Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.The Gram-negative bacterium  Bordetella pertussis, the causative agent of whooping cough. Producing vaccines against whooping cough requires growing B. pertussis in large volumes. However, its growth is relatively slow, final yields of biomass are relatively low and growth characteristics can be variable. Understanding B. pertussis metabolism has applications to improving vaccine production, as well as in understanding the basic biology of this organism.Metabolic flux models have been used to understand how organisms adapt their metabolism under different growth conditions, and are finding increasing application in synthetic biology and biotechnology. One barrier to progress in this field is the construction and curation of metabolic flux models for new organisms. Here we present a curated genome-scale metabolic flux model for  B. pertussis is a Gram-negative bacterium that causes whooping cough, a respiratory infection responsible for significant annual mortality worldwide [B. pertussis is described as a fastidious organism. It does not metabolise sugars as carbon source as it does not possess an intact glycolysis pathway [B. pertussis can grow using most of the amino acids as a carbon source, however alanine, proline and glutamate are utilized preferentially suggesting that amino acids that are degraded to α-ketoglutarate or pyruvate are oxidized rapidly. Several studies have demonstrated that glutamate is by far the most efficiently metabolized and is considered to be the main carbon source for growth of B. pertussis [orldwide , 2, espe pathway . Amino aertussis –5, whichB. pertussis. This stemmed from the inability of B. pertussis to utilise citrate as a carbon source along with observations of the build up of poly-hydroxybutyrate and release of free fatty acids in batch cultures. However, the B. pertussis genome contains genes that appear to encode a complete pathway [B. pertussis gave a clear indication that the TCA cycle is fully functional, although it remains unclear why citrate does not support B. pertussis growth [It was a long-held view that the TCA cycle was not completely functional in  pathway . Recentls growth .B. pertussis growth [B. pertussis in SS broth leads to an imbalance in N:C ratios leading to the formation of ammonium which is inhibitory to growth, resulting in relatively low final cell densities.Commonly used media for broth growth, such as Stainer-Scholte (SS) broth , contains growth . HoweverB. pertussis using either batch cultures or steady state cultures in bioreactors B. pertussis in culture are important limitations to the efficiency of B. pertussis vaccine production. In particular, at least five times more culture volume is required to generate one dose of an acellular pertussis vaccine compared to a whole cell one. Expansion of B. pertussis vaccination programmes using acellular vaccines, either into the developing world that for the most part use whole cell vaccines, or to increase the use of booster doses for adolescents/adults would place strain on global production of these vaccines. Increased efficiency of B. pertussis culture would help to alleviate these strains but this requires greater knowledge of the growth characteristics of B. pertussis.The slow growth and limited yields of m × n stoichiometric matrix, S. Each row of S represents a metabolite and each column gives the stoichiometry for a particular metabolic reaction. There are m metabolites and n reactions. The list of metabolites includes both so-called “internal metabolites”, which are not exchanged with the growth medium or environment, and “external metabolites”, which are. External metabolites include nutrients in the modelled growth medium, metabolites that diffuse in and out of the cell, and by-products of growth that leave the cell. FBA models make the approximation that the time scale of interest (hours or longer) is long enough that short-term transients in the kinetics of individual reactions  will have largely passed, so that reactions are running at steady state: there is no net production or consumption of  metabolites. Mathematically, each reaction is associated with a flux v; the steady-state approximation is the constraint Sv = 0. The specific growth medium and uptake rates mean that there are constraints on how fast the influx of nutrients can be; mathematically, this means that there are constraints on some or all of the reaction fluxes. Finally, FBA models describe the growth capacity of an organism using an objective function c: how much of the given objective could the metabolic network possibly produce, at steady state, under the given constraints? The objective is typically a biomass vector, c, describing the major components of the dry weight of the cells. FBA models then approximate the metabolic network’s capacity to produce this biomass under various conditions. FBA is performed by solving a linear programming problem:S is the stoichiometic matrix, v is a vector of reaction fluxes, c is the objective function, and ar and br are vectors of length n describing lower and upper constraints on the reaction fluxes. A growth medium is defined by setting constraints so as not to allow uptake of nutrients that are not present in the medium.Flux balance analysis (FBA) is an established approach for modelling the metabolic networks of organisms at the genome scale, and is a framework for integrating other ‘omics data layers with metabolism –16. BrieS and determining whether it describes realistic biochemistry, as well as examining the gene(s) associated with it, their annotation in the organism and whether the gene-reaction relationship is appropriate. This process requires considerable knowledge of the organism’s biochemistry, and is labour intensive [In principle, an FBA model can be constructed directly from an annotated genome; where a gene’s enzymatic function is known, the relevant reaction and metabolites can be added to the system and the stoichiometric matrix can be constructed so as to capture the  stoichiometries of the included reactions. In practice, genome annotation and functional prediction is imperfect, and FBA models require substantial curation . This tyntensive –20.B. pertussis were developed and demonstrated the utility of this approach for interrogating specific facets of B. pertussis metabolism. Here, we present the first published genome-scale metabolic reconstruction for B. pertussis. It is suited for flux balance analysis, and models B. pertussis’ metabolic reactions accordingly. We refer to this reconstruction as “the model” or “metabolic model” throughout. To demonstrate the use of the model to interrogate B. pertussis growth, we used it to predict reactions that are essential for growth on laboratory medium. We tested these predictions by performing a genome-wide screen for essential genes using a Transposon-directed Insertional Sequencing (TraDIS) approach [Previously, dynamic models of limited compartments of approach  and demoRecent advances in theory and computational power have allowed increasing automation in reconstructing full genome metabolic models. While still requiring considerable manual work to refine them, draft models can be produced rapidly and easily from annotated genomes. We used the Model Seed framework as the starting point for our model. The Model SEED integrates a range of existing approaches into a coherent pipeline, accessed through a web interface , 22.B. pertussis (Genbank accession number NC_002929.2). The genome sequence was then reannotated by the integrated RAST annotation servers, before this annotated version was used in the reconstruction of the model. The process is fully automated, undertaking a series of steps to ensure the resulting model is capable of producing the specified biomass vector under FBA simulation. Details of the steps in the Model SEED reconstruction are discussed below as pertinent to the steps in our manual curation, and full details can be found in the paper by Henry et al. [Our initial model was obtained from the SEED interface, uploading the genome sequence of the Tohama I strain of y et al. .B. pertussis biomass and used the results to define a biomass objective function for the model. The biomass objective function (BOF) is a special reaction in FBA which defines key biomass components, in specified ratios, that a metabolic network must produce in order for the bacteria to grow. By default, the Model SEED produces an organism-specific template biomass function, based on near-complete BOFs for all the organisms examined in their original study. The template reaction includes all universal biomass components. It also includes non-universal components but only if criteria are satisfied that specify the metabolic subsytems and functional roles a genome must contain for the component to be added to the biomass reaction template [We performed experiments to determine the composition of template . InevitaB. pertussis strain BP536 was used in these studies. BP536 is a streptomycin resistant derivative of the genome sequence strain Tohama I; they contain the same metabolic gene repertoire and metabolic network. Bacteria were grown in 100 mls of SS broth, supplemented with heptakis at 1g/L, for 48 hours. The OD600 of the cultures were recorded and the bacteria were pelleted by centrifugation in a microfuge from an appropriate volume of culture to give the equivalent cells for 20mLs of an OD600 = 1.0. These cells were freeze dried in preweighed tubes to enable measurement of the dry weight of cells in this culture volume. Appropriate volumes of culture were processed for measurement of DNA, RNA, protein, lipid and carbohydrate. Genomic DNA was extracted using a GenElute Genomic DNA kit . DNA was eluted using seven elution steps with 200μL of water each time. Optimisation trials demonstrated that DNA was eluted for up to 7 elution steps. Eluate was collected in a preweighed tube to enable accurate measurement of the total elution volume. RNA was extracted using stabilization with RNAprotect Bacteria reagent  and extraction using the RNeasy extraction kit (Qiagen Ltd). RNA was eluted using 250μL of water into preweighed tubes to allow accurate measurement of eluate volume. DNA and RNA were quantitated using a Qubit . Protein was measured using an assay based on the Bradford assay: the Bio-Rad Protein Assay  using BSA as the protein standard. Carbohydrate was measured using a phenol-sulphuric acid assay using glucose as the standard [standard . Total lstandard . The meaB. pertussis these checks are particularly important, since it has evolved through a process of genome reduction meaning that remnants of pathways may be encoded by the genome [The SEED algorithm  engages e genome .Part of the process of model curation is to ensure it can recreate known behaviour. We curated the model to ensure that its ability to grow on previously-defined media was correct. We compiled data on viable and non-viable growth media formulations , 8, 25. Electrons, in the form of H- hydride ions, are passed along the electron transport chain in a particular sequence. Although in principle every step of the chain is reversible, in practice problems arise if it is allowed to run in both directions. Energetically unfavourable reaction sequences are able to occur, leading to the possibility of free ATP production. These manifest themselves as sets of reactions that ‘freewheel’, feeding into one another and running at a rate much higher than the baseline level for the particular flux state. These loops can provide free sources of energy to the cell, and hence must be removed in order to ensure realistic results. As addressed in Thiele et al. , problemThe SEED Model included associations between reactions in the model and KEGG reactions. We manually examined the KEGG associations and used homology searches using BLAST algorithms to update associations. A number of these appeared to be associating different reactions to the same enzyme, for example similar reactions but operating in different pathways. These wrong associations arose from the annotation ascribing an incorrect (or too specific) EC number to an enzyme, or from misannotation of enzyme function. In total 170 KEGG associations were corrected .B. pertussis is noteworthy for containing an unusually high number (358) of pseudogenes arising during its evolution via genome reduction . Many genome annotations contain the full amino acid sequence of the pseudogene and the SEED model included these as functional enzymes. We removed reactions assigned to pseudogenes from the model.Pseudogenes are genes that have suffered a disabling mutation  rendering the gene non-functional; this has occurred recently enough for the coding sequence to be unchanged from the functional version except for the mutation. B. pertussis, as the growth rate is relatively insensitive to carbon source concentrations, with final yield of biomass varying rather than growth rate, for example [The model allows for both growth-associated (GAM) and non-growth-associated (NGAM) maintenance ATP costs, to capture the energy use of processes that are not explicitly described in the model , 27. In  example , 11. Thiμg/mL kanamycin and incubated at 37°C for 72 hours. Between 300 000 and 500 000 transposon mutants were harvested per library and processed for TraDIS. Insertion indexes were calculated for each gene and essentiality calculated using the cut off point described previously [Essential genes were identified using Transposon Directed Insertion-Site Sequencing (TraDIS) . Saturateviously .B. pertussis model.The FBA modelling approach allows the manipulation of a metabolic network of the cell to make predictions about what impact these interventions will have on growth rate. In particular, the link between reactions in the model and their catalysing proteins can be used to predict which genes will be fatal if knocked out from the genome. Comparing predictions about essentiality from the model with experimental essentiality data from real-world experiments allows a valuable test of the model . This teB. pertussis model on a rich growth medium, modelled on the charcoal agar used in the TraDIS experiment. To simulate the protein-rich growth environment we enhanced the minimal medium based on glutamate growth with free uptake of the full range of amino acids. We then ran through each gene in turn and removed all reactions from the model where annotations indicated the gene was required to catalyse the reaction. Optimizing for the biomass objective function, we ran FBA and normalised the achieved flux (c ⋅ v), dividing by that seen in the unmodified  network. This resulted in a relative growth rate for each knockout, ranging from 1  to 0 .We simulated growth of the B. pertussis, and in assessing the quality of the model’s performance, we treat this data as the “ground truth”. Our computational modelling of essentiality can be viewed as a classifier, giving each gene a score as to how essential it is. To convert this into a binary essential/non-essential classification, we need to choose a threshold on the relative growth rate. The standard approach to assessing performance of such classifiers is the Receiver Operating Characteristic (ROC) curve. If the threshold is smoothly varied, we can calculate the True Positive Rate (TPR) and False Positive Rate (FPR) for each point, and these are then plotted. The total area under this curve is an indication of the performance of the classifier.Results from the TraDIS experiment represent the best indicator of gene essentiality in B. pertussis were resuspended in SS broth. This suspension was used to seed two 30mls cultures in 250mls flasks. These were grown for 24 hours at 37°C with shaking. At this point cells were pelleted by centrifugation and washed in PBS. Cells were then resuspended in the various media at an OD600 of 0.1. Ten wells of 250uls of suspension in each media were seeded into a round bottom 96 well plate and grown at 37°C with shaking in a Fluostar Omega plate reader  until stationary phase was reached. The OD600 was measured every 15 minutes. At the end of growth, the culture was removed from five of the wells for each medium and the bacteria were pelleted in a microcentrifuge. The supernatant was removed and stored at -80°C. The concentration of ammonium in the supernatants was measured using an assay kit  as described in the manufacturer’s protocol.Media were prepared containing different ratios of glutamate:fumarate  in terms of contribution of carbon atoms. SS broth was used as the basal media for this. Plate grown B. pertussis (SS broth) were used to specify the available exchange reactions.The final model consists of 1152 reactions and 1191 metabolites, which are described in Extensive curation of the preliminary model was performed. Key changes are discussed below. The initial ModelSEED model file and a detailed curation history file are included as supplemental files to allow specific aspects of our curation and the effects of alternative curations to be investigated.B. pertussis, gap-filled reactions were removed to create true gaps , or genes identified that encode the probably missing function. This process left only 10 gap-filled reactions for which no gene assignment exists. These reactions are listed in Reactions that were automatically gap-filled were analysed. Based on known behaviours of xotrophy ), removeB. subtilis. However, there are no homologs of pdxT or pdxS in B. pertussis. An alternative well characterised pathway for PLP synthesis can occur via the activities of PdxB, PdxA and PdxJ. Clear homologs of both pdxA and pdxJ are evident in B. pertussis. PdxB is 4-phosphoerythronate dehydrogenase, an oxido-reductase enzyme. These enzymes generally show low levels of sequence conservation between homologs. Using BlastP of the E. coli PdxB sequence against the B. pertussis genome identified 4 putative dehydrogenases with scores in the range of 3e-10 to 5e-15. Thus, it was concluded that there are potential PdxB candidates in B. pertussis and as PLP synthesis is expected to be essential, gap-filling of the PdxB-catalysed reaction was more logical than that of the PdxT/PdxS reaction.PLP is an essential cofactor. The SEED model included two gap-filled enzymes corresponding to the PdxT/PdxS catalysed generation of PLP from glyceraldehyde-3-phosphate and ribulose-5-phosphate, as characterised in nadA and L-aspartate oxidase, encoded by nadB. There are no clear homologs of nadA or nadB in B. pertussis and this bacterium is auxotrophic for nicotinate, which is a component of the B. pertussis growth media. Thus, it is expected that the nicotinate synthesis pathway is incomplete in B. pertussis. These reactions were changed to true gaps in the model.The SEED model filled gaps in the reactions catalyzed by quinolinate synthase, encoded by B. pertussis there are no identifiable homologs of genes encoding the HemG or HemY members of this family of enzymes, although the remainder of the pathway appears to be present. In some other bacteria missing HemG/Y an alternative gene, hemJ, encodes this activity. BP2372 was identified as a potential hemJ homologue and was not associated with any other reaction in the model. Thus, the model was curated to include BP2372 as performing this step.The reaction catalyzed by this enzyme is a critical step in the synthesis of the cofactor heme. In E. coli in which ThiH catalyses the production of 4-hydroxy-benzylalcohol from tyrosine. However, in the model, 4-hydroxy-benzylalcohol is a dead-end metabolite, as it is not used in any pathway and the model constrains all fluxes producing dead-end metabolites to zero. There is no obvious homologue of ThiH in B. pertussis. It was reasoned that the biosynthesis more closely resembles the pathway described in B. subtilis involving ThiS, ThiF and ThiG for which there are obvious homologs in B. pertussis along with thiazole tautomerase, TenI (BP3809) and ThiE (BP0316). The model was curated to include this biosynthetic pathway.Thiamine phosphate is a crucial cofactor. The SEED model contained thiamine phosphate biosynthesis based on the pathways described in E. coli LPS structure. The structure of B. pertussis LPS is known, and the genetics of its biosynthesis is well-characterised [B. pertussis LPS molecule were substituted for the E. coli-based reactions, and the associated B. pertussis genes were assigned to these reactions. This involved modifying the reactants and products of two reactions, the addition of nine new reactions and removing thirteen of the E. coli LPS-specific reactions. LPS is most abundant molecule in the outer leaflet of the outer membrane of gram negative bacteria. Constructing an accurate B. pertussis LPS biomass component enhances the accuracy of the model.The SEED metabolic models include LPS biosynthesis based on the cterised –33. ReacSeveral reactions involving electron transfer were set by ModelSEED to operate in the opposite direction to the thermodynamically feasible direction for electron transport, producing unfeasibly large fluxes at no energetic cost. The direction of these transfers was reversed, B. pertussis [B. pertussis: pyruvate, L-aspartate, L-arginine’, L-alanine, L-glycine, L-histidine, 2-oxoglutarate, malate, L-lactate, ammonia.Previous studies have identified a number of carbon sources that either can or can not be metabolised by ertussis , 8, 25. B. pertussis model contains 301 singleton metabolites, which take part in a total of 199 reactions, consequently all blocked. Assuming the annotations and associated genes are correct, their presence points to further missing reactions, completing the pathways from which they come. Alternatively, these reactions are the remnants of pathways from which enzymes are missing due to the extensive gene loss that has been a feature of B. pertussis evolution [note annotation blocked:True.The requirement that all metabolites remain at a constant concentration is a central approximation in FBA, and this places a basic limit that all metabolites must appear at least twice in the model if they are to take an active part in any fluxes. As a direct consequence, any reaction that contains a singularly-appearing metabolite (a dead-end metabolite) has its flux constrained to zero, regardless of the state of the rest of the network. Removing these metabolites and reactions from the model entirely has no impact on the model’s results. Our curated volution . This exB. pertussis was measured using triplicate cultures /(TP + FP + TN + FN)). The reactions for each of these categories are listed in In B. pertussis metabolism, there remain uncharacterised proteins that may impact the performance of the network. Even accounting for errors in both TraDIS and the model, furthermore, FBA is an approach focused solely on the metabolic capabilities of an organism. There are regulatory and kinetic considerations that are beyond the scope of the FBA approach, but will nonetheless play a key role in the viability of knockout mutants. These considerations are likely to make perfect prediction an infeasible goal. Information on essential genes was used to refine some gene assignments for reactions. A number of reactions predicted to be essential had more than one possible gene assigned to them where it was not clear which gene was the correct assignment. In cases where one of the genes was shown to be essential, gene assignments were amended to show only this gene, as genes assigned to essential reactions also should be essential  mutants, and while our metabolic model reflects the current state of knowledge for ssential .B. pertussis utilises glutamate as its sole carbon source [A key use of metabolic models is to be able to make predictions of organism metabolism that can be investigated experimentally. To test our model, we sought to make predictions of changes to media formulations that decrease the production of growth inhibiting ammonia, without diminishing predicted growth rate. Ammonia production is thought to arise from an imbalanced N:C ratio when n source . To inveB. pertussis requires a nitrogen source to grow. If the uptake of ammonia as a source of nitrogen is prohibited then there is no growth in the model. However, as an increasing amount of glutamate is metabolised, with the corresponding decrease in fumarate metabolism, growth rate increases up to a point and the production of ammonia increases once a threshold ratio of glutamate:fumarate metabolised is reached. If this analysis is repeated allowing free uptake of ammonia, then the growth rate is unaffected by the ratio of glutamate:fumarate but ammonia is consumed up to a point when the metabolism of glutamate provides sufficient nitrogen, and ammonia is produced when the ratio of glutamate:fumarate metabolised reaches the point of imbalance between N:C . Extensive curation is typically required for genome-scale metabolic models [We have developed and curated the first published genome-scale FBA model for c models , and in E. coli. [While FBA models have extensive potential for applications, there are several remaining challenges. In particular, while genome annotation and function prediction are improving, the presence of genes classed as ‘hypothetical protein’ or with unknown function, and the presence of mis-classified genes, means that even with curation the accuracy of reconstructed models can be limited. This is a particular challenge for less-studied organisms; FBA models perform extremely well for well-characterized organisms such as E. coli. . Even ifE. coli. –38. For B. pertussis presents some unique challenges and opportunities for constraint-based metabolic modeling. For example, B. pertussis evolved from its ancestor  by a process of genome reduction and rearrangement [B. pertussis, SS broth, as inputs. Thus, the production of a metabolic model that mimics the known characteristics of the organism required extensive and laborious manual curation.angement . This haB. pertussis is considered a re-emerging pathogen, with pertussis disease resurgent in numerous countries [B. pertussis. Understanding the basic growth of the bacterium is key to this, and a genome scale metabolic model is a widely applicable tool towards this goal. In addition, millions of doses of pertussis vaccines are used globally each year. An increase in demand for these vaccines, through either replacement of whole cell with acellular vaccines in more parts of the world, or expanded use of booster vaccinations to combat resurgence, will generate considerable strain on the global vaccine supply. Enhancement of the vaccine production process through shorter production times and increased yields from production will be important to meeting any increased demand. Understanding, and the ability to manipulate, B. pertussis growth characteristics is important towards this aim. The genome-scale metabolic model described here provides a novel tool to investigate B. pertussis growth and physiology. In particular, it allows the effects of altered medium formulations or genetic manipulation of metabolism to be investigated in silico, enabling much more targeted experimental investigations than are currently possible. The alteration of B. pertussis growth by substituting fumarate for some of the glutamate in standard media demonstrate the validity of this approach.ountries . This haS1 Data(CSV)Click here for additional data file.S2 Data(CSV)Click here for additional data file.S3 Data(CSV)Click here for additional data file.S4 Data(CSV)Click here for additional data file.S5 Data(XML)Click here for additional data file.S6 Data(IPYNB)Click here for additional data file.S1 Table(TXT)Click here for additional data file.S2 Table(TXT)Click here for additional data file.S3 Table(TXT)Click here for additional data file.S4 Table(TXT)Click here for additional data file.S5 TableThese can also be queried directly in the SBML, for example by looking at all reactions associated with a specific gene. Genes are categorised as either True Positives, True Negatives, False Positives or False Negatives.(XLSX)Click here for additional data file.S6 Table(TXT)Click here for additional data file.


Synechococcus sp. PCC 7942 and Synechocystis sp. PCC 6803 show similar changes in the metabolic response to changed CO2 conditions but exhibit significant differences at the transcriptomic level. This study employs a systems biology approach to investigate the difference in metabolic regulation of Synechococcus sp. PCC 7942 and Synechocystis sp. PCC 6803. Presented multi-level kinetic model for Synechocystis sp. PCC 6803 is a new approach integrating and analysing metabolomic, transcriptomic and fluxomics data obtained under high and ambient CO2 levels. Modelling analysis revealed that higher number of different isozymes in Synechocystis 6803 improves homeostatic stability of several metabolites, especially 3PGA by 275%, against changes in gene expression, compared to Synechococcus sp. PCC 7942. Furthermore, both cyanobacteria have the same amount of phosphoglycerate mutases but Synechocystis 6803 exhibits only ~20% differences in their mRNA levels after shifts from high to ambient CO2 level, in comparison to ~500% differences in the case of Synechococcus sp. PCC 7942. These and other data imply that the biochemical control dominates over transcriptional regulation in Synechocystis 6803 to acclimate central carbon metabolism in the environment of variable inorganic carbon availability without extra cost carried by large changes in the proteome.Cyanobacteria  This process converts inorganic carbon (Ci) into sugars at the expense of light energy. The ability of autotrophic carbon assimilation makes cyanobacteria attractive for biotechnological application to produce bioenergy or chemical feedstock12Synechocystis sp. strain PCC 6803 (hereafter referred to as Synechocystis 6803)7Synechococcus elongatus strain PCC 7942 (hereafter referred to as Synechococcus 7942)2 atmosphere  to the ambient CO2 level of ambient air . These studies demonstrated that a high proportion of carbon is incorporated into glycogen under HC, whereas AC conditions induce a carbon drain from the Calvin-Benson cycle via glycolysis and the TCA cycle to sustain amino acids synthesis and activate the photorespiratory 2PG metabolism. These changes are accompanied by a transient accumulation of photorespiratory intermediates, the so-called photorespiratory burst11Whereas principal carbon assimilation was thoroughly studied in recent decades, we lack knowledge regarding how cellular functions are connected and regulated in photoautotrophic cells. For example, metabolomics was applied to analyse global changes in the central carbon and nitrogen assimilation in model cyanobacteria such as However, the handling of large data sets from “omics” experiments, such as transcriptomics, remains challenging. In the frame of systems biology, attempts were initiated to integrate those data into mathematical models13142 fixation and glycogen synthesis, allowing to predict the flux distribution. Common method employed within stoichiometric modelling is flux balance analysis (FBA)Synechocystis 680351819202324The current standard method for mathematical analysis of metabolism, functions of particular pathways or the effects of specific gene knock outs is stoichiometric modellingSynechococcus 7942142 conditionsSynechococcus 7942 exhibits major changes in expression of genes for many enzymes involved in central carbon metabolism after a shift from HC to ACWe have extended the traditional kinetic model, based on metabolic data from one steady state, to multi-level kinetic model  and applied this approach to analyse the metabolic regulation of cyanobacterium Synechocystis 6803 and its application to analyse metabolic alterations in cells shifted from HC (high CO2) to AC (ambient CO2) conditions. In contrast to Synechococcus 7942, Synechocystis 6803 cells did not show significant changes in the expression of enzymes involved in central carbon metabolism27Synechocystis 6803 carbon metabolism is rather regulated at the posttranslational, i.e. biochemical, level during the acclimation to different CO2 levels. In this study, we focus on three major topics: 1) differences in the metabolic regulation between Synechococcus 7942 and Synechocystis 6803, 2) the role of additional isozymes in Synechocystis 6803, and 3) the influence of gene expression on metabolic fluxes.Here, we report on a multi-level kinetic model for the cyanobacterium Synechocystis 6803 was used to compare central carbon metabolism and its acclimation to varying Ci conditions with that of Synechococcus 7942. These two unicellular cyanobacterial strains are often used as models to investigate molecular aspects of carbon acclimation30Synechocystis 6803, Synechococcus 7942 has an approximately 40% smaller genome (http://genome.microbedb.jp/cyanobase) and is strictly photoautotrophic. Hence, the metabolism of Synechocystis 6803 is more complex and flexible, thus making it more difficult to elucidate. The different genomic capacity also resulted in significant differences within the metabolic network between these cyanobacteria. In particular, Synechocystis 6803 runs an active OPP cycle under phototrophic conditionsSynechocystis 6803 also lacks some enzymes compared with Synechococcus 7942, such as the glycerate 3-phosphate kinase in the photorespiratory pathway, which is replaced by a glycerate 2-phosphate kinaseThe model of Synechocystis 6803 cells did not show significant changes in the expression of genes for enzymes of central carbon metabolism triggered by changing CO2 conditions27Synechococcus 7942 those changes mainly explained the observed changes in the carbon metabolism in HC compared to AC cells9Synechococcus 7942. Despite the unchanged expression, Synechocystis 6803 exhibited marked differences in metabolites under different Ci conditions  affect the behaviour of many metabolites. This effect is particularly visible in the homeostatic stability of 3PGA and 2PG, which increased by 275% and 215%, respectively , we could calculate an estimate from other fluxes involving F6P. The maximal flux via PKET could not be higher than 5 10−2 mmol h−1 g DW−1. This estimation is based on the mass conversation law of reactions included within the model. In reality the flux could be lower, since the model is not considering reactions such as mannose-6-phosphate or fructose synthesis. To clarify the exact flux via PKET, new labelling experiment would be required.Accordingly, we compared the calculated fluxes from our multi-level kinetic model of Synechocystis 6803 cells. To test whether the model provides conclusive flux calculation under different environmental conditions, we included flux values for cells cultivated in AC. Because there are no other resources to compare with, we rescaled the original values from Knoop stoichiometric modelHence, these comparisons indicated that our model can also be used for accurate calculation of fluxes from metabolic steady state data in Synechocystis 6803 under AC conditions. On this basis, the majority of fluxes are scalable for cells in changing CO2 conditions. We note that the stoichiometric model3− uptake as necessary for optimal growth yield.This attempt resulted in a good match for glycogen synthesis and a 4-fold improvement for phosphoketolase-driven flux under AC conditions . FurtherSynechocystis 6803 contain about 20-fold more glycogen than AC cells . Furthermore, phosphoketolase allows by passing of glycolysis and thus prevents the loss of CO2 due to pyruvate dehydrogenase, a feature not required in HC. These differences suggest that the redistribution of carbon sources at different CO2 level exclusively occurs in a few cardinal nodes of metabolic crossroads such as phosphoglycerate mutase, glycogen synthesis and phosphoketolase, see However, there are three exceptions to scalable metabolism: phosphoglycerate mutase (and following reactions), glycogen synthesis and phosphoketolase. Phosphoglycerate mutase regulates the crucial flux of carbon between the Calvin-Benson cycle and glycolysis  and it p−2 mmol h−1gDW−1measured in HC−2 mmol h−1gDW−1 via G6PD at AC conditions. This hypothetical high flux is very unlikely, since it did not allow to match the observed metabolic levels and it would led to further 34% decrease of the growth rate in AC. To obtain a good match between observed metabolite and growth changes with flux, it was necessary to modify the two values of weight factors from the transcriptional changes by newly estimated values of only 8% of the HC Vmax for G6PD  and 7% for phosphogluconate dehydrogenase  (see Another interesting result was found for the flux via glucose-6-phosphate dehydrogenase (G6PD), see ion) see  at AC. BFinally, our model provided significantly lower fluxes via phosphoglycerate mutase isozymes and the following part of glycolysis in AC cells compared with the published stoichiometric modelmax). We have addressed this question by testing the sensitivity of fluxes against changes of the transcriptome for Synechocystis 6803 and Synechococcus 7942. For this purpose, we collected the measured changes in transcriptome9Synechococcus 7942 was carried out by previous kinetic modelSynechocystis 6803.Modelling metabolic levels is sensitive to changes of the kinetic parameters within the model. However, fluxes must also fulfil the mass conservation law–fluxes in linear chains of reactions are the same or lower and split in the branching points in dependence of the kinetic properties of enzymes. Therefore the question arises to what degree are the fluxes influenced by the network topology and how transcriptomic changes influence the kinetic parameters  in gene expression yield almost identical results in fluxes for intermediates within the Calvin-Benson cycle after shifts from HC to AC conditions  to AC (0.04% CO2) shift from Synechocystis 6803 and Synechococcus 7942. The data were obtained fromSynechococcus 7942 andSynechocystis 6803. The cyanobacterial strains were grown under almost identical conditions in the two studies and sampling of cells after the HC to AC shifts were done at exactly the same time points. The relative metabolomic data for Synechocystis 6803 cells grown in HC and AC conditions were obtained from78Comparison of transcriptomic changes was performed for genes encoding enzymes involved in central carbon metabolism after HC  and 2). A summary of the Vmax values estimated for Synechocystis 6803 cells grown in HC together with weight factors (fold changes in transcriptome after shifting from HC to AC) is provided in the Synechococcus 7942 was previously describedThe multi-level kinetic model of Synechocystis 6803 was developed and simulations were executed using the SimBiology toolbox of MATLAB . The routine for parameter estimation was a hybrid genetic algorithm. The model for both high and ambient CO2 conditions is available in the 2) and 2) in SBML format L2V4 compatible with MATLAB 2010b and higher. We recommend to open the model either in MATLAB or user friendly COPASI (free academic license).The multi-level kinetic model for −1 ratio in HC and AC cells, which was maintained in a narrow range 0.74–0.78 as previously proposedSynechocystis 6803, e.g. ref. The final model  for AC wSynechocystis 6803 was generated to analyze the regulatory events and compare these events with Synechococcus 7942. The scope of the model includes the main parts of central carbon metabolism, such as the Calvin-Benson cycle, photorespiration, glycolysis, carbohydrate synthesis, and the OPP pathway. These metabolic reactions were coupled to simplified light reactions and Ci uptake as the main input parameters. Biomass production is one of the main output parameters . The initial sets of kinetic parameters were obtained from the model of carbon metabolism in C3 plants42Synechococcus 7942The starting point of modelling Synechocystis 6803 cells in HC conditions78Synechocystis 6803-specific kinetic parameters. For this purpose, we employed a routine called parameter estimation requiring the Global optimization and Optimization toolboxes within MATLAB. As a search method for parameter estimation, the genetic algorithm was used, which mimics natural selection by employing processes as inheritance, mutation, selection and crossover within a population of candidate solutions. This process runs numerous iterations to minimize the measure of the distance among the model predictions and the experimental data. However, parameter estimation can yield several kinetic parameter sets, all offering a good match with the experimental data. To substantially constrain the process of parameter estimation, we added metabolic data from Synechocystis 6803 cells grown under AC conditions7Initially, we assumed that the principal kinetic properties of the Calvin-Benson cycle are conserved among oxygenic phototrophs. However, the application of kinetic parameters from the previous models26max of each reaction, assuming a 1:1 ratio between a change in transcriptome and enzymatic amount. This assumption is supported by several studies with bacterial cells, e.g., E. coliSynechocystis 6803Is carbon metabolism scalable in response to a changing environment?). Because the resulting model describes two environmental conditions (HC and AC) and implements the transcriptome as well as metabolome data, we refer to it as the multi-level kinetic model of Synechocystis 6803.The essential step in identifying the optimized kinetic parameters involves implementation of the connection between both data sets (HC and AC), which, in contrast to traditional kinetic modelling, enabled estimation of one set of kinetic parameters for metabolic reactions describing both HC and AC. To connect the two data sets, we used fold changes in the mRNA levels of genes for metabolic enzymes in the model after shifting from HC to ACmax and KM for particular reaction if the substrate and product concentrations do not change significantly in HC and AC. Further model constraints as well as increased knowledge about metabolic regulation can be achieved by implementation of series of measurements based on single mutations (enforcing an adaptation of metabolic network), different light conditions, or other environmental factors affecting the central carbon metabolism.Multi-level kinetic modeling approach represents a significant advancement in parameters constraining in comparison to traditional kinetic modeling. This advancement is based on using a single set of kinetic parameters for more than one system state, e.g., multiple environmental conditions. Moreover, energetic and redox limitations are usually neglected in kinetic modeling, but our approach includes these biological important parameters. Our multi-level kinetic model works with flexible concentrations of ATP and NADPH as these compounds play a crucial role in metabolic regulation within multi-level kinetic modeling2 and ATP · (ADP + ATP)−1 ratio were used for identification of kinetic parameters. The next step included data from the high to ambient CO2 shift by applying the mean values of measured transcript changes as weight factors for each Vmax estimated for HC. Then, the third step simulated a transition to steady state in AC, calculation of biomass and evaluates the match with metabolic data from cells grown at AC and the growth rate ratio between HC and AC states. The deviation between the simulated and experimental metabolic data could reach up to 100% at this stage. This boundary has been empirically chosen to obtain a broader set of results followed by manual curation and tuning of parameter sets. If no match was found after hundreds of parameter estimation runs made in the first step, the model was re-evaluated and missing reaction(s) or regulatory steps, e.g., isoenzyme or OPP, were implemented. Finally, if the match was improved to deviation below 100% , the final tuning of kinetic parameters was initiated to improve the match between simulated and experimental data. This fine tuning was partially done by automatic parameter estimation routines and partially manually. Manual tuning and curation of kinetic parameters at the different levels of parameter estimation and data integration is an essential part of modelling, because it provides a deeper understanding of system behavior and helps to detect errors in the algorithms, routines and model structure.The integration of various omics data occurred in several steps. In the first step, metabolic and fluxomics data from cells grown at high COSynechocystis 6803, which could help to identify major regulatory processes. Our multi-level kinetic model approach allowed employing the same process of parameter estimation for Synechocystis 6803 as before for Synechococcus 7942 and resulted in a good match on the metabolic level. Furthermore, we demonstrate that the higher number of isozymes present in the larger Synechocystis 6803 genome compared with Synechococcus 7942 allow higher homeostatic stability on the metabolic level against changes in gene expression suggesting the possibility of a shift in metabolic regulation from genetic control in Synechococcus 7942 to enzymatic control in Synechocystis 6803. The only exception is G6PD, an enzyme from the OPP cycle that is active in photoautotrophic conditions as well as during the night. In this case, post-translational modifications are necessary to explain the regulation of G6PD.The main goal of this study is to achieve a mathematical description of the response of the central carbon metabolism to different inorganic carbon conditions in Synechococcus 7942 but not in the case of Synechocystis 6803. These findings may be relevant for future attempts to use cyanobacteria for biotechnological purposes. Host strains with greater genomes such as Synechocystis 6803 might be more difficult to engineer toward changes in carbon allocation, since the metabolic network is rather stable due to biochemical regulation. Thus, the introduction of single branches or the up- or down-regulation of specific enzymes will have lower impact than in cyanobacterial hosts with lowered genome size such as Synechococcus 7942, in which transcriptional changes via different promoter strength or the expression of additional enzymes will have a bigger impact on the cellular carbon allocation. Finally, our results indicate that the multi-level kinetic modelling of cyanobacterial central carbon metabolism can be used to analyse and optimize cellular carbon reallocation towards specific needs in green biotechnology.To support the notion that the two cyanobacteria control central carbon metabolism on different regulatory levels, we analysed the impact of random changes in gene expression on the fluxes. Our simulation revealed that the AC-induced fluxes at the branching points of the metabolic network are highly dependent on transcriptional changes in How to cite this article: Jablonsky, J. et al. Different strategies of metabolic regulation in cyanobacteria: from transcriptional to biochemical control. Sci. Rep.6, 33024; doi: 10.1038/srep33024 (2016).


Escherichia coli growing on glycolytic or gluconeogenic carbon sources to the selective pressure of three different antibiotics. Profiling more than 500 intracellular and extracellular putative metabolites in 190 evolved populations revealed that carbon and energy metabolism strongly constrained the evolutionary trajectories, both in terms of speed and mode of resistance acquisition. To interpret and explore the space of metabolome changes, we developed a novel constraint‐based modeling approach using the concept of shadow prices. This analysis, together with genome resequencing of resistant populations, identified condition‐dependent compensatory mechanisms of antibiotic resistance, such as the shift from respiratory to fermentative metabolism of glucose upon overexpression of efflux pumps. Moreover, metabolome‐based predictions revealed emerging weaknesses in resistant strains, such as the hypersensitivity to fosfomycin of ampicillin‐resistant strains. Overall, resolving metabolic adaptation throughout antibiotic‐driven evolutionary trajectories opens new perspectives in the fight against emerging antibiotic resistance.Despite our continuous improvement in understanding antibiotic resistance, the interplay between natural selection of resistance mutations and the environment remains unclear. To investigate the role of bacterial metabolism in constraining the evolution of antibiotic resistance, we evolved  Acquired through horizontal gene transfer or genetic mutations, the most effective antibiotic resistance mechanisms alter the antibiotic target, increase drug efflux, or overexpress drug modification enzymes  was used to systematically identify metabolites transitioning to a new steady state (Metabolite changes that are the result of adaptive mutations are expected to (i) exhibit clear monotonic transitions from wild‐type basal concentrations to new different steady‐state levels and (ii) to occur reproducibly across the four lineages evolved under the same selective pressure. We discriminated such metabolic adaptations from transient or stochastic fluctuations by fitting a sigmoidal curve to the relative metabolite abundances in the evolved populations from each lineage. The quality of fit , empirically we observed that a change in flux is often accompanied by changes of metabolites abundance  overrepresented among metabolites experimentally found to be altered during evolution of antibiotic resistance. By using shadow prices to interpret measured metabolite level changes, we implicitly assume that the newly evolved flux states will be reflected in altered steady‐state concentrations of metabolites that are limiting for the evolved metabolic functions. By systematically searching for reactions with an overrepresentation of altered limiting metabolites, we thus try to identify metabolic functions that if modulated can play an active role in the evolution of resistance or its compensation. We refer to these reactions as evolved metabolic characteristics  E. coli grown in glucose minimal medium, 1 h after treatment with the respective antibiotics . Consistently, we showed that: (i) chloramphenicol treatment selectively induces increased transcription of acrB  Action Fig , and  or oxidative phosphorylation inhibitors (e.g. carbonyl cyanide m‐chlorophenyl hydrazone (CCCP) or theophylline), and vice versa  7.52 g Na2HPO4·2H2O, 3 g KH2PO4, 0.5 g NaCl, 2.5 g (NH4)2SO4, 14.7 mg CaCl2·2H2O, 246.5 mg MgSO4·7H2O, 16.2 mg FeCl3·6H2O, 180 μg ZnSO4·7H2O, 120 μg CuCl2·2H2O, 120 μg MnSO4·H2O, 180 μg CoCl2·6H2O, 1 mg thiamine·HCl. Chloramphenicol, ampicillin, and norfloxacin were purchased from Sigma.600 was measured with a plate reader. The bacterial population that was able to grow (i.e. OD ≥ 0.5) at the highest of seven tested drug concentrations was used for the next passaging step. 9 μl of the selected bacterial culture (e.g. surviving to the highest drug concentration) was used for reinoculation. The number of generations during each passaging step was calculated by (i) measuring the final OD after a 48‐h growth cycle (ODfin), (ii) 9 μl of selected evolved populations was reinoculated in 900 μl of fresh medium yielding a 1/100 dilution for the new starting OD. At the end of the 48‐h growth cycle, OD was measured (OD*) and number of generation is calculated by the following formula: log2(OD*/(ODfin/100)). At each propagation step, an aliquot of the culture was frozen and stored at −80°C to obtain a library of mutants at different stages of the evolutionary experiment. At each reinoculation step, the highest drug concentration tested was adjusted to be at least double of the concentration where bacterial growth was detected in the previous passaging step. In parallel, a similar experimental setup was used to evolve two independent cultures in antibiotic‐free media. Evolutionary lineages on the two growth media, glucose (GLC) and acetate (ACE) M9, are referred to as lineage 1–12, where 1–4 evolved resistance to norfloxacin, 5–8 to ampicillin, and 9–12 to chloramphenicol. Similarly to other laboratory evolution strategies  for whole‐genome sequencing on an Illumina Gene Analyzer II× . Raw reads were aligned to the reference genome of E. coli K‐12 BW25133 using bowtie 2 .Evolved populations were inoculated from frozen stocks, grown overnight in LB medium, and chromosomal DNA was purified using commercial bacterial DNA isolation kits (QIAGEN DNeasy Blood & Tissue Kit). Isolated DNA was submitted to the functional genomics center Zurich  and quenched in a cold ethanol bath at −50°C for 7 s. After quenching, the samples were centrifuged for 2 min at 1,252 g and 0°C. The supernatant was discarded and the pellet was extracted with 100 μl of 60% (v/v) ethanol solution at 80°C, for 2 min. Samples were placed at −80°C and stored until further analysis. Eight samples for intracellular metabolite profiling were taken at different time points during exponential growth. Extracellular metabolites were collected by sampling 50 μl of cell culture, diluting 1:4 in 150 μl of water, centrifuging for 5 min at 4,000 rpm and 0°C, and storing at −80°C. Collected samples were directly injected into an Agilent 6550 time‐of‐flight mass spectrometer . Details are described in Fuhrer et al . For 64 metabolites, we could prove that intensities scales linearly with the corresponding metabolite abundance  in the two media (glucose and acetate M9), we selected eight different populations at intermediate stages during the evolutionary experiment. Overall, we selected 192 populations under antibiotic selective pressure. Two of these populations did not grow properly during the metabolome sampling experiment (i.e. AMP 5_3 and NOR_4_3 evolved in glucose M9) and were excluded from further analysis. We also profiled the endpoints of evolution for two er et al . This mebundance . Spectraet al , and (iii) the total sum of measured ion intensities. For each metabolite m, the relative change in sample u (FCm) was calculated as follows: Im is the measured intensity of metabolite m, ODu represents the optical density at the time of extraction of sample u, α represents the linear dependency between measured intensities and OD, βj represents the linear dependency of measured intensities with the temporal drift during injections in plate j, K is the injection sequence , γ is the linear dependency with Total Ion Counts (TIC) in plate j. The proportionality factors, α, β, and γ, were determined by multiple least square fitting analysis for each ion individually across all measured samples. For each evolved population(s), fold‐change and variability of metabolite m are, respectively, the average and standard deviation of FCm across collected samples  plate‐to‐plate variance, (ii) the intensity drift during sequential injection, and (iii) matrix effects. We modeled linear dependencies between measured ion intensities and (i) drift during sequential injection for each plate, (ii) amount of extracted biomass quantified  in the eight selected populations belonging to the same lineage are sorted according to the number of generations (g). A weighted least square fitting analysis is then used to fit a sigmoidal curve function: p1 is the minimum of the function values, p2 is the difference between maximum and minimum, p3 is the number of generations at which metabolite concentration reaches half of its maximum level, and p4 is the slope. Quality of the fitting is assessed by estimating the adjusted R2 values using the MATLAB function “fitnlm”, and weights used are the inverse of the fold‐change standard deviation. A sigmoidal model intrinsically captures slow and rapid changes from one basal state to a new different state. For those metabolites where a sigmoidal curve exhibits poor descriptive performance (e.g. low adjusted R2) either: (i) data are too noisy, (ii) there are no significant changes in the relative abundance of the metabolite during evolution of antibiotic resistance, (iii) changes are transient and reabsorbed to a normal basal level by the end of our evolutionary experiment.For each annotated metabolite, log®‐Xtra HS Method) following the suggested protocol. An absolute estimate of oxygen consumption was made by assuming the uptake rate of oxygen in wild type equals 14.93 mmol/gDW/h as reported in Covert et al . Selected populations were grown in glucose minimal medium, and supernatant samples were collected during exponential phase. Residual glucose was quantified using enzymatic assays kits (Megazyme), and acetate was quantified by high‐performance liquid chromatography (HPLC). HPLC analysis did not reveal significant secretions of other plausible fermentation products, such as citrate, succinate, fumarate, lactate, malate, oxaloacetate, or pyruvate. Relative oxygen uptake rates were measured using the Oxygen Consumption Rate Assay Kit  associated with each metabolite (j) for the systematic maximization/minimization of flux (vi) through each individual reaction (i) in the model. In matrix notation, if the primal problem is formulated in its standard form:The bTvMax Sv ≤ l, v ≥ 0,subject to the corresponding symmetric dual problem is as follows:lTwMin STw ≥ b, w ≥ 0,subject to w is the vector of dual variables.where vi:In our specific case, the primal FBA problems are for each flux cTvimin/max s.t. Sv = 0vmin ≤ v ≤ vmaxc is a vector with only one nonzero element corresponding to the flux to be optimized, S is the stoichiometric matrix, and vmin and vmax are the thermodynamic constraints. Hence, the dual problem can be formulated as follows: w are the dual variables associated with the mass balance constraints and lL and lU the dual variables to the thermodynamic inequality constraints. The CPLEX LP solver was used to find the corresponding dual solution to the FBA problems. Two sets of calculations were performed, corresponding to the two media conditions (glucose and acetate).where Practically, in a classical FBA analysis, where maximization of growth is assumed, a shadow price corresponds to the change in the biomass flux when one of the mass balance constraints is violated (e.g. metabolite deviating from steady state). Wasting of a metabolite (e.g. secretion) with a negative shadow price would have a negative impact on the objective, and hence decrease biomass production .R2 from the sigmoidal fitting analysis (Ω) were retained and used for the comparison. Results are qualitatively similar if the top 1% of metabolic changes are retained  is not discriminative in such a framework. For example, the same higher demand for a metabolite can induce two radically different scenarios: its overproduction and possibly accumulation, or an increased utilization, resulting in decrease metabolite levels. In both cases, the metabolite can still be limiting. Hence, the sign of the measured metabolic changes was not taken into account to establish the link with shadow prices.Overall, our procedure explores violation of mass balance constraints to predict the link between metabolite and flux changes. Given the medium composition, the stoichiometry of the system, and measurements of actual metabolic changes in evolved populations, we predicted evolved metabolic characteristics (EMC) as follows. Model‐based estimated shadow prices were compared to measured altered metabolites in evolved populations. For each condition, the 5% of metabolites with the highest retained . It is wi), the sum of shadow prices for selected metabolites is divided by the total sum of shadow prices. This results in a unique similarity score associated with maximization or minimization of flux i: SHoi denotes the observed statistics associated with maximization or minimization of reaction i, given the set of altered metabolites Ω. To avoid any assumption on the underlying background distribution and independence of categories, we tested the significance of the observed statistics using a permutation test. Associations between shadow prices and metabolites are randomized 1,000 times, yielding for each tested reaction 1,000 permuted statistics (SHP). Score significance is assessed as follows: For each objective reaction  in each of the six tested conditions are reported in For each reaction, the lowest acrAB was fused to GFP in the plasmid pMW82  then harvested by centrifugation, and cell pellets were re‐suspended in 50 mM Tris–HCl (pH 8.0). Protein extracts were prepared by sonication on ice with an MSE Soniprep 150  for four pulses of 30 s with a 30‐s pause between each pulse. A Bradford assay was carried out to quantify the protein concentration, and 10 μg of protein was run on 4–12 % NuPAGE® Bis‐Tris mini gels with NuPAGE® MES SDS running buffer . Protein was transferred to nitrocellulose transfer membranes , and analyzed by Western blotting using AcrB antibody at a 1 : 1,000 dilution. Blots were developed using anti‐rabbit IgG horseradish peroxidase‐linked antibody  at a 1 : 25,000 dilution and analyzed using the ECL detection system .Bacterial samples required for Western blotting were grown aerobically overnight in M9 minimal medium at 37°C. The following day cultures were subcultured and grown in M9 minimal medium at 37°C to approximately mid‐logarithmic growth phase .Elementary flux modes (EFM) are defined as the minimal reaction sets that are able to operate at steady state  that n growth .E. coli BW25113 was grown in glucose M9 minimal medium. Culture volumes of 5 ml were incubated at 37°C, and growth was followed via absorbance at 600 nm. When cell culture reached an OD600 of 1, cells were perturbed with the immediate addition of 200 ng/ml norfloxacin, 50 μg/ml chloramphenicol, and 50 μg/ml ampicillin. All samples were harvested after 1 h from drug exposure. Culture broth samples were transferred on a filter, supernatant was fast filtered, and metabolome was immediately extracted. To normalize the amount of biomass extracted, a total Volume × OD600 equal to 1 was maintained throughout all samples. The same mass spectrometry technique described in the “Processing of high‐throughput metabolome data” section was used. Relative fold‐changes were calculated for each of the 437 detected metabolites .Metabolome data are provided in MZ and US designed the project. MZ, VC and TE performed the experiments, MZ designed the analysis, and MZ and TE analyzed the data. VR and LP performed the AcrB protein quantification. All authors contributed to preparing the manuscript.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Review Process FileClick here for additional data file.


Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease associated with profound metabolic changes. The purpose of this study was to identify a distinctive metabolic signature from the training set with 29 PBC patients, 30 hepatitis B virus (HBV)-caused cirrhosis (HBC) and 41 healthy controls, and to validate the applicability and stability of the distinctive model from the validation set with 21 PBC patients, 7 autoimmune hepatitis (AIH) and 9 HBC. The sera were investigated using high resolution nuclear magnetic resonance (NMR) and the datasets were analyzed pairwise using pattern recognition methods. 45 distinguishable metabolites were identified and 15 metabolic pathways were reprogrammed. The altered metabolic pathways were associated with glucose, fatty acid and amino acid metabolites. Logistic regression and ROC analysis were used to establish a diagnostic model with the equated (p) = −12.22–3.46*log(4-hydroxyproline) + 6.62*log − 2.44*log(citraconate) − 3.80*log(pyruvate). The area under the curve (AUC) of the optimized model was 0.937 : 0.868–0.976) in the training set and 0.890 (95% CI: 0.743–0.969) in the validation set. These results not only revealed the potential pathogenesis of PBC, but also provided a feasible diagnostic tool for PBC populations through detection of serum metabolites. A population-based epidemiological study revealed prevalence rates ranging from 1.91 to 40.2 per 100,000 people that appeared to increase over time3. The etiology and pathogenesis of PBC are poorly understood, although the disease is characterized by a female predominance and anti-mitochondrial antibodies (AMAs) and is associated with autoimmune syndromes and histological features of chronic inflammation4.Primary biliary cholangitis (PBC), which until recently was named primary biliary cirrhosis, is a chronic autoimmune liver disease characterized by the progressive destruction of small intrahepatic bile ducts that can lead to chronic cholestasis, portal fibrosis, and cirrhosis6. The majority cases suffer from fatigue or pruritus in the early stage and are rarely suspected to have PBC. The diagnosis of PBC requires a combination of clinical features, auto-antibodies, abnormal liver biochemical patterns and liver histology; among them, the presence of serological AMAs and elevated alkaline phosphatase (ALP) are the most important and usually applied parameters7. However, liver chemical parameters and auto-antibodies are not pathogenic or required for the disease. AMA-negative PBC is difficult to diagnose because of the poor feasibility of liver biopsy in the suspected patients. Additionally, because many autoimmune diseases are associated with PBC, the differential diagnosis of PBC from other autoimmune liver diseases is very important. Therefore, improvement of the PBC diagnosis through the development of new methods to detect PBC accurately is important.The natural history of PBC varies greatly between patients, ranging from asymptomatic and slowly progressive to a symptomatic and rapidly evolving disease8. By comparing metabolic profiles and their dynamic changes, characteristic changes including pathological and treatment changes can be elucidated. The current metabolomics methods include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC/MS), and liquid chromatography-mass spectrometry (LC/MS). Recent reports have suggested that metabolic profiles may reveal novel characteristics of metabolic abnormalities of PBC and help improve the diagnosis11. Lv et al. revealed that gut microbiome was critical for the onset or development of PBC by interacting with metabolism and immunity12. Altamirano-Barrera et al. commented that high plasma concentrations of bile acids, in particular chenodeoxycholic acid, contribute to the induction of carcinogenesis via intrahepatic accumulation of bile acids13. Tang et al. showed that the levels of bile acids increased with the progression of PBC, while the levels of carnitines, such as propionyl carnitine and butyryl carnitine, decreased with the progression of PBC14. Moreover, cholestasis is associated with hyperlipidaemia, decreased fatty acid oxidation, increased glycolysis, and abnormal mitochondrial function15. These profound metabolic changes hamper the renewal of mitochondria and contribute to oxidative damage16. As a consequence, an inflammatory cascade response is triggered and eventually evolves on set of fibrosis17. Bell et al. identified four metabolic pathways that can be used as potential biomarkers to differentiate PBC from primary sclerosing cholangitis (PSC) patients, regarding protein/amino acid metabolism, lipid metabolism, oxidative stress/lipid peroxidation, and stress hormones18. Lian et al. and Wang et al. identified important differences of patients with PBC as compared to autoimmune hepatitis (AIH)20. These metabolites could provide insight into metabolic pathways that contribute to the pathogenesis of PBC. However, the sensitivity and specificity of these biomarkers have been challenged and require further verification.Metabolomics is a new “omic” discipline following genomics, transcriptomics and proteomics and is recognized as a promising technique for the evaluation of global metabolic changes1H-NMR metabolomics approach to identify a distinctive signature in serum samples from PBC patients and to reveal the reprogramming of metabolic pathways in PBC. We established a diagnostic model including 4 metabolites . Furthermore, we included 7 AIH patients, 9 HBC patients and 21 PBC patients in a validation dataset to validate the applicability and stability of the distinctive model. The results showed that the 4 novel metabolites were distinct for PBC, could differentiate PBC from HBC and AIH, and were not affected by ursodeoxycholic acid (UDCA) administration. These PBC-related distinctive metabolites could benefit from the development of novel categories of advanced disease biomarkers and assist in understanding the potential pathogenic mechanism.In the present study, we applied a high resolution We collected 29 patients with PBC, 30 patients with HBC and 41 healthy controls in the training set. Among the patients with PBC, 23 were initially diagnosed, 6 were treated with UDCA (13–15 mg/kg/d) for more than one year and all had biochemical response. The baseline clinical characteristics of the PBC and HBC patients are shown in Table 2(Y) of 0.950, an R2(Y) of 0.987, and a cumulative R2(X) of 0.852, indicating reliable differentiation between the groups.Representative NMR spectra with identified metabolites are shown in Fig. A pairwise comparison was performed between the PBC and HBC patients to discover the distinct potential biomarkers responsible for PBC among the thousands of variables. The PCA model for the classification of the two groups obtained satisfactory validation Fig. . The OPL2Y (cum) of 0.834 and a Q2 (cum) of 0.759 when the OPLS-DA model was used  for the treatment of PBC, but approximately 30 to 40% of patients may not achieve a biochemical response with UDCA treatmentp) >10. The biological pathway analysis revealed fifteen metabolic pathways, including taurine metabolism, arginine and proline metabolism, pyruvate and citrate metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism, glycerolipid metabolism, butanoate metabolism, glycolysis or gluconeogenesis, pentose phosphate pathway, lysine biosynthesis, valine, leucine and isoleucine biosynthesis, cysteine and methionine metabolism, pyrimidine metabolism, and primary bile acid biosynthesis  = −12.22–3.46* log (4-hydroxyproline) + 6.62* log  − 2.44* log (citraconate) − 3.80* log (pyruvate). The corresponding ROC curve had an AUC of 0.937 : 0.868–0.976) with a Youden index J of 0.754 . The performance of the designed model was evaluated by the AUC in the training set. Notably, 4-hydroxyproline, 3-hydroxyisovalerate, citraconate and pyruvate were present in the combined model. None of the other metabolites were included in the model, likely because of colinearity in the information provided by these compounds. Thus, the four metabolites contributed to the combined model. We also constructed receiver operating characteristic (ROC) curves for the four individual metabolites independently. Table To validate the applicability and stability of the distinctive model, we collected and analyzed serum samples from 21 PBC, 7 AIH and 9 HBC patients in an experiment using a different data set. The clinical baseline characteristics of the three patient groups are listed in Table 1H-NMR spectroscopy to identify a distinctive PBC signature. First, we detected 45 distinct metabolites from the comparative analysis and 15 metabolic pathways that exhibited reprogramming. To select and validate marker metabolites, a logistic regression and ROC analysis were used to establish a diagnostic model with 4 metabolites . The AUC of the optimized model was 0.937 (95% CI: 0.868–0.976), which indicated satisfactory sensitivity and specificity. Then, we performed an independent confirmatory experiment including 21 patients with PBC, 7 AIH patients and 9 patients with HBC and found that the combination of the above 4 metabolites effectively differentiated PBC from AIH and HBC. The sensitivity and specificity of distinguishing each sample were 95.24 and 75.00%, respectively, which indicated that the model had a high clinical value for the diagnosis of PBC.PBC has very diverse clinical and laboratory manifestations. Thus, the diagnosis of PBC is challenging, particularly its early diagnosis and differential diagnosis from other autoimmune liver diseases. Therefore, an early and accurate diagnosis of the disease is important to lower liver-related morbidity and mortality. In the current study, we used high resolution 22 and fatigue23. 3-Hydroxyisovalerate (3-HIVA) is produced from 3- hydroxy-3- methylglutary- CoA, which is a side product of increased ketogenesis24. A high 3-HIVA concentration occurs in ketoacidotic and isovaleric acidemia patients25. Citraconate is an isomeric carboxylic acid that is obtained from citrate, and recent studies have revealed that it is a competitive inhibitor of fumarate reduction26. Pyruvate is an intermediate compound in the metabolism of carbohydrates, proteins, and fats27, which could be regulated by pyruvate dehydrogenase. While AMA is directed against the lipoyl domain of the E2 subunit of pyruvate dehydrogenase28, the E2 subunit can be chemically modified and triggers the breakage of tolerance30, which will contribute to the pathogenesis of PBC31. These findings partially indicated that pyruvate metabolism specificities reflected aspects of the PBC induction phase. Therefore, the 4 metabolites have significance for PBC and are good biomarkers for the PBC diagnosis.4-Hydroxyproline is a specific amino acid in collagen protein that plays a key role in collagen stability and reflects the collagen content or fibrosis stage. Moreover, an increased serum or urinary hydroxyproline level is associated with depressionIn addition to the forty-five metabolites, the metabolic pathway analysis revealed fifteen reprogramming pathways, mainly including taurine metabolism, pyruvate and citrate metabolism, glycine, serine and threonine metabolism, glycerolipid metabolism, glycolysis or gluconeogenesis, and the pentose phosphate pathway. These changed pathways embody the potential pathological mechanisms of PBC and warrant further in-depth research.UDCA is effective for PBC treatment and can delay disease progression in most PBC patients. However, UDCA influences the bile acid assay in serum because UDCA itself is a type of bile acid. In our study, we found that UDCA treatment did not cause significant metabolite changes compared to the UDCA-naїve patients. This finding indicates that the metabolic profile does not reflect the therapeutic response or function as a metabolic marker, suggesting that the established metabolic diagnostic model can specifically distinguish PBC and is not associated with conventional therapy. We will expand the number of samples and compare metabolic differences between UDCA responders and non-responders in future studies.32. We found that hyperlipidemia and glucose metabolic disorders were common in PBC patients. Investigations of the overall metabolic changes have been quite limited, but the metabolic profile may reveal a distinctive PBC signature. 1H-NMR-based metabolomics can provide non-destructive, high-throughput metabolic profiles with minimal sample preparation. The technique recognizes metabolites by measuring the intrinsic properties of each proton and can distinguish low molecular weight metabolites through chemical shift, splitting patterns and coupling constants. 1H-NMR-based metabolomics is an unbiased method to distinguish metabolites and is extensively used in metabolic analyses. In the present study, high resolution 1H-NMR-based metabolomics was employed to reveal the metabolic profiles and global metabolites. A total of forty-five metabolites were changed in the three groups. Thus, the high-resolution NMR-based metabolomics approach was feasible for the present study.Nevertheless, we could not analyze the bile acids in the present study for the constraint of the NMR platform. However, abnormalities in lipoprotein and bile acid metabolism have been well describedIn conclusion, forty-five distinguishable metabolites were identified and fifteen metabolic pathways were reprogrammed. Logistic regression and ROC analysis were used to establish a diagnostic model with the equated (p) = −12.22–3.46* log (4-hydroxyproline) + 6.62* log  − 2.44*log (citraconate) − 3.80*log (pyruvate). The AUC of the optimized model was 0.937 (95% CI: 0.868–0.976) in the training set and 0.890 (95% CI: 0.743–0.969) in the validation set. Therefore, the distinctive signature of the four metabolites significantly increased the diagnostic performance of PBC.33. All participants provided written informed consent. Information regarding the age, gender, the duration of illness, laboratory indices, liver ultrasonic score, Child-Pugh score and previous diseases was collected. For ethical reasons, the patients continued to take their prescribed medications at the time of serum sampling. The full diagnostic assessment was based on all available sources of information, including patient interviews, case records, and if possible, interviews with the next of kin. To reduce inter-rater bias, a diagnosis decision based on all information available at admission was made by a consensus panel of board specialists in PBC. The general criteria for inclusion were as follows: at least 18 years of age and meeting the diagnostic criteria for PBC. The exclusion criteria were as follows: chemotherapy closest to the sample acquirement; cardiac, kidney, or liver failure; acquired immunodeficiency syndrome; inflammatory bowel disease; and systemic infection.Ethical approval was obtained from the Health Research Ethics Board at Shuguang Hospital Affiliated with Shanghai University of Traditional Chinese Medicine, and all methods were performed in accordance with the relevant guidelines and regulations. A total of 29 patients with PBC and 30 cases of HBC were recruited into the training set in July 2015 to June 2016 from the Department of Cirrhosis. A total of 41 healthy controls were randomly selected from our hospital health examination center. The validation set included an additional 21 PBC, 7 AIH and 9 HBC patients collected in May 2016 to November 2016. The patients with AIH were diagnosed according to the revised scoring system proposed by the International Autoimmune Hepatitis Group (IAIHG) in 1999g at 4 °C for 20 min to eliminate the supernatant serum and then quickly stored at −80 °C prior to the execution of the 1H-NMR experiment. To obtain robust and reproducible data, all samples were prepared according to the standard protocol34. Briefly, the samples were thawed at 4 °C for 2 h and then left at room temperature for 1 h. Then, 300 μL of thawed sample was mixed with 300 μL of phosphate buffer (pH 7.4) containing 10% D2O. After vortex mixing and centrifugation, 550 μL of the solution was transferred into 5 mm NMR tubes and kept at 4 °C prior to the NMR analysis.Morning fasting blood was collected in a vacuum tube and kept at room temperature for 30 min to allow clotting. The clotted blood samples were centrifuged at 3000 × 1H resonance frequency . Quality control tests were performed at the beginning of each measurement day. A representative sample was used for NMR probe tuning and matching, determination of the transmitter offset value for water pulse presaturation and 90 pulse adjustments. Each sample was locked and shimmed automatically; the receiver gain was set to 90.5 and the temperature to 310 K for all experiments. The CPMG (Carr–Purcell–Meiboom–Gill) pulse sequence was employed to obtain the low molecular signal with the equation-delay-90°-(t-180°-t) n - acquisition, where the relaxation delay was 2 s and t was the spin-echo delay of 400 μs. All signals were zero-filled to 32 k prior to Fourier transformation (FT). All spectra were acquired and performed using the TopSpin software package version 3.0 .All spectra were recorded using a Bruker AMX-600 NMR spectrometer operated at a 600.13 MHz 1H-NMR spectra of biological samples. Because of the overlap of signals from the 1H-NMR spectra, 2D-HSQC and J-Resolved spectra were employed for the metabolite identification and signal assignment. The 2D-HSQC spectrum provided excellent spectral dispersion along the indirect 13C dimension and allowed the separation of many of the peaks that overlapped in the 1D NMR spectrums. The J-Resolved spectra were acquired using 16 transients per increment for a total of 32 increments that were collected into 16 k data points with spectral widths of 6 kHz along the direct chemical shift axis and 50 Hz along the spin–spin coupling axis. Prior to Fourier transformation, the datasets were zero-filled to 128 points in the spin–spin coupling axis and 32,768 points in the chemical shift axis.The Chenomx NMR Suite software version 8.2  was used to identify metabolites from the 1H chemical shifts referred to the methyl doublet signal of lactate (δ 1.33). The corrected NMR spectra corresponding to the chemical shift range of δ 0.2–10.0 were imported into AMIX 3.9.5 , and all of the spectra were reduced into integral regions with equal lengths of 0.005 ppm. Regions of δ 4.7–5.1 that contained the resonance from residual water were set to zero. To reduce the concentration differences between samples, the data were normalized to the total spectral area (100%).The datasets were analyzed by pattern recognition methods using the software packages Simca-P, version 11.5 , and MetaboAnalyst 3.0 . To make the skewed distributions more symmetric, log transformations were used for nonlinear conversions of the data. To explain the maximum variation between samples, a PCA bilinear decomposition method was used to view the clusters within the multivariate data. To eliminate the effect of inter-subject variability among the participants and identify endogenous metabolites that significantly contributed to the classification, OPLS-DA was applied to remove linear combinations of variable X orthogonal to the Y vector. Cumulative R2(Y) and Q2(Y) values close to 1 indicated an excellent model. The specific metabolites between classes were interpreted using VIP and the correlation coefficient.Phase correction and baseline correction were carefully performed, and the To identify the most relevant metabolic pathways involved in PBC, a metabolomics pathway analysis was employed to perform the pathway enrichment and pathway topology analyze. The metabolites with VIP scores >1.0 in the partial least squares (PLS) or p1 >±0.05 in the S-Plot were examined and selected for their discrimination power by multiple statistical criteria. A global ANOVA was used to analyze the concentration values and identify subtle changes, and a relative betweenness centrality was used for the metabolite importance measurement. The changed metabolites in the network node were selected because they would trigger a more severe impact on the pathway than changes that occurred in marginal or relatively isolated positions.We designed a prediction model and constructed ROC curves to evaluate the accuracy of the model. First, the data set was split into training and validation. Then, a forward stepwise logistic regression model was constructed on the training sample set to design the best metabolite combination. ROC curves were used to evaluate the accuracy of this model in the training and validation sets and to evaluate the combined model following the DeLong method. ROC curve analyses were performed for the designed model. The global performance of each biomarker model was evaluated using the AUC and the determination of sensitivity and specificity at the optimal cut-off point defined by the minimum distance to the top-left corner.t-test, and differences involving more than two groups were analyzed using a one-way ANOVA followed by Tukey’s posthoc test. The level of significance was set at p < 0.05.Data entry and analysis were performed using MedClac software version 13.0.6.0 . After data collection, the data were checked manually for completeness and inconsistencies. The attribute data were expressed as the counts and analyzed using the non-parametric crosstab method. The measurement data are expressed as the mean ± SD (standard deviation). The differences between the two groups were analyzed using Student’s two-sided Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4