Scheffersomyces stipitis is an important yeast species in the field of biorenewables due to its desired capacity for xylose utilization. It has been recognized that redox balance plays a critical role in S. stipitis due to the different cofactor preferences in xylose assimilation pathway. However, there has not been any systems level understanding on how the shift in redox balance contributes to the overall metabolic shift in S. stipitis to cope with reduced oxygen uptake. Genome-scale metabolic network models (GEMs) offer the opportunity to gain such systems level understanding; however, currently the two published GEMs for S. stipitis cannot be used for this purpose, as neither of them is able to capture the strain’s fermentative metabolism reasonably well due to their poor prediction of xylitol production, a key by-product under oxygen limited conditions.S. stipitis, iBB814. After the modified GEM, named iDH814, was validated using literature data, it is used to obtain genome-scale understanding on how redox cofactor shifts when cells respond to reduced oxygen supply. The SID-based framework for GEM analysis was applied to examine how the environmental perturbation  propagates through the metabolic network, and key reactions that contribute to the shifts of redox and metabolic state were identified. Finally, the findings obtained through GEM analysis were validated using transcriptomic data.A system identification-based (SID-based) framework that we previously developed for GEM validation is expanded and applied to refine a published GEM for S. stipitis was shifted as a result of reduced oxygen supply. The systems level analysis identified the key contributors to the overall metabolic state shift, which were validated using transcriptomic data. The analysis confirmed that S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that directs the overall shift in metabolic states.iDH814, the modified model, was shown to offer significantly improved performance in terms of matching available experimental results and better capturing available knowledge on the organism. More importantly, our analysis based on iDH814 provides the first genome-scale understanding on how redox balance in The online version of this article (10.1186/s12934-018-0983-y) contains supplementary material, which is available to authorized users. Scheffersomyces stipitis has been recognized as an important yeast species in the field of biorenewables due to its desired capacity for utilizing xylose [Saccharomyces cerevisiae for both glucose and xylose conversion to produce ethanol [S. stipitis, making it a potential platform stain for producing various compounds derived from the shikimate pathway [g xylose , the sec ethanol , 3. In a pathway .S. stipitis, because the first two reactions in xylose assimilation pathway, i.e., xylose reductase (XR) and xylitol dehydrogenase (XDH), prefer different cofactors. XR prefers NADPH while XDH strictly depends on NAD+, which leads to redox imbalance. It has been suggested that a major cause for the limited growth performance and ethanol biosynthetic capacity of S. stipitis with xylose as substrate is the redox bottleneck, rather than enzyme activity deficiency that hinders specific metabolic pathways [For biological organisms, cofactor balances play critical roles in maintaining intracellular redox hemostasis, which has been recognized to be a prerequisite for robust growth and metabolism . This ispathways , 7. It iS. stipitis’ cellular metabolism, which is the foundation of various applications, two genome-scale metabolic models (GEMs), iSS884 [S. stipitis [Escherichia coli and Saccharomyces cerevisiae, have been successfully utilized in many applications, including metabolic engineering, model-driven discovery, prediction of cellular phenotypes, and others [To gain genome-wide understanding on , iSS884 , have bestipitis . A GEM istipitis , 11, whid others , 13.S. stipitis [Similar to models developed in various science and engineering fields, the quality of a GEM determines the successfulness of its applications. Therefore, model validation plays an important role in GEM development. Besides assessing its size and connectivity, the current standard approach for GEM validation is to compare model predictions with experimental data under different conditions . Most ofstipitis . In thatS. stipitis [Scheffersomyces stipitis.To address the shortcomings of point-matching validation approaches, we proposed a system identification (SID) based framework for GEM validation . In the stipitis . In thisEscherichia coli (since 2000) and Saccharomyces cerevisiae (since 2003) suggest that the development of the initial version of a GEM is a groundbreaking start; after which, significantly more efforts are needed to validate and refine the GEMs [S. stipitis, iSS884 and iBB814 represent significant steps forward in gaining a genome-scale understanding of the cellular metabolism of S. stipitis; and at the same time, it is natural that both models have certain limitations. For example, the prediction of xylitol production from both GEMs are quite different from the experimental observations. For iSS884, xylitol is not produced under any condition; while for iBB814, xylitol is only produced under conditions that are almost completely anaerobic, where no cell growth is predicted. Since xylitol is one of the major by-products under oxygen-limited conditions as observed in wet lab experiments, it is essential that the modified GEM can predict the production of xylitol under oxygen-limited conditions, before it is utilized to gain systems level understanding of S. stipitis’ fermentative metabolism.For any microorganism, obtaining a high-quality GEM is challenging and time consuming due to the scale and complexity involved at genome-scale . The stithe GEMs , 15. BeiCurrently, GEM refinement is typically accomplished through trial-and-error by modifying different reactions in the model and examining whether model predictions improve. This process relies heavily on the modeler’s knowledge and capability to sort out clues from various simulation results. Therefore, GEM refinement is usually labor intensive and time consuming , 15. To iBB814  was consS. stipitis (and other microorganisms), as it is well accepted that oxPPP should be active under aerobic conditions to generate reducing equivalents needed for cell synthesis.Identify the erroneous model behavior: one of the identified errors in iBB814 is that the oxidative branch of pentose phosphate pathway (oxPPP) is off under aerobic conditions  to identify the key reactions that contribute to this erroneous model behavior: a series of in silico experiments were conducted by forcing proportionally increasing flux through oxPPP with increasing OApply SID to identify which reactions respond most significantly to the forced change: Principal component analysis (PCA) identifies key reactions based on the magnitude of principal component (PC) loadings Fig. b. Seven Determine which candidate reactions and/or their constraints should be modified and/or what new reactions should be added: careful examination of the seven reactions showed that one reaction, malate oxidation to oxaloacetate, was carried out in the opposite direction as suggested by the literature. By increasing the lower bound of the malate oxidation reaction from − 1000 mmol/gCDW/h to 0 mmol/gCDW/h, oxPPP becomes active under aerobic conditions  play a key role in changing model behavior, and the point-matching validation does not provide information on such “hidden” relations. In this section, we present how the SID-based framework can be applied to identify candidate root causes for a particular model faulty behavior, and in doing so, expedite GEM refinement. Specifically, we use the refinement of iBB814 for one particular error as an example to illustrate the following four steps in the proposed SID-guided GEM refinement as shown in Fig. This example shows that the malate oxidation reaction, the root cause of the erroneous model behavior, i.e. inactive oxPPP under aerobic condition, is located quite away from oxPPP pathway. Without the guidance of the SID framework, it could be very difficult to quickly identify this source of error. However, with the SID-based analysis, the overall network response provides key information to help quickly identify the root cause of the error.Scheffersomyces stipitis CBS 5773 was obtained from ATCC. The frozen stock and culture media were the same as in Liang et al. [g et al. . All expEach cell sample was centrifuged for 5 min at 10,000 rpm at room temperature. After centrifugation, the samples were decanted and the remaining cell pellets were immediately submerged into liquid nitrogen. The frozen cell samples were stored in a − 80 °C freezer until all cell samples from both trials were collected. The frozen cell samples were shipped to the University of Wisconsin-Madison Biotechnology Center, which conducted the RNA extraction, and analysed the samples through Illumina Next-Generation Sequencing. The data set consists of raw count data, fragments per kilobase per million reads (FPKM), and transcripts per million (TPM). The raw reads were aligned to the CBS6054 genome using STAR aligner, and the aligned reads were quantified using RSEM , 19. AftThe gene-to-protein-to-reaction (GPR) rules provided in the GEM were used to calculate the reaction expression values  for different pathways. For reactions catalyzed by multiple isozymes, we use the sum of the expressions for the associated genes. If a protein consists of subunits, the protein expression is determined by considering the minimum expression of the subunits. These rules were also used in .S. stipitis under aerobic growth and oxygen-limited fermentation.In this section, we first provide detailed information on the differences between iBB814 (the base model) and iDH814 (the modified model) and evaluate the quality of iDH814. Specifically, we first subject iDH814 to phenotype phase plane (PhPP) Analysis and provide a detailed analysis along the line of optimality where cell growth is optimal; then we evaluate the model through the conventional point-matching approach by comparing the model predictions with published experimental results. Once validated, we use published experimental data to guide the design of in silico experiments and apply the SID-based framework to understand how cellular metabolism shift in response to reduced oxygen supply. In particular, we thoroughly examined the cellular redox cofactor consumption and production to obtain genome-scale understanding on the redox shift that contributes to the overall metabolic shift. Finally, the findings obtained through GEM analysis were validated using transcriptomic data collected on S. stipitis, we obtained a modified GEM, iDH814, based on iBB814. In summary, starting with iBB814, we modified 13 reactions, deleted 10 reactions and added 19 reactions to obtain iDH814. Additional file Following the procedure illustrated in Fig. 2, ethanol, and xylitol production over a wide range of xylose and oxygen uptake rates. Figure 2 production than iBB814, while ethanol secretion is predicted over a much narrower range of conditions compared to iBB814. More importantly, Fig. One major application of GEMs is to predict different growth phenotypes  under various genetic and environmental conditions. Developed by the Palsson lab, phenotype phase plane (PhPP) analysis is a powerful tool that utilizes flux balance analysis (FBA) with a GEM to provide a global perspective on the genotype–phenotype relationship, and to help characterize different metabolic phenotypes . Here weFigure Along the LO, since carbon and oxygen uptake rates increase proportionally at the fixed optimal ratio, it is expected that all activated pathways should carry proportionally increasing fluxes to achieve increased maximum cell growth. Therefore, when SID-extracted information along the LO is visualized, only blue colored (upregulating flux) and black colored (inactive reaction) reaction arrows are expected. Figure In this analysis, the LO is determined by changing the xylose uptake rate and allowing the oxygen uptake rate to be determined by FBA with maximizing cell growth as the objective. Figure 2 uptake reaction indicate the different slopes of the corresponding LO, as shown in Fig. 2, whereas iDH814 only requires 1.496 mol of O2. The larger loading for the CO2 exchange reaction in iBB814 indicates that it produces more CO2 than iDH814, which agrees with what is revealed in PhPP analysis, i.e., Fig. Figure It is worth noting that iDH814 was developed through the “knowledge-matching” approach only, without any attempt to fit available experimental data; in addition, the knowledge utilized to guide the model refinement is general to many different microorganisms, such as the example shown in Fig. 2 fluxes) were compared with experimental values at seven different conditions. Table To provide a fair comparison, we used the same datasets that were used in Damiani et al.  for iSS8With iDH814 validated through both knowledge-matching and point-matching approaches, we apply the SID-based framework to study the fermentative phenotype predicted by the GEM. The goal is to obtain system-level understanding on the strain’s fermentative metabolism.S. stipitis under fermentation. We have shown that iDH814 is able to predict xylitol production in a reasonable way, here we compared its prediction with experimental data available in literature on xylitol production [Xylitol has been shown to be a major by-product of oduction .S. stipitis, a redox imbalance is resulted and excess amount of NADH is produced. Since both routes of XR reactions that consume NADH and NADPH are included in the GEMs, without further constraints, the NADH-dependent route is always activated by FBA, as it will generate faster growth rate due to the balanced redox. To address this limitation, for the simulations conducted in this section, we added a constraint on the flux ratio between NADPH-dependent and NADH-dependent XR, to reflect the available knowledge on the XR cofactor preference [As noted previously, because of the different redox cofactor preferences of XR and XDH, when xylose is converted to xylulose in eference .Table Table To gain a better understanding on how the cellular redox balance shifts at genome-scale when oxygen uptake rate (OUR) decreases, we applied the SID-based framework to investigate the cellular response to the change in OUR using iDH814. As mentioned before, first a series of in silico experiments should be designed and carried out to help reveal how the cellular redox balance shifts when OUR decreases. Because iDH814 does not contain the gene regulatory mechanism on cofactor utilization, in silico experiments were carefully designed to reflect what is going on within the cell by integrating experimental results. In Fig. Saccharomyces cerevisiae, the two major electron shuttle pathways are external NADH dehydrogenase (NADHDH) and the glycerol-3-phosphate (GLYC3P) shuttles [S. stipitis also primarily utilizes the GLYC3P shuttle and/or the NADHDH shuttle. As revealed by Fig. Next, PCA was applied to analyze the resulting flux distribution matrix. One principal component (PC) captured 100% of the variance, which confirms that conditions along the linear segment shown in Fig. shuttles . Our anaS. stipitis has been suggested as a major cause for the limited growth performance and ethanol biosynthesis capacity of S. stipitis. Since cellular redox balance is sustained through an intricate network with multiple redox reactions, a genome-scale understanding on how cellular redox shift with reduced OUR will provide valuable insights for mutant design, especially for redox cofactor engineering. In this section, we use the modified model iDH814 to gain system-level understanding on how the key contributing reactions to the redox shift respond in a coherent fashion to the reduced oxygen supply and produce ethanol.It has been well recognized that redox balance is one of the prerequisites for robust growth and metabolism, and the redox imbalance caused by xylose assimilation pathway in Based on the in silico experimental results obtained in the previous section, we tabulated the total NADH, NADPH production/consumption in cytosol and the total Q6H2 production/consumption in mitochondria predicted by the GEM when the cells transition from condition 1 to condition 3. All reactions that contribute to the redox shift are listed in Additional file The detailed flux information provided in Additional file + transhydrogenase that converts cytosolic NADPH into mitochondrial NADH, which helps balance the significant shift from NADPH to NADH.Figure Regarding the redox shifts in the mitochondria, in Table This analysis suggests that to cope with the stress caused by reduced oxygen supply, cells rely on increased production of NADH, as well as converting cytosolic NADPH to mitochondrial NADH to achieve the drastic shift from NADPH to NADH as the reducing factor.In this section, we use transcriptomic data collected under aerobic growth and oxygen-limited conditions to validate the findings we obtained through GEM based analysis. Although the relationship between gene expression levels and the metabolic flux through the corresponding enzymatic reaction is not direct, transcriptomic data obtained through RNA-Seq analysis offers an opportunity to help gain genome-wide understanding on how an organism responds to environmental/genetic perturbations. It has been assumed that the trend of significant gene-regulation should align with flux changes, i.e., significant up-regulations would result in increased fluxes, while significant down-regulations would result in decreased fluxes . TherefoTable From Table In addition, the last column of Table Scheffersomyces stipitis. Through designed in silico experiments, the SID-based framework can identify candidate reactions to examine in order to correct erroneous prediction behaviors, and thus, expedite the GEM refinement process. Based on our previous results of GEM evaluation for S. stipitis, we applied SID-based framework on a previously published GEM model iBB814 to derive an improved GEM, iDH814. Through a series of examinations and comparison experiments, we showed that iDH814 offers improved performance in both traditional point-matching validations and the proposed knowledge-matching validations, although iDH814 was developed solely based on knowledge-matching.In this work, we expanded our previously developed SID-based framework for GEM validation to GEM refinement and demonstrated its effectiveness through developing an improved GEM for S. stipitis adjusts its cellular metabolism in response to a decrease in OUR through ethanol production. To compensate for the limitation that iDH814  does not contain a gene-regulatory mechanism to adjust XR cofactor preference, we used experimental data to derive additional constraints in order to reflect what happens in cellular metabolism when OUR decreases. The systems level analysis obtained through the SID-based framework identified the key reactions contributed to the metabolic shift trigger by reduced oxygen supply. In addition, the intricate network of redox reactions involved in the metabolic shift were identified as well. These findings offer key insight to the mutant design for cofactor engineering. Finally, the iDH814 predicted flux changes were validated by transcriptomic data, which showed 75–80% agreement between predicted changes by iDH814 and transcriptomic data.With the modified GEM iDH814 being capable of producing xylitol, we designed and performed additional in silico experiments to gain a more in-depth understanding of how S. stipitis uses a concerted approach to cope with the stress associated with reduced oxygen supply, and the shift of reducing power from NADPH to NADH seems to be the center theme that organizes the overall shift in metabolic states. It is worth noting that although the SID-based framework can only reveal correlations amongst the changes that happen to different pathways, we could derive certain causal relationships among them by integrating available information (such as introduced perturbation) and existing knowledge to derive testable hypotheses on causal relationships.The analysis results confirmed that Additional file 1: Table S1. iBB814 Reactions Modified to Produce iDH814.Additional file 2: Table S2. iBB814 Reactions Deleted to Produce iDH814.Additional file 3: Table S3. Reactions added to Produce iDH814.Additional file 4: Table S4. GEM metabolites.Additional file 5: Table S5. GEM reactions.Additional file 6: Table S6. All cytosolic reactions that use NADH as a cofactor with non-zero flux.Additional file 7: Table S7. All cytosolic reactions that use NADPH as a cofactor with non-zero flux.Additional file 8: Table S8. Genome-wide comparison for Trial I.Additional file 9: Table S9. Genome-wide comparison for Trial II.


Brachionus plicatilis). Metabolome profiling revealed ~5,000 features, 1,079 of which were annotated. Most of the features were represented at significantly higher levels in non-dormant than dormant embryos. A large number of features was assigned to putative functional pathways indicating novel differences between dormant and non-dormant states. These include features associated with glycolysis, the TCA and urea cycles, amino acid, purine and pyrimidine metabolism. Interestingly, ATP, nucleobases, cyclic nucleotides, thymidine and uracil, were not detected in dormant resting eggs, suggesting an impairment of response to environmental and internal cues, cessation of DNA synthesis, transcription and plausibly translation in the dormant embryos. The levels of trehalose or its analogues, with a role in survival under desiccation conditions, were higher in resting eggs. In conclusion, the current study highlights metabolomics as a major analytical tool to functionally compare dormancy across species.Numerous aquatic invertebrates survive harsh environments by displaying dormancy as encysted embryos. This study aimed at determining whether metabolomics could provide molecular insight to explain the “dormancy syndrome” by highlighting functional pathways and metabolites, hence offering a novel comprehensive molecular view of dormancy. We compared the metabolome of morphologically distinct dormant encysted embryos (resting eggs) and non-dormant embryos (amictic eggs) of a rotifer ( Despite numerous publications in the past decades, we still lack information on how organisms enter and exit from dormancy and how the latter state is maintained. The term dormancy refers here to a long-term phenomenon and differs from an annual or periodic event, which characterizes insect diapause. In insects, diapause is a genetically-programmed response and involves endocrine, neuroendocrine, metabolic, molecular, cellular, enzymatic and behavioral changes4. Dormancy or diapause may occur at any life developmental stage: egg, embryo, larvae or adults. Developmental arrest at embryonic developmental stages is well known in aquatic invertebrates such as Cladocera (Daphnia species), the brine shrimp Artemia, copepods and rotifers5, insects4, vertebrates such as fish6 as well as mammals11. Interestingly, artificially induced diapause in mouse embryos (or paused blastocysts) serves in embryonic cell pluripotency studies12. In many aquatic invertebrates exhibiting cyclic parthenogenesis, induction of sexual reproduction leads to the formation of dormant encysted or encased embryos in the form of resting eggs , cysts (e.g. Artemia sp.) or ephippia (e.g. Daphnia sp.).Dormancy is a complex physiological state that facilitates the survival of organisms during unfavorable environmental conditions and is characterized by an arrest of development and in most cases it is associated with significant metabolic alterations and adaptations. Development and metabolism are resumed by species-specific cues, indicating restoration of favorable environmental conditions. Dormancy occurs in a large number of evolutionary unrelated organisms - from prokaryotes to mammals13. A few developmental stages and/or adult invertebrate species survive desiccation for numerous years, including nematodes, bdelloid rotifers and tardigrades16, rotifer RE20 and insects24. Prior to desiccation in various organisms, numerous metabolites accumulate that may ameliorate the harmful effects of dehydration, including sugars such as glucose and trehalose, polyols as mannitol, erythritol and glycerol, as well as amino acids28. The role of trehalose in desiccation has been disputed. In numerous desiccated organisms, trehalose has been associated with the formation of the glassy state 29. However, it was not detected in desiccated bdelloid rotifers and tardigrades30. Only small amounts of trehalose (0.35% of the dry weight) were found in rotifer RE31, although two transcripts encoding for trehalase-6-phophate synthase, and four trehalose phosphate synthase enzymes, were detected in rotifer RE32, suggesting its biosynthesis in rotifers.In organisms where dormancy is associated with desiccation, there is a formation of a glassy or vitrified state, which entails an increase in cellular viscosity and leads to a dramatic reduction in biochemical reactions, including metabolism and other functional pathways38. Most amazing is the survival of Daphnia ephippia in a lake sediment cores for over six centuries39. The encased or encysted dormant embryos or eggs differ morphologically from the non-dormant embryos or eggs. This observation suggests an accumulation and enrichment of specific compounds that can support long-term dormancy40.There are, however, several examples of survival for decades or even centuries in a non-desiccated state in aquatic organisms such as rotifers, cladocerans or copepods, in the form of RE or ephippia42. Numerous studies describe transcripts and proteins in association with dormancy but there is little transcriptional similarity among dormancies across species43 and diverse transcriptional strategies for producing them were reported in insects44 (and references cited in this publication). The similarity in phenotypes can be partially explained by proteome profiling, which shows a low correlation with transcriptomics17. Metabolomics holds promise to offer additional information on dormancy as metabolites are downstream of gene transcripts and have a key impact on protein expression46.Surprisingly, similar phenotypes and common functional pathways have been identified, regardless of the diversity and complexity in the survival strategies of organisms displaying dormancy. These include repression of metabolic pathways, attenuation of cell cycle progression, changes in carbohydrate and lipid metabolism, resistance to stress and protection of cellular structures56. Very few studies examined dormant and non-dormant eggs of aquatic species; these include a chemical composition study on subitaneous and diapause eggs of a copepod57 and of Daphnia58, but not on Artemia or rotifers.A search using metabolomics on changes associated with the “diapause syndrome” has been reported for numerous insect species but the number of the identified metabolites is relatively low, ranging from 20 to 75 and therefore were not associated with full functional pathways48 and59). In parallel to the transcriptomics and proteomics, the set of metabolites synthesized by an organism constitute its metabolome45. The metabolites are the products of cellular processes and in comparative experiments metabolomics analyses define importance by the relative changes in metabolite abundance. Changes in metabolites can reveal biomarkers of the integrated response of an organism. Most importantly, metabolomics is applicable to all species without any prior knowledge of the genomic sequence52 and sampling is accomplished by extracting metabolites. The most common methods of analysis of the metabolites is by chromatographic analysis  or by nuclear magnetic resonance (NMR) spectroscopy. The large datasets obtained are subjected to principal components analysis (PCA) or other chemometric tools. Using this metabolomics approach, the present metabolites can be related qualitatively and quantitatively to the different physiological states and various treatments. The disadvantages of LC-MS and GC-MS, as well as MS or MS/MS, is the difference in detector response for metabolites, which means that for every single compound, relative changes between the different experimental conditions can be measured, but quantification is only possible by using a calibration curve for each single compound. All metabolomics methods have a major limitation in the identification of the compounds. For well-known compounds, direct comparison with standard compounds is possible, but for novel or rare compounds this represents a major problem. The advantages lie in the rather unbiased nature of the data gathering following strict protocols enabling the comparison of the results over many years and in between laboratories. The handling of big data with different chemometric/statistic tools allows the comparison of “omics” data, generating novel hypotheses in this systems biology type of approach. Even though one should keep in mind that living systems have four dimensions (three of space and one of time), a metabolic flux through biochemical pathways cannot be confidently deduced from metabolomics data, which only describes the whole organism at a single time point.Metabolomics is an emerging field concerned with the study of the organism’s physiological state at the metabolite level by offering means to obtain a comprehensive view of the changes in abundance of numerous low molecular weight compounds simultaneously (see the Introduction sections inBrachionus plicatilis. Proton nuclear magnetic resonance (1H NMR) spectroscopy and high-performance liquid chromatography coupled to a diode array detector (HPLC-DAD) were used in initial experiments. Afterwards, lipophilic and polar metabolites of dormant and non-dormant rotifer eggs and algae (Nannochloropsis sp.) fed to the cultured rotifers were analyzed with the use of LC-MS and GC-MS. The relatively large number of identified features facilitated a functional allocation to numerous biochemical pathways. The changes in features in these pathways suggest explanations for the reduced metabolism, lack of transcription and translation during dormancy. Moreover, features with putative functions in signal transduction pathways were also identified. Since rotifer RE are tolerant to desiccation37, we also searched for small molecules, with a possible role in the formation of a glass and protection of membranes and proteins. The current study demonstrates the functional importance of metabolomics in complimenting transcriptomics and proteomics in deciphering the molecular basis underlying dormancy. Our findings provide a novel view for explaining the long-term survival of dormant embryos.The aim of the current study was to determine whether metabolomics provides additional functional insights into explaining the “dormancy syndrome” by underscoring functional pathways and metabolites, enhancing a more comprehensive view of this phenomenon. Towards this end, we herein used the rotifer dormant embryos  as a model. We compared metabolomics profiles of dormant and non-dormant eggs  of the rotifer Morphological differences observed between AM and RE that were carried by females are depicted in Fig. 1H NMR spectra of AM and RE were calibrated to the chemical shifts of trimethylsilylpropionic acid (TSP). A preliminary analysis of the 1H NMR spectra showed a lower abundance of metabolites in RE , acetate, acetone, betaine, trehalose, proline, adenosine monophosphate (AMP) and creatine-like compounds . The PCA loadings plot  and a creatine-like compound . Extraction of the metabolites starts with the same ratio of dry material and solvent. This means that when most metabolites in RE are present at lower amounts than in AM . Binnedlot Fig.  and the  AM Fig. , the per AM Fig.  at low qhttp://www.hmdb.ca/metabolites/HMDB0000128; Table 60, the neurotransmitter dopamine and most notably adenosine triphosphate (ATP)  P) Table . Of the P) Table . ClusterP) Table  a large P) Table , most ofP) Table .Figure 3a)Carbohydrates: Surprisingly, numerous carbohydrates (sugars and polyols) that were associated with diapause or resistance to desiccation in other organisms were significantly more abundant in AM in comparison with RE (Table myo-inositol), organic acids and bases , including amino acids. Choi et al.61 hypothesized that these compounds may form Natural Deep Eutectic Solvents (NADES) that can strongly retain water and form a medium in which proteins can be conserved. Some candidates for these NADES were at higher abundance in AM in comparison with RE. Trehalose, with an established role in the formation of a glass at ambient temperatures was previously reported to occur at low levels in rotifer RE31; transcriptome and proteome profiling studies revealed the occurrence of enzymes in the trehalose synthesis pathway62. Our current results generally indicate a higher abundance of trehalose in RE, suggesting a functional role for this metabolite in dormant RE, in contrast to a previous publication31. Alternatively, trehalose may serve as a source of carbohydrates for metabolism in rotifers as in insects (reviewed previously63). Another possible candidate for forming the glassy state during desiccation in rotifer RE could be attributed, as in tartigrades16, to late embryogenesis abundant proteins. These intrinsically disordered proteins were identified in high abundance in RE65. Rotifer RE survive desiccation after a short (15–20 min) dehydration period17, therefore, it can be assumed that all the components that facilitate long-term survival in the dry form are within the RE at the end of their formation (between 2–5 days). It remains to be determined whether or not these components also contribute to the survival in the wet state as RE can be kept for at least 21 years in the lab in this state32.RE Table . Six feaRE Table . As discb)Vitamins: Vitamins were more abundant in AM than in RE, except for riboflavin which was solely detected in RE. α-tocopherol acetate (vitamin E) showed similar abundance in the two egg types Lipids: Relatively many lipids, lipid classes and fatty acids were identified in AM, RE and algae and fatty acid composition was determined or partially determined for 338 features . The generic “ceramide” family is comprised of >50 distinct molecular species, of which about 25 were identified in the current study Pigment metabolites. As abovementioned, a clearly visible difference in AM and RE is the yellow-orange colour of the latter Metabolites with a function in response to light. L-Kynurenine, a low molecular weight metabolite that is obtained from tryptophan metabolism was more abundant in AM than in RE . The lower abundance of kynurenine in RE may indicate a higher sensitivity to UV irradiation in RE. Since RE respond to irradiation by hatching after an obligatory dormant period and most RE hatch if exposed to 250–310 nm light, the lower abundance of kynurenine in RE may facilitate perceiving proper environmental cues including light for hatching71. Proteomics analysis17 identified four proteins in the tryptophan metabolism pathway Cellular and mitochondrial metabolism: As depicted in Fig. 73.b)The urea cycle: numerous features in this cycle were significantly higher in AM in comparison with RE Amino acid biosynthesis: The lower abundance of almost all amino acids (excluding aspartate and glutamate) in RE suggests lower translation in RE .d)Purine metabolism: A large number of features in this pathway was either lower or undetectable in RE. These include, most notably, ATP which was not detectable in RE. This is in contrast with Artemia cysts, where ATP is not depleted but the concentration of ATP is about five-fold lower compared with post-diapause embryos75. Artemia cysts placed under anoxia conditions, displayed a rapid decline in the cellular ATP pool which was nearly undetectable76, but the state of anoxia in the examined RE has not been determined; they were maintained in air exposed seawater during the period of collection (2–4 days) from the culture flasks. The following metabolites were not detected as well in RE: 3′5′-cyclic GMP, 3′5′-cyclic AMP, GDP, dADP, xanthosine and deoxyinosine  in response to the binding of membrane-impermeable peptide hormones to the external cell surface78. Artemia cysts contain massive amounts of guanine-containing nucleotides, with guanosine (5′) tetraphospho (5′) guanosine (Gp4G) as the most prominent member of this family. It was suggested that it serves as a source of free energy during anoxia. It may also serve as a source of adenine and guanine nucleotides, due to the apparent absence of the pathway required for the de novo biosynthesis of the purine rings in Artemia (reviewed in79).e)Pyrimidine metabolism: in this metabolic pathway too, features were higher in AM than in RE or were undetected in RE . Agmatine serves as the precursor for putrescine, the latter of which is the diamine precursor of polyamine metabolism including spermidine and spermine. Higher dopamine concentrations in the hemolymph and central nervous system in the silkworm larvae and pupae, contribute to the induction of diapause by enhanced production or release of the diapause hormone81. Dopamine was only detected in AM eggs suggesting that either it functions only as neurotransmitter in rotifer embryos or that the induction potential of dormancy in rotifers is limited to AM embryo’s or adults.The naturally occurring diamine, putrescine, plays a role in diapause induction and intensity in insects by enhancing juvenile hormone secretioned Table  in rotif17 and from numerous studies  on the “dormancy syndrome”53. In addition, the current study also highlights specific features in rotifers which possibly function in other aquatic dormant embryos. Surprisingly, most features were found at significantly higher levels in AM than in RE; these include carbohydrates with a function in resistance to desiccation, vitamins (except for riboflavin that was detected only in RE), fatty acids and lipid classes . The abundance of L-kynurenine, with an established function in blocking UV penetration and a possible function in hatching, was lower in RE. Our metabolomics results support the displayed phenotype and the common functional pathways that have been described in organisms exhibiting dormancy. As there is little similarity in the transcriptomic profiles among dormancies displayed by distinct species, the present study demonstrates large scale metabolomics as a promising research avenue for functionally comparing dormancy across species, including model and non-model organisms, since it is applicable without prior knowledge of the genomic sequence.In conclusion, the current metabolomics analyses provided a wealth of information on the abundance of a large number of features in rotifer embryos. Numerous features were assigned to putative functional pathways revealing differences between dormant embryos and non-dormant developing embryos in glycolysis, TCA cycle, amino acid biosynthesis, as well as purine and pyrimidine metabolic pathways. In fact, the results show on one hand a metabolically active egg (AM) and on the other hand an egg in full dormancy (RE) in which all biosynthetic pathways are turned off, including the production of ATP that is the canonical energy donor in a myriad of biosynthetic reactions. A large number of features with an association with these pathways were lower or were not detectable in RE when compared with AM. These suppositions are consistent and further support the conclusions reached from our previous proteome profiling studiesNannochloropsis sp.) prepared from cultures at Maagan Fish Farms, Israel  or from concentrated algae prepared by Galil Algae, Israel . Rotifers were induced to produce RE by transferring them from high salinity  to low salinity  culture conditions and RE production commenced synchronously, 3–5 days after this transfer and RE were collected after 9–15 days of culture.Rotifers (clone ATB4) were cultured at 24–26 °C in artificial sea water  at 40 ppt fed with concentrated algae , amictic females were collected from 10 ppt sea water rotifer cultures that were maintained at similar conditions to RE producing cultures. AM were removed from females and collected into Eppendorf vials (detailed description of the procedure is provided in Supplementary Information Text File (a) For In order to distinguish between metabolites found in rotifers and those originating from algae, three replicate samples of concentrated algae  were also collected, frozen in liquid nitrogen, stored at −80 °C and frozen samples were shipped on dry ice to metaSysX, Germany.Three samples of RE  were collected into 1.5 ml Eppendorf vials from three cultures (as described above) and similarly, three AM samples  were collected (as described above) from three cultures. Samples were weighed before and after incubation at 58 °C for 24, 48 and 96 hr. The dry weight did not change after 48 hr. The percent moisture was calculated according to: dry weight after 48 hr/wet weight × 100.1H nuclear magnetic resonance (NMR) spectroscopy was used due to its wide range of metabolic coverage and its ease of quantification of the constituents. This was followed by high-performance liquid chromatography (HPLC) coupled to a diode array detector (DAD), for the targeted analysis, especially for biomarker pigments such as xanthophylls. Detailed description of the extraction methods for NMR analysis, analysis of 1H NMR results and HPLC sample preparation and analyses is provided in Supplementary Information Text File 1H NMR spectra were scaled to total intensity and reduced to integrated regions of equal width (0.04 ppm) from 0.30–10.0 ppm and automatically binned by AMIX software . The regions of δ 4.70–5.00 and δ 3.28–3.34 were excluded from the analysis due to the residual signals of water and methanol, respectively. In order to cluster the samples, Principle Components Analysis (PCA) was performed with SIMCA-P software . Scaling was based on Pareto82.The NMR spectra were binned and subjected to multivariate analysis. Thereby, the shared and discriminant markers could be identified. For multivariate data analysis, 83. After extraction, the volume collected for further analysis was adjusted to the number of eggs . LC-MS, MS/MS and GC-MS measurements, data processing, annotation and normalization, are described in Supplementary Information Text File AM and RE were extracted according to a modified protocol previously described2 transformed before performing student’s t test between the AM and RE samples. The p-values were adjusted with use of Benjamini-Hochberg (BH) correction procedure84. The fold-changes of all features were calculated by dividing mean of normalized intensity of AM samples by mean of normalized intensity of RE samples. Unless otherwise indicated, the Figures and Tables in the manuscript use the normalized dataset  in the un-normalized data were submitted to hierarchical clustering in Partek Genomics Suite using Pearson’s dissimilarity and complete linkage  Median group-wise normalization where the median of log transformed intensities of each sample was first subtracted from the log transformed intensities of each feature and then the median of intensities of the whole group was added to the log transformed intensity of each feature Table . This tyet Table , and (b)et Table  and inclet Table . Featureage Fig. . Prior tList of Supplementary FilesTable S1Table S2Table S3Table S4


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating noncommunicable disease brandishing an enormous worldwide disease burden with some evidence of inherited genetic risk. Absence of measurable changes in patients’ standard blood work has necessitated ad hoc symptom-driven therapies and a dearth of mechanistic hypotheses regarding its etiology and possible cure. A new hypothesis, the indolamine-2,3-dioxygenase (IDO) metabolic trap, was developed and formulated as a mathematical model. The historical occurrence of ME/CFS outbreaks is a singular feature of the disease and implies that any predisposing genetic mutation must be common. A database search for common damaging mutations in human enzymes produces 208 hits, including IDO2 with four such mutations. Non-functional IDO2, combined with well-established substrate inhibition of IDO1 and kinetic asymmetry of the large neutral amino acid transporter, LAT1, yielded a mathematical model of tryptophan metabolism that displays both physiological and pathological steady-states. Escape from the pathological one requires an exogenous perturbation. This model also identifies a critical point in cytosolic tryptophan abundance beyond which descent into the pathological steady-state is inevitable. If, however, means can be discovered to return cytosolic tryptophan below the critical point, return to the normal physiological steady-state is assured. Testing this hypothesis for any cell type requires only labelled tryptophan, a means to measure cytosolic tryptophan and kynurenine, and the standard tools of tracer kinetics. Diagnostic measurements for a given disease are most definitive, most specific, and most useful when the measurements are closely related to the molecular basis of the disease. Compare, for example, the ancient diagnosis of diabetes based on excessive urine output versus a diagnosis based on the measurement of plasma glucose and plasma insulin. For a disease like myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), whose mechanistic basis is unknown, a specific diagnostic is difficult to identify. Medicine thus resorts to complex pattern recognition in lists of symptoms ,2, statiThe idea of a metabolic trap arises from the well-established concept of bistability in nonlinear systems. A system, such as a metabolic pathway, is said to be nonlinear if a doubling of its input does not yield a doubling of its output. Saturation of enzymatic catalysis, characterized by the classic Michaelis-Menten equation, is one source of biological nonlinearity. A more important source of biological nonlinearity is feedback control whether it be transcriptional regulation, allosteric control mediated by binding of a specific metabolite, post-translational modification mediated by, for example, a kinase, or physiological regulation mediated by hormones, cytokines, and their receptors. Importantly, some nonlinear systems, unlike all linear systems, can settle into multiple different steady-states, depending on external conditions or perturbations. This is called bistability. If one of those steady-states is pathological, we refer to it as a metabolic trap because organisms are vulnerable to external perturbations that precipitate a shift from normal physiology to pathophysiology, a shift that is not easily reversed. If we now turn to the specific case—the IDO metabolic trap developed here—we find that this new theoretical model rests on three ideas: (1) the potential importance of common damaging mutations, (2) a possibly detrimental aspect of the phenomenon in enzyme kinetics known as substrate inhibition, and (3) the bistable metabolic system that can result.combinations of triggers are, themselves, rare. Outbreaks are then explained by a geographically localized combination of factors superimposed on a genetic predisposition that is common in the population. Thus, it is the existence of ME/CFS outbreaks that pointed to the potential importance of common damaging mutations. This inference from the existence of outbreaks does not limit the model to patients who became sick as part of an outbreak.Genetics must hold clues to ME/CFS because, like other chronic diseases, there is evidence that this disease can run in families, but it is clearly not a disease one has at birth. Rather, there appears to be a genetic propensity that lies hidden until a particular collection of triggering circumstances arises in the patient’s microbial, dietary, micronutrient, physiological, emotional or physical environment. One clue that distinguishes ME/CFS from most other chronic diseases is its long history epidemics, outbreaks or clusters . HistoriM. This unusual behavior has been a part of the enzyme kinetics literature for decades  increases above ~30 µM. Thus, what IDO2 can do that IDO1 cannot is to catalyze Trp + O2Three important points emerge from examination of These features of IDO kinetics can, when the supply of substrate Trp does not decrease when IDO1 is substrate inhibited, create an untoward metabolic situation that can be referred to as a metabolic trap. To provide a quantitative description of this IDO metabolic trap, we can consider the abbreviated metabolic model illustrated in s values for amino acid uptake are in the 15 µM range, while Ks values for amino acid export are in the mM range [Tryptophan is an essential amino acid; for humans, its only source is dietary. Transport across the intestinal epithelium is dominated by the LAT2 transporter while crossing the capillary endothelium, the blood-brain barrier, and the serotonergic neuronal plasma membrane is mediated by the heterodimeric transporter, LAT1. LAT1 is an obligate antiport; it transports a large neutral amino acid (often leucine) out of the cell each time it transports tryptophan in. An important feature of LAT1-mediated transport is that KmM range ,43.To capture this kinetic asymmetry, a reversible Michaelis-Menten rate law  for the Next, we need a quantitative expression for the flux through the substrate-inhibited enzyme, IDO1. This can be written using a standard substrate inhibition rate law:A full kinetic model would also include rate laws for IDO2, for tryptophan hydroxylase (TPH1 or TPH2 depending on the cell type being modeled) and for protein translation and proteolysis. Here, we can consider the case where IDO2 is disabled by common damaging mutations, where serotonin production is negligible compared to the kynurenine pathway flux, and where (in a steady-state) the flux of protein synthesis is equal to the proteolytic flux and thus there is no net flux between cytosolic tryptophan and cellular protein. These must be relaxed when it comes time for data analysis, but to establish bistability only the principal input of Trp, Thus, we can write the differential equation for cytosolic tryptophan as:In Steady-states labeled A and C are stable, and the steady-state labeled B is unstable. These conclusions can be drawn directly from critical point. If cytosolic tryptophan exceeds the value defined by this critical point, the system falls inexorably into steady-state C. This extremely simple model is thus bistable. It is capable of both a normal physiological steady-state (A) in which kynurenine production is supported by LAT1-mediated import of tryptophan and cytosolic tryptophan is ~2 × 107 molecules/cell, and a pathological steady-state (C) in which kynurenine production is nearly abolished and cytosolic tryptophan is ~5 × 109 molecules/cell, more than two orders of magnitude greater than normal for a cell expressing the kynurenine pathway.But steady-state B is different. It is unstable because stochastic variation in 14C or 13C in its indole ring and, optionally, in its alpha and carboxyl carbons as well. Tryptophan labeled with either isotope can be used in animal studies and freshly isolated or cultured cells, while the stable isotope, 13C, is, today, widely preferred for human metabolic studies. For isolated cells, the choice usually depends on local expertise and training.Every hypothesis needs an experimental test capable of rejecting or corroborating it. For the IDO metabolic trap hypothesis, the natural test is based on metabolic tracer kinetics  and the 13C-tryptophan for a specified loading period. At specified times thereafter multiple cold washes both stop the cellular reactions and remove the extracellular medium so that intracellular measurements are not contaminated with extracellular unlabeled or labeled tryptophan or secreted serotonin or kynurenine. It would then be possible to measure cytosolic 13C-tryptophan as well as the incorporation of labeled tryptophan into cellular kynurenine and serotonin. If transients in 13C-kynurenine and 13C-serotonin are measured after removing the tryptophan tracer from the medium. A great advantage of stable isotope kinetics is that the corresponding endogenous 12C compounds are measured simultaneously and quantified separately by the liquid chromatography-mass spectrometry workflow and thus further constrain the computational analysis of the data. The hypothesis will be corroborated if the flux through IDO to kynurenine is substantially reduced in cells from ME/CFS patients compared to healthy control subjects. The hypothesis will be rejected for the cell type tested if there is no difference in the IDO fluxes measured in the two experimental groups.A useful test requires methods for isolating intracellular compounds after exposing the cells to The IDO metabolic trap hypothesis for ME/CFS thus suggests that four cell types are at risk of being driven into the pathological steady-state C : (1) antConsequences of being in this abnormal steady-state depend on many factors. For example, if the normal flux through the kynurenine pathway is the major route of tryptophan oxidation/removal, then cytosolic tryptophan will increase dramatically if IDO1 becomes substrate inhibited. In turn, increased cytosolic tryptophan will drive excessive serotonin production in cells, like serotonergic raphe neurons, that express TPH2 (human chromosome 12)  with itsreg cells [Even if serotonin and melatonin production are unperturbed, the absence of kynurenine and its metabolites can have untoward effects. For example, kynurenine is spontaneously converted to trace condensation products called TEACOPs that are potent activators of the aryl hydrocarbon receptor (AHR), a transcription factor that controls the development of Teg cells . In addieg cells .+, especially if dietary nicotinate is limited [+ is insufficient to maintain a high glycolytic flux through glyceraldehyde 3-phosphate dehydrogenase, the resulting increase in triose phosphates will divert the kynurenine pathway away from neuroprotective picolinate and toward quinolinate/NAD+. This regulatory cross-talk between glycolysis and the kynurenine pathway will certainly fail in trapped cells.If instead we follow the main branch of the kynurenine pathway, additional consequences of insufficient kynurenine production appear. First, the spontaneous synthesis of quinolinate from 2-amino-3-carboxymuconate semialdehyde will decrease. Not only is quinolinate itself neuroexcitatory, but it is also the precursor of nicotinate mononucleotide, which is an important source of NAD limited . Second, limited . Thus, iIn It may be important to recognize that at some increased level of LAT1 expression, only one steady-state is possible. And that steady-state is the pathological one. Conversely, there are decreased levels of LAT1 expression that are only compatible with a normal physiological steady-state.Another implication of the critical point is that therapeutic perturbations need only reduce cytosolic tryptophan below the critical point. Below that point a return to the normal physiological steady-state is inevitable. It must be kept in mind, however, that the trap persists, and subsequent triggers can cause relapse.It remains to be determined whether this bistable system is also capable of limit cycle oscillations or deterministic chaos, which could explain relapsing-remitting forms of ME/CFS and the seeming impossibility of multiple metabalomics studies converging on the same plasma signature.Again, we emphasize that all these points are based on a hypothesis. Without experimental corroboration of that hypothesis and, optimally, a clinical trial, these statements about therapy are not actionable. They are intended to provoke research.+, and others) will occur in any cell whose IDO1 becomes substrate inhibited. Implicit in this scenario is that IDO2 activity provides insufficient backup at high cytosolic tryptophan. This, of course, is the rationale for the attention paid to common damaging mutations in IDO2. Similarly, the IDO metabolic trap cannot occur in any cell type that also expresses tryptophan-2,3-dioxygenase (TDO).Deficits in kynurenine production and decreases in the abundance of its downstream metabolites  can play both activator and inhibitor roles for LAT1-mediated tryptophan import.A final assumption of the IDO metabolic trap model is that tryptophan uptake by LAT1 has the features described in the literature , namely While it is possible that the IDO metabolic trap lays bare the etiology of ME/CFS, the probability that this is so is small. When we search for common damaging mutations in the human genome, we, by definition, find many. Consequently, the hypothesized increase in damaging alleles in ME/CFS will be difficult to demonstrate without a much larger population of carefully diagnosed ME/CFS patients whose genome sequences are available. For a given trap hypothesis, such as the IDO metabolic trap, it is likely that targeted sequencing of the relevant gene is a more efficient and cost-effective approach. Furthermore, one could question whether a classical genetic family study would be fruitful in this context because the presence of a predisposing mutation need not correlate with the presence of disease. If a common damaging mutation is predisposing , its allele frequency in a given population may be far greater than the prevalence of the disease for which it is predisposing. The mutation(s) will have low penetrance because it is one or more rare combinations of environmental circumstances that trigger the disease and determines its prevalence. If this is true, the fraction of the world’s population at risk for ME/CFS may be vastly greater than its 2% estimated prevalence of the disease.Another model of chronic disease is the comprehensive healing cycle (HC) model of Naviaux . The HC without requiring a chronic infection or chronic exposure to a toxin. Furthermore, nothing need be broken or dysfunctional. The possibility of this chronic disease state is inherent in the detailed molecular mechanism of substrate inhibition.The IDO metabolic trap is a prototype for a different class of systems-level problems. Like the HC model, the trap model implies that chronic disease is not the result of chronic exposure to a pathogen or toxin. Unlike the HC model, the trap model does not suggest a failure of healing or, indeed, a failure of anything at all. Instead, the trap hypothesis identifies substrate inhibition, an inherent and well-studied feature of some enzymes that can, in response to pathogenic triggers, unmask metabolic bistability. Bistability is a phenomenon unique to nonlinear dynamics that has been studied for decades ,24,58,59Because substrate inhibition is relatively common , the feaTo summarize, the IDO metabolic trap hypothesis for ME/CFS suggests that substrate inhibition of IDO1 creates the possibility of metabolic bistability in cells expressing the kynurenine pathway. Transition from the normal physiological steady-state to the alternative steady-state can be initiated by any trigger that increases cytosolic tryptophan concentration above the critical point (point B in


Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.A genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.We have constructed a manually curated genome-scale metabolic model of  Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is present in the human gut and is an important probiotic organism [L. reuteri also has most of the genes encoding for the enzymes needed for biosynthesis of 1,2-propanediol and 1-propanol, both of which are industrially relevant chemicals. These compounds are, however, not produced under normal conditions by L. reuteri, requiring improved engineering- and optimization strategies to achieve commercial level cell factories and production processes [organism . There iorganism , 44, dueorganism . The speorganism , of whicorganism . L. reutrocesses .Escherichia coli and Saccharomyces cerevisiae, but models for several LAB species are also available, including Lactobacillus plantarum [Lactobacillus casei [Lactococcus lactis [Streptococcus thermophilus [L. reuteri [Leuconostoc mesenteroides [L. reuteri [L. reuteri have been previously published [L. reuteri models were used along with transcriptomics data to identify qualitative metabolic differences between the two strains as well as to analyze their probiotic properties [Genome-scale metabolic models are highly useful for directing metabolic engineering strategies, as well as to improve understanding of the physiology and metabolism of the target organism , 52. So lantarum , Lactobaus casei , Lactocos lactis , 38 and mophilus   knockout strain to constrain, validate, and test the model. Lastly, we use the model to test predictions for metabolic engineering strategies. The model as well as the experimental data are available in Additional files.Here, we set out to reconstruct the metabolic network of Strains used in this study are listed in Table De Mann Rosa Sharp (MRS) medium (incl. 20 g/L glucose) was obtained from VWR and prepared according to the manufacturer’s instructions.Chemically defined medium (CDM) was used as described in , 60 with600 was measured and cells were transferred to 12 mL CDM with or without glycerol in a 15 mL Falcon tube to a starting OD600 of 0.08. After 4 h of growth, OD600 was measured and cultures were transferred to a starting OD600 of 0.05 in 100 mL pre-warmed CDM with or without glycerol in a 100 mL Schott flask. Samples for OD600 measurement and HPLC were taken directly after inoculation (t = 0 h) and at 2, 3, 4, 5, and 6 h; cultures were swirled for mixing prior to taking samples. The 6 h samples were also used for protein and amino acid determinations. The time points used were all during exponential growth, ensuring a pseudo steady state . Next morning, cultures were washed 3× with sterile 0.9% NaCl, after which OD600 of 0.1. The other two reactor cultivations were performed in CDM, with and without glycerol, at 37 °C in 0.4 L reactors with a 0.5 L working volume, 50 rpm agitation and sparged with N2 at 15 mL/min for 1 h prior to inoculation. The pH was controlled at 5.8 using 5 M NaOH. Fermenters were inoculated to an initial OD600 of 0.05 from an exponentially growing culture on CDM without glycerol. As can be seen in Additional file 2 prior to fermentations and those that were not and hence we decided to treat these as replicates.All bioreactor cultivations were performed in batch mode and samples were taken during exponential/pseudo-steady state  and ODProtein concentration of the cells was determined in the 6 h samples as described above, via a BCA protein assay (Merck-Millipore cat. 71285) according to the manufacturer’s protocol. Prior to the BCA assay, cell pellets were washed once in 0.9% NaCl and resuspended in 0.25 mM Tris–HCl pH 7.5 and sonicated on ice with an Ultrasonic Homogenizer 300VT (BioLogics) for 3 × 30 s at 40% power, with 30 s breaks on ice.Amino acid composition of the cells was determined by Ansynth BV (The Netherlands) on washed cell pellets of a 6 h CDM culture as described above.2SO4, a flow rate of 0.6 mL/min and an oven temperature of 60 °C; 2) a Shimadzu LC-20AD equipped with refractive index and UV (210 nm) detectors, with an injection volume of 20 µL. A Shodex SH1011 8.0 mmIDx300mm column was used with a mobile phase of 5 mM H2SO4, a flow rate of 0.6 mL/min and an oven temperature of 50 °C. All amino acids, ornithine and GABA were quantified using a Dionex Ultimate 3000 (Thermo Scientific), for which the procedure is as follows: 20 µg/mL 2-aminobutanoic acid and sarcosine were used as internal standards for dilution of the samples; derivatization was performed in the autosampler. 0.5 µL sample was added into 2.5 µL of (v/v) 3-mercaptopropionic acid in borate buffer , mixed and incubated for 20 s at 4 °C to reduce free cystines. Then 1 µL of 120 mM iodoacetic acid in 140 mM NaOH was added, mixed and incubated for 20 s at 4 °C to alkylate reduced cysteines. 1.5 µL of OPA reagent  was then added to derivatize primary amino acids. The reaction was mixed and incubated for 20 s at 4 °C. 1 µL of FMOC reagent (2.5 mg 9-fluorenylmethyl chloroformate/mL in acetonitrile) was added, mixed and incubated for 20 s at 4 °C to derivatize other amino acids. 50 µL of Buffer A  at pH 7.8) at pH 7 was added to lower the pH of the reaction prior to injecting the 56.5 µL reaction onto a Gemini C18 column  with a guard column . The column temperature was kept at 37 °C in a thermostatic column compartment. The mobile phase had the following composition: Buffer A: see above, pH 7.8; Buffer B: 45% (v/v) acetonitrile, 45% (v/v) methanol and 10% (v/v) water; flow rate 1 mL/min. Derivatized amino acids were monitored using a fluorescence detector. OPA-derivatized amino acids were detected at 340ex and 450em nm and FMOC-derivatized amino acids at 266ex and 305em nm. Quantifications were based on standard curves derived from dilutions of a mixed amino acid standard (250 µg/mL). The upper and lower limits of quantification were 100 and 0.5 µg/mL, respectively.Substrates, products and amino acids secreted and taken up during the cultivations were quantified using HPLC. Glucose, glycerol, ethanol, lactate, acetate, citrate, 1,2-propanediol, 1,3-propanediol, 1-propanol, 2-propanol, pyruvate, succinate and malate were quantified with either one of two HPLCs: 1) a Dionex Ultimate 3000 (Thermo Scientific) containing an LPG-3400SD pump, a WPS-3000 autosampler, a UV–visible (UV–Vis) DAD-3000 detector, and an RI-101 refraction index detector. Injection volume was 20 µL. An Aminex HPx87 ion exclusion 125-0140 column was used with a mobile phase of 5 mM HL. reuteri JCM 1112, GenBank accession nr AP007281, annotated with Prokka version 1.11) were identified using Breseq (version 0.31.0) [L. reuteri JCM 1112 from NCBI was used. During the reconstruction, several genes were re-annotated, based on BLAST and physiological data. A list of all genes in the JCM 1112 genome can be found in Additional fie For genomic DNA (gDNA) isolation, overnight cultures of DSM 20016 and SJ 11774 were grown in MRS and the pellet was used for gDNA isolation using the Epicentre MasterPure™ Gram Positive DNA Purification kit according to the manufacturer’s protocol. Subsequent genome sequencing was performed at the sequencing facility at the NNF Center for Biosustainability. Library preparation was performed using KAPA HyperPlus Library Prep Kit (ROCHE) with Illumina-compatible dual-indexed PentAdapters (PentaBase). The average size of the library pool was 317 bp. Sequencing was performed on MiSeq (Illumina) using the MiSeq Reagent Kit v2, 300 Cycles (Illumina). The libraries were loaded to the flow cell at 10 pM and sequenced using paired-end reads of 150 bp. Read quality check was performed with FastQC version 0.11.5. Mutations relative to reference  was estimated using one of the data sets   was used to analyze the genome-scale metabolic model , 53 by cy (PKP) “” sectionious FBA  which idmentclasspt{minima package  was usedadhE gene were predicted by temporarily deleting it from the network. Where the effects of an active 1,2-propanediol pathway were predicted, a methylglyoxal synthase (MGS) was added to the model and optimized for growth.To predict growth rates the model was constrained with uptake rates of glucose, glycerol and five amino acids , and with the secretion rates of ethanol, lactate, acetate and 1,3-propanediol. Effects of knocking out the l-alanine, an l-alanine dehydrogenase was added to the model. The production of ethyl lactate required the addition of a lactate acyl transferase and a reaction for the condensation of lactoyl-CoA with ethanol [+ and the flux predictions were therefore repeated in the presence of an active MGS. To simulate a non-limiting phosphofructokinase, the flux constraint involving vPFK above was omitted.To predict the theoretical maximum yields of selected target compounds, a reaction enabling the secretion of the corresponding metabolite was added to the model, unless an exchange reaction already existed, and flux through the reaction maximized. The glucose uptake rate was 25.2 mmol/gDW/h, based on experimental data, and free secretion of by-products was allowed. For the production of  ethanol . To prodL. reuteri suitable for use in cell factory design and optimization, we built upon a draft metabolic model of L. reuteri JCM 1112 described in [To reconstruct a genome-scale metabolic model of ribed in  that we ribed in  was usedReactions and metabolites were abbreviated according to the BiGG database nomenclature where applicable and annotations with links to external databases included. Genes from the JCM 1112 genome were identified with locus tags from the GenBank file, and annotations were included which contain: the old locus tag which is often found in older literature, the NCBI protein ID, gene annotation and the protein sequence. Apart from general network curation, organism-specific information obtained from laboratory experiments and from available literature was integrated by reviewing reactions, genes and gene-protein-reaction (GPR) rules.L. reuteri F275 [The two most well-known strain names and origins for the type strain are DSM 20016 and JCM 1112 from the DSMZ and JCM culture collections, respectively. These two are derived from the same original human faeces isolate eri F275 , which weri F275 . Both geeri F275 , which weri F275 . The fireri F275 . As the L. reuteri type strain. We sequenced the DSM 20016 strain that we obtained from DSMZ and this showed that its genome is identical to that of JCM 1112 instead, meaning it contained the two islands missing in DSM 20016. A similar result of these strains being ‘swapped’ was obtained by others based on whole genome sequencing [L. reuteri type strain perform PCR on the two islands or perform resequencing to validate the presence or absence of the genes in the two islands.During the preparation of our model, it became clear that there are inconsistencies in naming and hence gene content of the quencing  and PCR quencing . This inquencing . Contrarquencing . Both stBased on our sequencing results, we have included all genes in the two islands in our metabolic reconstruction and model. The genes in the model are identified with the locus tags obtained from the JCM 1112 strain’s genome in NCBI (NC_010609). As many other publications refer to genes in the DSM 20016 strain or use the old locus tags from the JCM 1112 genome, we have included a table  instead of the Embden–Meyerhof–Parnas pathway (EMPP) for glucose consumption [pfk was lacking from a distinct monophyletic group formed by mainly (87%) obligatively and otherwise facultatively heterofermentative Lactobacillus spp., including L. reuteri DSM 20016 and L. panis DSM 6035 [fba, which has traditionally been linked to the presence of the EMPP. Despite the absence of pfk, EMPP activity has been observed in several L. reuteri strains and in some strains it appears to play a major role compared to the PKP, depending on the growth phase, and showing strain-specific differences [Obligately heterofermentative lactobacilli like sumption  Fig. 1)L. reuterDSM 6035 . Contrarferences . For modLactobacillales and showed that L. reuteri (strains ATCC 55730 and DSM 20016) is one of the four exceptions that do not have a pfkA gene where this is located in all other species. Nevertheless, they detect PFK and EMPP activity in strain ATCC 55730 and subsequently identify two genes (GenBank accession nrs EF547651 and EF547653) for orthologues of pfkB, a minor PFK-variant in E. coli [L. reuteri, Kang et al. [L. panis PM1 with 82% similarity to the pfkB gene identified in L. reuteri ATCC 55730 from Årsköld et al. (74% in our own BLAST search).Årsköld et al.  comparedg et al.  identifipfkB protein sequence of L. panis PM1 (GenBank accession nr AGU90228.1) and L. reuteri ATCC 55730 (GenBank accession nr ABQ23677.1) against L. reuteri JCM 1112 resulted in 81% and 99% identity, respectively, to JCM 1112 gene number LAR_RS02150, which is annotated as ribokinase rbsK_2. On a gene level, this gene shares 97% identity with L. reuteri ATCC 55730 and 73% with L. panis PM1. The same identities were found in L. reuteri DSM 20016 for gene LREU_RS02105 . A second gene annotated as “ribokinase rbsK_3” (locus tag LAR_RS06895) showed only limited query coverage and identity and hence rbsK_2 is the most likely homolog of pfkB. The growth experiments conducted in the present study with JCM 1112 are in line with the findings of Burgé et al. [pfkB. The average flux through the EMPP in all cultures was 7.0%  as energy source for transport [L. reuteri JCM 1112, we could identify the two common proteins of the PTS system, Enzyme I (Lreu_1324) and HPr (Lreu_1325). Some sugar-specific parts were present, but no complete PTS was identified. As a result, all sugar transport in the model takes place via secondary transporters and the PMF.Transport of carbohydrates can be mediated by ATP-Binding Cassette (ABC) transporters, phosphotransferase systems (PTS), or secondary transporters  . PTS systive LAB . This shransport . In L. rLactobacillus reuteri, like many lactobacilli, is known to be unable to grow on glycerol as a sole carbon source, but can use it as an alternative electron acceptor, providing a means to gain energy on a variety of carbon sources [L. reuteri is among the best native producers of large amounts of 3-hydroxypropionaldehyde  from glycerol that are currently known [L. reuteri, depending on the conditions used) that is known to be toxic and produced in a microcompartment [L. reuteri genome [L. reuteri glycerol dehydrogenase also has activity as 1,3-PDO:NAD-oxidoreductase, whereas in for example Klebsiella pneumoniae, which does produce glycolytic end products from glycerol, these are two different enzymes [L. reuteri is the reduction of 3-HPA to 1,3-PDO, rather than glycerol to DHA conversion, explaining the lack of growth on glycerol [ sources , 57. L. ly known . This isi genome . FurtherL. reuteri strain, based on sequence comparisons with the L. casei strain used by Landete et al. [L. reuteri JCM 1112 possesses a malate dehydrogenase and PEP carboxykinase, and cannot utilize citrate; malate (and fumarate) is converted to succinate [Most heterofermentative LAB possess a malolactic enzyme but no malic enzymes , which ie et al. . Based ouccinate .L. reuteri possesses all enzymes needed for these pathways, except methylglyoxal synthase (MGS), the step of the pathway converting dihydroxyacetone phosphate into MG [L. reuteri JCM 1112 cultures or when a heterologous mgs is expressed, all the subsequent metabolites are formed [mgs in the L. reuteri genome, this homolog is clearly not active under normal conditions since no 1,2-PDO was observed in our experiments. Hence, all the genes in these pathways except mgs were included in the reconstruction. For methylglyoxal reductase, mgr, we also identified several aldo/keto reductases as possible homologs, based BLAST comparison to genes identified in [From a biotechnological perspective, an interesting branch point of central carbon metabolism is the conversion from methylglyoxal (MG) to 1,2-propanediol , which can then be further metabolized into 1-propanol and propanoate.  into MG , 55. It e formed . Althougified in . Howeverified in , which hified in .L. reuteri is a native producer. Vitamin B12 is important as a cofactor in for example the 3-HPA pathway but is also of relevance for biotechnological and medical/health applications (e.g. when produced by probiotic strains). The structure and biosynthetic genes have been studied in detail [Whereas many LAB are auxotrophic for vitamin B12, n detail , 50. TheL. reuteri biomass was also measured experimentally. The remaining macromolecular fractions were derived from L. plantarum [L. lactis [L. reuteri was obtained from literature [L. plantarum. The composition of lipoteichoic acid [L. reuteri were obtained from literature. Peptidoglycan composition was adopted from L. plantarum and glycogen was assumed to be negligible [A biomass objective function (BOF), which contains all necessary components for biomass biosynthesis, is commonly used to predict growth rate in metabolic models. Ideally, the BOF should be constructed based on organism-specific experimental data, mainly the fractional composition of the macromolecules  and their individual building blocks , as well as the energy necessary for their biosynthesis . The prolantarum  and L. l. lactis . Nucleotterature , while poic acid  and exopoic acid  in L. regligible , 9.L. reuteri. These parameters have been estimated from experimental data for several other LAB, including L. plantarum, and reported in literature [L. reuteri and L. plantarum are relatively closely related, adopting these parameters from L. plantarum can negatively affect the quality of model predictions. When the differences in physiologies of L. plantarum and L. reuteri are considered, it is possible that L. reuteri requires less energy: (1) The genome is only ~ 2 Mb, while L. plantarum’s genome is 3.3 Mb. (2) L. reuteri is an obligate heterofermenter, which means it uses almost solely the PKP  LAB in general have low catabolic capabilities, and for L. reuteri this includes auxotrophy for several amino acids. This, combined with the fact that macromolecular biosynthesis is already accounted for in the model reactions, supports the claim that adopting energy parameters from L. plantarum can negatively affect model predictions, as we also observed when evaluating this in our model. We decided to use one of our experimental datasets (Table L. plantarum (“Energy required for growth (GAM) and cell maintenance (NGAM) are important parameters in metabolic models, and can be estimated from ATP production rates, which can be calculated from experimental data obtained at different dilution rates . Unfortuterature . Even thantarum “” section L. reuteri specific experimental data obtained in this study.The sensitivity of the predicted growth rate to changes in biomass and energy components was investigated by varying the coefficient of each component, one at a time, by 50% while varying the glucose uptake rate. The components tested were protein, polysaccharide, DNA, RNA, lipid, GAM and NGAM. The analysis showed that predicted growth rate was sensitive to changes in the protein and GAM components of the biomass, compared to the other components . The model predicts slightly higher growth rates than observed in vivo for the SJ strain , which is to be expected based on inspection of redox balance [L. reuteri uses practically only the PKP and not the EMP for glucose fermentation. In the PKP, two extra NAD(P)H molecules are formed compared to the EMP, which are regenerated to NAD(P)+ by AdhE through the formation of ethanol  but does yield one ATP, enabling a higher growth rate [adhE gene has dramatic effects on the metabolism when glucose is the sole carbon source, both in vivo and in silico  in L. reuteri can activate the pathway to 1,2-PDO and 1-propanol production [L. reuteri for using the 1,2-PDO pathway towards 1-propanol is that L. reuteri produces vitamin B12, which is needed as a co-factor for the B12-dependent diol dehydratase step and has been suggested to be a limiting factor in this pathway in for example C. glutamicum [ E. coli strain harboring this pathway [ L. reuteri  as well as growth-coupled production of 1-propanol (14.7 mmol/gDW/h). Given the good agreement between in silico predictions and in vivo measurements in “mgs gene in the 1,2-PDO pathway at a sufficiently high level in vivo is expected to result in a relatively fast-growing 1-propanol-producing cell factory, which is also in agreement with existing literature [While optimizing in silico for 1-propanol production as described above is a good way to assess suitability of different engineering strategies, optimizing for growth gives more biologically realistic results. Hence, next the model was used to maximize growth while constrained with experimental uptake rates of glucose and the 5 amino acids from the WT grown on glucose , a food additive , biofuels (1-propanol and ethanol), chemical building blocks  and an environmentally friendly solvent (ethyl lactate). The last compound has recently been produced in an engineered E. coli strain [L. reuteri since it is a condensation product of the two major products of glucose fermentation via the phosphoketolase pathway, lactate and ethanol.The heterofermentative nature of i strain  and is aL. reuteri for producing a particular compound was assessed in terms of the maximum carbon yield, using a fixed glucose uptake rate .In this study, we have established a manually curated genome-scale metabolic model of Additional file 1. Growth curves and measured uptake- and secretion rates of metabolites for experimental datasets.L. reuteri JCM 1112.Additional file 2. Gene list for Additional file 3. Code used for simulations.Additional file 4. Escher metabolic maps.Additional file 5. Memote report.Additional file 6. Genome scale metabolic model: Lreuteri_530.Additional file 7. Biomass reaction.L. reuteri, L. plantarum and L. lactis.Additional file 8. Unique EC-numbers for