the reactions in an in silico metabolic model of the organism. It is assumed that the metabolism of the organism works towards the specified objective function. A common objective is the maximization of biomass. However, this goal is not suitable for situations when the bacterium is exposed to antibiotics, as the goal of organisms in these cases is survival and not necessarily optimal growth. In this paper we propose a new approach for defining the FBA objective function in studies when the bacterium is under stress. The function is defined based on protein expression data. The proposed methodology is applied to the case when the bacterium is exposed to the drug mefloquine, but can be easily extended to other organisms, conditions or drugs. We compare our method with an alternative method that uses experimental data for adjusting flux constraints. We perform comparisons in terms of essential enzymes and agreement using enzyme abundances. Results indicate that using proteomics data to define FBA objective functions yields less essential reactions with zero flux and lower error rates in prediction accuracy. With flux variability analysis we observe that overall variability due to alternate optima is reduced with the incorporation of proteomics data. We believe that incorporating proteomics data in the objective function used in FBA may help obtain metabolic flux representations that better support experimentally observed features.We present a study of the metabolism of the  This is, for instance, the approach followed by the E-flux method  is the flux vector, and c is the vector of coefficients of the linear combination. The dimension N of both vectors is equal to the number of reactions (i.e. metabolic fluxes) in the model. In the case of biomass maximization, vector c is an all-zero vector except for a one (1.0) in the position corresponding to the biomass reaction:vbio is the corresponding flux for biomass in the model. In our method we propose to use the same linear combination of fluxes for the objective function, but instead of maximizing the biomass flux, we calculate the coefficients ck from proteomics data.In this section we describe our proposed objective function. We explain how proteomics data can be used to determine an objective function for FBA in cases where the organism is under drug-induced stress, and thus optimal growth should not be assumed as the metabolic goal of the cell. We propose to define the objective to be a linear combination of fluxes as in , 21. Howp containing the quantitative values of protein levels obtained with the proteomics experiment , the number K of identified proteins is only a subset of the whole proteome of the cell, a difficulty stemming from two facts; (1) the method performs identification by comparison of peptide fragments against a database, so that only previously identified proteins are accounted for and (2) not all proteins may be susceptible to the cleavage procedure.We first define a vector ment cf.  for one 6h control but not in sample day 1 mefloquine), we set the levels of all proteins absent from a sample to zero. It is also important to note that while the proteome experiment quantifies both enzymes and signaling proteins, vector p contains only the subset of proteins with enzymatic activity and that are present in the in silico model. Although this leaves a number of important regulatory proteins not accounted for, we propose to include regulatory information in a subsequent analysis.Moreover, since some proteins may not appear in a specific sample . For this reaction the catalytic proteins are combined in the following boolean expression:OR and AND respectively. The proteins in this expression are the two transferases Rv2612c (IGY178), Rv1822 (I6X2EA) and the synthase Rv0046c (I6X8D3), all involved in inositol synthesis , since the AND expression is calculated using the minimum of the two values in As is clear from the objective function We note that by using data from different time points it is possible to define different objective functions for different time points, something that it is not possible when either the more common objective biomass yield or any other time-independent objective function is used. In this work we assume that our proteomics data for the control group should not be significantly different even for different time points, thus we use an average value of these data to perform FBA for the control condition. Our proposed methodology focus on defining an objective function by using proteomics data. Therefore, our FBA constraints are unaltered and are the same as the ones used with the regular FBA procedure. This way, when we present results of comparisons between our method and the E-flux method, we are comparing two variants of the plain FBA procedure modified by the introduction of proteomics data: one method (E-flux) uses proteomics data to redefine flux constraints and a biomass objective function, while the method proposed here uses proteomics data to define the objective function and unconstrained internal fluxes (as in the regular FBA method).and are catalyzed by essential enzymes needed for growth as presented by [essential reactions .In this study we have performed comparisons of the proposed methodology with the alternative method E-flux . This meented by . Here weAs a validation step, we performed comparison of prediction error between our proposed method and the E-flux method for a control condition and treatment conditions in different time points. We perform FBA for both methods using randomly chosen subsets of our proteomics data and the remaining subset of proteins to evaluate the prediction of the model in terms of enzyme abundances. This cross-validation step was performed as follows: For one specific time point and experimental condition the set of proteomics data was randomly split in two groups using a 80/20 rule, i.e. 80% of the proteins were used to defined the objective function in our proposed method and to define normalized constraints for the E-flux method, while 20% of the proteins were used as a validation set to compare fluxes and enzyme abundances. This procedure was repeated 16 times with different random splits. The enzymes in the validation set were evaluated in the boolean expressions and the resulting values were used for comparison with the corresponding fluxes obtained with FBA. Normalized squared prediction error was used to evaluate prediction accuracy of the two methods. In doing so, we normalized the error by the number of reactions in the validation set, as without normalization different splits would result in a different number of reactions depending on the boolean expressions evaluated. The validation procedure was repeated and results obtained in different realizations are in agreement. In order to obtain some insight into the metabolic behavior for alternative optima, we perform a final step of flux variability analysis, again comparing both methods. Our results are presented and discussed in the next section.in silico genome-scale metabolic network of the Mycobacterium tuberculosis presented in [Using the reconstructed ented in , we perfented in . For eacessential reaction we mean a metabolic reaction catalyzed by enzymes considered essential for growth according to Sassetti et al. [First we compare the results of FBA for the biomass objective function and the objective function defined using quantitative proteomics data from the control condition only. We must check whether using a proteomics objective function instead of the more common biomass goal, still produces sound flux results. One possible way is to observe the number of reactions carrying zero flux that are considered essential, and where by an i et al. . In the i et al. , the numi et al. ). The rein silico model is the following:Mycobacterium tuberculosis. The names of metabolites in this stoichiometric equation follow the nomenclature of convenience used in the GSMN model and can be found in the additional data file 6 from [PE (L-1-phosphatidyl-ethanolamine), TAGbio (triacylglycerol), PIMs (phosphatidylinositol mannosides), LAM (lipoarabinomannan), MAPC , P-L-GLX (poly-L-glutamate-glutamine), CL (cardiolipin), LM (lipomannan), PI (phosphate).It is important to discuss further the need for a biomass constraint. In the GSMN, the biomass reaction is a convenience equation defined as a weighted linear combination of macromolecule precursors such as DNA, RNA, protein, other small molecules, essential cell wall components, etc. The biomass reaction used in our simulations with the GSMN-TB Since the biomass function above is an external convenience transport reaction in the model, when we modify the objective function from biomass to the one defined in terms of the proteomics data (which affect mainly intracellular reactions which are precursors of the components in the biomass equation), the consumption and production of biomass components will no longer be maximized, and the resulting optimal flux vector will be different from the one obtained when we assume biomass maximization. Moreover, since the proteomics objective function may produce a metabolic state with biomass flux zero even under the control, it is important to constrain the biomass equation to a minimum value so that conclusions are not off-target. In the above comparison we constrained biomass yield to the same value obtained with regular FBA when maximizing a biomass objective.It is important to note though, that our goal is not to use proteomics-defined objective functions as a replacement for biomass maximization, but rather for situations of drug stress. As a source of validation however, it is reasonable to expect that, by using control data to define the objective function, the metabolic functioning of the cell should be similar. That is why we performed comparisons for the control condition as well as for the treatment condition.t-test on the difference of the errors for the two methods and report in the last row the p-values for a significance level of 95% and 15 degrees of freedom. We observe that even for higher p-values, the error difference is consistently lower in the case of using the proposed methodology for all conditions. In After having compared essential reactions, we compared our proposed methodology with the E-flux method in terms of prediction error. To this end we performed several runs of FBA with a subset of the proteomics data and validated the results with the remaining subset, as explained in the previous section. Results for mean squared error of prediction are presented in In constraint-based optimization it may be possible that optimal solutions are not unique. Alternative optima represent situations where the metabolic system reaches the same optimal objective function value using a different set of reactions, which is possible due to redundancies inherent to the metabolic network. Therefore, we next use flux variability analysis (FVA)  with ourAlternative optima are responsible for the situation where different uses of the underlying network of metabolic reactions correspond to the same cellular function . By defiWe also observed, for each experimental condition, the flux variability in reactions catalyzed by essential enzymes according to the essentiality criterion in . It is iThe FVA simulations reveal that, besides reducing the number of reactions that show large variability, using the proteomics objective function also reduces the magnitudes of these variabilities when compared to using the biomass function. The obtained results indicate that there are clear advantages to use objective functions defined by experimental proteomics data, mainly in situations of cellular stress (e.g. antibiotics exposure), where biomass maximization may not be the best assumed objective. We believe that more research in this direction is required to further evaluate this new and promising approach to FBA.The metabolic model comprises 856 reactions for which an optimal flux is obtained with FBA. When we modify the objective function from biomass to one defined by proteomics data, out of 856 reactions, a small number (10%) of the reactions in the control condition present significant change in their optimal flux value (i.e. 76 reactions). It is important to note that by using an objective function defined in terms of proteomics data, as mentioned above, implicitly assumes that metabolic fluxes are mainly regulated by enzyme concentrations, disregarding the fact that their activity may also be regulated by metabolite concentrations or other signalling proteins. Nonetheless, it is a useful option whenever none of these extra data is used.katG—Rv1908c (P9WIE5), for peroxidase-catalase activity, in the control condition. All reaction names and formulae are available in [The intracellular fluxes that most greatly varied from one objective function to the other are part of metabolic pathways related to carbon source (glucose) degradation and energy production, as can be seen in the plot for the region with lower indices . Other two reactions with fluxes that varied significantly are R134, R363, due to the presence of high levels of protein lable in  in the akatG observed in proteomics may not translate into high enzymatic activity due to the presence of inhibitor molecules or other regulator proteins that are not being accounted for in the simple FBA optimization.Many proteins present both enzymatic as well as signal transduction roles in the cell, but in FBA only the enzymes are taken into account. It is therefore possible, that some proteins having high levels in the proteome for roles other than enzymatic activity, will produce spurious results in FBA. One important factor that may cause difficulties when using proteomics data to define the objective function is that enzyme activity is not simply a function of enzyme levels, but is also regulated by metabolite concentrations and other regulation mechanisms. This is actually a fact that can help with the understanding of metabolic regulation. If a proteomics-defined objective function is not close to the one obtained when biomass is maximized, it is possible that the main differences are largely due to lack of information on metabolite concentrations and enzyme regulation. As an example, the high levels of The incorporation of transcription factors  and reguin silico metabolic model itself (GSMN-TB) is not complete and many blanks are left in those parts of the metabolic network that are either not known, not implemented in the model, are presently not associated with specific enzymes or even have annotation inconsistencies. These factors, together with the lack of signalling information, may account for the major differences observed when proteomics is used to define an objective function.Lastly, two other facts may also cause interference in the FBA results. First, the limited number of proteins that a proteomics experiment can identify compared to the complete proteome of a cell. Further, the After having compared the metabolic flux results in the control condition, we now turn to compare the changes in metabolic configuration using only objective functions defined in terms of proteomics data for both control and drug conditions. We observe the resulting fold-change differences in metabolic fluxes between the two conditions. We limit the comparison to fluxes presenting fold-change larger than 2 or smaller than 0.5. We discuss the significance of the obtained fluxes by comparison with biological knowledge on the behavior of the bacterium when exposed to mefloquine, as presented in .The fold-change in flux is presented for each time point in Rv0904c (I6WZQ9), Rv3411c (I6X784) and Rv3515c (I6YCB1) show large fold-change values. None of these three genes are present in our proteomics data. However, the pathways in which these enzymes participate are correctly identified by FBA. For example, large metabolic fold-change is observed in reactions R156, R163 and R495, all pertaining to pathways for lipid biosynthesis, like the FAS1, where Rv0904c is the gene encoding the catalyzing enzyme. We note that these are predicted by FBA only on day 2 and day 4, but not on the 6 hours time point. Gene Rv3411c, encoding an enzyme participating in the pathway for nucleotide (purine) biosynthesis, although not available in proteomics, catalyzes a reaction in a pathway that is correctly predicted by FBA (in time point 6 hrs) to have larger fluxes upon mefloquine exposure. A similar conclusion can be drawn for the protein encoded by the gene Rv3515c. These results indicate that FBA can correctly predict reactions in pathways that have been shown to be more active upon mefloquine exposure.According to Table II in , upon exFBA results also show a large increase in biotin synthesis , which is a precursor molecule in the FAS1 system  of lipidRv2249c (I6X3P8) and Rv3302c (I6Y352) are not in the proteomics data, so that the flux is driven indirectly by the expression of other enzymes in the model.By using a proteomics-defined objective function for FBA allowed us to obtain a metabolic flux configuration on a per-time point basis, something that is not possible with maximization of biomass or any other function that is not time-dependent. We have observed this advantage with the biotin reactions discussed above. As another example, we observe that reaction R003, in the pathway for glycerol metabolism, presents flux changes over time. In the 6 hour sample this flux has fold-change 0.2, changing to 0.5 in day 2 and back to 0.2 in day 4. It is interesting to note that the genes coding for the enzymes that catalyze this reaction, Rv1127c (O06579), which is not in the proteomics data, however flux through it increases over 100 fold from time point 6hr to time point day 2.Another reaction with dynamic behavior is observed for reaction R069 in pyruvate metabolism. The enzyme catalyzing this reaction is encoded by R080), threonine biosynthesis , biotin biosynthesis , peptidoglycan  biosynthesis .In terms of metabolic pathways, pathways that consistently present large fold-change are degradation of branched-chain amino acids such as valine and isoleucine, of which several reactions are present in the top reactions in all three time points. In the case of studying metabolism to identify metabolic pathways used for survival one should observe the later time points, when bacteria are closer to death. In these time points, large fold-change are observed in pathways for propanoate metabolism . The proposed approach allows the derivation of a specific objective function for each treatment condition. Then, an optimal steady-state metabolic phenotype can be obtained by performing FBA for each specific time point.cn+1. This weighting could be used, for example, to limit the biomass contribution if biomass maximization is not expected to be the main metabolic goal of the cell under a specific experimental condition. Biomass yield can also be constrained directly using minimum and/or maximum reaction constraints in the linear program.The proposition of using proteomics in the definition of objective functions for FBA can be combined with other strategies. For instance, the proposed objective function can be complemented with a biomass term weighted by a suitable coefficient In this article we evaluated the possibility of using proteomics data to determine an experimental condition-dependent objective function for flux balance analysis. Three main advantages of using proteomics data to define the objective function for FBA are the following:Comparisons were performed in terms of number of reactions and enzymes essential for growth Click here for additional data file.S1 File(ZIP)Click here for additional data file.S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(CSV)Click here for additional data file.


There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective.Computational models using metabolic reconstructions for  Type 2 Diabetes Mellitus (T2DM), the most common form of Diabetes in America, is becoming a global pandemic with the greatest increase in cases in many developing countries. The pathophysiology of T2DM primarily involves defects in three organ systems– liver, peripheral target tissues , and pancreatic β-cells In the patients with T2DM, withdrawal of insulin treatment has been shown to be associated with increased levels of branched-chain amino acids (BCAAs) in the plasma It is also known that elevated free fatty acid (FFA) levels in plasma is linked to T2DM in patients in silico genome scale metabolic reconstruction models to study the causes of various metabolic disorders in silico. Accounting for more than 3000 human metabolic reactions, Recon1 provides a firm basis for studying human metabolism and metabolic disorders such as cancer, diabetes, obesity, and inherited gene and enzyme deficiencies In contrast to the wealth of knowledge available for the concentrations of circulating BCAAs and FFAs in T2DM patients, the actual mechanisms leading to these changes at the metabolic and genetic levels are less understood. With the emergence of systems biology tools associated with high-throughput data, it is now feasible to create Following Recon1 coming into existence, several tissue-specific models have also been generated in silico model to T2DM phenotype. The current study introduces a comprehensive multi-tissue-specific model to study interdependence of hepatocytes, mycoytes, and adipocytes in the T2DM condition. Insulin resistance leading to Type 2 Diabetes Mellitus (T2DM) is regulated by more than one tissue system requiring analysis at multi-tissue level. Major tissues involved in T2DM are skeletal muscle, liver, adipose, pancreas, brain, and gastrointestinal tract h) consists of 1110 reactions and 1593 metabolites. The final medium confidence reaction list (Cm) consists of 1099 reactions and 1643 metabolites. The final low confidence reaction list (Cx) consists of 4433 reactions and 3679 metabolites. The algorithm required six iterations before completing to give multi-confidence level (MCL) multi-tissue model with 4704 reactions and 3131 metabolites. Completion is indicated by the fact that the objective function for the whole model is at a maximum for a particular set of flux distributions and no more reactions can be added in subsequent iterations .Before generating the model, an algorithm was applied to generate a list of high confidence, medium confidence, and low confidence reactions, based on the source of a particular reaction. The high confidence list comprised reactions from published literature and was not altered by the algorithm; the medium confidence list comprised of reactions from online databases such as HPRD, etc.; and the low confidence list comprised of all remaining reactions that weren't present in high and medium confidence reaction lists, as shown in in silico. S-Adenosylhomocysteine hydrolase is associated with AHCY gene deficiency in silico model was run to predict the phenotype of the disease associated with removing that particular gene(s). The predicted phenotype was then compared to the actual phenotype from the OMIM database. Exchange reactions tell about the concentration and the exchange flux balances indicate the increase or decrease in concentration of a metabolite in the model in the blood/extracellular compartment. This analysis was performed on the MCL multi-tissue model, Recon1, and the multi-tissue version of Recon1 . The results from simulations of Recon1 and the multi-tissue version of Recon1 served as benchmarks to compare to the simulations of MCL multi-tissue model, as shown in receiver-operator curves (ROC) in From the OMIM database Recon1 and the multi-tissue version of Recon1 have an identical curve which is understandable as a multi-tissue version of Recon1 is just a combination of Recon1 models of the three tissues and the external exchange compartment; however, it is necessary to test the differences between the two because the number of type III cycles can increase in the multi-tissue version of Recon1. A type III cycle is one of three types of extreme pathways that can exist in a reaction network  and could cause the behavior of the multi-tissue version to be different than the single tissue version of Recon1. in silico models. The microarray data of the two sets was compared and genes that had statistically significant  fold changes were tabulated. These differentially expressed genes were then mapped to Recon1 and the MCL multi-tissue model to determine sets of upregulated and downregulated reactions. The bounds of these reactions were changed according to the procedure defined elsewhere . The resulting differences in the exchange reactions flux bounds in both models were compared with amino acid data for the Zucker diabetic fatty rat from the literature Subsequent validation analysis involved the application of microarray data from MKR T2DM mice to the aforementioned The difference between the two pairs (Recon 1 and MCL multi-tissue model) of columns in The first two rows in These ROC curves clearly demonstrate the differences between Recon1 and MCL multi-tissue model by providing the clear differences in the area under the curve (AUC) between the two models. In Recon1 was highly inaccurate for transport reactions at each threshold, which demonstrates the difficulty with predicting increases or decreases of exchange from gene expression fold changes. The fact that the MCL multi-tissue model displays an AUC that is roughly 7.5 times greater than the AUC produced by Recon1 provides further validation for the MCL multi-tissue model in these analyses, at least compared to other systems available.in silico model which was less comprehensive in terms of the number of metabolic reactions used in the one presented in this study. In order to use the MCL multi-tissue model for predicting physiological changes in fully T2DM MKR mice as compared to healthy mice, gene expression data was first generated for liver, skeletal muscle, and fat tissues for healthy and diabetic mice. Statistical tests on gene expression data suggested that several genes were differentially regulated between MKR and healthy mice. The statistical test – Fisher's exact test, providing significance of the association of the experimental gene expression values and the Ingenuity Pathway Analysis canonical pathways, was done using the Ingenuity software (www.ingenuity.com). From gene expression fold change analysis in the three tissues, it was found that for BCAA degradation and FA oxidation pathways, some of the genes are upregulated and some are downregulated in liver, while the same were predominantly downregulated in the T2DM MKR mice's fat and muscle tissues when compared to their healthy euglycemic littermates, as shown in After the two extensive validations outlined in sections 2.1 and 2.2, the model was used to study physiological behaviors during T2DM condition – higher plasma concentration of branched-chain amino acids (BCAAs) and free fatty acids (FFAs) in T2DM subjects. Branched-chain amino acid metabolism has been studied in T2DM condition previously The liver tissues' FA degradation pathway genes did not provide a clear explanation of high concentration of FFAs in the plasma of T2DM subjects. However, the biosynthesis pathway from Ingenuity Pathway Analysis  for fattin silico T2DM condition simulation. Then, flux variability analysis is performed to find out the reactions without fluxes, which are then removed. The remaining reactions then represent an active context-specific metabolic network. This active model was used to find out the differences between biochemical reaction activity between T2DM and healthy states.In order to perform systems level analysis of the gene expression data, to achieve a better understanding of BCAA and FA metabolism, we next used the gene expression data along with the MCL multi-tissue model to generate flux predictions. Gene expression data was used for generating a context-specific MCL multi-tissue network using an algorithm which uses gene expression data to remove the reactions associated with no gene expression, thereby creating a context-specific metabolic network for the Cholesterol metabolism is upregulated in adipose tissue  – D and It is important to note that in most of the research work done on studying behavior of these subsystems deal with studying differences in gene expression in the two physiological conditions; however, in this study we present differences in fluxes in the biochemical reactions in the two physiological conditions . Figures S1–S15 in In the present study, microarray technology was used to elucidate the relation of free fatty acid and branched-chain amino acids levels under T2DM and normal condition and the gene expression profiles in three different tissues – liver, muscle, and adipose of diabetic MKR mice. The findings from microarray analysis show that MKR mice have a downregulated fatty acid oxidation profile in muscle and adipose tissues. In liver, the entire fatty acid oxidation profile is not downregulated. On the contrary, fatty acid biosynthesis in liver tissues is considerably upregulated as shown in Wang et al. Subsequently, an algorithm for building a multi-tissue model is presented in the current paper. The algorithm presented in this paper uses as few linear programming solutions as possible to achieve an optimal solution, so that the solution can be found in a reasonable amount of time. The model provides users with the ability to predict changes in metabolite levels in the medium after deleting specific metabolic genes, as well as the ability to predict changes in metabolite concentrations in the plasma/medium after applying fold changes to specific genes. Other capabilities include using a quadratic programming solver to apply experimentally derived fluxes to specific reactions in the model and determining the remaining fluxes to examine the behavior of the overall model in given by the experiment The positive aspect of this approach is that metabolic fluxes can be predicted; this is important because metabolic fluxes are difficult to measure in mammalian tissues, and metabolic fluxes provide essential information used for characterizing phenotypes of cellular systems. Flux information can help predict metabolic biomarkers in blood/plasma Figures S7–S12 in in vivo methods of metabolic flux measurement, we did find evidences supporting our results. For example, it is reported that gluconeogenesis flux in humans is decreased in Type 2 Diabetes Statistical significance of the differential regulation of the subsystems was calculated by using a two sample t-test  to determine the statistical significance of the subsystem's deviation from random behavior. For example, if the subsystem is 100% downregulated with two reactions, then, intuitively, this is not very statistically relevant because a random selector can make this selection 1/9 of the time . Thus, sorting by the t-score of each subsystem provides a better understanding of the extent of differential regulation of the subsystems. Figures S1–S12 in The most negative t-score in adipose and muscle tissues is for the carnitine shuttle subsystem. The ‘Nucleotides’ subsystem is also strongly downregulated in both muscle and adipose tissue. Interestingly, liver tissue displays more upregulation than the other tissues, with the carnitine shuttle subsystem showing upregulation. Thus, the carnitine shuttle subsystem appears to be the subsystem most affected in T2DM. The carnitine shuttle is responsible for transport of long chain fatty acids into mitochondria for oxidation. So, a downregulated carnitine shuttle in adipose and muscle tissues implies lower metabolism of the FFAs in those two tissues. Even though the FFA metabolism is higher in liver because of upregulated carnitine shuttle, the biosynthesis pathway of fatty acids is also elevated . This exin silico predictions via constraint-based modeling has facilitated better understanding of the reason behind high BCAA and FFA levels in plasma of T2DM subjects. Prior systems biology studies have shown the ability of constraint-based models to predict metabolic biomarkers In summary, the integration of microarray data and Type 2 diabetes is characterized by two major defects: beta-cell dysfunction and insulin resistance in peripheral tissues. The exact alterations in molecular pathways associated with beta-cell dysfunction in insulin-resistant and diabetic states are not clearly understood. Most of the studies involving T2DM and insulin resistance utilize data from insulin-resistant or diabetic animal models, such as Zucker fatty (ZF) and Zucker diabetic fatty rats (ZDF) In a mammalian system, the specific objective of a particular function of a cell, at a given time and under certain conditions, is difficult to identify, whereas in unicellular systems, the objective function can be assumed to be the maximization of cell growth. This is because mammalian systems are much more complex, especially when attempting to define models for different tissues, because each tissue has a different objective. One may consider that there is an over-arching objective function when considering a system that defines the overall human body as a linear combination of partial Recon1's associated with each tissue, but this again is difficult to define, because the objective of maximizing biomass in unicellular organisms only applies during the exponential growth phase. Thus, maximizing biomass in mammalian systems may be applicable, but only during the growth phase of the mammal.All animal study protocols were approved by the Mount Sinai School of Medicine Institutional Animal Care and Use Committee (IACUC). Mice were housed in The Mount Sinai School of Medicine Center for Comparative Medicine and Surgery, an Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) and Office of Laboratory Animal Welfare (OLAW) accredited facility, where animal care and maintenance were provided.2. Liver, Skeletal muscle, and fat tissues were flash frozen in liquid nitrogen, stored at −80°C, and shipped on dry ice to NIDDK/NIH facility for further processing.Male 10-weeks old FVB/N – MKR mice were used for the microarray studies. Generation and characterization of MKR mice have been described elsewhere Total RNA from the liver, fat, and skeletal muscle tissues was isolated for three biological replicates for both diseased and healthy animal subjects. Qiagen's Microarray Tissue Mini Kit  was used for RNA isolation purpose according to the manufacturer's instructions. Purified total RNA was quantified using a spectrophotometer (Thermo Scientific Ltd). The absorbance values at 260 and 280 nm were used for assessing the quality of the sample. Only the samples with greater than 1.80 260/280 absorbance ratio were used for microarray analysis +2 hours in hybridization oven rotating at 60 rpm at 45°C . Arrays were processed using Affymetrix 450 Fluidic stations using wash and stain kit (Affymetrix Inc.). Chips were scanned using Affymetrix GeneChip scanner 3000 operated by Gene Chip Operating Software, version 1. 4 (GCOS 1.4) and generated.CEL.CHP and RPT files. To access the efficiency of cDNA synthesis Poly A controls  was spiked to the samples and hybridization controls  were added to monitor labeling efficiency according to the manufacturer's instructions.RNA quality was tested using bioanalyzer  and RIN (RNA Integrity Number) values were above 8.0 for all the samples. 100 ng of RNA from each sample was amplified to generate cDNA using NUGEN Applause WT – Amp ST system , according to the manufacturer's instructions. 2.5 µg of cDNA was fragmented and biotinylated using Encore Biotin module . Resultant sample mix with hybridization reagents (Affymetrix Inc. CA) and injected into Affymetrix Mouse Gene 1.0 ST arrays and incubated for 18The microarray raw data was analyzed using software Partek genomics suite, version 6.3 Copyright 2008 Partek Inc., St. Louis, MO, USA. Raw data were subjected to Robust Multichip Average (RMA) quantile normalization to remove biases introduced by technical and experimental effects. All expression data were log base 2 -transformed to get near normal distribution for accurate statistical inference. Quality control by visualizing the data using Principal Component Analysis cluster plot ensured that no outliers were included for the analysis. Next, two-way ANOVA analysis was performed to obtain a set of differentially expressed genes. A filter of P-value <0.05 and Fold-change >1.5 times was applied to get the significantly differentially expressed genes list. The results of the microarray analysis have been deposited to National Center for Biotechnology Information (NCBI) repository and can be accessed with Gene Omnibus Expression accession: GSE51866.www.ingenuity.com) for finding biological inference. Ingenuity Pathway Analysis is an online software used to study relationship between genes, proteins, and biological reactions. More technical details about the Ingenuity Pathway Analysis' capabilities can be accessed from the Ingenuity Systems website. Statistically significant genes from Partek analysis were overlaid on the Ingenuity Pathway Analysis global molecular network, which is based on information from other databases such as KEGG, HumanCyc, etc. in the Ingenuity knowledge base after applying a filter on species type (mouse) and tissue type (e.g. adipose).The significantly differentially expressed genes list was exported to – Ingenuity Pathway Analysis  was downloaded from the BiGG database in SBML format The algorithm for trimming down the general three tissue version of Recon1 is built by minimizing the number of linear programming problems needed to trim the reconstruction The overall goal of this algorithm is: (1) maintain all high confidence reactions and (2) maximize the number of medium confidence reactions minus 0.5 multiplied by the number of low confidence reactions in the final trimmed version of the reconstruction. Statement (2) can be considered the objective function of the algorithm as shown below –The value 0.5 reflects equal probability of obtaining the most parsimonious model as well as including a maximal number of moderate probability reactions in the partial model using above algorithm p is a partial subset of reactions from Recon1 that defines the solution space for the tissue-specific reconstruction, CM and CX are the medium and low confidence set of reactions, respectively. The algorithm makes more efficient use of all of the information gained from a single linear programming (LP) solution. Every reconstruction can be broken down into elementary flux modes; these can be thought of as the simplest possible flux distributions. Therefore, every reconstruction is a linear combination of elementary flux modes, and thus, each linear programming solution is a smaller linear combination of elementary flux modes. Ideally, the algorithm would identify all elementary flux modes in a reconstruction or even LP solution and find the optimal combination from the elementary flux modes, but as of now, this is computationally infeasible due to combinatorial explosion RThe next step of the algorithm consists of providing scores for each flux distribution. The scores are calculated by subtracting half the number of active low confidence reactions from the number of active medium confidence reactions. If a score is positive, it means that adding the reactions associated with this flux distribution will add to the value of the objective function of the algorithm. Scores are calculated for each column in the matrix. This marks the beginning of the third step of the algorithm. This step involves addition of flux distributions associated with the high confidence set of reactions. It starts by ordering the list of scores associated with the flux distributions from high to low values. Then, the flux distribution with the highest score is checked for any active high confidence reactions; if there are no high confidence reactions, then the flux distribution with the second highest score is checked for high confidence reactions. This procedure continues until the highest scoring flux distribution containing at least one high confidence reaction is found. All of the active reactions within this flux distribution are added to a final list of reactions. Also, any high confidence reactions added to the final list of reactions are removed from the list of remaining high confidence reactions. Then, the rows of the fdMatrix which are associated with the set of active reactions that were recently added to the final list of reactions are set to rows filled with zeros. This is done because these reactions are now in the final list of reactions and therefore do not contribute to the objective score anymore. Changing the values in the rows changes the scores associated with each column/flux distribution in fdMatrix, thus the scores must be re-calculated to reflect the changes in the rows. These re-calculated scores are sorted again to reveal the columns/flux distributions with the highest scores. Then, the highest scoring column/flux distribution with at least one reaction that is in the list of remaining high confidence reactions is selected, and the whole cycle repeats itself. This cycle is repeated until there are no reactions left in the list of remaining high confidence reactions, which means that all of the high confidence reactions were added to the list of final reactions, which is one of the requirements of the algorithm. Also, it's important to note that the high confidence reactions were added in a way that maximizes the score/objective function of the algorithm.The next step of the algorithm involves adding other flux distributions that increase the value of the objective function. After adding all of the high confidence reactions, the number of medium confidence reactions added to the reconstruction was optimized. The process for adding flux distributions that adds a positive value to the objective function is very similar to the process that added all of the high confidence reactions. Each of the remaining flux distributions is assigned an objective score based on the number of medium confidence reactions and low confidence reactions remaining within that distribution. These scores are sorted to find the distribution with the highest objective value. If the highest objective score is above zero, then the reactions of that distribution are added to the reconstruction. Then, the rows that correspond to the reactions that were just added are filled with zeros to prevent double counting toward the objective function. This process is repeated as long as the highest objective score is greater than zero. This means that reactions are added only if the corresponding distributions increase the objective function, and reactions are no longer added when the objective function cannot be increased by the randomly created distributions, meaning that the objective function is at a maximum for this particular set of flux distributions.Then, this whole process is repeated again; a new set of random flux distributions is created based on the partial model this time , and reactions from the high confidence list are added along with their associated reactions determined by the flux distributions, and medium confidence reactions are optimized. As part of the model building algorithm during the reconstruction process, maximizing the flux to ascertain activity/inactivity of a reaction is needed. A random flux distribution is used only in the reconstruction process which is subjected to our algorithm to find out whether each reaction adds to biological meaning or not. This process repeats itself until the model doesn't change in size from one iteration to the next. The time required to solve a problem associated with a three tissue system varies from 0.05–0.1 seconds. The number of reactions maximized in each iteration is variable; in the first iteration, all reactions are maximized. In the second iteration, all reactions that had zero flux in the solution to the first linear programming iteration are maximized, and so on. This continues until either all reactions are proven to carry a flux (have a non-zero solution to any linear programming problem), or some set of reactions are proven to be unable to carry flux (the set of reactions is maximized/minimized and no flux profiles result). The latter is explicitly proven by performing distinct linear programming problems and maximizing/minimizing each reaction in the set individually as explained in f tissue-specific model building algorithm.Robustness analysis of the algorithm was performed by generating 5 replicate models with starting point as 5 different randomized stoichiometric matrices. All 5 of the replicate models were similar in size . After the lower bounds are changed, flux variability analysis is performed on the model For reaction ‘i’:Disease reaction flux < wild-type reaction flux if,Disease reaction flux > wild-type reaction flux if,Where:i’ are the set of exchange reactions of metabolites that have been identified in the phenotype descriptions of the OMIM database. In this validation, they are amino acids. The variables diseaseMax and diseaseMin are the flux bounds of the amino acids in the disease version of the model with particular genes knocked out. The variables refMin and refMax are the flux bounds of the amino acids in the healthy model that has no genes knocked out. The variable ε represents the threshold value used for the receiver-operator characteristic (ROC) curves.The set of reactions represented by the variable ‘The results of this validation are depicted via ROC curves in the Results section. In this simulation, the threshold value is varied to distinguish between very large changes in flux bounds and very small changes in flux bounds. Thus, for a given threshold value and disease, upregulated and downregulated predictions are made for each amino acid exchange reaction. These predictions are checked against the known phenotype of that specific disease. This generates a list of true positive, false positive, true negative, and false negative predictions for the specific disease at a particular threshold. This analysis is repeated using the same threshold value for each disease in the validation. All of the predictions from each disease are added together, to provide a number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) for that particular threshold value. Then, the false positive rate (FPR) and the true positive rate (TPR) are calculated as below.o is proportion of unchanged metabolites in the data; Where, TPR  =  true positive rate; (number of true positives)/.FPR  =  false positive rate; /.These values for FPR and TPR are calculated for a range of threshold values; typically from 0 to 1000, and plotted with FPR on the x-axis and TPR is on the y-axis. The area under the curve (AUC) of this plot is the indicator of the quality of the reconstruction network. An AUC of 0.5 represents a random classifier; any AUC over 0.5 represents a system that is better than a random guessing.The gene expression of adipose, liver, and muscle tissue for MKR mice model was examined. The MKR mouse model was developed by over-expressing the IGF-I receptor in skeletal muscle max in enzyme kinetics equations. Vmax is equal to kcat multiplied by enzyme concentration; therefore, a lower enzyme concentration decreases the maximum reaction velocity . Reactions that are determined to be affected by gene regulation have upper bounds initially set to 100 and lower bounds initially set to −100 or 0 depending on reversibility. Unaffected reactions have normal reaction bounds (upper bounds are 1000 and lower bounds are −1000 or 0). Initial FVA is then performed. Then, upregulated reactions have bounds doubled downregulated reactions have bounds halved. Then, FVA is performed, and resulting bounds are compared to initial FVA results for comparison and analysis. The reasoning for initially changing affected reaction bounds from 1000 to 100 was to magnify the effect of upgregulating reactions. If reaction bounds of affected reactions are kept at normal 1000 values, then doubling the reaction bounds due to upregulation may not display downstream upregulation or display the far-reaching effects of gene upregulation. The lower bound and upper bound for exchange reactions are 1 and 1000 respectively.Transcription levels of genes do not directly correspond to reaction fluxes, but a few assumptions can be made to demonstrate the effect of the levels. The main assumption is that relative gene transcription levels correlate with relative protein concentrations. With this assumption, the effect of increased or decreased protein concentration manifests itself in the upper and lower bounds of the reactions that are associated with the protein. The reason for this is because enzyme concentration only affects VThe first step is to identify all upregulated and downregulated reactions, and change the associated bounds to one-tenth of the initial value. This is done so that the effect of increases in reaction bounds due to upregulation will have an effect in a steady-state model. Then, FVA is performed on the model to establish a control set of bounds. After that, the bounds of upregulated reactions are doubled and downregulated reactions are halved. Then, FVA is performed again to generate a disease-state set of bounds. Then, the difference between the two sets of bounds is determined by the following:For a given reaction ‘Literature results were obtained for specific concentrations of amino acids in the plasma for T2DM rat versus healthy rat. The effect on each of the transport fluxes of each amino acid were considered to determine an increase or decrease in concentration in the plasma. This was done instead of simply inspecting the exchange reactions for each amino acid because the full effect of the gene expression changes may not be observed on an exchange reaction if an unaffected pathway exists that involves the exchange reaction in question. Taking all transport reactions into account generates a more complete picture of the uptake or secretion of metabolites.File S1Supporting figures. Figure S1. Adipose Chart 1: Percentage of Reactions Retained by Subsystem in Adipose Tissue – 1. Figure S2. Adipose Chart 2: Percentage of Reactions Retained by Subsystem in Adipose Tissue – 2. Figure S3. Liver Chart 1: Percentage of Reactions Retained by Subsystem in Liver Tissue – 1. Figure S4. Liver Chart 2: Percentage of Reactions Retained by Subsystem in Liver Tissue – 2. Figure S5. Skeletal Muscle Chart 1: Percentage of Reactions Retained by Subsystem in Skeletal Muscle Tissue – 1. Figure S6. Skeletal Muscle Chart 2: Percentage of Reactions Retained by Subsystem in Skeletal Muscle Tissue – 2. Figure S7. Adipose Fold Change 1: Fold Change Analysis in Adipose Tissue – 1. Figure S8. Adipose Fold Change 2: Fold Change Analysis in Adipose Tissue – 2. Figure S9. Liver Fold Change 1: Fold Change Analysis in Liver Tissue – 1. Figure S10. Liver Fold Change 2: Fold Change Analysis in Liver Tissue – 2. Figure S11. Skeletal Muscle Fold Change 1: Fold Change Analysis in Skeletal Muscle Tissue – 1. Figure S12. Skeletal Muscle Fold Change 2: Skeletal Fold Change Analysis in Muscle Tissue – 2. Figure S13. OMIM gene data analysis: OMIM data analysis including random selector. Figure S14. OMIM gene data analysis – closeup: OMIM data analysis including random selector zoomed in version. Figure S15. Robustness analysis results: Table in the figure shows number of reactions in each replicate models and the bar plot gives clear visualization of the same.(PPT)Click here for additional data file.File S2(XLS)Click here for additional data file.


Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further  The ability of a plant to grow and survive is linked to its metabolic network  and by the relative ease of sequencing a collection of cDNAs as opposed to predicting gene models in plant chromosomes  and the PlantSEED resource  an expanded and improved biomass composition; (ii) improved gene-protein-reaction associations where low confidence gene-reaction mappings based on poor evidence or purely computational predictions have been removed; and (iii) improved compartmentalization of reactions to subcellular organelles based on a combination of literature evidence, curation, and gapfilling algorithms. The improved gene-reaction associations in our new model were critical to our use of maize transcript profiles  sets the bounds on the flux through reaction i, where vi is the flux and zi is a binary use variable equal to zero when the flux is zero and equal to one otherwise. Equation (3) ensures that the forward and backward components of the same reaction may not both be active at the same time; in our formulation, this constraint is the sole reason for using binary variables. Equation (4) establishes the growth conditions for the gapfilling analysis; metabolites present in the growth media  have a γi of 1 in Equation (4). Otherwise γi is zero.Equation (1) represents the mass balance constraints, where In addition to these standard constraints, we applied a new constraint that introduces a slack flux for all reactions found in the original un-gapfilled model:i  and the slack flux for reaction i (δi) must be greater than or equal to 0.01. As a result of this constraint, a reaction can only have a net flux of zero if the corresponding slack flux is 0.01. Thus, the slack flux is a variable used to identify reactions that carry no flux in the model. We utilize this new slack flux for this purpose in the objective function for our gapfilling.Equation (5) states that the sum of the net flux through reaction activate, i, dictates the cost of leaving a gene-associated reaction inactive, while the gapfilling coefficient, γgapfill, i, dictates the cost of adding a gapfilled reaction to the model. In our gapfilling studies, we set γactivate, i equal to one for all gene-associated reactions, while we computed γgapfill, i as described in our previous work  –(9), the reaction expression score, Reactions with an expression score falling below 0.2 were categorized as being “low expression.” Biologically, a score of 0.2 means that the critical genes associated with the reaction are expressed at 20% of their average expression across all 37 conditions included in our transcriptomics data. This represents a conservative calling of “low expression” genes. We then applied the gapfilling algorithm as described in Equations (1)–(6) with two modifications: (i) the mass-balance constraints encoded by Equation (1) only included the stoichiometry of the reactions in the gapfilled full genome model (stoichiometry was not expanded to include the entire biochemistry database as done in full gapfilling); and (ii) the objective function was altered to maximize the high expression reaction activity while minimizing flux through low-expression reactions (Equation 10).a in our objective function, which scales the cost of leaving some high expression reactions inactive against the cost of activating some low expression reactions. We explored values for a ranging from 0.01 to 0.25, with only minimal effect on the solutions produced. We found an a of 0.1 generated the most well-balanced solutions.Similar to our gapfilling formulation, this objective function minimizes the flux through the low expression reactions while also minimizing the slack fluxes associated with all high expression reactions. This maximizes the number of high expression reactions with a non-zero flux while setting the flux through as many low expression reactions as possible to zero. Again, we use a scaling factor In order to calculate how well the metabolic model can match experimentally measured flux data for a list of specific reactions, we applied a QP where we minimized the distance between the predicted fluxes and the experimentally measured fluxes. The QP utilized the standard FBA constraints:Nmodel is the stoichiometry matrix for all model reactions and v is the vector of fluxes through all model reactions. Unlike our gapfilling formulation, in this study, reversible reactions were not decomposed. Equation (12) represents the bounds on the flux through each reaction, with the lower bound vmin, i being zero if a reaction is irreversible and −1000 is a reaction is reversible. As in our gapfilling formulation, Equation (13) sets the bounds of uptake of nutrients from the environment.Equation (11) represents our mass balance constraints, where iv) from the experimentally measured fluxes :In the quadratic objective function of our QP, we minimize the deviation of our predicted fluxes , and we added reactions to the appropriate compartment whenever they were associated with a localized gene. We find that the gene localization data led to more than 700 reactions being placed in new locations that are not otherwise designated in the databases and published models used as sources; the “None” column in Figure By using the protein localization data collected from various sources, we were able to confirm the presence of ~2000 reactions in eight compartments . We collected gene localization data for 12,398 As stated above, we wish to refine our Full metabolic reconstructions to only contain reactions with reliable evidence for gene-reaction associations. Almost every gene-reaction association found in KEGG, and in any plant BioCyc databases that is not AraCyc, are computationally predicted  and Mevalonate diphosphate decarboxylase (MVD) localize to the peroxisomes  that attempts to generate biomass and simultaneously activate all reactions with associated genes. The pathway gapfilling recommended reactions to add to our models to produce biomass and improve the function of all the pathways included in the model. We tested our gapfilled models by simulating growth on heterotrophic media in the KBase environment before applying the transcript profiles.http://qteller.com/, downloaded on 02/04/2014). The data consists of 37 experiments from nine sources, covering a range of cells, tissues, organs, and conditions. As an initial exploration of how the transcript profiles may affect a transcriptome-based model, we computed, for each of the datasets, and at 10 different thresholds, the number of reactions in the genome-scale Full and Evidenced metabolic models for maize that would be active in the organ or tissue, and conditions from which the transcript profiles were retrieved , which is an important reminder that, when performing in silico experiments using transcript profiles, one must ensure they come from the same source.We built transcriptome-based metabolic reconstructions of maize, derived directly from the gapfilled genome-scale Evidenced metabolic model, such that each transcriptome-based model will be a subset of the Evidenced metabolic model. To generate these transcriptome-based metabolic reconstructions, we used RNA-Seq data collated at qTeller . Finally, further evidence was used to deduce the biomass fraction of inositol Teas, .The cell wall of maize is considered to contain two main types of phenolic derivatives: p-coumaric acid and ferulic acid . We used these three transcript profiles in particular because they came from the same experiments and, therefore, were processed in a similar manner.We used the novel transcriptome-based gapfilling approach  along with three separate transcript profiles to generate metabolic models that are specific to the leaf, endosperm and embryo, which are named “Leaves 20-day old seedling – field,” “Endosperm 25 days after pollination,” and “Embryo 25 days after pollination”  to generate three separate metabolic models that can grow in heterotrophic media. All three metabolic models contained an average of 2302 reactions Table , which iWe have described a process that generates and refines metabolic models in three steps, generating metabolic models at each step. We can now show how these metabolic models not only compare with fluxomics data, but how that comparison improves at each step, resulting in transcriptome-based models with the closest fit to the original fluxomics data.14C labeling in two different tissues, the embryo and endosperm . In succession, we created two Full metabolic reconstructions for Our approach allows us to create relatively large metabolic reconstructions that compare favorably to the prior published metabolic models, albeit with a smaller set of gene-reaction associations. This enables us to apply transcriptome data with a high degree of confidence. The approach is validated by the fact that the embryo and endosperm models retained nearly every reaction of central carbon metabolism. This was done both by the body of evidence available for the gene-reaction associations, and the pathway gapfilling method which included reactions with a low expression score, but were essential to the models. Finally, it was shown that the same models can be active and able to replicate the activity observed in published experimental fluxomics datasets. To date, we believe we are the first to apply such wide-ranging body of evidence to the generation of large-scale metabolic reconstructions.http://kbase.us/), an open software and data platform that aim to enable researchers to predict and ultimately design biological function. The data is publicly available within KBase workspaces named “Maize_Tissue_Models”  and also via the PlantSEED website (http://plantseed.theseed.org). The KBase software environment allows researchers to copy the individual metabolic models and to explore the models using the suite of modeling tools available.All of our work was carried out through the DOE Systems Biology Knowledgebase (KBase; The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.