Chlamydia trachomatis (Ct). Here we infer the flux differences for Ct from proteome and qRT-PCR data by comprehensive pathway modeling. We compare the comparatively inert infectious elementary body (EB) and the active replicative reticulate body (RB) systematically using a genome-scale metabolic model with 321 metabolites and 277 reactions. This did yield 84 extreme pathways based on a published proteomics dataset at three different time points of infection. Validation of predictions was done by quantitative RT-PCR of enzyme mRNA expression at three time points. Ct’s major active pathways are glycolysis, gluconeogenesis, glycerol-phospholipid (GPL) biosynthesis (support from host acetyl-CoA) and pentose phosphate pathway (PPP), while its incomplete TCA and fatty acid biosynthesis are less active. The modeled metabolic pathways are much more active in RB than in EB. Our in silico model suggests that EB and RB utilize folate to generate NAD(P)H using independent pathways. The only low metabolic flux inferred for EB involves mainly carbohydrate metabolism. RB utilizes energy -rich compounds to generate ATP in nucleic acid metabolism. Validation data for the modeling include proteomics experiments (model basis) as well as qRT-PCR confirmation of selected metabolic enzyme mRNA expression differences. The metabolic modeling is made fully available here. Its detailed insights and models on Ct metabolic adaptations during infection are a useful modeling basis for future studies.Metabolic adaptation to the host cell is important for obligate intracellular pathogens such as  Chlamydia trachomatis (Ct) is one of the medically significant gram-negative species in the genus Chlamydia, an obligate intracellular pathogen in humans   . Ct is a and HIV .Ct is characterized by a biphasic developmental cycle: the elementary bodies (EBs), an infectious form which is metabolically inactive, and reticulate bodies (RBs) which is a non-infectious form that is metabolically active. After internalization into the host cell, EBs differentiate into RBs in an intracellular vacuole called inclusion body. In the inclusion, RBs replicate and subsequently reorganize back to EBs , the fatty acid biosynthesis and the tricarboxylic acid (TCA) cycle , the gene encoding ribose-phosphate pyrophosphokinase (EC: 2.7.6.1)  cycle . HoweverA) cycle . The gen2.7.6.1) . In this2.7.6.1) , 2016 an2.7.6.1) . For man studies . Althougnnotated , little tabolism .Ct developmental forms during its biphasic life cycle. Transcriptional profiling of Ct was performed  to study the metabolic difference between EBs and RBs during different infection stages. Extreme pathway modeling is one approach in MFA, which is based on constraints and tolerance to input data. Extreme pathway modeling is often used in microbial engineering and host-pathogen interaction to study metabolic flux changes under different conditions. In this study, we provide the first Ct metabolic model based on detailed reannotation using the protein profiling data with time courses from Ct during infection.To understand the biphasic metabolism of Chlamydia trachomatis D/UW-3/CX (accession: NC_000117)  and internal metabolites processed by the enzyme network. Our model provides both the KEGG reaction number and EC number as available, and these are provided in SBML format.A genome-scale metabolic network reconstruction was based on available genome information of the model strain _000117) , accordi_000117)  and BREN_000117) , NCBI an_000117) . Gap filS) with the size of m * n (S = m * n). The rows and columns of the matrix correspond to internal metabolites in the model (m) and involved reactions or enzymes (n), respectively. For each column, the entries describe a stoichiometric correlation with involved metabolites. All possible phenotypes are involved in the non-negative linear combinations of the stoichiometric matrix. The solution of the model was based on the steady state of the network, which is described by the equation: S * v = 0. The solution of this equation describes the complete set of different flux modes satisfying the equation. Each flux mode balances all internal metabolites which are involved in this specific flux mode.Extreme pathways  were calculated by YANA square  and with1, data-set PXD003883.After the steady state was calculated, we estimated enzyme activities from the proteomics data and calculated the optimal pathway fluxes in a dynamic state according to the different metabolic phenotypes at different time points of chlamydial infection. The original data we used were measured in Flux analysis is a mathematical modeling method to quantitatively simulate the flow of metabolites through the metabolic network. It is widely used in genome-scale metabolic network reconstruction . MetabolBased on flux constraints, the stoichiometric approach limits the solution space for the system by enumerating all available limiting pathways . Their linear combination predicts all available metabolic network states for a given set of enzymes. With experimental data , an optiCt textbook pathways are partially incomplete . Any metabolic state of the metabolic network in Ct is a linear combination of these pure pathways balancing all involved internal metabolites. These pathways are solution space limiting and hence called “extreme” pathways (or P for short). On the other hand, these calculated extreme pathways often cover only part of a textbook pathway, in the results section we refer to this situation as “contributing to a textbook pathway,” when only a subset of all enzymes used in the textbook pathway are involved in this extreme pathway. Moreover, the P numbers given in the following always refer to calculated pathways and not to any directly measured metabolites. There is no direct metabolomics data-set available or given in this study. Instead, using the proteome data as basis and qRT-PCR data for validation, we calculate fluxes according to the extreme pathway system. As these are many flux modes and as these extreme pathways are only contributing to textbook pathways but are not identical to them, it is necessary to number them.These optimal pathway fluxes were calculated by using least square fitting. Our focus was the metabolic difference between EB and RB. Least square fitting by the gradient descent method was generated by YANA square to calculate the best-matched pathway fluxes, including identification of enzymes not presented in proteomic data. Extreme pathways shown are stable, balanced, limiting flux modes of the metabolic system. They represent all adaptation and metabolic pathway capabilities the Chlamydia have, even if the ® CCL2.1TM) and human endothelial cells, HUVEC cells (ATCC® CRL-1730TM) were used for propagating bacteria and for basic experiments. HeLa229 cells were grown in RPMI1640 + GlutaMAXTM (GibcoTM 72400-054) with 10% heat inactivated FCS (Sigma-Aldrich F7524). HUVECs were grown in M200 media with growth supplements LSGS (Invitrogen). Chlamydia trachomatis (serovar L2/434/Bu) was used in the study. Chlamydia was prepared as previously published (Chlamydia were grown in HeLa229 cells at a MOI (multiplicity of infection) of 1 for 48 h. The cells were lysed using glass beads (15 mm) for 3 min and centrifuged at 2000 g for 10 min to remove the cell debris. The supernatant -containing bacteria was collected and centrifuged at 24,000 g for 30 min at 4°C. The pellet was washed and resuspended in SPG buffer , aliquoted and stored in −80°C. Chlamydia EBs and cell lines used in the study were verified to be free of Mycoplasma contamination via PCR. The bacteria were titrated to have a MOI of 1 and were used further in the experiments.Human epithelial cells, HeLa229 . RNA was reverse transcribed using a Revert Aid First Strand synthesis Kit (Fermentas) according to the manufacturer’s instructions and diluted 1:10 with RNase free water. qRT-PCR was performed as previously described , and manual curation. It contains 321 unique metabolites and 277 chemical reactions. Theoretically, there could be more metabolites and more reactions: This depends on the number of enzymes identified from all available sources for this organism (here: Ct) and the known compounds these enzymes involve. However, we focus here on the primary metabolism and consider only those reactions relevant to the primary metabolism. Nevertheless, an extensive check of all available data is necessary for an accurate estimate :We initially established a genome-scale model of Chlamydia trachomatis D/UW-3/CX (accession: NC_000117) and compared the model to the available proteomics dataset for the curation of the annotation. The curation involved monitoring of any obvious pathway gaps and re-examining the function of the involved enzymes  were removed. The result is a first genome-scale metabolic model of the central and primary metabolism of Ct. The model is described in SBML (Level 2) format (We started with the well-annotated genome sequence of ) format , a simplThe model with full annotation was next transformed into a stoichiometric matrix considering all enzymes and their reversibility while differentiating between internal metabolites and metabolic sources and drains . This matrix  was nextCt at that time. Roughly at 8 hpi, the EBs start to transform to RB. At 20 hpi, RBs dominate compared to EBs. During the time from 36 hpi to 40 hpi, the RB form of Ct converts back to EB. Hence, at this stage, the number of RB is supposed to decrease and the number of EB’s to increase. The comparison of the quantitative fluxes of EB, RB, and EB + RB at three different time points is a very dense, data-rich figure.Considering the EB + RB data together at each time point allows us to describe the general features of the It is hence shown in detail only as The extreme pathways calculated are all stable, balanced limiting flux modes of the metabolic system . They reveal which adaptation and metabolic pathway capabilities Chlamydia have, even if textbook pathways are partly incomplete . Any metabolic state of this network is a linear combination of these pure  pathways (see P numbers). On the other hand, these calculated pathways frequently cover only part of a textbook pathway, they only contribute to it (see above). From the modeling, we can infer that there are a few central pathways which carry most of the metabolic flux during infection, such as the strongest flux path from pyruvate to malonyl-ACP (pathway number P65), followed by the downstream part of glycolysis , upstream part of fatty acid biosynthesis (extreme pathways number P66 and P67), and a calculated flux pathway involved in biotin metabolism (P65). Cysteine biosynthesis also presents a strong flux as calculated from the proteome data (P16). Many reactions in nucleotide metabolism present strong intensities . Only medium activity was inferred from the proteome data for glycolysis and glycerophospholipid pathway , PPP (P84), and folate biosynthesis or conversion . Always only very low activity was inferred for fatty acid biosynthesis , phenylalanine and tyrosine biosynthesis , and porphyrin and chlorophyll metabolism (P29).So, based on the proteome data by Ct are summarized in Ct and represented in the metabolic model with their respective fluxes. Pathways involve the PPP, the glycolysis Emden-Meyerhof-Parnass pathway; EMP) and glycerol-phospholipid (GPL) pathway as well as fatty acid (FA) biosynthesis. Moreover, there is the citric acid or tricarboxylic acid (TCA) cycle and a cycle formed by two phosphofructokinases (pfkA_1 and pfkA_2).The central metabolic pathways present in Ct during infection. Central carbohydrate pathways, such as PPP, EMP/GPL, and pfkA presented higher intensities in both EB and RB when compared to the flux carried by TCA and FA. EB shows carbohydrate pathway activity, in particular regarding PPP, EMP/GPL, and to a lesser extent even TCA. As far as can be inferred from the data provided by Ct metabolism.Phosphofructokinase, as the pacemaker of glycolysis, presented an average intensity of 0.32 in EB and 0.11 in RB . This suCt has the capability of synthesizing its own phospholipids and fatty acids (though in low intensity) in both EB and RB groups  directly from the host cell rather than rely on producing the energy independently. TCA is not much active in EB when compared to the other pathways and even less active in RB. This may also suggest that RB may take more energy from the host. Fatty acid biosynthesis has two pathways, FA1 (using 3-oxoacyl-ACP synthase II) and FA2 (using 3-oxoacyl-ACP synthase II and III). FA1 was observed to have an intensity of less than 0.04 . This suggests RB relies more on ATP consumption when it is easier to utilize from both the host and the self-production by activated nucleotides metabolism. There are other folate-related pathways with less ATP consumption in P55 and P56 albeit with lower intensity. P55 was the transformation among folate, tetrahydrofolate, and di-hydro-folate. And P56, although not as active as P33 and P51, could be an alternative pathway for folate biosynthesis, especially for EB.Folate pathways are also central pathways for pathways . The fluCt or at least in the folate pathways. Also, it is likely that folate metabolism is tightly correlated with the availability of carbon/nitrogen sources and energy production in Ct  in the outer membrane. In order to compose T3SS, cysteine is assumed to accumulate more in the infectious form , the oxaloacetate could exchange with aspartate accompanied by barter between glutamate and 2-oxoglutarate. However, the flux prefers to drive from glutamate to aspartate [The TCA cycle in Ct presents glutamine-fructose-6-phosphate transaminase , whose protein product converts glutamine and fructose 6-phosphate to glutamate and glucosamine 6-phosphate. The existence of CTP synthase , should allow to transform glutamate into glutamine. PyrG seems not to be involved in long essential pathways such as glmS, as no full pyrimidine metabolism is formed in Ct. Both glmS and pyrG were detected with expressions in the proteomics data used , glutaminase (EC 3.5.2.1), and glutamate synthase (EC 1.4.1.13/1.4.1.14) are important for switching between glutamate and glutamine. Normally, glutamine could be converted to glutamate after taken from the environment or host cell. Without encoding genes for these enzymes, ata used .Ct, it is still unknown whether Ct imports only glutamine or both glutamine and glutamate from the host cell. Interestingly, three glutamate-related transporters, CT_216, CT_230 and CT_401 are encoded in the genome, and both CT_216 and CT_401 are expressed in the protein level , an important precursor in nucleotide biosynthesis, is not available in e genome . Whetherpathways . Also, spathways .Ct were selected and examined by RT-qPCR  and pyruvate kinase (pykF) were slightly increased . This indicates that glycolysis is used strongly by Ct no matter whether EB or RB. In accordance with this, the combined data (EB + RB) show an increase in glycolysis in Ct, pfkA_2 is more active in the glycolysis of Ct and it uses diphosphate as substrate. In contrast, the substrate ATP is used by pfkA_1, with a different sequence and representing a different phosphofructokinase type. According to these qRT-PCR data, in the GPL pathway, the expression of most of the enzymes for phospholipid biosynthesis was reduced during infection (plsC), which increased at 24 hpi.In HeLa229 cells , most ofaraD) and transketolase (tktB) decreased by nearly 50% from 12 to 24 hpi, while transaldolase  was slightly reduced. Expressions of the genes in TCA shows a complex picture of comparative activities: The sucABCD exhibited about twofold upregulation at 24 hpi, while fumarate hydratase (fumC) and malate dehydrogenase (mdhC) decreased at 24 hpi and almost invisibly expressed at 48 hpi. Moreover, the malonyl-CoA-ACP transacylase (fabD) was involved in the upstream of fatty acid biosynthesis (glmS) and CTP synthase (pyrG) are able to irreversibly convert glutamine to glutamate. glmS increased its expression by more than a 1.5-fold change at 24 hpi compared to that at 12 hpi, and it was still very active even at 48 hpi, the lysis phase. pyrG presented around 50% activity in both 24 and 48 hpi when compared to the 12 hpi. We assume that both of the enzymes may be actively functional in the transfer of glutamine to glutamate. glmS could serve for both EB and RB, while pyrG is mainly activated in RBs rather than EBs. Three genes in folate biosynthesis were validated. They were significantly decreased by even more than 50% in both 24 and 48 hpi.According to our calculations, the biosynthesis of GPL relies more on the downstream part of glycolysis and acyl-CoA, rather than proceeding directly from GAPDH in the upstream part of glycolysis . Expressynthesis  and assoynthesis . It was ynthesis . In addiCt infection to make a comparison  was upregulated in HUVECs and expressed two times more at 24 hpi than at 12 hpi. In the PPP, the transketolases tktB and tal showed almost the same expression, but the ribulose-phosphate 3-epimerase (araD) was slightly upregulated in HUVECs. In TCA, the succinyl-CoA synthetase (suc) genes were overexpressed in both 24 hpi and 36 hpi when compared to those in HeLa229. The other genes involved in glycerophospholipid biosynthesis, fatty acid biosynthesis, glutamine/glutamate transformation, and folate biosynthesis, showed different regulation in HUVECs than in HeLa229. The glycerol-3-phosphate acyltransferases plsB, plsC, plsX together with the acyl-carrier-protein S-malonyltransferase (fabD) highly increased the regulation at 24hpi and 36hpi, which suggested the intensive activity of synthesizing glycerophospholipid in HUVECs. The active biosynthesis was also visible by the increased regulation of the dihydrofolate reductase (folA), the dihydropteroate synthase (folP), and the dihydroneopterin aldolase (folX) for folate synthesis, which were differential expressions compared to those in HeLa229. In addition, the expression of glmS was similar in both cell lines. However, pyrG, which was downregulated in HeLa229, displayed twofold higher upregulation at both 24 and 36 hpi in HUVECs. This suggests the Ct’s pyrG may be able to convert glutamine to glutamate as well as glmS.The human umbilical vein endothelial cells (HUVECs) were also used as the host for mparison . Target Ct’s genome-scale metabolic network was first reconstructed based on the constraints, with 321 unique metabolites, 171 enzymes and 277 reactions. Many efforts have been made to elucidate the metabolic properties of Chlamydia trachomatis developmental forms during its biphasic life cycle  were used as the host for Ct infection. Ct generally showed more upregulated gene expressions in HUVECs than in HeLa229 cells, especially in the biosynthesis of glycerophospholipid and folate (pls and fol).The famous HeLa229 cell line is frequently used as the host of ost cell . When inCt in a natural infection is still an open question. Nevertheless, one approach to improve the metabolic study of Ct infection is to use a 3D human tissue model instead of cell lines. A 3D tissue model restores more appropriately the environment for studying the infection of an obligate human pathogen, and offers safer and more efficient support for therapeutic strategies and drug design in the future when compared to animal experiments or human cell lines.The different gene expressions in different cell lines drive unstable and unpredictable deviations especially in studying pathogen metabolism. Whether the previous metabolic observations  in HeLa2Ct lacks a clear transcriptome response during adaptive change if the carbon source is switched from glucose to glutamate or α-ketoglutarate  are encoded in the genome (gltT (CT_401) in Ct’s carbon metabolism, however, transport of glutamine is still unclear. On the other hand, two enzymes encoded by gene glmS and pyrG provide capabilities of utilizing glutamine to transfer to glutamate in both cell lines of HeLa229 and HUVECs . This includes glycolysis/gluconeogenesis (P5), folate biosynthesis (P33 and P51), glycerophospholipid biosynthesis (P83), and PPP (P84). According to the available pathways, pfkA_1 (CT_205) and pfkA_2 (CT_207) respectively. The PfkA_2 is similar to PfkA_1, but utilizes diphosphate instead of ATP to catalyze a reversible reaction. Pfk_2 has been found in higher plants, some other eukaryotes, bacteria, and archaea, however, not in Homo sapiens. In human cells, this phosphorylation reaction is catalyzed by fructose-1,6-bisphosphatase (EC 3.1.3.11) instead. Hence, Ct’s PfkA_2 may be a good drug target for treating Ct infection, as this enzyme is not present in humans.Two phosphofructokinases are involved in glycolysis, 6-phosphofructokinase 1 (EC 2.7.1.11), and diphosphate-fructose-6-phosphate 1-phosphotransferase (EC 2.7.1.90). These are encoded by Ct, F1P cannot be transformed from fructose. Instead, F1P could possibly be generated as an intermediate in glycolysis, though it is unclear whether the 1,6-fructose bisphosphate aldolase will supply this. A similar situation concerns ribose 1-phosphate produced by ribose 5-phosphate isomerase A (EC 5.3.1.6) which is encoded by rpiA (CT_213). Whether F1P and R1P exist in Ct and what their functions are is still an open question.The expression of 1,6-fructose biphosphate aldolase (EC 4.1.2.13) is also able  to reversibly exchange fructose 1-phosphate (F1P) with dihydroxyacetone-P (old name: glycerone-P) and glyceraldehyde. However, due to the lack of fructose kinase in Chlamydia trachomatis and measured different time points of the infection. The differences in the gene expression show the adaptability of the pathogen depending on the availability of the metabolites and these conclusions should be general for different serovars. Although other serovars have a different genome, their metabolic genes are highly conserved, and so is the mode of intracellular parasitism in this respect. Hence, it is reasonable to expect for these similar results regarding metabolism.How general are our findings? Well, HeLa229 cells are transformed cervical epithelial cells, while Huvecs are primary endothelial cells derived from human umbilical cord. We tested infection of both these cell lines with Ct metabolism after meticulous curation of all available data . However, there are no direct metabolite or metabolomics data available. The model takes all extreme pathways accessible for Ct into account. It infers detailed information on the pathway changes during infection including differences between EB and RB. Ct’s major active pathways are glycolysis, gluconeogenesis, GPL biosynthesis (support from host acetyl-CoA), and the PPP, while its incomplete TCA and fatty acid biosynthesis are inferred to be less active. The modeled metabolic pathways are much more active in RB than in EB. EB and RB utilize folate to generate NAD(P)H using independent pathways. The only low metabolic activity of EB involves the carbohydrate metabolism. RB utilizes energy rich compounds to generate ATP using nucleic acid metabolism enzymes. Experimental data to infer the metabolic model include proteomics experiments (model basis) as well as RT-PCR confirmation of selected enzyme mRNA expression differences at three time points of infection. The metabolic model delivers detailed insights into Ct metabolic adaptations during infection and is made available for detailed studies in Chlamydia infection biology.We have described a first model of All datasets generated for this study are included in the manuscript/MY did all the bioinformatics analyses, supervised by TD. KR did all the experiments with initial input from MY. TR provided experimental expert advice. TD provided bioinformatics expert advice. TD led and guided the study. MY and TD drafted the manuscript. All authors participated in the data analysis, refinement of the data , and manuscript writing, and agreed to the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Current understanding of many animal-microbial symbioses involving unculturable bacterial symbionts with much-reduced genomes derives almost entirely from nonquantitative inferences from genome data. To overcome this limitation, we reconstructed multipartner metabolic models that quantify both the metabolic fluxes within and between three xylem-feeding insects and their bacterial symbionts. This revealed near-complete metabolic segregation between cooccurring bacterial symbionts, despite extensive metabolite exchange between each symbiont and the host, suggestive of strict host controls over the metabolism of its symbionts. We extended the model analysis to investigate metabolic costs. The positive relationship between symbiont genome size and the metabolic cost incurred by the host points to fitness benefits to the host of bearing symbionts with small genomes. The multicompartment metabolic models developed here can be applied to other symbioses that are not readily tractable to experimental approaches. Sulcia, that has codiversified with the insects, and a coprimary symbiont of distinct taxonomic origin and with different degrees of genome reduction in each insect species . Our simulations reveal extensive bidirectional flux of multiple metabolites between each symbiont and the host, but near-complete metabolic segregation  between the two symbionts, a likely mode of host control over symbiont metabolism. Genome reduction of the symbionts is associated with an increased number of host metabolic inputs to the symbiont and also reduced metabolic cost to the host. In particular, Sulcia and Hodgkinia with genomes of ≤0.3 Mb are calculated to recycle ∼30 to 80% of host-derived nitrogen to essential amino acids returned to the host, while Baumannia and Sodalis with genomes of ≥0.6 Mb recycle 10 to 15% of host nitrogen. We hypothesize that genome reduction of symbionts may be driven by selection for increased host control and reduced host costs, as well as by the stochastic process of genomic decay and relaxed selection.Various intracellular bacterial symbionts that provide their host with essential nutrients have much-reduced genomes, attributed largely to genomic decay and relaxed selection. To obtain quantitative estimates of the metabolic function of these bacteria, we reconstructed genome- and transcriptome-informed metabolic models of three xylem-feeding insects that bear two bacterial symbionts with complementary metabolic functions: a primary symbiont,  Buchnera in aphids is a major nutritional sink, estimated to consume 11 times more nitrogen than it provides to the insect host  from bacterial symbionts that are localized to specialized cells (bacteriocytes) and are transmitted vertically via the ovary of the female insect , 14. The15–Philaenus spumarius, the sharpshooter Graphocephala coccinea, and the cicada Neotibicen canicularis. These insects possess the primary symbiont Sulcia muelleri (Bacteroidetes [henceforth referred to as Sulcia]), which produces 7 or 8 essential amino acids  in cicadas (Baumannia cicadellinicola (gammaproteobacterium [henceforth Baumannia]) in sharpshooters  in spittlebugs of the tribe Philaeninae , the coprimary symbiont, and the insect host for the three xylem-feeding insects (Escherichia coli (Sulcia are highly conserved across the three insects (Sulcia network (see Hodgkinia (in the cicada) representing just 14% of the 370 reactions in Baumannia (in the sharpshooter) and 9% of the 578 reactions in Sodalis (in the spittlebug)  . Overallttlebug) . The metttlebug)  to f.10.1128/mBio.01433-18.2FIG S1FIG S1, PDF file, 0.02 MB.Overview of reactions and metabolites for xylem feeder bacterial symbionts. Reactions and metabolites are colored red , blue , and green (unique to a single bacterium). Download Copyright © 2018 Ankrah et al.2018Ankrah et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01433-18.5TABLE S1Sulcia and Sodalis) in three-compartment spittlebug model. (f) List of inputs and outputs and their predicted fluxes for bacterial partners (Sulcia and Baumannia) in three-compartment sharpshooter model. (g) List of inputs and outputs and their predicted fluxes for bacterial partners (Sulcia and Hodgkinia) in three-compartment cicada model. (h) Metabolites shared between bacterial partners (part i) and unique metabolites transported by individual bacterial partners (part ii). (i) Nitrogen utilization by bacterial symbionts. Download Table S1, XLSX file, 0.24 MB.(a) Comparison of reaction and metabolite content of bacterial genome scale metabolic models. (b) Flux variability analysis for spittlebug symbiosis. (c) Flux variability analysis for sharpshooter symbiosis. (d) Flux variability analysis for cicada symbiosis. (e) List of inputs and outputs and their predicted fluxes for bacterial partners . Flux ranges are calculated as the difference between the maximum and minimum flux distributions resulting in the same growth objective value. Download Copyright © 2018 Ankrah et al.2018Ankrah et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01433-18.4FIG S3FIG S3, PDF file, 0.01 MB.Variation in symbiont transport flux relative to optimal (maximum) flux in spittlebug, sharpshooter, and cicada symbioses. Variation around maximum is calculated by dividing the range of flux variation (obtained by FVA) by the optimal flux  and is expressed as a percentage. Download Copyright © 2018 Ankrah et al.2018Ankrah et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Sulcia and the two remaining EAAs (histidine and methionine) by the coprimary symbiont . The metabolic models supported the net release of every EAA synthesized by each bacterium in the sharpshooter and the cicada symbioses: 8 EAAs by symbiont  and b. Ty Sulcia . In our −1 h−1 for the spittlebug symbiosis, 0.12 mmol g dry weight−1 h−1 for the sharpshooter, and 0.13 mmol g dry weight−1 h−1 for the cicada symbioses , tryptophan (2%), and threonine (6%).In our simulations, the total flux of EAA release was 0.26 mmol g dry weightymbioses . The hosymbioses  to f. Thymbioses . All theymbioses  to c. HiOur models also revealed that the symbionts release a range of metabolites in addition to EAAs. The total number of metabolites released was 18 to 21, independent of genome size  to c. HoSulcia in all three symbioses released the same set of five central carbon metabolites: succinate, fumarate, xylulose-5-P, glycerate-1,3-P, and dihydroxyacetone P  and Hodgkinia (0.015 mmol g dry weight−1 h−1), while acetate accounted for the highest flux of material released from Baumannia (0.23 mmol g dry weight−1 h−1) (cetone P  to g. Thcetone P  to g. Int−1 h−1)  to g. InThe overlap in outputs from the primary and coprimary symbionts in each association comprised up to two metabolites: ammonia and threonine in the spittlebug, acetate in the sharpshooter, and acetate and AMP in the cicada , part i.Sulcia to the coprimary symbiont Baumannia, one contributing to Baumannia peptidoglycan synthesis and two to products Baumannia delivered to the host  . Five cross-fed metabolites were identified, each unique to a single symbiosis . The shathenate) . The twothenate) . The cicthenate) .Sulcia, the amino acid with the highest import flux was glutamate (utilized in reactions in EAA synthesis), while fructose 6-phosphate and malate were the chief central carbon imports  to 37 inputs to the bacterium with the smallest genome, Hodgkinia (0.15 Mb genome). In parallel, the number of host-derived metabolites shared between the primary and coprimary symbionts increased with reduced genome size of the coprimary symbiont, from two shared metabolites for the spittlebug symbiosis, through 8 for the sharpshooter, to 15 for the cicada symbiosis. The two shared metabolites in the spittlebug symbiosis, glutamine and tyrosine, were also shared between the primary and coprimary symbionts in the other symbioses.  were capped at the observed uptake fluxes in the three-compartment model by setting the lower bounds of the uptake reactions to the predicted uptake fluxes with both symbionts present. Our simulations indicated that the cost of maintaining bacterial partners by the host decreased with declining bacterial genome size .Sulcia, EAA output was equivalent to 66 to 80% of host-derived N  than for the sharpshooter association with Baumannia (26 inputs) and the spittlebug association with Sodalis (14 inputs). These metabolic controls may operate in conjunction with both controls over transport across the host-symbiont interface and also host effector molecules, including immune-related products, to regulate symbiont growth yields and nutrient release fluxes. Such mechanisms have not, to date, been investigated in xylem-feeding insect symbioses, but they have been identified in other intracellular symbioses. For example, an amino acid transporter expressed in the aphid bacteriocyte has been functionally characterized in the aphid symbiosis  is very nutrient poor, especially in organic carbon and nitrogen. With respect to carbon, the coprimary symbiont oduction  , informally known as the meadow spittlebug, and Graphocephala coccinea , a sharpshooter informally known as the red-banded leafhopper, were collected from vegetation surrounding Beebe Lake, Ithaca, NY, in June 2014 and July 2015, respectively. Mature nymphs of the dog-day cicada Neotibicen canicularis  were collected from tree trunks at Lansing, Ithaca, NY, and retained in the laboratory for up to 3 days after they had molted to adulthood. Species identification was carried out using taxonomic keys  were transferred to RNAlater (ThermoFisher) and RNA was extracted with the RNeasy kit (Qiagen) “tissues” protocol, including treatment with RNase-free DNase I (Qiagen) for 15 min at room temperature, following the manufacturer’s instructions. The final product was eluted in 50 µl RNase-free water.Adults of mic keys –53  was fragmented using an S2 ultrasonicator (Covaris) to obtain 700-bp fragments, which were end repaired with the End repair mix LC (Enzymatics) and A-tailed with the Klenow 3′→5′ exo-enzyme (Enzymatics). Universal Y-shaped adaptors were ligated using A-T ligation, adaptor-ligated DNA was purified and size-selected using AMPure XP beads (Agencourt), and DNA was subjected to 14 cycles of PCR amplification with barcoded Illumina index primers . BLASTn (BLAST version 2.2 ) searcherium see  to generrium see , using Gtic code . The conNA246493 ). For th10.1128/mBio.01433-18.7TABLE S3Table S3, XLSX file, 0.01 MB.List of reference genomes. Download Copyright © 2018 Ankrah et al.2018Ankrah et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the + RNA was purified using Dynabeads oligo(dT) (Life Technologies) according to the manufacturer’s protocol and fragmented by incubation at 94°C for 2 min to generate long fragments (>700 bp). cDNA was synthesized using Superscript II reverse transcriptase (Invitrogen) following the manufacturer’s protocol, and the resulting cDNA was purified using RNA Clean XP magnetic beads (Agencourt). Strand-specific libraries were generated with dUTP for second-strand synthesis. Double-stranded cDNA was end repaired, A-tailed, and ligated to adaptors as for the DNA library preparation (described above), and the resultant cDNA was purified and size selected to obtain 750-bp fragments. The uracil-containing second strand was then digested using uracil DNA glycosylase (Enzymatics), and cDNA was subjected to 15 cycles of PCR amplification using barcoded Illumina index primers  libraries were generated from 2 µg total RNA per replicate, using a published protocol  with min primers . The finhttps://transdecoder.github.io/) with default settings. The transcriptome was annotated using the Trinotate pipeline version 2.0.1 (https://trinotate.github.io/) and local BLAST (https://github.com/trinityrnaseq/trinityrnaseq/). The reads from each sample were aligned against the reference transcriptome using the align_and_estimate_abundance.pl script with bowtie2 as the aligner and RSEM   see  to i. Orada) see  were addada) see , and modada) see  run in Mada) see .10.1128/mBio.01433-18.1TEXT S1Text S1, DOCX file, 0.02 MB.Supplementary methods. Download Copyright © 2018 Ankrah et al.2018Ankrah et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.01433-18.9TABLE S5Sulcia (spittlebug symbiosis) metabolic model (i) reaction list and (ii) metabolite list. (b) Stand-alone Sodalis metabolic model (i) reaction list and (ii) metabolite list. (c) Stand-alone Sulcia (sharpshooter symbiosis) metabolic model (i) reaction list and (ii) metabolite list. (d) Stand-alone Baumannia metabolic model (i) reaction list and (ii) metabolite list. (e) Stand-alone Sulcia (cicada symbiosis) metabolic model (i) reaction list and (ii) metabolite list. (f) Stand-alone Hodgkinia metabolic model (i) reaction list and (ii) metabolite list. (g) Stand-alone spittlebug metabolic model (i) reaction list and (ii) metabolite list. (h) Stand-alone sharpshooter metabolic model (i) reaction list and (ii) metabolite list. (i) Stand-alone cicada metabolic model (part i) reaction list and (part ii) metabolite list. (j) Orphan reactions in spittlebug, sharpshooter, and cicada metabolic models. (k) Three-compartment Sulcia-Sodalis-spittlebug model (i) reaction list and (ii) metabolite list. (l) Three-compartment Sulcia-Baumannia-sharpshooter model (i) reaction list and (ii) metabolite list. (m) Three-compartment Sulcia-Hodgkinia-cicada model (i) reaction list and (ii) metabolite list. (n) Transcriptome constraints applied to insect compartment reactions. (o) Insect (bacteriocyte) media used for simulations. Download Table S5, XLSX file, 0.90 MB.(a) Stand-alone Copyright © 2018 Ankrah et al.2018Ankrah et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the −1 h−1 (with lower bounds of −10 mmol g dry weight−1 h−1 for reversible reactions). Approximately 66% of all host-constrained reactions carried flux under optimal conditions  wanditions .−1 h−1) and a minimal external medium (insect hemolymph) comprising glucose, ammonia, and sulfate as carbon, nitrogen, and sulfur sources, respectively, universal metabolites present in the external medium of all three insect models, and nicotinate d-ribonucleotide (spittlebug model medium), fructose (sharpshooter model medium), and thiamine diphosphate, nicotinate d-ribonucleotide, dihydropteroate, pyridoxine 5-phosphate, pantothenate, and cobalt (cicada model medium). The maximum uptake flux for each reaction was capped at 100 mmol g dry weight−1 h−1. Amino acids were excluded as nutrient sources in all model simulations  Objective function components in Sulcia, Sodalis, and spittlebug metabolic models. (b) Estimation of amino acid percentage in spittlebug protein. (c) Calculation of amino acid stoichiometry for spittlebug model. (d) Objective function components in Sulcia, Baumannia, and sharpshooter metabolic models. (e) Estimation of amino acid percentage in sharpshooter protein. (f) Calculation of amino acid stoichiometry for sharpshooter model. (g) Objective function components in Sulcia, Hodgkinia, and cicada metabolic models. (h) Estimation of amino acid percentage in cicada protein. (i) Calculation of amino acid stoichiometry for cicada model. Download Table S6, XLSX file, 0.08 MB.  and fluxFor analyses of symbiont maintenance costs, flux through the biomass equation for a primary or coprimary symbiont was fixed to zero, while allowing flux through all other symbiont-associated reactions , and the cost was computed as the difference between host growth yields in the presence and absence of symbiont biomass production. Applying these constraints allowed the costs associated exclusively with symbiont maintenance to be decoupled from the costs of meeting the EAA demands of the host. For all maintenance cost simulations, the uptake fluxes for the main sources of C, N, P, and S  were capped at the observed uptake fluxes in the three-compartment model .NJPN00000000, NKXM00000000, MIEN00000000, NZ_NJPO00000000, and NJHQ00000000 for the bacterial genome sequences and PRJNA341855, PRJNA342845, and PRJNA343314 for the insect transcriptomes.The GenBank accession numbers of the sequences described here are https://github.com/Bessem06/Hemipteran). SBML files of the models have also been submitted to the BioModels database (All models have been provided in three formats—SBML (.xml), MATLAB (.mat), and Excel (.xls)—and deposited in GitHub (database  with the


Alterations from the normal set of chromosomes are extremely common as cells progress toward tumourigenesis. Similarly, we expect to see disruption of normal cellular metabolism, particularly in the use of glucose. In this review, we discuss the connections between these two processes: how chromosomal aberrations lead to metabolic disruption, and vice versa. Both processes typically result in the production of elevated levels of reactive oxygen species, so we particularly focus on their role in mediating oncogenic changes. Aneuploidy, an abnormal set of chromosomes, was first identified as a biomarker for epithelial cancer in the late 1800s . It has Metabolic pathways interact with almost every oncogenic process, but there are three areas in which the contribution of metabolic mutations to oncogenesis has been particularly well characterized: the elevation of glycolysis, the generation of oncometabolites and the production of reactive oxygen species ,7.Glycolysis was the first metabolic process known to be aberrantly elevated in cancer. This Warburg effect was initially interpreted as a shutdown of oxidative phosphorylation that was oncogenic in itself . HoweverA problem with some of these models is that we do not know what the flux is through each of the possible output pathways from glycolysis, and they can be mutually exclusive. For example, every molecule of lactate produced is also one molecule of glucose that was not made into a nucleotide. However, despite the need for nucleotides, production of lactate is frequently elevated in cancers. Recent flux analysis of glycolysis in cancer cells suggests that a surprising fraction of glycolysis is ‘wasted’ as lactate, which is secreted, then used as fuel by other cells ,13,14. HGlucose can also be used to produce reducing power in the form of NADPH via the pentose phosphate pathway. NADPH facilitates lipid synthesis, and may be critical for maintaining redox homeostasis, as will be seen when we consider the production of reactive oxygen species. It is worth noting that the demand for new nucleotides (via the pentose phosphate pathway) will lead to a build-up of NADPH that will block the first step in the Pentose Phosphate Pathway (PPP) unless the NADPH is used up. This step is tightly regulated, being induced by Ras and PI3-kinase and inhibited by p53 and AMPK among many others that are typically altered in tumorigenesis to promote this pathway . Since pThe metabolic changes described to this point could be considered enabling changes that potentiate high rates of proliferation, rather than specifically driving oncogenesis. However, there are other metabolic changes that can specifically promote cellular transformation . MutatioThe elevated production of reactive oxygen species (ROS) is a common metabolic change seen in almost all cancer types, and is currently a focus for metabolic therapies ,32,33. RDisruption to the genome is a hallmark of cancer, and in the vast majority of cases, this is not just point mutations or minor indels, but extends to the point at which changes to the karyotype can be seen ,39. ThesThe role of tetraploidy in promoting oncogenesis has been clinically validated in Barrett’s oesophagus, in which a genome doubling is a well characterized step towards adenocarcinoma in precancerous lesions that have lost p53 . SimilarIt should be noted that a change from stable diploidy to a stable aneuploidy is not the normal course of cancer evolution. Instead, it is common to find ongoing chromosomal instability (CIN) that generates significant subclonal diversity of the chromosome number in tumours . Given tHaving briefly reviewed the effects of metabolism and aneuploidy on tumorigenesis, we now move to consideration of how these two processes are related.Aneuploid cell populations can exhibit heterogeneous phenotypes due to the wide range of possible gene dosage alterations . The ens2O2, thus preventing mitosis [Aneuploidy alters several metabolic pathways compared to diploid controls ,79. Both mitosis . One stu mitosis . Therefo mitosis ,87. Inde mitosis .The increase in glycolysis and mitochondrial activity may be partly to accommodate the energy deficit caused by the additional protein synthesis, folding and clearance required in a high-ROS environment ,78,88. TSome of the above-mentioned stresses likely stem from gene dosage changes imposing a proteomic burden on aneuploid cells ,75, althSustained stress on the ER from misfolded proteins can also result in triggering unfolded protein response (UPR) mechanisms ,96. Prol2O2 production, which causes sustained activation of AMP-activated kinase (AMPK) [2O2 in response to growth factors [Aneuploidy is readily generated by any flaw that results in chromosome segregation before complete decatenation of the replicated DNA or before proper amphitelic attachment to the mitotic spindle has been established . Howevere (AMPK) . Nutriene (AMPK) . In somee (AMPK) . Additio factors . Hypoxia factors ,108.2O2-treated yeast have mis-localisation of Bub1 from kinetochores, resulting in the inactivation of the SAC and premature exit from mitosis [2O2 also causes centrosome amplification [Drosophila and fission yeast leads to genomic instability [The accumulation of ROS is thought to impact aneuploidy primarily by causing double-strand breaks in DNA, which increases the frequency of mitotic errors ; however mitosis . Exogenofication , which ofication . Similarfication . ROS hasfication ,115. Evefication . Howevertability ,118. Thitability . Deletiotability . Similartability .While disruptions to metabolism that generate oxidative stress promote aneuploidy by increasing the error rate in mitosis, metabolic changes can also select for aneuploidy. Cells that are metabolically damaged may be benefitted if a mitotic error leads to a favourable chromosomal alteration. For example, yeast carrying a thiol peroxidase deficiency gained spontaneous duplications in chromosome 11 . Thiol pIt is no longer enough to know that tumours are likely to be aneuploid and have a disrupted metabolism. Specific genetic and metabolic changes have become commonplace markers for cancer prognosis, so these processes clearly contribute to driving tumour progression and significantly impact the likelihood of effective chemotherapy. We are beginning to understand how the genome and metabolome are interlinked, particularly via the generation and clearance of ROS. A recurring theme is that metabolism can act as an amplifying sensor for genetic alterations, reacting to relatively minor changes in gene dosage by generating feedback loops that should promote further cellular disruption, leading to cell death. While cancers may hijack these protective responses and avoid cell death, they nonetheless represent a point of significant difference between the tumour and normal somatic tissue. There are ongoing efforts to target both genetic and metabolic differences in cancer cells as a chemotherapeutic approach ,126,127.


Metabolic flexibility is the ability of an organism to switch between substrates for energy metabolism, in response to the changing nutritional state and needs of the organism. On the cellular level, metabolic flexibility revolves around the tricarboxylic acid cycle by switching acetyl coenzyme A production from glucose to fatty acids and vice versa. In this study, we modelled cellular metabolic flexibility by constructing a logical model connecting glycolysis, fatty acid oxidation, fatty acid synthesis and the tricarboxylic acid cycle, and then using network analysis to study the behaviours of the model.We observed that the substrate switching usually occurs through the inhibition of pyruvate dehydrogenase complex (PDC) by pyruvate dehydrogenase kinases (PDK), which moves the metabolism from glycolysis to fatty acid oxidation. Furthermore, we were able to verify four different regulatory models of PDK to contain known biological observations, leading to the biological plausibility of all four models across different cells and conditions.These results suggest that the cellular metabolic flexibility depends upon the PDC-PDK regulatory interaction as a key regulatory switch for changing metabolic substrates.The online version of this article (10.1186/s12263-019-0647-5) contains supplementary material, which is available to authorized users. Metabolic flexibility is the ability of an organism to switch between substrates for energy metabolism, adapting to the changing nutritional state and needs of the organism . In compγ) nuclear receptor mediated transcriptional regulation [In a recent review , we explgulation .In another study of ours , we used3 M −1s−1 to greater than 109 M −1s−1 [Based on these observations, we hypothesise that this regulatory interaction, termed by us as the PDC-PDK regulatory switch, is a key regulator of cellular metabolic flexibility. As such, we focused on the changing of the metabolic substrate in response to the PDC-PDK regulatory interaction in this study. We have used logical modelling  to construct a regulatory model to test this hypothesis, and show how the various perturbations designed to derail cellular metabolic flexibility are propagated through the malfunctioning of the PDC-PDK regulatory switch. We opted to use logical modelling, as opposed to more complex and complicated quantitative modelling primarily because of the high resolution of data required to accurately model cellular processes quantitatively . Molecul M −1s−1 , 11–13.We applied a well-established pipeline of logical modelling and analysis of biological pathways and networks using discrete/qualitative models , 14–16. In logical modelling, the model represents a system by using discrete values of 0 and 1 for OFF and ON states of the entities comprising the system. The dynamics of the system are then defined by step functions that change these values. In our study, we have employed the René Thomas Kinetic Logic formalism  and refeIn the toy example, we have a regulatory network consisting of three entities, P1, P2 and P3. P1 and P3 have a reciprocal relationship where P3 is an activator of P1, and P1 the inhibitor of P3. An activator implies that the source entity has a positive effect on the concentration and/or activity of the target entity, whereas an inhibitor has a negative effect. We also see P2 as an activator of P3, and by extension, having an indirect effect on the activity of P1 mediated via P3. The regulatory network for the toy example is shown in Fig. P2 is the activator of P3.P3 is the activator of P1.P1 is the inhibitor of P3.As multiple entities can affect the same target entity simultaneously, a set of logical parameters was selected which define the rules and precedence governing the evolution of the target entities in the network. These logical parameters are based on the behaviours observed, measured or inferred using data and experiments. In the toy example, we define the logical parameters based on the interactions that we have in the regulatory network, We see that the logical parameters for P1 are trivial as it has only one activator and no inhibitor. Generally, in the absence of their activators, the entities are assumed to degrade over time. Thus, P1 would be reduced to level 0 in the absence of P3, whereas P2 would reduce to 0 as its activator has not been modelled in the regulatory network. It is also possible to assume an implied activator that would activate P2, but generally, such assumptions are made only if an inhibitor of the entity in question has been explicitly modelled in the regulatory network to balance the activity of the said entity. Since we do not have any such an inhibitor for P2 in the regulatory network, we assume otherwise. Lastly, the logical parameters for P3 are non-trivial since we have both an activator (P2) and an inhibitor (P1) that can act simultaneously on P3. Here we model a precedence for the inhibitor, and assume that P3 would always be inhibited by P1 whenever P1 is present in the system, otherwise P3 would rely on P2 to become activated. These parameters are tabulated as Table lvln, where lvl represents the maximum discrete level plus 1 , and n represents the number of entities having that level. In the toy example, this formula would be 23, totalling eight states, as we can see in Fig. The logical parameters govern the behaviour of the regulatory network, from which it is possible to generate a graph of all possible behaviours. This graph is called a State Transition Graph (STG), consisting of states (nodes) and transitions (edges). Each state represents a particular configuration of the complete regulatory network, where a configuration is defined as a unique combination of the discrete levels of the entities of the regulatory network. Thus, any two states would have a different discrete level for at least one entity. The maximum number of states is defined by the formula The transitions represent the changes in the discrete levels of the entities, and thus the transitioning of the regulatory network from one configuration to another. The transitions are constrained by the logical parameters, and can only exist between two states if the source state satisfies the discrete levels of the target state through the logical parameters. This imparts directionality to the behaviours represented in the STG, generating cyclic and acyclic paths in the graph. A behaviour can then be defined as a path in the STG, essentially a series of states connected by transitions between them. Finally, we utilise asynchronous transitions, which only allow one entity to change its discrete level between two successive states. The logical parameters given in Table ϕ, these quantifiers are; ϕ: From a given state, all states (G) along all paths (A) must satisfy ϕAG ϕ: From a given state, at least one future state (F) along all paths (A) must satisfy ϕAF ϕ: From a given state, all states (G) along at least one path (E) must satisfy ϕEG ϕ: From a given state, at least one future state (F) along at least one path (E) must satisfy ϕEF ϕ: From a given state, all (A) immediate successor states (X) must satisfy ϕAX ϕ: From a given state, at least one (E) successor state (X) must satisfy ϕEX Using the STGs generated by a regulatory network, it is possible to reverse engineer sets of logical parameters that allow certain known behaviours of the system to exist in the regulatory network. By extension, this allowed our modelled system to be verified against known biological observations or to find logical parameters that satisfy those conditions. We utilised a model checking technique called computational tree logic (CTL) to identify known behaviours . CTL allP2=1) → EX(EG(P2=1))) ∧ ((P2=1) → EX(EG(P2=0)))For the toy example, we formulate the CTL formula as, , essentially linked cyclic paths) contained within the STG itself. Towards this end, GINsim allows us to collapse these sub-networks and patterns in the network to generate Hierarchical Transition Graphs (HTGs) –21. The We then proceeded to collapse the acyclic paths into single nodes, and finally merged all edges between these collapsed nodes based on the edges present between the respective states in the STG. Figure In addition to the HTG, we also performed the perturbation analysis, where we restricted the parameters of one or more entities to represent knockouts or ectopic activities. The STG constructed with these restrictions establishes the propagation of their effects through the rest of the system. Figure It is possible to couple a perturbation with a defined initial state to fine-tune the behaviours of the system in response to known restrictions, or predict outcomes of new restrictions. In Fig. Logical modelling, network and perturbation analyses were performed using GINsim v3.0 . The sysIn a review done previously , we had Figure γ) triggered by fatty acids in the cytoplasm [The inhibitor, Pyruvate, always blocks PDK activity when it is present in the system.γ), is absent from the system. Thus, the activators collectively override Pyruvate mediated inhibition.The inhibitor, Pyruvate, only blocks PDK activity when at least one of its activators, Acetyl-CoA (mediating activation via increased BADH and ATP) or Fatty Acids  of each model consisted of 1,024 states, and 6,144 transitions between the edges. The only difference between the STGs of the four models were the transitions between the states governing the regulation of PDK, which was expected as per the logical parameters. The STG of model 1 is provided in Additional file We then proceeded to collapse the four STGs into their respective HTGs , to compare the behavioural patterns and substructures in the models. We observed that due to the regulation of Glucose and Circulating Fatty Acids as inhibitory self-loops, the majority of the states from the STGs, 992 to be exact, are collapsed into a single node in the HTG with the remaining nodes showing fluctuations of Glucose and Circulating Fatty Acids without any effect on the cellular environment. Essentially, the various groups of system dynamics were being integrated together by the fluctuation of the glucose and circulating fatty acid input nodes. The self-inhibitory loop on each of the input nodes would switch them between 0 and 1, thereby linking all different strongly connected components together to form a single strongly connected component. Although this behaviour does show how interconnected the biological behaviours are, it makes the analysis of these behaviours that much complex. To remedy this situation, we opted to remove the self-inhibitory loops and restricted the input nodes to the four combinations of 0 and 1 to see how the system behaves for these particular input conditions. The only difference is that the edges that connected the pairs of states differing in only the level of either glucose or circulating fatty acids are no longer connected due to the absence of the self-inhibitory loop governing the change in level, neatly dividing the previously large 1,024 state STG into four smaller 256 state STGs for detailed analysis. We then proceeded with comparing the HTGs of each model with the respective input combination discussed below. Figure This input combination models the availability of circulating glucose and the unavailability of circulating fatty acids. All four models of PDK regulation collapse the STG into a two node HTG containing all 256 states pertaining to this input combination. The root node is an irreversible component that does not contain any incoming edges, and consists of either 31, 33, 29 or 39 states for models 1, 2, 3 and 4 respectively. This root node shows a situation from which the system is able to recover to stable behaviours, but is unable to return to the original situation (hence the irreversible component). The remaining states are collapsed into the leaf node, which represents a terminal SCC. The system can remain in the terminal SCC indefinitely, following the cyclic behaviours it represents. In case the input conditions change, the system would then be able to move to complementary state in one of the HTGs representing the new input combination.γ signalling is able to bypass the TCA cycle. This results in stable cyclic behaviours in the terminal SCC untying from each other and dispersing into smaller groups of cyclic behaviours, accompanied by irreversible components. However, the non-terminal SCC remains the same as those of models 1, 2 and 3. In essence, the larger structure of the HTGs remains intact.In this input combination, both glucose and fatty acids are available for cellular metabolism. Models 1, 2 and 3 generate a four node HTG each, all consisting of two irreversible components (one of which is a root node), and two SCCs . Model 4 generates ten nodes, consisting of five irreversible components and five SCCs. We observed that the smaller HTGs of the first three models represent very similar sets of dynamics because the logical parameters for these models tie the regulation of PDK completely or partially to the TCA cycle. In model 4, this dependency is nullified as the Fatty Acid mediated activation via PPAR In this input combination, only circulating fatty acids are available for metabolism. All four models show strong divergence in their respective HTGs, with different types, number, and sizes of nodes. The only conserved pattern in the HTGs is the terminal SCC, along with three more non-terminal SCCs leading to the terminal SCC. This sub-network maintains both the number of states, as well as the edges between the respective SCCs across all four models, suggesting that although the change in PDK regulation has a strong effect in the upstream behaviours in the HTGs, the system converges to the same behaviours and patterns.This input combination represents an extreme scenario where neither circulating glucose nor circulating fatty acids are available to fuel metabolism. As such, the system moves towards cell death, which can be seen as the only deadlocked state  in the all four HTGs, labelled as ‘ss-0001000010’. Models 2, 3 and 4 show very similar behaviour patterns in terms of the node types and the edges between them, whereas model 1 shows some behaviours as separate nodes instead. However, as with the previous input combination, this one also has a sub-network conserved between all four HTGs consisting of an SCC, an irreversible component, and the deadlocked state.In the perturbation analysis, we opted to perturb the non-metabolite inhibitors of the regulatory network to either ectopic or knockout levels to observe how their effects propagate through the system. The reason for selecting non-metabolite inhibitors is that the metabolite inhibitors are derived from the metabolic processes themselves, thus perturbing those would create a self-fulfilling prophecy in terms of gauging the effects of the regulators on cellular metabolic flexibility. The only two entities in our regulatory network fulfilling this criterion are PDK and AMPK.We started with perturbing PDK to knockout level by restricting it to level 0. We then initialised the system with all entities at level 0 and tested it with all four input combinations. We observed the same behaviours as those in the network analysis for all input combinations, except for when both circulating glucose and circulating fatty acids are available for metabolism. For this input combination, we observed that when PDK is locked to level 0 the STG shows that the Acetyl-CoA is derivable from both glucose and fatty acid sources simultaneously, contrary to the known biological properties which were also checked via CTL model checking. When we allowed the system to change the PDK level to 1, we immediately observe only fatty acid driven Acetyl-CoA production.We then proceeded to test PDK at ectopic level by restricting it to level 1 and testing again with the four input combinations . We observed that, like previously, the behaviours were similar to those observed in the network analysis except for one input combination, this time it being the availability of circulating glucose and the absence of circulating fatty acids. The STG of this combination showed that there was no Acetyl-CoA production as the ectopic activity of PDK was barring Pyruvate conversion to Acetyl-CoA, and there were no available fatty acids to fuel metabolism. When we allowed the system to change the PDK level to 0, glucose driven metabolism resumed.Lastly, we perturbed AMPK using the same method used for PDK. We did not observe any changes when AMPK was restricted to level 0. However, when restricted to level 1, we observed that changes in the behaviours generated when both the circulating glucose and circulating fatty acids are available. For this input combination, the ectopic activity of AMPK led to a fatty acid preferred metabolism, which, in turn, led to ectopic PDK levels, leading solely to fatty acid driven metabolism, instead of any switching behaviour. Here, we observed that AMPK perturbation still acted through PDK mediated regulation.γ signalling pathways. The selection of the logical parameters for all but one of the entities was relatively straightforward as the biological processes being represented have been well studied [In this study, we have utilised a logical modelling and network analysis workflow to assess the hypothesis of the PDC-PDK regulatory switch being a key regulatory mechanism behind cellular metabolic flexibility. We start with the construction of the regulatory network by abstracting the cellular network of metabolic flexibility reviewed earlier f. The regulatory network, consisting of ten entities and nineteen interactions, covers both the glucose and fatty acid oxidative metabolism pathways, merging them with the fatty acid synthesis pathway along with AMPK and PPAR  studied –40. The  studied .The interactions in the regulatory network are both simplified and abstracted so we relied on system verification using model checking to test four possible logical parameter sets for PDK regulation. CTL formulae were used to formulate both oxidative metabolisms and the known behaviours of the PDC-PDK regulatory interactions. The model checker, SMBioNet  verifiedAs an additional verification step, we constructed the larger cellular metabolic flexibility network from our review  in the sThe perturbation analysis, done by restricting certain components to knockout or ectopic expressions, allowed us to test the propagation of regulation in the regulatory network when the negative regulators in the system malfunctioned. We elected to perturb the non-metabolite negative regulators, namely PDK and AMPK, because we wanted to test the regulation of the switching of metabolism from glucose to fatty acids independent of the increase or decrease in metabolite concentrations implied in logical modelling. The results of the perturbation analysis reveal that malfunctioning of PDK has a direct effect on the switching of metabolism, which is in line with our hypothesis. What is more interesting is that the perturbation of the only other non-metabolite inhibitor modelled in our regulatory network, AMPK, also propagated its effects through persistent activation or availability of PDK, providing additional support to our hypothesis. The results of the perturbation analysis, coupled with the conserved dynamics in all four models reinforces the hypothesis that the PDK isoenzymes are a key regulatory element of cellular metabolic flexibility via the PDC-PDK interaction.β and other toxins, thereby mitigating some of the mechanisms underlying Alzheimer’s disease progression [This propagation of the regulatory effect places the PDK enzymes squarely in the middle of perturbed metabolism, as can be seen in cancer studies , 43 whergression . When taγ is taking place, indicating the absence of cellular fatty acid mediated PPAR γ signalling. Interestingly, there is expression of Stearoyl-CoA desaturase 1 (SCD), an enzyme from the fatty acid (re)synthesis pathway. However, studies have shown that SCD in endothelial cells plays a vital role in mitigating laminar stress [In cell types reliant on a single substrate for oxidative metabolism, the role of the metabolic switch is slightly different, likely to regulate the rate of oxidative metabolism instead of metabolic flexibility. One example of such cell types are Endothelial cells which utilise glucose. We took the network resource developed in our previous study  and visur stress , 52, theIn essence, all four models of PDK regulation analysed in this study, coupled with the network and perturbation analyses, strongly suggest the PDK mediated inactivation of PDC as a key switching mechanism of cellular metabolism from glucose to fatty acids, and therefore, a key regulator of cellular metabolic flexibility. However, the model has its limitations, not least of which is that logical modelling is time and quantity independent. This means that that although our models suggests the PDC-PDK switch as a key regulator, it does not tell us anything about the intensity and duration of this regulation. In addition, wet-lab experimentation is still required to validate our findings, the design of which is difficult in itself since many of the interactions and regulations modelled in the system happen at extremely short time scales, the accurate measurement of which is tricky at best. These design constraints themselves are one of the reasons why quantitative data was sparse and lacking at the time of this study, resulting in the logical modelling conducted in this study. While our study focuses on the substrate switching in metabolism and specifically the enzymatic regulation of this substrate switching, there are other factors/mechanisms of the regulation of cellular metabolism that are not considered in our current model. One of these is the ratio of deuterium to hydrogen in the cellular environment, which has been shown to affect cellular metabolism , 54. AltIn this study, we have modelled and analysed cellular metabolic flexibility using logical modelling and network analysis. The results of our models strongly suggest that the PDC-PDK regulatory switch plays an important role in the regulation of cellular metabolic flexibility, revolving around the TCA cycle and the oxidative metabolism of glucose and fatty acids. The results support the hypothesis that this regulatory switch relies on the regulation of PDK itself, and thus PDK regulation acts as the pivot balancing cellular metabolic flexibility between available nutrients.Additional file 1Archive containing the SMBioNet and GINsim source files for toy example (ZIP 1.66 kb).Additional file 2PDF showing a generic abstraction/reduction process for regulatory networks (PDF 573 kb).Additional file 3Tabulated logical parameters for the regulatory network of cellular metabolic flexibility (PDF 60.1 kb).Additional file 4Explanation and conversion of the biological properties into computation tree logic (CTL) (PDF 97.5 kb).Additional file 5Archive containing the SMBioNet and GINsim source file for the regulatory network of cellular metabolic flexibility (ZIP 10.1 kb).Additional file 6Cytoscape session file of the STGs of Models 1, 2, 3 and 4 (CYS 967 kb).Additional file 7Cytoscape session file of the HTGs of Models 1, 2, 3 and 4 (CYS 82.4 kb).Additional file 8Archive containing alternative verification models from the review article constructed in GINsim (ZIP 11.3 kb).Additional file 9Cytoscape session file visualising the RNA-seq expression data of endothelial cells (CYS 58.6 kb).


A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users. Metabolism is key to the production of energy (catabolism), the generation of cellular building blocks (anabolism) as well as the activation, detoxification, and elimination of metabolic by-products or xenobiotics. Over the past 100 years, considerable effort has gone into determining the precise molecular details of primary metabolism—i.e. the metabolic processes associated with the production and breakdown of essential metabolites  ”), and carboxyl groups =O”). The molecular descriptors were all computed with the CDK library. The structural features are represented as binary features in a custom chemical fingerprint to encode their absence (0) or presence  + ions extracted from the experimental QToF MS data collected from the rat urine samples, ranging from 53.4896 to 969.8669 Da. Monoisotopic masses were generated by subtracting 1.00727 Da from the ions extracted from the MS dataset. Details regarding the data extraction process are provided in Additional file We also tested whether BMIT could identify any of the remaining 38 known metabolites (corresponding to 26 unique masses) previously reported, but not observed in our study, or not selected by our data treatment parameters. The 26 unique masses were provided to BioTransformer as input, and the identification was performed using the same mass tolerance as before (0.01). BMIT was able to suggest 28 molecules for 19 unique masses. Among those, 21 compounds corresponding to 18 unique monoisotopic masses had previously been reported as epicatechin metabolites .BioTransformer is a software tool that combines both a knowledge-based approach and a machine learning approach to predict the metabolism of small molecules, and to assist in metabolite identification. The knowledge-based system consists of a biotransformation database (MetXBioDB), a knowledgebase (the reaction knowledgebase), and a reasoning engine. MetXBioDB is a unique resource that is freely available, and covers a wide range of enzymatic reactions that take place in human tissues, the human gut and the environment (soil and water microflora). In contrast to most publicly available databases, MetXBioDB provides detailed biological and chemical information about all of its biotransformations, including the catalyzing enzymes, the substrates, the products, and the biotransformation rule(s) that is/are applied. MetXBioDB describes the metabolism of > 2000 compounds catalyzed by ~ 15 enzyme families. For each biotransformation, at least one scientific source or reference is provided. MetXBioDB is stored as a JSON document, which can be easily parsed.One potential application of MetXBioDB is in the design of biotransformation rules with narrow specificity, which can be used for in silico metabolism prediction. In fact, this resource has already been used (in addition to other data) to successfully design > 300 biotransformation rules, which were used to annotate the biotransformations in the database and predict metabolites via the BioTransformer Reasoning Engine. Despite the aforementioned strengths of MetXBioDB, the database still has a number of limitations. Although it covers a large number of enzymatic reactions, it is clear that more data is needed in order to cover an even larger set of reactions (e.g. oxidation reactions) catalyzed by enzymes other than CYP450s. It is also clear that there is a need to define more constraints and/or build additional models that would increase the quality of the predictions. Moreover, users could benefit from data about the different sites of metabolism for each specific biotransformation, as it would serve as a training set for the development of models for the prediction of sites of metabolism (SoMs). For the current version of MetXBioDB, the intent was simply to provide an easily readable and comprehensible data set. However, providing MetXBioDB in a database format that can be parsed and queried in a more sophisticated way (e.g. SQL) would make the database much more useful to a broader number of users. Efforts are underway to do so for the next release of MetXBioDB. We welcome and encourage contributions in regard to the curation, improvement, and expansion of this resource.In our first test, BioTransformer was evaluated against Meteor Nexus (v.3.0.1). Meteor Nexus is a commercially available software tool that is considered to be the gold standard for predicting biotransformations of xenobiotics. While BioTransformer achieved a better prediction (49%) and recall (88%) than Meteor Nexus at the equivocal level of confidence , Meteor Nexus’ precision improved significantly at the plausible (56%), and probable (59%) levels. The increase in Meteor Nexus’ precision matched our expectations, as the minimum likelihood threshold for metabolite selection increased, thus reducing its probability of selecting unconfirmed metabolites. However, the 68% increase in precision  led to an 82% decrease in recall. As a consequence, while Meteor Nexus’ predicted a higher percentage of true metabolites at these levels, compared to BioTransformer, it returned a significantly lower number of true metabolites.It is worth noting that BioTransformer heavily relies on the selective nature of the biotransformation rules and other structural constraints, in addition to its implementation of relative reasoning. On the other hand, Meteor Nexus combines the continuous absolute scoring of biotransformations with relative reasoning, providing binned data for different levels of reasoning through a more dynamic scoring system. Overall, the performance of BioTransformer suggests that the freely accessible BioTransformer tool could be used to assist scientists in various drug discovery and environmental safety studies.In our second test, we evaluated BioTransformer’s performance in predicting single-step human and human gut microbial metabolism of 20 endobiotics and xenobiotics. Overall, 69% of BioTransformer predictions matched experimentally confirmed metabolites. Moreover, BioTransformer was able to predict 87% of all reported  metabolites. The better performance, compared to the first test, can be partly explained by the fact that some endobiotics, such as sphingo- and glycerophospholipids, follow very classical and well-known metabolic pathways  shows that on average, about half of their predictions matched experimentally confirmed metabolites. However, BioTransformer was able to predict 90% of all experimentally confirmed metabolites, which is significantly higher than the 61% predicted by ADMET Predictor.Overall, the first three tests demonstrate BioTransformer ability to accurately predict human and human gut microbial metabolism for a very diverse set of metabolites, covering endogenous metabolites, pharmaceuticals and personal care products, food compounds, as well as other exogenous compounds. The comparative assessments of BioTransformer with Meteor Nexus and ADMET Predictor show that while BioTransformer is slightly slower, it consistently performs better, and it also addresses some of their shortcomings. In particular, BioTransformer is open access, and it covers a much wider range of chemical substrates and metabolic biotransformations.In order to evaluate BioTransformer’s ability to predict environmental metabolism, we compared its prediction results with the EAWAG-BBD/PPS system. It is worth noting that the biotransformation and preference rules we encoded in BioTransformer were based on the same set of rules defined by the EAWAG-BBD/PPS. The key difference was that the rules were encoded in the same common SMIRKS/SMARTS format used by all of BioTransformer’s other transformer tools. Based on the sample tests provided in the Results section, it is clear that BioTransformer was able to accurately replicate the predictions provided by the EAWAG-BBD/PPS system. These results suggest that BioTransformer could also be used to accurately predict environmental microbial metabolism.superbio option simulates only one conjugation step, as it is often sufficient to make a molecule stable and hydrophilic enough for excretion .In a fourth test, we evaluated BioTransformer’s ability to identify metabolites using its BMIT module. This task tacitly relies on the metabolism prediction task, and BioTransformer was able to suggest 37 metabolites matching 20 masses from a list of 260 monoisotopic masses extracted from the MS analysis of urine samples collected after exposure to epicatechin , BMIT predicted two isobaric metabolites, but only one peak (retention time = 5.94 min.) was found in the spectra, indicating that only one metabolite was present in the sample or that the analytical conditions did not allow the resolution of isobaric compounds -2--5,7-diOH-2,4-DBP Fig. a, and otWe believe the examples used here nicely demonstrate the ability of BioTransformer to accurately predict a wide range of metabolic reactions, for a number of different types of small molecules (endogenous and xenobiotic compounds) and a number of different biosystems . BioTransformer is unique in its ability to cover almost all aspects of non-essential metabolism . This makes it particularly useful for the wide-ranging applications seen in metabolomics and other small molecule studies. Furthermore, the accuracy, coverage, precision and recall of BioTransformer appear to be as good as, or even much better than some of the most highly regarded metabolic prediction systems now available. It is also notable that BioTransformer, unlike most of its competitors, is freely available.Certainly a more extensive analysis of a much larger set of query compounds would likely better illustrate the strengths and weaknesses of BioTransformer. However, it is important to remember that there are relatively few experimentally validated, comprehensive sets of metabolic “biotransformation trees” and that the examples selected here  cover a good portion of the better known trees.While there are a number of strengths to BioTransformer, we believe that certain improvements could still be made to the program. First, the addition of more biotransformation data would certainly provide additional reaction “fodder” to create more biotransformation rules. Additional biotransformation data would also provide further statistical evidence to fine tune the reaction preference rules (relative reasoning) and occurrence ratios for absolute/relative reasoning. In particular, adding an option for absolute reasoning would give BioTransformer the ability to select candidates with a set cut-off score. Currently BioTransformer’s biotransformation database (MetXBioDB) and its reaction knowledgebase cover only a small portion of gut microbial metabolism (i.e. metabolism of plant-derived polyphenols). As many xenobiotics as well as endogenous compounds are known to be metabolized in the gut , 89–92, https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, BioTransformer is also freely accessible as a web service at www.biotransformer.ca. The web service provides users with the possibility to manually or programmatically submit queries, and retrieve data generated by the BioTransformer software tool.In this work, we have presented BioTransformer, a freely available, open access software tool that supports the rapid, accurate, comprehensive prediction of metabolism of small molecules in both mammals and environmental microorganisms. BioTransformer can also assist in metabolite identification using experimental MS data. BioTransformer can be used either as a command-line tool or as an imported library. The Java executable and Java library are open access, and freely available at Within mammals, we have shown that BioTransformer was able to accurately predict single-step biotransformations for a diverse set of xenobiotics, including drugs, pesticides, and food compounds. The reactions that BioTransformer predicts cover Phase I and Phase II metabolism in mammals, as well as the human gut microbial metabolism. Overall, BioTransformer was shown to perform better than Meteor Nexus and ADMET Predictor, two highly regarded commercial software tools for in silico metabolism prediction. Unlike most other metabolic prediction tools, BioTransformer also supports the prediction of metabolism of small molecules by environmental microbes. The integration of environmental metabolism with endogenous human and gut microbial metabolism allows BioTransformer to address many of the predictive metabolic needs of metabolomics or exposomics researchers, which tend to span a much wider range than, say, drug researchers, food chemists or environmental scientists.Despite its strengths, BioTransformer is not without some limitations. Addressing these would certainly make the program much more flexible, more accurate, and more comprehensive. Obvious improvements for the current version of BioTransformer include: (1) the validation of BioTransformer’s predictions for a larger and more diverse test set of molecules; (2) the experimental validation of BioTransformer’s BMIT predictions for a larger set of molecules and experimental data; (3) the expansion of the reaction knowledgebase to cover more reactions, and (4) the addition of new options for metabolite prediction/ranking.Additional file 1. Cited structures.Additional file 2. Additional-Notes-Introduction-Methods-Evaluation.Additional file 3. Phase-II-Filter-Features.Additional file 4. Predictions: BioTransformer vs. Meteor Nexus.Additional file 5. BioTransformer—human and human gut microbial metabolism.Additional file 6. BioTransformer vs. ADMET Predictor.Additional file 7. Epicatechin metabolite identification tables.Additional file 8. Epicatechin metabolites identification part 1.Additional file 9. Epicatechin metabolites identification part 2.