Sphingopyxis granuli strain TFA is an α-proteobacterium that belongs to the sphingomonads, a group of bacteria well-known for its degradative capabilities and oligotrophic metabolism. Strain TFA is the only bacterium in which the mineralisation of the aromatic pollutant tetralin has been completely characterized at biochemical, genetic, and regulatory levels and the first Sphingopyxis characterised as facultative anaerobe. Here we report additional metabolic features of this α-proteobacterium using metabolic modelling and the functional integration of genomic and transcriptomic data. The genome-scale metabolic model (GEM) of strain TFA, which has been manually curated, includes information on 743 genes, 1114 metabolites and 1397 reactions. This represents the largest metabolic model for a member of the Sphingomonadales order thus far. The predictive potential of this model was validated against experimentally calculated growth rates on different carbon sources and under different growth conditions, including both aerobic and anaerobic metabolisms. Moreover, new carbon and nitrogen sources were predicted and experimentally validated. The constructed metabolic model was used as a platform for the incorporation of transcriptomic data, generating a more robust and accurate model. In silico flux analysis under different metabolic scenarios highlighted the key role of the glyoxylate cycle in the central metabolism of strain TFA. Sphingomonadales order, which includes the Sphingomonadaceae and Erythrobacteraceae families, are known as degraders of a large diversity of recalcitrant compounds1. Within the Sphingomonadaceae family, the sphingomonads group, composed of the ubiquitous genera Sphingomonas, Sphingobium, Novosphingobium and Sphingopyxis2, contains metabolically versatile bacteria capable of using a large number of recalcitrant compounds, mainly aromatic compounds and their derivatives, as sole carbon and energy source3. In addition, the genus Sphingopyxis includes organisms adapted to oligotrophic environments, which show specific genomic characteristics such as a low number of rRNA operons, prophages or CRISPRs sequences4. Moreover, oligotrophs show overrepresentation of Clusters of Orthologous Groups (COGs) for lipid transport and metabolism (I) and secondary metabolite biosynthesis, transport, and catabolism (Q) among others4.Thus there is a need to better understand the metabolism of these degradative bacteria and to potentially reveal new insights into their degradative capabilities and lifestyle. To this end, we set out to establish a GEnome-scale metabolic Network REconstruction (GENRE) for a member of this genus that was likely to prove a powerful tool for future studies8. A GENRE is a database with specific information about the species of interest generated from genomic data, and that contains detailed information about metabolism, the network of reactions, including stoichiometry and reversibility, the Gene-Protein-Reaction (GPR) associations and other available biochemical and physiological data9. The development of COnstraint-Based Reconstruction and Analysis (COBRA) methods10 has allowed the generation of quantitative GEnome-scale metabolic Models (GEMs), that can be used to understand phenotypes in terms of metabolic fluxes. GEMs allow the integration of additional information, such as gene expression data, compartmentalisation of the pathways and probabilistic transcriptional regulation data, resulting in more robust metabolic models with enhanced predictive capabilities. Nowadays there are tools that automatically generate GEMs, like Path2Models11, CarveMe12 or AGORA13. However, manual curation, although tedious and time-consuming, always provides a high-quality and more reliable metabolic model. A recent review of GEMs shows that only 1.9% of the bacterial models have been manually reconstructed8. Some applications of well-curated GEMs include the design of microorganisms for the production of chemicals, the prediction of enzyme functions or the discovery of drug targets in pathogens8.Members of the 14, 198 and 813 genome sequences of Erythrobacteraceae and Sphingomonadaceae, respectively, are available. However, despite the environmental significance of these bacterial groups, only one metabolic reconstruction using the genomic information of Zymomonas mobilis, iEM439, has been generated thus far15. This species is a well-known member of the Sphingomonadaceae family because of its ability to produce ethanol in a broad pH range, its ethanol tolerance and its use in other biotechnological applications16. In this report, we present a genome-scale metabolic model of another Sphingomonadaceae, Sphingopyxis granuli strain TFA, a small, rod-shaped, facultative anaerobic, streptomycin-resistant bacterium that possesses the capability to grow using tetralin, a volatile toxic compound that consists of an aromatic and an alicyclic ring, as sole carbon and energy source17. The metabolic pathway for tetralin degradation of strain TFA is the only one completely characterized at both biochemical and genetic levels18. This pathway, which seems to have been horizontally acquired by TFA, represents a paradigm of how a bicyclic compound can be metabolised with one set of genes mainly involved in the degradation of aromatic compounds. Moreover, the mechanisms involved in the regulation of the expression of the pathway structural and regulatory genes at both transcriptional and translational levels have been elucidated18 showing that genes are induced in the presence of tetralin but subjected to carbon catabolite repression in the presence of preferential carbon sources. The genome of strain TFA has been sequenced, assembled into a single circular DNA molecule and functionally annotated19. No free plasmids have been detected in TFA either experimentally or during genome sequencing19. In this report, biochemical data and gene annotations have been put together to generate a GEM of strain TFA, that has been manually curated and whose characteristics and predictive potential are presented. New potential carbon and nitrogen sources and metabolic pathways that could support growth of strain TFA have been discovered. Moreover, the metabolic model has been improved by incorporating transcriptomic data obtained under two different growth conditions. Flux Balance Analysis (FBA) has revealed the importance of the glyoxylate cycle in the central metabolism of this bacterium. Overall, the model presented here constitutes the first GEM generated for a member of the Sphingopyxis genus and the second within the Sphingomonadales order.According to the NCBI Genome database (Genome List)iIG743, was constructed following a procedure that includes four steps . It contained 810 reactions, classified into 84 functional subsystems, 952 metabolites and 386 genes. In this initial draft, 260 reactions and 407 metabolites were added indistinctly from iJN1411 or iJO1366 based on orthologous genes for those reactions present in both models and in the TFA genome. Interestingly, 410 reactions and 421 metabolites were assigned exclusively from iJN1411 whereas only 140 reactions and 124 metabolites came from iJO1366. In total, 670 reactions and 828 metabolites were common between iJN1411 and the initial TFA draft, whilst 400 reactions and 531 metabolites were shared with iJO1366. The higher number of reactions, genes and metabolites taken from iJN1411 suggests that TFA is metabolically more similar to P. putida KT2440 than to E. coli K12 , the production of the compatible solute ectoine, and the biosynthesis of sphingolipids as components of the cell envelope, which is a characteristic of the Sphingomonadales order. To model tetralin degradation, all available biochemical and genetic data were used18 -3-hydroxybutyryl-CoA originates from two molecules of acetyl-CoA via acetoacetyl-CoA23. Additionally, since strain TFA is able to grow on 3-HB as carbon and energy source, an additional pathway that directly activates 3-HB into its CoA derivative in a reaction catalysed by putative acyl-CoA ligases (SGRAN_2644 or SGRAN_3728) was included  that however lacked a gene for the expected L-aspartate-β-semialdehyde dehydrogenase  , a PVA degradation pathway has been included in iIG743.Secondly, an exhaustive revision and manual curation of reactions was carried out based on the careful analysis of each GPR included in the draft (see Methods). Therefore, each individual GPR was evaluated based not only on sequence identity, but also considering physiological evidences. For instance, the transport reaction 3_4DHBZ1t_pp_  included in the draft from iJN1411 was eliminated since no enzymes for 3,4-dihydroxybenzoate catabolism were found in TFA. Similarly, the transport reactions for 3-hydroxybutyrate (3-HB) and sebacic acid were included as it is known that TFA grows using these fatty acids as sole carbon and energy sources. Then, the initial model was converted into a mathematical representation and was tested for biomass production (a detailed description as to how biomass composition was determined can be found in the methods section). Finally, the network gaps across the metabolic pathways were filled in by adding new reactions based on the information stored in biological databases, scientific literature and in the functional annotation of strain TFAiIG743 contains a large number of reactions and metabolites. Moreover, the manual curation resulted in high or very high (3 or 4) confidence values for 27.8% of the reactions included in iIG743. Reactions were classified into fourteen major categories  were essential except thnN, thnO and thnP, which code for a glutaryl-CoA dehydrogenase and an α- and β-flavoprotein subunit, respectively. However, other non-essential genes such as those involved in β-oxidation of pimelic acid  that are dispensable for growth with tetralin or pimelic acid were not predicted. The large number of β-oxidation pathways and aredundant pimelic semialdehyde dehydrogenase activity (ThnG) found in strain TFA28 might explain this discrepancy.Only 2.6% of the reactions were assigned to the catabolism of aromatic compounds, such as those involved in the tetralin degradation pathway, the catabolism of the aromatic amino acids phenylalanine, tyrosine and tryptophan, and the degradation pathway of 3-phenylpropanoate and 3-(3-hydroxyphenyl) propanoate. Analysis of essential genes to predict growth on tetralin showed that all the genes identified previously experimentally to be involved in tetralin degradation19 and the corresponding pathways included into iIG743. However, no growth of strain TFA was supported by any of these compounds (Table Genes encoding enzymes for the utilization of 3-phenylpropanoate and 3-(3-hydroxyphenyl) propanoate as carbon and energy sources, converting these compounds into pyruvate and acetyl-CoA, were also foundds Table .Table 2PiIG743, a minimal medium was established (see Methods) and the flux through the exchange reaction of each metabolite, including O2, was not limited. To test whether iIG743 was able to predict cellular growth, the biomass reaction was selected as the objective function and was maximised through the Flux Balance Analysis algorithm (FBA) implemented in the COBRA package. The model was able to predict growth on compounds previously shown to be carbon and energy sources for strain TFA, such as tetralin, 3-HB and sebacic acid, supporting the absence of gaps in the predicted catabolic pathways. Furthermore, the model was able to predict growth in anoxic conditions using nitrate as electron acceptor and 3-HB or sebacic acid as carbon and energy source as had been shown experimentally19. To evaluate quantitatively the predictive capability of iIG743, the uptake rate of three carbon sources  for the growth of strain TFA in oxic conditions was experimentally calculated  propanoate and fructose. In the case of glucose, the model predicts growth only if a transporter, which has not been annotated in the genome of strain TFA, is artificially included. For growth on PVA, the model predicts that PQQ has to be included as a component of the minimal medium (see above) and shows that for a fixed PVA uptake, PQQ uptake rate determines TFA growth rate on PVA.Like other members of the genus 4, where the overrepresentation of genes involved in lipid transport and metabolism in oligotrophic bacteria was related to the higher ATP yield obtained from fats compared to sugars4.When predicted growth rates were normalized according the numbers of carbon atoms present in each carbon source, the model strongly suggested fatty acids as the preferred and more efficient carbon and energy sources over amino acids and other compounds such as lactate or tetralin  was used as constraint in the exchange reactions. The uptake of the rest of the metabolites in the minimal medium was not limited, except O2, whose uptake was set to 0. Additionally, since a lower value for the non-growth associated ATP maintenance reaction (NGAM) under anoxic conditions has been described in E. coli30, NGAM was reduced from 0.92  to 0.15 mmol·gDW−1·h−1 to give predicted growth rates of 0.038 h−1 on 3-HB and of 0.037 h−1 on sebacic acid, while the experimentally calculated values were 0.055 h−1 and 0.020 h−1, respectively. Concomitant nitrite secretion during anaerobic growth of strain TFA using nitrate as electron acceptor has been experimentally demonstrated19. To establish the predictive capacity of iIG743, the uptake of nitrate and 3-HB and the excretion of nitrite were simulated by dynamic FBA (dFBA) throughout the cultivation time. For the simulation, the initial concentrations of nitrate and 3-HB were set to 20 mM and 40 mM, respectively, as previously used under experimental conditions19, and metabolite uptake rates were set as described above. Initial biomass was experimentally calculated to be 0.038 g·L−1.Strain TFA is a facultative anaerobe that can grow using either oxygen or nitrate as the terminal electron acceptors19. Similar results were obtained when anaerobic growth with sebacic acid was simulated (data not shown). The results show that iIG743 model contains all the information to simulate TFA growth under anoxic conditions.As visualized in Fig. 31. We thus included available transcriptomic data for strain TFA grown aerobically with tetralin and 3-HB32 in an attempt to optimize iIG743 predictions for the strain grown on each of the two carbon sources. To do this, we reduced the solution space of iIG743 by using the GIMME method33. The expression of each gene was used as a new constraint in the metabolic model since only genes with a FPKM value higher than the cut-off (see Methods) were considered as expressed under each growth condition. Thus, GIMME constructed condition-specific models by removing reactions encoded by unexpressed genes in each condition and finding a flux distribution consistent with the biological objective. To explore the feasible metabolic states, random sampling was applied to each new model. In this process, the possible fluxes and the probability of each one were determined for all of the reactions in the network,  was now predicted to be an essential gene for growth on tetralin since glyoxylate became essential for the synthesis of glycerol-3-phosphate as source of glyceraldehyde-3-phosphate  and the conservation of the genetic organization of TFA PVA-degradation genes with those present in the well-characterised PVA-degrader Sphingopyxis sp strain 113P3 support the existence of that pathway in TFA. However, more experimental assays, such as the use of PVA with different degree of polymerization, are needed since we cannot rule out that those are silent genes. Genes encoding upper pathways  that converge in 2-oxopent-4-enoate were annotated in the genome of strain TFA. This compound could be further metabolised to pyruvate and acetyl-CoA by the mhpDEF gene products. The lack of growth of strain TFA with 3HPP and 3PP could be explained by (i) the absence of appropriate transport systems, (ii) the lack of a functional regulatory system that allowed gene expression, and/or (iii) inaccuracy in gene annotation on enzyme specificity resulting in an incorrectly predicted substrate for a metabolic pathway. The latter could mean that strain TFA might use the predicted pathways to metabolize other similar compounds. Correct substrate prediction is one of the major challenges of automatic GEM generation and requires extensive experimental data. Interestingly, adaptive laboratory experiments (ALE) have shown that some in silico predicted phenotypes can be achieved in vivo after short-term evolution37 by activating putative silent pathways. Alternatively, the presence of these silent metabolic pathways might represent the first step of an evolutionary process to lose the ability to degrade particular aromatic compounds. Consistent with this, it has been proposed that Sphingopyxis are specialised to degrade particular compounds rather than exhibiting the ability to utilise a wide range of aromatic substrates38.Other compounds identified as carbon sources by the model were PVA, 3-(3-hydroxyphenyl) propanoate (3HPP) and 3-phenylpropanoate (3PP). Although PVA-supported TFA growth was no detected even in the presence of a PQQ producer, the PVA degradation pathway was included in in silico prediction proposed growth with compounds that have been verified in vivo, in some cases, such as the use of 3-HB in oxic conditions, in silico growth rates were higher than the experimentally calculated. A possible explanation for this discrepancy could be that these compounds were not immediately respired in vivo but captured in an intracellular pool of compounds, such as PHB granules, as it has been described in S. alaskensis RB2256 during growth with glucose39. In fact, 3-HB catabolism implies its conversion into acetoacetyl-CoA and acetyl-CoA 42, where the dispensability of gnyA for L-isoleucine utilization and of scoA and scoB for growth on 4-hydroxybenzoic acid has been recently demonstrated by Dunhpy et al.43. In E. coli, aspC, argD, and gltA were incorrectly predicted as essential since isoenzyme functions have been described44. Thus, as a dynamic process, a genome-scale metabolic reconstruction is expected to undergo continual refinement as inconsistences are found between predictions and experimental data.thn genes repressed in the presence of preferential carbon sources such as 3-HB or sebacic acid18. Interestingly, iIG743 is able to simulate this phenomenon when oxygen uptake is limited to values predicted by the model when using different carbon sources. For instance, when oxygen uptake was limited to -5 mmol·gDW-1·h-1, the model discriminates between a good carbon source for TFA, such as 3-HB, and tetralin and establishes a hierarchy of use . However, it is known that in vivo strain TFA shows similar growth rates and final biomass accumulations in the presence of 20 or 40 mM nitrate as electron acceptor19. This may be due to the inhibitory effect of nitrite concentrations higher than 20 mM in the growth of strain TFA19. In fact, the difficulty of predicting the toxicity of metabolic intermediates or final products of pathways is one of the major drawbacks when heterologous pathways are incorporated into the metabolism of a target host45.Anaerobic growth of strain TFA using nitrate as electron acceptor could also be modelled using 46, which is the case for tetralin, 3-HB and sebacic acid. Moreover, induction of the glyoxylate shunt by oxidative stress has been shown in P. aeruginosa47. This could be the case in strain TFA when grown on tetralin since this compound is toxic because of the formation of hydroperoxides in the cell48. Moreover, the high demand for oxygen during its degradation, which requires high oxygenase activity, might also lead to the formation of hydrogen peroxide49 resulting in the induction of glyoxylate synthesis to deal with the generated oxidative stress47.The incorporation of transcriptomic data into metabolic models was expected to result in a more accurate metabolic scenario by defining which genes will be transcribed under a certain environmental condition, which could push fluxes into some specific pathways or reactions. When expression data were incorporated into the model, pathways of central metabolism were modified in different ways depending on the carbon source. However, in all cases, the central role of the glyoxylate shunt was maintained or even enhanced. This shunt bypasses the Krebs cycle conserving carbon atoms for gluconeogenesis and diminishing the number of electrons funnelled into respiration. It is known that this cycle is upregulated when acetyl-CoA is the direct product of a metabolic pathwayIn conclusion, we have developed and enhanced a powerful tool for further metabolic studies of strain TFA that will allow us to better explore and exploit the unstudied metabolic characteristics of this oligotrophic bacterium.Sphingopyxis granuli strain TFA was used as a model organism for this work17. Strain TFA was grown aerobically at 30 °C in MML rich medium and in MM minimal medium17 containing an appropriate amount of carbon source 21, using the GEMSiRV-MrBac server as described in50. Two lists of orthologous genes, one between strain TFA and K12 and another between strain TFA and KT2440 were obtained by Reciprocal Best Hits using BlastP . These lists and the corresponding model in SBML format were used as input files for the GEMSiRV-MrBac server to obtain two draft models of strain TFA in SBML format which included the reactions present in iJO1366 and iJN1411 assigned to the orthologous genes identified in TFA. Both drafts were then merged into one model eliminating redundancies.Firstly, two initial models of strain TFA were constructed based on 51 and/or Pseudomonas database (http://www.pseudomonas.com/). To increase the number of GPRs, proteins of strain TFA were considered orthologous to the best hit in the BLASTP alignment when the e-value was less than 1e-5, the identity higher than 30% and the coverage of both proteins in the alignment higher than 70%52. Additional criteria such as the absence or presence of a proper catabolic pathway were used to eliminate or to include transport reactions for some metabolites. Furthermore, a confidence value (between 1 and 4) was assigned to each reaction, which indicated the level of biological knowledge. This value was established according to Thiele and Palsson6 with 4 being the highest level of knowledge about the metabolic reaction. Metabolic/biochemical databases such as KEGG53, BRENDA54, MetaCyc55 and BiGG56 were used to find the information for the confidence value assignation. In addition, notes and bibliographic references were associated to some GPRs, which were used, together with the BLASTP result and the confidence value, to evaluate the inclusion of a reaction into the network.Secondly, as part of the manual curation, all the gene-protein-reaction (GPR) associations were manually reviewed. The assignment of each gene-protein to a reaction was validated by BLASTP against the Swiss-Prot databaseiJN141157 and iJO1366. Since experimental data for macromolecular composition were not available for TFA, standard values were considered for the biomass , the latter was removed from TFA biomass and the synthesis of sphingolipids was added in the form of the precursor sphinganine . Furthermore, the synthesis of all biomass components by the metabolic network was evaluated. If any one component was not produced, the corresponding biosynthesis pathway was manually revised to find gaps and to fill them in. In the same way, the reactions within each subsystem were analysed to corroborate that essential metabolites were properly synthetized. When possible, a gene was assigned to the newly added reaction using the criteria described above. Otherwise, it was incorporated as an orphan or spontaneous reaction based on available literature. Other non-gene associated reactions were also added, such as exchange reactions for all the external metabolites, sink and demand reactions and the non-growth ATP maintenance reaction (NGAM), as described by Thiele and PalssoniIG743 model can be found in the Supplementary Tables The reactions and metabolites included in the final 20The metabolic reconstruction of strain TFA consisted of a network of reactions in which fluxes were constrained in several ways. Firstly, the lower and upper fluxes of each reaction were limited. Secondly, the metabolic matrix S was imposing constraint through the stoichiometric coefficients. And, thirdly, the stationary state was considered in the simulation, which is defined by the equation Sv=0, where v represents the fluxes through the reactions58 was used to evaluate the biomass production (growth prediction) once the biomass reaction was fixed as the objective function . The result when executing FBA was the growth rate (h-1) predicted under the specified media conditions. All the exchange reactions were sequentially tested as potential carbon, nitrogen, or both carbon and nitrogen source, establishing an uptake (vi) of -10 mmol·gDW-1·h-1. For each test, the uptake of the rest of nutrients in the defined minimal medium, including oxygen, was set up to -30 ≤ vi ≤ 0 mmol·gDW1·h-1 to avoid uptake limitation other than the tested carbon and/or nitrogen source. For the carbon sources, the growth rate was further normalized considering the number of carbons of each source.The Flux Balanced Analysis (FBA)59 under anoxic conditions. For that, the initial concentration of 3-HB and nitrate were set to 40 and 20 mM and the uptake to -3 and -2.82 mmol·gDW-1·h-1, respectively. As required for anoxic simulation, the entrance of oxygen was set to 0 mmol·gDW-1·h-1. The initial biomass was experimentally calculated to be 0.038 grams, the timestep was establish in 1 and the number of steps in 200.Furthermore, the concentration of metabolites in the extracellular space and the biomass formation along the cultivation time were analysed through the dynamic FBA (dFBA)60 implemented in COBRA package61.The distribution of feasible fluxes in the condition-specific models was calculated by Markov chain Monte Carlo sampling-1·h-1 to avoid limitation, except for those for which the uptake was experimentally calculated  and the carbon source in which the predictions were to be done. To apply more specific constraints to the model in MM plus tetralin and MM plus 3-HB, transcriptomic data were incorporated following the protocol described31. A flux of 0 through a reaction was considered when the expression of the associated gene(s), measured in FPKM (Fragments Per Kilobase of transcript per Million mapped reads), was lower than the value of the first quartile of the total FPKM values. However, several cut-offs were analysed around the first quartile value, as recommended37.The L-lactate uptake, a lactate dehydrogenase assay was carried out to determine the concentration of L-lactate in the supernatant during cultivation. The initial concentration was set to 53 mM. Briefly, 5 µL of supernatant was added to 100 µL of 2x reaction buffer  and H2O was added to reach 200 µL. 1.5 U L-lactate dehydrogenase (Roche) was used to start the reaction and the formation of NADH was measured in a Tecan fluorimeter after 1 h of incubation at 30 °C. Lactate concentration was calculated based on a standard curve62. To express the fluxes in mmol·gDW-1·h-1, the biomass of strain TFA was experimentally measured in DW (Dry Weight) units. To do that, 100 mL of culture were taken at different times along the growth curve in MM plus the corresponding carbon source, centrifuged for 15 min at 5000 rpm and the pellet resuspended in 2 mL of phosphate buffer  and centrifuged for 5 min at 13000 rpm. Finally, the pellet was dried at 80 °C and weighed on a precision balance.Concentrations of 3-HB and sebacic acid were measured by GC-MS (gas chromatography–mass spectrometry), establishing an initial concentration of 40 mM and 16 mM, respectively. Briefly, 1 mL of sample collected at different time points during growth was centrifuged at 13000 rpm for 5 minutes. 100 µL of the supernatant were evaporated with nitrogen (50 °C) and resuspended on 100 µL of a 0.5 mg/mL solution of citric acid in pyridine. 200 µl of BSTFA + TMCS (99:1) were added to the sample, and the mixture was stirred for 5 seconds and then incubated at 100 °C for 45 minutes. Finally, 1 µL of sample was injected in a DB-17 column (Agilent) and the areas of the picks corresponding to the carbon source and to citrate, which was used as internal standard, were obtained. For Supplementary Information.Supplementary Information 2.


Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen.Multidrug-resistant (MDR)  Acinetobacter baumannii has become a critical threat to human health globally [A. baumannii as one of three top-priority pathogens that urgently require novel antimicrobial therapeutics [Multidrug-resistant (MDR) globally . It has globally . Recentlapeutics .A. baumannii infection in vivo [Antibiotics target essential components of bacterial growth, such as DNA replication, translation, and peptidoglycan biosynthesis . However in vivo ,13; howeA. baumannii MDR strain AB5075. By incorporating in vivo transcriptomics data as constraints, our model was able to identify the significant metabolic changes of AB5075 during infection in mice. This is the first integrative modeling of A. baumannii infection in vivo and provides key mechanistic information regarding bacterial metabolic changes in response to the host immune system. Such information will facilitate optimization of antibiotic therapy for infections caused by A. baumannii. Genome-scale metabolic modeling (GSMM) is increasingly used to decipher the metabolic changes in pathogens under infection or antibiotic treatment conditions ,15,16. IA. baumannii AB5075 was obtained from the University of Washington and stored at −80 °C in tryptone soy broth  with 20% glycerol. Prior to experiments, AB5075 was sub-cultured onto nutrient agar and incubated at 37 °C overnight. A single colony was then selected and grown overnight in 20 mL of cation-adjusted Mueller-Hinton broth , from which a 1:100 dilution was performed in fresh broth to prepare mid-logarithmic cultures (OD600nm = 0.4 to 0.6). All broth cultures were incubated at 37 °C in an open-air shaker (200 rpm). Final bacterial suspensions were concentrated to 1 × 1010 CFU·mL−1 in sterile saline. Hypervirulent MDR ad libitum.Animal experiments were approved by Monash University Animal Ethics Committee. For all animal experiments, Swiss mice  were obtained from Monash Animal Services. Animals were handled, fed, and housed according to the criteria of the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes . Food ann = 3 per group) were injected with 10 μL of bacterial suspension (approximately 1.0 × 109 colony-forming units (CFUs) in early logarithmic phase) via an intravenous injection at 0 h and then placed onto a warm pad for rapid recovery. Our preliminary studies showed that bacterial infection established in mice after approximately 2 h post bacterial inoculation, and bacterial load peaked at approximately 4 h post bacterial inoculation. Therefore, we used 2 and 4 h post inoculation in this study to represent the establishment of bacterial infection and the maximum bacterial load, respectively. Samples were collected for RNA extraction and sequencing at Genewiz . Raw reads were submitted to the Sequence Read Archive Database (accession number: SRS7522398) [A non-neutropenic murine bacteremia infection model was employed in this study. Mice were briefly anesthetized via placement into an isoflurane induction chamber. Anesthetized mice were placed on a Perspex support in a vertical upright position, which allowed the mice to be temporarily immobilized. Two independent groups of mice (7522398) .A. baumannii AB5075  was obtained from the PGAT database [The genome annotation of database  and a drdatabase . Furtherdatabase .iAB5075 was then employed to predict the bacterial growth on a chemically defined media with 190 individual carbon sources and 95 nitrogen sources using flux balance analysis (FBA) method with COBRA toolbox 3.0 [−1·h−1 under aerobic condition [m metabolites and n reactions. Each flux AB5075 was subcultured onto nutrient agar and incubated at 37 °C for 20 h. Biolog phenotype microarrays  were employed to test the utilization of 190 carbon and 95 nitrogen sources, with 3 independent biological replicates. Bacterial growth was detected after 18 and 24 h of incubation at 37 °C by measuring the optical density at 595 nm using an Infinite M200 microplate reader . Readings with ≥1.5-fold of blank media controls were considered as utilization of nutrients. The constructed GSMM lbox 3.0 . Biomassescribed . BrieflyThe Matthews correlation coefficient (MCC)  was thenA. baumannii AB5075 was employed as a reference to assess the prediction accuracy as previously described [In silico single-gene deletion was conducted using both FBA and minimization of metabolic adjustment (MOMA) algorithms . FBA preescribed . iAB5075 using INIT (Integrative Network Inference for Tissues) algorithm, which is formulated as a mixed integer linear programming problem (MILP) shown below [The in vivo RNA-Seq data (accession number: SRS7522398) of AB5075 were incorporated to wn below :max∑i∈Rwε is an arbitrarily small positive number, which was set by default in COBRA toolbox; iw is the weight of the ith reaction calculated using transcriptomic data; and iy is an integer variable indicating either including (iy = 1) or excluding (iy = 0) of the ith reaction in the extracted model [w) [The parameter ed model . The optodel [w) , which ww [ith gene in the jth condition is higher than its average across all the samples, i,jw is positive. Otherwise, i,jw is negative. An weights-containing objective function is then maximized to achieve the best agreement of the calculated metabolic fluxes with the transcriptomic data. Moreover, to further improve the prediction, additional protein crowding constraints were incorporated into the extracted model. The total abundance of metabolic enzyme (P) was set to 0.25 g⋅gDW−1 according to previous Escherichia coli data due to the limited quantitative proteomics data in A. baumannii [kMW) of kth protein was calculated based on its amino acid sequence: kkcat, and enzyme molar abundance A. baumannii, an averaged kcat (65 s−1) in E. coli was used unless specific kcat values were available in BRENDA database  calculated from the bacterial viable counts. Metabolic solution space was sampled with 10000 random points with the ll-ACHRB  algorithm and linear programming solver Gurobi 9.0 [Z-score method. Differential metabolic fluxes were filtered with FDR < 0.05 and fold change > 2.Specifically, RPKM (reads per kilobase per million) values were calculated using edgeR and employed to calculate the weights w . Brieflyaumannii . The molescribed ,29. Due  R_HXAND , 1 s−1 fPTALAGLN , 11.14 s_ALAALAr , 34 s−1  R_NADS1 ). FBA wa R_NADS1  in MATLArobi 9.0 . StatistA. baumannii [A. baumannii, 15 reactions and 20 metabolites involved in acinetobactin biosynthesis were added to the draft model. Altogether, a final model iAB5075 was obtained that involved 1530 metabolites and 2229 reactions , iAB5075 represents the most comprehensive GSMM for A. baumannii determined thus far  addition of transport and exchange reactions to enable nutrient uptake and by-product secretion, and (ii) filling pathway gaps. Acinetobactin is the essential siderophore for iron uptake in aumannii . To makeeactions . iAB5075ups COG, ; the majups COG, . iAB5075artments . Comparethus far .iAB5075 predicted bacterial exponential growth at 1.87, 2.64, 0.97, and 0.72 h−1 in Luria-Bertani (LB) media, Mueller-Hinton (MH) media, M9 media supplemented with citrate (M9C), and M9 media supplemented with succinate (M9S), respectively; these specific growth rates are consistent with experimental observations  and two aminopeptidase reactions  to enable in silico utilization of these two nutrients. Further prediction under anaerobic conditions was undertaken using iAB5075. The prediction results showed no growth under anaerobic conditions and it is consistent with the fact that A. baumannii is an obligate aerobe. The overall prediction accuracy of nutrient utilization achieved was high  compared with Biolog assay in which AB5075 grew on 59 carbon sources and 46 nitrogen sources , the differentially expressed genes were mainly enriched in the following pathways: intracellular trafficking, secretion and vesicular transport; secondary metabolites biosynthesis, transport, and catabolism; inorganic ion transport and metabolism; and translation, ribosomal structure, and biogenesis. Among those genes mapped to iAB5075, 283 were identified as being involved with transport and metabolism of carbohydrates, nucleotides, and amino acids; energy production and conversion; and cell envelope metabolism, indicating their critical roles in adaption to the host environment.nfection . However iAB5075 . Based oThe transcriptomic data were then incorporated in the model as flux constraints to accurately predict metabolic fluxes using the INIT algorithm. In addition, constraints on bacterial growth rate and protein crowding were also imposed on the model. Specifically, 478 flux-carrying reactions were detected at 2 h post infection, while 502 reactions had non-zero fluxes at 4 h post infection. Compared to 2 h post infection, 198 significantly changed fluxes  were identified at 4 h post infection, including 146 increased and 52 decreased fluxes . The inc−1 from malate to oxaloacetate 4 h post infection, whereas the flux from oxaloacetate to malate was 3.25 mmol·gDW·h−1; the two metabolites  were utilized by the gluconeogenesis pathway, resulting in significantly increased fluxes through gluconeogenesis (d-ribose 5-phospahte towards d-sedoheptulose 7-phosphate was significantly decreased (d-ribose 1-diphosphate (PRPP) synthase was dramatically increased (0 mmol·gDW·h−1 at 2 h post infection versus 0.24 mmol·gDW·h−1 at 4 h post infection), and the increased PRPP was then utilized in the biosynthesis of purine and pyrimidine nucleotides.In the TCA cycle, six of eight metabolic fluxes were significantly increased at 4 h post-infection compared to 2 h post-infection, while the flux through fumarate hydratase was significantly decreased a. Notablogenesis b. At theecreased b. Remarkde novo biosynthesis  glucosamine N-acyltransferase, UDP-2,3-diacylglucosamine diphosphatase, lipid-A-disaccharide synthase, and tetraacyldisaccharide 4’-kinase were upregulated, resulting in a dramatic increase in the production of lipid A . At this time, most fluxes within peptidoglycan biosynthesis from UDP-ecreased d. In lip lipid A d. In addition to the increased fluxes through the above metabolic pathways, fluxes through fatty acid biosynthesis and amino acid metabolism were also significantly impacted at both 2 and 4 h post infection. In fatty acid biosynthesis, most fluxes were significantly increased at 4 h compared to 2 h , whereasA. baumannii is a Gram-negative opportunistic pathogen which has imposed a heavy burden on the global health care system [A. baumannii responds to the host immune system during infection. GSMM has been increasingly employed to predict key genetic targets in order to guide therapeutic interventions and decipher the mechanism(s) of antibiotic killing and resistance. Unfortunately, few studies have previously examined bacterial metabolic responses in vivo using GSMMs, and none of these studies examined A. baumannii. We report here, for the first time, the development, validation, and application of a GSMM named iAB5075 for the MDR clinical isolate A. baumannii AB5075.e system . There iA. baumannii are for strains ATCC 19606 and AYE [A. baumannii to investigate virulence and multidrug resistance [iAB5075 (iCN718), iAB5075 contains a larger number of unique genes, metabolites, and reactions, and has a much higher prediction accuracy . These differences are mainly attributable to the different compartment settings, transport and exchange reactions contained within the two models  4 h post infection compared to 2 h post infection. In addition to lipid A biosynthesis, increased fluxes were also detected in peptidoglycan biosynthesis via the conversion of UDP-N-acetyl-α-d-glucosamine to meso diaminopimelate containing lipid II. It has previously been shown that genes involved in the biosynthesis and maintenance of LPS and peptidoglycan contribute to the fitness of A. baumannii during bacteremia [A. baumannii [njugates . An impocteremia , and thacteremia ,51. Duriaumannii . Comparepenta/tetra-guanosine phosphate) are key bacterial regulators involved in adaptations to conditions of limiting or non-optimal carbon and energy resources [In addition to surface glycoconjugates, other strategies also contribute to bacterial defense against host immune systems, including secreted proteins and multiple other regulators and metabolic pathways . Nucleotide second messengers (e.g., cAMP, cyclic di-GMP, and esources ,54. Cyclesources . IncreasOverall, fluxes were significantly changed through multiple pathways in AB5075 at 4 h post infection compared with 2 h post infection. Marked fluxes through central metabolism, nucleotide metabolism, and fatty acid and cell envelope biosynthesis might contribute to adaptations to the host environment and enhanced infection during bacteremia. These impacted pathways may be potential therapeutic targets for future drug development and therapy optimization.iAB5075, for a modern model strain of MDR A. baumannii, namely AB5075. This is the first study to integrate in vivo transcriptomic data with GSMM, and importantly, the modeling provides a precise understanding of metabolic changes in A. baumannii in response to a host immune system during bacteremia. Model iAB5075 provides a unique >in silico platform for predicting bacterial metabolic responses to different treatments at the network level, which may assist in the optimization of antibiotic treatment in patients.In summary, we constructed and validated the first GSMM,


Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value. In the third stage, allogeneic and metabolites are removed from liver and intestinal cells. After these three stages, exogenous substances such as drugs may be transformed into non-physiological active substances or toxic metabolites. 70% clinical drugs are removed by the body’s metabolic system, so as part of drug development, it is also necessary to conduct in-depth research on drug metabolism  and metabolite structure are two main research directions of computer-aided metabolic prediction methods, which can provide decisive support and guidance for experimenters. SOM prediction methods usually have high prediction accuracy. The program MetaSite estimate the possibility of metabolic reactions at an atomic site using protein structure information, GRID-derived MIFs of protein, and ligand and molecular orbital calculations . The proin-vitro ADME assays, called the Biological fingerprint  and the type of metabolic reaction correctly. Current methods for predicting metabolite structure tend to have high false-positive rates and can only be used for specific enzymes without covering all the metabolic enzymes involved in human reactions. In view of the above problems, we mainly designed a deep learning algorithm combined with drug metabolism characteristics.In this work, by combining metabolic reaction template and Deep Learning, we have established a model to predict the main metabolites of drugs Figure 2We collected metabolic reaction data from MDL Database (2011 edition). Here we used only human metabolic reactions with effective substrates and metabolites. The data were filtered to remove unreasonable structures, such as reactants and products containing R groups, free radicals, metal chelating, and structural errors, which could make it impossible to distinguish the reaction records of reaction sites. The pretreatment resulted in 7,380 reaction records, of which 74 reactions had only chiral changes. We randomly selected 300 response records from them as standby for external test sets.The process of constructing the metabolic reactions template library is shown in The above-mentioned metabolic reaction templates are stored in the database for subsequent production of positive and negative potential metabolites. For each atom mapping reaction in the dataset, the reaction center is defined by determining which product atoms are different from the corresponding reactant atoms. The reaction center is expanded to include the surrounding environment, and then other factors that play a role in the reaction is found out. Adjacent atoms are defined as non-hydrogen substituents, where high coverage is achieved at the expense of low specificity. Metabolic Templates are defined with SMART format strings encoding reaction centers. The reaction template generated in this way does not depend on manual extraction, marking, or sorting.As shown in n vectors. We use keras wrapper to realize each vector, that is, each individual potential metabolite is fully connected independently, which increases the ability of the model to achieve one-to-many and many-to-many. The probability of all potential reaction products is finally mapped out by the output layer activation function softmax, so that the total probability of all potential metabolites is 1. According to the score of the output layer, it is most likely to describe which metabolites actually exist. Deep neural network models are trained here to solve problems similar to classification problems: given hundreds of possible classes , predicting real classes (recording reaction products), each metabolic reaction may correspond to multiple classifications. We use cross-entropy as the loss function during training. This objective function can be understood as the negative logarithm of probability allocated to the true class (true metabolites). During the training period, we use five-fold cross-validation to divide all the data sets into five parts, one of them is taken as the validation set without repetition, and the other four are used as training model of training set. Cross-validation can avoid over-fitting and under-fitting, and the final results are more convincing.For deep learning and supervised learning, we need to input eigenvalues. What we need to consider is using what molecular descriptors to characterize the whole metabolic process. Here we choose molecular fingerprints to describe the atomic and functional characteristics of metabolic reactions. The abstract representation of molecular fingerprints, which encodes molecular transformation into a series of vectors, makes it easier for molecules to compare with each other. If two molecules are similar, there must be many common fragments between them. Then molecules with similar fingerprints will have a high probability of being similar in 2D structures. Here we use RDKit to generate 1,024-dimensional Morgan molecular fingerprints. Molecular fingerprint ECFP is suitable for machine learning because it contains more molecular structure details. The metabolites generated above through the metabolic reaction template will be scored separately by Deep Learning model. Here we use Python library of Keras. The input layer consists of molecular fingerprints of products and reactants, with a total of 2,048 dimensions. One reaction corresponds to multiple potential metabolites. Thus, the input layer generates a matrix of 2,048 dimensions with Following the above steps, we cross-validated the model with five folds by using 200 epochs. The training set, validation set, test set segmentation was 7:1:2. The objective of the training period is to minimize the cross-entropy loss of classification, which is the natural logarithm of probability allocated to real metabolites. Considering that there may be more than one metabolic reaction product for a drug, we believe that the top ten predicted products may have more reference value. As shown in Here, we conducted external tests on 300 reaction records that were not used for model training. It is also compared with the rule-based prediction method SyGMa. The accuracy rates of Top-1, Top-3, Top-6, and Top-10 are 35, 55, 67, and 78% respectively for our method Figure 5As can be seen from The amount of our data is small for Deep Learning to learn more information. The more reaction records that focused on a specific reaction, the more accurate the prediction of the reaction is. Thus we need to expand the data set for training. Next, we will collect more and more metabolic reactions from KEGG and other databases to train models, so as to improve the prediction accuracy of the models.We retrieved Morgan molecular fingerprints with radius 3 from potential metabolic reaction products in training set and retrained them with AutoEncoder algorithm Figure 7As shown in Here we take Zileuton as an example to analyze its prediction results of metabolites. It is an inhibitor of 5-lipoxygenase for the maintenance treatment of asthma. The main metabolic pathways of Zileuton are hydroxylation of benzene ring, oxidation of sulfur atoms on sulfur-containing heterocycles, and hydrolysis of nitrogen atoms on amide groups  ; the National Natural Science Foundation of China ; the National Science & Technology Major Project “Key New Drug Creation and Manufacturing Program,” China (Number: 2018ZX09711002); the Fundamental Research Funds for the Central Universities, Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) under Grant No.U1501501. SL is also sponsored by Shanghai Sailing Program (No. 18YF1405100). HL is also sponsored by National Program for Special Supports of Eminent Professionals and National Program for Support of Top-notch Young Professionals.Author WL was employed by company Jiangzhong Pharmaceutical Co., Ltd.The remaining 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.The reviewer MZ declared a past co-authorship with one of the authors HL to the handling editor.


Down syndrome (DS), is the most common cause of intellectual disability, and is characterized by defective neurogenesis during perinatal development. To identify metabolic aberrations in early neurogenesis, we profiled neurospheres derived from the embryonic brain of Ts1Cje, a mouse model of Down syndrome. High-throughput phenotypic microarray revealed a significant decrease in utilisation of 17 out of 367 substrates and significantly higher utilisation of 6 substrates in the Ts1Cje neurospheres compared to controls. Specifically, Ts1Cje neurospheres were less efficient in the utilisation of glucose-6-phosphate suggesting a dysregulation in the energy-producing pathway. T Cje neurospheres were significantly smaller in diameter than the controls. Subsequent preliminary study on supplementation with 6-phosphogluconic acid, an intermediate of glucose-6-phosphate metabolism, was able to rescue the Ts1Cje neurosphere size. This study confirmed the perturbed pentose phosphate pathway, contributing to defects observed in Ts1Cje neurospheres. We show for the first time that this comprehensive energetic assay platform facilitates the metabolic characterisation of Ts1Cje cells and confirmed their distinguishable metabolic profiles compared to the controls. Clinical manifestations that are prominent in individuals with DS include intellectual disability, craniofacial abnormalities and muscle weakness.Down syndrome (DS), or Trisomy 21, is a genetic disorder that results from the partial or full triplication of human chromosome 21 (HSA21), which leads to multiple phenotypes with varying complexity. The frequency of DS worldwide is approximately 1 in 1000 live births . The cells were then cultured in complete neurosphere culture medium in T25 flask at a density of 5x105 cells/ml and incubated at 37°C in a humidified incubator with 5% CO2. After 5 to 7 days in vitro (DIV), the proliferating NSPCs generate non-adherent spherical clusters of cells, termed neurospheres, which measure 100 to 200 μm in diameter. At this stage, the neurospheres can either be passaged or subjected to downstream metabolic profiling experiments.For timed breeding, two WT C57BL/6 females were housed with a Ts1Cje male for overnight. The female mice were observed for the presence of a vaginal plug the following morning. The day a vaginal plug was observed was considered as embryonic day 0.5 (E0.5). Developmental stages of embryos were further determined based on the Theiler Stage Definition from The e-Mouse Atlas  followed by incubation in Biolog’s Omnilog PM instrument at 37°C. The OmniLog reader was set to measure the tetrazolium reduction, at 15-minute intervals for 48 hours. The data collected by the OmniLog® Phenotype MicroArray (PM) system was organised using the OmniLog® PM software . Subsequently, the File Management/Kinetic Analysis program was used to assemble the data for each microplate into an integrated dataset and was subjected to a downstream data analysis pipeline.A total of twelve different E15.5 embryos per genotype were obtained from five separate litters, to generate neurospheres for Phenotype MicroAssay experiments. For each PM-M plate, the neurospheres generated from wild type (n = 3) and Ts1Cje (n = 3) embryos were derived from three different litters. Neurospheres were trypsinised using 0.05% trypsin-EDTA  and were mechanically dissociated into a single-cell suspension. The cells were pelleted, resuspended in MC-0 assay media . The Biolog PM assay was performed in biological triplicates. Metabolic profiling of embryonic neurospheres were performed using four mammalian Phenotype MicroArray (PM-M) microplates; PM-M1, PM-M2, PM-M3 and PM-M4. These 96-well microplates are pre-coated with a total of 367 oxidizable carbon sources. PM-M1 is pre-coated with various simple reducing sugars, carbohydrates and carboxylates, whereas PM-M2, PM-M3 and PM-M4 wells are pre-coated with L-amino acids and various dipeptides . For eacwww.r-project.org, version R-3.2.0) based on the R script (http://www.helsinki.fi/bsg/software/R-Biolog/) as previously described [t = asym/1+exp (xmid-x)/scal, t = time point at which a metabolic signal is measured; asym = metabolic signal as t→ ∞; xmid = time at which Asym/2; scale = inverse of the maximum growth rate), whereas the non-active profiles were fitted to a linear model .Data analysis for Biolog PM results was executed in the R statistical software  was used to measure the diameter of each neurosphere from all four micrographs. This quantitative analysis was repeated for all three biological replicates for each group, Ts1Cje and WT neurospheres. Data shown represent mean ± S.E.M. Two-way ANOVA followed by Sidak’s multiple comparisons correction analysis of the neurosphere diameter mean values for each biological replicate were performed using Prism 7 . Corrected p-values <0.05 were considered statistically significant.Ten milligrams of 6-phosphogluconic acid trisodium salt (6PGA)  were reconstituted in autoclaved distilled water to achieve a stock solution with a final concentration of 50 mM. Neurospheres measuring 100 to 200 μm were dissociated into a single cell suspension and were seeded at a density of 2.5 x 10The ability of WT and Ts1Cje neurospheres to utilise basic elemental nutrients such as carbon, nitrogen, phosphorus and sulfur-based substrates was determined using four types of PM-M microplates pre-coated with a total of 367 substrate nutrients, as indicated by colour changes throughout the 48-hour incubation period . RepreseThe effect identification step of the data analysis pipeline was performed to identify substrates that were significantly differentially utilised by Ts1Cje neurospheres as compared to that of WT. Values fitted to either logistic or linear curves  at 8 different time points  serve as data for the Bayesian hierarchical variance analysis. Based on the threshold value of 20, the statistical significance of the differentially utilised substrates was observed for a total of 23 substrates over the period up to 48 hours of incubation . Raw absIn PM-M1, which contains primarily carbohydrate and carboxylate substrates, the metabolism of D-glucose-6-phosphate, α-D-glucose-1-phosphate and α-D-glucose were significantly poorly utilised by Ts1Cje neurospheres compared to WT neurospheres between 24 and 30 hours. PM-M2 to PM-M4, on the other hand, contain lipids and protein-derived nutrients, primarily amino acids and dipeptides. In PM-M2, a total of 6 substrates were significantly differentially utilised by neurospheres of both genotypes. Ts1Cje neurospheres showed lower metabolism of L-serine than that of WT neurospheres. In contrast, Ts1Cje neurospheres demonstrated stronger metabolism of Tween 80, aspartyl-glutamate (Asp-Glu), glutamyl-tryptophan (Glu-Trp), glutamyl-tyrosine (Glu-Tyr) and glycyl-aspartic acid (Gly-Asp) when compared to that of WT neurospheres. The highest number of significant differentially utilised substrates was detected in PM-M3, with 9 dipeptides that were less metabolised by Ts1Cje neurospheres as compared to WT. These substrates include histidyl-leucine (His-Leu), histidyl-valine , isoleucyl-asparagine (Ile-Asn), isoleucyl-glycine (Ile-Gly), isoleucyl-serine (Ile-Ser), isoleucyl-tyrosine (Ile-Tyr), isoleucyl-valine , leucyl-tryptophan (Leu-Trp), lysyl-leucine (Lys-Leu). For PM-M4, 4 dipeptides, namely prolyl-tryptophan (Pro-Trp), tryptophyl-tryptophan (Trp-Trp), tyrosyl-alanine (Tyr-Ala) and tryptophyl-leucine (Trp-Leu) were significantly poorly metabolised by Ts1Cje neurospheres as compared to WT neurospheres. On the other hand, Ts1Cje neurospheres demonstrated significantly stronger metabolism in the well containing prolyl-hydroxyproline (Pro-Hyp) as compared to WT neurospheres .in vitro, and their size was measured  is an intermediate in the breakdown of glycogen (glycogenolysis) and is converted to glucose-6-phosphate (G6P) by the enzyme phosphoglucomutase . G6P canOther significant differentially utilised substrates include those that are involved in glucose metabolism, specifically glycolysis and PPP. Metabolism of α-D-glucose and D-glucose-6-phosphate were significantly lower in Ts1Cje neurospheres compared to WT neurospheres, with a significantly larger difference in metabolism at later time points. Glucose is the main energy-producing substrate in the brain. Glucose metabolism provides precursors for the synthesis of neurotransmitters and ATP for the brain’s metabolic demands . Low expAnother prominent finding in this current study is the significantly reduced metabolism of L-serine in the Ts1Cje neurospheres. L-serine is a non-essential amino acid that exhibits essential functions in the central nervous system, particularly protein synthesis and cell proliferation . For exaThe present study has implicated that five isoleucine-containing dipeptides  were utilised significantly lower in the Ts1Cje neurospheres compared to the WT neurospheres. Lower level of isoleucine was previously reported in the plasma of adults with DS compared to healthy controls . IsoleucClinically, reduced level of leucine was documented in the plasma of adults with DS as compared to healthy subjects, although the direct implication of this remains unclear . The pre+) and neuroactive intermediates such as quinolinic acid, the NMDA receptor agonist, kynurenic acid, the NMDA receptor antagonist and picolinic acid, a neuroprotectant [In the present study, four tryptophan-containing dipeptides  were also found to be significantly differentially utilised by Ts1Cje-derived NSPCs. Tryptophan is an essential amino acid, as it cannot be synthesised in the body and is therefore obtained through diet. It is predominantly used for protein synthesis and as a precursor for kynurenine (KYN) and serotonin metabolic pathways . Dysreguotectant .+, which is a precursor of NADH [Powers and colleagues  reported of NADH . This suWhile a couple of substrates were significantly poorly utilised by Ts1Cje neurospheres, some dipeptides , containing aspartic acid (Asp), proline (Pro), hydroxyproline (Hyp), alanine (Ala) and glutamate (Glu) were significantly more utilised in the Ts1Cje neurospheres instead. Although the specific function of each of these amino acids in neural progenitor cells of Ts1Cje mice are not known, it is postulated that the dipeptides are degraded to release the individual amino acids for utilisation in downstream metabolic pathways. Glutamate, on the other hand, is a known excitatory neurotransmitter in the brain that has been suggested to regulate the proliferation of neural precursor cells in developing rodent and human brains –46. The The Biolog PM analysis also revealed a lower utilisation of G6P in the Ts1Cje embryonic neurospheres compared to the WT neurospheres, suggesting that altered G6P metabolic pathways could contribute to defects in Ts1Cje embryonic NSPCs. G6P is involved in glycolysis and pentose phosphate pathway, which have been known to play a role in the survival and maintenance of NSPCs. There is limited knowledge on alterations in energy metabolic pathways, specifically in G6P metabolism in NSPCs of embryonic Ts1Cje neurospheres. Therefore, neurosphere supplementation assay was used to explore the role of G6P metabolic pathways in governing Ts1Cje NSPCs. 6PGA, an intermediate of the pentose phosphate pathway, was used as supplementation for neurosphere culture. The diameters of the neurospheres were then measured to assess the growth capacity at different concentrations of supplementation. Without 6PGA supplementation, the size of Ts1Cje neurospheres is significantly smaller than that of WT neurospheres, suggesting that the lower neurosphere-generating potential in Ts1Cje could be associated with a reduction in NSPC proliferation. Upon increasing concentration of 6PGA supplementation, the mean diameter of Ts1Cje is getting similar to that of WT neurospheres suggesting the ability of 6PGA in improving the proliferative capacity of NSPCs proliferation and neurosphere-generating potential through the energy-producing PPP.Altered PPP may potentially contribute to the defective proliferation of embryonic Ts1Cje NSPCs and neurosphere generation, but the mechanistic contribution of PPP requires further investigation. The reliance of embryonic NSPCs on PPP is also supported by a previous study through inhibition of PPP with 6-aminonicotinamide (6-AN), which prevents G6P from entering PPP, leading to an approximately 60% decrease in survival of embryonic NSPCs . The oxi57 PK is an enzyme involved in glycolysis, which converts phosphoenopyruvate to pyruvate, yielding one molecule of ATP. Altogether, previous findings provide transcriptomic and proteomic evidence to support the involvement of disrupted energy metabolism observed in this present study. However, whether the abberations in metabolic pathways is a direct effect of the dysregulated genes in the trisomic region of Ts1Cje mice is still unclear and therefore warrants further investigation.Findings from this present study support previous results that indicate the contribution of dysregulated metabolic pathways in impaired proliferation and differentiation of Ts1Cje neurospheres. Functional enrichment analysis of probes that were overexpressed in adult Ts1Cje neurospheres revealed genes involved in cellular energy production and metabolism, suggesting a role for dysregulated metabolic pathways in Ts1Cje cells . In a laThere has been studies reported on the genetically inspired biological distinctions between male and female mouse model, including Down syndrome mice such as Trisomic Dp(10)1Yey  and Ts65In summary, the present study has demonstrated differences in substrate utilisation pattern between the embryonic Ts1Cje and WT neurospheres through the use of Biolog PM technology. A prominent finding from this global metabolic profiling is the decreased utilisation of G6P, which is part of the PPP. The potential involvement of altered PPP in Ts1Cje NSPCs is subsequently confirmed by the ability of 6PGA to rescue neurosphere-generating potential in the embryonic Ts1Cje neurospheres. These results introduce the potential role of disrupted energy metabolism in the deficits observed in embryonic NSPCs derived from Ts1Cje mice. Furthermore, metabolic profiling also highlights alternative sources that can be utilised by embryonic NSPCs for energy production. This present study only provides a general picture of the metabolic activity of Ts1Cje neurospheres and insights on underlying dysregulations in neurogenesis during early brain development in DS, which to a certain extent, complement previous genomic, transcriptomic and proteomic characterisation of Ts1Cje neurospheres. To better understand energy metabolism in Ts1Cje neurospheres, further validation of the significant differentially utilised substrates is required. A more comprehensive investigation using the metabolomics approach could enhance the understanding of the causes of defects in proliferation and differentiation of embryonic NSPCs derived from Ts1Cje mice. This could facilitate future research on nutritional intervention for the management of neurocognitive phenotypes observed in individuals with DS.S1 File(XLSX)Click here for additional data file.S1 Raw data(XLSX)Click here for additional data file.S2 Raw data(XLSX)Click here for additional data file.S3 Raw data(XLSX)Click here for additional data file.S4 Raw data(XLSX)Click here for additional data file.


Zymomonas mobilis ZM4, an industrially relevant ethanologenic organism with extremely high glycolytic flux and low biomass yield. Gene essentiality predictions made by the draft model were compared to data from individual pooled mutant experiments to identify areas of the model requiring deeper validation. Subsequent experiments showed that some of the discrepancies between the model and dataset were caused by polar effects, mis-mapped barcodes, or mutants carrying both wild-type and transposon disrupted gene copies—highlighting potential limitations inherent to data from individual mutants in these high-throughput datasets. Therefore, we analyzed correlations in fitness scores across all 492 experiments in the dataset in the context of functionally related metabolic reaction modules identified within the model via flux coupling analysis. These correlations were used to identify candidate genes for a reaction in histidine biosynthesis lacking an annotated gene and highlight metabolic modules with poorly correlated gene fitness scores. Additional genes for reactions involved in biotin, ubiquinone, and pyridoxine biosynthesis in Z. mobilis were identified and confirmed using mutant complementation experiments. These discovered genes, were incorporated into the final model, iZM4_478, which contains 747 metabolic and transport reactions (of which 612 have gene-protein-reaction associations), 478 genes, and 616 unique metabolites, making it one of the most complete models of Z. mobilis ZM4 to date. The methods of analysis that we applied here with the Z. mobilis transposon mutant dataset, could easily be utilized to improve future genome-scale metabolic reconstructions for organisms where these, or similar, high-throughput datasets are available.Genome-scale metabolic models have been utilized extensively in the study and engineering of the organisms they describe. Here we present the analysis of a published dataset from pooled transposon mutant fitness experiments as an approach for improving the accuracy and gene-reaction associations of a metabolic model for  Zymomonas mobilis in order to improve a newly developed genome-scale metabolic model. Data from individual experiments within this dataset were compared to predictions made by our draft model to determine areas of inaccuracies in the model. Correlations within the dataset were investigated in the framework of metabolic modules in the model enabling us to identify, and subsequently experimentally confirm, genes for several reactions in the model that previously lacked an associated gene. Finally, we looked at which metabolic modules correlate poorly, highlighting where metabolic knowledge gaps in Z. mobilis may still reside. Our metabolic model, iZM4_478, is one of the most complete for Z. mobilis ZM4 to date, and we expect it will be a valuable tool for investigating the unique, streamlined metabolism of this organism. The data analysis approaches that we used can be easily applied by other investigators in the development of metabolic models for other organisms with similar datasets, leading to more complete assignment of genes to reactions and more accurate metabolic models.We have used several methods of analysis of a high-throughput transposon library dataset for the organism  Zymomonas mobilis is an aerotolerant, Gram-negative, alpha-proteobacterium known for its ethanol production capabilities and exceptionally high glycolytic flux. Glucose uptake through facilitated diffusion coupled with high expression of the Entner-Doudoroff (ED) pathway results in uptake rates exceeding most common organisms, such as Escherichia coli, by several fold . We. We2H anMFA) see , along wZymomonas mobilis ZM4 identified a limited number of utilized substrates [Z. mobilis and we did not confirm their use in this study, reactions enabling growth for these nitrogen sources were not added to our model.A phenotype microarray study of bstrates . Only hein silico predictions of gene essentiality were compared to the experimental results from individual experiments in previously published pooled Tn5 transposon mutant fitness experiments originally constructed to investigate chemical stresses from complex plant hydrolysates, and later extended to investigate mutant phenotypes [During the development of our model, enotypes ,26. Whilin silico model predictions and pooled phenotypes. A lower fitness score cutoff increases the number of false negatives , and a higher cutoff increases the number of false positives . Further complicating the selection of a fitness score cutoff, the two experiments in the dataset may disagree regarding a given gene’s essentiality due to differences in fitness scores, giving rise to genes with an inconsistent pooled phenotype.To investigate the completeness of the metabolic network and the predictive capability of our model, predictions under anaerobic minimal media conditions were compared against two replicate experiments, specifically Exp. 633 and Exp. 638 in the dataset. In order to compare to the model, the mutants in the experiments must be categorized as having either a growth phenotype or a non-growth phenotype. We selected a cutoff of -0.6 as the gene fitness score above which a mutant is considered to have a growth phenotype in the pooled mutant experiments. The fitness score cutoff influences the error rate between Growth but experimental results indicate No Growth) and 5.26% (20/380) being false negatives . The relationship between model predictions and experimental growth phenotypes is statistically significant (chi-squared test statistic yields p < 0.001). There were 49 genes included in the model for which a mutant was not present in the Tn5 mutant collection, likely representing genes that are essential in the aerobic rich media in which the collection was made [We selected a fitness score cutoff that minimized the total error between the model and the pooled mutant datasets thereby providing a higher confidence in identifying true mispredictions made by the model . With a was made . RoughlyIn addition to the anaerobic minimal media condition, we modeled and similarly analyzed data from individual experiments using aerobic minimal media, alternative nitrogen sources , and supplementation experiments with methionine or casamino acids. We found that the model performed similarly well at predicting growth/no growth phenotypes in these experiments, except for an experiment with no added nitrogen source. Except for that experiment, model predictions agreed with the fitness data for an average 77.2% of the genes, with a 14.3% false positive and 8.5% false negative rate. We found 26 genes were consistently classified as false positives (GNG) and 16 genes consistently classified as false negatives (NGG). A summary of these additional simulations is included in the supplemental Z. mobilis, Skerker et al. noted that in some mutants PCR amplification of the mapped insertion region resulted in two bands, one corresponding to the wild-type gene and the other corresponding to the transposon disrupted gene (30). Therefore, to confirm that the differences between our draft model’s predictions and the dataset were true errors, we investigated 12 mutants from the collection. We selected five NGG mutants , four GNG mutants , two mutants predicted to grow but inconsistent experimentally (GI mutants ZMO1556::Tn5 and ZMO1598::Tn5), and one mutant the model and data agreed should grow .Discrepancies between model predictions and experimental results can arise due to errors in the model, errors in the library or dataset, or as the result of the selected fitness cutoff used to assign experimental mutant growth phenotypes. During their analysis of the constructed transposon library in To confirm these mutants had transposons disrupting the gene to which the barcode was mapped, we amplified the mapped loci using PCR. Only five of the 12  mutants tested showed the expected product of the transposon disruptions of the mapped genes. An additional five mutants showed bands corresponding to both a disrupted and a wild-type copy of the gene, while gel imaging of the PCR products for the remaining two mutants revealed only the wild-type gene at the mapped loci. A summary of the PCR results is included with the strain list in Supplementary Zymomonas Minimal Medium Glucose (ZMMG). After one day, ZMO0938::Tn5 and ZMO1307::Tn5 reached an OD600 over 1.0, while ZMO0114::Tn5 and ZMO0172::Tn5 showed only weak growth. These latter two strains were diluted, transferred to fresh ZMMG medium, and incubated for an additional two days. ZMO0172::Tn5, a GG mutant in thiamine biosynthesis, grew with supplemented thiamine agreeing with both the model and experimental dataset. ZMO0114::Tn5, a NGG mutant involved in folate biosynthesis did not grow agreeing with our model prediction that this gene is essential, but not the dataset. It is possible that the ZMO0114::Tn5 mutant grew in the pooled fitness experiment by cross-feeding with other mutants, or using residual carryover from rich media precultures. ZMO0938::Tn5, a folate biosynthesis mutant, and ZMO1307::Tn5 a fumarase mutant both grew, agreeing with the experimental dataset, but not our initial model’s prediction that these genes are essential. The initial model’s ZMO1307 misprediction was caused by imbalanced flux through the fumarate node of the network. We found that due to five reactions that must occur in a fixed ratio due to the biomass equation and the lack of a complete TCA cycle in Z. mobilis, when the fumarase reaction was deleted from the model, no steady-state solution could be found  remains the same between the draft and final model and highlights that folate biosynthesis represent an area for further investigation and future improvements.For four of the five verified mutants with a pooled experimental “growth” phenotype, anaerobic growth experiments were performed in Hungate tubes with be found . Noting in silico media, allowing for the uptake or excretion of any metabolites with transport reactions included in the model, we identified 157 fully coupled and six partially coupled modules, containing between two and 19 reactions  or fixed (fully coupled) ratios . This aniZM4_478. In order to determine if a given module showed a significant correlation in its gene’s fitness scores, we calculated threshold cofitness values representing the 95th percentile of average cofitness scores of 100,000 sets of randomly sampled genes for a given module size . The Pearson’s correlation for all pairs of genes’ fitness scores (cofitness) was calculated for all 492 experiments in the dataset. The mean cofitness was 0.07 for all genes and 0.15 for genes included in ule size . Thirty-Z. mobilis ZM4 grows without histidine supplementation. In order to identify candidate histidinol-phosphatase encoding genes, we searched for additional genes in the dataset whose fitness were highly correlated with known histidine biosynthesis genes. With the exception of ZMO0421 where a transposon insertion had a polar effect on a downstream gene , which catalyzes both the sixth and eighth reaction steps in histidine biosynthesis. Expression of ZMO1503 (associated with the sixth step in Z. mobilis) and ZMO1518 rescued growth of an E. coli ΔhisB mutant in minimal medium lacking histidine  or pyridoxal 5’-phosphate (M45) biosynthesis modules, we would be able to identify strong gene candidates for OZ. mobilis transports glucose via a facilitated diffusion process under most conditions, extracellular gluconate accumulation, the result of a periplasmic glucose dehydrogenase, has been described as a result of oxygen exposure [We found that module 8, representing the transport and phosphorylation of gluconate, has an average cofitness of 0.750 within the full dataset, but only 0.214 within the subset of anaerobic experiments. While exposure . The pooZ. mobilis genes likely responsible for other gap-filled reactions—chorismate-pyruvate lyase (encoded by ubiC in E. coli) and erythronate-4-phosphate dehydrogenase (encoded by pdxB in E. coli)—and to confirm the hypothesized pimeloyl-ACP methyl ester esterase (encoded by bioH in E. coli)  becomes essential in the E. coli host strain when grown in the paired selective medium. The corresponding gene in Z. mobilis was identified via a selection experiment after transformation into the host strain a plasmid-based library containing fragments of the Z. mobilis genome. Plasmids from the Z. mobilis library that rescue the E. coli host strain’s growth on the selective medium should contain Z. mobilis gene(s) responsible for the reaction of interest and were analyzed by DNA sequencing.We used our model-enabled gene search (MEGS) approach  to identE. coli) . MEGS waE. coli) . FollowiE. coli knockout mutants ΔubiC, ΔpdxB, and ΔbioH were used as MEGS host strains and paired selective media was MOPS minimal medium with 20 mM malate (ΔubiC), MOPS minimal medium with 20 mM glucose (ΔpdxB), or M9 minimal medium with 20 mM glucose (ΔbioH), respectively. Plasmids that contained ZMO1008 or ZMO1916 complemented growth of the ΔpdxB and ΔbioH E. coli mutants, respectively. Therefore, ZMO1008 (annotated as a FAD linked oxidase domain protein) likely encodes an erythronate-4-phosphate dehydrogenase and ZMO1916  likely encodes a pimeloyl-ACP methyl ester esterase. ZMO0562 and ZMO0563 were found together on the plasmid complementing the growth of the ΔubiC E. coli mutant. These two genes were cloned separately into the E. coli ΔubiC strain and only ZMO0563 (annotated as chorismate mutase) complemented growth of the mutant, indicating that it is likely the enzyme responsible for chorismate-pyruvate lyase activity.Z. mobilis ZM4 to improve the accuracy and gene assignments included in the model. Candidate genes for several gap-filled reactions were identified via correlations in the pooled fitness dataset or MEGS, and experimentally validated, allowing us to further improve the GPR assignments in our new genome-scale model iZM4_478. Prior models of Z. mobilis contained large numbers of reactions without GPR associations. The model reported here has better gene coverage, with only 19 metabolic reactions lacking GPR associations in the final model  of nuscript .Zymomonas Minimal Medium Glucose (ZMMG) were set to -1000 mmol/gDW/h allowing for uptake of these metabolites in the model. Glucose and oxygen exchange flux lower bounds were set to -61.5 and 0 mmol/gDW/h, respectively [Gene essentiality was predicted using FBA and a gene was considered essential if the maximum flux through the biomass reaction was predicted by FBA to be zero. In all simulations, the lower bounds on the exchange fluxes for biotin, thiamine, and all components found in Z. mobilis model. In our analysis, all exchange fluxes were allowed to take positive or negative values and individual sink reactions for each biomass component and terminal products of complete metabolic pathways were added so that smaller modules of reactions coupled to individual biomass components (rather than aggregate biomass) could be found.The Flux Coupling Finder  was usedin silico modules, and across the 41 anaerobic experiments for the anaerobic condition-dependent modules. For a given set of genes, the average cofitness was calculated as the average of the cofitness scores between all pairwise combinations of genes in the set. To determine a significance cutoff for the average cofitness for a gene module of size k, we first sampled k genes randomly from the set of 423 metabolic genes whose mutants were included in the pooled fitness datasets, calculated the cofitness between each pairwise combination, and then calculated the average of k choose two pairwise cofitness scores. We repeated the random sampling of k genes 100,000 times and chose the average cofitness at the 95th percentile as the cutoff. Similar analysis of the 41 anaerobic experiments was performed to determine 95th percentile cutoffs for the anaerobic condition-dependent modules.Cofitness was calculated as the Pearson’s correlation between the fitness scores for two genes across all 492 experiments for the full E. coli BW25113 was obtained from E. coli genetic stock center and single knockout E. coli strains were obtained from the Keio collection (Open Biosystems) [Z. mobilis ZM4 was obtained from ATCC (ATCC 31821). Z. mobilis ZM4 Tn5 mutants were obtained from the mutant collection generated by Skerker et al. [systems) . Strainssystems) , prior tsystems)  with sinr et al. . StrainsZ. mobilis genes in E. coli and Z. mobilis. The RBS sequence in front of ZMO1518 in pRL814_ZMO1503_ZMO1518 was designed using the Salis RBS Calculator [Gibson cloning  was usedE. coli strains were grown at 37°C with shaking and Z. mobilis strains were grown at 30°C without shaking. E. coli strains were grown aerobically on LB agar plates, in LB broth, 2 g/L glucose M9 minimal medium (MM), or MM supplemented with 0.25 g/L aspartate, 0.2 g/L leucine, 0.15 g/L isoleucine and 0.15 g/L valine (MM++). Z. mobilis strains were grown in Zymomonas Rich Medium Glucose (ZRMG), Zymomonas Minimal Medium Glucose (ZMMG), Zymomonas Minimal Medium Glucose with reduced iron sulfate (ZMMG_0.25Fe), ZRMG agar plates, or ZRMG agar plates supplemented with 0.33 g/L each of histidine, tyrosine, and phenylalanine (ZRMG++ agar). When necessary, kanamycin or spectinomycin were added at final concentrations of 50 μg/mL for E. coli and 100 μg/mL for Z. mobilis. Media recipes can be found in supplemental Unless otherwise noted, 600). All plate reader growth experiments were performed in triplicate in 96-well microplates at 200 μL volume. The OD600 of the E. coli cultures was measured by an Infinite M200 plate reader (Tecan) shaking at 37°C for 24 hours. For Z. mobilis, the OD595 was measured by an Infinite F500 plate reader (Tecan) placed inside an anaerobic chamber shaking at 31.5°C for 48 hours.Strains were streaked onto the appropriate rich medium agar plates (LB or ZMRG) with the necessary antibiotics. Single colonies were precultured aerobically overnight in 3 mL rich medium with the appropriate antibiotics, centrifuged, washed once in minimal medium, and inoculated to a starting optical density of ~0.01 at 600 nm  equipped with electrospray ionization operating in negative-ion mode. Chromatography was carried out on a 2.1 x 100 mm ACQUITY UHPLC BEH C18 column with 1.7 μm particle size (Waters) at 25°C. Solvent A was 97:3 water:methanol with 10 mM tributylamine adjusted to pH 8.2 with 10 mM acetic acid and solvent B was 100% methanol. The separation run time was 25 minutes following the protocol: 0 min, 5% B; 2.5 min, 5% B; 17 min, 95% B; 19.5 min, 95% B; 20 min, 5% B; 25 min 5% B. Full MS-SIM (single ion monitoring) parameters include scanning between 70 and 1000m/z, automatic control gain target of 1e6, maximum injection time of 40 ms, with a resolution of 140,000 full width at half maximum. Data analysis performed using the MAVEN software package with fumarate identified by retention time matched to the standard curve. Fumarate excretion was simulated in MATLAB 2017a as growth associated product formation based on the excretion rate determined by FBA, and integrated to OD600 = 1.0. OD600 values were converted to gDW based on a published conversion factor of 0.41 gDW OD600-1 L-1 [Biological triplicates of ZMO1307::Tn5 grown in ZMMG were washed and inoculated into fresh ZMMG to a starting OD00-1 L-1 E. coli strains and paired media sets were designed using FBA with the E. coli iJO1366 model [Z. mobilis was prepared using fragments of extracted genomic DNA generated through sonication and ligated into the HinCII multiple cloning site of the pZE21MCS1 vector [Growth coupled recipient 66 model  as descr66 model . The gen1 vector . The aveS1 Text(DOCX)Click here for additional data file.S1 Model(XML)Click here for additional data file.S2 Model(XLSX)Click here for additional data file.S1 Appendix(XLSX)Click here for additional data file.S2 Appendix(XLSX)Click here for additional data file.S1 FigiZM363, iZM411, and iZmobMBEL601 were not available with their respective publications.Comparison of model predicted (solid lines) and reported experimental (data points) specific growth rates (top) and ethanol production rates (bottom) against glucose uptake rates for published nitrogen fixation experiments conducted in anaerobic glucose minimal media experiments. Simulation ready models for (TIF)Click here for additional data file.S2 Figin silico growth rate of 1 hr-1. Due to the composition of the biomass equation, several reactions producing fumarate from aspartic acid, or using fumarate as an electron acceptor (shown in red) are constrained to a fixed ratio with the biomass reaction since their products lead to ATP, GTP, UTP, CTP, NAD or arginine synthesis. Only fumarase (νFUM) and fumarate transport reactions  are unconstrained and may carry the balance of flux (0.6474 mmol/gDW/hr) necessary to satisfy the steady state assumption around the fumarate (or fumaric acid) node. (B) Bar graph of extracellular fumarate concentration of ZMO1307::Tn5 culture at an OD600 = 1.0, model predicted concentration based shown in light blue, experimentally measured supernatant concentration shown in dark blue, error bar represents one standard deviation of triplicate data.(A) Diagram of reactions included in the model producing or consuming fumarate. For simplicity, fluxes shown were calculated with an (TIF)Click here for additional data file.S3 Figth percentile module cutoffs. ZMO1008, the gene identified via MEGS experiments, was identified as the gene with the third highest average cofitness score. ZMO1916, identified as the pimeloyl-ACP methyl ester esterase via MEGS experiments is shown on the far right of the boxplot and highlighted via a green bar in the bargraph.Boxplots of cofitness values of individual genes with the module genes and bar graphs showing genes ordered by average cofitness scores to module genes for modules 8, 42, and 46 . Cofitness of the genes in the module with the other genes in the module are shown in the left of the boxplot, candidate genes or genes identified via MEGS are shown to the right of the plot. Orange bars in the bargraph represent the module genes, blue bars the candidate genes included in the left panel, and green bars genes identified via MEGS that do not overlap with candidate genes. Horizontal green lines represent the average cofitness score of the known genes  and the red lines represent the 95(TIF)Click here for additional data file.S4 FigGrowth of the wild-type and each tested mutant, or mutant with plasmid complement, shown in minimal media (ZMMG) (blue), and in minimal media with histidine supplementation (orange) over 48 hours. The solid line represents the average (n = 3) optical density at 595nm (OD595) over time, and the shaded band indicates one standard deviation.(TIF)Click here for additional data file.S5 FighisB E. coli strain carrying an empty plasmid (blue), pRL814_ZMO1503 (orange), pRL814_ZMO1518 (green), and pRL814_ZMOM1503_ZMO1518 (red) in minimal media without supplementation. The solid line represents the average (n = 3) optical density at 600 nm (OD600) over time, and the shaded band indicates one standard deviation.Growth curves of Δ(TIF)Click here for additional data file.S6 FigΔaspC ΔtyrB ΔilvE triple E. coli knockout with and without plasmid complementation of ZMO0421 grown in minimal media with no supplementation, supplementation with tyrosine, phenylalanine, or both. (D) Summary of the growth phenotypes for the ZMO0421::Tn5 mutant harboring different plasmids in minimal media with different supplementations. Phenotypes are categorized as growth (++), weak growth (+), and no growth (-).(A) Genomic region containing ZMO0421 and ZMO0420, which are in a single operon. (B) Partial pathways for tyrosine and histidine biosynthesis, showing the steps catalyzed by ZMO0421 and ZMO0420. (C) Summary of growth phenotypes of a (TIF)Click here for additional data file.