Yet, a systematic 1H NMR based metabolomics characterisation of most tissues in this animal model has not been published. Here, we provide a systematic organ-specific metabolomics analysis of this widely employed model using NMR spectroscopy.The rapid expansion of Type 2 Diabetes (T2D), that currently affects 90% of people suffering from diabetes, urges us to develop a better understanding of the metabolic processes involved in the disease process in order to develop better therapies. The most commonly used model for T2D research is the db/db mice in 18 relevant biological matrices.The aim of this study was to characterise the metabolic modulations associated with T2D in 1H-NMR and 2D-NMR spectroscopy were applied to 18 biological matrices of 12 db/db mice  aged 22 weeks, when diabetes is fully established.High-resolution 61 metabolites associated with T2D were identified. Kidney, spleen, eye and plasma were the biological matrices carrying the largest metabolomics modulations observed in established T2D, based on the total number of metabolites that showed a statistical difference between the diabetic and control group in each tissue (16 in each case) and the strength of the O-PLS DA model for each tissue. Glucose and glutamate were the most commonly associated metabolites found significantly increased in nine biological matrices. Investigated sections where no increase of glucose was associated with T2D include all intestinal segments . Microbial co-metabolites such as acetate and butyrate, used as carbon sources by the host, were identified in excess in the colonic tissues of diabetic individuals.1H NMR-based metabolomics will represent a useful resource to explore metabolic pathways involved in T2D in the db/db mouse model.The metabolic biomarkers identified using The online version of this article (10.1007/s11306-019-1548-8) contains supplementary material, which is available to authorized users. T2D is also an increasing clinical issue among children and adolescents, who suffer more aggressive complications than adults or paediatric T1D, including hypertension, proteinuria, peripheral and autonomic neuropathy, renal disease and retinopathy  is a complex metabolic disorder characterised by insulin resistance and systemic hyperglycaemia  T2D mouse model: BKS.Cg-Dock7 < m > +/+ Lepr < db >/J is missing. Previous reports have characterised relevant biological matrices such as urine, plasma and kidneys, showing an increase in glucose levels and modulations in the tricarboxylic acid cycle (TCA cycle), branched-chain amino acids (BCAAs) levels, homocysteine-methionine metabolism and ketone and fatty acid metabolism at different stages of the disease. However, a systematic metabolomics characterisation of this animal model in a large number of biological matrices has never been published. Therefore, we herein provide a useful resource to progress in the understanding of organ-specific metabolic alterations in established T2D in the db/db mouse model  and bedding from each environment was mixed on weekly basis to minimise cage effect. After one week of acclimatisation, body weight was recorded on a weekly basis starting from week six. Animals were humanely euthanized by neck dislocation, according to the specifications of the United Kingdom Animals (Scientific Procedures) Act, 1986 (ASPA), when they were 22 weeks old. The procedure was performed first time in the morning.In order to characterise the metabolic fingerprint of T2D, twelve four-week-old mice  from the strain BKS.Cg-Dock7 < m > +/+ Lepr < db >/J and their corresponding WT controls were acquired from Charles River Laboratories, Italy. Animals were allocated into two different homogenous environments, diabetic and control, according to their genetic background , muscle, heart, intestinal sections , urine and blood were aseptically collected and immediately frozen in liquid nitrogen to be later on kept at − 80 °C until the day of the analysis. NMR sample preparation is detailed in S1.1H NMR spectra from all biofluids and extracts, except the liver, were acquired on a Bruker Avance HD 700 MHz  with a TCI Cryoprobe and equipped with a cooled SampleJet sample changer from the same manufacturer. For liver samples, NMR spectra were acquired on a Bruker Avance III 500 MHz NMR spectrometer  equipped with a High-Resolution Magic Angle Spinning 1H NMR probe from the same manufacturer at a rotational speed of 5000 Hz.2 filter was set at 39 ms. Additionally, one Correlation Spectroscopy (1H–1H COSY) was acquired on a selected representative sample from each bio-fluid and liver  NMR spectrum (for each tissue), a total of 64 scans were accumulated into 64 K data points with a spectral width of 13 ppm. Two types of 1D experiments were recorded, using standard pulse sequence: Carr–Purcell–Meiboom–Gill  (Meiboom and Gill i at time x − body weight i at time 0), liver and WAT relative weight (tissue weight i/body weight i) using RStudio .Descriptive statistics and one-way ANOVA (factor = genetic background) were carried out on body weight, body weight gain  using 7-fold cross-validation. Random permutation testing (300 randomisations) was then applied to validate the models and calculate a p value, which is the probability of obtaining such model purely by chance. Aliphatic and aromatic regions from urine datasets, where glucose signal is not present, were further studied applying a normalisation under total area , the Biological Magnetic Resonance data bank  and published literature  with manual phasing followed by automatic baseline correction using the Whittaker smoother algorithm and manual multipoint baseline correction when appropriate. Chemical shift calibrations were carried out relative to TSP (δ 0.00) for all tissues, except for liver and plasma where the glucose anomeric peak (δ 5.223) was used. NMR spectra were imported into Matlab version R2015b  and analysed using the statistic toolbox and algorithms provided by Korrigan Toolbox version 0.1 . In Matlab, residual water (δ 4.70–5.10) and noise (regions before δ 0.5 and after δ 9.5) were removed prior to matrix normalisation using a median-based probabilistic quotient method  , in particular during the first 6 weeks (p < 0.001), when animals were between 5 and 11 weeks old and increased body weight gain more rapidly and variability was smaller. During the last week, diabetic individuals had 210% higher body weight gain than controls. WAT weight was significantly higher (434.8%) in the diabetic group (p < 0.001) .The twelve animals of the study arrived at week four of age and were monitored until they were just over 22 weeks old, when they were euthanized. Body weight gain was significantly higher in diabetic animals . Leucine decrease was consistent with previous observations showing that ketogenesis is altered in the db/db mouse model. In addition, it has also been reported that BCAAs decreased in the late stages of the disease, which is consistent with the 22 weeks of age of the animals used in this work, effectively corresponding to a well-established disease . The O-PLS DA analysis of skeletal muscle identified higher levels of glucose, glycerol and lipids and lower levels of anserine, creatine and IMP in diabetic individuals   Fig. . Thus, aspleen O-PLS DA  identified that diabetic individuals had higher levels of choline, fumarate, glucose, glycerol, isobutyrate and NADH. Conversely, diabetic individuals had lower levels of aspartate, creatine, glutamate, hypoxanthine, lactate, O-phosphoethanolamine, serine, taurine, threonine and uracil  allowed the identification of metabolites differing between diabetic and control individuals , Gas Chromatography-Mass Spectroscopy (GC/MS)  is a leading cause of death and one of the major reasons of end stage renal disease  were driven by higher levels of triglycerides in diabetic individuals while minor changes in polar metabolites were also observed .cerebrum , it was still possible to identify some metabolites that increased amongst healthy individuals, including choline, glutamate, hypoxanthine and N-acetylglutamate. By contrast, diabetic individuals were associated with higher levels of lactate and sn-glycero-3-phosphocholine . Neurotransmission in the ventromedial hypothalamus is mediated by GABAergic neurotransmission. The suppression of GABAergic neurotransmission is necessary to activate the counter-regulatory responses to hypoglycemia  3-phosphoglycerate dehydrogenase deficiency, (ii) 3-phosphoserine phosphatase deficiency and (iii) phosphoserine aminotransferase deficiency. These enzyme defects result in severe psychomotor retardation and microcephaly . Diabetic retinopathy was previously linked to an increased activity of polyol synthesis pathway   is produced when In total, 61 distinct metabolites were identified associated with diabetic modulations. Glucose and glutamate were the most commonly associated metabolites, and they were significantly increased across nine biological matrices. Kidneys, spleen, eye and plasma, clustering all in the same super group in the heatmap (S6), were the organs and fluids that displayed the most varied metabolic changes. This clustering was partially due to a decrease in amino acids. The large heterogeneity in the metabolic response that is strongly organ-specific prevented further grouping of the organs.In total, 16 metabolites were found modulated in kidney and spleen, and 15 in eye and plasma. Table db/db mouse model BKS.Cg-Dock7 < m > +/+ Lepr < db >/J and their wild type control, identifying over 60 metabolites modulated between these two groups. This study represents the most comprehensive tissue-specific metabolic characterization of this model and is intended to be used as a reference for further research in this area. Kidney, spleen, eye and plasma were the organs that showed the most metabolic modulations between control and diabetic individuals. In total, across all the tissues and biofluids studied, 61 biomarkers were found associated with diabetes.The present study reports qualitative differences in 16 tissues between diabetic Some limitations of this study included a restricted coverage of some potentially important metabolites, such as bile acids and lipids, due to the nature of the methods employed and further studies are necessary to uncover these modulations. The use of a small number of mice of both genders, which impeded a study of gender specific changes is another limitation. Future studies, should also consider the impact of diet and environment on the metabolic modulations associated with diabetes. Hence, diabetic studies should be addressed as part of an integrative approach considering metabolomics along other ‘omics’ technologies such as metagenomics.Supplementary material 1 (DOCX 121 kb)Supplementary material 2 (DOCX 234 kb)Supplementary material 3 (DOCX 62161 kb)Supplementary material 4 (DOCX 13453 kb)Supplementary material 5 (XLSX 36 kb)Supplementary material 6 (DOCX 250 kb)Supplementary material 7 (ZIP 49544 kb)Below is the link to the electronic supplementary material.


Trypanosoma brucei parasite. The tsetse fly vector is of interest for its potential to prevent disease spread, as it is essential for T. brucei life cycle progression and transmission. The tsetse’s mutualistic endosymbiont Sodalis glossinidius has a link to trypanosome establishment, providing a disease control target. Here, we describe a new, experimentally verified model of S. glossinidius metabolism. This model has enabled the development of a defined growth medium that was used successfully to test aspects of S. glossinidius metabolism. We present S. glossinidius as uniquely adapted to life in the tsetse, through its reliance on the blood diet and host-derived sugars. Additionally, S. glossinidius has adapted to the tsetse’s obligate symbiont Wigglesworthia glossinidia by scavenging a vitamin it produces for the insect. This work highlights the use of metabolic modeling to design defined growth media for symbiotic bacteria and may provide novel inhibitory targets to block trypanosome transmission.Human African trypanosomiasis is caused by the  Trypanosoma brucei parasite, the causative agent of human African trypanosomiasis. The colonization and spread of the trypanosome correlate positively with the presence of a secondary symbiotic bacterium, Sodalis glossinidius. The metabolic requirements and interactions of the bacterium with its host are poorly understood, and herein we describe a metabolic model of S. glossinidius metabolism. The model enabled the design and experimental verification of a defined medium that supports S. glossinidius growth ex vivo. This has been used subsequently to analyze in vitro aspects of S. glossinidius metabolism, revealing multiple unique adaptations of the symbiont to its environment. Continued dependence on a sugar, and the importance of the chitin monomer N-acetyl-d-glucosamine as a carbon and energy source, suggests adaptation to host-derived molecules. Adaptation to the amino acid-rich blood diet is revealed by a strong dependence on l-glutamate as a source of carbon and nitrogen and by the ability to rescue a predicted l-arginine auxotrophy. Finally, the selective loss of thiamine biosynthesis, a vitamin provided to the host by the primary symbiont Wigglesworthia glossinidia, reveals an intersymbiont dependence. The reductive evolution of S. glossinidius to exploit environmentally derived metabolites has resulted in multiple weaknesses in the metabolic network. These weaknesses may become targets for reagents that inhibit S. glossinidius growth and aid the reduction of trypanosomal transmission.The tsetse fly is the insect vector for the  Paenibacillaceae and Serratia marcescens in the Asian malarial vector Anopheles stephensi (Aedes aegypti (Trypanosoma brucei parasites that cause human African trypanosomiasis (HAT)  .Glossina, also hosts a limited bacterial microbiome alongside the parasitic T. brucei. The microbiome consists of a primary, obligate symbiont Wigglesworthia glossinidia and typically, a secondary facultative symbiont Sodalis glossinidius  and flux balance analysis (FBA) to model S. glossinidius in silico. This is a powerful method when based on a well-annotated genome and the ability to test in silico hypotheses experimentally  carboxylase gene (ppc), preventing the conversion of PEP to oxaloacetate for the tricarboxylic acid (TCA) cycle. The pseudogenization of components of the l-arginine biosynthesis pathway indicate the requirement of an external source of l-arginine to supplement growth in silico. They concluded that exogenous l-arginine is required both as a biomass component and to form succinate via putrescine in order to supplement the TCA cycle in the absence of ppc. The common hexose sugar d-glucose is given as the sole carbon source. Importantly, this construction of iEB458 was limited by the lack of a well-annotated relative from the same genus, which is no longer an issue since the discovery of S. praecaptivus.To define an mentally . Analysia et al. , who desiLF517, and describe how it has enabled the development of an entirely defined medium that supports S. glossinidius growth in vitro (SGM11). Our data indicate the use of a carbon source lacking in the blood meal, namely, N-acetyl-d-glucosamine (GlcNAc). This suggests a complex nutritional interaction of S. glossinidius with the tsetse chitinous peritrophic matrix. Degradation of this by a microbe-derived chitinase might explain the increased persistence of the trypanosome when S. glossinidius is present , whereas the gene in S. glossinidius contains multiple frameshifts and premature stop codons  is split across three reading frames due to frameshift mutations and contains two premature stop codons. Download FIG S1, TIF file, 4.4 MB.Analysis of a portion of the Copyright © 2019 Hall et al.2019Hall et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the l-arginine to succinate is not supported in our analysis, with little or no evidence for these genes being present in S. glossinidius , aminobutyraldehyde dehydrogenase (ABUTD) and 4-aminobutyrate transaminase (ABTA) (S. glossinidius (patA gene in S. praecaptivus (Sant_1573). Similarly, there is no functional orthologue of patD for ABUTD, nor of puuE or gabT for ABTA. l-Arginine can still be converted to agmatine and then to putrescine via arginine decarboxylase  and agmatinase . This is the only route to the synthesis of this biomass component, meaning a source of l-arginine in the cell is still predicted to be essential. However, it is not supporting the proposed additional anapleurotic function.The proposed anapleurotic link from e (ABTA)  (Data See (ABTA) . PatA issinidius . There i10.1128/mBio.02106-18.7DATA SET S1iLF517 for Sodalis glossinidius. Compounds are listed in the first tab, reactions in the second tab, and the biomass composition in the third tab. Download Data Set S1, XLSX file, 0.1 MB.Metabolic model of Copyright © 2019 Hall et al.2019Hall et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the ppc. Removal of the ABTA reaction from iEB458, breaking the link of putrescine to succinate, results in zero biomass production. This can be rescued by the in silico addition of metabolites that can be introduced easily into the TCA cycle: the amino acid l-aspartate or l-glutamate or the organic acid succinate, fumarate, or α-ketoglutarate. The loss of ppc therefore requires the addition of a second organic substrate in addition to glucose for growth, suggesting an adaptation to an amino acid-rich environment that results from the tsetse blood diet. Important components, including l-aspartate and l-glutamate, are predicted to be present at high concentrations.An alternative organic compound must therefore serve the role of supplying TCA cycle intermediates in the absence of S. glossinidius genome enabled the construction of an independent metabolic model, iLF517. This model has significant differences to iEB458 . This model can be viewed and analyzed through a web-based FBA browser on DETOXbase (www.detoxbase.org/publications/iLF517).A systematic reanalysis of the o iEB458 . Growth a et al. . Full deiLF517 was analyzed via FBA to investigate metabolite essentiality and the presence of predicted auxotrophies. Eighty reactions are included in iLF517 that were absent in iEB458, and 32 have been removed . This value was selected originally for E. coli and indicates highly aerated growth conditions in a chemostat , to create SGM11, resulted in higher yields than with LB alone, although this difference was not statistically significant  for an OD650 value of 0.28  gene ptsG had been pseudogenized. It is intact in S. praecaptivus (Sant_2470), suggesting that there may be weak selection for its retention within the tsetse. While the nonspecific ManXYZ could substitute for this loss of function   is also retained −1 h−1 for d-glucose, GlcNAc, and mannitol, respectively.This investigation highlighted immediately the presence of a GlcNAc-specific PTS transporter gene, (SG0859)  Fig. 3)nagE, in nfection , 37, 38.retained  S. glossinidius was grown experimentally in LB and LB supplemented with a selection of carbon sources to test the hypothesis that GlcNAc and mannitol may be suitable alternatives. Of the saccharides tested, only d-glucose, GlcNAc, and mannitol increase growth significantly in comparison to LB alone (P < 0.01 by one-way ANOVA). Approximately two times greater endpoint growth is exhibited with an equimolar amount of GlcNAc compared to d-glucose (P < 0.05 by one-way ANOVA) . This demonstrates that the difference in growth between d-glucose and GlcNAc is not a result of the additional carbon. S. glossinidius grows significantly better on GlcNAc than mannitol (P < 0.05 by one-way ANOVA), reflecting the in silico results qualitatively.LB alone  (P < 0.0y ANOVA) . Normalil-arginine, l-glutamate, and a carbon source, namely, GlcNAc, does not produce a positive biomass output in iLF517. It is likely that S. glossinidius requires supplementation from certain vitamins that it cannot synthesize. During the transition to symbiosis, S. glossinidius may lose genes that encode components of the vitamin and cofactor biosynthetic pathways in favor of retaining transporters. There is also thought to be a connection between aspects of the tsetse microbiome in terms of vitamin biosynthesis  is used as a comparison. The loss of this pathway in S. glossinidius may be a specific adaptation to the tsetse. To investigate this further, three other Sodalis-allied symbionts were examined: “Candidatus Sodalis pierantonius” strain SOPE from the rice weevil Sitophilus oryzai   . A thiamy ANOVA) . This adr et al.  that S. S. glossinidius in SGM11 enables the thorough testing of amino acid usage in iLF517. Metabolites can be removed individually, and the effect on biomass production can be measured. iLF517 requires either l-glutamate or l-aspartate to produce a positive biomass output. Both l-glutamate and l-aspartate likely enter the cell through the GltP transporter SG2121  are dicarboxylate/amino acid:cation symporter (DAACS), amino acid-polyamine-organocation family (APC), branched-chain amino acid symporter (LIVSC), alanine or glycine:cation symporter (AGCS), and hydroxyl/aromatic amino acid permease (HAAAP). Download Copyright © 2019 Hall et al.2019Hall et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the l-aspartate or l-glutamate from SGM11 individually resulted in a significant decrease in the growth yield achieved by S. glossinidius after incubating for 72 h (P < 0.05 by one-way ANOVA) (l-aspartate and l-glutamate abolished growth completely (P < 0.001 by one-way ANOVA). This confirms that an exogenous source of one of these amino acids is essential for S. glossinidius growth. Examination of iLF517 reveals that l-glutamate feeds directly into the TCA cycle through deamination to α-ketoglutarate. The direct route to feed l-aspartate into the TCA cycle via fumarate (l-aspartase) is however missing in S. glossinidius , producing l-glutamate and oxaloacetate. The resulting biomass output was reduced from 0.35 to 0.31 g DW (mmol glucose)−1 h−1, demonstrating that S. glossinidius can use l-aspartate if l-glutamate is not available, although the latter may be preferred. l-Glutamate is therefore likely an important energy source in iLF517 both to form l-aspartate and to replenish the TCA cycle at α-ketoglutarate (Removing y ANOVA) . Removinsinidius . When iLlutarate .S. glossinidius appeared to confirm existing opinion  was grown in M9 medium alone (solid line) or with added l-arginine (dashed line), l-glutamate  or 5× concentration l-glutamate  for 24 hours in a microplate reader. This was compared to growth in the same medium combinations for argA (pink), argD (orange), and argG (green) gene deletion mutants from the Keio collection  to assess its completeness in comparison to S. praecaptivus. This revealed that argB, argE, argF/argI, and argH are full length and therefore likely functional  has a fragment (argG_2) that is almost full length in comparison to its S. praecaptivus orthologue. This indicates that argG is also likely functional in spite of its description as a pseudogene in the previous annotation.The new annotation suggests that the argA gene appears in two fragments: SGGMMB4_04654 (argA_1) and SGGMMB4_04655 (argA_2). It has been demonstrated in Pseudomonas aeruginosa that the two separate ArgA protein domains can be expressed individually (l-glutamate is provided (argA_2) fragment of this gene has a GTG start codon and may therefore be functional under the conditions shown in The vidually , 49. Theprovided . The SGGS. glossinidius argD orthologue also appears in two pieces: SGGMMB4_05438 (argD_1) and SGGMMB4_05439 (argD_2). Lal et al.  or hemL (SG0500), that may provide functional alternatives.The l et al.  showed tllection  were grollection  alone orarginine , confirmS. glossinidius can synthesize l-arginine, surviving when it is not supplied exogenously. Therefore, it is not a true auxotroph and could instead be described as a relic of a prototroph transitioning to auxotrophy.The data here suggest that under certain conditions, S. glossinidius metabolic network and demonstrates its application in designing defined growth media for the symbiont. The carbon source for iLF517 is GlcNAc, as opposed to d-glucose in iEB458. It has been verified empirically that S. glossinidius achieves a significantly better growth yield with GlcNAc than with d-glucose  medium subsequently removes any limitations caused by insufficient L-glutamate.rnithine . An excetabolite . The argnotation . S. glosripennis . The expe tissue , 60. SGMl-arginine biosynthesis pathway is undergoing the process of inactivation and will become entirely pseudogenized over evolutionary time. This is supported by the complete pathway for l-arginine biosynthesis in S. praecaptivus, suggesting that these genes may have been lost within the tsetse environment as a result of selection pressure or drift. It implies that l-glutamate is not limiting inside the tsetse, allowing relaxed selection pressure on the l-arginine biosynthesis genes. It also emphasizes strongly the importance of using in vitro experiments to test in silico assertions. This is particularly relevant in symbiotic bacteria where the functionality of broken or fragmented genes is not certain. Indeed, a recent report implies that some S. glossinidius genes thought to be pseudogenized are in fact under transcriptional control      and Aedeungunya) –75. Whilnfection –79, the prolixus , and Aceipalpis) .iLF517 and the S. glossinidius metabolic network has enabled the design of a defined growth medium that supports growth of the symbiont. While several FBA models for insect symbionts have been published  iEB458  was asse−1 h−1 in order to simulate a reduced oxygen environment. The uptake of ammonia, water, phosphate, sulfate, potassium, sodium, calcium, carbon dioxide, protons, and essential transition metals was unconstrained. Uptake of all other metabolites was set at zero, with the exception of those used in the analyses which have been set at 2 mmol g DW−1 h−1 GlcNAc and l-glutamate, 0.5 mmol g DW−1 h−1l-arginine, and 0.01 mmol g DW−1 h−1 thiamine. Cofactor constraints were implemented by introducing these metabolites to the biomass functions at small fluxes (0.00001 mmol g DW−1 h−1) (−1 g DW (mmol glucose)−1 h−1. Futile cycles, identified as reactions carrying biochemically unsustainable flux, were altered to the correct reaction stoichiometry where possible with guidance from EcoCyc and BiGG. The full description of the model is provided in The flux balance analysis (FBA) solutions were generated using the GNA linear programming kit (GLPK) integrated with custom software in Java. Oxygen uptake was constrained to 12 mmol g DWW−1 h−1) . The pheS. glossinidius strain GMM4 was obtained from the University of Liverpool. Working stocks were established by growing starter cultures on brain heart infusion (BHI) (Sigma-Aldrich) plates under microaerophilic conditions generated by Oxoid CampyGen sachets (Thermo Fisher Scientific) until growth was visible. Colonies were then transferred to liquid BHI medium and incubated for 4 to 7 days in cell culture flasks at room temperature until growth was visible. S. glossinidius from the working stock was then transferred to 5 ml fresh Luria-Bertani (LB) medium (Sigma-Aldrich) and supplemented with either d-glucose or GlcNAc (Sigma-Aldrich).iLF517 and the S. glossinidius metabolic network were used to design in silico an entirely defined medium, SGM11, in which to grow the bacterium. S. glossinidius from the working stock was transferred to 5 ml of M9 minimal medium . Metabolites were omitted from SGM11 individually and in combination to test the model predictions.l medium  containiS. glossinidius growth was measured at an optical density of 650 nm. Preliminary experiments using variable sampling times indicated that this was the most appropriate sampling time for S. glossinidius to reproducibly capture the final steady state but still retain a proxy for growth rate to guide the modeling results.The culture flasks were incubated for 48 h (LB) or 72 h (SGM11) at 25°C in a temperature-controlled water bath. Gentle agitation was achieved using magnetic stir bars to achieve a suitable balance between oxygenation, settling, and disturbance, and a stirring speed of 500 rpm was used. Intermediate time points were found to compromise the sterility of the cultures, and therefore, destructive sampling was the only reliable method of investigation, negating the possibility of higher-resolution temporal data. Endpoint increase in E. coli gene deletion mutants were obtained from the Keio collection . Error bars show standard errors of the means, and statistical significance was assessed using one-way analysis of variance (ANOVA).All statistical analyses were performed using SciPy in Python (version 2.7.10,


Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI ( Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints into genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid to understanding the interactions between different types of omics data.The recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in high-throughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between “data generation” and “data integration.”. In this study, we present a new method named REMI ( Genome-scale metabolic models (GEMs), which essentially associate an organism’s genotype with its phenotype, integrate genomic information with known information about metabolite levels to comprehensively describe an organism's metabolism [The turnover rates of metabolites through a pathway are called fluxes, and genome-wide intracellular metabolic fluxes are the ultimate regulator of cellular physiology. Perturbations on the normal physiology, such as those that occur in a disease state, directly influence the metabolic fluxes. The well-established experimental approach for determining these metabolic fluxes is tabolism . Due to tabolism .With the growing availability of high-throughput data for different organisms under a wide range of genetic or environmental perturbations, GEMs became popular because of their ability to incorporate omics data as additional regulatory constraints for FBA problems. Because GEMs associate a genotype with a phenotype, it is essential to understand that a single genome can result in thousands of different physiologies through different regulatory mechanisms. Therefore, the integration of static snapshots of the metabolism, obtained from transcriptomic and metabolomic data, provides more biologically relevant constraints for the system and helps to increase the precision of the flux prediction, therefore better deducing the observed physiology. However, despite the high number of methods that have been introduced in recent years for the integration of omics data into constraint-based metabolic models, the enhanced prediction of flux profiles using omics data, particularly in cases using multi-omics data, is still far from being resolved. Recently, these methods, their scopes, and limitations were extensively reviewed , and theThe existing methods for integrating gene-expression data into GEMs can be classified into two categories with the first relying on the integration of absolute gene-expression data into GEMs. This includes techniques such as gene inactivity moderated by metabolism and expression (GIMME)  and the et al. [The increasing availability and quality of metabolomic data have promoted the development of methods that can be integrated into GEMs to refine model reconstruction, to reduce the solution space of feasible fluxes, and to better predict the physiological state of a system. These methods, their scope, and their limitations have been reviewed by Töpfer et al. . One of et al. , 4. Anotet al. . Howeveret al. . Howeveret al. , and it For multi-omic data, methods have recently been introduced for integrating different layers of data, such as genomic, transcriptomic, proteomic, and fluxomic, into metabolic models  or multiRelative Expression and Metabolite Integration (REMI), to integrate relative expression and relative metabolite abundance data into thermodynamically curated GEMs. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We demonstrate that REMI’s ability to integrate different layers of constrictive data significantly reduces the solution space of feasible fluxes. REMI also extensively enumerates alternative optimal and sub-optimal solutions, bringing a robustness and flexibility to the flux distribution analysis. We applied REMI to a GEM of E. coli to estimate the central carbon metabolism flux measurements that were determined by 13C metabolic flux analysis (13C-MFA) and were provided by two independent experimental studies [To address this deficiency, we herein propose a novel method, termed  studies , 21. Alt studies ), we alsWe designed REMI as the first method to integrate relative gene-expression and metabolite abundance data into thermodynamically curated GEMs, reducing the solution space of optimal fluxes to provide results that are better at predicting cell physiologies closer to the experimental observations than can be reached using existing methods. The REMI workflow along with an illustration of the method performed on a toy model is presented in E. coli transcriptomic and metabolomic data obtained from two studies under 8 [E. coli GEM iJO1366 and to estimate the differential steady-state fluxes. We call the data and information from [E. coli GEM iJO1366 (REMI-Gex). The comparison of REMI-Gex and REMI-TGex highlights the significance of the thermodynamic constraints in reducing the solution space of flux analysis. We also performed some studies with only metabolite changes with thermodynamic constraints (REMI-TM) and without thermodynamic constraints (REMI-M).The REMI framework was applied to integrate the  under 8  and 3 [2 under 8  differenion from  “Datasetion from  “Datasetion from , though The underlying assumption of the REMI method is that the perturbation of gene-expression and metabolite levels influences the flux levels in the metabolic network. To this end, REMI maximizes the consistency between relative experimentally observed changes in gene expression and metabolite abundance with the flux levels (the objective function of the REMI constraint-based method). The maximum consistency is then calculated as an integer number, called the maximum consistency score (MCS). This represents the maximum number of constraints that can be incorporated into a FBA model from a given set of constraints (gene-expression or metabolite abundance levels) while ensuring that the model still achieves the required metabolic functionalities and remains feasible. MCS is a unique number, however, in that the complex nature and interconnectivity of metabolic networks can result in several alternative solutions for a given MCS, meaning that numerous combination of different constraints from the input data could result in the same MCS. The theoretical maximum consistency score (TMCS) indicates the number of genes (or metabolites or both) with available experimental data that can potentially be integrated into the model, and MCS indicates the number of these available constraints that could be consistently integrated into the model.et al. [E. coli model. After integrating the gene-expression data of each condition into the model and comparing it with the reference model, we computed TMCSs, MCSs, and the number of alternative solutions for the REMI-Gex method (without thermodynamic constraints) and the REMI-TGex method (with thermodynamic constraints). In contrast to other methods, REMI finds all possible alternative solutions of a given maximum consistency score, which involves all possible combinations of the given set of constraints that always result in a feasible model. These alternative solutions provide flexibility in the biological interpretation of the results as they are equally consistent with the provided experimental data (applied as constraints to the model). Note that in the GEM analysis, the alternate flux solutions are conventionally considered as equivalent phenotypic states [We first applied REMI to the integration of eight datasets from Ishii et al. , which ic states . In thisE. coli model using REMI-TGexM and obtained the MCS for the integrated metabolites as well as the global maximum consistency score (GMCS), which encompasses both genes and metabolites . Different conditions (mutations) alter the cell metabolism differently, leading to different levels of metabolic adaptations and metabolic flux rerouting. Hence, we speculate that the differences in flux rerouting across conditions results in differences in the numbers of alternative solutions across the seven relative conditions. Note that for REMI-TM and consequently for REMI-M, the constraints for all the deregulated metabolites were consistently integrated into the model, so we found only one solution for the REMI-TM models without any alternative solution.We further integrated the available metabolomics measurements into the abolites . Althougabolites . As Tablabolites , which svs Ref, gapC vs Ref, zwf vs Ref, wt5 vs Ref, and wt7 vs Ref comparisons. We observed that the number of alternative solutions for these five cases was identical between REMI-TGexM and REMI-TGex. This implies that the relative expression constraints and the relative metabolite constraints were not contradictory for these five cases. However, in rpe vs Ref and pgi vs Ref, all the metabolic data were integrated in the model, but the number of alternative solutions differed (and in the case of rpe vs Ref was noticeably reduced) between REMI-TGexM and REMI-TGex. To see if this indicated a contradiction, further investigation into the alternative solutions revealed that in the rpe vs Ref and wt5 vs Ref comparisons, REMI-TGexM and REMI-TGex have the same set of constraints, which means that the constraints from metabolomics and expression data were not contradictory. However, we found that the metabolomics integration resulted in a reduction in the number of alternative solutions  analysis.The results of this analysis indicate the core constraints that consistently operate in all the alternative solutions . Meaning that such core constraints certainly perturb fluxes within each pair of conditions. Therefore, these constraints could potentially be the indicators of the regulators of the condition-specific metabolism, which assist biologist in determining which metabolic subsystems to deregulate or to mutate. We believe that the capability to analyze and identify these regulators is a key advantage of REMI.E. coli because they were found to be deregulated in all mutant conditions.As shown in the To study the effect of thermodynamics on the model, we compared the reduction in solution space for the predicted flux profiles from the REMI-TGex and REMI-Gex methods when coupled with the gene-expression data . The MCSvs Ref as a case study, where we obtained MCS = 56 in REMI-Gex and MCS = 49 in REMI-TGex  that do not necessarily involve the same set of deregulated genes.To further benchmark REMI with the available experimental data, we used a second data set (2 overexpression compared to the ref condition) from an independent study where the role of metabolic cofactors, such as NADH and ATP in different aspect of metabolism is studied by overexpressing NADH oxidase (NOX) and the soluble F1-ATPase in E. coli . REMI in [et al.  into thevs. Ref case and the lowest BDR reduction for the NOX vs. Ref case, which therefore indicates more reduction in the feasible flux solution space in the rpe vs. Ref case compared to the NOX vs. Ref case. For the all comparisons, we found a further reduction in BDRs upon the integration of relative metabolomic data into the REMI-TGex model. In most of the cases, we found a similar decrease in BDRs, which means that the metabolomic data further constrained the solution space. Except for the wt7 vs Ref case, we observed a decrease in BDRs for all cases that were constrained by metabolites and expression data together (GexM) as compared to only expression (Gex) data. Unexpectedly and unlike all the other cases, by incorporating metabolomics data for the wt7 vs Ref case, we found an increase of one reaction in the BDRs. This suggests that for the wt7 vs Ref case the integration of gene expression and metabolites reroutes fluxes through the metabolic networks differently compared to other cases. As expected, we consistently find a reduction in BDRs for the REMI-TM model (thermodynamics and relative metabolomics) compared to without thermodynamics (the REMI-M model). This is in agreement with the fact that integrating thermodynamic constraints into a model eliminates infeasible reaction directionalities and consequently the flux feasible optimal space.To investigate the influence of thermodynamic constraints on flux ranges, we identified the overlapping constraints (HFCs) across all the alternative solutions and then enforced them to be active to build the most consistent model. An active HFC satisfies differential gene expression (or metabolite levels) between two conditions form a given experimental data. Thus, for each condition, we built the most consistent model despite having many alternatives. We next performed FVA on the REMI-Gex and REMI-TGex models. As REMI is based on pair-wise relative constraints (for two conditions) and builds two models that are then compared, as opposed to modifying one solution based on a given condition, we obtained two FVA solutions, i.e. one for each condition. We identified less bidirectional reactions (BDRs) in the REMI-TGex case compared to the REMI-Gex case , which mvs. Ref case as an example to demonstrate the average relative flexibility (ARF) reduction at a global  level as well as at the subsystem level.To further illustrate the positive influence of thermodynamic constraints in reducing the optimal solution space, we performed a relative flexibility  analysis using the REMI-TGex and REMI-Gex methods. To perform a relative flexibility analysis, a reference model is compared to a target model to investigate the relative flux reduction. For a reference, we used the iJ01366 model without integrating any data, meaning that the reference model implies only mass balance constraints. We took the pgi vs. Ref case, we found a 10%, 20%, 50%, 77%, and 80% reduction in the global ARF in REMI-M, REMI-Gex, REMI-TGex, REMI-GexM, and REMI-TGexM models compared to the reference model, respectively  the Pearson correlation between the predicted and measured intracellular fluxes, and 2) the average percentage error  showed a considerably improved flux prediction for the REMI-Gex, REMI-TGex, REMI-TGexM, and REMI-GexM models as compared to the GX-FBA method, indicated by Pearson correlation and average percentage error (vs. Ref and gapC vs. Ref cases (vs. Ref and ATPase vs. Ref), REMI-TGex predicted better correlation than REMI-Gex and GX-FBA, and the average percentage error of GX-FBA was higher than that of REMI-TGex and REMI-Gex  [et al. (3 datasets) [Eleven total sets of experimental data that had been previously integrated into the genome-scale model (GEM) of m et al.  and wereatasets)  and Holmatasets)  were useet al. [E. coli, cells overexpressing NADH oxidase (NOX), and cells overexpressing the soluble F1-ATPase (ATPase). The eight datasets from Ishii et al. [E. coli cells cultured at different growth rates of 0.2, 0.6, and 0.7 per hour along with single-gene knockout mutants of the glycolysis and pentose phosphate pathway .The three datasets from Holm et al.  comprisei et al.  include E. coli [https://github.com/EP-LCSB/remi. Mixed-integer linear programming (MILP) problems were solved using the CPLEX solver on an Intel 12-core desktop computer running Mac.All analyses were performed using IJO1366, the latest GEM of E. coli . The modIt has been previously shown that thermodynamic constraints not only effectively reduce the solution space of FBA by eliminating the thermodynamically infeasible fluxes from the solution space, but also allow the integration of metabolite concentrations. This provides important links between mass and energy balance and the phenotypic characteristics of the organism. The thermodynamic constraints, as depicted in Eq , were inn method .xj) of each metabolite j given the pH, the metabolite concentration range, and the ionic strength at the cellular compartment where the reaction occurs.For each reaction of a GEM, the Gibbs free energy of the reaction (E. coli GEM to translate the relative gene-expression levels (being relatively up- or downregulated) to the differential and relative flux values of corresponding reactions. GPRs are not mapped as one gene to one reaction, meaning there are many cases in which one gene is mapped to several reactions and multiple genes are mapped to a single reaction, which are depicted with “and” and “or” affiliations, respectively.We used the gene-protein-reaction (GPR) association rules acquired from the To capture this, we followed the same procedure for the mapping between GPRs and reactions fluxes introduced by Fang et al . In thatIn REMI, if the reaction R is associated with two genes (g1 “and” g2), the expression level ratios for genes g1 and g2 in the two corresponding conditions are calculated to obtain the geometric mean of the g1 and g2 ratios. Whereas, if the reaction R is associated with two genes (g1 “or” g2), the arithmetic mean of the obtained expression data ratios is calculated. Thus, from GPR associations, REMI computes the so-called tentative “reaction flux ratios” to further constrain the model. For the metabolomic data, the ratio of metabolite concentration for each metabolite (if available) is calculated for any two given conditions.To evaluate whether a reaction or metabolite was up- or downregulated, we sorted the ratios , and selected the top 5% as upregulated and the bottom 5% as downregulated. The fold changes greater than two are considered as significant in many studies. For comparison purposes, we used the two-fold change as the cut-off threshold to identify the significant gene expression and metabolite changes. We found that across all mutants, the set of significant changes identified with our threshold of the top and bottom 5% encloses the corresponding set identified with the two-fold change, meaning that the proposed cutoff criterion is conservative. However, this threshold is a user-defined parameter and one could use different threshold cutoff.R reactions and M metabolites, bidirectional reactions are decomposed into forward and backward reactions to allow all fluxes to have positive values. Assuming that S is a stoichiometry matrix, Smr is the stoichiometric coefficient associated with the metabolite m  in reaction r . Positive and negative stoichiometric coefficients of metabolites signify the substrate or products of a reaction. A binary variable zr was assigned to each reaction r to ensure a positive flux vr  throughusing Eq  to ensurIn REMI, two models are described for each given condition. For both models, we constrained the cellular growth rate to be at least 0.1 mmol g-1 DW-1 h-1, to ensure that the model is able to synthesize all the biomass building blocks required for the cellular growth. Throughout the manuscript, the terms “wildtype” and “mutant” are used to better differentiate between the two conditions (or models) when describing the REMI framework. REMI can, however, be used for any two given conditions and is not restricted to the wildtype and mutant labels. Eq  specifieu and d, respectively, where n is the total number of up- and downregulated reactions. For the upregulated reactions, a higher flux was enforced in the mutants as compared to the wildtype, while for downregulated reactions, a higher flux was enforced for the wildtype as compared to the mutant.The relative information about the gene-expression levels or metabolite levels between the two given experimental conditions was formulated as additional constraints and integrated into the two representative models of the conditions. To do this, binary variables for the up- and downregulated reactions were assigned as u upregulated and d downregulated reactions, a total of n binary variables were generated , where Bi = 1 indicates the up- or downregulation of a reaction. Next, n constraints  than a wildtype flux, where p is a reaction ratio between the wildtype and mutant (computed from gene-expression ratio). Constraints were added  w wu upregints (Eq ) were addded (Eq ) for d dx. In Eq , n constd in Eqs  and 9),,u upreguIn GEMs, gene-level perturbations can mediate both reactions and their subsequent metabolites. Available studies show a correlation between gene changes and metabolite changes and infer that perturbations at the metabolite level are formed from perturbations in genes or reaction levels , 28. Thuϕp in condition 2 is greater than the ϕp in condition 1 or the sum of consumption ϕc in condition 2 is less than the ϕc in condition 1  of up- and downregulated metabolites, where u’ and d’ are up- and downregulated metabolites, respectively. For an up-regulated metabolite i (i.e. in the mutant vs. wildtype), either more production or less consumption is enforced in the mutant compared to the wildtype using Eqs (Bi) is introduced, which switches to production if Bi = 1 and to consumption if Bi = 0. Similarly, for downregulated metabolites i, less production or more consumption is enforced in the mutant compared to the wildtype n’) of upsing Eqs , a binarype  was forAn aforementioned mathematical optimization model (objective function 15) subject to Eqs 5 subjectThe left-hand side of Eq  determinn and n’ represent a total number of up- and downregulated metabolites and up- and downregulated genes, respectively.To concurrently integrate both the relative gene-expression data and the relative metabolite levels, an integrated mathematical optimization model was built with a global objective function ). T). Tin sivi and vm are the in silico and measured vectors of the fluxes, respectively. The correlation coefficients +1 and -1 indicate a strong positive and negative linear relationship between vi and vm, and the 0 correlation coefficient indicates no linear relationship between vi and vm.In Eq , vi and r is used to measure the relative deviation between the two fluxes in two conditions, where x and y correspond to the flux of a given reaction in condition 1 and condition 2, respectively. Since |dr| lies between 0 and 1, one can consider dr as a percentage flux change from condition 1 to condition 2. The average (per reaction) percentage error, e, in the predicted in silico fluxes was calculated using Eq  for reaction i is calculated using the following equation:i at each of the two conditions, one condition is usually designated as a reference condition or reference state, such as when comparing the relative flexibility of a metabolic system with (condition 1) and without (condition2) thermodynamic constraints. The value of RF that is computed for each reaction i reflects the relative changes in the flux variability range of one condition compared to the other condition. The global relative flexibility change between two given condition is then computed by averaging the Fi values for each reaction i that carry flux in the reference state.The We developed the computational tool, REMI, which combines gene-expression, metabolomics, and thermodynamics constraints with the mass balance constraints imposed in metabolic models to predict phenotypic changes in an organism upon environmental or genetic perturbations. As the integration of these three additional physiological constraint results in a highly reduced flexibility of the predicted optimal flux profiles, REMI enhances the quality of the computationally predicted fluxes. REMI’s novel formulation permits the extensive enumeration of alternative solutions because there exist several alternative sets of pathway that result in the same phenotype due to the complexity and interconnectivity of metabolic networks, meaning that the results provided by REMI more accurately reflect natural biological states than previously existing methods. Within several examples, we showed the effectiveness of incorporating thermodynamic data with gene-expression and metabolomics in reducing the flexibility of predicted optimal flux profiles. This means that we can obtain manageable set of physiological consistent hypothesis and physiological interpretations which have a higher confidence as they are consistent with a larger set of data. Applying REMI to experimental data has shown that there is not always a full consistency between gene-expression and metabolomic data, which shows that there is still much to learn about how gene expression and metabolism are linked.E.coli or the Warburg effect in the cancer cells. Furthermore, REMI can also be used to investigate metabolism of diseased states compared to the healthy one, where numerous sets of omics data are available.The application of REMI goes beyond the study of physiology of a mutant versus a wild-type cell presented in this work. With a slight modification in the formulation, REMI can be employed for investigating the physiology of several mutants simultaneously against the wild type physiology within a single optimization. Although in this study we showcased REMI for constraining internal fluxes, REMI can also be applied to study the perturbation of external fluxes and metabolites whenever omics data are available. This has a potential application in studying the overflow metabolism, e.g., the acetate overflow in fast-growing Various REMI methods introduced in this work permit a wide range of applications depending on the type of available data . However, whenever gene-expression, metabolite abundance, and thermodynamic data are available, our results suggest that the most extensive data integration method, REMI-TGexM, provides the best results and the most reduced optimal solution space. As systematic multi-omics integration remains a challenge, REMI opens the possibility of not only multi-omics integration, but also the identification of the crosstalk between the various omics present in a system.S1 Text(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 FigThe bars represent the flux variability range, with a longer bar indicating greater flexibility of feasible flux ranges. Flux ranges are calculated based on the flux variability analysis of the REMI-TGex and REMI-Gex models at different consistency scores. For more clarification, two reactions out of the 45 are shown as examples, where it can easily be seen that there is a much greater variability in the Gex-10 and Gex-49 models by the length of the bar. This indicates that these two models are more flexible compared to the TGex-49 model. This means an equal consistency score (Gex-49) or a lower consistency score (Gex-10) in the absence of thermodynamic constraints (instead of Gex-56) provides a greater flexibility compared to TGex-49.(EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 Fig(A) The comparison between the mutant and wildtype for the pgm, pgi, gapC, zwf, and rpe mutants. (B) The comparison of NOX overexpression and ATPase overexpression against the wildtype.(EPS)Click here for additional data file.S4 FigComparison of the experimental values and predicted fluxes for (A) pgm and (B) zwf cases.(EPS)Click here for additional data file.


Plasmodium falciparum is an auxotroph for purines, which are required for nucleic acid synthesis during the intra-erythrocytic developmental cycle (IDC) of the parasite. The capabilities of the parasite and extent to which it can use compensatory mechanisms to adapt to purine deprivation were studied by examining changes in its metabolism under sub-optimal concentrations of hypoxanthine, the primary precursor utilized by the parasite for purine-based nucleic acid synthesis.The malarial parasite P. falciparum to hypoxanthine deprivation.The concentration of hypoxanthine that caused a moderate growth defect over the course of one IDC was determined. At this concentration of hypoxanthine (0.5 μM), transcriptomic and metabolomic data were collected during one IDC at multiple time points. These data were integrated with a metabolic network model of the parasite embedded in a red blood cell (RBC) to interpret the metabolic adaptation of P. falciparum parasites were observed after the 24-h midpoint of the IDC. Parasites grown under these conditions experienced a slowdown in the progression of the IDC. After 72 h of deprivation, the parasite growth could not be recovered despite supplementation with 90 µM hypoxanthine. Simulations of P. falciparum metabolism suggested that alterations in ubiquinone, isoprenoid, shikimate, and mitochondrial metabolism occurred before the appearance of these vacuole-like structures. Alterations were found in metabolic reactions associated with fatty acid synthesis, the pentose phosphate pathway, methionine metabolism, and coenzyme A synthesis in the latter half of the IDC. Furthermore, gene set enrichment analysis revealed that P. falciparum activated genes associated with rosette formation, Maurer’s cleft and protein export under two different nutrient-deprivation conditions (hypoxanthine and isoleucine).At a hypoxanthine concentration of 0.5 μM, vacuole-like structures in the cytosol of many P. falciparum invokes specific purine-recycling pathways to compensate for hypoxanthine deprivation and maintains a hypoxanthine pool for purine-based nucleic acid synthesis. However, this compensatory mechanism is not sufficient to maintain long-term viability of the parasite. Although P. falciparum can complete a full IDC in low hypoxanthine conditions, subsequent cycles are disrupted.The metabolic network analysis presented here suggests that The online version of this article (10.1186/s12936-019-2720-3) contains supplementary material, which is available to authorized users. Plasmodium falciparum, the most lethal malaria parasite, is responsible for 99% of malaria-related deaths in sub-Saharan Africa [In 2016, there were 216 million cases of malaria and 445,000 malaria-related deaths worldwide [n Africa , which gP. falciparum parasites [In contrast to most human cells, arasites  and RBCsarasites  lack thearasites , but thaarasites . Therefoarasites –8. Figurarasites , 5. Prevarasites .Fig. 1ScP. falciparum was first cultured under a range of hypoxanthine concentrations to identify appropriate nutrient-deprived conditions. At a concentration of 0.5 μM, transcriptomic and metabolomic data were first collected and then integrated with metabolic network models of uninfected and parasite-infected RBCs to predict the metabolism of P. falciparum under hypoxanthine-deprivation conditions. Model simulations of uninfected and parasite-infected RBCs were validated by comparing them with relevant experimental data. Lastly, the metabolic adaptations of the parasite under hypoxanthine-deprivation conditions were investigated and gene set enrichment analysis (GSEA) was performed to identify biological processes that were altered significantly beyond metabolic adaptations. The GSEA results were used to identify pathways and processes invoked by the parasite in response to hypoxanthine deprivation. These pathways and processes were then contrasted with those invoked in response to a different nutrient-deprivation condition [To determine how hypoxanthine deprivation affects parasite metabolism, ondition , to idenPlasmodium falciparum Nf54attB parasites  were cultivated in RBCs at 2% haematocrit (HCT) under a mixed gas atmosphere  at 37 °C. The parasite cultures were maintained in Roswell Park Memorial Institute (RPMI)-1640 medium  supplemented with 25 mM HEPES, 90 µM hypoxanthine, 0.3% sodium bicarbonate, 25 µg/mL gentamicin, 0.5 µM R-lipoic acid, and 0.5% AlbuMAX II . This medium is referred to as the hypoxanthine-rich medium.g, 5 min) to pellet the RBCs within 0–2 h after they were infected. The pellet was washed once in medium lacking hypoxanthine and then seeded into a medium containing a hypoxanthine concentration of 0, 0.5, 2, 5, or 90 µM. Parasite morphology and parasitaemia were monitored by Giemsa staining of blood smears at 24 and 40 h. To assess re-infection of RBCs, a tenth of each culture was transferred into fresh hypoxanthine-rich medium with 2% HCT at 40 h. Blood smears were prepared at 72 h for these diluted cultures to assess re-infection of RBCs.To determine the hypoxanthine-deprivation conditions, experiments were first carried out in 24-well culture plates to identify the concentration range in which parasite viability was affected. RBCs containing late-stage parasites were collected by Magnetic Activated Cell Sorting (MACS). Parasite-infected RBCs were purified and diluted in fresh hypoxanthine-rich medium to approximately 0.8% parasitaemia with 2% HCT. The culture was centrifuged  and 0.5 (hypoxanthine-deprived) for over a period of 120 h, and blood smears were prepared at 24, 40, and 48 h. A tenth of the hypoxanthine-deprived culture was transferred to hypoxanthine-rich medium at 40, 48, 60, 72, and 96 h, while the hypoxanthine-rich culture was transferred at 40 h to serve as a control. Twenty-4 h after transfer, the cultures were assessed for parasitaemia using a blood smear test. All growth experiments were carried out in triplicate wells for each condition and the procedure was repeated at least twice.P. falciparum metabolic network model, which consists of 1025 metabolic reactions and 923 metabolites [P. falciparum Nf54attB strain maintained in RPMI-1640 medium during the IDC, as determined by (1) ultrahigh-performance liquid chromatography (UPLC) tandem mass spectrometry (MS/MS) [The present study used the latest version of a abolites . This moabolites  of 273 mr, the level of expression of an enzymatic reaction, which is a function of the gene(s) transcribing the enzyme(s) catalyzing a given reaction . For a OR’ gate .Fig. 2ScFigure A previously described computational framework was used for simulating hypoxanthine deprivation . BrieflyP. falciparum during the IDC, the following equations were solved:R denotes the set of all metabolic reactions of P. falciparum, M represents the set of metabolic reactions influenced by the metabolomic data, S represents a matrix containing the stoichiometry coefficients of all reactions, vg represents the metabolic reaction governing the growth of the parasite, ith metabolic reaction. ith value of N denotes the set of reactions transporting nutrients across the parasite plasma membrane and N. The other variables are as defined above.To obtain the metabolic flux distribution of entclass1pt{minimavref, which were modulated using the time-dependent transcriptomic and metabolomic data to obtain the temporal profile of P. falciparum metabolism. The time-dependent transcriptomic and metabolomic data were incorporated by solving the following:G denotes the set of all intracellular reactions of P. falciparum, jth reaction at time t, t for the ith reaction, and the bar above m denotes normalization of metabolite abundance by its median over the IDC. jth and ith values, respectively, of The optimization problems shown in Eqs.  and 2)  2) yieldThe method to solve the optimization problems in Eqs. –4) was  was 4) wμ) and standard deviation (σ) of the absolute values were calculated based on the absolute fold-change values of all genes. The average score of a given gene set  in response to parasite infection under hypoxanthine-rich and -deprived conditions. A complete list of the RBC metabolic pathways perturbed under these two conditions is provided in Additional file Parasite-infected RBC metabolism did not significantly change in response to hypoxanthine deprivation. In fact, the metabolic pathways of a parasite-infected RBC were perturbed to a similar degree regardless of the culture medium. Table In the culture conditions used here, malaria parasites had access to only hypoxanthine as their sole purine source, which was kept at a pre-determined low concentration of 0.5 µM. Figure P. falciparum to synthesize DNA, RNA, and protein. The extent to which the proposed model could capture the time-dependent changes in DNA, RNA, and protein synthesis over the course of the IDC in the hypoxanthine-deprived condition was examined , intracellular signalling, Maurer’s cleft, rosette formation, protein export, and mitosis.Thus far, the analysis has only focused on identifying alterations in the metabolism of the parasite. This section presents the results of GSEA, which includes analyses of all biological processes of the parasite. Figure P. falciparum, along with their enrichment level at each time point and deprivation condition, is provided in Additional files To ascertain whether enrichment of these gene sets constitutes a general response or a response specific to hypoxanthine deprivation, GSEA was also performed on published data obtained under isoleucine deprivation at the 30-h mark, when parasites enter a hibernatory state . Figure P. falciparum is capable of effectively responding to short-term nutrient stress.Parasites deprived of hypoxanthine displayed morphological and developmental aberrations during the first IDC and could not be rescued after two IDCs in the deprived medium. The formation of vacuole-like structures was evident in many parasites at 24 h into the IDC. Such vacuole formation, which has previously been observed in parasites exposed to heat stress, is attributed to autophagic programmed cell death . The hypBased on model simulations, it can be inferred that the hypoxanthine-deprived parasite invokes mechanisms to maintain a sufficient pool of purine bases, which thereby supports its growth under limited availability of hypoxanthine. Model-predicted reaction fluxes that support this hypothesis are presented below Fig. . First, S-adenosylhomocysteine (SAH). The cycle is futile because SAH requires S-adenosylmethionine (SAM), which cannot be synthesized without ATP. Therefore, although the parasite can synthesize hypoxanthine through this pathway, it must also redistribute (enhance or suppress) reactions associated with ATP to maintain the synthesis of purine-based nucleic acids. The deprived parasites suppressed metabolic fluxes through ATP-consuming reactions, such as those of phosphoenolpyruvate carboxykinase (PPCK), adenylate kinase and carbamoyl-phosphate synthase, while also increasing metabolic fluxes through certain ATP-consuming reactions, such as those of glutathione synthase and guanylate kinase  and is essential for ubiquinone synthesis. Therefore, an increase in flux through SAM should also increase flux through reactions associated with ubiquinone synthesis and create a demand for the precursors necessary for ubiquinone synthesis, such as chorismate. One of the top pathways significantly altered under hypoxanthine deprivation in the simulations was that of shikimate biosynthesis , and, consequently, influence other reactions that require PEP. PEP is also an important precursor in the methylerythritol phosphate pathway (MEP) pathway of isoprenoid synthesis, and as with shikimate synthesis, the flux through the MEP pathway significantly increased under hypoxanthine deprived conditions Table . InteresFigure Plasmodium falciparum lacks the machinery to synthesize purine bases de novo and relies on purine salvage to meet its need [P. falciparum utilizes pathways to recycle available purine and maintain DNA replication. However, enhanced flux through the pathways that recycle purines causes an imbalance in redox and mitochondrial metabolism, which is detrimental to the parasite. These findings suggest the following conclusions:P. falciparum survival during the blood stage;Maintenance of redox reactions and mitochondrial function within their normal range is essential for Plasmodium falciparum redistributes metabolic reactions to maintain synthesis of purine-based nucleic acids under limited concentrations of hypoxanthine;P. falciparum by making them unviable for replication after 72 h of deprivation, despite supplementation with a hypoxanthine-rich medium.The compensatory mechanisms, induced under hypoxanthine-deprivation conditions, cause irreparable damage to its need . The preAdditional file 1. Supplementary computational details Text S1–S2 and additional Figures S1–S3.Additional file 2. Significantly altered erythrocyte reactions in response to parasite infection and hypoxanthine concentration.Additional file 3. List of metabolites detected under hypoxanthine-rich and -deprived conditions.Additional file 4. Significantly altered metabolic genes and reactions of Plasmodium falciparum in response to hypoxanthine deprivation.Additional file 5. Gene set enrichment analysis of transcriptomic data from hypoxanthine-deprived Plasmodium falciparum.Additional file 6. Gene set enrichment analysis of transcriptomic data from isoleucine-deprived Plasmodium falciparum.


Protocell multilevel selection models have been proposed to study the evolutionary dynamics of vesicles encapsulating a set of replicating, competing and mutating sequences. The frequency of the different sequence types determines protocell survival through a fitness function. One of the defining features of these models is the genetic load generated when the protocell divides and its sequences are assorted between the offspring vesicles. However, these stochastic assortment effects disappear when the redundancy of each sequence type is sufficiently high. The fitness dependence of the vesicle with its sequence content is usually defined without considering a realistic account on how the lower level dynamics would specify the protocell fitness. Here, we present a protocell model with a fitness function determined by the output flux of a simple metabolic network with the aim of understanding how the evolution of both kinetic and topological features of metabolism would have been constrained by the particularities of the protocell evolutionary dynamics. In our model, the sequences inside the vesicle are both the carriers of information and Michaelis-Menten catalysts exhibiting saturation. We found that the saturation of the catalysts controlling the metabolic fluxes, achievable by modifying the kinetic or stoichiometric parameters, provides a mechanism to ameliorate the assortment load by increasing the redundancy of the catalytic sequences required to achieve the maximum flux. Regarding the network architecture, we conclude that combinations of parallel network motifs and bimolecular catalysts are a robust way to increase the complexity of the metabolism enclosed by the protocell. The protocell hypothesis conjectures the existence of a vesicle containing catalytic and replicating sequences as the primordial cellular organization during the early stages of the evolution of life. Mathematical models of protocells traditionally consider RNA sequences being encapsulated and having both an informational and a catalytic role in the same molecule. Because of this dual function, the protocell sequences are evolutionarily constrained. Mathematical models have been extensively used to study the evolutionary dynamics of protocells with a focus on the processes, like mutation or stochastic sequence assortment upon division, that affect the protocell information capacity in terms of the coexistence of different sequence types. Here we introduce a simple model of metabolic networks whose output determines the survival of the protocell with the aim of studying the effect of modifying the kinetic and architectural properties of the network on sequence coexistence. We find that stochastic assortment and mutation limit the architectures able to be encapsulated by the protocell with a given fraction of the population harbouring all possible sequence types. Trying to address this issue led to the consideration of distributing the information encoded by the sequences in a mutualistic community of replicators known as the hypercycle model  and [S] denote the concentrations of enzyme and substrate respectively and kcat is the turnover number  of the enzyme and Km its Michaelis constant. We will also study bimolecular reactions with mechanisms of the following form:a1 and a2 can be determined from either the forward and the backward rates of the underlying reactions or from thermodynamic potentials. Special values of these constants correspond to the cases of random order, definite order, non-interacting. We will model the depletion reactions as exponential decay or diffusionδi is the decay rate or diffusion constant for S.We will model the metabolic activity of our protocell as a well-stirred reactor in which ribozymes, environmental inputs, and metabolites interact. More specifically, the metabolic dynamics will be modelled as Michaelis-Menten enzyme dynamics where each flux is given by the expressionwithin the protocell may vary due to the permeability of the membrane). We assume that the intermediates that leak out to the environment are degraded and do not accumulate. We assume that the characteristic timescale of the metabolic reactions is sufficiently faster than the timescale of sequence replication that we may ignore initial transients, approximate the metabolic reactions by their (dynamic) equilibrium solution, and describe the metabolic outputs by their averaging their rates of production over a metabolic timescale.We assume that even though the protocell development will be accompanied by an increase in the vesicle volume, this will be negligible and will not dilute significantly the concentrations of the catalysts and intermediates of the reaction network. Therefore we can identify the catalysts’ concentrations with the catalyst number in our model of metabolism. We will augment this hypothesis with three further assumptions. We assume that the supply of this input in the environment is high enough that we can disregard depletion and treat its concentration as constant (v − kcat[E]) = 0, which has the solutions v = j and v = kcat[E]. The choice of solution depends upon the value of j and is determined by the conditions that the concentration is positive; thus, when j < kcat[E], we should choose the solution v = j and, when j ≥ kcat[E], we should choose v = kcat[E]. As a result, the zeroth order term in the expansion of the the velocity is a piecewise linear function of the incoming flux,v = min. The higher order terms can be regarded as corrections which account for deviations from piecewise linearity. Most notably, they replace the sharp peak at the maximum with a smooth peak whose maximum value is lower.The first class of motifs consist of a unimolecular reaction in which an enzyme E reacts with molecule of substrate S1 and a molecule of substrate S2 to produce one or more products Pi. This is described by the reactionsS1] and [S2] and collecting powers of δ leads to the equationδ → 0, this reduces to (v − j1)(v − j2)(v − kcat[E]) = 0. As previously, the choice of root is determined by the values of j1 and j2 through the condition that the concentrations [S1] and [S2] not be negative. This leads to a piecewise linear form of the leading term in the expansion of the solution,v = min.The second class of motifs consist of a bimolecular reaction in which an enzyme A, two enzymes α and β, an intermediate metabolite W and an output metabolite M and reactionsα, referred in the text as the keystone, is given by the Michaelis-Menten formula. However, since we assume that the input A is plentiful in the environment, we expect this reaction to be happening well within the saturation regime, hence will approximate its velocity by the limiting velocity:β (referred in the text as the recycler) takes part are an instance of the first type of motif, so we havej is the flux of W. Since the stochiometry of the first reaction produces one molecule of W for every molecule of input, we have j = vα, henceM will include the output of both reactions:As a simple illustration of how to use these motifs, consider a network consisting of an environmental input Using exactly the same techniques and assumptions as in the foregoing simple example, we calculated the flux-fitness functions for several more complicated networks involving three and four enzymes. The results of these calculations were then inserted into the evolutionary population model described below to study the effects of network architecture.x and the scaled flux ϕ:α to β remains constant during development. Since ϕ only depends upon α and β through their ratio, ϕ will also remain constant. As per the “slow-fast” minority control hypothesis of Kaneko and Yomo [M and W will approach an equilibrium values after an initial transient. Combining the equations, we conclude thatOur aim is to enrich the evolutionary population genetic models with relevant biochemical features rather than go on the direction of a full-blown simulation of a cellular system. However, in order to introduce these features realistically, we will derive them from a simplified “whole protocell” model in which the metabolic reactions described above are augmented with reactions that describe the replication of the sequences:and Yomo , we shalg, that, in principle will be of cardinality ω, that will not contribute to the metabolic activity.The general protocell model for our study will be built following a similar approach to the package model , 32. We N independent samplings with replacement from the initial contents of the vesicle to represent the replication of sequences. Let ji denote the number of number of sequences of type i present at the beginning of the cycle. The probability of sampling a sequence of type i equals ji/N. The probability that a replication will produce a sequence of non-error type i equalsω equalski offspring of the sequence type i is given asmi sequences of type i and an offspring with ki − mi sequences of type i equals toWe will model the protocell development process as 2F(ji) which is the frequency in the population of protocells which began their development process with ji copies of the i-th sequence. Let W(ji) denote the survivability of a typical protocell which begins its development process with ji copies of the i-th sequence. Such a population may be modelled either as a branching process to describe a growing population or as a Moran process to describe a population which has reached its carrying capacity. In the limit of large population size, the dynamics of either of these models become deterministic and the equilibrium frequencies of both models are determined by the same eigenvalue equationK(mi|ji) are non-negative, this equation may be solved numerically by using Perron-Frobenius iteration for small ploidy values (up to 100) and few sequence types (up to three). For higher ploidy values, both the Perron-Frobenius iteration or the Moran Process become cumbersome, so an agent based simulation which also approaches the same limit for large population size was used instead. As in [https://github.com/jopejor/protocell_metabolism.Our population is comprised of a number of non-interacting protocells each of which undergoes a lifecycle as described above. To describe this population, we introduce the summary statistic d. As in , the aged. As in  and the S1 FigThe figure shows the dependence of the average mean metabolic flux of the stationary population with the ploidy and the replication accuracy for the case in (PDF)Click here for additional data file.S2 FigThe figure shows the dependence of the average mean metabolic flux of the stationary population with the ploidy for the case in (PDF)Click here for additional data file.S3 FigThe curves show the dependence of the average defective type frequency in the stationary population with the ploidy and the replication accuracy for the case in (PDF)Click here for additional data file.S1 TextChemical reactions, network diagrams and fluxes for each of the networks studied in this work.(PDF)Click here for additional data file.