Salmonella enterica serovar Typhimurium and identified pairs of reactions (cut sets) predicted to be required for growth in vivo. We termed such cut sets synthetic auxotrophic pairs. We tested whether these would reveal possible combined targets for new antibiotics by analyzing the performance of selected single and double mutants in systemic mouse infections. One hundred and two cut sets were identified. Sixty-three of these included only pathways encoded by fully annotated genes, and from this sub-set we selected five cut sets involved in amino acid or polyamine biosynthesis. One cut set (asnA/asnB) demonstrated redundancy in vitro and in vivo and showed that asparagine is essential for S. Typhimurium during infection. trpB/trpA as well as single mutants were attenuated for growth in vitro, while only the double mutant was a cut set in vivo, underlining previous observations that tryptophan is essential for successful outcome of infection. speB/speF,speC was not affected in vitro but was attenuated during infection showing that polyamines are essential for virulence apparently in a growth independent manner. The serA/glyA cut-set was found to be growth attenuated as predicted by the model. However, not only the double mutant, but also the glyA mutant, were found to be attenuated for virulence. This adds glycine production or conversion of glycine to THF to the list of essential reactions during infection. One pair (thrC/kbl) showed true redundancy in vitro but not in vivo demonstrating that threonine is available to the bacterium during infection. These data add to the existing knowledge of available nutrients in the intra-host environment, and have identified possible new targets for antibiotics.Bacterial infections remain a threat to human and animal health worldwide, and there is an urgent need to find novel targets for intervention. In the current study we used a computer model of the metabolic network of  Despite substantial progress in infection biology research over the last decades, bacterial infections remain a major threat to human and animal health worldwide. With the rapid evolution and spread of antimicrobial resistance Bacterial metabolism constitutes a recent area of interest with regard to host-pathogen interactions because of the potential of identifying novel anti-infective targets that inhibits pathogen survival in the host System level approaches describing the metabolic capacities of bacterial pathogens has revealed that bacterial metabolism can be highly robust with extensive redundancies, not only within the pathogen but also between the pathogen and the host Salmonella enterica serovar Typhimurium (S. Typhimurium) using a published genome-scale metabolic model (GSM) et al.Exhaustive identification of synthetically auxotrophic pairs using purely experimental approaches would involve constructing and characterizing many thousands of mutant strains, which is, if not infeasible, cumbersome. Also, since this approach would be limited to combinations of two genes, possible combinations of pairs of enzymes encoded by redundant genes would be missed. An alternative, explored here, is to use a computer model of the metabolic network and identify pairs of reactions that when removed from the network renders it incapable of synthesizing biomass, i.e. cut sets. Here we investigate the metabolic network of in silico metabolic modeling to identify predicted redundancies in pathways in the metabolism with a targeted validation by infection experiments with selected synthetic auxotrophic pairs involved in amino acid and polyamine biosynthesis. Specifically, we have used S. Typhimurium as a model to investigate the role of 5 cut sets, suggested by computational analysis. Results have disclosed specific information about the available nutrients in the host environment, and we have identified potential novel combined targets for antimicrobial therapy.In the current study we have combined S. Typhimurium 4/74 was used as wild-type strain in all experiments. This strain has been described previously and its virulence is well defined S. Typhimurium KP1274 was used as primary recipient for plasmids S. Typhimurium strains were maintained in LB media (Oxoid). For solid medium, 1.5% agar was added to give LB agar plates. Chloramphenicol (10 µg ml−1), kanamycin (50 µg ml−1) or carbenicillin (75 µg ml−1) was added as required. Growth phenotypes were investigated in LB (Difco), M9 minimal media  or M9 minimal media supplemented with 50 mg/l alanine, thiamine, glycine, glutamine, proline, arginine, aspartate, glutamate (M9+) as suggested Escherichia coli Top10 competent cells were used for DNA cloning and were grown in LB media or on LB agar plates at 37°C with appropriate selection.asnA/asnB was achieved by cloning asnA into pSLD64, while genetic complementation of other genes was achieved by PCR amplification and subsequent cloning of genes into pACYC177, essentially as described XhoI and BamHI. Enzymes were used according to the manufacturer’s recommendentation (Thermo Scientific). All constructs were verified by restriction analysis and sequencing.Gene deletions and concomitant insertions of an antibiotic resistance cassette were constructed using Lambda Red mediated recombination as described elsewhere in vivo fitness of bacterial strains as previously described 3 bacteria of each strain. The exact c.f.u. and ratio between wild type and mutated strains were enumerated by plating as described below. Mice were killed at 6 days post-inoculation by cervical dislocation. Severely affected animals were sacrificed early to this time point for animal welfare reasons, but otherwise treated as the rest of the group. The spleens were removed aseptically and bacteria recovered and enumerated after plating a dilution series on to LB agar. One hundred colonies were randomly picked and tested for resistance to the relevant antibiotic to determine the proportion of mutant strains. The competitive index was calculated as the mutant/wild type ratio of the output versus the mutant/wild type ratio of the inoculum.Female C57/BL6 mice (Nramp-) (20–25 g) were used to assess Mice experiments were conducted with permission from the Danish Animal Experiments Inspectorate, license number: 2009/561–1675.Statistical significance of the differences between wild type and mutant strains was determined using GraphPad Prism version 5.0 (GraphPad software) with one-sample t-test analysis. Grubb’s outlier test was performed to exclude outliers with a significance of 0.05.The model used, which contains 1099 reactions, has been described elsewhere The model was analyzed using Flux Balance Analysis i) all cut sets of size one were identified by attempting to solve t); (ii) all two-reaction combinations which were not supersets of reactions identified in (i) were sequentially set to zero and combinations which rendered the problem infeasible were recorded. For each cut set the cause of the lethality was analyzed by identifying which biomass components were not produced when the pair was removed from the network. This was done by attempting to solve the FBA (with a given pair fixed to zero flux) for each component of vector t fixed to a non-zero value, whilst fixing all other components to zero.All model analysis was carried out using the ScrumPy package et al.The gene associations of the selected reaction pairs were obtained from the BioCyc database S. Typhimurium is an important intracellular pathogen of both humans and animals causing self-limiting enteritis and life threatening systemic infections. Replication and dissemination of S. Typhimurium inside the host is essential for establishing a systemic infection and is dependent on the ability to acquire and utilize the nutrients available in this niche in addition to the ability to synthesize essential nutrients that are only present in limited concentrations S. Typhimurium causes a systemic disease in susceptible mice, thus serving as a suitable model for studying bacterial systemic infections S. Typhimurium S. Typhimurium at different points during the systemic stage of infection is largely unknown, but contains glucose and certain amino acids S. Typhimurium during infection S. Typhimurium exhibits carbon catabolite repression A metabolic model of The damage analysis resulted in a list of 102 reaction pairs hampering the production of essential biomass components . The numS. Typhimurium during systemic mouse infection prsA, argD, fbp, adk and nadE.A number of genes were predicted by the analysis to be essential  transporter reaction of a molecule synthesized by the reaction see . Five ofprsA) has been reported to be conditional essential in E. coliE. coli null mutants require additional mutations and several additional metabolites to be viable Salmonella infection, prsA was not reported, suggesting that prsA mutants are not viable S. Typhimurium under the conditions used for construction of mutants.The homolog of STM1780 , minimal media with glucose (M9) and M9 additionally supplemented with specific amino acids (M9+). The composition of the latter medium corresponded to the input provided in the computational cut set analysis. According to the model analysis, single mutants should grow like the wild type strain, whereas double-mutants were expected to be unable to grow in M9+ as they would be auxotrophic for at least one biomass component. Genes in the selected reactions were subjected to mutational analysis. Initially we tested growth of the mutants asnA/asnB; serA/glyA and thrC/kbl) confirmed the cut set analysis since single mutants grew well in M9+, while the double mutant did not grow. Contrary to this, even the double mutant in the pair speB/speF,speC was not attenuated for growth, while both single and double mutants of trpA/trpB were affected for growth  was calculated for each infection pair  indicating that these reaction pairs contributed significantly to the fitness of the bacterium during infection, while the double mutant thrC/kbl was unaffected. Interestingly the single glyA mutant also proved attenuated, despite its ability to grow in vitro  requires either threonine or isoleucine for growth. The fact that isoleucine can support growth of the thrC mutant without being converted to threonine is an indication that the threonine flux from glycine is enough to supply threonine-charged tRNAs for protein synthesis. Since the flux of threonine from glycine was not sufficient to support growth of the thrC mutant in the absence of Isoleucine, we expected that either the LtaA reaction or the Kbl/Thr path was nonfunctional. We considered the thrC/kbl pair suggested by the model analysis as a cut set. In our strain we found that a thrC mutant grew somewhat slower than the wild type, while a kbl strain had no growth related phenotypes. The thrC/kbl double mutant however was unable to grow in minimal medium without threonine supplements, showing that the mutant pair is truly auxotrophic. Interestingly, however, we did not observe any clear infection defect as shown by the C.I. values of either single or double mutants. This confirms that the host must supplies sufficient threonine during all phases of the infection, as recently reported Threonine can be synthesized in two ways aside from the threonine biosynthesis pathways. Both of these auxiliary reactions use glycine as precursor. Threonine may be formed in a single step from glycine and acetaldehyde catalyzed by the speA and speB gene products or directly from ornithine by either of the speC or speF gene products. Putrescine can subsequently be converted into spermidine by the co-action of the speE and speD gene products  or the two M9 media  when complemented by the wild type allele in trans. As expected, complementation of the double mutant with single genes was unsuccessful. Single gene knock-outs were without impact on fitness in vivo, while the double mutant could not compete with the wild type strains. The most obvious explanation for this observation is that tryptophan is not provided by the host during S. Typhimurium infection. However, the observation contradicts a previous observation by Steeb et al.S. Typhimurium during infection. In support of our conclusion, Henry et al.S. Typhimurium in cell cultures, observed that lack of TrpC, which performs the step above the one by TrpB/TrpA in the biosynthesis of trypophane, caused formation of filamentous cells, and thus this enzyme appeared essential for in vivo growth. However, further studies are indicated to clarify this matter.Finally,  cut set . They fo+ medium , suggestS. Typhimurium required for intra host survival. Our results revealed that asparagine, tryptophan and most likely glycine are not present in sufficient amounts to support growth while threonine and serine are. These data add to the existing knowledge of available nutrients in the intra-host environment et al.glyA mutant and the trpA;trpB double mutant had reduced competitive fitness compared to the wild type. The conclusion regarding the glyA mutant should be further substantiated by studies comparing GlyA and GCV performance. Furthermore, we additionally found that asparagine and putrescine are only present in limiting amounts during infection. This is particularly interesting since the SpeB, SpeC, and SpeF enzymes, facilitating putrescine biosynthesis, were not detected in Salmonella bacteria isolated from spleens S. Typhimurium in vitro, indicating that they are more specifically related to virulence than to central metabolism. The putrescine biosynthetic genes constitute a novel target for anti-infective therapy with potential of broad effect against several intracellular pathogens.In the present study we have used genome scale metabolic modeling to identify pairs of reactions in Figure S1Growth phenotypes of five selected cut sets in M9 media with supplements (see text). The media corresponds to the one used as input in the cut set analysis using the S. Typhimurium genome scale model.(PDF)Click here for additional data file.Figure S2The transformation of L-asparatate to L-asparagine catalyzed by the reactions of AsnA and AsnB. AsnA uses ammonia as a reactant where AsnB predominantly uses glutamine.(PDF)Click here for additional data file.Figure S3The biosynthesis of serine. Serine can be produced from glycine through the action of GlyA and also from 3-phospo-glycecerate (3PG) by the action of SerA/B/C.(PDF)Click here for additional data file.Figure S4The polyamine biosynthesis pathways in Salmonella. Putrescine can be formed from arginine through the reactions catalyzed by SpeA and SpeB and from ornithine through the reaction catalyzed by SpeC/SpeF. Spermidine is formed from putrescine through the reactions of SpeE and SpeD.(PDF)Click here for additional data file.Table S1Primers used for construction of mutants and for cloning purposes.(PDF)Click here for additional data file.Table S2A list of 102 reaction pairs identified as unfeasible for production of essential biomass in the damage analysis.(PDF)Click here for additional data file.


Karenia brevis, responsible for early harmful algal blooms in the Gulf of Mexico, produces many secondary metabolites, including potent neurotoxins called brevetoxins (PbTx). These compounds have been identified as toxic agents for humans, and they are also responsible for the deaths of several marine organisms. The overall biosynthesis of these highly complex metabolites has not been fully ascertained, even if there is little doubt on a polyketide origin. In addition to gaining some insights into the metabolic events involved in the biosynthesis of these compounds, feeding studies with labeled precursors helps to discriminate between the de novo biosynthesis of toxins and conversion of stored intermediates into final toxic products in the response to environmental stresses. In this context, the use of radiolabeled precursors is well suited as it allows working with the highest sensitive techniques and consequently with a minor amount of cultured dinoflagellates. We were then able to incorporate [U-14C]-acetate, the renowned precursor of the polyketide pathway, in several PbTx produced by K. brevis. The specific activities of PbTx-1, -2, -3, and -7, identified by High-Resolution Electrospray Ionization Mass Spectrometer (HRESIMS), were assessed by HPLC-UV and highly sensitive Radio-TLC counting. We demonstrated that working at close to natural concentrations of acetate is a requirement for biosynthetic studies, highlighting the importance of highly sensitive radiolabeling feeding experiments. Quantification of the specific activity of the four, targeted toxins led us to propose that PbTx-1 and PbTx-2 aldehydes originate from oxidation of the primary alcohols of PbTx-7 and PbTx-3, respectively. This approach will open the way for a better comprehension of the metabolic pathways leading to PbTx but also to a better understanding of their regulation by environmental factors.The toxic dinoflagellate  Karenia brevis (K. brevis), a dinoflagellate responsible for recurrent harmful algal blooms in the Gulf of Mexico. PbTxs have been found to affect marine ecosystems, being associated to massive fish kills, bird deaths or marine mammal mortalities, and may induce human food or respiratory intoxication through the bioaccumulation of the toxins by comestible shellfishes or aerosols -acetate as a radiolabeled precursor and this toxin as our main target. We performed five feeding experiments. A first set of experiments  inoculated together with increasing concentrations of [U-14C]-acetate without antibacterial agent. The second set of experiments was performed with addition of antibacterial agents just after the exponential stage followed by feeding of two different concentrations of the same acetate precursor the next day (A and B). These experiments allowed us to assess the effect of the concentration of acetate on the metabolomic profile. The decision to add some antibacterial agents in the medium was made because cell death was observed with higher concentration of acetate. Additionally, quantification of the specific activity of the labeled compounds in experiment B afforded some information on the controversial metabolic pathway of these toxins.PbTx-2 is the most abundant metabolite usually produced by K. brevis NOAA1 strain were measured in each experimental condition.The growth rates of 10k medium, the control culture exhibited a growth rate of 0.29 div·day−1. With an initial concentration of [U-14C]-acetate of 100 kBq·L−1 , the rate was 0.31 div·day−1. This rate decreased to 0.07 div·day−1 when the concentration of acetate was raised to 500 kBq·L−1  and a rapid death of the culture (within three days after inoculation) was observed with a concentration of 1000 kBq·L−1 . As previously reported, the increase in acetate concentration induced a bacterial proliferation and consequently a decrease in the pH from 8.1 to 7.6, which is unsuitable for the algal culture [In the first set of experiments performed in 1 L f culture . Because14C]-acetate, 200 kBq·L−1,  and [U-14C]-acetate, 500 kBq·L−1  respectively, the growth rates were 0.17 ± 0.02; 0.16 ± 0.01 div·day−1. In this case, neither significant change in the pH nor cell death was observed in the culture media, confirming our hypothesis on the bacterial proliferation inducing cell death. The repeatability of the growth curve was rather satisfactory for each experiment, the lag phase ended 12 days after inoculation and the subsequent exponential phase lasted 15 days.In the second set of experiments performed in 2 L volume media where antibiotics penicillin G and streptomycin were added at day 26  and the cultures fed with + Δ + 0.33 ppm). A second purification step on a C18 column was necessary to isolate pure PbTx-2 from the t6 fraction, the first coeluting peak being inactive in the RBA. PbTx-3 was identified in t4 without any further purification (m/z 897.4999 [M + H]+ Δ + 0.49 ppm). On the basis of HRESIMS data, we were also able to identify PbTx-1 (m/z 867.4941 [M + H]+ Δ + 5.9 ppm) and PbTx-7 (m/z 869.5050 [M + H]+ Δ + 0.54 ppm) in t8 and t5’ respectively.PbTx-2 was easily located in fraction t6 by coelution with a commercial standard and additional HRESIMS analysis , other compounds highly incorporated radiolabeled acetate. HRESIMS did not allow the identification of these highly radiolabeled compounds because the detected mass did not correspond to already known metabolites from this species but several explanations can be found for this high incorporation. First, highly radiolabeled compounds, such as t9, t10, and t12, may be intermediates in the biosynthesis of PbTxs. Alternatively, these rather non polar non identified metabolites may originate from other metabolic pathways involving acetate as a precursor like usual fatty acid pathway. Indeed, recent studies evidenced the presence of fatty acid synthases in K. brevis -CH3COONa to have respectively in media A0, B0 and C0, a total radioactivity of 100 (0.026 µm), 500 (0.13 µm) and 1000 (0.26 µm) kBq·L−1 . Cultures were harvested 18 days after inoculation. Growth rates were measured for the exponential stage in div·day−1 between days 5 and 15. Between these two dates the formula for growth rate is:N1 and N2 are the number of cells at day 5 and day 15 after inoculation.. 26 days after inoculation, penicillin-G (53.3 mg) and streptomycin sulfate (55.5 mg) were added in media A and B. The following day, the culture was enriched with [U-14C]-CH3COONa to have respectively, for media A and B, a total radioactivity of 200 (0.052 µm) and 500 (0.13 µm) kBq·L−1. After 72 h, the culture was harvested. Growth rates were measured for the exponential stage in div·day−1 between days 12 and 27.Cultures were grown in 2 L volumes following the same conditions as described in Section 3.1N1 and N2 are the number of cells at day 12 and day 27 after inoculation.We used the same formula as above where in this case, The whole culture was filtered using gravity or mild vacuum pressure <5 kPa) on 1 µm polycarbonate filters . Filters were pooled and extracted with 20 mL methanol into a 50 mL conical Falcon tube  and then stored overnight at –20 °C -acetate into the toxins was monitored by Radio-Thin Layer Chromatography . In a typical experiment, the fractions were deposited on a 20 cm × 20 cm silica plate and radio-analyzed during one hour. For each fraction, 20 µL of the methanolic solution described above were spotted on the plate for the radio-counting. The analysis was followed in real time by Gina Star software and stooped after 1 h of counting. To assess the radioactivity in Bq of the fractions collected after HPLC, a standard curve of [U-14C]-proline was used. The specific activity (SA) was then assessed dividing the radioactivity by the corresponding quantity obtained by HPLC-UV.Incorporation of [U-14C-acetate appears as a powerful method to address the metabolic activity but also some metabolic pathways of toxic dinoflagellates producing polyether derivatives. It allows working at close to natural conditions avoiding changes in the metabolism of these microalgae induced by the presence of large quantity of precursors or antibacterial compounds. We were able to demonstrate that the metabolic activity of brevetoxins started early during the cell growth of Karenia brevis and this result should be taken into account for further feeding experiments. The measurement of the specific activity of the major PbTxs gave important results on the chronology of a key metabolic reaction. The specific activities of both major toxins, PbTx-1 and -2, featuring a key terminal aldehyde and representing both types of brevetoxins, were found to be twice lower than the corresponding alcohols PxTx-7 and PbTx-3. This result implies that PbTx-1 and PbTx-2 should be produced by oxidation of the allylic alcohol of PbTx-7 and PbTx-3 respectively, a result in contradiction with the accepted hypothesis that several brevetoxin congeners are derived from the aldehydes analogues. This result underlines the importance of feeding experiments with radioisotopes to assess the metabolic pathways of these complex toxins but also the chronology of the involved biochemical steps.Several results should be highlighted from our study. First, radiotracing with


Genome-wide sensitivity screens in yeast have been immensely popular following the construction of a collection of deletion mutants of non-essential genes. However, the auxotrophic markers in this collection preclude experiments on minimal growth medium, one of the most informative metabolic environments. Here we present quantitative growth analysis for mutants in all 4,772 non-essential genes from our prototrophic deletion collection across a large set of metabolic conditions.in silico predictions and hitherto unavailable experimental data.The complete collection was grown in environments consisting of one of four possible carbon sources paired with one of seven nitrogen sources, for a total of 28 different well-defined metabolic environments. The relative contributions to mutants' fitness of each carbon and nitrogen source were determined using multivariate statistical methods. The mutant profiling recovered known and novel genes specific to the processing of nutrients and accurately predicted functional relationships, especially for metabolic functions. A benchmark of genome-scale metabolic network modeling is also given to demonstrate the level of agreement between current Saccharomyces cerevisiae and its response to the most basic of environments. While choice of carbon source has the greatest impact on cell growth, specific effects due to nitrogen source and interactions between the nutrients are frequent. We demonstrate utility in characterizing genes of unknown function and illustrate how these data can be integrated with other whole-genome screens to interpret similarities between seemingly diverse perturbation types.These data address a fundamental deficiency in our understanding of the model eukaryote  Saccharomyces cerevisiae due to efforts in the yeast community to assemble a near complete collection of non-essential single-mutant strains .S. cerevisiae deletion collection is composed of uncharacterized ORFs [Approximately 13% of the zed ORFs , 692 of FMP32. The fmp32Δ0 strain displays dramatically decreased fitness under arginine and proline conditions. While the protein product of FMP32 has been detected in highly purified mitochondria [fmp32Δ0 strain was included in our liquid confirmation assay and these sensitivities were confirmed in this independent, small-scale assay  is responsible for the conversion of proline into glutamate for further use as a nitrogen source. Our analysis suggests that FMP32 is similarly involved in the respiratory response under proline, though the reason for its additional sensitivity under arginine remains unclear. These examples show the utility of interactions between genes and simple environments in uncovering the function of both individual uncharacterized genes and genes without a previously observed fitness defect in more complete media.The genes with the highest profile similarity to UT1, PUT3) and mitPUT3[RRF1). PUT3 iJust as gene-gene correlation predicts functional similarities, we expect a high correlation between condition pairs to reflect a substantial overlap in the cellular machinery required to utilize the provided carbon and nitrogen sources. When our matrix of z-scores is hierarchically clustered in both the gene and condition dimensions, a structure clearly driven by carbon sources emerges Figure , as is oFMP32) exhibited an effect in multiple instances of a particular nitrogen source , but without a specific pattern with regard to carbon source (or vice versa). This is expected behavior for genes required for the utilization of a particular carbon/nitrogen source regardless of the context. In order to more formally extract a list of sensitivities for each source of carbon or nitrogen regardless of its partner, we employed a method known as non-negative matrix factorization (NMF) [Further examination of gene and environmental profiles after clustering revealed cases where a gene  ,35 to deGAP1 is transcribed at equal levels in cells grown on urea and glutamate, permease activity in urea grown cells is 100 times higher than glutamate-grown cells [One example of a decomposed signature involves genes that are sensitive when glutamate is chosen as a nitrogen source. These genes are enriched for annotations relating to endocytosis, endosome and vacuole related transport, and retrograde transport  and genes involved in acetyl-CoA biosynthesis that appear to be specifically sensitive under ribose (P < 1.4 × 10−6). As more complex environments are mapped, multivariate statistical techniques will become increasingly important in determining which environmental constituents are actually relevant to which experimental observations, and care should be taken when designing experiments to ensure their successful application .Many mutants (92) appear in both the galactose and ribose signatures, and overlapping GO enrichments in these conditions reveal many of these genes to have known involvement in various aspects of respiration. For example, enrichment for GO terms relating to mitochondrial organization and translation, as well as 'aerobic respiration', appear highly significant in both of these signatures . The strength and specificity of this similarity is not driven by a handful of mutants in the collection, but instead by trends across a much larger set of genes. We speculate that the profile similarity in this case may be due to accumulation of aspartate, which is upstream of homoserine and threonine biosynthesis, and is excreted in part through urea production. Growth on urea in the setting of the respiratory growth of galactose may result in the accumulation of aspartate.Beginning to test the immense space of possible environmental and chemical conditions combined with experiments that have queried the space of genetic perturbations  allows uet al. first apet al., we founet al. P < 10−18[et al. P < 10−9[The idea of comparing environmental and genetic perturbations can be generalized to other genome-wide perturbation data as well. For example, we observe significant correlations between our glutamate signature and a rapamycin sensitivity profile as measured by two different chemical genomic screens . The en P < 10−9. Thus, tHIS3, URA3, LEU2, and MET15. The resource used in this study differs in that URA3, LEU2, and MET15 are in their native genomic locations, with the exception of HIS3, which is provided by Schizosaccharomyces pombe HIS5 under the SGA reporter [The creation of the original yeast deletion collection has had a profound impact on the way in which reverse genetic experiments are performed. Yet despite a staggering number of successful studies, the inherent auxotrophies create a major blind-spot in a fundamental area of cellular function, and previous reviews of the topic have called for the creation and use of standardized prototrophic strains for metabolic experiments . Recentlreporter . WithoutThe use of a genome-wide prototrophic strain collection enables truly informative sensitivity screening in metabolically controlled conditions. This represents a first step in probing how nutrients in the environment jointly affect cellular response with or without additional genetic perturbation. This study demonstrates that much work is yet to be done to understand growth in even simple environments. A solid grasp of the surprisingly complex responses to simple environments will add much needed context to studies done in more complex environments.S. cerevisiae. Central to that goal is a version of the deletion collection that is unhindered by historical auxotrophic requirements.This study has demonstrated the potential of this collection, when screened against simple environments, to uncover phenotypes for hundreds of mutants that are phenotypically normal in standard lab conditions. We believe that the stock of simple experiments that might reveal a phenotype for these mutants has not yet been exhausted and expect that this whole-genome prototrophic collection will be an invaluable resource to the community. The rising number of metabolomics studies, fueled in part by the increasing accuracy of experimental mass-spectrometry, as well as the growing interest in metabolism as central to many common ailments in humans, make it more important than ever to properly design metabolically relevant experiments in the model eukaryote in silico predictions from constraint-based metabolic models, it is far from a fully understood system. Our results show a generally weak ability to predict condition-specific sensitivities, though performance is clearly above a random baseline. The prediction of condition-specific sensitivities is admittedly more difficult than the prediction of sensitivities in general, but it was our estimation that FBA and MoMA would be well suited to approximate our observations given our simple experimental setup. Their only moderate success in doing so demonstrates the current limitations of constraint-based modeling and the difficulty of relating models built from biomass predictions to quantitative growth rate data. There might be several possible reasons for the discrepancy between in silico and in vivo results. First, the success of predicting growth defects hinges on the proper formulation of biomass composition. While a single biomass composition is used for all our simulations, it likely changes across environmental conditions. Future studies could address this issue by measuring the composition of yeast cells under different nutrient settings. A second limitation of purely flux-based models is their inability to make predictions about components that have an indirect effect on metabolism. Consider, for example, the enrichment for transport-related genes whose deletion confers glutamate-specific sensitivities. Their putative role in nutrient sensing and signaling reflects the fact that, despite its constrained nature, the metabolic network operates as part of a much larger and more dynamic network. More generally, the basic constraint-based modeling approaches ignore regulatory mechanisms. Several attempts have been made to bridge this gap and they rely either on 'omic' data to constrain the activity of specific reactions [For example, while central metabolism is unrivaled among cellular processes with respect to our ability to make eactions -44 or oneactions -47. We fP < 6.2 × 10−5). We believe this shift may be explained by fundamental cellular rate limitations. Failure to segregate chromosomes in the midst of even moderate growth  can have very severe consequences, ultimately limiting growth rate, whereas comparatively slow growth  affords additional time for slowly segregating mutants to complete segregation. These mutants grow faster than we expect despite no apparent link between carbon metabolism and chromosome segregation. Thus, growth rates under one condition disclose information about the interplay between a wide variety of cellular subsystems, giving us a readout of the internal cellular state. Similarly, a mutant profile across many environments gives us information about how essential that gene may be in any of those various cellular states, in addition to elucidating any direct role that gene may have in direct utilization of the provided nutrients. Analysis of our growth data recapitulated the role of vesicle trafficking in the regulation of the amino acid permease Gap1, relating growth on glutamate to the drug rapamycin. This broader view of whole-genome screen information then allows for integration of profiles across different perturbation types , and should ultimately aid us in applying knowledge gained in one arena to observations made in another.Central to the understanding of the network as a whole is the idea that a whole-genome screen reveals indirect as well as direct consequences of the perturbation tested. Positive gene-environment interactions under ribose conditions may well illustrate this point. The median z-score for the 166 genes annotated to 'chromosome segregation' in GO is negative for all seven galactose conditions, yet positive for all seven ribose conditions  [MATα can1Δ::STE2pr-SpHIS5 his3Δ1 lyp1Δ0 strain, creating diploids . These were sporulated and successive pinnings on selective media were used to select prototrophic MATa strains carrying each deletion allele. These prototrophic strains were organized into an array of 16 plates including one entire plate of the wild-type strain (hoΔ::KanMX), with additional wild-type replicates in each row and column of every plate . The entire prototrophic collection is available upon request, as is the individual SGA-ready prototroph strain for crossing into other collections.As recently described , the strMinimal growth media were prepared using yeast nitrogen base  with the specified carbon and nitrogen sources. Carbon sources included glucose, galactose, ribose, and glycerol. Nitrogen sources included ammonium, allantoin, arginine, glutamate, glutamine, proline, and urea. Carbon sources were provided at a concentration of 2%; nitrogen sources were 3.8 mM with respect to nitrogen.Sixteen 16 × 24 well plates were grown in 28 chemical conditions for 24 to 48 hours. Plates were scanned on a flatbed transparency scanner at 0, 5, 10, 24 and, in the case of glycerol, 48 hours. Each condition is composed of one carbon source and one nitrogen source. In total, 4,772 mutants were grown, and colony areas were extracted from tiff images by CellProfiler  and precA) and the remaining five replicates were LOWESS smoothed (window size = 50% of available data) and normalized by:Six replicates of the glucose:ammonium combination were merged to form a reference condition, establishing a baseline score for each deletion. The six replicates were first normalized to each other to control for differences in the overall scale of growth rates, then averaged together according to the following procedure. For each array plate (p) the glucose:ammonium replicate with the fewest missing data points was held out , each plate was LOWESS smoothed (window size = 50% of available data) against the constructed glucose:ammonium reference plate, then normalized:In certain cases, a growth rate of NaN was assigned to a colony due to insufficient data being collected by CellProfiler. In an effort to recover any good data, these cases were visually inspected by five researchers operating independently and a vote was taken to determine whether to leave it as missing data (NaN) or assign it a growth rate of 0, indicating that the colony appeared to be correctly plated but non-viable. In total, 1,362 of 2,601 colonies were recovered this way.For each array plate, at each position, a strain-wise standard deviation is calculated across the residuals of the six glucose:ammonium (GA) replicates.Similarly, a plate-wise standard deviation is calculated that accounts for the general growth variation on the plate, separately for each condition. These are then combined, and a z-score measure is calculated for each strain on each experimental plate:These z-scores are an expression of the difference in magnitude and direction between the growth observed at each position of a plate under a given condition from the same position (and hence deletion) under the reference GA model.The plate level spatial smoothing filter is similar to that found in . First, P-values for the experimental z-scores could be calculated. This information allowed the use of Benjamini-Hochberg procedure to establish condition-specific effect thresholds as a function of a desired FDR  divided by the time to saturation. A simple model was favored in order to robustly accommodate drastic differences in curve characteristics between fast growth and slow growth conditions .We adjusted the liquid growth scores by dividing the mean of mutant growth slopes by the mean of wild-type growth slopes in the relevant condition. We further normalized these scores by dividing them by the corresponding adjusted mutant score in glucose:ammonium so they would reflect condition-specific effects, similar to our modified z-score derived from the agar experiment.GO and KEGG annotations were downloaded in January 2011 ,50.S. cerevisiae metabolic models were used for mutant biomass prediction. The Yeast Consensus Reconstruction version 5.35 (Yeast5) [Two (Yeast5)  and iMM9(Yeast5) . Yeast5 Wild-type biomass production flux for each condition was obtained using FBA  in MATLAFBA and MoMA biomass fluxes were correlated with both raw and normalized (z-score) experimental growth rates using the Spearman rank correlation. Predicted biomass fluxes were also normalized for comparison to experimental growth rate z-scores (separately in each condition):Prediction of positive z-scores was also carried out, though performance was generally below random expectation  were removed, as were the columns involving growth on glycerol. We then defined a Coefficient matrix that related Condition rows in the data to their component Sources. This matrix then had C columns and S rows. For example, the glucose-urea column has a 1 in the glucose row and a 1 in the urea row. Our task is then to find a Signatures matrix (Genes × Sources) such that the difference between the Data matrix and the Signatures-Coefficients product is minimized:Growth data were decomposed using a variant of NMF . FollowiCoefficient matrix, we removed all but one glucose:ammonium (glucose:ammonium01) column, removing the same columns in the Data matrix. Traditional NMF would use a multiplicative update algorithm applied to both the Signature and the Coefficient matrix to find the best fit to the data; however, we chose to fix the Coefficient matrix at the initial defined values (0 or 1). This gives each Signature column equal weight and prevents over-fitting caused by the sparsity of the Data matrix and the dramatically different number of non-zero elements from one column to the next. The multiplicative update was applied for 20 iterations, though in practice the results converged in fewer than 10, and repeated trials from different random initializations of the Signature matrix showed the results to be quite stable. Genes were added to the signature list in Additional file To ensure linear independence among the columns of the P-values were Bonferroni corrected to account for the number of terms/pathways tested against.For the comparison to auxotrophic SGA data represented in Figure Figure FBA: flux balance analysis; FDR: false discovery rate; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MoMA: minimization of metabolic adjustment; NMF: non-negative matrix factorization; ORF: open reading frame; SGA: synthetic genetic array.The authors declare that they have no competing interests.DCH, OGT, CLM, and AAC conceived the study. DCH and AAC performed the laboratory experiments. CN provided experimental resources for the liquid growth assay. BJV, CP, and TS analyzed the raw data. BJV, CP, and EK designed and performed further computational analysis and experiments. BJV, DCH, BS, BP, CLM, and AAC wrote the manuscript. All authors read and approved the final manuscript.Raw growth rates for all 28 conditions and all 4,772 strains.Click here for fileRaw growth rates for 701 wild-type replicates in all conditions.Click here for filez-score data for 21 conditions.Click here for fileFDR 10% and 20% thresholds for z-score data.Click here for fileA list of 565 galactose-sensitive genes as well as overlap details between this set and three other full genome studies using the auxotrophic collection on galactose.Click here for fileSummary of fast/slow prediction accuracy of FBA models (sheets 1 and 2). Also contains KEGG pathway enrichments for sets of genes predicted to be sensitive but not observed and vice versa (sheets 3 and 4).Click here for fileList of genes in each signature and GO enrichments.Click here for file


Arabidopsis thaliana. Applying a method for kinetic parameter normalization, several millions of probable enzyme kinetic parameter constellations were simulated and evaluated with regard to the stability information of the metabolic homeostasis. Information about the stability of the metabolic steady state was derived from real parts of eigenvalues of Jacobian matrices. Our results provide evidence for a differential stabilizing contribution of different subcellular compartments. We could identify stabilizing and destabilizing network components which we could classify according to their subcellular localization. The findings prove that a highly dynamic interplay between intracellular compartments is preliminary for an efficient stabilization of a metabolic homeostasis after environmental perturbation. Further, our results provide evidence that feedback-inhibition originating from the cytosol and plastid seem to stabilize the sucrose homeostasis more efficiently than vacuolar control. In summary, our results indicate stabilizing and destabilizing network components in context of their subcellular organization.Biochemical regulation in compartmentalized metabolic networks is highly complex and non-intuitive. This is particularly true for cells of higher plants showing one of the most compartmentalized cellular structures across all kingdoms of life. The interpretation and testable hypothesis generation from experimental data on such complex systems is a challenging step in biological research and biotechnological applications. While it is known that subcellular compartments provide defined reaction spaces within a cell allowing for the tight coordination of complex biochemical reaction sequences, its role in the coordination of metabolic signals during metabolic reprogramming due to environmental fluctuations is less clear. In the present study, we numerically analysed the effects of environmental fluctuations in a subcellular metabolic network with regard to the stability of an experimentally observed steady state in the genetic model plant The online version of this article (doi:10.1007/s12064-016-0225-y) contains supplementary material, which is available to authorized users. A characteristic feature of eukaryotic cells is a compartmentalized subcellular structure resulting in numerous cellular reaction environments. Due to this compartmentalization, various biochemical reaction conditions are established in a cell enabling highly coordinated and tightly regulated reactions and reaction sequences. Particularly, such highly compartmentalized subcellular structures are a characteristic feature of plant cells Lunn . The firAs sessile organisms, plants have to cope with fast and fluctuating changes in environmental conditions, e.g. a sudden increase/decrease in light intensity or temperature. To prevent a disbalance of primary and secondary photosynthetic reactions, which could easily cause the generation of reactive oxygen species (ROS) and the irreversible damage of membrane systems and cells, a new and stable metabolic homeostasis has to be established within a relatively short time period. In biochemical networks, the metabolic homeostasis results from a concerted orchestration of enzymatic activities which determine metabolic fluxes, i.e. rates of metabolic interconversion and membrane transport. The coordinate reprogramming of a metabolic homeostasis, which finally results in a new acclimated stable homeostasis, is a complex process comprising numerous enzymatic reactions and regulatory molecular circuits. Decades ago it was outlined that by linear approximation of enzymatic reaction chains, i.e. the assumption that enzyme velocities linearly depend on substrate concentrations, analytical solutions can be obtained for metabolite concentrations and metabolic fluxes at steady state conditions  were interpreted as a pool of all involved membrane transporters, even though some of these transporters might still have to be characterized . Environmental fluctuations α, e.g. occurring due to sudden changes in light intensity or temperature, directly affected the flux F of v1 as a constant scalar (v1 = αF). The pool of plastidial sugar phosphates was substrate for two reactions: v2, the export to the cytosol (P-SugCyt), catalysed by membrane transporters, and v10, representing the intraplastidial conversion to starch (StaPla). Cytosolic sucrose, SucCyt, was the substrate for the export reaction via T4 supplying the pool of extracellular sucrose, SucExt. Reaction v5 indicated the transport of sucrose from the cytosol (SucCyt) to the vacuole (SucVac). In both compartments, hydrolytic cleavage of sucrose via invertase yielded free hexoses [v6 (HexVac) and v7 (HexCyt)]. Vacuolar hexose transport to the cytosol was described via transport process T8 resulting in a circular structure. Degradation of plastidial starch (StaPla) to hexose equivalents and their transport to the cytosol was described by transport T11 and reaction v11. Reaction v9 described the phosphorylation of cytosolic hexoses via hexokinase activity . Finally, reaction v12 which was coupled to transport T12, described the transport of apoplastic sucrose, SucExt, to sink organs (SinkExt).Based on approaches of mathematical modelling in previous publications , steady state concentrations of different metabolites  and their fluxes νj. Experimentally determined steady state concentrations of metabolites, which are provided in the supplements (Supplementary Table S1), were derived from previous studies (Nägele and Heyer Ext), which were not experimentally determined in these studies, were set to a low (0.01 mM), medium (15 mM) and a high (45 mM) concentration when compared to the cytosolic sucrose concentration (15 mM). With these settings, three possible scenarios were simulated: (1) a sucrose concentration gradient from the intracellular to the extracellular space (SucExt low), (2) no sucrose gradient (SucExt medium), and (3) a sucrose concentration gradient from the extracellular to the intracellular space (SucExt high). To define a steady state equilibrium, the input flux v1 and output flux v4 were set to an equal value (αF). The flux F was set to be constant (F = 1). For simulation of random environmental fluctuations and the response of the system, alpha (α) and the proportion characters beta (β) and gamma (γ), were randomly varied in the interval . While β quantified the relative proportion of carbon flux from chloroplast to cytosol, γ quantified the proportion of carbon flux from cytosol to vacuole. Based on the SKM approach  of the considered metabolic system, normalized to steady-state fluxes ν(c0) and metabolite concentrations ci0, . A detailed explanation of these entries in context of enzyme kinetics, such as Michaelis–Menten kinetics, was provided previously (Reznik and Segrè A non-zero element Λ and θ matrices derived in the present study is provided in the Supplements (Supplementary files S2 and S3) while a short and very fundamental explanation of eigenvalues in context of a differential equation is provided in the following paragraph.A detailed and explicit formulation of the J characterize a metabolic steady state with regard to its stability. The steady state concentration c0,i of a metabolite ci, is related to the time-dependent concentration ci(t) by a fluctuation term Eigenvalues of the Jacobian i(t) Eq. :4\documeThe first element of a Taylor expansion reveals the linearization around the considered steady state Eq. :5\documeC represents constants which depend on the initial fluctuation conditions. The complex number λi represents the eigenvalues of the Jacobian matrix J  < 0, the fluctuation term λi) > 0 results in an exponential increase (instability). While the imaginary part of λi, im(λi), is necessary to exactly classify the local dynamics and to differentiate a node from a focus or a saddle . To simulate different activation and inhibition scenarios, the θ matrix was adapted accordingly. In general, for an activation, the corresponding 6 iterations were calculated to reduce the probability of numerical coincidences. For reactions v2 to v12, the enzyme-substrate interaction was described by entries in the elasticity matrix θ indicated by the subscript index . Elasticities were chosen randomly in the interval of  for every iteration process to reveal the system behaviour over all 106 iterations. The source code which was applied for calculations is provided in the supplements (Supplementary file S5).Stability characteristics of steady states were evaluated by the maximum real part of the eigenvalue  or doubled .In the next step, instances of metabolic activation and feedback-inhibition were incorporated into the model, belonging to the central cytosolic carbohydrate metabolism as described previously in a simplified model structure  by extracellular sucrose , the combination of cytosolic and plastidial feedback-inhibition was found to yield a stable system Λ derived for the metabolic structure provided in Fig. Matrix θ derived for the presented metabolic structures (PDF 195 kb)Matrix Detailed Information about eigenvalue real part distributions (XLSX 35 kb)® script for calculation of eigenvalues (ZIP 2 kb)Matlaba) were performed 106 times for low concentration of extracellular/apoplastic sucrose concentrations and for F = 0.5 (b-e) and F = 2 (f-i). Steps of metabolic activation are indicated by grey filled circles, steps of inhibition are indicated by grey filled diamonds and dashed lines. Particular settings in the θ matrix are indicated within the single histograms (TIFF 2445 kb)Histograms of maximum eigenvalue real parts with cytosolic activation and inhibition under varying steady state fluxes. Calculations for the shown model configuration (Below is the link to the electronic supplementary material.


Saccharomyces cerevisiae glycolysis pathway. Metabolic fluxes were quantified by the COPASI tool using the kinetic models of Hynne and Teusink at varied concentrations of external glucose. The enzyme sequences were taken from the UniProtKB and the average amino acid (AA) properties were computed using the set of Georgiev's uncorrelated scales that satisfy the VARIMAX criterion and specific AA indices that show the highest correlations with those. Multiple linear regressions  were found between the values of metabolic fluxes and the selected sets of the average AA properties. The hydrophobicity, α-helicity, and net charge were pointed out as the most influential characteristics of the sequences. The results provide an evidence that metabolic fluxes of the yeast glycolysis pathway are closely related to certain physical-chemical properties of relevant enzymes and support the view on the interdependence of catalytic, binding, and structural AA residues to ensure the efficiency of biocatalysts and, hence, physiologically adequate metabolic processes.Metabolic fluxes are key parameters of metabolic pathways being closely related to the kinetic properties of enzymes, thereby could be dependent on. This study examines possible relationships between the metabolic fluxes and the physical-chemical/structural features of enzymes from the yeast  Saccharomyces cerevisiae glycolytic enzymes and the AAC of corresponding sequences has been found [The general concepts and methods of systems biology become increasingly important in modern microbiology research. In terms of “systems microbiology,” this approach allows to analyze and describe as a whole the molecular interactions that occur within a microbial cell or community –3. The cen found , as wellen found . At the en found .However, the possible solutions have been defined as rather difficult task, arising from the sequence metrics problems , 15. In  Saccharomyces cerevisiae glycolysis pathway and the physical-chemical/structural features of enzyme sequences.Therefore, following our previous line of research, the goal of the present study is to test the possible relationship (QSAM) between the flux distribution through the yeast Saccharomyces cerevisiae glycolysis pathway: low-affinity glucose transporter , hexokinase , glucose-6-phosphate isomerase , 6-phosphofructo-2-kinase , fructose-biphosphate aldolase , triose-phosphate isomerase , glyceraldehyde-3-phosphate dehydrogenase , 3-phosphoglycerate kinase , phosphoglycerate mutase , enolase , pyruvate kinase , pyruvate decarboxylase , and alcohol dehydrogenase , as well as the main branches that include glycogen synthase  and glycerol-3- phosphate dehydrogenase  together with enzymes involved into the turnover of ATP: plasma membrane ATPase 1 , adenylate kinase 1 .The data set consisted of the amino acid (AA) sequences, representing the enzymes/carriers for the core reactions of the yeasthttp://www.uniprot.org) database under accession numbers as indicated in the brackets above. The AA composition (frequencies of AA occurrence) of sequences (AAC) was computed using ExPASy/ProtParam (http://web.expasy.org/protparam/) tool.The protein AA sequences were taken from the UniProtKB ( Saccharomyces cerevisiae glycolysis pathway were estimated using the kinetic models of Hynne [http://www.ebi.ac.uk/biomodels/)—BIOMD0000000061 and BIOMD0000000064, respectively. Simulation experiments were performed for both models using the COmplex PAthway Simulator tool  at perturbed initial concentrations of external glucose .Metabolic fluxes within the yeastof Hynne  and Teusof Hynne  from thePave(i) is the average AA property for each sequence and P(j) is the property value for jth residue and the summation over N, the total number of residues in a protein. The transformation of sequences into fixed-size numerical feature vectors was performed in two steps. First, the average AA property for each sequence was estimated using the generalized numerical scores in respect of individual AA [http://dx.doi.org/10.1155/2014/817102. Second, the average AA property for each sequence was computed using the narrower sets of specific AA indices from generalized AA property groups with a highest correlation to the VARIMAX-derived scales [The enzyme AAC was converted into a feature-based numerical representation using the average AA property for each sequence and computed according to the standard formula  (see 1)1):(1)Paidual AA . Appropridual AA . This led scales . The relThe accession numbers (AAindex database), description, and designation of specific AA indices which were used for this purpose are represented in R-square value besides keeping the variance inflation factor (VIF) below the threshold value of 3.3 [R-square criterion of regression models. This process was repeated to obtain four-variable and larger models until no variables could increase the adjusted R-square value.The data representing metabolic fluxes, as the dependent variables and the sets of numerical property vectors for respective enzymes, as the independent variables  were processed by correlation analysis (parametric and nonparametric) using the Statgraphics Plus  and SPSS 11.0 for Windows  and subjected to the multiple linear regression analysis using the same software. Explanatory variables in the models were subsequently checked by stepwise forward selection procedures thus finding the significant one-variable models as well as significant two-variable models to arrange all the variables in groups of 2 at a time for each model. The best three-variable models were formed by adding another variable one by one from the remaining variables, and the variables that yielded the greatest increase in the adjusted e of 3.3  or, in eF-test for analysis of variance (ANOVA) was employed to evaluate the statistical significance of regression models and Student's t-test was used to check the significance of regression coefficients. The leave-one-out cross-validation (LOOCV) procedure was used to validate developed regression models [R-square values.In addition, the corrected Akaike's (AICc) information criterion  was usedn models . The linP values < 0.05 were considered to be statistically significant for parametric and nonparametric tests.The  Saccharomyces cerevisiae glycolysis pathway and the values of individual average amino acid (AA) properties for respective enzymes expressed according to VARIMAX scales [Statistical analysis of the data set  revealed a number of significant pair correlations  between X scales  and, theR-square values  caused a marked decrease in actual R-square levels, as well as an increase of AIC c, which indicates a certain decline in the quality of extended models , and the measure of linker propensity (group 5) [Thus, in the present case seven (model I) or eight (model II) variables proved to be adequate to provide statistically robust multiple linear regression models linking the values of metabolic fluxes predicted by different kinetic models , 21 withgroup 5) .R-square values of corresponding Ridge regression, as described above, led to statistically robust multiple linear regressions  of the metabolic fluxes estimated by kinetic models against those predicted by regression models Tables . The higs Tables  adequateR-square values (Tables P < 0.00001). Besides, the observed values of the variance inflation factor (VIF)   showed o) Tables  and 3 in Saccharomyces cerevisiae glycolysis pathway appear as closely related to the sequence-derived properties of implicated enzymes.The obtained results indicate that the metabolic fluxes determined by Hynne's and Teusink's full-scale models for the yeastThe relevant multivariate regression models Tables  and 3 shOn the other hand, such an approach meets the general lines for the multiscale nature and, consequently, the scale-space representation of real-world objects , which iDifferent sets of the predictor variables in the regression models Tables  and 3, aThis study in the most direct way continues our previous research , 12 therThe results thus provide further evidence that the enzyme operation and hence the metabolic fluxes are directly dependent on the general physical-chemical and structural properties of the full enzyme sequences. Therefore, it might be useful for structure/function studies to look more beyond the active centre composition of the enzyme but carefully assess the overall physical and chemical properties of biocatalysts. The more so that the modest success of creating artificial enzymes also points to currently unknown, probably crucial, parameters that could significantly affect enzyme catalysis . In part Saccharomyces cerevisiae kinetic model [It is clearly understood that the estimates of metabolic fluxes currently obtained by the kinetic models represent an approximation, albeit realistic enough, of their “true” values which could involve multiple regulatory mechanisms including gene expression and posttranslational modification , 25, 35.The obtained sequence-activity relationships in the form of quantitative models (QSAM) will allow to assess the contribution of individual AA properties to the overall compositional features of proteins and thus to specify potential targets within enzyme sequences in order to attain a purposeful modification of biocatalysts and, hence, the metabolic fluxes in microorganisms, particularly if there is a need to include or replace  any additional enzyme currently not represented in a given metabolic pathway, which is essential for metabolic engineering and synthetic biology.Supplementary information 1:Table S1-1: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average amino acid (AA) property for each enzyme sequence .Table S1-2: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence .Table S1-3: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence .Table S1-4: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence .Supplementary information 2:Figure S2: The linear plots for the metabolic fluxes of the yeast Saccharomyces cerevisiae glycolysis pathway estimated by kinetic models against those predicted by linear regression models I-IV and by the leave-one-out cross-validation (LOOCV) of the models.Table S2-1: The variance analysis of the regression models.Table S2-2: Standard errors and confidence intervals for the linear regression models.Supplementary information 3:Table S3: Elements and the statistical indices for multiple linear regression model which links the values of metabolic fluxes and the average AA properties of the yeast Saccharomyces cerevisiae enzyme sequences expressed according to the specific AA indices.Figure S3: The linear plot for the metabolic fluxes of the yeast Saccharomyces cerevisiae glycolysis pathway estimated by kinetic models against those predicted by linear regression model.


Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic  Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment, which wipes out the normal intestinal bacterial community. We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C. difficile infection. We use this model to identify bacteria that may inhibit C. difficile growth. Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C. difficile growth. This result suggests that B. intestinihominis could potentially be used as a probiotic to treat or prevent C. difficile infection.The community of bacteria that live in our intestines  is important to normal intestinal function, and destruction of this community has a causative role in diseases including obesity, diabetes, and even neurological disorders.  The achieved bacterial density—area under the growth curve (AUC)—in a culture was calculated by integrating over the growth curve in each experiment using the “trapz” function in Matlab. It can be thought of as representing the total biomass produced over time. The simply additive null model was calculated by fitting a Lotka-Volterra model [B. intestihominis and C. difficile. The null model of co-culture (assuming zero interaction between species) was simulated by using the parameters from single culture, and summing the predicted OD870 values. chamber . Single  chamber . Maximumra model  to the shttps://bitbucket.org/gutmicrobiomepaper/microbiomenetworkmodelpaper/wiki/Home.All scripts used to analyze the data are available at To capture the dynamics of inter-genus interactions in the intestinal tract we employed a pipeline  which trLachnospiraceae nodes and Barnesiella is strongly influenced by clindamycin whereas the other subnetwork is largely independent of the first, except for the single edge between Barnesiella and C. difficile  in the clindamycin treatment group, but they do not appear in the final states of any of the treatment groups (see Firmicutes (Lachnospiraceae) and Bacteroidetes (Barnesiella) are devastated by antibiotic administration [C. difficile colitis: loss of normal gut flora, which normally suppresses opportunistic infection (clindamycin cluster), and the presence of C. difficile at a minimum inoculum (C. difficile cluster) [Barnesiella and C. difficile.Boolean rules  were infifficile . In factoups see . This nestration ,52. Furtcluster) ,53. The Barnesiella on C. difficile is in agreement with recently published findings in which Barnesiella was strongly correlated with C. difficile clearance [Barnesiella as an inhibitor of another pathogen (vancomycin-resistant Enterococci (VRE)) has been shown in mice [Barnesiella and Enterococcus  to determine all the possible steady states of the microbiome network model. In a 12 node network, there are 212 possible network states. We employed model simulations using the synchronous updating scheme to visit all possible network states and identify all fixed points of the model. Exploration of the steady states of this network reveals 23 possible fixed point attractors  independent of the clindamycin subnetwork. The third grouping has the clindamycin treated steady state (Attractor 7) and genera in the C. difficile subnetwork that can survive in the presence of the clindamycin. Group 4 contains the clindamycin plus C. difficile steady state (Attractor 12) and its subsets in which one or both of the source nodes Mollicutes and Enterobacteriaceae are absent. Group 5 contains attractors in which clindamycin is absent and C. difficile is present. Even if clindamycin is absent, our model suggests that C. difficile can thrive if Lachnospiraceae and Barnesiella are absent, i.e. these states represent a clindamycin-independent loss of Lachnospiraceae and Barnesiella. Lastly, group 6 attractors have both clindamycin and C. difficile as OFF. Blautia and Enterococcus are always abundant in these attractors. Indeed, because of the mutual activation between Blautia and Enterococcus they always appear together. Attractors in this group may also include the abundance (ON state) of the source nodes Mollicutes and Enterobacteriaceae.The attractor landscape can be divided into six groups based on abundance patterns they share . Group 1C. difficile treatment after clindamycin treatment was removed. Thus, we considered the clindamycin-treated steady state (Attractor 7 in C. difficile treated steady state (Attractor 12) as initial conditions and assumed that clindamycin treatment was stopped. Our simulations, employing asynchronous update (see Methods), indicate that for both initial conditions, only the state of clindamycin changes after the treatment is stopped; these steady states become Attractor 1 and Attractor 19, respectively .We next explored the perturbation of genera in the gut microbiome network model. We considered the clinically relevant question of which perturbations might alter the microbiome steady states produced by clindamycin or clindamycin+ectively . In otheectively , top rowdy state . From thEnterobacteriaceae contained the most reactions on average (1335), while those from Mollicutes contained the least (485) (Barnesiella and Enterococcus reconstructions contained the most unique reactions (Lachnospiraceae and Barnesiella had the next highest degree of overlap (424 reactions). Mollicutes and Coprobacillus had the least degree of overlap (363 reactions) (Species-level reconstructions from the genus st (485) . The Bareactions  and, inteactions . Lachnosactions) . Note thC. difficile is highly enriched for reactions in cyanamino acid metabolism compared to all other genera. Lipopolysaccharide (LPS) biosynthesis and cyanoamino acid metabolism subsystems are differentially enriched between C. difficile and both Barnesiella and Lachnospiraceae. Between Barnesiella and Enterococcus, Barnesiella is more highly enriched for d-glutamine and d-glutamate metabolism, pantothenate and CoA biosynthesis, LPS biosynthesis. With respect to Enterococcus, Barnesiella is less highly enriched in pyrimidine metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis.Enrichment analysis was performed for the 99 unique subsystem annotations that were observed in the community. 22 subsystems displayed interesting enrichment patterns with respect to the structure of the interaction network . The subLachnospiraceae genera were treated as metabolically identical, and the “Other” group was excluded. We grouped pairs of genera into five groups based on being connected by a positive or negative edge, a negative or positive path (meaning an indirect relationship), or no path. A positive relationship was found between competition score and edge type in the interaction network (i.e. positive edges tend to have a higher competition score), which was not statistically significant, perhaps due to the small sample size  . The two um test) . The mutum test) . All paium test) . Barnesicile see . Barnesi network .The positive relationship between edge type and competition score suggests that more metabolic similarity between genera tends to foster positive interaction. The converse is also true, where less metabolic similarity tends to foster negative interactions . Here, “Barnesiellaintestinihominis was chosen as a representative species for the genus Barnesiella for the in vitro experiments. C. difficile grew more slowly in B. intestinihominis spent media   (C. difficile area under the growth curve (AUC), a measure of the achieved bacterial density over the experiment, was not statistically different between growth in fresh media and B. intestinihominis spent media . However, the co-culture displayed a much lower AUC than expected under a null model of interaction (in which the two species do not interact) (um test) . The co-um test) . C. diffnteract) . Examininteract) .Here we have developed a novel strategy for generating a dynamic model of gut microbiota composition by inferring relationships from time series metagenomic data . To our C. difficile infection on gut microbial community structure. We generate a microbial interaction network and dynamic model based on time-series data from a population of mice. We validate a key edge in this interaction network between Barnesiella and C. difficile through an in vitro experiment. Consistent with the literature, our model affirms that solely inoculating a healthy microbiome with C. difficile is insufficient to disrupt the healthy intestinal tract microbiome. Additionally, our results demonstrate that clindamycin treatment has a tremendous effect on the microbiome, greatly reducing many microbial genera, and that during the time C. difficile is present, a certain subset of bacteria come to dominate the microbiome  is unknown, but unlikely given the AUC data.We experimentally validated a key edge in the interaction network, and showed that nificant . The facC. difficile AUC did not significantly change between fresh media and spent media  to a state highly similar to the original healthy state (loss of C. difficile). This result is particularly interesting from a therapeutic design standpoint. In this case, the model results indicate that Barnesiella may serve as an effective probiotic. Model-driven analysis can be used to identify candidate organisms for probiotic treatments. Recent work by Buffie et al. performed a proof-of-concept study in which they used statistical models to identify candidate probiotic organisms, which were then tested on a murine model of C. difficile infection [et al., the integration of computational models in probiotic design has been shown to be a feasible, effective approach. Improved tools, such as the perturbation analysis presented here, will surely accelerate the probiotic design process and shorten the path to the clinic.Computational perturbation analysis showed that forced overabundance of nfection . This monfection . The modGenome-scale metabolic network reconstructions can be used to estimate the interactions between microbes in a complex community based purely on genome sequence data. Our use of genus-level metabolic network reconstructions  may not reflect the unique, species-level interactions and heterogeneity within a community. This higher-level model will only capture broad trends and the possible extent of metabolic capacity within a genus. Furthermore, the draft status of these models precludes the effective application of flux balance analysis (FBA) to estimate interactions among genera. This is due to the established lack of precision in draft reconstructions in predictions of growth rates and substrate utilization patterns , and theThe application of competition scores demonstrated here  could poC. difficile, Barnesiella showed the second lowest , but rather to some non-metabolic mechanism. The similarity in reaction content between Barnesiella and Enterococcus indicates similar network structure (Enterococcus does not inhibit C. difficile in the inferred interaction network (Barnesiella (65 unique reactions) and Enterococcus (36 unique reactions) are hints at the mechanism of interaction, or metabolism does not play a significant role in C. difficile inhibition in the environment of the gut. For example, enrichment analysis showed that that, with respect to Enterococcus, Barnesiella is more highly enriched for d-glutamine and d-glutamate metabolism, pantothenate and CoA biosynthesis and LPS biosynthesis. With respect to Enterococcus, Barnesiella is less enriched in pyrimidine metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis. The possible role of LPS is discussed further on. The possible roles of these other metabolic pathways in C. difficile inhibition is unclear.Understanding this relationship between competition score and interaction type leads to the conclusion that negative interactions are probably not caused by metabolic competition. Of all the genus competition scores with d lowest . In othetructure , and yet network . Either Barnesiella  may combat pathogen overgrowth through non-metabolic mechanisms. As a first step, it has been shown that VRE can grow in sterile murine cecal contents—indicating the presence of sufficient nutrition to support VRE—but is inhibited in saline-treated cecal contents—indicating that live flora are needed to suppress VRE growth, and that this suppression is not through nutrient sequestration [B. intestinihominis has been demonstrated to prevent and cure VRE infection in mice [C. difficile infection in mice [Barnesiella plays a key role in pathogen inhibition, and pathogen inhibition can be caused by mechanisms other than nutrient competition.There is experimental evidence that stration . Further in mice , and is  in mice . Clearlyin vitro that B. intestinihominis can inhibit C. difficile growth rate  may prevent pathogen overgrowth by stimulation of a host antimicrobial response [Barnesiella can activate host toll-like receptor TLR signaling, which activates host antimicrobial peptide production. For example, LPS and flagellin have been shown to stimulate the host innate immune response through toll-like receptor (TLR) signaling and production of bactericidal lectins [Barnesiella shows enrichment for LPS biosynthesis pathways Barnesiel lectins ,69. Barnpathways . Howevernesiella . An indinesiella . Regardlin silico perturbation, which leads to fast candidate selection for probiotic design. We further describe the use of genome-scale metabolic network reconstructions to explore the metabolic potential attributed to community members, and to estimate metabolic competition and cooperation between members of the microbiome community. Analysis of genome-scale metabolic network reconstructions indicates that Barnesiella likely inhibits C. difficile through some non-metabolic mechanism. We present empirical in vitro evidence that B. intestinihominis does in fact inhibit C. difficile growth, likely by a non-metabolic mechanism, and our findings are in good agreement with published results. We present this work as a demonstration of the use of dynamic Boolean models and genome-scale metabolic reconstructions to explore the structure, dynamics, and mechanistic underpinnings of complex microbial communities.We demonstrate that dynamic Boolean models capture key microbial interactions and dynamics from time-series abundance data in a murine microbiome. We show that this computational approach enables exhaustive S1 FigC. difficile (at t = 0 days) and did not undergo observable microbial changes, Population 2 received a single dose of clindamycin (at t = -1 days), and Population 3 received a single dose of clindamycin (at t = -1 days) and, on the following day, was inoculated with C. difficile spores (at t = 0 days). Genus abundances were measured at 0, 2, 3, 4, 5, 6, 7, 9, 12, 13, 16, and 23 days; however, not all samples had measurements at all the time points. B) Cubic spline interpolation of data points was performed such that all the same time point measurements of bacterial abundance occurred in all samples and that single day intervals were present in all datasets.A) Genera abundance information for the nine samples. The “Healthy” population received spores of (TIF)Click here for additional data file.S2 FigIterative k-means binarization was completed on all the samples 1000 times and average binarization is shown for each genus at each time point in each of the nine samples. If a node (genus) is binarized as 0 (OFF) at a time step, then it is colored blue, and if a node (genus) is binarized as 1 (ON) at a time step, then it is colored yellow. This figure represents the average of 1000 replicates of IKM binarization. Intermediate cell colors represent cases where a genus abundance at a time point was binarized to 1 (ON) in a fraction of the replicates.(TIFF)Click here for additional data file.S3 FigThe most probable binarized state of each genus at each time point. If the average genus abundance binarization  was grea(TIFF)Click here for additional data file.S4 FigC. difficile steady state. These three steady states are directly corroborated by experimental metagenomic data.There are 23 predicted steady states in the Boolean model of the gut microbiome. Each attractor is a column in the heatmap and is made up of the state of each genus in the network model (rows). Each genus can be present above an activity threshold  or below an activity threshold . The steady states in the model are grouped based on their similarities to other steady states in the same group. The first steady state of group 2 (Attractor 2) is the healthy steady state, the first steady state of group 3 (Attractor 7) is the clindamycin treated steady state, and the first steady state of group 4 (Attractor 12) is the clindamycin + (TIFF)Click here for additional data file.S5 FigBarnesiella and C. difficile corresponds to the highest mutualism score.A) Competition score values for all classes of paths through the network, including direct edges, directed paths, and no directed path. A positive relationship was found between competition score and direct edge type in the dynamic network (self-edges were excluded), which was not statistically significant, perhaps due to the small sample size , but is worthy of note. B) Mutualism score values for all classes of paths through the network, including direct edges, directed paths, and no directed path. C) Competition and mutualism score plot for the interaction edges in the network. All the interactions reflect moderate to high competition scores and relatively low mutualism scores. All the interactions have a higher competition score than mutualism score. The two negative interactions (red circles) do not have higher competition scores, nor lower mutualism scores, than the positive interactions. In fact, the negative interaction between (TIF)Click here for additional data file.S1 TableThe ruleset inferred from metagenomic sequencing information using Boolnet.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableAkkermansia is represented by a single species-level reconstruction, while several genera are represented by 10 species-level reconstructions. The average network overlap within a genus refers to the average number of shared reactions between any two pairs of species within the genus. Similarly, the average fraction of unique reactions refers to the average subset of reactions in a given species that are unique within the genus.In this table we characterize the genus-level metabolic network reconstructions. Average model size refers to the average number of reactions in the component species reconstructions within each genus. (DOCX)Click here for additional data file.S4 TableBarnesiella contains 167 reactions that the reconstruction for C. difficile does not. Conversely, the reconstruction for C. difficile only contains 30 unique reactions that the reconstruction for Blautia does not already contain.The genus in each row has n reactions that the genus in the columns do not have. For example, the genus-level reconstruction for (DOCX)Click here for additional data file.S5 TableThe upper portion of the table contains the number of shared reaction content between all genus-level metabolic network reconstructions.(DOCX)Click here for additional data file.