Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosyl-homocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism.Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across  E. coli growth rate, quantitatively vary across members of a species. Among the most medically and agriculturally important traits are levels of cellular metabolites, such as cholesterol levels in humans or starch in food crops. Metabolic variation in yeast also holds practical importance with some Saccharomyces strains better suited to making ethanol for biofuel and others tailored to making flavorful wine. This metabolic heterogeneity can be used to gain insight into general principles of metabolic regulation which effect metabolite abundance in eukaryotes. To this end, we examined inter-strain differences in metabolism in over 100 closely related S. cerevisiae strains. We identified over 50 genetic loci that control the levels of specific metabolites, including not only loci that encode metabolic enzymes, but also those that encode global cellular regulators. For example, differences in the sequence of ira2, an inhibitor of Ras, lead to differences in central carbon metabolite levels, and polymorphisms in slt2, a poorly characterized MAP kinase, alter levels of sulfur-containing metabolites. These findings provide insights into the mechanisms cells use to control metabolite concentrations.Many traits, from human height to  Inter-individual differences in metabolism are of substantial biological importance. In humans, they underlie susceptibility to type II diabetes Over the past decade, the relationship between the metabolome and the genome has been increasingly studied, most thoroughly in the plant community A. thaliana and yeast Metabolomic methods have been applied to determine how levels of metabolites are associated with gene segregation across intercrosses of mice, ira2. Interestingly, while the expression QTLs linked to ira2 were enriched for central metabolic enzymes, the variant of ira2 that promoted high metabolite concentrations favored low enzyme transcript levels. This dichotomy can be explained because ira2-linked transcripts are primarily involved in oxidative metabolism, while linked metabolites are mainly associated with fermentation. These findings reveal the utility of mQTL analysis for identifying metabolic regulatory mechanisms.With the goal of discovering potential novel regulators of primary metabolism, we examined 74 metabolites involved in highly conserved core metabolic pathways of central carbon metabolism and nucleotide and amino acid biosynthesis. We found 52 significant linkages and experimentally verified the genes underlying three major linkage hot spots, including two linked genes responsible for altering S-adenosyl-methionine levels, neither with known metabolic roles. Additionally, we compared our metabolite results with the expression QTL results for the same cross To identify genetic loci responsible for inter-individual differences in the metabolome, we used a well-studied cross between a laboratory strain of yeast, BY4716, and a vineyard isolate, RM11-1a . These strains have both been sequenced, and they differ at Intracellular metabolites were harvested from yeast growing exponentially on aerobic, glucose-containing minimal medium by direct quenching and extraction in cold organic solvent Many of these compound's levels show patterns of inheritance consistent with a complex underlying genetic basis. Based on the methods described for transcripts in Brem et. al. 2005 We tested for linkage with R/qtl The mQTLs were not uniformly distributed along the genome; rather, most fell within 8 “hot spots” with 3 or more compounds linking to each . To imprWe compared the metabolite linkage results with those for transcript abundance in the same cross on test) . Two metTo determine whether changes in metabolites tend to be linked to genes with known roles in metabolism, we carried out functional enrichment analysis of genes located in mQTL confidence intervals. Genes were classified as “metabolic” based on inclusion in the iMM904 metabolism model tabolite . Over haUra3, a pyrimidine biosynthesis gene which carries an engineered deletion in the RM strain, is contained within the hot spot and lies within the 95% mQTL confidence intervals for all five compounds (ura3 in the pathway show the greatest differences in abundance (as much as 128-fold), and particularly strong linkages  is linked to levels of three metabolites: thiamine, S-adenosyl-methionine (SAM), and S-adenosyl-homocysteine (SAH) . The oveslt2 had significantly higher levels of SAM and SAH . However, in the RM background, the BY allele of slt2 resulted in a three-fold decrease for both SAM and SAH  from slt2, and thus the alleles of the two genes segregate together as a haplotype. We used the slt2 allele replacement strains to create strains in which both genes were replaced with the alternative alleles. In the BY background, replacing both slt2 and erc1 with the RM alleles led to a significant increase in SAM (p  =  0.019) compared to the original BY strain, but the level of SAM was still much lower than in RM  at the same locus in this cross Ira2 is a Ras-related GTPase ira2-catalyzed GTP hydrolysis leading to inactivation of Ras. The eQTL expression patterns suggested that ira2 is hypoactive in the BY strain.A mQTL hot spot on chromosome XV (m6) is linked to five central carbon metabolites: glucose-6-phosphate (G6P) and its isomers (which were not distinguished by the LC-MS method used here), fructose-1,6-bisphosphate (FBP), sedoheptulose 7-phosphate (S7P), dihydroxyacetone phosphate (DHAP), and (iso)citrate. The overlap among the 95% confidence intervals of the mQTL for each compound covers a region containing all or part of 13 genes . We focuira2 showed higher levels of all five linked metabolites than those that inherit the RM allele citrate, the linked metabolites are indicative of active fermentation. The accumulation of (iso)citrate in the BY strain is consistent with the lower expression of the primary isocitrate consuming enzyme (idh1) from the BY allele of ira2. Taken together with the data showing that the BY allele of ira2 promotes glucose fermentation, one obtains a coherent view: ira2 activity is lower in the BY strain. This leads to decreased expression of genes required for respiration, more need for fermentative ATP production, and higher levels of the glycolytic intermediates G6P, FBP, and DHAP.Insight into this paradox is provided by the nature of the regulated genes: 28 of the 32 central carbon metabolism genes that link to A. thaliana have suggested moderate heritability of metabolite traits across globally-distributed strains A. thaliana cross. Greater levels of heritability across metabolites are associated with an increased number of detected mQTLs (p  =  0.014); this is evident in We can only relate metabolite abundance variation to genetic heterogeneity across segregants when there is substantial genetic variation affecting metabolite levels in the first place. Previous estimates of broad-sense heritability ira2 allele swap.We used high-throughput metabolite phenotyping in a cross of two divergent strains of yeast to find 52 linkages for 34 metabolites. We have detected linkages for a majority of compounds with significant differences between parental strains, as well as for a few compounds without such differences. Many metabolites show transgressive segregation, with levels in progeny strains outside the range of the parents; the parental strains likely carry alleles with opposing effects, with some segregants that inherit combinations of alleles that result in extreme metabolite levels, as has been observed for transcript levels Ira2 is a regulator of cell signaling, not metabolism per se. Nevertheless, allelic differences in ira2 have a broad impact on central carbon metabolism at the level of transcripts, metabolites and flux. The hypoactive variant of ira2 found in the BY strain is associated with decreased expression of oxidative metabolism transcripts, higher levels of citrate, glycolytic intermediates, and sedoheptulose-7-phosphate, as well as higher glycolytic flux. These observations are consistent with active ira2 inducing oxidative metabolic genes, which in turn decrease the glycolytic flux required to fulfill ATP production. This raises the intriguing possibility that, due to the efficiency of oxidative ATP production, the extent of residual oxidative phosphorylation during yeast fermentative growth is a major determinant of glycolytic flux. More direct inhibition of glycolysis by the BY variant of ira2, e.g., through inhibition of phosphofructokinase-2, is also a possibility.slt2 and erc1, with no previously known metabolic regulatory role. These two proteins interestingly segregate as a complex haplotype. SAM and SAH are key metabolites from the perspective of epigenetics; they are substrates and products, respectively, in DNA and histone methylation. Through epigenetics or other mechanisms, SAM and SAH may impact a broad range of diseases, e.g., of the cardiovascular system Slt2 is part of a MAP kinase cascade responsible for maintaining cell wall integrity, and thus contributing to fitness during osmotic stress. Erc1 was identified for conferring ethionine resistance slt2 nor erc1 is regulated transcriptionally in response to sulfur availability slt2 have been associated with the cell cycle  slt2 and erc1 polymorphisms regulate SAM and SAH levels remains, however, to be elucidated. The discovery of the underlying mechanisms, may in turn, inform the overall interplay between metabolism, epigenetics, and the cycle cell. Thus, mQTL analysis provides a powerful tool for integrative systems biology.Perhaps the most exciting use of yeast mQTL mapping is to discover novel regulators of metabolism. In this respect, we have found linkages between levels of SAM and SAH and two proteins, The BY ura3, slt2/erc1, and ira2. In Zhu et al., the ura3 auxotrophy was implicated through its linkage with orotate and dihydroorotate elevation; we have confirmed these effects both statistically and through direct experimental manipulation of ura3, and also expanded them to other metabolites in the pathway. In both studies, SAM and SAH were linked to the slt2/erc1 locus, but Zhu et al. did not discuss this hot spot, and they did not identify or propose underlying genes. Zhu et al. also mapped the abundance of glycerol, lysine, tyrosine and trehalose to the region containing ira2 and pmh7. They concluded that variation in pmh7 was the causal source of these metabolic alterations, but this conclusion was based on a weak knockout phenotype, rather than on an allele replacement. Of these metabolites, we were only able to quantify lysine, which was not linked to this region in our study. It is therefore difficult to determine whether ira2 and phm7 function as a complex locus, similar to slt2/erc1, with both genes playing a role in variation of the same or different sets of metabolites, or whether ira2 is the only gene in the region that influences metabolite variation.Three hot spots are shared between these two studies: those which we have shown are due to variation in The remaining mQTL hot spots of Zhu et al. were associated with amino acid metabolism and were not observed in our study, perhaps because of differences in growth conditions: synthetic compete medium in Zhu et al. vs. supplemented minimal medium in this study. Such mQTL hot spot dependence on growth conditions would be analogous to gene-environment interaction eQTLs (gxeQTL) previously identified in the BY lys2leu2 ura3We used strains generated from the cross between BY4716 (MATThe cell-loaded filter membrane was quenched by placing it cell-side down in 2 mL of acetonitrile/methanol/water (40∶40∶20) at Two different LC separations were coupled by electrospray ionization (ESI) to Thermo TSQ Quantum triple quadrupole mass spectrometers operating in multiple reaction monitoring (MRM) mode. Positive-mode ESI was coupled to hydrophilic interaction chromatography (HILIC) on an aminopropyl column; negative-mode ESI was coupled to reversed-phase chromatography with an amine-based ion pairing agent Raw LC-MS/MS data from both runs were analyzed using the MAVEN software For linkage analysis, compounds detected in fewer than 25% of samples were discarded; for the remaining compounds, when signal was not detectable, raw ion counts were floored to 32, which is approximately the lower limit of detection. Duplicate samples of the same strain were averaged and then divided by the associated OD at extraction to normalize for any sample-size differences.Each day the RM11-1a strain was also run under this method. To correct for inter-day variance in raw signal intensities, log-ratios between segregant and the same-day RM values were used for each compound.For each compound's abundance data, an ANOVA of the form phenotype To determine which metabolites may appear abundant by virtue of the extraction procedure, we compared metabolite levels from mock extracted cells to the parental strains using a one-tailed t-test and we found six compounds at levels comparable to biological samples. Four of these metabolites were included in the media as vitamins or supplements: leucine/isoleucine, nicotinate . For metabolites with multiple linkages, each confidence interval was examined separately.To look at pathway-specific metabolic genes for each metabolite, we compared the SGD list of genes in all pathways for that metabolite with the list of all metabolic genes in that metabolite's confidence interval  was assigned to a bin. A bin was considered to have an excess of linkages if the number exceeded the number expected by chance by Poisson distribution. Given the number of metabolite-linkages (52) and bins (1216) we have For each metabolite, segregants with two quantifiable biological replicates were isolated and the variance within replicates was compared to the total across all samples. This is effectively subtracting the environmental variance from the total phenotypic variance to yield the genetic variance. The ratio of genetic variance to phenotypic variance is the broad sense heritability (1)The association between the number of QTLs found for a metabolite and the metabolite's heritability was found by modeling the number of detected QTLs as an approximately poisson trait and predicting this value using poisson regression.Figure S1Accumulation of metabolic genes in random distributions of intervals. Confidence intervals were randomly permuted across the yeast genome. All genes classified as metabolic that overlapped with a confidence interval were counted . This was repeated 10,000 times and the distribution seen is shown. The red arrow shows were the actual count is relative to the distribution (471), where only 0.7% of permuted values were greater.(EPS)Click here for additional data file.Figure S295% Confidence Intervals of Chromosome V-linked metabolites. 95% Confidence intervals were computed using the bayesint function in R/QTL. Shown in black is the interval, the red marks are the location of the specific marker with the highest LOD score for the respective metabolite. In blue are the ORFs of local genes. URA3 is the closest gene to all five markers and within all five intervals.(EPS)Click here for additional data file.Figure S395% Confidence Intervals of Chromosome VIII-linked metabolites. 95% Confidence intervals were computed using the bayesint function in R/QTL. Shown in black is the interval, the red marks are the location of the specific marker with the highest LOD score for the respective metabolite. In blue are the ORFs of local genes.(EPS)Click here for additional data file.Figure S495% Confidence Intervals of Chromosome XV-linked metabolites. 95% Confidence intervals were computed using the bayesint function in R/QTL. Shown in black is the interval, the red marks are the location of the specific marker with the highest LOD score for the respective metabolite. In blue are the ORFs of local genes. IRA2 is within all five intervals.(EPS)Click here for additional data file.Figure S5Impact of IRA2 allele on glycolysis. Segregants inheriting the RM allele of IRA2 show significantly lower citrate, dihydroxyacetone phosphate, hexose phosphate and sedoheptulose 7-phosphate levels. Relative metabolite concentrations (mean (EPS)Click here for additional data file.Table S1Metabolites and their linkage LOD-scores. All 52 linkages are listed, sorted by metabolite name. Metabolites with multiple linkages are sorted by LOD-score. The chromosome and position of the closest marker are also given. For metabolites detected in both parental strains, the p-value of metabolite level differences between the parents is also shown. FDR of 5% corresponds to a p-value of 0.0898. * Same compound but in different ionization modes. (PDF)Click here for additional data file.Table S2Examining confidence intervals for pathway genes. Compounds are shown with the number of pathway genes and metabolic genes captured in their confidence intervals. Pathway genes for each compound are specified in the third column. For compounds with multiple linkages, metabolic gene number and pathway gene names are broken down by the chromosome of the linkage. Glutathione and glutathione-disulfide are combined, as are the positive and negative mode measurements for S-adenosyl-homocysteine. * While alcohol dehydrogenase (ADH1) is not specified as a gene in the same pathway as these metabolites, it is mentioned due to its role in glycolysis. (PDF)Click here for additional data file.Table S3www.yeastgenome.org. Genes with eQTLs containing IRA2 are marked whether the eQTL was detected in media containing either ethanol or glucose as a carbon source. Each genes average expression levels were also compared dependent on the allele of IRA2 and noted.eQTLs containing IRA2 from carbon cycle genes. eQTLs were taken from Smith et al. (XLSX)Click here for additional data file.


Saccharomyces cerevisiae with the aid of in silico analysis of a genome-scale metabolic model. First, FUM1 was selected as the target gene on the basis of extensive literature mining. Flux balance analysis (FBA) revealed that FUM1 deletion can lead to FA production and slightly lower growth of S. cerevisiae. The engineered S. cerevisiae strain obtained by deleting FUM1 can produce FA up to a concentration of 610±31 mg L–1 without any apparent change in growth in fed-batch culture. FT-IR and 1H and 13C NMR spectra confirmed that FA was synthesized by the engineered S. cerevisiae strain. FBA identified pyruvate carboxylase as one of the factors limiting higher FA production. When the RoPYC gene was introduced, S. cerevisiae produced 1134±48 mg L–1 FA. Furthermore, the final engineered S. cerevisiae strain was able to produce 1675±52 mg L–1 FA in batch culture when the SFC1 gene encoding a succinate–fumarate transporter was introduced. These results demonstrate that the model shows great predictive capability for metabolic engineering. Moreover, FA production in S. cerevisiae can be efficiently developed with the aid of in silico metabolic engineering.Fumaric acid (FA) is a promising biomass-derived building-block chemical. Bio-based FA production from renewable feedstock is a promising and sustainable alternative to petroleum-based chemical synthesis. Here we report on FA production by direct fermentation using metabolically engineered  Fumaric acid (FA) is widely used in food, pharmaceutical and chemical industries, and is attracting increasing attention because it can be converted into therapeutic drugs and is a starting material for polymerization and esterification. FA is mainly produced petrochemically from maleic anhydride at present. Increasing petroleum prices, concerns about climate change and advances in the field of metabolic engineering have fueled renewed interest in the production of organic acids by microbial fermentation S. cerevisiae. In the first, FA can be produced via a reductive tricarboxylic acid (TCA) cycle, which provides a maximum theoretical yield of 2 moles of FA per mole of glucose. Moreover, this process involves biological CO2 fixation instead of release, which is of great interest because of increasing concerns about climate change. In our previous study, an exogenous fumarate biosynthetic pathway involving reductive reactions of the TCA cycle was successfully introduced in S. cerevisiae via a series of simple genetic modifications and pyruvate carboxylase was identified as one of the factors limiting fumarate production –1 at a yield of 0.11 moles of succinic acid per mole of glucose was achieved for oxidative production of succinic acid in yeast by deletion of the SDH1, SDH2, IDH1 and IDP1 genes At least two metabolic strategies can be used for FA production by Recent advances in genomics and other -omics technologies combined with computational analysis have opened new avenues for strain improvement In the present study, the target gene for FA production in S. cerevisiae was identified via literature mining. Then iND750, a validated genome-scale metabolic model (GSMM) of S. cerevisiae To search for target genes for FA production in S. cerevisiae, extensive mining of the literature on FA and S. cerevisiae was carried out. The results of this literature survey revealed that fumarase defects or FUM1 deletion can lead to FA production. Thus, FUM1 was selected as the target gene to be manipulated.–1 DCW h–1 for the modified model, whereas FA was not produced in the original iND750 model. Robustness analysis of the rates of D-glucose uptake and growth for the original and modified models showed that FUM1 deletion leads to slightly lower growth of S. cerevisae (–1) was only ∼1.95% lower than for iND750 (0.973 h–1). Thus, the in silico simulation indicated that FUM1 deletion should not only result in FA accumulation, but should also have no obvious influence on S. cerevisiae growth.FBA analysis revealed that FUM1 deletion can lead to FA production at a rate of 0.357 mmol gerevisae ; the grofum1-deleted mutant was obtained by double-crossover replacement. To confirm that the disruption cassette was correctly integrated in the genome and replaced the target FUM1 gene, PCR analysis was performed using two primer sets  after 120 h of cultivation, while no FA was detected in the broth of parent strain during fermentation  to the theoretical maximum value using iND750 and an FBA algorithm. According to the results, three types of intracellular flux profiles occur: increased, decreased and irregular  preventing channeling of the carbon flux to FA must be identified. First, the central metabolic flux of CEN.PK2-1CΔFUM1 was analyzed with cell growth as the objective when FA production successively increased from the control value   . As showEfficient export of FA is also important to further enhance its production; on the contrary, insufficient transport capacity might impede efficient FA production. It has been reported that SFC1 encodes a succinate–fumarate transporter in S. cerevisiae –1 at 96 h, representing a 47.6% increase in comparison with the engineered strain FMME-002ΔFUM1+↑RoPYC. However, as shown in Therefore, we investigated the potential of constitutive coexpression of RoPYC and SFC1 to further improve FA production. The gene expression levels of RoPYC and SFC1 were increased by 6-fold and 13-fold respectively . ResultsThus, metabolic engineering of S. cerevisiae based on GSMM analysis and transporter engineering successfully yielded a genetically defined FA overproducer.S. cerevisiae is an excellent platform for biologically based chemicals such as organic acids. The aim of the present study was to construct a genetically engineered S. cerevisiae strain that can produce FA. First, the target gene FUM1 was identified in an extensive literature search and then FBA was used to predict the effect of FUM1 disruption using the S. cerevisiae iND750 GSMM. The simulated results revealed that FUM1 deletion could lead to FA accumulation with only a slight influence on cell growth (∼1.95% lower). Then gene deletion was carried out and engineered S. cerevisiae cells produced FA at concentrations up to 610±31 mg L–1. Meanwhile, cell growth and glucose consumption were slightly lower compared to the parent strain, in accordance with the simulated result. Simulated results also showed that pyruvate carboxylase could be one of the factors limiting higher FA production, and an improved FA yield was obtained when the RoPYC gene was introduced. Furthermore, a significant improvement in FA production was achieved when the SFC1 gene was introduced. The final FA concentration obtained was 1675±52 mg L–1. Thus, the engineered strain provides a potential new route for FA production. However, the concentration and yield are low in comparison with R. oryzaeNone of the natural fumarate-producing microorganisms seem to be suitable for large-scale commercial production although high FA yields have been obtained S. cerevisiae, metabolic engineering strategies aided by GSMM have led to improved production of various metabolites such as bioethanol, purine, proline/pyrimidines and vanillin FUM1 deletion or fumarase deficiency on fermentation profiles (growth and glucose uptake rate) has not been studied in detail. In the present study, the phenotypic trait of slightly lower growth caused by FUM1 deletion in S. cerevisiae was successfully predicted by FBA analysis; this trait is important for metabolic engineering because unwanted side effects can be induced. Metabolic models are also useful in identifying targets for further strain improvement. We identified pyruvate carboxylase as a factor restricting higher FA yield. A higher FA yield was obtained by increasing the flux through pyruvate carboxylase. The increased flux induced by overexpression of pyruvate carboxylase is linked to increased transport of cytosolic oxaloacetate into mitochondria and supply to oxidative reactions SFC1 were coexpressed, a higher growth rate and FA yield were obtained, which suggests that insufficient FA export is another controlling step that would lead to higher FA production in steady-state metabolism.The number of GSMMs available is increasing sharply in silico predictions and experimental results. The discrepancy between experimental and predictive yields was primarily caused by lack of model knowledge for yeast metabolism, regulatory mechanisms and feedback inhibition, which requires specific further experimental investigation.The model showed some restrictions; however, the physiological characteristics observed for engineered organisms can be used to update the model. In the present study, there was very good accordance between S. cerevisiae were rationally engineered for FA production with the aid of in silico simulations. The strategy described here can be useful for improved production of organic acids and other metabolites by direct microbial fermentation from renewable resources.In conclusion, the metabolic pathways in S. cerevisiae CEN.PK2-1C , which was obtained from Euroscarf . Plasmid pUG27 was constructed by replacing the kanMX marker in pUG6 with the his5+ marker from pFA6a-HIS3MX6 (which complements the S. cerevisiae his3 mutation) using BglII and SacI restriction sites, as previously described Pyrobest DNA polymerase for PCR was purchased from TaKaRa Biotechnology .The strains and plasmids used are listed in kanMX gene flanked by loxP sites, then the Cre plasmid pSH47 is removed from this yeast strain. Primers used in confirming the loss of selectable marker are listed in The Cre-expressing plasmid pSH47 was used for marker rescue S. cerevisiae, a literature search was carried out. The literature database consisted of articles downloaded from public online databases, including ScienceDirect (http://www.sciencedirect.com/), Springer Link(http://www.springerlink.com/), PubMed(http://www.ncbi.nlm.nih.gov/pubmed/), and ISI Web of Science (http://www.isiknowledge.com). Manual surveys of these papers were then carried out to obtain objective information.To identify genes associated with FA production in in silico by FBA using the S. cerevisiae iND750 GSMM. To simulate single gene deletion, the flux of the reactions in iND750 catalyzed by target genes was set to zero . Then FBA was carried out to predict optimum growth in minimum medium in which D-glucose was the sole carbon source. The rate of D-glucose uptake was set to 10 mmol g–1 DCW h–1 and the biomass equation was taken as the objective function. The rates of growth and FA production were simulated and compared with values for the non-modified iND750 model under the same culture conditions. A robustness analysis of the rates of D-glucose uptake and growth between the original and modified models was also conducted. FBA simulations were performed using COBRA Toolbox v2.0 in MATLAB 2010b, with GLPK as the linear programming solver The impact of gene deletion on cell growth and FA production was evaluated FUM1 gene (1467 bp) was performed via a one-step inactivation method S. cerevisiae CEN.PK2-1C. After plasmid preparation, a fragment of pUG27 was amplified by PCR to obtain a cassette consisting of loxP-+his5-loxP. Primers were constructed  was obtained as previously described RoPYC gene were designed according to GenBank sequences for R. oryzae using Primer Premier v5.0 software  and HindIII-R(SFC1). The PCR fragment and pY26TEF-GPD vector were digested with BamHI and HindIII and ligated to create pY26TEF-GPD-SFC1.cDNA of software . ThermalPlasmids were introduced into yeast cells using a Frozen-EZ yeast transformation II kit  according to manufacturer’s protocol. Transformants were selected on agar plates of synthetic complete (SC) selection medium lacking leucine and uracil as auxotrophic markers.260] of 1 equals 40 µg mL–1 RNA). Reverse transcription was carried out using an iScript™ cDNA Synthesis Kit (Bio-Rad) according to the manufacturer’s instructions. Amplification of cDNA via quantitative real-time PCR was carried out using an iTaq™ Universal SYBR Green Supermix Kit (Bio-Rad). The reaction mix consisted of 10 µL iTaq™ Universal SYBR Green Supermix (2×), 1 µL each of forward and reverse primers (10 µmol), 2 µL of cDNA (50 ng), and 6 µL H2O. Primers used in the transcriptional analysis are listed in ACT1 reference gene. Each sample was tested in triplicate in a 96-well plate .For RNA extraction, flask culture cells in early stationary phase were harvested by centrifugation (8000 rpm at 4°C for 5 min). Total RNA was isolated using an RNAprep pure plant kit according to the manufacturer’s instructions. The amount of isolated RNA was determined photometrically  was used for plasmid purification from E. coli JM109. The fermentation medium (pH 5.0) contained (per liter) 50 g of glucose, 2 g of CO(NH2)2, 5 g of KH2PO4, 0.8 g of MgSO4⋅7H2O, and 10 mL of trace metal solution . In flasks, 40 g L–1 CaCO3 (dry-heat sterilized at 160°C for 30 min) was used as a buffer for the medium. Flasks were incubated at 30°C in an orbital shaker at 200 rpm. YPD medium  was used for seed culture. The seed culture was inoculated with yeast growing well on an agar slant and was incubated for 24 h in a 250-mL flask containing 20 mL of seed medium. The broth was centrifuged, the pellet was resuspended in isometric fresh fermentation medium, and the cell suspension was inoculated into a 250-mL shaker flask containing 50 mL of fermentation medium.Luria-Bertani (LB) medium (5 g L600) spectrophotometrically. Cell concentration, defined as grams of dry cell weight (g DCW) per liter, was calculated from a standard curve relating OD600 to dry weight (1 OD600 = 0.35 g DCW L–1). Extracellular concentrations of fumaric acid, ethanol and glucose were determined by HPLC using an Aminex HPX-87H column (Bio-Rad) eluted with 0.0275% (v/v) H2SO4 at a flow rate of 0.6 mL min–1 at 35°C. Fumaric acid was monitored using an Agilent  1200 series DAD detector at 210 nm. Ethanol and glucose were monitored using a 1200 series Agilent refractive index detector.Cell growth was monitored by measuring the absorbance at 600 nm  and 13C NMR  spectra were recorded on an Avance III 400-MHz digital NMR spectrometer.Cell cultures of the engineered strain were centrifuged and the supernatant was adjusted to pH 1.0 by addition of HCl. Following acidification, fumaric acid precipitated out of the solution and was recovered by drying in a rotary dryer. The sample obtained was processed in parallel with a fumaric acid reference standard for FT-IR and Figure S1PCR analysis to confirm correct integration of the HIS marker gene disruption cassette at the YPL262W locus and confirm the loss of selectable marker. The size of the expected PCR products is given below each lane. Wild type, nontransformed wild type yeast strain; Mutant I, mutant yeast strain carrying the HIS marker gene disruption cassette; Mutant II, mutant yeast strain without marker gene.(TIF)Click here for additional data file.Figure S2The IR spectra of fumaric acid. (a) sample; (b) the fumaric acid standard.(TIF)Click here for additional data file.Figure S31H and 13C NMR spectrum of sample and the fumarate standard.The  (a) 1H spectrum of the fumarate standard; (b) 13C spectrum of the fumarate standard; (c) 1H spectrum of sample; (d) 13C spectrum of sample.(RAR)Click here for additional data file.Table S1The central metabolic flux of CEN.PK2-1CΔFUM1 was analyzed using iND750 and an FBA algorithm. The genome-scale analysis was divided into three types of intracellular flux profiles occur: increased, decreased and irregular. The “increased” represents that the flux increases when FA production successively increased, the “decreased” represents that the flux decreases when FA production successively increased, and the “irregular” represents that the flux is unchanged or ruleless when FA production successively increased.(XLSX)Click here for additional data file.


Methylotrophic microorganisms are playing a key role in biogeochemical processes - especially the global carbon cycle - and have gained interest for biotechnological purposes. Significant progress was made in the recent years in the biochemistry, genetics, genomics, and physiology of methylotrophic bacteria, showing that methylotrophy is much more widespread and versatile than initially assumed. Despite such progress, system-level description of the methylotrophic metabolism is currently lacking, and much remains to understand regarding the network-scale organization and properties of methylotrophy, and how the methylotrophic capacity emerges from this organization, especially in facultative organisms.Methylobacterium extorquens AM1, a valuable model of methylotrophic bacteria. The genome-scale metabolic network of the bacterium was reconstructed and contains 1139 reactions and 977 metabolites. The sub-network operating upon methylotrophic growth was identified from both in silico and experimental investigations, and 13C-fluxomics was applied to measure the distribution of metabolic fluxes under such conditions. The core metabolism has a highly unusual topology, in which the unique enzymes that catalyse the key steps of C1 assimilation are tightly connected by several, large metabolic cycles . The entire set of reactions must operate as a unique process to achieve C1 assimilation, but was shown to be structurally fragile based on network analysis. This observation suggests that in nature a strong pressure of selection must exist to maintain the methylotrophic capability. Nevertheless, substantial substrate cycling could be measured within C2/C3/C4 inter-conversions, indicating that the metabolic network is highly versatile around a flexible backbone of central reactions that allows rapid switching to multi-carbon sources.In this work, we report on the integrated, system-level investigation of the metabolic network of the facultative methylotroph M. extorquens AM1 is adapted to its lifestyle not only in terms of enzymatic equipment, but also in terms of network-level structure and regulation. It suggests that the metabolism of the bacterium has evolved both structurally and functionally to an efficient but transitory utilization of methanol. Besides, this work provides a basis for metabolic engineering to convert methanol into value-added products.This work emphasizes that the metabolism of  Methylotrophs are microorganisms able to grow on reduced C1 compounds such as methane and methanol as sole source of carbon and energy. Methylotrophy has gained increasing interest over the past decade for both basic and applied purposes, since methanol can be produced from diverse renewable sources and represents a valuable feedstock for biotechnological applications . Recent 2). In addition, alternative dissimilation mechanism exist in which formate is not involved. The assimilation of C1-units can be achieved by different mechanisms starting from either formaldehyde , CO2  or methylene-tetrahydrofolate (Me-THF) + CO2 (serine cycle). Novel enzymes, biochemical mechanisms, and metabolic pathways have been discovered, resulting in a more complete description of methylotrophic pathways and their diversity. Interestingly, these findings do not modify our current view of the general organization of C1 pathways, but show that many more alternative enzymes or pathways than initially assumed carry out each of the basic steps of methylotrophy. Moreover, a growing number of newly discovered methylotrophs have been investigated, showing that different combinations of the various pathways can be found in nature, and leading to the concept of a modular metabolism 7]. The aE.coli , using aE.coli -methanol experiments were performed like in [2 to remove the 13CO2 produced by the bacteria from [13C]-methanol. Under this condition, only 4.6 ± 0.4% of total CO2 was found to derive from [13C]-methanol oxidation.Batch cultivations  of eviously . Cultiva like in . The culM. extorquens. Calculation of EFMs in the methylotrophic network was performed using the solver EFMTool -methanol. The distribution of 13C-isotopomers in metabolites can be accurately determined by NMR or MS [13C-isotopomers of proteinogenic amino-acids were measured by the two methods. NMR spectra were monitored as described in [M. extorquens central metabolism, according to the topology of the methylotrophic network . RP and SM performed the NMR measurement. RP and PK performed the MS measurement. RP performed the data treatment. RP performed the metabolic flux analysis and statistical analysis. RP and JCP analyzed the results. RP, JV and JCP wrote the manuscript. RP, JV and JCP conceived the study. All authors read and approved the final manuscript.RP performed the reconstruction and the modeling. KS and PK performed the determination of the biomass composition. RP performed the Work flow of the reconstruction and reduction processes. The work flow of the reconstruction was performed similarly to the protocol recommended for the generation of high-quality reconstruction .Click here for fileList of reactions of the reconstruction (iRP911)Click here for fileList of metabolites of the reconstruction (iRP911)Click here for fileDetailed biomass compositionClick here for fileM. extorquens AM1 from experimentally observed phenotype and Flux Balance Analysis using the genome scale network (iRP911)Table of substrate usage by .Click here for fileM. extorquens AM1Electron flow through the metabolic network of . The schemas represent the reactions involved in electron flow in M. extorquens AM1like it appear from the network reconstruction (iRP911). Detail on the reaction, given with identifiers of the type R-XXXX, can be found in the Additional file Click here for fileMethylotrophic network. The reactions of the GS network were included or excluded from the methylotrophic network based on multi-criteria analysis. The list of considered criteria is given here as well as the score for each reaction of the GS network. The list of reactions included in the methylotrophic network are indicated as '1' in the 'methylotrophic network' column.Click here for fileEFM analysis of the primary assimilation pathways and connectivity of biomass precursors. The EFMs were calculated for the conversion of methanol into 13 key carbon precursors. The main properties of the calculated EFMs are given.Click here for fileReaction essentiality in the methylotrophic network. The graph displays the essentiality or dispensability of reactions and showx the experimental evidence for gene essentiality. Reaction essentiality was analyzed using Minimal Cut Set calculation [culation  applied culation  allowingculation . ReactioClick here for fileTable of published mutant phenotypes and associated genes and reactionsClick here for fileGrowth parameters measured for the 3 replicate 13C-methanol labeling experimentsClick here for fileFitting of the isotopomers data collected during 13C-methanol labeling experimentsClick here for fileFlux distributions and sensitivity analysis. Only C1 assimilation was considered during flux calculation due to the high difference in range of C1dissimilation and assimilation. Measured methanol uptake rate was considered subsequently.Click here for fileQuality of isotopomer fitting. Comparison of experimental and collected isotopomer values for the three biological replicates. The isotopomer data include both LC-MS and 2D-NMR (HSQC and TOCSY) data. Flux calculation and fitting were performed using the software 13CFlux . A) Experimental values (+/- standard deviation) are plotted against theoretical values. B) Residuum of the calculated data.Click here for fileFlux variability in the 3 biological replicates. Comparison of the flux distribution obtained for the three biological replicates. The flux calculation and the sensitivity analysis were performed using the software 13CFLUX . The fluxes were normalized by the flux of entry of the C1-units in central metabolism (SHMT: serine hydroxymethyltransferase). Flux distributions were found to be similar except slight changes through the C3/C4 interconversions (pyruvate kinase (PK), pyruvate dikynase (PPDK), malic enzyme (ME) and the phosphoenolpyruvate carboxykinase (PEPCK)), and through the Entner-Doudorof pathway.Click here for fileComputer model written in Systems Biology Markup Language of the genome-scale metabolic network iRP911.Click here for fileFtbl file describing the network used for flux calculation.Click here for file


Elementary mode (EM) analysis is ideally suited for metabolic engineering as it allows for an unbiased decomposition of metabolic networks in biologically meaningful pathways. Recently, constrained minimal cut sets (cMCS) have been introduced to derive optimal design strategies for strain improvement by using the full potential of EM analysis. However, this approach does not allow for the inclusion of regulatory information.E. coli. Our study showed that up to 70% of the predicted cMCS contained non-enzymatic, non-annotated reactions, which are difficult to engineer. These cMCS are automatically excluded by our approach utilizing simple weight functions. Finally, due to efficient preprocessing, the binary program remains computationally feasible.Here we present an alternative, novel and simple method for the prediction of cMCS, which allows to account for boolean transcriptional regulation. We use binary linear programming and show that the design of a regulated, optimal metabolic network of minimal functionality can be formulated as a standard optimization problem, where EM and regulation show up as constraints. We validated our tool by optimizing ethanol production in E. coli.We used integer programming to predict efficient deletion strategies to metabolically engineer a production organism. Our formulation utilizes the full potential of cMCS but adds additional flexibility to the design process. In particular our method allows to integrate regulatory information into the metabolic design process and explicitly favors experimentally feasible deletions. Our method remains manageable even if millions or potentially billions of EM enter the analysis. We demonstrated that our approach is able to correctly predict the most efficient designs for ethanol production in  Arguably the most successful methods in computer aided strain design are based on constraint-based modeling . These mAn alternative way of predicting optimal strain design is to use elementary mode analysis (EMA) -13. EMA A rigorous formulation – constrained minimal cut sets (cMCS) – for generating NMF has recently been put forward . It reliHere we present an alternative formulation to predict the optimal engineering strategy for the design of MNF. We formulate an optimization problem and show that cMCS can be easily calculated by binary linear programming (BLP) for which commercial and non-commercial solvers are readily available. The scope of our approach is similar to the algorithm presented by  but it im internal metabolites and n reactions, i.e. S denotes the m×n stoichiometric matrix of the network, and n-dimensional flux vector through the network.We consider the standard steady-state problem of a metabolic network with ê be an EM flux vector , it is always possible to find at least one solution.Equation (4) may either have no or a finite number of solutions. In the first case, no knockout strategy accommodates all constraints. However, if the constraints are relaxed, i.e. EM are shifted from xj)( of equation (4) by adding . In ourified by , H=0, angiven by . Again, given by . Howevergiven by , who anngiven by  as “undein vivo implementation of the smallest, fully annotated, biologically feasible MCS requires seven gene deletions. We found eight alternate MCS. In comparison, the experimentally implemented strain by [By using a weight function  Without any weight function and additional constraints, at least eleven reaction deletions are required to reach the design goal. In total we found 55,488 MCS, 1.440 of which require the minimal number of eleven reaction deletions.  are, however, no deletions, but in fact the result of the regulation. As expected, we found that all MCS were characterized by a down-regulation of R_GLB1 and R_GG13.Elementary mode analysis has been identified as a promising tool for metabolic engineering. However, the analysis of millions or billions of EM still poses difficulties. Recently  introducWe partitioned EM into three categories: goal modes, kill modes, and helper modes. The first group contains the desired functionality. All modes in this group will also be present in the final NMF. Kill modes, on the other hand, are all those modes which will definitely get deleted from the network. The third and final group collects all other modes, which may or may not be present in the final design. With respect to the design criterion this last group of modes neither contributes to nor counteracts the design goal. We then reformulated the problem of calculating MCS to generate NMF as a linear optimization problem. Our approach is very intuitive and structurally reminiscent to ordinary flux balance analysis. The matrix of EM replaces the stoichiometric matrix. Constraints are not set by the mass-balance but by design requirements. More specifically, binarized EM show up as constraints on the admissible flux space. By optimizing the admissible flux space, maximal or minimal intervention strategies (with respect to the number of deletions) can be predicted.G –, while in cMCS EM are chosen automatically form a pool of desirable EM, D, such that at least n modes survive. However, if the surviving modes are known an identical BLP problem can be set up.In the case of optimal ethanol production we demonstrated that BLP is able to give identical results as compared to cMCS . In factE. coli and S. cerevisiae curated transcriptional regulatory networks are readily available [The major advantage of our reformulation is its easy integration of (binary) transcriptional regulation. Regulatory information may simply be included as additional constraints. We have shown that our formulation allows a regulatory coupling between reactions and between genes and reactions alike. In Table vailable . HoweverE. coli we demonstrated that our predictions are robust against variations in the weights. In particular we found that our choice of weights is very conservative and far from the limits detected in the robustness analysis . A NMF can be easily generated by either knocking out either R4 or R6. The metabolic functionality of the resulting networks, however, differs significantly. By deleting R4 the only “surviving” mode is the desired goal mode. Thus the network is in fact a NMF, as no other functionality is available. On the other hand, by deleting R6 the network still has the desired properties, but retains additional functionality (conversion of C to B) without compromising the original design criterion. Alternatively we may define a kill-matrix K and calculate the resulting network of minimal or maximal functionality. BLP is able to predict solutions with the smallest/largest set of EM contributing to the metabolic functionality and distinguish between those two extremes without enumerating the full solution space. This feature therefore opens a way to include secondary objectives in the design process.Part of the flexibility of our approach is its ability to optimize metabolic functionality. This can be easily demonstrated in a simple example as illustrated in Figure E. coli model [An integer programming problem sits at the heart of our algorithm. Integer programs are inherently difficult to solve . Neverthli model  (see aboli model .We tested various off-the-shelf software packages to solve the BLP problem. The implementation of our algorithm merely required setting up the input parameters for those solvers. We found that our approach is computationally modest and scalable. In fact, we were able to successfully repeat the analysis for the much larger core metabolic network of  with itsIn summary, we have demonstrated an efficient and easy to implement method to rationally predict engineering strategies for the improvement of production hosts. Optimal pathways were identified using elementary mode analysis. Based on integer/binary programming we were then able to predict all minimal intervention strategies to design a strain with desirable metabolic capabilities. Our method is based on the concept of constrained minimal cut sets, but offers much more flexibility in the prediction of engineering targets, including most prominently the possibility of easily integrating gene regulation.efmtool[http://www.gurobi.com/ for solving the BLP problem. efmtool is open source and freely available; Gurobi offers a free academic license.We used efmtool to calcuBLP: Binary linear programming; cMCS: Constrained minimal cut set; EM: Elementary modes; EMA: Elementary mode analysis; MCS: Minimal cut set.We declare that we have no competing interests.CJ and JZ designed, analyzed and wrote the paper. Both authors read and approved the final manuscript.Supplementary material. A pdf containing all additional data, figures and tables.Click here for file


Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature.Efforts to improve the computational reconstruction of the Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file  Saccharomyces cerevisiae biochemical reaction network and to refine the metabolic models that can be derived from such a reconstruction have continued since the first yeast genome scale metabolic model was published [Efforts to improve the computational reconstruction of the ublished . The disublished ) and derublished ) remainsublished , and theublished . The hisublished .Salmonella Typhimurium LT2 [Though a GEM may be considered finished when it is sufficient for a particular modeling application, the effort to build a complete and accurate GENRE is ongoing as biochemical research continues . Reflecrium LT2 .The Yeast Consensus Reconstruction has benefited from the continued involvement of the broader research community. Previous updates to the Yeast Consensus Reconstruction  have focTherefore, we decided to undertake an update to the Yeast Consensus Reconstruction to refine the biochemical reactions included in the GENRE, particularly reactions involved in sphingolipid metabolism ,21; to rhttp://yeast.sf.net/). In addition to the GEM and GENRE SBML files, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications, such as simulating aerobic and anaerobic metabolism with Yeast 5. These scripts, which provide an independent tool for evaluating the performance of various yeast GEMs, are included as additional files.We have incorporated the results of these efforts to the consensus reconstruction to produce Yeast 5. Yeast 5 expands and refines the yeast GENRE and improves the predictive accuracy of the yeast GEM. Further, it differs from previous reconstructions and models by emphasizing the distinction between the yeast GENRE and GEM, and makes both available as separate systems biology markup language (SBML) files . ThroughAs a consensus reconstruction and model, Yeast 5 will depend upon community use and suggested modifications for future improvement. The Yeast 5 GENRE may serve as a resource for construction of new models, of both genome and smaller scale. Thus, in addition to the emphasis on standards compliance, the Yeast 5 SBML files include coding conventions to facilitate use by popular software. Recognizing that MATLAB is a commonly used platform for systems and computational biology , we incl1Saccharomyces cerevisiae genes. In comparison, Yeast 4 includes 1481 metabolites, 2030 reactions, and 924 genes. The Yeast 5 GENRE does not include genes annotated in the Saccharomyces Genome Database (SGD) as “dubious” or “uncharacterized”, while Yeast 4 includes 4 such genes . Yeast 4 included reactions annotated with 29 open reading frames which are not included in the Yeast 5 GENRE. However, the Yeast 5 GENRE includes 23 open reading frames which are not included in Yeast 4. The 29 open reading frames which are present in Yeast 4 but not Yeast 5 are: YAL014C, YAL030W, YAR042W, YCR073W-A, YDL019C, YDR313C, YDR331W, YDR468C, YEL011W, YEL013W, YER093C, YFR055W, YGR199W, YHR005C, YHR073W, YMR068W, YNL006W, YNR034W, YOL078W, YPL145C, YPL275W, YPL276W, YOR237W, YIL105C, YJL058C, YJR160C, YKL203C, YML082W, and YNL047C. These open reading frames were removed from reaction annotations because of inadequate literature evidence supporting the Yeast 4 annotation. The 23 open reading frames included in the Yeast 5 GENRE but not in Yeast 4 are: YBR001C, YBR058C-A, YBR161W, YBR199W, YDR196C, YDR367W, YGR138C, YGR277C, YMR241W, YMR278W, YMR298W, YPL023C, YPL053C, YPL189W, YPR156C, YOR175C, YGL084C, YIL083C, YJL200C, YKL088W, YKL132C, YML056C, and YNL029C. Additional annotation information about these ORFs is provided in Additional file The Yeast 5 GENRE is an evidence-driven biochemical knowledge-base. It does not include the low-confidence or hypothetical reactions and metabolites required to conduct FBA, nor does it include constraints on reaction reversibility which may be added in the course of model-building. Since it does not include compounds such as “biomass”, reactions such as “growth”, or hypothetical intercompartmental transport reactions, the Yeast 5 GENRE is more specific than Yeast 4, which did not differentiate between GENRE and GEM. It contains 1418 metabolites which participate in 2110 reactions, catalyzed by 918 verified In addition to being more specific, the Yeast 5 GENRE is also more complete than Yeast 4. Sphingolipid metabolism has been acknowledged to be a poorly reconstructed portion of the yeast metabolic network since the first yeast GEM, iFF708 . Yeast 5in vivo.The Yeast 5 GEM includes biomass demand functions and low-confidence reactions such as intercompartmental transport reactions which enhance network connectivity and enable FBA. It also includes reaction directionality constraints to improve the accuracy of model phenotype predictions and exchange reactions which allow model users to simulate a growth medium. The Yeast 5 GEM includes more reactions and metabolites than previously published yeast GEMs Table, though Since the Yeast GEM includes both general classes of compounds (e.g.“fatty acid”) and specific members of these classes (e.g. “octanoate”), we use non-reversible encapsulating reactions called “isa” reactions to provide pathways from specific to generic compounds . The use of “isa” reactions is discussed further in the Discussion section. To accommodate the more specific biochemistry included in the Yeast 5 GENRE, the Yeast 5 GEM includes 261 “isa” reactions, compared to 162 in Yeast 4. Additionally, the Yeast 5 GEM includes 2 different lipid pseudoreactions, which create the “lipid” portion of biomass , but that biomass could still be produced following deletion of 113 genes included on those lists . Thus, the model has a 47% specificity for this list of essential genes. If the model simulation predicted that biomass could not be produced following a gene deletion, the deleted gene was listed as essential or auxotroph-inducing in 83.5% of the cases .Comparing Yeast 5 GEM knockout simulations with our list of essential and auxotroph-inducing genes yields a geometric mean overall predictive accuracy . In 17 cases, model simulation predicted that biomass could be produced in minimal media, and thus did not accurately predict the auxotrophic phenotype. In 2 cases, model simulation predicted that biomass could not be produced in either minimal or maximal media, and so the model incorrectly predicted that the gene deletion could not be saved by media supplementation.Auxotroph phenotypes have not previously been a metric used to evaluate yeast GEMs. We found that simulation with the Yeast 5 GEM had better agreement with observed auxotrophic phenotypes than simulations conducted with other previously published GEMs Table.in vivo[Optimal solutions found when conducting FBA of the Yeast 5 GEM to maximize biomass flux include fluxes through key internal reactions, a flux distribution which better matches fluxes observed in vivo than solRecognizing recent efforts to investigate system-level organization of cellular metabolism via the phenotypic effects of multiple gene deletions using yeast GEMs -36, we cResearch to expand our understanding of yeast metabolism and biochemistry is ongoing, and the process of integrating established biochemical knowledge into computational reconstructions lags research advancements. Thus, the Yeast 5 GENRE is not a complete reconstruction of the yeast biochemical network, and though it offers improvements over earlier models, simulations using the Yeast 5 GEM do not fully reflect observed biological phenomena. GENRE and GEM limitations suggest opportunities for future efforts to improve computational reconstruction of established biochemistry and can highlight portions of metabolism that are ripe for further research .A limitation of the Yeast 5 GENRE which suggests future opportunities for improving the Yeast Reconstruction is that due to the lack of information about enzyme specificity or the metabolic significance of variation among similar chemical species, the Yeast 5 GENRE uses general classes of chemical compounds rather than the enumeration of many similar compounds. For example, Yeast 5 generalizes the many possible triglyceride compounds which may be synthesized from fatty acyl moieties of varying length  into a sAs with other reconstructed metabolic networks, the continued existence of blocked pathways Table highlighin situ rather than transported from the cytoplasm, and model growth simulations incorrectly predict that yeast is not a pantothenate auxotroph or a nicotinic acid auxotroph in anaerobic conditions. Additionally, although the model predicts that biomass can only be produced anaerobically if the biomass definition is modified , the reason that simulated anaerobic biomass production using an unmodified biomass definition is blocked is not because of the biological requirement of yeast fatty acid desaturate for oxygen. Instead, simulated anaerobic biomass production is blocked due to other, as yet unidentified limitations in the reconstruction of phospholipid and sterol biosynthesis. Refining the solution space to more closely match observed biological phenomena through improved reconstruction or expanded constraints remains an ongoing research effort for reconstructed metabolic networks.Optimal solutions found when conducting FBA on the Yeast 5 GEM may include fluxes that differ from those observed in yeast: we have found optimal solutions in which mitochondrial coenzyme A is synthesized Due to varying interpretations of experimental evidence, uncertainty of metabolic mechanism, and varying approaches taken as modelers work to reconstruct different portions of metabolism, it is likely that the Yeast 5 consensus reconstruction has additional limitations which will be discovered as it is used. These limitations provide opportunities for continued research to improve the computational reconstruction and simulation of the yeast biochemical network. Thus, though Yeast 5 consists of a more complete reconstruction and more accurate model of yeast metabolism than previous efforts, the goal of building a complete and accurate computational reconstruction of yeast metabolism must remain an ongoing community effort.Yeast 5 is the most recent update to the consensus reconstruction of the yeast metabolic network. It consists of a genome-scale reconstruction (GENRE), a genome scale model (GEM), and MATLAB scripts designed to facilitate evaluation of yeast GEMs and to demonstrate simulation and analysis using the COBRA and SBML toolboxes. This update improves the consensus reconstruction’s coverage of established biochemical knowledge, and improves the predictive ability of simulations using the yeast GEM. The included scripts lower the barriers for the research community to use the model and to contribute to the collaborative effort to improve the computational reconstruction of yeast metabolism.We emphasize the distinction between a reconstruction, or GENRE, and a model, or GEM, to more clearly delineate the established biochemical knowledge of a reconstruction from the assumptions and hypotheses that are required for modeling and simulation. This distinction makes the modeling process more transparent and reproducible, essential attributes for community-based scientific efforts such as the consensus reconstruction of yeast metabolism.As evidence of improvements to the consensus yeast reconstruction, we have presented a comparison of viability, auxotrophy predictions, and genetic interaction effects, produced using FBA of the Yeast 5 GEM and other metabolic models Table. Howeverin vivo. Thus, a metabolic model based upon a reconstruction with improved coverage of established biochemistry of isoenzymes would make less accurate predictions of individual gene essentiality than a model based upon a less complete reconstruction.Expanding the reconstruction of a metabolic network would be expected to increase both dead-end pathways and network redundancy. Dead ends would be introduced through the inclusion of established knowledge regarding pathways that are not fully elucidated. In such pathways, the production of intermediates may be established, but their fate is not yet known. In such cases, expanding the reconstruction of established knowledge would not be expected to improve a model’s ability to predict single-gene mutant viability, and so such metrics would not reflect the expanded scope of the reconstruction. Another example of network expansion which may not be reflected in single-gene deletion metrics is expanding the reconstruction’s coverage of isoenzymes. Network redundancy increases through expanded inclusion of isoenzymes, which introduce parallel metabolic paths for the production of chemical intermediates or products. In the absence of regulatory constraints (which are beyond the scope of a metabolic reconstruction), these parallel pathways would increase the rate of false positive prediction since a metabolic pathway from substrate to product would exist in the reconstruction, even if a given isoenzyme were individually insufficient to support growth A second problem with metrics based upon lists of essential genes is that gene essentiality is dependent upon strain, media, and environmental conditions  identifiers. New reactions should be supplied with primary evidence for their mechanism and catalysis, via PubMed identifiers. Reactions without evidence should have clear reasons for their proposed addition.http://yeast.sf.net/. Assumptions and constraints should be documented, for example with code documenting how to build the GEM from the Yeast GENRE. Such models may be submitted for publication separately from updates to the Yeast GENRE, and may follow the iNNXXX naming convention which has been previously used for identifying GEMs [We also invite researchers to submit models derived from the yeast consensus reconstruction for hosting at ing GEMs .http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible. The Yeast Consensus Reconstruction remains a community-based resource which emphases standards compliance and biochemical accuracy via evidence-based selection of reactions.The Yeast 5 expansion of the Yeast Consensus Reconstruction refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction (GENRE) and stoichiometrically constrained model (GEM). This update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. Yeast 5 differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file Though Yeast 5 consists of a more complete reconstruction and more accurate model of yeast metabolism than previous efforts, the goal of building a complete and accurate computational reconstruction of yeast metabolism must remain an ongoing community effort. As Yeast 5 limitations are identified, they provide opportunities for continued research to improve the computational reconstruction and simulation of the yeast biochemical network.S. cerevisiae reference strain SC288C. Since multiple yeast genome sequences are now available, future updates to the Yeast Consensus Reconstruction may become strain specific.The scope of Yeast Consensus Reconstruction was originally determined by the data sets used for its construction: the iMM904  and iLL6reconstruction of yeast metabolism, an evidence-driven biochemical knowledge-base, and its corresponding model, which relies on a number of assumptions to make quantitative flux predictions [We must maintain a distinction between the dictions . We discdictions . Specifidictions .The Yeast 5 GEM includes conventions to facilitate model analysis using the COBRA Toolbox, such as standardized representation of exchange reactions, though such conventions are not required in the SBML specification. However, we have chosen to rely upon the SBML standard for encoding reaction and metabolite annotation, rather than the nonstandard custom notes field currently used by the COBRA toolbox. This information, which includes metabolite ChEBI identifiers and literature references supporting reaction inclusion, is encoded in the model .sbml file and is available to MATLAB users via the SBML toolbox. An additional model convention is that the Yeast 5 GEM includes biomass as a species in the model to help make our approach to simulating biomass production more explicit.Exchange reactions in the Yeast 5 GEM follow a convention of using compounds in the model as reactants, leading to exchange reactions of the form “reactant ->”, with an entry of +1 in the stoichiometric matrix. Thus, positive flux values for exchange reactions represent compounds produced in FBA simulation, and negative flux values represent compounds consumed. Since reactions must include both substrate and product in SBML, we have followed the COBRA toolbox convention of denoting exchange reaction species which lay outside the model with the subscript “_b”. These species are not loaded into the COBRA toolbox data structure, and serve only as placeholders for exchange reaction substrates.We have chosen to include biomass as a species in the Yeast 5 GEM. Thus, when conducting FBA on the Yeast 5 GEM, the biomass exchange reaction can be selected if biomass optimization is the desired objective function. For modeling purposes, the species “biomass” is produced in the Yeast 5 GEM via a reaction which consumes 37 biomass precursor compounds to produce biomass, ADP, protons, and phosphate. The biomass precursors include water, polysaccharides, nucleotides, amino acids, riboflavin, sulfate, and the general species “lipid”. The “lipid” species in the Yeast 5 GEM serves a function similar to Zanghellini et al.’s “virtual membrane particle”  or NookaThe Yeast 5 GEM has over 300 more directionally constrained reactions than Yeast 4. Such constraints incorporate thermodynamic information into the GEM, and often serve in part to limit Type III cycling, which arises from the linear programing approach, but are thermodynamically infeasible . It is nThe Yeast 5 GEM includes 170 exchange reactions, each of which defines a compound that can be included as a medium component for simulation purposes. As distributed, the simulated media is a glucose-limited minimal aerobic medium with constrained limited uptake of glucose, and unconstrained exchange of oxygen, ammonium, protons, iron(2+), phosphate, potassium, sodium, sulfate and water. To simulate anaerobic growth, the oxygen exchange reaction may be constrained to disallow oxygen uptake. Simulating anaerobic growth also requires that the simulated media be supplemented by allowing exchange of ergosterol, lanosterol, zymosterol and phosphatidate, and the biomass definition be changed by removing 14-demethyllanosterol and ergosta-5,7,22,24(28)-tetraen-3beta-ol from the “lipid” definition. These requirements reflect the observation that yeasts require sterols ,49 and fGrowth simulations were performed using the COBRA toolbox . We havehttp://www-sequence.stanford.edu/group/yeast_deletion_project/data_sets.html. Since this data set screened in complex media (which is only incompletely accounted for with FBA simulation due to the limited number of exchange reactions), we refined the list of “essential” genes first by removing any ORFs which have not been reported as verified in the SGD database [Gene deletion simulations were performed using the COBRA toolbox . To compdatabase , and thedatabase  from thixy-WxWy[. In this definition, Wx and Wy are fitness scores for organisms with a mutation in genes x and y, respectively, and Wxy is the fitness of the organism with both mutations present. Any ϵ ≠ 0 indicates a genetic interaction under the assumption that both of the genes independently and multiplicatively contribute to fitness. Following the example of [Genetic interactions were quantified with the nonscaled multiplicative definition of epistasis, ϵ = Wxy-WxWy. In thisample of , we quanample of , we simumodelToReconstruction.m: A script which can be used to generate the Yeast GENRE from the Yeast GEM; testYeastModel.m.m.; A script which can be used to compare various yeast GEMs ; fluxDistribution.m: A script demonstrating aerobic and anaerobic growth simulations, and comparing the fluxes through glycolysis, the TCA cycle, and the pentose phosphate pathway.yeast_5.01_model.xml: The Yeast GEM; yeast_5.01_recon.xml: The Yeast GENRE.model To Reconstruction.m, A script which can be used to generate the Yeast GENRE from the Yeast GEM; test Yeast Model.m, A script which can be used to compare various yeast GEMs ; flux Distribution. m, A script demonstrating aerobic and anaerobic growth simulations, and comparing the fluxes through glycolysis, the TCA cycle, and the pentose phosphate pathway; yeast_5.01_model.xml, The Yeast GEM; yeast_5.01_recon.xml, The Yeast GENRE.model To Reconstruction.m, A script which can be used to generate the Yeast GENRE from the Yeast GEM; test Yeast Model.m, A script which can be used to compare various yeast GEMs ; flux Distribution. m, A script demonstrating aerobic and anaerobic growth simulations, and comparing the fluxes through glycolysis, the TCA cycle, and the pentose phosphate pathway; yeast_5.01_model.xml, The Yeast GEM; yeast_5.01_recon.xml, The Yeast GENRE.The authors declare that they have no competing interests.BDH contributed sphingolipid updates to the GENRE, added constraints to improve GEM flux predictions, compiled the essential and auxotroph-inducing gene lists, and wrote the manuscript. KS conducted the FBA for model evaluation and comparison, wrote the Matlab scripts included as supplemental data, and maintains the Yeast SBML and database files. BB conducted the epistasis analysis. PM leads the Computational Systems Biology & Biochemical Networks Modeling Group at the University of Manchester. LPW leads the Biomass Conversion Group at Cornell University. All authors have contributed to writing and have approved the manuscript.Function testYeastModel.m.m.Click here for fileFunction modelToReconstruction.m.Click here for fileFunction fluxDistribution.m.Click here for fileYeast metabolic network reconstruction.Click here for fileYeast metabolic network model.Click here for fileTable S1. ORFs in Yeast 4 not in Yeast 5.Click here for fileTable S2. ORFs in Yeast 5 not in Yeast 4.Click here for fileTable S3.Genes considered essential.Click here for fileTable S6. Biomass Definition.Click here for fileTable S4. Auxotroph-inducing genes.Click here for fileTable S5. Gene interaction analysis.Click here for file


Escherichia coli for producing recombinant proteins, biofuels etc. However, high growth rate aerobic E. coli cultivations are accompanied by acetate excretion i.e. overflow metabolism which is harmful as it inhibits growth, diverts valuable carbon from biomass formation and is detrimental for target product synthesis. Although overflow metabolism has been studied for decades, its regulation mechanisms still remain unclear.The biotechnology industry has extensively exploited E. coli was continuously monitored using advanced continuous cultivation methods (A-stat and D-stat). The first step in acetate overflow switch (at μ = 0.27 ± 0.02 h-1) is the repression of acetyl-CoA synthethase (Acs) activity triggered by carbon catabolite repression resulting in decreased assimilation of acetate produced by phosphotransacetylase (Pta), and disruption of the PTA-ACS node. This was indicated by acetate synthesis pathways PTA-ACKA and POXB component expression down-regulation before the overflow switch at μ = 0.27 ± 0.02 h-1 with concurrent 5-fold stronger repression of acetate-consuming Acs. This in turn suggests insufficient Acs activity for consuming all the acetate produced by Pta, leading to disruption of the acetate cycling process in PTA-ACS node where constant acetyl phosphate or acetate regeneration is essential for E. coli chemotaxis, proteolysis, pathogenesis etc. regulation. In addition, two-substrate A-stat and D-stat experiments showed that acetate consumption capability of E. coli decreased drastically, just as Acs expression, before the start of overflow metabolism. The second step in overflow switch is the sharp decline in cAMP production at μ = 0.45 h-1 leading to total Acs inhibition and fast accumulation of acetate.In the current work, growth rate dependent acetate overflow metabolism of E. coli shown by proteomic, transcriptomic and metabolomic levels coupled to two-phase acetate accumulation: acetate overflow metabolism in E. coli is triggered by Acs down-regulation resulting in decreased assimilation of acetic acid produced by Pta, and disruption of the PTA-ACS node.This study is an example of how a systems biology approach allowed to propose a new regulation mechanism for overflow metabolism in  Escherichia coli has not only been the prime organism for developing new molecular biology methods but also for producing recombinant proteins, low molecular weight compounds etc. in industrial biotechnology for decades due to its low cost manufacturing and end-product purification and its ability to reach high cell densities grown aerobically [E. coli cultivation on glucose at high growth rates-formation and accumulation of considerable amounts of acetic acid i.e. overflow metabolism. In addition to being detrimental for target product synthesis, accumulated acetate inhibits growth and diverts valuable carbon from biomass formation [obically ,2. Howevormation ,4.Km of 200 μM for acetic acid) ACS scavenges acetate at low concentrations whereas the low affinity PTA-ACKA pathway (Km of 7-10 mM) is activated in the presence of high acetate concentrations [The acetate synthesis and utilization pathways  can be strations .et al. [ca 20%) and elevated levels of formate and lactate (ca 30-fold) [poxB disruption causes reduction in biomass yield (ca 25%) and loss of aerobic growth efficiency of E. coli [acs knock-out strain [E. coli genetically modified strains engineered to diminish acetate production in batch cultures have not fully succeeded in avoiding acetate accumulation together with increasing target product production yields and rates [The phenomenon of overflow metabolism has been studied widely over the years and it is commonly believed to be caused by an imbalance between the fluxes of glucose uptake and those for energy production and biosynthesis ,8. Severet al.  have bee30-fold) ,16-18, w E. coli . The latt strain . It shound rates . Additiond rates ,20,21.E. coli metabolism at proteomic, transcriptomic and metabolomic levels was investigated using continuous cultivation methods prior to and after overflow metabolism was switched on. Usually, chemostat cultures are used for steady state metabolism analysis, however, we applied two changestat cultivation techniques: accelerostat (A-stat) and dilution rate stat (D-stat), see Methods section for details [e.g. start of overflow metabolism, maximum specific growth rate) and enable to monitor the dynamic patterns of several metabolic physiological responses simultaneously which could be left unnoticed using chemostat. Secondly, it is possible to collect vast amount of steady state comparable samples and by doing so, save time. Thirdly, both A-stat and D-stat enable to quantitatively study specific growth rate dependent co-utilization of growth substrates. Latter advantage was applied for investigating acetic acid consumption capability of E. coli at various dilution rates in this study. Combining changestat cultivation methods enables to study metabolism responses of the same genotype at different physiological states in detail without encountering the possible metabolic artifacts accompanied when using genetically modified strains.Acetate overflow is a growth rate dependent phenomenon, but no study has specifically focused on growth rate dependency of protein and gene expression regulation, intra-and extracellular metabolite levels using also metabolic modeling. Describing the physiology of an organism on several 'omic levels is the basis of systems biology that facilitates better understanding of metabolic regulation . In this details ,24. ThesE. coli proteome, transcriptome and metabolome in continuous cultures together with metabolic modeling allowed us to propose a new theory for acetate overflow: acetate excretion in E. coli is triggered by carbon catabolite repression mediated down-regulation of Acs resulting in decreased assimilation of acetate produced by Pta, and disruption of the PTA-ACS node.Results obtained by studying specific growth rate dependent changes in -1 to achieve steady state conditions, continuous increase in dilution rate with acceleration rate (a) 0.01 h-2 (0.01 h-1 per hour) was started. Continuous change of specific growth rate resulted in detecting several important changes in E. coli metabolism as demonstrated in Figure i.e. overflow metabolism switch) at μ = 0.27 ± 0.02 h-1 (average ± standard deviation) and a two-phase acetate accumulation pattern was observed . The nature of these fluctuations will be studied further and not covered in the current publication. All A-stat results were reproduced with relative standard deviation less than 10% with the exception of acetate production per biomass (YOAc-)  cultivation experiments, after the culture had been stabilized in chemostat at 0.10 h-1 with concomitant change in specific CO2 production rate  signal molecule cAMP (YcAMP) rose from steady state chemostat level 2.45 ± 0.26 μmol/g dry cellular weight (DCW) (μ = 0.10 h-1) to 3.55 ± 0.32 μmol/g DCW (μ = 0.30 h-1) after which it sharply decreased to 1.30 ± 0.44 μmol/g DCW at μ = 0.45 h-1  and erythrose-4-phosphate (E4P) was detected with increasing specific growth rate which could point to possible limitation in RNA biosynthesis during growth Figure . PTA-ACSe Figure  with ince Figure .-1 i.e. before acetate overflow was switched on. At the same time, there was a concurrent 10-fold repression of the acetic acid utilization enzyme acetyl-CoA synthetase (Acs). This substantial difference (5-fold) between the acetate synthesis and assimilation pathways expression suggests that the synthesized acetic acid cannot be fully assimilated with increasing growth rates  and pyruvate oxidase (POXB) were down-regulated, both on gene and protein expression levels, from μ = 0.20 h4-dicarboxylates (DctA) and acetate   and Cra  induction prior to acetate accumulation in parallel with Acs down-regulation. This was indicated by down-regulation (3-fold on average) of CCR-mediated components: alternative (to glucose) substrate transport and utilization systems like galactose (MglAB), maltose , galactitol (GatABC), L-arabinose (AraF), D-ribose (RbsAB), C) Figure . Moreoved uptake ) were re-1 , acetate (YOAc-), cyclic AMP (YcAMP) and carbon dioxide (YCO2) production per biomass from A-stat and chemostat are all fully quantitatively comparable. The latter results enable to use A-stat data for quantitative modeling calculations. In addition, the two continuous cultivation methods were examined at transcriptome level using DNA microarrays. Transcript spot intensities from quasi steady state A-stat sample at μ = 0.48 h-1 and chemostat sample at μ = 0.51 h-1 showed an excellent Pearson product-moment correlation coefficient R = 0.964  which produced Pearson correlation coefficients for two biological replicates in the indicated pairs of comparison in the range of R = 0.788-0.917  were calculated. Comparison of gene and protein expression changes (between respective specific growth rates) revealed that components of the PTA-ACS node were regulated at transcriptional level as the absolute majority of the studied transcripts and proteins indicated by the good correlation between transcriptome and proteome expression profiles . It hasE. coli, we studied specific growth rate dependent proteomic, transcriptomic and metabolomic patterns combined with metabolic modeling using advanced continuous cultivation methods, which has not been carried out before. Continuous monitoring of the specific growth rate effect on E. coli metabolism enabled us to precisely detect important metabolic shift points, the most important being the start of acetate overflow at μ = 0.27 ± 0.02 h-1  0.22 h-1 whereas acetate overflow was started in wild-type at a higher D = 0.35 h-1 [acs [acs together with glyoxylate shunt repressors iclR and fadR mutant resulted in a significant reduction in acetate accumulation in glucose batch fermentations [E. coli increased acetate consumption capability by over-expressing Acs (not AckA) [A similar regulation for overflow metabolism of acetate was posed for th rates . The hyp0.35 h-1 . Furtherh-1 [acs  and consntations . Adams aot AckA) ,46, furtE. coli has not evolved towards maintaining sufficient Acs levels for acetate assimilation in all growth conditions. Growth conditions in E. coli native environments are rough as concentrations of utilizable carbon sources including acetate are in the low mg L-1 range and access to nutrients is troublesome [E. coli to make its metabolism ready for scavenging all possible carbon sources including acetate. However, in nutrient rich laboratory conditions, E. coli focuses on anthropic growth [E. coli. It seems that maintaining high expression levels of acetate assimilation components  is energetically not favorable at higher growth rates. Moreover, as the space on cell membrane is limited and as E. coli achieves more rapid growth probably by increasing the number of glucose transport machinery components on the membrane, using area for alternative substrate transport proteins is not beneficial for faster growth. Interestingly, even in one of its natural environments-urinary tract-where a continuous dilution of acetate occurs, it has been shown that metabolizing acetate to acetyl-CoA by Acs is not essential for normal E. coli colonization as PTA-ACKA pathway and maintenance of a proper intracellular acetyl-P concentration are necessary for colonizing murine urinary tract [As Acs down-regulation is responsible for triggering overflow metabolism and the resulting accumulation of acetate is detrimental to cellular growth, it bears questioning why ublesome . These hc growth  and biomc growth . This inry tract .tar-tap-cheRBYZ) was more intensively expressed at lower growth rates , pathogenesis, biofilm formation etc. This hypothesis is consistent with the fact that the flagellar assembly and regulation operon . Secondly, disruption of the PTA-ACS node decreases the energy needed for expression of this cycle's components. As the disruption of PTA-ACS cycle is CCR-mediated, repression of other alternative substrate transport and utilization enzymes by CCR enables to save additional energy. This could all lead to the decrease of ATP production as was indicated by the diminishing TCA cycle fluxes Figure . Hence, et al. for S. cerevisiae [Finally, it was demonstrated that highly reproducible A-stat data are well comparable to chemostat at the level of major growth characteristics and transcriptome, hence quasi steady state data from A-stat can be considered steady state representative Table . Furtherrevisiae , chemostE. coli: acetate overflow is triggered by carbon catabolite repression mediated Acs down-regulation resulting in decreased assimilation of acetate produced by Pta, and disruption of the PTA-ACS node. The practical implications derived from this could lead to better engineering of E. coli in overcoming several metabolic obstacles, increasing production yields etc.This study is an excellent example of how a systems biology approach using highly reproducible advanced cultivation methods coupled with multiple 'omics analysis and metabolic modeling allowed to propose a new possible regulation mechanism for overflow metabolism in E. coli K12 MG1655  strain was used in all experiments. Growth and physiological characteristics in accelerostat (A-stat) cultivations were determined using a defined minimal medium as described before by Nahku et al. [-1 Antifoam C  was used. The latter was also used in dilution rate stat (D-stat) experiments as the main cultivation medium. In addition, a second medium was used in D-stat where the main medium was supplemented by acetic acid and prepared as follows: 300 ml medium was withdrawn from the main cultivation medium and supplemented with 3 ml of glacial acetic acid (99.9%). One A-stat experiment (referred to as two-substrate A-stat) was carried out with the same medium as other A-stats, but in addition supplemented with acetic acid .The u et al. , except 2, CO2 and temperature sensors. The bioreactor was set on a balance whose output was used as the control variable to ensure constant culture volume (300 ± 1 mL). Similarly, the inflow was controlled through measuring the mass of the fresh culture medium.The continuous (both A-stat and D-stat) cultivation system consisted of 1.25 L Biobundle bioreactor  controlled by an ADI 1030 biocontroller (Applikon Biotechnology B.V.) and a cultivation control program "BioXpert NT" (Applikon Biotechnology B.V.). The system was equipped with OD, pH, pOA-stat cultivation system and control algorithms used are described in more detail in our previous works ,51,56. D-1) with an agitation speed of 800 rpm. Four A-stat cultivations were performed with acceleration rate (a) 0.01 h-2. Duplicate D-stat experiments were performed at dilution rates 0.10; 0.30; 0.505 ± 0.005 h-1 and single experiments at 0.19; 0.24; 0.40; 0.45 h-1. The acetic acid addition profile was set to achieve 32 ± 6 mM and 58 ± 5 mM in 7 hours inside the bioreactor for experiments at dilution rates 0.10-0.24 h-1 and 0.30-0.51 h-1, respectively. The growth characteristics of the bacteria were calculated on the basis of total volume of medium pumped out from bioreactor (L), biomass (g DCW), organic acid concentrations in culture medium (mM) and CO2 concentration in the outflow gas (mM). Formulas were as described in a previous study [2 concentrations could be error-prone due to measurement difficulties. However, this does not influence the dynamic pattern of specific CO2 production rate (rCO2) during specific growth rate increase.All continuous cultivation experiments were carried out at 37°C, pH 7 and under aerobic conditions , ethanol and glucose in the culture medium were determined by HPLC and cellular dry weight (expressed as DCW) as described by Nahku et al. .-1 (chemostat point prior to the start of acceleration in A-stat); 0.20 ± 0.01; 0.26; 0.30 ± 0.01; 0.40 ± 0.00; 0.49 ± 0.01 h-1 and batch sample grown on medium containing 15NH4Cl as the only source of ammonia. Secondly, the ratios between the mentioned specific growth rates with chemostat point (μ = 0.10 ± 0.01 h-1) for two biological replicates were calculated to yield protein expression levels for respective specific growth rates. Protein (and gene) expression measurement results are shown in Additional file http://www.ebi.ac.uk/pride under accession numbers 12189-12199 . The data was converted using PRIDE Converter http://code.google.com/p/pride-converter[Refer to Text S1 in Additional file converter.-2), and gene expression ratios between specific growth rates 0.21; 0.26; 0.31; 0.36; 0.40; 0.48 h-1 and μ = 0.11 h-1 were calculated. Transcript spot intensities of chemostat sample (sample from D-stat prior to acetic acid addition) from μ = 0.51 h-1 and A-stat μ = 0.48 h-1 were used for the two method's comparison at transcriptome level. Gene (and protein) expression measurement results are shown in Additional file DNA microarray analysis of 4,321 transcripts was conducted with the Agilent platform using the data from one A-stat cultivation  together with end-capped HSS C18 T3 1.8 μm, 2.1 × 100 mm column for compound separation coupled to TOF-MS with an electrospray ionization (ESI) source was used for detection . The details of the procedure are provided in Text S1 in Additional file KV, KA, and RV drafted the manuscript. RN, PL, and LA helped in preparing the manuscript. KV, RN, and PL designed and performed the experiments. KV analysed the experimental data. RN, PL, and LA carried out the 'omics analysis. KV, KA, and RV guided and coordinated the project. All authors read and approved the manuscript.Detailed Methods (Text S1); calculation of acetate reconsumption (Text S2); Supplementary Figures S1-S5.Click here for fileEscherichia coli K12 MG1655Growth rate dependent gene (one A-stat) and average protein expression changes of two A-stat experiments with . Transcriptome and proteome analysis results, also with standard deviations.Click here for file-1 and chemostat at μ = 0.51 h-1 experiments with Escherichia coli K12 MG1655Gene spot intensities of A-stat at μ = 0.48 h. Data for A-stat and chemostat transcriptome comparison.Click here for fileSimplified metabolic flux analysis. Detailed description of model calculations with simplified metabolic flux analysis.Click here for file