Mycoplasma species: Mycoplasma flocculare, Mycoplasma hyopneumoniae and Mycoplasma hyorhinis. While colonization by M. flocculare is virtually asymptomatic, M. hyopneumoniae is the causative agent of enzootic pneumonia and M. hyorhinis is present in cases of pneumonia, polyserositis and arthritis. The genomic resemblance among these three Mycoplasma species combined with their different levels of pathogenicity is an indication that they have unknown mechanisms of virulence and differential expression, as for most mycoplasmas.The respiratory tract of swine is colonized by several bacteria among which are three in silico.In this work, we performed whole-genome metabolic network reconstructions for these three mycoplasmas. Cultivation tests and metabolomic experiments through nuclear magnetic resonance spectroscopy (NMR) were also performed to acquire experimental data and further refine the models reconstructed M. hyorhinis, which in turn may also explain why this species is a widely contaminant in cell cultures. In addition, the myo-inositol catabolism is exclusive to M. hyopneumoniae and may be an important trait for virulence. However, the most important difference seems to be related to glycerol conversion to dihydroxyacetone-phosphate, which produces toxic hydrogen peroxide. This activity, missing only in M. flocculare, may be directly involved in cytotoxicity, as already described for two lung pathogenic mycoplasmas, namely Mycoplasma pneumoniae in human and Mycoplasma mycoides subsp. mycoides in ruminants. Metabolomic data suggest that even though these mycoplasmas are extremely similar in terms of genome and metabolism, distinct products and reaction rates may be the result of differential expression throughout the species.Even though the refined models have similar metabolic capabilities, interesting differences include a wider range of carbohydrate uptake in M. flocculare if compared to the highly pathogenic M. hyopneumoniae may be related to its incapacity to produce cytotoxic hydrogen peroxide. Moreover, the ability of M. hyorhinis to grow in diverse sites and even in different hosts may be a reflection of its enhanced and wider carbohydrate uptake. Altogether, the metabolic differences highlighted in silico and in vitro provide important insights to the different levels of pathogenicity observed in each of the studied species.We were able to infer from the reconstructed networks that the lack of pathogenicity of The online version of this article (doi:10.1186/s12864-016-2644-z) contains supplementary material, which is available to authorized users. Mycoplasma species: Mycoplasma flocculare, Mycoplasma hyopneumoniae, and Mycoplasma hyorhinis [Mycoplasma species have been isolated from the respiratory tract of both healthy and diseased pigs [The respiratory tract of swine is colonized by several bacteria, among which are three common yorhinis –3. Even sed pigs –6.M. flocculare is usually described as a commensal bacterium [M. hyopneumoniae and M. hyorhinis are considered pathogenic. Enzootic pneumonia, caused by M. hyopneumoniae, is widespread in pig populations and is considered a major cause of economic loss in the pig industry [M. hyorhinis is frequently present in cases of polyserositis and arthritis and M. flocculare has high prevalence in swine herds worldwide, but up to date, no disease has been per se associated with this species [Mycoplasma hyosynoviae, the primary agent of non-purulent arthritis, can occasionally colonize in the lower respiratory tract when pneumonic lesions pre-exist [While acterium , M. hyopindustry ; M. hyor species . In addire-exist .M. hyopneumoniae is by far the most costly disease in pig industry, and this bacteria is usually seen as an essential component to the successful establishment of a pathogenic community in the host [M. hyopneumoniae infections take longer to cause lesions and to be successfully eliminated than infections from other pathogens [Besides mycoplasmal pneumonia, the Porcine Respiratory Disease Complex (PRDC) has emerged as an economically signigicant respiratory disorder characterized by the slow growth, fever, cough, loss of appetite, lethargy and dyspnea in pigs , 11. Evethe host . Also, Mathogens .M. hyopneumoniae, M. flocculare and M. hyorhinis in close proximity within the hyopneumoniae clade by phylogenomic analysis [While mycoplasmal diseases in swine have been extensively studied, their causative agents have not been explored from a mathematical and computational point of view, mostly because their genome sequences were not available until recently –21. Althanalysis , which canalysis , it is nM. hyopneumoniae and M. hyorhinis [M. hyopneumoniae has been reported to be important for the interaction with the host cells in one single study [Mycoplasma species develop less severe microscopic lesions of pneumonia if compared to normal animals [Pathogenic determinants such as adhesion to the host cell and evasion from the immune response have already been well-described in the literature for both yorhinis –27. The le study . Several animals –31. ThisM. hyopneumoniae and M. flocculare harbor similar sets of adhesion proteins [M. hyopneumoniae to cause disease if compared to M. flocculare might not be directly related to adhesion.Even if these topics are of utter importance for understanding swine respiratory tract mycoplasmal diseases, what has yet to be better understood is the direct participation of metabolism in the development of them. For instance, although adhesion factors are related to pathogenicity, proteins , and havproteins . Thus, tMycoplasma spp. range from 580 kb  to more than 1,358 kb (Mycoplasma penetrans), representing an important example of genome reduction  during the evolutionary process. It is possible that in an initial symbiotic phase, the host provided a broad range of metabolites for these bacteria. This, together with the ability of the bacteria to uptake such compounds, made several activities dispensable for the bacterial life. Over the course of evolution, these bacteria would have lost some of the genes that became unnecessary for life in an environment conditioned by another genome [Furthermore, the genome sizes of r genome .M. hyopneumoniae, M. hyorhinis and M. flocculare to better understand their different life-styles. Based on the reconstructed networks, we propose that one of the mechanisms that may explain why M. hyorhinis and M. hyopneumoniae are pathogenic while M. flocculare is not, is their ability to use glycerol as a carbon source, thus enabling the production of the highly toxic hydrogen peroxide. This trait may be directly involved in cytotoxicity, as already described for two lung pathogenic mycoplasmas, namely Mycoplasma pneumoniae in human [Mycoplasma mycoides subsp. mycoides in ruminants [In this way, we aimed at studying the reduced metabolism through genome-scale metabolic model reconstructions of in human  and Mycouminants .in silico models show that M. hyorhinis and M. hyopneumoniae have extra sets of enzymes that may help them overgrow the non-pathogenic species M. flocculare. M. hyorhinis seems to have a wider carbohydrate uptake range, and probably for this reason is seen as a well-known contaminant of cell cultures [M. hyopneumoniae, on the other hand, might use myo-inositol as a carbon source producing acetyl-coenzyme-A as a byproduct. This feature is of particular interest for the production of cofactor Coenzyme-A in this species, since most enzymes from the biosynthetic pathway are missing in all species.Additionally, growth rates and fitness have been correlated in the past to virulence in several other organisms –39 and mcultures . M. hyopin silico reconstructed metabolic networks with the in vivo metabolic characteristics of the three species. For this reason, we also performed nuclear magnetic resonance spectroscopy (NMR) analyses to detect metabolites consumed and produced in both complex and defined media. These experiments corroborated with the reconstructed models and suggested two new features in particular: (i) the uptake of myo-inositol in M. hyopneumoniae might be related to a higher acetate production, and (ii) M. hyorhinis showed a surprisingly reduced ability to convert pyruvate to acetate in the growth conditions used in this study. All these in silico and in vivo metabolic differences might influence the different levels of pathogenicity in each of the species studied here.Nevertheless, there is little metabolic experimental data available for the three species, which makes the reconstruction of a reliable metabolic model an extremely time-consuming work. Together with the fact that these mycoplasmas are generally grown in complex media, with high serum concentrations, we needed additional experimental data in order to compare the We present in this work the metabolic networks reconstructed from the annotated genomes of the three species and the comparison done with growth rates and metabolomic experiments performed in vitro in order to better understand the basis of the pathologies caused by these bacteria, which might help prevent their development in the future.M. hyopneumoniae , M. hyorhinis  and M. flocculare (ATCC 27716 and ATCC 27399). The semi-automated reconstructions were generated by the Pathologic tool from the Pathway Tools software [We reconstructed the metabolic networks for all available strains isolated from swine so far of software  using thPathway Tools automatically associated genes with reactions, based on the annotation names, Gene Ontology (GO) terms and enzyme code (EC) numbers contained in the GenBank files. The software assembled reactions into pathways by comparing them with the reference database, MetaCyc. The software automatically added missing reactions from the reference database to the model, creating many orphan reactions (reactions that do not have an enzyme associated). In a gapfilling step, we used the Pathway Hole Filler tool, included in the Pathway Tools software, to identify possible candidate genes associated with these orphan reactions. Orphan reactions in the metabolic network were allowed only to allow known functionalities of the organisms or biomass production in the reconstructed models. We incorporated the minimal number of orphan reactions into the models to avoid adding a behavior that was never described.−10 [−5). Homolog proteins with equivalent functional domains were assigned as isozymes to a particular reaction, while proteins with distinct functional domains were assigned as subunits of a multi-protein complex.In our work, the refinement of the networks was made in a subsystems approach  simultan−10 . WheneveNon-metabolic reactions such as DNA polymerization, protein synthesis and RNA synthesis were explicitly deleted from the network but implicitly included in the biomass assembly. Generic reactions were either specified or excluded from the models. The resulting models were further refined in accordance with a detailed protocol from Palsson and Thiele . ReactioWe analyzed the network topology to identify compounds that were only produced or consumed in the network, the so-called dead-end metabolites (DEM). Whenever a DEM was found, we either (i) added or validated an orphan reaction to reconnect it to the rest of the network, or (ii) removed the reaction from the model when both substrate(s) and product(s) were disconnected from the network and/or did not affect the overall metabolism. Transporters were predicted at first from genome annotation using the Transport Identification Parser, from Pathway Tools . TranspoM. hyopneumoniae and M. flocculare, which were reported to be absent in M. hyorhinis [In order to simulate growth, we had first to estimate the average cell composition of these mycoplasmas (biomass composition). To this end, we used a general macromolecule mycoplasma cell composition from Razin and collaborators . Dependiyorhinis –58. Baseyorhinis , 60. TheThe metabolic networks were exported as a mathematical Systems Biology Markup Language (SBML) models . They weM. pneumoniae strain 129 by Yus and collaborators (2009), and (ii) commercial media CMRL with no glutathione (Invitrogen). Since we had no information on metabolism of swine mycoplasmas, we decided to supplement the defined Yus medium with all amino acids (from now on the supplemented version will be referred to as Yus+). We also supplemented the CMRL-1066 medium with other peptone and/or other cofactors (from now on these will be referred to as CMRL+ and CMRL+/Pep). Information on the composition of all defined media is available in Additional file rpm).Complex media comprised (i) Friis media   and defined Yus+ medium . The medium was collected at the following time intervals: 0 μL of a mixture containing 1.25 MKH2PO4 phosphate buffer  in D2O with 2 mMNaN3 and 0.1 % trimethylsilyl propionate (TMSP) was added to 540 μL supernatant samples. Both solutions were mixed thoroughly and 550 μL were then transferred to 5 mm NMR tubes and sorted in 96-tubes racks.Sample preparation was as follows: 60 MHz NMR spectrometer equipped with a 5 mm TXI probe and a SampleJet autosampler, enabling high-throughput data acquisition for large collections of samples. The temperature was controlled at 27 °C throughout the experiments, and the samples were kept refrigerated at 4 °C during a waiting time of less than 24 h in the autosampler, before the NMR analysis. Standard 1H 1D NMR pulse sequence nuclear Overhauser effect spectroscopy (NOESY) with z-gradient and Carr-Purcell-Meiboom-Gill (CPMG) with water presaturation (Bruker pulse program noesygppr1d and cpmgpr1d) were applied on each sample to obtain the corresponding metabolic profiles. A total of 128 transient free induction decays (FID) were collected for each experiment with a spectral width of 20 ppm. The relaxation delay was set to 4 s. The NOESY mixing time was set to 10 ms. The total acquisition time of each sample was 12 min 34 s. The CPMG spin-echo delay was set to 300 ms, for a total filter of 77 ms, allowing an efficient attenuation of the lipid and protein NMR signals. The 90° pulse length was automatically calibrated for each sample at around 10 μs.All NMR experiments were carried out on a Bruker 800 Hz line-broadening factor, prior Fourier transformation. 1H-NMR spectra were manually phased and referenced to the TSP signal (δ= - 0.016 ppm at pH 7.4) using Topspin 3.1 . Extraction of a data matrix for multivariate statistical analysis from the 1H NMR profiles was done using the Statistics toolbox of AMIX (Bruker Biospin). Spectra were integrated from 0.3 to 10 ppm at a step of 0.01 ppm but excluding the regions of residual water at 4.68-4.88 ppm. No normalization of the intensity was performed. The resulting data matrix contains 947 NMR variables.Data processing: All FIDs were multiplied by an exponential function corresponding to a 0.3 Multivariate data analysis: Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were performed using SIMCA-P 13  with scaling based on the Pareto method.1H−13C HSQC, 1H−1H TOCSY and J-resolved experiments. Absolute quantification of the metabolites was performed using Chenomx NMR Suite .Metabolites identification and quantification: Metabolite identification was achieved by comparing spectra with databases such as HMDB . IdentifM. hyopneumoniae, 4 strains of M. hyorhinis and 2 strains of M. flocculare, we reconstructed 16 genome-scale metabolic models: one model for each strain separately, one for each species and a pan-reconstruction for all three species  was initially composed by 829 reactions. Duplicate reactions arose from the fact that Pathway Tools is based only on the annotations contained in the GenBank files. We removed generic and duplicate reactions from the models and replaced them with the specific and validated ones. Non-metabolic reactions were also excluded at this point, along with absent capabilities of these mycoplasmas, such as heme, quinone or cytochrome dependent reactions [moonlight enzymes) from these organisms may indeed use some of the excluded DEMs; however, since we have no experimental evidence at present, we could not assess their interference in the metabolic models.The Pan-swine eactions . The netThe remaining DEMs consisted mainly of cofactors and biomass precursors  disconnected from the rest of the network. They were solved along with the biomass precursor check; 141 reactions were added to allow growth: 1 biomass reaction, 14 enzymatic reactions, 1 drain-synthetic reaction, 105 transport reactions and 5 spontaneous reactions. Transport reactions were considered as such even if an enzymatic activity was also present (i.e. the import of sugars with a concomitant phosphorylation of substrate). In order to correctly assign possible and specific transporters, we performed an extensive literature search and reciprocal BLAST alignments to characterized transporters in other species. These results have to be experimentally confirmed, since assignment of the correct substrate based only on sequence homology remains an open problem in genomic annotation.We also changed inconsistencies of reversibility and cofactor usage. Even after all efforts, four DEMs still remained: TTP, hexulose-6-phosphate, deoxyinosine and xanthosine-5-phosphate. After the final addition of 101 exchange reactions, we ran FBA tests to check the consistency of all models. If a reaction was essential for biomass growth and no homolog gene was found in the genome, an orphan reaction was validated or added to the reconstruction for modeling reasons only. The resulting refined pan-network had a total of 457 reactions and 258 GPR associations. A comparison between the species models may be found in Fig. The overall characteristics of each reconstruction can be seen in Table M. hyorhinis was computed as follows: CH1.57O0.36N0.21P0.02S0.02, with traces of calcium, chlorum, cobalt, copper, iron, potassium, magnesium, manganese, molibdenium and zync and molecular weight (MW) on a C-mole basis of 23.89 g/ C−mol. We had a slightly different composition for M. hyopneumoniae and M. flocculare: CH1.59O0.34N0.21P0.16S0.02, with the previously mentioned trace compounds and MW on a C-mole basis of 23.31 g/ C−mol.The biomass equation drained all precursors  into biomass. Biomass composition according to the “Mycoplasma species studied so far [M. flocculare was the only species that did not show any exclusive metabolic activities in the models, although this could not be verified in vivo due to lack of biochemical studies for this species. M. hyopneumoniae had 10 exclusive reactions, linked to the myo-inositol metabolism and alcohol dehydrogenase activity. M. hyorhinis had 24 exclusive reactions; most of them corresponded to carbohydrate metabolism. A global model comparing each species enzymatic capabilities can be seen in Figs. Similar to most d so far –48, all in silico based on sequence similarity to known transporters. We were able to identify in all species complete putative ABC transporters for multidrug/toxin efflux and for import of sugars, oligopeptides, peptides, spermidine/putrescine, phosphonate/phosphate/thiamine, cobalt, manganese/zinc and glycerol. In the search of a possible nucleotide transporter, we came across with an unpublished work from Lee [Mycoplasma bovis. These results may be highly speculative, and need further verification, but we were able to find homolog genes in the genomes of all species. M. hyopneumoniae seems to have an extra ABC transporter proposed for myo-inositol and M. hyorhinis has a unique predicted ABC transporter for maltose/maltodextrins. Three complete phospho-transferase transport systems (PTS) were common to all species. Although specificity of PTS is difficult to ascertain, we have tried to compare conserved domains from validated PTS substrate-specific enzymes II . M. hyorhinis and M. hyopneumoniae seem to have an extra IIB component specific for glucose, and along with the other components of the general sugar PTS were proposed to form a complete glucose PTS.Transporter assignment was made from Lee  in whichnsportDB ) and canM. pneumoniae, was recently described to act on the uptake of glycerophosphodiesters [M. hyopneumoniae and M. hyorhinis adjacent to the gene coding for the enzyme responsible for the metabolization of these substrates (GlpQ) [M. hyorhinis has two extra transport systems: one for sialic acid  and one sodium/phosphate co-transporter. Details of the transport search throughout all species is summarized in Additional file The protein GlpU, coded by gene MPN241 in diesters . We founs (GlpQ) . In MHR_s (GlpQ) ) and a mM. hyorhinis may be able to convert dihydroxyacetone (DHA) directly to dihydroxyacetone-phosphate (DHAP) with the action of DHA kinase (2.7.1.29), but the mechanism of DHA uptake is not clear.As previously described in , all genLactobacillus casei [In agreement with previously described literature , 18, 72,us casei . Out of Only one enzyme is missing from the typical pathway: L-ascorbate-6-phosphate-lactonase (UlaG). This enzyme is responsible for the turnover of L-ascorbate-6-phosphate to 3-keto-gulonate-6-phosphate (EC 3.1.1.-). From the gene context, MHP_7448_0377 from MHP_7448 and its homologs from all other organisms may encode the missing enzyme. The gene was annotated as a conserved hypothetical protein with a proposed phosphotriesterase activity.M. hyorhinis has an extra set of enzymes enabling the conversion of fructose-1-phosphate into fructose-1,6-biphosphate by the action of 1-phosphofructokinase (EC 2.7.1.56). Only in M. hyopneumoniae the conversion of glyceraldehyde into glycerol might be possible through the moonlight activity of alcohol dehydrogenase (EC 1.1.1.372), although up to date no experimental evidence can sustain that assumption. The presence of a unique transcriptional unit (TU) with carbohydrate metabolism-related genes in M. hyorhinis probably enables the uptake and metabolization of isomaltose (EC 3.2.1.10), maltose (EC 2.4.1.8), trehalose (EC 2.4.1.64 and EC 3.2.1.93) and sucrose (EC 3.2.1.48) into either glucose-6-phosphate or F6P. This may be related to the fact that M. hyorhinis can overgrow the other two species in several growth media.While the uptake of fructose, mannitol and mannose is similar in all species and they all can be fed into glycolysis as fructose-6-phosphate (F6P) and DHAP, M. hyopneumoniae strains, with the exception of the enzyme 5-dehydro-2-deoxy-phosphogluconate aldolase . The gene encoding this enzyme in other organisms is similar to the fructose-biphosphate aldolase (Fba) from glycolysis (EC 4.1.2.13). Since there are two copies annotated for Fba in M. hyopneumoniae, we proposed one of them as candidate for this activity based on sequence alignments with both genes from Bacillus subtilis. Inositol can be used as a carbon source and also produces acetyl coenzyme-A (AcCoA), which can be a source of cofactor coenzyme-A (CoA).A TU for the myo-inositol catabolism is present in all M. hyopneumoniae and M. flocculare models could uptake and convert GlcNAc to N-acetyl-glucosamine-6-phosphate (GlcNAc6P); M. hyorhinis models imported sialic acid and converted it to GlcNAc6P in three steps that are unique to M. hyorhinis . At first, the genes coding for these three enzymes were absent from MHR_HUB1 and MHR_GDL1. However, based on chromosome alignment and synteny with other genes, we were able to find the regions and correctly annotate all genes.M. hyopneumoniae was able to produce oxaloacetate, malate and 2-oxoglutarate (2KG) [M. hyorhinis extracts [Mollicutes lactate dehydrogenase  could also function as malate dehydrogenase . M. hyopneumoniae is the only organism among the three to have a gene coding for an alcohol dehydrogenase, probably enabling the turnover of AcCoA to acetaldehyde (EC 1.2.1.1 0) and ethanol (EC 1.1.1.1). Conversion of pyruvate to formate by pyruvate formate-lyase (EC 2.3.1.54), malate to fumarate by fumarate hydratase (EC 4.2.1.2) and SucCoA to succinate by succinyl-CoA synthetase (EC 6.2.1.5) were added to the models in a final step to accommodate the results from the metabolomics experiments.Out of fifteen enzymatic reactions in this pathway, only three were not found in any organism. Conversion of pyruvate to lactate and acetate were possible in all species. From literature data, we knew that te (2KG) , 75. Decextracts . Althougextracts  suggesteM. hyorhinis). Extracellular cholesterol and sphyngomyelin were incorporated unmodified directly into biomass. While M. hyopneumoniae seems to have four different ways to uptake and metabolize glycerol  M. hyorhinis lacks one and M. flocculare lacks two of them  were found; however, all Mollicutes definitely synthesize this metabolite, and hence must use a hitherto undetected enzyme for this step [M. hyorhinis and may act as a fatty acid and CoA donor in these organisms. Orphan reactions for the production of glycolipids (EC 2.4.1.46 and EC 2.4.1.241) were added to models for M. hyopneumoniae andM. flocculare based on the presence of these metabolites in vivo [The enzyme responsible for the turnover of glycerol-3-phosphate into acyl-sn-glycerol-3-phosphate  was found only in the genomes of his step . The acy in vivo .The import of amino acids was added in two forms: oligopeptide import and cleavage or single amino acid import. tRNA charging accounts for most of the reactions in this pathway (23 reactions). Production of the biomass precursor S-adenosyl-methionine from methionine is possible in all strains . Moreover, since the enzyme adenosylhomocysteine nucleosidase (EC 3.2.2.9) is present in all species, we added two reactions to restore connectivity between these two subpathways (EC 2.1.1.- and EC 4.4.1.21). This addition resulted in the production of two metabolites essential for quorum sensing and cell communication in other species: S-rybosyl-homocysteine and autoinducer-2.de novo purines and pyrimidines; therefore, they have only salvage pathways and interconversions to supply the cell with nucleic acid precursors. The three species have the same enzymatic capabilities, except for the presence of thymidylate synthetase (EC 2.1.1.46) in M. hyorhinis, which is also important for cofactor metabolism and is responsible for the conversion of dUMP and 5,10-methylene-tetrahydrofolate (MeTHF) to dTMP and dihydrofolate. Overall, the nucleotide metabolism consists in the uptake of guanine, adenine, uracil, thymine and cytidine and produces all deoxy-ribonucleotides  and ribonucleotides .The mycoplasmas in this study cannot synthesize M. hyorhinis), serine hydroxymethyltransferase  and formate-tetrahydrofolate ligase . This may also account for the better fitness of M. hyorhinis when compared to the other two species. Although no homologs for NAD kinase were found, two reactions were added (EC 2.7.1.23 and EC 2.7.1.86) to allow the presence of NADP and NADPH, which are cofactors for several essential reactions. The NADPH and NADH produced from the degradation of glucose and other carbohydrates are recycled by the action of two enzymes: NADH oxidase  and thioredoxin disulfide-reductase (EC 1.8.1.9).Around 60 % of the enzymes that comprise the cofactor metabolism of the reconstructed models were not found in any species. For instance, thiamine-pyrophosphate is imported unchanged directly into biomass, while pyridoxal is imported and converted to the cofactors pyridoxal-phosphate by the action of pyridoxal kinase . From the pantothenate/coenzyme-A metabolism, we only found homologs for the conversion of 4’-phosphopantetheine into dephospho-coenzyme-A by pantetheine-phosphate adenylyltransferase (EC 2.7.7.3). Three forms of folate are incorporated to the biomass: MeTHF, 5,6,7,8-tetra-hydrofolate and 10-formyl-tetra-hydrofolate. We assumed that folate was imported and converted to these three biomass cofactors by: dihydrofolate reductase . The presence of peptone appeared to have a negative effect on M. hyorhinis growth, but more tests should be performed to verify this hypothesis. Yus+ medium did not allow proliferation in any species, but maintained cell concentration and viability even after 5 days of culture (if inoculated afterwards in complex media). These results are in agreement with a previous work performed by Bertin and colleagues [M. mycoides subsp. mycoides that shows that CMRL-1066 contains all components to support cellular metabolism but not growth. Here, the supplemented versions CMRL+ or CMRL+/Pep allowed proliferation only for M. hyorhinis during the first 24 h of growth. It seems that after this period of time, one or more essential metabolites initially present were no longer available. By comparing the models reconstructed in this work with the media composition tested for the three species, it might seem plausible that key cofactors might not be delivered to mycoplasmas in the correct form. For example, thiamine pyrophosphate should be directly delivered to all species instead of its precursor thiamine, and pyridoxal-5-phosphate might be the cofactor of choice for the media composition in place of pyridoxal. A list of predicted biomass precursors from the models versus the actual precursors found in the Yus+ medium can be seen in Additional file M. hyorhinis, M. hyopneumoniae and M. flocculare.We cultivated the three species of mycoplasmas in complex and defined media to gather experimental information and compare to wth Fig. . In the lleagues  in M. myNMR analysis of the culture media was performed in order to detect possible differences in the metabolism of the three species. All data measured in defined and complex media can be seen in Additional file M. hyorhinis produced higher quantities of pyruvate at the end of 48 h in complex medium. As a result, pyruvate conversion to acetate was detected in low quantities for this species in both media. M. hyopneumoniae and M. flocculare, on the other hand, produced higher amounts of acetate (this was even more pronounced for the growth of M. hyopneumoniae in complex medium). Low amounts of formate, fumarate and succinate were also detected, and this indicates the presence of genes encoding the enzymes pyruvate formate-lyase (EC 2.3.1.54), fumarate hydratase (EC 4.2.1.2) and succinyl-CoA synthetase (EC 6.2.1.5) in these genomes. As previously mentioned, all models were thus modified to accommodate these activities. Formate production seems to be independent from acetate production in M. hyopneumoniae and M. flocculare. However, the similar low concentrations of acetate and formate in both growth media from M. hyorhinis might be an indication that both are produced concomitantly by the action of pyruvate formate-lyase (EC 2.3.1.54), phosphate acetyl-trasnferase (EC 2.3.1.8) and acetate kinase (EC 2.7.2.1).The major differences in both complex and defined media were related to the metabolism of pyruvate Fig. : M. hyorM. hyorhinis, we compared the predicted proteins PdhA, PdhB, PdhC and PdhD from all species. The known active sites of ODP2 (homolog of PdhC) and OLDH1 (homologs to PdhC and PdhD respectively) from Bacillus subtilis were analyzed in search for a possible mutation. No active sites have been described for PdhA and PdhB so far, but the enzymes for all three swine mycoplasmas do not present significant differences. Although not all active sites from PdhD seem to exist in mycoplasmas, all predicted proteins from the species analyzed  are similar in terms of active sites, when these are present. The only difference between M. hyorhinis and the other two species was found in PdhC, in the protein portion related to the binding to PdhA and PdhD. This portion is the most variable between the species. An enzyme characterization should be made in order to verify if this complex is not active in this species, or if it is responsible for another unforeseen activity.To check if the lower concentrations of acetate were a result of an impaired activity of the pyruvate dehydrogenase complex in M. hyopneumoniae in complex medium, if compared to M. flocculare. Since the myo-inositol pathway produces AcCoA, the recovery of a molecule of CoA from myo-inositol is possible and may be directly linked to the production of acetate. Since the myo-inositol catabolic pathway is one of the few distinctions between the metabolic models of M. hyopneumoniae and M. flocculare, it is possible to assume that the differences in acetate concentration in vitro may indeed arise from the ability of M. hyopneumoniae to uptake myo-inositol. Indeed, when no source of myo-inositol is present (defined medium), no difference in the concentration of acetate is observed for these two species.The presence of myo-inositol in swine serum  might beM. hyorhinis seemed to have lower concentrations of most amino acids at the end of the growth curve if compared either to the control medium or to the other species. This may be related to several factors, such as lower rates (in comparison to the other two species) of peptone degradation by membrane proteases or higher uptake rates of amino acids by M. hyorhinis. At this point, we could no longer verify which hypothesis is more suitable for this particular distinction, however, since peptone was not necessary for the maintenance of the viability of M. hyorhinis in the defined medium, it is possible that this species may harbor transporters with higher specificity or effectiveness for single amino acids.The analysis of the amino acid uptake was not trivial since both media contained peptone, which at first was not measured through the CPMG experiment but, in the course of time, was degraded into single amino acids, and thus changed the overall signal of the NMR spectra. To address this issue, we only took into account the Yus+ medium at a single time point after 72 h of growth. The results as concerns the other metabolites indicate for the three species the uptake of glucose and nucleotides.in silico medium that contained all biomass precursors. Similarly to the in vitro growth results, in the presence of oxygen, acetate was the main product and NADH was recycled by the conversion of molecular oxygen into water. In the absence of oxygen (not tested in vitro), all models were able to grow but the main product became lactate. As expected, the availability of myo-inositol in the in silico medium allowed an alternative source of CoA and higher concentrations of acetate as a final product in the models for M. hyopneumoniae. Hydrogen peroxide production was linked to the metabolism of glycerol in both M. hyopneumoniae and M. hyorhinis, while the models for M. flocculare did not produce toxic levels of this metabolite. Since we have no information on gene essentiality for any species, we checked reaction essentiality in the pan-network. This means that we deleted individual reactions  even if they did not have a GPR association. A total of 111 reactions  were essential for growth in the pan-network . FBA simulations were performed with an M. genitalium (382 genes) or M. pneumoniae (310 genes) [M. genitalium and M. pneumoniae. The lack of experimental information on gene essentiality is also a setback for the validation of the models created here. Further experiments on this matter should help us better refine and complete the networks.In this work, we created a metabolism as realistic as possible for the three known mycoplasmas present in the respiratory tract of swine. It is essential to point out that, although we had to include 30 % of orphan reactions in order to allow growth, we only used about half of the genomes of these organisms. The other half consists either of hypothetical or of conserved hypothetical proteins , which m0 genes) , 82. GenMycoplasma species studied [As for most of the  studied –48, all  studied , 84 and M. hyorhinis, M. hyopneumoniae and M. flocculare. We also wanted to compare the metabolism of the three species and find possible links to virulence, host-colonization capacity and life-style. M. hyorhinis, for instance, is not only a pathogen, but also a common contaminant in many mammalian cell cultures [M. hyorhinis to grow in diverse sites inside the host [M. hyopneumoniae has been detected in other sites such as brain, liver and spleen [M. flocculare has never been detected outside of the respiratory tract of swine.Our main objective was not only to reconstruct the metabolic models for cultures . This mathe host  and eventhe host , 86. M. d spleen , 88, butM. hyopneumoniae is up to date the only Mycoplasma species with sequenced genome that has the genes for the catabolism of myo-inositol. Myo-inositol is readily abundant in the bloodstream of mammalian hosts, and can be used as a secondary carbon source for energy production [Mycoplasma iguanae has been described to produce acid from inositol [M. hyopneumoniae, the NMR results we obtained suggest that myo-inositol might be directly related to the higher production of acetate in complex medium of M. hyopneumoniae if compared to M. flocculare. This assumption is based on two factors: first, a previous work has detected myo-inositol in swine serum, which is a component of the complex medium [M. hyopneumoniae to grow in diverse sites if compared to M. flocculare. This is particularly interesting when we take a close look into the composition of lung surfactant from several mammalian models [M. hyopneumoniae is one of the reasons for the high virulence of this species when compared to M. flocculare and M. hyorhinis.One may wonder what could explain this if both organisms are so similar. oduction . Mycoplainositol . Howeverx medium ; and secn models , which lM. hyorhinis to produce acetate. This was not foreseen in the model reconstruction, but predicted protein sequences from the pyruvate dehydrogenase complex of the three species showed a particular distinction in PdhC, more specifically in the region responsible for binding to other complex components. We could not verify at this point if this complex is not active in the particular growth conditions of our work in M. hyorhinis or if it is responsible for another unforeseen activity. However, a transcriptome profiling of M. hyorhinis has detected both pdhA and pdhB in the pool of genes with the highest number of transcript reads [M. hyorhinis extracts [A striking result from the NMR results was the reduced capability of pt reads , indicatextracts , and it M. hyorhinis seems to be able to uptake folate directly while the other two species might need intermediate metabolites. This might also explain why M. hyorhinis is extensively found as a cell culture contaminant. Even though we supplemented CMRL-1066 (CMRL+) with all missing components present in the Yus+ medium, cells maintained viability for longer periods in the latter. Cofactors present originally in both media existed in lower concentrations in the CMRL-1066. This means that possibly one or more cofactors with higher concentrations in the Yus+ medium are essential for the viability of the three species. Common cofactors with higher concentration in Yus+ medium included: choline, folate, pantothenate, pyridoxal, thiamine and spermine.Cultivation tests in vitro also showed that none of the species was able to grow in none of the defined media. This is probably due to a lack of the correct cofactors as previously mentioned. For instance, thiamine, pyridoxal/pyridoxine, pantothenate, spermine and folate were the actual media components tested, but the reconstructed models were not able to convert them to biomass precursors . Only M. hyopneumoniae and M. flocculare lack one or more amino acid transporters. This may be due to two things: (i) both remained viable for longer periods whenever CMRL+ was supplemented with peptone, and (ii) we detected a higher amino acid accumulation in the defined media if compared to M. hyorhinis. M. hyorhinis on the other hand may have all the transporters for single amino acids, but this hypothesis needs to be further verified. From sequence analyses, we did not find significant differences among the species that would explain this distinct behavior. Moreover, since M. hyorhinis seemed to overgrow in the first 24 h of culture in both CMRL media (CMRL+ and CMRL+/Pep), it is possible that one or more compounds were missing in the Yus+ medium in order to allow growth of this species. Possible candidates for such (present in CMRL-1066 and absent in the Yus+ medium) would be: CoA, nicotinamide, ascorbate and/or pyridoxine.Other differences among the species seem to indicate that M. hyopneumoniae. However, this is particularly important because in this study Tajima and Yagihashi [M. hyopneumoniae play a key role in the interaction between pathogen and host. Moreover, it has been reported that some strains of M. hyopneumoniae become less pathogenic in broth culture and, after serial passages, lose their ability to produce gross pneumonia in pigs [Mycoplasma species, it is not yet known if the material is synthesized by the bacteria or imported from the medium for most species [Escherichia coli strains seem to have practically nonimmunogenic capsular material, due to the fact that these antigens are similar or identical to the ones found in the host [E. coli capsules is basically GlcNAc and glucuronic acid (GlcA) in a molar ratio of 1:1 [M. hyopneumoniae and M. flocculare might be able to import GlcNAc unchanged, they may use it for the composition of its capsule; M. hyorhinis, on the other hand, imports sialic acid, and might use it directly to produce its own. Of course this is highly speculative, and we are not sure if all these hypotheses indeed happen in vivo.It is still not clear if any of the swine mycoplasmas studied here indeed have a polysaccharide capsule, given that only one study has assesed its presence in agihashi , as disc in pigs . In seve in pigs  and it d in pigs . Further species . Recent  species , 95. Howthe host . The stro of 1:1 . Erlingeo of 1:1  identifiM. hyopneumoniae and M. hyorhinis and the non-pathogenic M. flocculare. Such metabolism and the production of hydrogen peroxide by the action of GlpO are essential for the cytotoxicity of M. pneumoniae [M. mycoides subsp. mycoides [M. mycoides subsp. mycoides is due to the translocation of the hydrogen peroxide into the host cells. This is only possible because of the close proximity to the host cells along with the membrane-bound enzyme. Highly conserved homolog genes to this enzyme were only found in the genomes of the pathogenic species studied here. Whether or not these enzymes from M. hyopneumoniae and M. hyorhinis are indeed capable of the same activities as GlpO is not yet confirmed; however, the high similarity may be an indication that this trait is essential for the pathogenicity of these two species. This may also explain why both M. hyopneumoniae and M. flocculare can adhere to the cilia of tracheal epithelial cells, but only the adhesion of M. hyopneumoniae results in tissue damage [M. hyorhinis has in the reconstructed models three ways of uptaking glycerol, M. hyopneumoniae seems to have five, and this may reflect in its enhanced pathogenicity.The metabolism of glycerol is the final major difference between the pathogenic eumoniae  and M. mmycoides . Particue damage . MoreoveM. hyopneumoniae, M. hyorhinis and M. flocculare using different approaches. The reconstructed models showed some distinctions among the species, namely the myo-inositol metabolism for M. hyopneumoniae, the wider uptake of carbohydrates for M. hyorhinis and the usage of glycerol as a carbon source for the two pathogenic species. The models also served as a basis for all the assumptions made for the experimental data. Metabolic profiling of both complex and defined media pointed to new differences that we were not able to identify based solely on the sequenced genomes. The major ones were related to the pyruvate conversion to acetate, which appeared to be higher in M. hyopneumoniae and M. flocculare than in M. hyorhinis. After we performed growth tests using defined media that hypothetically would lead to bacterial growth in silico, we assessed that some cofactors or metabolites were probably being delivered in the wrong form to the mycoplasmas. The environmental context may explain the differences between the in silico models and the in vivo behavior.We presented in this work an overview of the differential metabolism of M. hyopneumoniae may help the commensal species M. flocculare to better survive inside the host.Whether the main differences among the species we reported here .All models from this work are available in Additional file


Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA) that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data.The coccolithophorid unicellular alga  Emiliania huxleyi is a species of unicellular coccolithophorid algae. As a coccolithophore, it forms highly-structured calcium carbonate plates (coccoliths). E. huxleyi blooms can be seen as a major carbon sink in the global carbon cycle [et al. [E. huxleyi sampled all over the world. The genome of strain CCMP 1516 was annotated as a reference genome. To make this data usable for metabolic engineering techniques in the form of constraint-based analysis, the genome-scale metabolic network needs to be reconstructed.on cycle . They haon cycle . This bi [et al. . This stEscherichia coli [Saccharomyces cerevisiae [Arabidopsis thaliana [Chlamydomonas reinhardtii [E. huxleyi is available, yet.Genome-scale reconstructions of many organisms are available today and are still being updated. For example, models of hia coli ,5, Sacchrevisiae , Arabidothaliana  and Chlanhardtii  are avaiConstraint-based analysis of metabolic networks with methods, like flux balance analysis (FBA), phenotypic phase plane (PhPP) analysis and dynamic FBA, is widely used in systems biology ,11,12,13E. huxleyi is adapted to diurnally-changing environmental conditions with alternating resource availability, an approach considering the diurnal change is required. Therefore, using the organism’s currently-available resources as efficiently as possible towards biomass production might not be sufficient. A more complex optimization goal has to be stated for these organisms, namely “Maximize biomass production and produce enough storage metabolites to survive a well-defined starvation period”.An important task while setting up FBA problems is to define an appropriate objective function that results in realistic flux distributions. Typically, objectives, such as “maximize biomass production”, “maximize ATP production” or “minimize substrate uptake”, are chosen . These aIn contrast, previous publications applying the classical FBA to plants and algae fix the proportion of storage metabolites in the biomass function for the light metabolism ,8. As a Many extensions of the FBA approach were developed, for example the dynamic FBA, which allows the dynamic simulation of biological processes by balancing the external metabolites via uptake and excretion kinetics . The dynet al., who duplicated the fluxes for a stoichiometric model of Arabidopsis thaliana to simulate day and night metabolism in a single optimization problem [The approach presented in this work, a diurnal flux balance analysis (diuFBA), is similar to the integrated FBA proposed by Cheung  problem .This is related to the dFBA DOA, but contains some simplifying assumptions. The approach is based on dividing the diurnal cycle into periods of quasi-steady-state environmental conditions, namely a light and a dark period, and to solve an optimization problem over a complete diurnal cycle. The time steps are merged into a single stoichiometric matrix and linked by explicit Euler integration steps, making it possible to formulate a convex optimization problem that is similar to a classical FBA problem.E. huxleyi and apply the diuFBA formalism. We demonstrate the diuFBA approach on an example model. The diuFBA is then applied to the genome-scale reconstruction to simulate a diurnal cycle, with the focus on the biomass composition at the transition from day to night.In this study, we introduce a genome-scale reconstruction of Modeling was performed using the COBRA Toolbox 2.0  with MATE. huxleyi CCMP 1516 genome was reconstructed and annotated [E. huxleyi presented in this work was built according to the protocol by Thiele and Palsson [Recently, the nnotated . Based o Palsson . In ordeThe general idea of the approach is to have storage metabolites that can be transferred between daytime and nighttime, while each time step is considered as a flux balance problem with varying environmental exchange fluxes. Resources can be exchanged between the two time steps via storage metabolites. This is illustrated in To depict the behavior of a photosynthetic organism over the course of one day with the diuFBA, we assume that all time steps can be considered to be in a quasi-steady state within themselves. This assumption is valid if we consider the environmental conditions constant and the substrate is abundant within each time step. This is a realistic assumption for cultivation experiments in bioreactors or other monitored environments, but also for beginning blooms, in which no nutrient limitation is present.r reactions, s species, which contain p storage metabolites. To describe the connection between day and night metabolism, an extended stoichiometric matrix We consider a metabolic network with The transfer reactions are modeled as reversible reactions, since the transfer of storage metabolites is possible from day to night, as well as from night to day.For each time step, one instance of the original matrix This definition also includes the shortened transition matrix As a result, the reaction vector In this equation, v (mmol /gDCW /h) (gDCW: grams of dry cell weight) over a time step, e.g., the light phase, results in a change of concentration Concentration changes are used due to the time discretization, which results in the reaction vector now containing the integrated stationary fluxes. The integration of a flux With the extended stoichiometric matrix A common tool for the analysis of the metabolic capabilities of a stoichiometric model and its distinct patterns of pathway utilization is phenotypic phase plane (PhPP) analysis ,20. For phenotypePhasePlane, which is supplied by the COBRA Toolbox [As a result, the value of the objective function can be plotted over the reactions of interest. In this paper, we used a modified version of the function  Toolbox . It takeAn abstract example model was created that had the minimal features that are necessary to demonstrate the diuFBA. The model is depicted in Escherichia coli reconstruction iAF1260 [Arabidopsis thaliana reconstruction AraGEM [Chlamydomonas reinhardtii reconstruction AlgaGEM [et al. [The model iEH410 resulting from the analysis of the annotated genome and gap filling comprises 410 reactions and 363 metabolites. The model created consists of five compartments: cytoplasm, mitochondria, chloroplasts and coccolith vesicles and the Golgi apparatus. Most reactions are located in the cytoplasm, mitochondrion and chloroplast, while the coccolith vesicle and the Golgi apparatus only contain reactions related to calcification. The sources for the reactions are depicted in n AraGEM  and the  AlgaGEM . The cal [et al. , and the [et al. .Chlamydomonas reinhardtii [E. huxleyi was taken from the literature [E. huxleyi uses mannitol as a storage metabolite [et al. in the model.The biomass composition was divided into the precursor metabolite proteins, long chain molecules, lipids and low molecular weight molecules . The pronhardtii . Long chterature , and theterature . Since ttabolite ,28, we iThe carbon pool inside a cell is often divided into particulate inorganic carbon (PIC) and particulate organic carbon (POC) ,30. We c−2s−1) and low (50 μE m−2s−1) light irradiation conditions.The model considers biomass compositions for three metabolic scenarios and two environmental conditions: biomass compositions for a 16-h light phase, for an 8-h dark phase and a combined 24-h resulting biomass. Irradiation is given in Einstein (E), which corresponds to the molar amount of photons. The two experimental conditions comprise high , but 0.125 M of storage metabolites were produced. This is just enough to satisfy maintenance demands in the second time step, resulting in a final biomass concentration of 0.775 M. The biomass composition after the first time step is 86.1% functional biomass and 13.9% storage metabolites B.In the second column, the optimization is repeated for the second scenario with a longer night period. For the diuFBA, this results in an increased production of storage metabolites, altering the biomass composition at the transition from the first to the second phase in favor of more storage metabolite, namely 72.2% functional biomass and 27.8% storage metabolites.E. huxleyi. The biomass composition after a 24-h cycle was considered to be the functional biomass, whereas lipid biomass precursors and mannitol [In silico, this leads to an increased protein production during the day and a reduced transfer of mannitol, since the carbon for the protein production in the night phase does not need to be transferred. This result can be explained by the phenotypic phase plane (PhPP) plot depicted in vs. 0.923 1/d). At the same time, the production of mannitol during the day needs to be increased to provide the resources for protein production during the night. This was further illustrated in another diuFBA scenario, in which the protein production during the day was fixed to literature values. The result is also depicted in The diuFBA was applied to the model of mannitol ,28 were mannitol  is not dThis flexibility regarding the choice of storage metabolites was also analyzed using PhPP plots. In Mode I, where both mannitol and cytoplasmic lipids are limiting, chloroplastic lipids are produced and exported to the cytoplasm. The chloroplastic lipids are stored for the night, where they are used for energy production and the production of cytoplasmatic lipids. The cytoplasmatic lipids are used during the day for biomass production. In Mode II, enough mannitol is produced during the day to satisfy maintenance during the night. The additionally-transferred mannitol is used to produce cytoplasmatic lipids during the night, which could not be produced during the daytime. Additionally, chloroplastic lipids are still transferred, as the transferred mannitol is not sufficient to satisfy the carbon requirements during the night. Mode III corresponds to a carbon distribution where enough mannitol is available to satisfy the carbon requirements for the night. As not enough cytoplasmatic lipids are produced, yet, mannitol is used to produce them during the night. At the optimal point, the cytoplasmatic lipid production is just high enough to satisfy biomass requirements, and mannitol is used as the sole storage metabolite. Mode IV describes a phase where mannitol production is higher than necessary to fulfil cytoplasmatic lipid production, as well as storage metabolite production for night maintenance. Excessive mannitol is consumed in futile cycles, leading to a decrease in the growth rate. Mode V corresponds to an abundance of cytoplasmatic lipids. These lipids need to be consumed in futile cycles to achieve mass balance. Mode VI corresponds to an overproduction of both cytoplasmatic lipids and mannitol. All excessive metabolites are consumed in futile cycles.E. huxleyi as a long-term storage, but are consumed due to mass balance constraints.These metabolic modes include the biologically-meaningful Modes II and III, where a combination of lipids and mannitol are used to satisfy both energy storage and functional biomass requirements. Mode I corresponds to an underproduction of both lipids and mannitol that is compensated by an alternative lipid production path. Modes IV, V and VI correspond to an overproduction of possible storage metabolites, which might be used by In the case of the optimal carbon distribution, only mannitol is used as the storage metabolite, and all biomass precursors are produced during day metabolism. We can see in the PhPP plot depicted in Although it would have been interesting to compare the results of the diuFBA with the results of the dFBA algorithm, as was done in the example model, this cannot be easily accomplished. In the second phase, the optimization problem for the example model was only solvable due to the starvation term that allowed the conversion of biomass into energy for maintenance. Such a reaction can be easily postulated for a proof-of-principle model, but for real organisms, the response to substrate limitation is much more complex.E. huxleyi strain CCMP 1516, an organism with a strong impact on the global carbon cycle, to create the genome-scale model iEH410. It can be used to determine the energy balance of the organism in periodically-changing light conditions. Understanding the energy distribution for the cellular processes, the build-up of storage metabolites and substrate requirements also makes this organism more accessible for biotechnological purposes. Direct applications of the model include the possibility to map transcriptomic data to the model. Furthermore, the model in combination with the diuFBA approach allows the search for optimized growth media by identifying bottlenecks in the metabolism of E. huxleyi.We used the available data of the annotated genome of the E. huxleyi strains, Read et al. point out that no single strain can be considered to be the “typical” E. huxleyi strain [Regarding the high genetic variability of i strain . This miA common problem in the analysis of stoichiometric models is the ambiguity of the solution. Methods analyzing this flexibility usually require an optimization problem to be solved repeatedly. In this case, the advantageous formulation of the presented optimization problem as a convex program allows one to derive a quick solution even for large-scale models. Moreover, the resulting optimization problem is in structure identical to the classical flux balance problem, making all methods developed for the classical FBA, such as the phenotypic phase plane analysis, flux variability analysis or elementary modes, easily applicable.The evolutionary optimality criterion for photosynthetic organisms differs from that of heterotrophic organisms: while the latter have to react to unpredictable substrate availabilities, the former adapted to a deterministic pattern of periodically-changing energy availability. Therefore, a strictly-stationary approach, like the classical FBA algorithm, is unable to correctly depict the evolutionary conditions of photosynthetic organisms. Workarounds, such as different biomass compositions for day and night metabolism, are able to describe measurement data , but lacet al. [The diuFBA approach generalizes the FBA formalism presented by Cheung et al. . The reaet al. was assembled manually.The script published in et al. is the shortened transfer matrix E. huxleyi, it might be important for the analysis of higher plants, where metabolites produced in leafs are consumed and stored in other parts of plants.Another important extension of the formalism by Cheung With the diuFBA, the conflicting goals of functional biomass and storage metabolite production can be described as part of the optimization problem to produce as much biomass as possible while producing just as much storage metabolites as necessary. As shown in the example model, the optimal biomass composition is now the result of the optimization process and no longer a constraint imposed on the basis of measurement data. Moreover, the biomass composition is variable depending on the environmental conditions. As demonstrated with the example model, the ratio of functional and storage metabolites is dependent on the duration of the light and the dark phase. If we applied classical FBA methods, where storage metabolites are included into the biomass function, every new experimental condition would require us to manually adjust the biomass function.E. huxleyi that proteins are produced during the light phase, but measurements show that the production is distributed over the diurnal cycle, with more production during the dark phase. This discrepancy can be elucidated using PhPP plots of the protein transfer from day to night, representing the ratio of proteins produced in the according phases. This analysis shows that a considerable amount of protein can be produced during the night phase with only minimal influence on the growth rate. This shows the metabolic flexibility regarding protein production. Flexibility regarding the choice of the storage metabolite can be analyzed using the PhPP plot of mannitol and lipids, depicted in et al. suggest that E. huxleyi uses this flexibility by producing lipids as storage metabolites instead of relying on mannitol [The diuFBA predicts for mannitol . This ilE. huxleyi CCMP 1516 under a circadian rhythm. These insights might prove useful for metabolic engineering, where they can be used to divert resources to economically-interesting products, such as highly-structured calcium carbonate in the form of coccoliths or lipids for nutritional/feedstock supplements or petrochemical replacements [The model and simulation approach can now describe the metabolic processes in wild-type acements .


Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions. One of the greatest challenges for scientists is to understand the genetics, physiology, and biochemistry of plants and the interaction of genes with the environment in order to provide strategies to manipulate these processes to improve plant growth and performance and to prevent diseases. There have been immense advances in the past decades allowing scientists to sequence genomes of organisms addressing many questions, such as how the genome determines a plant’s response to any environmental stimuli. Simultaneously, the development of analytical technologies allows us to take a comprehensive and unbiased glance at the gene products, such as gene transcripts (mRNA), proteins, lipids, and metabolites. The post-genomics era was born with the establishment of transcriptomics, proteomics, lipidomics, and metabolomics, each with their associated computational advances providing the path for data analysis, visualization, and integration to establish the relationships between the genome and gene products under certain conditions.Metabolites are synthesized by enzyme-catalyzed reactions in any living cell. They are important for the maintenance and survival of cells, most importantly for energy storage and provision, and they also contribute to building and maintaining the cell’s structural components. Metabolites and their functionalities are indispensable in the interaction of a cell with the environment, and it has been argued that the metabolome of any biological system represents the final “read-out” of the expression of many genes in that system in a particular situation, and reflecting gene × environment relationships -based separations, researchers now commonly use two types of separation chemistries, such as C18 reverse phase, which separates the more hydrophobic complement of a metabolite extract, such as many secondary metabolites and lipids, and hydrophilic interaction chromatography, which is better suited for the polar metabolites , which is known for its superior separation power and reproducibility , a feature used for structural elucidation of unknown compounds. MS also has excellent capabilities to detect and distinguish isotopic patterns of each ion under analysis. This is particularly important for stable isotope labeling experiments used for metabolic flux analysis. MS analysis is able to determine enrichments in stable isotope labels in all the analyzed compounds and therefore allows determining metabolic fluxes through particular pathways. Most commonly 13C labeled metabolic precursors are used, and the distribution and enrichment of the label across pathways of interest are quantified , which also represents an important platform for untargeted, non-destructive metabolite profiling  is the most commonly used detector in metabolomics approaches. The power of MS is that it can distinguish the size of the ionized molecule (by determining the mass to charge ratio of each detected ion) and allows the determination of the number of individual ions detected. The mass to charge ratio determined by MS is used for compound identification and the number of ions detected can be related back to initial concentration of the molecule in the metabolite extract. Another advantage of MS is that it can fragment an ion once (MS/MS) or multiple times , min–max, and Pearson’s and Spearman-rank correlation are examples for univariate data analysis. Principal component analysis (PCA), partial least squares regression (PLS), and multivariate analysis of variance (MANOVA) are commonly used for multivariate data analysis. Self-organizing maps (SOM), support vector machines (SVM), and k-means are popular methods for cluster analysis. Generated results can be visualized using different kinds of diagrams such as plots, histograms, cluster diagrams, and heat maps.To perform the above-mentioned analytical methods, several software tools can be used. There is a distinction between low-level tools to provide the actual set of algorithms, which are in turn used by high-level tools to provide user-friendly application of those algorithms. Table Graphical representations of metabolic pathways and networks have been used for long time to represent knowledge about metabolic processes, and with the availability of pathways in databases, several tools have been developed to visualize metabolic data in the context of networks. These tools mostly support tasks such as data mapping and network analysis but often also try to help with layout and exploration of data. The latter touches the field of visual analytics for metabolic information , location, and developmental stage.Examples for multispecies plant metabolic databases are:For a long time, the use of biological organisms for the production of chemicals was limited by the repertoire of biosynthetic pathways naturally present in these organisms. Microbial low-molecular-weight metabolic end products have long been used as commodity chemicals for medical applications: for example, antibiotics, such as penicillin, or immunosuppressive drugs, such as cyclosporine, have been discovered in 1929 and 1972, respectively Drews, . MicrobeWith the advent of recombinant DNA technology in the early 1990s, it was possible to engineer specific genes in biological organisms, which has significantly reduced the time required for mutagenesis and selection of desirable traits. Genetic engineering made it possible to use heterologous hosts for the production of chemical compounds that are not naturally present in the organism. The emergence of the clustered, regularly interspaced, short palindromic repeat (CRISPR) and related technologies that use targeted genome editing via engineered nucleases are the latest developments to introduce alterations of genome sequences and gene expression, which can be ultimately used to also introduce modifications to existing metabolic pathways and to transfer novel traits in agricultural crops  as well as computational analysis and modeling methods .The development of new sophisticated genomic sequencing and other enabling technologies for synthetic biology facilitated the production of naturally present chemicals at levels that made extraction economically feasible, and the field of metabolic engineering began to emerge. Metabolic engineering is defined as the targeted modification of metabolic pathways of biological organisms for metabolite overproduction or the improvement of cellular properties Lessard, . Since tThe ultimate goal of synthetic biology is the efficient design of biological systems  to stoichiometric models  to detailed kinetic models (which model changes of metabolite concentration over time). A current review  sharing and publication of models, (2) reusability of models, and (3) surviving of models beyond the lifetime of the software used to create them. The SBML web page currently lists more than 270 software applications that support SBML, and thousands of SBML-encoded models are available from public repositories such as BioModels including Path2Models  species , (2) compartments , and (3) reactions . For the analysis and simulation of a model, more properties need to be defined such as stoichiometries, rate laws, local and global parameters, as well as units on quantities. A formal description of SBML can be found in the detailed specification  allow for the representation of different aspects of biological systems at different levels of detail as SBGN maps, thus providing corresponding views on the underlying biological system. The PD language  has been developed. It is a graphical notation allowing depiction of the structure of a design using glyphs to specify genetic parts, devices, modules, and systems.The SBOL web page currently lists more than 20 software applications supporting SBOL and SBOL Visual. Some of these applications allow the generation of SBML models from synthetic biology designs in SBOL (Roehner et al., in vivo will be very important next steps to further enhance the predictive capabilities of existing metabolic models. Continuous development of more user-friendly software, databases, languages, and computer models that incorporate and interpret complex information will be crucial to handle the acquired data and to aid interpretation in a biological context. We are just at the beginning of a new area of synthetic biology in plants based on metabolomics and metabolic modeling.The total number of metabolites in the plant kingdom is estimated to be between 100,000 and 200,000 and can be highly variable depending on the physiological and environmental conditions as well as the genetic background of the plant (Hill et al., 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.


Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. 13C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values.Two variants of pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction.The major differences between the two variants occurred at the formate node as well as within C3–C4 inter-conversion pathways. Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants  contains supplementary material, which is available to authorized users. Methylobacterium extorquens AM1 is a facultative α-proteobacterial methylotroph, that has been studied intensively over 50 years  overlapping with the serine cycle in key intermediate metabolites nodes (such as 2-O-Phosphono-D-glyceric acid (2PGA) and phosphoenolpyruvate (PEP). Some of the C3 and C4 metabolites were involved in more than one pathway in this interconnected set of cycles. For instance, 1) 2PGA is the branch node for the serine cycle and gluconeogenesis, 2) PEP and pyruvate are both in the serine cycle and anaplerotic pathways, 3) Malate and OAA were both in the serine cycle, TCA cycle and anaplerotic pathway. This complexity and redundancy offers the cell alternatives for the connections between the serine cycle and EMC pathway under environmental perturbation and gene manipulation.The central carbon metabolism pathway model used for in silico simulation, Influx_s was used [Methanol uptake rates and biomass yields were measured for individual conditions. Extracellular metabolite secretion was measured using 1H NMR with a previously described protocol . For in was used .Methylobacterium extorquens AM1; HY, hypho medium with 1.35 μM cobalt; HYC, Hypho medium with 6.31 μMAM1,


The group of filamentous fungi contains important species used in industrial biotechnology for acid, antibiotics and enzyme production. Their unique lifestyle turns these organisms into a valuable genetic reservoir of new natural products and biomass degrading enzymes that has not been used to full capacity. One of the major bottlenecks in the development of new strains into viable industrial hosts is the alteration of the metabolism towards optimal production. Genome-scale models promise a reduction in the time needed for metabolic engineering by predicting the most potent targets in silico before testing them in vivo. The increasing availability of high quality models and molecular biological tools for manipulating filamentous fungi renders the model-guided engineering of these fungal factories possible with comprehensive metabolic networks. A typical fungal model contains on average 1138 unique metabolic reactions and 1050 ORFs, making them a vast knowledge-base of fungal metabolism. In the present review we focus on the current state as well as potential future applications of genome-scale models in filamentous fungi. Aspergillus niger as the major source of citric acid production. Furthermore, reflecting the saprobic lifestyle of many filamentous fungi, they harbor a great variety of biomass-degrading enzymes natively produced in high amounts. Additionally, the large diversity of bioactive compounds produced by filamentous fungi is just being recognized as a valuable reservoir of promising new natural compounds. Exploration of the biosynthetic capabilities of these organisms has been facilitated by the availability of genome sequences, thereby enabling the discovery of secondary metabolite clusters being inactive under standard laboratory conditions.Filamentous fungi have been used for decades in industrial biotechnology exploiting their ability to utilize various sources of nutrients and tolerating adverse growth conditions. For example, tolerance of low pH and the endogenous property of producing citric acid in high amounts have led to the establishment of A key requirement for the transition of a new compound into a viable commercial product is the availability of a host producing the compound in sufficiently high amounts. As the organisms are, in general, not evolutionarily optimized to produce a single compound in optimal amounts, process optimizations as well as genetic modifications have to be performed. The rational approach of modifying the metabolism of an organism in order to improve product output constitutes the field of metabolic engineering. Due to the lack of information in the pre-genomic era, the scope of metabolic engineering has been limited to individual pathways not considering inherent interdependencies in the metabolic network. The availability of genome sequence information provides the opportunity to expand the scope of metabolic engineering to the whole metabolism transforming the field towards systems biotechnology. As the process of metabolic engineering represents a bottleneck in the development of many cell factories, accurate predictions by metabolic models could help to reduce the time and costs involved by guiding the efforts towards the most promising set of modifications.Haemophilus influenza 20 years ago  in filamentous fungi started considerably later with the first genome being published in 2003 for These reconstructions differ considerably in their content of legacy information included, reflecting different strategies of model establishment. The process of manual reconstruction tends to be laborious, as a maximum amount of information is considered leading to the generation of a structured knowledge base. The complementary approach aims at establishing genome-scale reconstructions (semi-)automatically based on sequence comparisons and gene assignments, enabling the prediction of genome-scale networks for less covered species. While these models generally move towards a larger number of genes and reactions included as models are progressively improved, a qualitative comparison with respect to the numbers of reactions and genes included in the resulting models of these different strategies is not directly possible. The majority of automatically generated models contain dead-ends and/or unconnected reactions that have been removed from manually created and curated models, resulting in higher number of genes included. The results from both of these approaches are fragmentary summaries of the metabolic capacities of the organisms requiring additional curation in the form of gap-filling in order to make computational analysis of the networks feasible. The mathematical background of the underlying modeling approach has been reviewed elsewhere  has been used to assign genes to metabolic pathways in different strains  and level of characterization of the individual organisms. A key aspect of metabolism in eukaryotes is the separation of metabolic reactions in different compartments. The correct assignment of metabolic enzymes to these compartments requires detailed information not readily available for a number of components in filamentous fungi metabolism. Even though this imprecision can limit the accuracy of predictions by these models, they still provide a valuable tool for data interpretation besides being a collection of information that is easily accessible. The unique feature of genome-scale network reconstructions is the integration of different types of information from various sources including databases such as KEGG and Swissprot as well as primary literature data. This ordered collection of metabolic reactions is curated with genomic information and literature citations which results in a structured knowledgebase for the specific organism. While organism specific databases pursue a similar goal, the strength of genome-scale reconstructions is the easy accessibility for computational analysis.While genome-scale metabolic modeling in E. coli and S. cerevisiae, enabling multiple genetic modifications in the same host, but have only recently become available in filamentous fungi (Oakley et al. Although genome-scale models have been developed for various filamentous fungi (see Table E. coli where accuracies of >90 and 80 % have been achieved for single- and double gene knockouts respectively (Monk and Palsson Additional factors potentially hampering the final development of these models into predictive tools for genetic modifications are the low percentage of experimentally verified gene assignments, unknown subcellular localization of the different pathways, as well as insufficient data for model validation. Due to advancements in the field of high throughput omics-technologies this necessary data for modeling is becoming more and more available facilitating the prospective use of genome-scale models as well as the model building process. The extension of the knowledge being available for incorporation will furthermore improve the prediction accuracy of existing models rendering more advanced applications possible. This improvement of prediction accuracy over time has already been demonstrated by the genome-scale model of Continuous extension of the models towards more detailed representations while improving their predictive power demonstrates the iterative nature of model development. The availability of predictive models for important organisms used in industrial biotechnology will provide the opportunity to choose the best performing host platform for a new biotechnological process by simulation, as optimal production of an individual compound requires a very specific metabolic performance. This comparability of genome-scale reconstructions between different species has been severely hampered by the non-standardized modeling practice of the different research groups in the past. With the development of modeling standards (Le Novère et al. During the past years, life sciences and biotechnology has begun the transformation from data-poor to a data-rich discipline introducing the need for new tools of analysis and interpretation of the generated data. Genome-scale metabolic networks have been proven useful in contextualizing and analyzing the data acquired by the different omics-techniques. At the same time, the increase in availability and quality of omics data pave the way for the generation of more detailed and context-specific metabolic networks based on the presence of individual metabolic activities. This usage of omics data as a surrogate for the customization of genome-scale models will increase in importance as the sensitivity of the omics technologies is improving. The combination of multi-omics measurements with metabolic modeling provides a powerful tool for the detection and elucidation of unknown relations and interactions. Improvements in the molecular toolbox for genetic engineering will allow for a faster realization of model predictions in the future thereby increasing the speed of model improvement. Taken together the recent developments in the metabolic modeling as well as experimental techniques for generating relevant data are promising for an increased use of model-driven decision making in future.