S. cerevisiae and E. coli. While these organisms may in many cases be more difficult to work with as “chassis” strains for synthetic biology than certain heterotrophs, the unique advantages of autotrophs in biotechnology applications as well as the scientific importance of improved understanding of photosynthesis warrant the development of these systems into something akin to a “green E. coli.” In this review, we highlight unique challenges and opportunities for development of synthetic biology approaches in cyanobacteria. We review classical and recently developed methods for constructing targeted mutants in various cyanobacterial strains, and offer perspective on what genetic tools might most greatly expand the ability to engineer new functions in such strains. Similarly, we review what genetic parts are most needed for the development of cyanobacterial synthetic biology. Finally, we highlight recent methods to construct genome-scale models of cyanobacterial metabolism and to use those models to measure properties of autotrophic metabolism. Throughout this paper, we discuss some of the unique challenges of a diurnal, autotrophic lifestyle along with how the development of synthetic biology and biotechnology in cyanobacteria must fit within those constraints.Photosynthetic organisms, and especially cyanobacteria, hold great promise as sources of renewably-produced fuels, bulk and specialty chemicals, and nutritional products. Synthetic biology tools can help unlock cyanobacteria's potential for these functions, but unfortunately tool development for these organisms has lagged behind that for  As compared with eukaryotic algae and plants, cyanobacteria are much easier to manipulate genetically and grow much faster. They have been engineered to produce a wide and ever-expanding range of products including fatty acids, long-chain alcohols, alkanes, ethylene, polyhydroxybutyrate, 2,3-Butanediol, ethanol, and hydrogen. These processes have been reviewed recently  or in trans (through plasmid addition) and synthetic biology experiments have used both approaches. We discuss advantages and disadvantages of each approach, as well as recent technical developments below. While even the best cyanobacterial model systems are still far from being a “green E. coli,” many tools are already available and more are being developed. The future holds great promise for this field.Several strains of cyanobacteria are known which are readily amenable to genetic modification  resistance cassette as well as any sequence of interest. Streptomycin-resistant, kanamycin-sensitive markerless mutants can be recovered in a second transformation .Until recently, it has been difficult to create mutants at high throughput in cyanobacterial strains, as transposon-based methods developed for use in other strains can work poorly in cyanobacterial hosts. However, libraries can be created in other strains and subsequently transferred to a cyanobacterial host via homologous recombination. A Tn7-based library containing ~10,000 lines was recently created to screen for strains with increased polyhydroxybutyrate (PHB) production . This move toward standardization of genetic parts is a critical aim for synthetic biology, independent of the chassis organism or method of transformation. However, a limited number of plasmids are available for expression in cyanobacterial hosts. Plasmid assembly for expression in cis or in trans in cyanobacterial hosts has generally been performed in E. coli because of the longer growth times that would be associated with assembling vectors in cyanobacterial hosts  means it is critical to have high-fidelity assembly methods to avoid a time-consuming screening process.Although transgene expression s Figure . This reSynechocystis sp. PCC 6803  for the iron-stress inducible protein, which when translated, forms a ring around trimers of photosystem I, preventing their activity and thus oxidative stress in the absence of sufficient iron  use 2-component systems, and even combine 2-component systems with non-coding RNA to control system dynamics  It will not be activated in the absence of inducer. (2) It will produce a predictable response to a given concentration of inducer or repressor. This response may be digital  or graded change with different concentrations of inducer/repressor. (3) The inducer at saturating concentrations should have no harmful effect on the host organism. (4) The inducer should be cheap and stable under the growth conditions of the host. Finally, (5) the inducible system should act orthogonally to the host cell's transcriptional program. Ideal transcriptional repressors should not bind to native promoters and if non-native transcriptional machinery is used (such as T7 RNA polymerase) it should not initiate transcription from native promoters. Promoters must perform as ideally as possible in order to be used in the construction of more complex genetic circuits  and PziaA (Cd2+ and Zn2+), both from Synechocystis sp. PCC 6803 and inducers can also activate multiple promoters, such as Cd2+ inducing ziaA and isiA  is structurally different between E. coli and cyanobacteria. In cyanobacteria the β ' subunit of the RNAP holoenzyme is split into two parts, as opposed to one in most eubacteria, creating a different DNA binding domain . The best performing promoter induced a 290-fold change in response to 1 μg/ml aTc .Success of FBA greatly depends on the quality of the metabolic network reconstruction as well as the availability of regulatory constraints under a given environmental or growth condition. For instance, constraints can be added that disable or limit fluxes due to known regulatory constraints or substrate availability  via this non-fermentative alcohol production pathway. These active areas of research will help to better define cyanobacterial metabolism and allow the generation of models that can more accurately predict cellular phenotypes. While newer fluxomics techniques can yield powerful results in well-characterized strains, developing a “green E. coli” will also require expanded knowledge of biochemistry that to date can only come from older methods of single gene or single protein analysis.The understanding of several key pathways in cyanobacteria has been recently revised. Zhang and Bryant  identifiin silico growth phenotype with available experimental data on the viability of single or multiple gene knockouts (Thiele and Palsson, in vivo gene essentiality data are available only for Synechocystis 6803 among the cyanobacteria in the CyanoMutants database (Nakamura et al., Synechocystis 6803, and the proportion is much less for any other strain. While a genome-wide library of knockout mutants has been created in Synechococcus 7942 (Chen et al., The quality or accuracy of any genome-scale metabolic model can be tested by contrasting the Cyanothece 51142 exhibits one of the highest rates of nitrogen fixation and is the first unicellular diazotrophic cyanobacterium to be completely sequenced (Welsh et al., Cyanothece 51142, iCce806, is recently developed (Vu et al., iCyt773 contains an additional 266 unique reactions spanning pathways such as lipid, pigment and alkane biosynthesis (Saha et al., iCyt773 also models diurnal metabolism by including flux regulation based on available day/night protein expression data (Stockel et al., Genome-scale models contain detailed Gene-Protein-Reaction associations, a stoichiometric representation of all possible reactions occurring in an organism, and a set of appropriate regulatory constraints on each reaction flux. They are differentiated from more basic FBA models simply by their completeness—they span all or nearly all of the metabolic reactions encoded in a genome. Thus, these models can have greater predictive value than those of only central metabolism. Among cyanobacteria, Synechocystis 6803 is a model cyanobacterial strain, it has long been the target for modeling of photosynthetic central metabolism (Yang et al., iSyn731, the latest of all Synechocystis 6803 models, integrates all recent developments and supplements them with improved metabolic capability and additional literature evidence. As many as 322 unique reactions are introduced in iSyn731 including reactions distributed in pathways such as heptadecane and fatty acid biosynthesis (Saha et al., iSyn731 is the first model for which both gene essentiality data (Nakamura et al., Synechococcus sp. PCC 7002 (Hamilton and Reed, Since in silico models are great tools for generating hypotheses on how to use synthetic biology interventions to alter metabolism, they need to be complemented by fluxomics methods that allow in vivo measurement of metabolic fluxes to assess these interventions. Such a suite of tools allows the closure of the design-build-test engineering cycle in synthetic biology (Figure 13CO3 and the labeling patterns of metabolic intermediates are followed over a time-course to determine relative rates of metabolic flux. Previous studies (Yang et al., 13C labeled glucose and metabolic fluxes are inferred from labeling patterns of proteinogenic amino acids. These studies have been extremely useful in identifying fluxes that exist in vivo, but have previously been regarded as wasteful or futile cycles, such as the oxidative pentose phosphate pathway and RuBP oxygenation. Comparisons between flux measurements (Young et al., Synechocystis 6803 have revealed the necessity of additional regulatory information for accurate in silico predictions of phenotype. These modeling and fluxomics efforts have resulted in deeper understanding of the metabolic capabilities of the modeled strains and of cyanobacteria in general.While y Figure . To thisy Figure  have devE. coli.” Metabolic modeling is a complementary tool that can help guide the creation of synthetic strains with desirable phenotypes. By developing the tools for strain manipulation and control, synthetic biologists can unlock a bright future for the biotechnological use of abundant light and CO2.Cyanobacterial synthetic biology offers great promise for enhancing efforts to produce biofuels and chemicals in photoautotrophic hosts. While several cyanobacterial chassis strains have been used in synthetic biology efforts, the tools for their manipulation and analysis need greater development to unlock this potential and develop a “green The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in  E. coli across a set of 24 different media that are significantly correlated with experimental measurements, while existing state-of-the art stoichiometric modeling approaches fail to do so. These results suggest that a bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, identifying the factors that determine microbial growth rate and the prediction of growth rates under various conditions is still an open challenge. Here we present a metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts growth rates by integrating standard stoichiometric modeling with prior data on enzyme turnover rates and enzyme molecular weights, considering a physiological bound on total enzymes' concentration. The method is based on a finding that enzymes catalyzing high flux reactions tend to be more efficient in terms of having higher turnover numbers. MOMENT predicts growth rates of  Traditional metabolic modeling techniques involve the reconstruction of kinetic models based on detailed knowledge on enzyme kinetic parameters for all enzymes in a certain system Flux Balance Analysis (FBA) is a commonly used CBM approach that enables to predict biomass production yield (in units of gram biomass/gram nutrient) based solely on reactions' stoichiometry and directionality (i.e. without measurements of nutrient uptake rates). Given information only on reactions' stoichiometry and directionality, the prediction of biomass yield works by searching for a feasible flux distribution with maximal flux through the biomass production reaction, considering an arbitrary upper bound on the uptake rate of the carbon nutrient. The maximal biomass production rate predicted by FBA reflects optimal yield metabolism and is equal to the assumed uptake rate multiplied by maximal biomass yield. The prediction of actual growth rate by FBA is theoretically possible when experimental measurement of nutrient uptake rates is available and is used to constrain the uptake flux in the model . However, experimental studies have shown that microorganisms exhibit non optimal-yield metabolism under various conditions, for example, in the case of over-flow metabolism where excess nutrient uptake is metabolized inefficiently E.coli across a small set of growth media (without given measurements of nutrients uptake rates), as well as under conditions of over-flow metabolism FBA with Molecular Crowding (FBAwMC) is a recently developed extension of FBA which was shown to enable the prediction of growth rates of E.coli under a diverse set of growth media that are significantly correlated with experimental measuements, without requiring measured nutrient uptake rates, significantly outperforming the prediction accuracy of FBAwMC. Furthermore, MOMENT is shown to markedly improve the prediction performance of various metabolic phenotypes, including metabolic fluxes and expression level of metabolic genes. We begin our analysis by exploring the relation between enzyme kinetic parameters and measured metabolic flux, showing a design principle in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers (hence requiring lower concentration to achieve a certain flux rate). This suggests that a a physiological constraint on total cellular enzyme concentration, which underlies MOMENT, significantly affects cellular metabolism and the evolution of enzyme kinetic parameters.In this paper, we present a method, MetabOlic Modeling with ENzyme kineTics (MOMENT), for predicting metabolic fluxes and growth rates by accounting for the maximal cellular capacity for metabolic enzymes without the requirement of experimentally determined uptake rates. Extending upon FBAwMC, MOMENT accurately quantifies the enzyme concentrations required for catalyzing each metabolic reaction based on known kinetic constants, accounting for isozymes, protein complexes and multi-functional enzymes. MOMENT is shown to predict growth rates for E.coliAn enzyme turnover number is defined as the maximal number of molecules of substrate that the enzyme can convert to product per catalytic site per unit of time. We extracted enzyme turnover numbers for 251 reactions from BRENDA E. coli and measured flux rates, we found that enzymes catalyzing high flux reactions have high turnover number, with statistically significant Pearson correlations of 0.45  and 0.46  between turnover rates and fluxes under conditions of low and high growth rates, respectively  was also found between metabolic flux rates and enzyme molecular weights, indicating higher flux rates for enzymes with high molecular weights  with metabolic flux rates, suggesting that each parameter contributes independently to explaining flux rates , suggesting a potentially stronger selection pressure for efficient metabolism under aerobic conditions. These results suggest that data on enzyme kinetics and metabolic flux may provide valuable insight into organisms' natural environment, in line with previous attempts to do so via other molecular data sources such as codon usage and gene expression Having shown that enzyme turnover numbers are significantly correlated with measured flux rates under glucose minimal media, we set to examine the correlation between enzyme turnover numbers and flux rates under a diverse set of growth media. Towards this end, we applied FBA to predict likely flux distributions under a set of media  of cellular mass  (suggesting that only 48% of protein mass is taken by metabolic enzymes). Hence, practically, in order to scale the predicted growth rates to the correct range, prior knowledge on the total mass of metabolic enzymes should be used. In this case, the identified fraction of proteins mass devoted to metabolic enzymes in E. coli is further supported by analyzing gene expression data Protein mass was previously shown to account for 56% is markedly lower than that of the measured growth rates (0.054 for predicted rates versus 0.14 for measured rates). A potential explanation for the differences between the standard deviations in the observed and predicted growth rates could be that the fraction of protein mass devoted to metabolic enzymes increases under high growth rates. Notably, checking this hypothesis would require high-throughput protein concentration data measured under various growth rates, though this kind of data is currently unavailable. Overall, utilizing additional experimental data on the total enzyme concentration in a specific growth condition of interest is expected to further improve MOMENT's predictive performance.p-value = 0.026, representing the fraction of random samplings, which have led to a higher correlation with the measured growth rates than that achieved with the known turnover numbers).To evaluate the importance of the utilized enzyme turnover numbers, we repeated MOMENT's growth rate predictions with randomly shuffled turnover numbers, which were found to provide significantly lower prediction accuracy , although a significant correlation between measured and predicted growth rates was reported for a smaller set of 10 media by Beg et al. To benchmark our new method, we tested the prediction performance of the previously developed FBAwMC. Here, FBAwMC was provided with the very same enzyme turnover rates given to MOMENT, while performing a sampling procedure for missing parameters as described in Beg et al did not improve the predicted performance (data not shown). We found that the growth rate prediction achieved by FBAwMC are not significantly correlated with the measured ones . We found that changes in these enzyme concentrations predicted by MOMENT between the low and high growth rates achieve a Pearson correlation of 0.84  with the measured changes in gene expression .To further evaluate the predictive performance of MOMENT, we extracted data from pression , even thpression , with a E. coli according to whether they are used in the optimal growth solutions (as this classification was previously shown to correlate with changes in gene expression following laboratory-evolved E.coli straints that increased their growth rates) p-value = 0.06).Notably, naïve FBA was not evaluated here as it cannot be applied to predict differential metabolism across different growth rates. As a further benchmark, we applied a recently developed method called Parsimonious enzyme usage FBA (pFBA), to classify genes in E. coli under various growth media that are significantly correlatred with experimental measurements, and to improve the prediction accuracy of several metabolic phenotypes including intracellular fluxes, and gene expression of enzyme-coding genes. The method is based on an identified design principle of metabolism, in which enzymes catalyzing high flux reactions across different media tend to have higher turnover numbers.Computational prediction of microbial growth rates represents a major challenge. Here, we present a novel computational approach, MOMENT, that addresses this challenge by integrating genome-scale metabolic modeling with enzyme kinetic parameters. MOMENT is shown to predict growth rates for E. coli. Specifically, kinetic data on various membrane transporters is missing from both BRENDA E. coli are fully saturated, following nd law of thermodynamics as done in While MOMENT enables genome-scale prediction of metabolic phenotypes it is bound to make simplifying assumptions that in some cases may lead to false predictions: (i) MOMENT requires as input information on the fraction of total protein concentrations that is devoted to metabolic enzymes. Since this information is difficult to obtain for each modeled condition, here we assumed that this fraction remains constant across a variety of growth media, which is expected to bias the predictions. (ii) MOMENT does not take into account several important factors that affect growth rate such as the cost of protein synthesis by ribosomes and local substrate turnover numbers, etc From an applicative standpoint, the improved metabolic modeling performance achieved by MOMENT is expected to significantly contribute to metabolic engineering applications and specifically to optimal strain design. Specifically, the additional constraints employed by MOMENT on the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, can be integrated and potentially improve the accuracy of computational metabolic engineering methods such as OptKnock, RobustKnock, OptStrain, etc 2HPO4, 110 mM KH2PO4, 42.8 mM NaCl, 56.7 mM (NH4)2SO4, autoclaved), 10 mL of trace elements , 1 mL 0.1 M CaCl2 solution (autoclaved), 1 mL 1 M MgSO4 solution (autoclaved), 2 mL of 500× thiamine solution  and 0.6 mL 0.1 M FeCl3 solution (filter sterilized) were added. The resulting solution was filled up to 1 liter with water. Carbon source were added to the medium from stock solutions adjusted to pH 7. All media were filtrated prior to use . All chemicals used were obtained from Sigma-Aldrich if not indicated otherwise.The wild-type Escherichia coli K-12 strain BW25113 was used for all experiments. Cells were cultivated in M9-minimal medium with different carbon source added. The carbon sources were added so that the amount of reducible carbon equaled the amount present in a concentration of 2 g/L glucose. M9 minimal medium was prepared in the following way: To 700 mL of autoclaved, purified water, 200 mL of 5× base salt solution (211 mM NaThe growth rates were determined using an automated cultivation device (Tecan Infinite 200 Pro plate reader). Cells were grown to a steady state in the respective growth medium in shake flasks, then washed twice in minimal medium. 4 µL of the washed cells were inoculated into a well on a 96-well plate  with 196 µL of medium. The plate was covered with a transparent plastic cover and sealed with parafilm. Cultivation was done at the maximal linear shaking speed  and the following settings for optical density (OD) measurement  18 s; number of flashes 1; wavelength 600 nm, bandwidth 9 nm). The cells were grown to stationary phase or for at least 50 hours to ensure observation of steady state growth. The measured OD-values were corrected for the non-linearity of the device using an empirical function derived from samples with known OD-values (measured by spectrometry) from 10 to 0.001.E.coli metabolic model by Feist et al. Enzyme turnover rates were extracted from BRENDA v (mmol/gDW/h) with maximal growth rate (i.e. flux through the biomass production reaction), satisfying mass-balance and reaction directionality constraints based on the following linear constraints:Similar to FBA, MOMENT searches for a feasible flux distribution vector S denotes a stiochiometric matrix S (NxM) composed of N metabolites and M reactions (ijS corresponds to the stoichiometric coefficient of metabolite i in reaction j) and lbv and ubv represent known lower and upper bounds, respectively, on flux rates. Here, lbv is set to either −inf for reversible reactions or 0 for irreversible reactions, and ubv is set to +inf for all reactions. In addition to searching for a flux distribution, MOMENT searches for a vector of enzyme concentrations, denoted g (mmol/gDW), such that each flux rate in v has a sufficiently high enzyme concentration to catalyze it. To associate flux rates with enzyme concentrations, we utilize the Boolean gene-to-reaction mapping that is included in the E.coli model of Feist et al. Where j catalyzed by single enzyme i, we use the equation:For a reaction j catalyzed by two isozymes a OR b, we use the equation:For a reaction j catalyzed by an enzyme complex consisting of gene products a AND b, we use the equation:For a reaction a&bg that is constrained to be smaller than both ag and bg.This can be formulated in a linear equation by defining an auxiliary variable E. coli . The Pearson correlation between enzyme turnover numbers and the averaged flux distribution across conditions in green  is shown to be higher than those obtained under the different media. (B) Pearson correlations between enzyme turnover numbers and predicted fluxes whose flux rate is uniquely determined by FVA, under a set of single carbon and energy source media under either aerobic versus anaerobic conditions. As shown, under most growth conditions, the correlation between enzyme turnover numbers and fluxes is higher when fluxes are predicted under aerobic conditions.(A) Histogram of Pearson correlations between enzyme turnover numbers and predicted flux rates, uniquely determined by Flux Variability Analysis (FVA), under different single carbon and energy source media in blue Click here for additional data file.Figure S2The prediction of growth rates by FBAwMC. FBAwMC predicted growth rates achieving a Pearson correlation of 0.29 with the measured growth rates.(TIF)Click here for additional data file.Figure S3E.coli under glucose minimal media based on MOMENT. Linear regression line in red. (B) MOMENT prediction of differential gene expression levels in E.coli under glucose minimal media, between low and high growth rate conditions (with the high growth rate condition involving overflow metabolism). Linear regression line in red.Metabolic flux and gene expression level predictions via MOMENT. (A) The prediction of flux rates in (TIF)Click here for additional data file.Table S1Correlations of enzyme turnover numbers, enzyme molecular weights, gene expression levels, and combinations of the latter via a linear regression model with metabolic flux rates.(XLSX)Click here for additional data file.Table S2E. coli under glucose minimal media and differential gene expression.The contribution of kinetics parameters to the prediction of metabolic flux in (XLSX)Click here for additional data file.Table S3Measured and predicted growth rates.(XLSX)Click here for additional data file.Table S4The prediction accuracy of growth rate predictions made by MOMENT versus FBAwMC.(XLSX)Click here for additional data file.Table S5E. coli under glucose minimal media.Metabolic flux predictions in (XLSX)Click here for additional data file.Table S6E. coli under glucose minimal media.Prediction of differential gene expression levels in (XLSX)Click here for additional data file.Table S7Turnover numbers.(XLSX)Click here for additional data file.Text S1FVA formulation for MOMENT and the relation between growth rate and the total enzyame mass.(DOC)Click here for additional data file.


Scheffersomyces stipitis is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as Saccharomyces cerevisiae, the onset of fermentation in S. stipitis is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though S. stipitis has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of S. stipitis during aerobic growth on glucose under batch and chemostat cultivations.13C-based flux analysis the fully respiratory metabolism of S. stipitis when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for S. cerevisiae under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with S. cerevisiae. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through in silico transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways.Starting from the analysis of physiological data, we confirmed through S. stipitis under aerobic batch and chemostat cultivations, which shows similarities to the oxidative metabolism observed for S. cerevisiae under chemostat cultivations. Through metabolome analysis and genome-wide transcriptomic analysis several differences were identified. Interestingly, in silico analysis of transciption factors was useful to address a different regulation of mRNAs of genes involved in the central carbon metabolism. To our knowledge, this is the first time that the metabolism of S. stiptis is investigated in details and is compared to S. cerevisiae. Our study provides useful results and allows for the possibility to incorporate these data into recently developed genome-scaled metabolic, thus contributing to improve future industrial applications of S. stipitis as cell factory.The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree negative yeasts. Based on physiological data and flux analysis we identified the presence of one metabolic condition for  Scheffersomyces stipitis, commonly known as Pichia stipitis, is a Crabtree negative, homothallic yeast, found mainly in haploid form. S. stipis has greater respiratory capacity than S. cerevisiae due to the presence of an alternative respiration system donating electrons directly to O2 from ubiquinone, branching out before the cytochrome C complex[S. cerevisiae. This Crabtree negative yeast is well known for its ability to ferment pentose sugars to ethanol, having one of the highest native capacity for xylose fermentation with yields on substrate between 0.35/ 0.44 g g-1[S. stipitis has therefore been studied and exploited as a source of genes for the engineering of xylose metabolisms in other microorganisms and it has also been considered as a platform cell factory for production of fuels and chemicals from lignocellulose. Differently from the Crabtree positive yeast S. cerevisiae, the regulation of fermentation in S. stipitis depends on the oxygen levels, where ethanol production sets in only when oxygen becomes limiting. There are two genes responsible for ethanol production in S. stipitis: ADH1 and ADH2, encoding the alcohol dehydrogenase complex (ADH)[S. cerevisiae where ethanol production takes place under glucose excess conditions, regardless of the availability of oxygen[The yeast  complex,2 and toex (ADH),7. Underex (ADH). This bef oxygen.S. stipitis has been performed using xylose as the sole carbon source or in mix with other sugars and no detailed physiological studies of this organisms growing on glucose are available. Such data will be important in terms of further exploiting S. stipitis as a platform cell factory for production of fuels and chemicals, particularly as glucose is a dominant sugar in biomass.In light of its attractive feature to ferment pentoses, most of the work available on S. stipitis[S. stipitis. Some regulatory proteins such as SNF1, GNC1 and HAP5 are known to share similarity to S. cerevisiae[Recently a genome-scale metabolic network was reconstructed for  stipitis,11 provierevisiae, but regerevisiae.S. stipits during aerobic growth on glucose and to compare its patterns to the Crabtree positive yeast S. cerevisiae under batch and chemostat cultivations, using a systems biology approach. Besides measurement of traditional physiological parameters, we analyzed the flux distribution, intracellular metabolites levels and provide RNA-seq data to analyze gene expression levels. Additionally, to highlight the differences in regulatory network between the two yeasts, we performed in-silico analysis of known transcription factors. This work represents an attempt to integrate data from different systems biology tools to gain insight into the metabolism of S. stipitis during growth on glucose.In this study, we sought to provide an insight into the metabolism of S. stipitis and S. cerevisiae were grown on glucose as the sole carbon source under both chemostat and batch conditions. For S. cerevisiae, a Crabtree positive yeast, there is a remarkable metabolic difference during these growth conditions; S. cerevisiae shows respiro-fermentative metabolism in the batch cultures, when glucose is available in excess, while a purely respiratory metabolism in the chemostat cultures when glucose is limiting, is observed. We were interested to see how S. stipitis respond to these differences in glucose concentration, and to establish eventual differences between the two different yeasts.S. stipitis shows a specific growth rate of 0.47 hr-1 and a specific glucose consumption rate of 26.7 C-mmol (g Dry Weight)-1 hr-1, whereas S. cerevisiae shows a specific growth rate of 0.40 hr-1 and a specific glucose consumption rate of 84.5 C-mmol (g DW)-1 hr-1. For S. stipitis, which shows respiratory metabolism in the batch culture, no extracellular metabolites were secreted and the main products were biomass and CO2 with the yields on substrate listed in Table-1. For S. cerevisiae, which shows respiro-fermentative metabolism in the batch culture, ethanol and smaller amounts of glycerol, pyruvate and acetate were formed as by-products; as a consequence, the biomass yield during the glucose growth phase is much lower as shown in TableS. stipitis shows a high dependency on oxygen, having an oxygen transfer rate (OTR) of 0.12 g (g cell)-1 hr-1; the OTR and carbon transfer rate (CTR) of S. stipitis are reported in FigureS. stipitis consumes higher amount of oxygen compared with S. cerevisiae. The physiological patterns observed suggest that under aerobic batch cultivations S. stipitis shows fully respiratory metabolism. This is also confirmed by the Respiratory Quotient (RQ) calculated to be below 1 (data not shown) during the exponential growth phase.The growth curve of the two strains during batch cultures is shown in FigureS. stipitis to the presence of glucose excess and identify possible differences between growth in batch and chemostat, we performed chemostat cultivations at a dilution rate of 0.1 hr-1. Physiological analysis under this condition allowed us to establish that the yields on substrate of S. stipitis do not differ from those reported during batch cultivations. We could therefore conclude that S. stipitis shows the same behavior under the two cultivation modes and that this pattern is indeed similar to the respiratory mode observed during purely respiratory growth of S. cerevisiae.To investigate the response of S. stipitis and compared them to that of S. cerevisiae by cultivating the two yeasts on 13C labeled glucose under aerobic batch and chemostat conditions.To address at a metabolic level the gross physiological patterns presented above, we quantified the intracellular flux distributions of S. stipitis has been previously reported using a different method for flux resolution[Metabolic network analysis of solution, howeverS. stipitis; this was calculated based on the summed fractional labeling (SFL) of proteinogenic amino-acids in combination with metabolic flux analysis (MFA). The distribution of the metabolic network of S. stipitis under the two cultivations conditions was compared to S. cerevisiae, addressing relevant differences and similarities that are shown in FigureS. stipitis) and FigureS. cerevisiae). The fluxes shown are net fluxes, normalized on the glucose uptake. The flux values reported for S. cerevisiae are in good agreement with those reported by Gombert et al.[S. stipitis does not show significantly different values between batch and chemostat conditions  during batch and chemostat, however the flux towards the pentose phosphate pathway was estimated to be slightly lower during chemostat cultures. Additionally, no external flux of metabolites (metabolites secretion) was reported, in agreement with the measured physiological parameters. The flux values of the pyruvate dehydrogenase (PDH) and of the pyruvate transport into the mitochondria are comparable between the two conditions, as well as fluxes through the TCA. Our results indicate that the metabolic fluxes of S. stipitis under the two cultivations conditions do not show remarkable differences. In contrast, in S. cerevisiae, the flux distribution in batch and chemostat conditions shows extremely different patterns, reflecting the metabolic states arising as a consequence of regulatory phenomena due to glucose repression[In pression,15.S. cerevisiae highly different values are found in batch and chemostat, as previously reported[S. stipitis high values of the TCA fluxes are found regardless of the cultivation mode, however, in S. stipitis, the TCA fluxes during chemostat cultures are slightly higher than during batch culture. In S. stipitis, the flux through the oxidative part of the pentose phosphate pathway (PPP) is higher, similarly to S. cerevisiae during respiratory conditions. The flux distribution at the pyruvate branch point in S. stipitis also shows substantial differences with S. cerevisiae during batch cultures. First, the flux through the pyruvate decarboxylate reaction (PDC) in S. cerevisiae shows remarkably high values, indicating a flux towards acetaldehyde formation during glucose excess conditions; instead, during oxidative growth, this flux presents a lower value, comparable to that reported for S. stipitis. Additionally, during respiro-fermentative growth, S. cerevisiae shows secretion of intracellular metabolites , as previously mentioned.Remarkable differences are found in the tricarboxylic acid cycle (TCA): while in reported, in S. sS. stipis, whereas in S. cerevisiae this flux is almost 10 times higher in the chemostat culture compared with the batch culture, however, when the flux between yeasts is compared, in S. stipitis is slightly lower than these reported during oxidative growth in S. cerevisiae. A similar pattern is observed for pyruvate import into the mitochondria: this flux is low during fermentative growth of S. cerevisiae and about 16 times higher in the chemostat, while in S. stipitis this flux shows constitutively high values. Other differences are found in the pyruvate dehydrogenase (PDH) flux: in S. cerevisiae during respiro-fermentative growth acetyl-CoA formation mainly occur through the PDH-bypass , while, during oxidative growth, acetyl-CoA is mainly generated through the PDH reaction[S. stipitis shows similar flux values at the two growth conditions and, in S. stipitis, the PDH flux is slightly higher than that found in S. cerevisiae during oxidative growth, suggesting that in S. stipitis mitochondrial acetyl-CoA formation mainly occurs through the pyruvate dehydrogenase reaction.The flux corresponding to the anaplerotic reaction of pyruvate carboxylase (PYC) is also comparable within the two growth conditions for S. stipitis and that of S. cerevisiae during oxidative growth is found in the malic enzyme flux (MAE); the flux reported by S. stipitis, comparable in the two conditions, is higher than that presented by S. cerevisiae in batch cultivations, but lower than that found during oxidative growth of S. cerevisiae[S. stipitis, in agreement with the results reported by Fiaux et al.[A difference between the flux distribution of erevisiae. This reux et al..S. stipitis, of one main metabolic mode, showing similar patterns to those observed for S. cerevisiae during respiratory growth; however, minor differences between the respiratory growth of S. stipitis and the respiratory growth of S. cerevisiae were identified. In S. cerevisiae instead, as previously well characterized, physiology and flux network distribution differ substantially between the two conditions as a consequence of regulatory phenomena not occurring in S. stipitis.Intracellular flux distribution analysis supported the presence, in S. stipitis and S. cerevisiae under glucose excess conditions, we compared metabolome profiles of the two yeasts under batch cultivations. Metabolic fingerprinting allows the quantification of intracellular metabolites, providing realistic information about the metabolic state of the cell under a certain condition. After statistical analysis, it was possible to identify different representation of metabolites involved in different pathways between the two yeasts, as shown in TableS. stipitis vs S. cerevisiae) of metabolites with significant p-values, listed accordingly to their super and sub-pathway. Changes in metabolites belonging to the amino acids pattern are reported in TableS. stipitis. Adenine content, instead, is remarkably increased in S. stipitis , a metabolite involved in the leucine biosynthetic pathway, synthesized from pyruvate and acetyl-CoA, shows a high fold-change in S. stipitis; for this reason, we sought for the presence of a reaction generating citramalate in S. stipitis: despite a reaction leading to the formation of citramalate is present in the recently published genome-scale metabolic model iSS884, the gene coding for this reaction has not been identified and its activity never proved. We sought the presence of citramalate synthase by blasting the protein sequence from microorganisms having this reaction  against the translated proteins from the genome sequence of S. stipitis. We found high similarity to 2-isopropylmalate synthase  and we thus suggested that the isopropylmalate synthase might have a role in generating citramalate.To gain further insight into the difference observed between is Table probablyAspergillus niger demonstrated that polyols are synthesized under oxygen limiting conditions, acting as carbon storage compounds but also having a role in the maintenance of the osmotic and redox balance[S. stipitis to utilize the arabinose assimilation pathway in the opposite direction, producing arabitol and ribitol via the PPP pathway[S. stipitis; the increased presence of this metabolite, together with the increased adenine content compared to S. cerevisiae, might be linked to the increased biomass production. This observation is in agreement with previous results suggesting that Crabtree negative yeasts accumulates reserve carbohydrates upon glucose pulse[S. stipitis whereas succinate and malate are present in slightly lower amount. Pyruvate and acetyl-CoA are also less abundant in S. stipitis, however it is unfortunately not possible to distinguish between the cytosolic and mitochondrial fraction.Table balance,18. Base pathway. The conse pulse. MetabolS. cerevisiae[S. stipitis. 2-hydroxyglutarate, originating from the TCA intermediate 2-oxoglutarate is found to be present at higher levels in S. stipitis[Intracellular levels of lipids are shown in Tableerevisiae. Choline stipitis.Intracellular metabolome analysis highlighted several differences in metabolites patterns of carbohydrates, energy and fatty acids metabolism. Despite some of these results might be directly connected to what observed at a phenotypic level, other differences could not be captured by physiological analysis, thus proving the validity of metabolome analysis in providing useful information for metabolic characterization.S. stipitis and only a few works describing gene expression levels in S. stipitis have been performed. To determine gene expression levels during growth on glucose, we analyzed the transcriptome of S. stipitis in a high-throughput fashion, using RNA-seq to compare mRNAs extracted from aerobic batch and chemostat cultivations to those of S. cerevisiae[et al. analyzed gene expressions through NimbleGen expression arrays, comparing the transcriptional response to oxygen limitation on glucose and xylose[et al. sequenced the transcriptome of S. stipitis growing on glucose and xylose, identifying 214 ORFs whose expression is changed during growth on the two different carbon sources[Not much is known about the regulatory pathways of erevisiae. Jeffried xylose,24 and s sources.S. stipitis CBS6045 and to S. cerevisiae S288c genome. We used RNA-seq data to describe the transcriptome in a comparative fashion between the two yeasts within the two conditions. To this end, we firstly used RNA-seq data in a qualitative way to identify significant Gene Ontology terms . This parameter quantifies the expression level of a certain mRNA according to its abundance and normalized it by the number of reads per samples and the gene length.S. cerevisiae, the expression level of HXK1 appears to be sensitively glucose repressed, as its mRNA in batch cultivations showed remarkable lower expression  and a similar behavior is observed for GLK1. In S. stipitis, where glucose repression is absent, there is almost no difference in mRNA levels of these genes under the two conditions. Other glycolytic genes that show to be sensitive to the presence of glucose in S. cerevisiae are ENO1 and TDH1. Interestingly, in S. stipitis, TDH1 and TDH2 genes are less expressed than in S. cerevisiae. A lower expression is also reported for the genes FBA1 and TPI, despite the difference is not as remarkable as that reported for TDH1 and TDH2. Pentose phosphate pathway genes (data not shown) do not show significant changes, accordingly to what is known about the housekeeping function of ZWF1[TKL1 and GND1[S. cerevisiae it is possible to observe differences in mRNA levels between batch and chemostat cultivation due to the inhibitory presence of glucose; mRNA levels of TCA genes, besides IDP1, are higher during chemostat cultivations when glucose repression is relieved. Differently, in S. stipitis, the FPKM of these genes do not change remarkably. A similar trend is observed for the main glyoxylate genes MLS1 and ICL1: their mRNA levels in S. cerevisiae are overexpressed during chemostat cultivations, whereas in S. stipitis this difference is not remarkable, however, in S. stipitis, expression of MLS1 is higher than that reported by S. cerevisiae in batch. The gene ACS1, involved in acetyl-CoA metabolism, behaves in a similar fashion: in S. cerevisiae its mRNA is more abundant during chemostat cultivations while its expression does not change significantly in S. stipitis; additionally, mRNA of ACS1 appears to be, similarly to MLS1, more abundant in S. stipitis during batch compared to S. cerevisiae. Genes of the PDH complex PDA1 and PDB1 are present in higher amount in S. stipitis than in S. cerevisiae. Expression of the PDC complex is also different as mRNAs corresponding to the S. stipitis genes PDC1 and PDC2 are not as much expressed as the corresponding S. cerevisiae genes. The mRNAs of genes involved in anaplerotic reactions show different patterns of expression between S. cerevisiae and S. stipitis. While PCK1 and PYC1 are increased in chemostat for S. cerevisiae, in S. stipitis it not possible to observe the same trend.We searched for genes whose expression changed in the two conditions between the two yeasts. Transcript levels of genes involved in central carbon metabolism are shown in Figuren of ZWF1 and the  and GND1. DiffereRNA-seq data provided information about the different levels of expression of genes of the central carbon metabolism between the two conditions, indicating that transcriptional regulation of central carbon metabolism differ, qualitatively and quantitatively, between the two yeasts.in-silico analysis of Transcription Factors (TF) of the two yeasts. We used the Fungal Transcription Factor Database[S. cerevisiae and S. stipitis by performing clustering based on orthologs families[S. cerevisiae and 115 for S. stipitis. The analysis of TFs found unique for S. stipitis uncovered that most of the TFs in these families are poorly characterized ORF. Most of them are putative or predicted proteins sharing conserved domains with Debaromyces hansenii, Spathaspora passalidarum or members of Candida spp., however, even in these yeasts, their function remains unknown. Many of the predicted transcription factors are Zn finger protein having GAL4-like domains. Some are involved in tRNA processing and maturation. Among the annotated ones we found a transcriptional activator of nitrogen-regulated genes, a beta-1,6-N-acetyl-glucosaminyl-transferase involved in transcriptional processes and the subunit A of HAP3.1 transcription factor which directly interacts with the more characterized regulator HAP2. Moreover transcription factors involved in arginine metabolism, lactose metabolism and fluconazole resistance (FRC1) have also been uniquely found in S. stipitis.To gain insight into the regulation of the Crabtree negative yeast we performed a comparative Database to identfamilies Figure6S. cerevisiae involved in central carbon metabolism that are reported in TableS. cerevisiae, might suggest a different regulation of the phospholipid and fatty acids biosynthetic pathway. The ORF YOR344C, corresponding to the transcription factor SGC1 was identified as unique in S. cerevisiae; interestingly, this TF seems to function as a transcriptional activator in Ty1-mediated gene expression[S. cerevisiae might be linked to the finding that RGT1, a TF involved in regulating expression of glucose transporters (HXT) during growth on glucose, is also unique for S. cerevisiae. Additionally, the protein MIG2 and MIG3 are also uniquely found in S. cerevisiae. Among specific TF of S. cerevisiae we also found GSM1, a poorly characterized transcriptional regulatory protein (YJL103C) probably involved in energy metabolism[S. cerevisiae are proteins involved in cell cycle progression, tRNA and mRNA maturation and processing, sulfite resistance, arginine and methionine metabolism, hypoxic genes regulators, nitrogen metabolism and filamentous growth. The finding that TFs involved in controlling the expression of glycolytic and gluconeogenic genes were found to be uniquely present in S. cerevisiae indicated that the two yeast show a different regulation exerted by glucose.To attempt uncovering the reason at the origin of a different transcriptional regulation of genes involved in the central carbon metabolism, we looked at unique transcription factors of pression, showingtabolism and two tabolism,38, VID2S. stipitis during aerobic growth, we performed a systems-level comparison between the Crabtree negative yeast and S. cerevisiae during aerobic growth on glucose. To our knowledge, this is the first time that the metabolism of S. stipits is investigated in details during batch and chemostat cultivations on glucose. Despite the differences in genome evolution and metabolism of these two yeast species[S. stipitis. This behavior is very different from S. cerevisiae where is the amount of glucose to determine its metabolic mode (respiratory or fermentative). This is in agreement with what has previously been reported for S. stipitis concerning the onset of ethanol fermentation, which is dependent on oxygen availability[S. stipitis under both conditions show similarities to that observed for S. cerevisiae during purely respiratory growth, however, minor differences during batch and chemostat cultivations condition were identified[13C-labeling uncovered differences through the anaplerotic reactions of pyruvate carboxylase and malic enzyme, showing lower values in S. stipitis compared to those showed by S. cerevisiae during oxidative growth. Interestingly, the flux through the oxidative part of the PPP is found to be higher during batch cultivations and higher than that reported in S. cerevisiae during oxidative growth. This might be explained with a higher biomass yield on substrate reported for S. stipitis. S. stipitis showed increased pyruvate import into the mitochondria, increased flux through the PDH and TCA reactions, indicating that the Crabtree negative yeast mainly fuel the TCA through the conversion of pyruvate to acetyl-CoA via the PDH reaction.With the aim of describing the metabolism of  species we soughlability. The fluentified. The estS. stipitis while adenine content is found to be higher. This result might be in agreement with the observed increase in biomass yield and higher specific growth rate. Carbohydrate metabolism shows several differences, mainly in the content of polyols. This might indicate that these compounds are used as storage carbohydrate or, as observed in Aspergillus nidulans, can play a role in maintaining the redox balance[S. stipitis, which is not present in S. cerevisiae. Differences in fatty acids and phospholipids are also found. The increased choline content could suggest a different regulation of the phospholipid metabolism as, through the analysis of unique transcription factors, we found the TFs INO2 and INO4 to be absent in S. stipitis. Analysis of RNA-seq data uncovered different transcriptional regulation of central carbon metabolism of the two yeasts, indicating that, in S. stipitis, transcriptional regulation is not dependent on glucose concentration. Most mRNAs of S. stipitis do not change their expression under the two conditions, in contrast to S. cerevisiae where, in the shift towards respiratory conditions, a significant number of genes change remarkably their expression[S. cerevisiae potentially involved in controlling the onset of fermentative metabolism (Rgt1 and Sgc1). Several families of transcription factors were found to be unique for S. stipitis; unfortunately, even though the S. stipitis genome was sequenced in 2007, most of these ORF have not been assigned a function yet.Through metabolome analysis it was possible to assess and quantify differences in the levels of intracellular metabolites of the two yeasts during batch cultivations. Total amino-acid levels are slightly lower in  balance. Relevanpression. The anaS. stipitis, sharing similarities to the patterns found in S. cerevisiae during purely respiratory growth. However, through integrated system-level analysis, it was possible to identify differences between the main metabolic mode of S. stipitis and the oxidative growth of S. cerevisiae, to highlights non-obvious difference during batch cultivation conditions and identify potential transcription factors involved in controlling the different response to glucose excess.Despite the non-direct relationship between mRNA levels, metabolites and fluxes, our attempt to integrate data coming from these different techniques uncovered a robust consistency in identifying a one-mode phenotype of Scheffersomyces stipitis strain used in this work was CBS 6054 obtained by T. Jeffries. The Saccharomyces cerevisiae strain CEN.PK113-7D was used as wild-type strain for comparison.The 4)2SO4, 5 g; KH2PO4, 3 g; MgSO4·7H2O, 0.5 g; Antifoam 289 , 0.05 mL; trace metals, 1 mL and vitamins, 1 mL trace metal solution. The trace metal solution consisted of (per liter): EDTA , 15 g; ZnSO4·7H2O, 0.45 g; MnCl2·2H2O, 1 g; CoCl2·6H2O, 0.3 g; CuSO4·5H2O, 0.3 g; Na2MoO4·2H2O, 0.4 g; CaCl2·2H2O, 0.45 g; FeSO4·7H2O, 0.3 g; H3BO3, 0.1 g and KI, 0.1 g. The pH of the trace metal solution was adjusted to 4.0 with 2 M NaOH prior to heat sterilization. The vitamin solution contained (per liter): biotin, 0.05 g; p-amino benzoic acid, 0.2 g; nicotinic acid, 1 g; Ca-pantothenate, 1 g; pyridoxine-HCl, 1 g; thiamine-HCl, 1 g and myo-inositol, 25 g. The pH of the vitamin solution was adjusted to 6.5 with 2 M NaOH. The vitamin solution was filter sterilized and stored at 4°C[-1 of Glucose as carbon source. The precultures were used to inoculate the fermentors to an initial OD600 of 0.05.The cultivations have been conducted in DasGip fermentors  in batch and chemostat modes, for each mode triplicates cultures were grown. A mineral salt medium was usedd at 4°C. The med2 concentration by DasGip fedbatch pro® gas analysis systems with the off gas analyzer GA4 based on zirconium dioxide and two-beam infrared sensor.The batch cultures were performed in 1.0 L DasGip stirrer-pro® vessels with a working volume of 0.7 L. Agitation was maintained at 600 rpm using a magnetic stirrer integrated in the BioBlock®, which maintained the temperature at 30°C. The aeration was set to 1 vvm. The pH of the medium was maintained at 5.0 by automatic addition of 2 M KOH. The temperature, agitation, gassing, pH and composition of the offgas were monitored and controlled using the DasGip monitoring and control system. Dissolved oxygen was monitored with an autoclavable polarographic oxygen electrode . The effluent gas from the fermentation was analyzed for real-time determination of oxygen and CO-1) was fed with a constant dilution rate of 0.1 hr-1 and aeration was set to 1 vvm. The working volume was kept at 0.5 L by a peristaltic effluent pump. Samples were taken after a metabolic steady state  was achieved.For the chemostat cultivations, the medium described above  first and an isotopic steady state (at least 5 residence time after feeding with labeled glucose) were reached.Metabolic flux distribution was analyzed after feeding the cultivations with 13C labeled glucose. The media used and the fermentation system was the same as described in paragraph 2.2 but glucose concentrations was 5 gL13C-labeled glucose during batch and chemostat cultivations. For batch cultures, the medium was added with 5 gL-1 of 100% D-Glucose-1-13C  and samples from triplicate cultures were withdrawn from mid-exponential phase . For chemostat cultures, after reaching a metabolic steady state, the original medium containing 2gL-1 naturally labeled glucose was replaced by chemically identical medium, but where the glucose was replaced by 100% D-Glucose-1-13C .Flux distribution analysis was performed upon growth on et al.[The labeling pattern of proteinogenic amino acids was determined calculating the summed fractional labeling (SFL) as described in Gombert et al.. This caet al..Cell dry weight was measured by filtering a known volume of the culture through a pre-dried and pre-weighed 0.45 μm pore size nitrocellulose filter . The filters with the biomass were washed with water, dried for 15 min in a microwave oven at 150 W and weighed again. The optical density was determined at 600 nm using a Genesys 20 Spectrophotometer, Thermo Scientific, Wilmington, DE, USA).2SO4 as mobile phase at a flow rate of 0.6 mL min-1. Glucose, glycerol and ethanol were measured with a refraction index detector whereas succinate, acetate and pyruvate with an ultraviolet–visible light absorbance detector.The concentrations of glycerol, ethanol, acetate, succinate and pyruvate were analyzed by an isocratic HPLC  with an Aminex HPX-87 H ion exchange column  at 65°C using 5 mM HSample from each condition were withdrawn from mid-exponential phase  and added to a 75% v/v cold methanol buffered with 12.5 mM Tricine pH 7.4, kept at −40°C in an Ethanol Bath. Pellet were harvested by centrifugation 4 min at 10,000 g at −20°C and froze on liquid nitrogen. The quenched samples were sent to Metabolon  for extraction based on methanol/chloroform and analysis through GC-MS and LC-MS-MS. Metabolome profiling was performed as previously described,44. The 600 = 2.5). The sample was cooled immediately in ice and centrifuged at 2°C and 5000 rpm for 5 min, the supernatant discarded and the biomass immediately frozen in liquid nitrogen. The total RNA was extracted from cells through mechanical disruption with glass beads, digested with DNAse and purified using the RNeasy kit . The quality of the RNA was assayed using a BioAnalyzer . 10 ng of total RNA were sent to Lab for Life for RNA sequencing based on the Illumina Solexa platform. For construction of the library the Illumina TrueSeq kit was used.For RNA sequencing, a 25 mL sample was withdrawn from the fermentor in the mid-exponential phase  and Cuffdiff to determine differential gene expression,51. The S. stipitis CBS6045 and S. serevisiae S288c were used to identify orthologous group through OrthoMCL software version 1.4 using default parameters[The protein sequences based translated from the genome sequence of rameters. The resrameters, and, baThe authors declare no competing financial interests.MP, JN and IN designed the experiments, MP performed the experiments, MP and IN analyzed the data, IN, MP, MU and JN discussed and interpreted the data. All authors read and approved the final manuscript.The orthologous family analysis result from OrthoMCL software.Click here for file


As a critical analysis step in these studies, biological pathways are reconstructed based on the genes predicted from metagenomic shotgun sequences. Pathway reconstruction provides insights into the functionality of a microbial community and can be used for comparing multiple microbial communities. The utilization of pathway reconstruction, however, can be jeopardized because of imperfect functional annotation of genes, and ambiguity in the assignment of predicted enzymes to biochemical reactions . Considering that metabolic functions in a microbial community are carried out by many enzymes in a collaborative manner, we present a probabilistic sampling approach to profiling functional content in a metagenomic dataset, by sampling functions of catalytically promiscuous enzymes within the context of the entire metabolic network defined by the annotated metagenome. We test our approach on metagenomic datasets from environmental and human-associated microbial communities. The results show that our approach provides a more accurate representation of the metabolic activities encoded in a metagenome, and thus improves the comparative analysis of multiple microbial communities. In addition, our approach reports likelihood scores of putative reactions, which can be used to identify important reactions and metabolic pathways that reflect the environmental adaptation of the microbial communities. Source code for sampling metabolic networks is available online at  We present a probabilistic sampling approach to profiling metabolic reactions in a microbial community from metagenomic shotgun reads, in an attempt to understand the metabolism within a microbial community and compare them across multiple communities. Different from the conventional pathway reconstruction approaches that aim at a definitive set of reactions, our method estimates how likely each annotated reaction can occur in the metabolism of the microbial community, given the shotgun sequencing data. This probabilistic measure improves our prediction of the actual metabolism in the microbial communities and can be used in the comparative functional analysis of metagenomic data. Metagenomics aims to analyze the microbial communities directly extracted from their living environment, bypassing the requirements of isolating and culturing the microbes. With the recent progress of the next generation sequencing (NGS) technologies, the shotgun sequencing of a whole microbial community has become a routine exercise. As a result, the list of metagenomics studies is growing rapidly de novo, and then predicts the functions of the predicted proteins based on similarity searches To understand the functional and metabolic potential of a microbial community given the sequencing data, a key analysis is to predict - from raw NGS reads or assembled contigs - protein coding genes and their functions. Functional annotations are often achieved by similarity search  and the overlaps among different pathways Differential functions or biological pathways can be identified by comparing annotations of metagenomes, providing insights into the differences of functionality of various microbial communities metabolic reactions catalyzed by predicted genes from the dataset, instead of the genes themselves. By directly working on reactions in the context of a global network, our method is immune to the problem of pathway reconstruction caused by the overlaps between pathways - pathways are important for understanding the biological processes, however, their definition can be rather arbitrary, and the overlaps between pathways are artificially created. More importantly, our new method computes the likelihood of each reaction for all potential reactions catalyzed by predicted functions. Clearly, using all potential reactions can lead to an unfaithful estimation of the functionality of a microbial community: functional predictions are noisy and contain mistakes; on the other hand, there are genes that indeed have multiple functions In this paper, we present a computational method for inferring the functional activities in a metagenome on the basis of the likelihood of each reaction in a metagenome-scale metabolic network given predictions of enzymes. Our method computes the marginal probability of each reaction observed in a collection of randomly sampled subnetworks from the metagenome-scale metabolic network. In these subnetworks, for each annotated gene family, there exists at least one reaction that is carried out by the product of the gene (i.e. the enzyme). However, if the product of a gene is annotated to catalyze multiple reactions, some of these reactions may be excluded from the sampled subnetwork, as long as at least one of these reactions is included. We note that, according to this condition, each sampled subnetwork represents a putative reconstruction of the collective metabolic network of the metagenome, among which we assume the subnetworks containing fewer metabolites are more likely to represent the actual metabolism of the microbial community than the ones containing more metabolites. Based on this parsimony concept, we devised a Markov Chain Monte Carlo algorithm We propose a probabilistic approach to estimate the A microbial community adapts its collective metabolic profile to its living environment. Therefore, the similarity measure based on either protein content or metabolic activities in metagenomes can be used to cluster the metagenomes, consistent to similarity of the environments From the IMG/M metagenome repository We clustered the 44 samples based on different similarity measures of their enzyme contents or metabolic reactions. Five types of measures were used to estimate the distance between the metagenomics samples and compare the clustering results  using the five distance measures are shown in   and   has a higher probability to occur in the metabolism of microbial communities from Antarctic deep lakes , which iIn this paper, we focus on the analysis of metagenomics samples based on the metabolic reactions annotated to be catalyzed by the predicted genes in the metagenomes. We proposed a method that assigns marginal probabilities to reactions to estimate the likelihood of the reactions to occur in the metabolism of a microbial community. The Markov Chain Monte Carlo (MCMC) sampling algorithm establishes a framework that can be used to study the different aspects of metabolic networks. The subnetwork universe can be sampled by the MCMC algorithm with different constraints based on various assumptions. For example, in recent work, flux balance analysis (FBA) is used to constrain the viability of the sampled genome-scale metabolic networks in a MCMC based method The marginal probabilities are assigned by our method to reactions that are catalyzed by catalytically promiscuous enzymes. As shown in the results, the reactions occurring in a microbial community are a better representation of the metabolism in the community, because one reaction may be catalyzed by different enzymes encoded by different microbial organisms in the community. An extension of this method is to compare the likelihood of the reactions to be catalyzed by the same enzyme and allow us to investigate how promiscuous enzymes function in different environments.By applying the parsimony assumption, our method successfully takes several intrinsic properties of the metabolic network into consideration. It should be noted that this method indirectly favors highly connected metabolic networks, where the number of non-terminal metabolites that can be produced and consumed by the microbial community is maximized. Similar assumptions have been used in other studies. For example, in the metabolic network reconstruction, gaps in metabolic paths are usually filled to decrease the number of isolated reactions or metabolites supra-organism), ignoring the boundaries between species altogether We note that our method lacks the compartmentalization of the biochemical reactions and the resolution of individual species. In previous studies, multi-species models have been applied to investigate the interactions within the community or between host and microbiomes dead-end metabolites in the network, the metabolites that are not the product of the other reactions or are not used by other reactions as substrates. In our model, they are misinterpreted as the metabolites that connect two reactions, which could decrease the accuracy of the reaction and pathway annotated by our method. Another issue is that this method does not consider the abundance of enzymes predicted from the metagenomic sequences. We observed large variance in the abundance of the same enzyme among samples as well as pairs of enzymes that share same metabolites as substrates or products. We are working on a revision of our current method to take the abundance of enzymes into account.There are, however, several limitations of the method that are worth noting. In our metabolic network definition, the reactions are considered to be indirect, which indicate that all reactions are reversible. However, conditions in the cell are often such that it is thermodynamically infeasible for flux of reactions to flow in certain direction so the reaction becomes irreversible. Therefore, there might be We start the problem formulation with a formal definition of a metabolic network.A reference metabolic network is a labeled undirected graphwhererepresents a set ofvertices, each labeled by a metabolite (compound), andrepresents a set ofedges, each labeled by a biochemical reaction. The graph topology is represented in a matrix formannotated global metabolic network as follows.We then annotate the enzymes corresponding to the predicted genes in a metagenomic dataset on the reference metabolic network ,andis a submatrix ofAn annotated metabolic subnetworkof a annotated global metabolic networkis called valid, ifvalid subnetworksThe set of valid subnetworks from an annotated global metabolic network constitutes the universe of  theorem :(1)whereDue to the complexity of metabolic networks, it is intractable to compute necessary compounds need to be taken from its environment, while the other compounds can be synthesized through metabolic reactions inside the microbial community. Based on these observations, we adopt a parsimony assumption: the valid subnetworks involving fewer compounds are more likely to represent the metabolism of the microbial community.Before we discuss the details of the MCMC algorithm, we introduce an inequality of likelihood of the metabolic subnetworks based on a parsimony assumption. We observe that within microbial communities, metabolic enzymes work collectively to catalyze a series of reactions to transform some compounds that are available in their living environment into other compounds that can be utilized to maintain their cellular functions. Although the enzymes act on different substrates and products, the products of some reactions are usually used as substrates in the subsequent reactions, resulting in a sequence of reactions devising the complex but efficient metabolic network. The adaption of a living microbial organism to its living environment often leads to a unique and nearly optimal metabolic network, in which only a small number of Equivalently, given two valid subnetworks candidate probability), where We construct a Markov Chain (MC) of annotated subnetworks to sample the valid annotated metabolic subnetwork in the universe ergodic, i.e., any pair of subnetworks can be connected by a finite series of such transitions. The candidate probability ensures that the random walk samples the subnetworks based on the parsimony assumption, as defined in It is straightforward to show that this type of transition is subsampling  technique to reduce the correlation and to approximate independence between the successive samples of the Markov chain i.e., the burn-in period, to make the random walk to start from a better point in the subnetwork probability space The number of variables we are trying to estimate equals the number of reactions that are catalyzed by promiscuous enzymes. Metabolic networks constructed from metagenomic data normally contain hundreds of such reactions. Here we discuss several methodological issues of the Markov chain caused by the large number of variables in the sampling universe. First, every step in the random walk only changes the state of at most one reaction, therefore, the correlation between the two consecutive sampled subnetworks is obviously large. We use the lgorithm  are not  sampled . To imprbnetwork . For a lThe formal Metropolis-Hastings algorithm is shown in After sampling, we can compute the estimated marginal probability In addition to the marginal probability For any pair of metagenomes The taxonomic distances, denoted as We used the Ward's minimum variance method as the linkage criteria in our hierarchical clustering, which tries to minimize the total within-cluster variance. Note that when applying other common linkage criteria in the hierarchical clustering, even though the performance varies, the order of performances using the four distance measure was still observed.Fisher's exact test We used the independent two-sample t-test to determine whether the quantities of enzymes are statistically different in two environments under the assumptions that these quantities are independent and normally distributed, and the distributions of the quantities in the two groups of samples have the same variance. So for each enzyme, we consider its quantities in samples in the two environments as two groups of values, and the t-statistic determines whether the means of the two groups of values are different. Similarly, we also used t-test for analyzing whether each reaction has a different probability in the two environments.http://omics.informatics.indiana.edu/mg/MetaNetSam/.The MCMC sampler of metabolic networks is implemented in Java, and based on the HYDRA MCMC library Figure S1Reactions catalyzed by muconate cycloisomerase (KEGG Orothlog K01856). All five reactions are found in all samples in the metagenomics samples from the Antarctic deep lake and Alaska permafrost studies. KEGG reactions: (a) R05300; (b) R05390; (c) R06989; (d) R08116; (e) R09229.(PDF)Click here for additional data file.Table S1IMG/M metagenomics samples used in our study. Data were downloaded on 4/10/2012.(PDF)Click here for additional data file.Table S2Reactions in Antarctic deep lake and Alaska permafrost samples. The first p-value is from the Fisher's test that checks if the occurrences of the reactions in the two groups of samples are different. This p-value equaling 1 means the reaction occurs in all the samples in the two groups. The second p-value is the t-test based on the marginal probability of the reactions. The last two columns are the average marginal probabilities of the reactions in the two groups of samples.(PDF)Click here for additional data file.