Chlorella protothecoides under heterotrophic condition. It combines both physiology and flux information in a kinetic approach. Cell nutrition, growth, lipid production and almost 30 metabolic intermediates covering central carbon metabolism were included and simulated.Microalgae have been proposed as potential platform to produce lipid-derived products, such as biofuels. Knowledge on the intracellular carbon flow distribution may identify key metabolic processes during lipid synthesis thus refining culture/genetic strategies to maximize cell lipid productivity. A kinetic metabolic model simulating cell metabolic behavior and lipid production was first applied in the microalgae platform Chlorella protothecoides cells’ biology. The dynamic metabolic flux analysis using the model showed a reversible starch flux from accumulation to decomposing when glucose reached depletion, while net lipid flux shows a quasi-constant rate. The sensitive flux parameters on starch and lipid metabolism suggested that starch synthesis is the major competing pathway that affects lipid accumulation in C. protothecoides. Flux analysis also demonstrated that high lipid yield under heterotrophic condition is accompanied with high lipid flux and low TCA activity. Meanwhile, the dynamic flux distribution also suggests a relatively constant ratio of glucose distributed to biomass, lipid, starch, nucleotides as well as pentose phosphate pathway.Model simulations were shown to adequately agree with experimental data, which is suggesting that the proposed model copes with C. protothecoides.The model described not only experimental data, but also unraveled intracellular carbon flow distribution and identify key metabolic processes during lipid synthesis. Most of the metabolic kinetics also showed statistical significance for metabolic mechanism. Therefore, this study unravels the mechanisms of the glucose impact on the dynamic carbon flux distribution, thus improving our understanding of the links between carbon fluxes and lipid metabolism in The online version of this article (10.1186/s12934-019-1163-4) contains supplementary material, which is available to authorized users. Arghrospia platensis, Synechocystis sp. PCC 6803 [Chlamydomonas reinhardtii [Chlorella protothecoides [Chlorella sp. [Chlorella sp., a shift in intracellular flux distribution was predicted during transition from nutrient sufficient phase to nutrient starvation phase of growth [Microalgae have been proposed as potential platform to produce lipid-derived products, such as biofuels. Previous studies have shown that most of microalgae cells accumulate lipids when the cell division is blocked or inhibited while carbon can still continue to be fixed (such as nitrogen shortage stress conditions), which results in reduced biomass growth and in tum constraints the total lipid yield –4. Thus,PCC 6803 , Chlamydnhardtii , 12, Chlhecoides  and Chloella sp. . For insf growth . Anotherf growth , 16. Sucf growth . Howeverf growth .Chlorella protothecoides cellular metabolism was developed to describe heterotrophic culture mode. Unlike most dynamic models coping with algae physiology or steady-state metabolic level models (FBA), the current model combines both physiology and flux information in a kinetic approach. Cell growth, lipid production and almost 30 metabolic intermediates covering glycolysis, pentose phosphate pathway and TCA cycle and energetic metabolism were included and simulated. Multiple Michaelis–Menten equations are used to introduce metabolic kinetics of reaction rates and the Monod equation is used to describe the cell specific growth state, mass balances for each intermediate were considered in a dynamic profile. It can thus be used as an in silico platform for characterizing the cell lines as well as to search for ‘‘optimal’’ culture strategy through identify key metabolic processes during lipid synthesis.In the present work, a kinetic metabolic model describing Chlorella protothecoides (the Culture Collection of Alga at the University of Texas) culture in the dark was carried out in 2.8-L glass flasks with 10 g L−1 glucose as the carbon source and the modified basal medium (MBM), thus imposing a strict heterotrophic metabolism. Glucose concentration in the medium was analyzed by a biochemistry analyzer . Intracellular metabolites extraction was performed as described in previous work [Details about algae species and culture conditions can be found in a previous work . Brieflyous work  and theious work .Chlorella protothecoides cells were considered as a unique compartment with no specific intracellular compartments such as mitochondria, chloroplast, vacuoles, vesicles and nucleus. Energy metabolism was considered as a global reaction where de novo synthesis and substrate level phosphorylation were combined in a unique pathway. Reversible reactions involving storage carbon such as starch and lipid catabolism were described. The stoichiometry of the biochemical reactions of the network is based on the flux balance analysis on Chlorella protothecoides [A kinetic metabolic model was developed to describe the central carbon metabolism of a microalgae platform, including glycolysis, TCA (tricarboxylic acid) cycle, pentose phosphate pathway, total lipid synthesis, starch synthesis, amino acids metabolism, energy metabolism and biomass synthesis. The metabolic network Fig.  was firshecoides . A full The Michaelis–Menten kinetic equation is used to describe each flux rate Table . The celMi is the concentration of each metabolite at time t, a is the input flux number, b is the output flux number, Vinput and Voutput are the flux rates at each metabolite node. Vgrowth is the specific growth rate. So For each metabolite in the model, a mass balance equation Eq.  includedX is the biomass concentration. The cell specific growth rate All the ordinary differential equations of metabolites were provided as in Additional file mentclass2pt{minimThe model has 77 parameters, which include 34 maximum flux rates, 38 enzyme half-saturated constants, and 5 growth coefficients for the 5 growth precursors contributing to biomass synthesis. Initial metabolite concentrations (i.e. at t = 0) were taken from experimental data, which include 24 intracellular metabolites distributed in 8 pathways covering 30 metabolic reactions  or from Vmax, Km) for each flux and enzymes in the model were taken within ranges found in the enzyme database BRENDA (http://www.brenda-enzymes.org), and the respective units (mmol/L) were converted to comply with the model (mmol/gDW) by dividing 10 gDW/L biomass obtained in our culture. First estimates of maximal flux rates (Vmax) have been calculated from experimental data [m at time k, where the weight is the experimental data for each state variable, was used to quantify simulation error.Initial kinetic parameter values , glycine flux (VGHMT) as well as cell specific growth rate (Vgrowth)  ceased more than 24 h prior to growth cessation. Fluxes of PPP pathway and starch synthesis  that was slightly affected at glucose depletion and two catabolic fluxes (VLipase and VGPAT) which stayed quite constant (11.87–12.04 mmol gDW−1 day−1 and 0.05 mmol gDW−1 day−1 respectively)   Fig. a, it is sis Fig. c, d origsis Fig. a. For in−1DW h−1 glucose , G6PDH of 12.15 (leading to PPP pathway) and PGM of 13.83 (leading to starch), compared to 49.8 mmol g−1 DW h−1, 32.04 and 17.3 respectively in our work. The total flux to G6P obtained from our model is of 92.26 mmol g−1 DW h−1 compared that of 99.22 in literature. The net flow From F6P to GD (PFK minus FBPase) was of 73.47 mmol g−1DW h−1 compared to 70.35 mmol g−1 DW h −1 (from F6P to GAP), and the flux from GD to PEP was of 150.51 mmol g−1 DW h−1 in our model versus 148.25 (from GAP to PEP) in literature. The fluxes of nucleotides synthesis (from RX to ADP) was of 1.36 mmol g−1DW h−1 compared to 0.67 mmol g−1DW h−1 (from PRPP to DNA and RNA). Biomass synthesis rate was of 9.19 compared to 7.36 in [A closer view of flux rates were estimated at 48 h before glucose depletion in the exponential growth phase. For comparison purposes, all the flux values were normalized to an uptake flux of 100 mmol g 7.36 in .Fig. 4Fl−1DW h−1 compared to 86.14 g−1DW h−1 in literature. Although a similar total flux around the TCA cycle was obtained, with 73.93 mmol g−1DW h−1 at lipid branch and 0.08 at TCA branch, different results were reported in [−1DW h−1 at TCA cycle and 4.82 mmol g−1DW h−1 at lipid branch. This discrepancy may rely on a high lipid level (13.13% DW) in our cell culture compared to that in [13C metabolic flux analysis was accompanied with flux balance analysis in Chlorella protothecoides [Chlorella is mainly due to low TCA split-flow. In our result, lipid was fully accumulated at 72 h, so the metabolic fluxes at 72 h was also extracted from the dynamic flux profile. Along with the glucose deleption, most of the metabolic fluxes decreased in different extent, some even decreased up to 78.87% (EMP pathway fluxes). However, the re-arrangement of flux distribution from starch and energy catabolism to lipid synthesis was obvious. At 48 h, starch and ATP were accumulating (VPGM is 13.83 mmol g−1DW h−1) along with glucose assimilation. However, after glucose deleption at 72 h, starch was catablising (VPGM is − 23.25 mmol g−1DW h−1) as a storage pool and ATP was also consuming (VPPRiBP is − 0.01 mmol g−1DW h−1) to maintain other metabolism. Lipid synthesis flux (VFASN), decreased a little bit from 73.93 to 63.89 mmol g−1DW h−1 (decreased 13.51%), which was less impacted by glucose deleption compared with other fluxes on EMP pathway. This may due to a constant feeding flux converting from starch and ATP catabolism. Therefore, the energy stored in starch and ATP seams to be converted to a more stable storage pool as lipid after glucose deleption.Furthermore, downstream fluxes to AcCOA, the sum of the downstream lipid and TCA cycle flux was of 74.01 mmol gorted in  with 81. that in  (1% DW).id level .13% DW iVHK) and the glycolytic fluxes went down to a very low level after day 2.6, we have thus analyzed their related flux ratios only before glucose depletion (< 2.6 days). First, we evaluated that 6% of the glucose flux contributes to biomass synthesis and growth (Vgrowth-to-VPK ratio)   shows two successive constant values at around 60% increasing at 80% at mid-exponential growth phase (1.5 d)     Fig. a, a valuio) Fig. b. Howeve phase 1. d  to be more sensitive than affinity constants . For the final calibrated model 21 parameters, 15 maximum flux rates and 6 enzyme affinity constant  and TCA cycle parameters, showed a low sensitivity level. Since the major intersections from glycolysis including starch synthesis, PPP pathway which finally leads to nucleic acids, and TCA cycle which is related to protein synthesis, and the fatty acids synthesis which leads to lipids. The sensitive flux parameters on starch and lipid metabolism suggested that starch synthesis is the major competing pathway that affect lipid accumulation in C. protothecoides. It has also been reported in Chlamydomonas reinhardtii, when starch biosynthesis is blocked (sta6 mutant), the lipid content could be greatly boost, some can reach up to 30-fold [Vmax,FASN, Vmax,Lipase and Vmax,GPAT are also sensitive. As Vmax,FASN and Vmax,Lipase are related to lipid synthesis and degradation, they are responsible for the balance of cellular lipid pool. Meanwhile, Vmax,GPAT is in charge of providing glycerone-phosphate as the neutral lipid skeleton. The sensitivity of these fluxes gave us light on the genetic strategy for lipid yield promotion. Interestingly, there are two highly sensitive affinity constants , referring to the importance of lipid for cell biomass growth. Algae cell is a great platform accumulating lipids, some algae species could accumulate lipids up to 70% of their biomass. In C. protothecoides, the lipid content could reach 36% under heterotrophic condition, although in our work lipid content only reached 13% DCW, the sensitive of lipid affinity constant to growth suggest a huge potential to optimize the enzyme activity. Some reactions or pathways (i.e. their kinetic parameters) such as the maximum specific growth rate, PPP pathway and TCA cycle, showed a low sensitivity level, which suggest these are robust pathways. Final parameter values and the 95% confidence intervals for the sensitive parameters are shown in Table The most sensitive parameters are  30-fold . Except Chlorella protothecoides cell metabolic behavior under heterotrophic condition and describing metabolic network flux kinetics and energetic states has been developed and calibrated. Simulation results show adequate fit with experimental data. Flux analysis is also in high agreement with literature data. A sustained high lipid synthesis metabolic activity was further confirmed from model simulations with higher lipid flux and lower TCA activity. The model was also used to analyze the dynamic distribution of the carbon source to the main carbon pathways, such as PPP pathway, starch synthesis, lipid synthesis and nucleotides synthesis. In addition, as the model included a high number of parameters, it described not only experimental data, but also most of the metabolic kinetics that showed statistical significance. It can thus be used as an in silico platform for characterizing the cell lines as well as to search for ‘‘optimal’’ culture strategy either by management through rational adjustment of the main nutrient concentrations that affect glucose and/or glycine concentration with time or by genetic manipulation of certain predicted critical enzymes. However, much work remains to be done: it would be of interest to add more metabolic reactions from extracellular multiple nutrients, like ions; and separating the lipids pools to more interest classes; get larger data sets including both extra- and intracellular experimental data, to test and validate the platform as a predictive tool.A model simulating Abbreviations of metabolites names and parameter symbols were detailed in Tables Additional file 1: Table S1. Mass balances of state variables in the model.


Tetrahymena thermophila. We also provide the tools and resources to simulate different growth conditions and to predict metabolic variations. CiliateGEM can be extended to other ciliates in order to set up a meta-model, i.e. a metabolic network reconstruction valid for all ciliates.The study of cell metabolism is becoming central in several fields such as biotechnology, evolution/adaptation and human disease investigations. Here we present CiliateGEM, the first metabolic network reconstruction draft of the freshwater ciliate Ciliates are complex unicellular eukaryotes of presumably monophyletic origin, with a phylogenetic position that is equal from plants and animals. These cells represent a new concept of unicellular system with a high degree of species, population biodiversity and cell complexity. Ciliates perform in a single cell all the functions of a pluricellular organism, including locomotion, feeding, digestion, and sexual processes.After generating the model, we performed an in-silico simulation with the presence and absence of glucose. The lack of this nutrient caused a 32.1% reduction rate in biomass synthesis. Despite the glucose starvation, the growth did not stop due to the use of alternative carbon sources such as amino acids.The future models obtained from CiliateGEM may represent a new approach to describe the metabolism of ciliates. This tool will be a useful resource for the ciliate research community in order to extend these species as model organisms in different research fields. An improved understanding of ciliate metabolism could be relevant to elucidate the basis of biological phenomena like genotype-phenotype relationships, population genetics, and cilia-related disease mechanisms.The online version of this article (10.1186/s12859-018-2422-9) contains supplementary material, which is available to authorized users. Caenorhabditis elegans to predict genes essentiality glucose than in the same experiment with [l4C]glycerol, i.e. 43.9–64.2 nmol/106 cells versus 19.8–19.9 nmol/106 cells. Stein et al. [l4C]glucose than from pyruvate, glutamate and glycerol. The measured values of the labelled carbon incorporation into the CO2 product were as follows: [l4C]glucose (106–255 nmol/106 cells); [l4C]pyruvate (55.4–121 nmol/106 cells); [l4C]glutamate (1.90–3.96 nmol/106 cells); [l4C]glycerol (11.0–13.6 nmol/106 cells). Then, they tested glycogen as product, and the [l4C]glucose resulted the preferred substrate with the highest incorporation rates (313–496 nmol/106 cells). A comparable incorporation rate between glycerol and pyruvate was reported (~ 6.5 nmol/106 cells), as well as an absent incorporation from glutamate (< 0.2 nmol/106 cells). The results differed from those of CO2 among the secondary substrates , but agreed on the highest used substrate (glucose).One of the first experimental in vitro studies of metabolic pathways in ciliates were performed using organism –30. Tetrz et al.  reportedOur initial simulation showed the maximum growth when glucose, glutamate, glycerol and pyruvate were used as growth media. In agreement with literature , 30, simAeropyrum pernix K1  and R00381 [ATP + DNA(n) + 5’-Phospho-DNA(m) < => AMP + Diphosphate + DNA(n + m)] showed the highest positive and negative values. Both reactions are catalysed by DNA ligases during DNA repair or recombination. These two reactions differ only on ATP or ADP cofactors. ATP is the phosphorylated form of ADP and is the preferred cofactor because phosphoanhydride bonds store a high amount of energy. Since ATP is synthesised from ADP during glycolysis by phosphorylation, we can speculate that in absence of glucose DNA ligation by ligase is performed by using ADP molecules as an alternative. DNA ligase using ADP has been reported from the aerobic hyper-thermophilic archaeon ernix K1 .Other opposite values were reported for reactions R02549  and R01986 , both involving in the arginine-catabolism pathway. Like R10822 and R00381 they only differ on cofactors, in this case NAD and NADP. While the structural difference between these two molecules is only the phosphate group, NADH participates in catabolic reactions, i.e. reactions that break down molecules to release energy, while NADPH participates in anabolic reactions, namely those that consume energy in order to build up or synthesise larger molecules.Finally, reactions R04241 (ATP + THF-polyglutamate(n) + L-Glutamate <= > ADP + Orthophosphate + THF-polyglutamate(n + 1)) and R04242 (THF-polyglutamate + n H2O < => Tetrahydrofolate + n L-Glutamate) showed an increase of biomass production in the simulation without glucose. Both reactions are involved in folate biosynthesis, an essential cofactor for DNA and amino acid synthesis.Tetrahymena have led ciliate researchers to landmark discoveries on cellular mechanisms, including the first cytoskeletal motor [Studies on al motor  and progal motor . Some otal motor , has beeal motor .CiliateGEM and we constructed a metabolic model draft of T. thermophila, made freely available to researchers. We checked the model robustness by simulating the organism cell growth in the presence or absence of glucose (the preferred carbon source utilised by this organism). Then we compared glucose to glutamate, pyruvate and glycerol consumption. Despite the organisms shows a decreased rate of biomass production, the growth without glucose is maintained by using alternative carbon sources, as amino acids and the tetrahydrofolate synthesis . This metabolic variation appears to increase the activity of ADP dependent DNA ligase I, possibly involved in DNA repair or recombination (R10822). We also speculated that in absence of glucose, ADP may be the preferred energy molecule instead of ATP, and the metabolism is switched towards catabolic reactions in order to obtain energy for growth. Our results showed consistency with previous studies on Tetrahymena pyriformis [In this study, we set the basis of the open project riformis –30, therCiliateGEM is freely available for academics and can be fully customised and updated. Our long-term objective is to achieve a “meta-model” that can be applied for metabolic simulations using other ciliates genomic resources , and consequently to optimise the design of experimental protocols. Although this study focuses on T. thermophila, the findings may be shared with other ciliates as Paramecium and Euplotes species, including Antarctic strains [CiliateGEM are common in algae). Moreover, the complex systems of ciliates membranous compartments associated to phagocytosis and trafficking is strictly correlated with nutrients uptake and metabolism [ strains . In the  strains  , the availability of “omic” resources allows reliable modelling of metabolic fluxes. Tools and resources obtained for Tetrahymena can then be applied to other ciliates and can elucidate genotype-phenotype mapping. A key point of this project is to create a collaborative network useful to expand and validate CiliateGEM with in vitro experiments on metabolic reactions already collected inside the model, and ultimately add new reactions. Collaboration between research teams in this type of projects is essential to speed up the process and to join the expertise of different research groups. A consensus modelling approach, already successfully adapted for many metabolic reconstructions including human and yeast, might help researchers to understand the ciliate differences at genera, species, subspecies and populations level. Furthermore, with a combination of shared resources, it will be possible to study full ecosystems . These models, coupled with multi-omic approaches [In conclusion, our work confirms that proaches , could aCiliateGEM was created using the protocol described by Thiele and Palsson [Tetrahymena protein-encoding genes from the macronuclear genome assembly and then to assign the functional roles through accurate annotation with EC (Enzyme Commission) number. Next, a list of metabolic reactions was generated from KEGG and converted into a stoichiometric matrix. KEGG was used to search for Tetrahymena pathways and all the reactions were manually assembled. The specific links are listed in Additional file Tetrahymena metabolism , was us studies –30. This studies .S ⋅ v = 0 holding for internal metabolites , where the vector v represents the flux distribution. An internal metabolite constitutes the model but cannot be imported or exported directly. An “exchange” counterpart of that metabolite, and an exchange reaction are required for modelling import/export reactions. Exchange metabolites can be imported and exported from the system, so they do not satisfy the steady state assumption. The flux distribution v is a vector of reaction rates and represents a feasible flux of metabolites through the reaction network, where under the principle of mass conservation, the total amount of metabolite consumed and total amount of metabolite produced are equal to zero.FBA requires reactions to be represented as a stoichiometric matrix (S), with dimensions of m × n, where the metabolites (m) are represented as rows and the reactions (n) are represented as columns. The stoichiometric matrix is a numerical matrix of stoichiometric coefficients for each metabolite participating in a reaction. FBA assumes the system to be in a pseudo-steady state Vmax) and lower (Vmin) bounds on the range of values that the flux of each reaction can have (Vmin ≤ v ≤ Vmax). These constraints define the space of allowable flux distributions at which every metabolite is consumed or produced by each reaction in the system. Such flux bounds, coupled with gene-protein-reaction association rules, can also be used to map multiple environmental conditions, using binary threshold-based or continuous valve-based approaches [v, and a vector of weights c, measuring how each component in the network contributes to the production of a biologically desirable phenotype.In FBA, directionality and capacity constraints are placed on individual reactions by defining the upper , and 84 boundary steps (which represent the input and output of metabolites from extracellular space and a biomass reaction). The biomass reaction defines a unique objective function to effective growth. This describes the rate at which all of the biomass precursors are made in the correct proportions. Linear programming was used to calculate the optimal flux distribution that maximizes growth. The model was encoded in SBML format [L format  and fulfL format . It was L format  toolbox Additional file 1:Databases and bioinformatics resources for ciliates –54. Additional file 3:Links of the KEGG reactions. (XLSX 11 kb)Additional file 4:List of reactions. (XLSX 98 kb)Additional file 5:Simulation file. (XLSX 226 kb)Additional file 6:SBML version of the model. (XML 716 kb)Additional file 7:Matlab version of the model. (MAT 48 kb)


Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen’s adaptation to the intra-host hypoxic environment.M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen.The newly identified HPIs suggest the pathogen’s ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection.The comprehensive host-pathogen interaction network, a Boolean model of The online version of this article (10.1186/s12864-018-4947-8) contains supplementary material, which is available to authorized users. Mycobacterium tuberculosis can evade the immune response and remain in a dormant (or latent) stage within the host cell for prolonged periods , without causing any symptoms before reactivation [M. tuberculosis undergo several physiological and metabolic changes [M. tuberculosis against hypoxic (low oxygen) conditions is one of the primary features characterizing its state during latency [M. tuberculosis survival strategies, including changes in gene regulatory cascades as well as metabolism, would therefore remain incomplete unless studied in context of the host-pathogen interactions.Invasion by pathogens is known to trigger an array of immune responses in the host. Pathogens, in turn, try to counter-act, subvert, or alter these responses , 2. The tivation , 5. To a changes . An elab latency –9. MycobM. tuberculosis inside human macrophage cells have been investigated in this work. (I) A comprehensive host-pathogen interactome network, depicting the potential interactions between an invading M. tuberculosis cell and a human macrophage, has been built. (II) A Boolean model of the gene regulatory network controlling hypoxic response in M. tuberculosis has been constructed and simulated to understand the progression of hypoxia and its downstream regulatory effects. (III) The metabolic changes experienced by a M. tuberculosis cell during hypoxic conditions have been simulated using FBA  models of M. tuberculosis metabolism. Subsequently, the cross-talks between these aspects have been investigated to understand how the cellular and metabolic changes are driven by the gene regulatory events as well as the host-pathogen interactions.The following aspects pertaining to intra-cellular hypoxic survival of A template protein-protein interaction (PPI) library was constructed by collating PPI related information from different public resources and potential HPIs between human and Mtb proteins was obtained by interlogs mapping approach  were investigated in context of the KEGG pathways , in ordedosR regulon and enduring hypoxic response (EHR), was identified to be involved in hypoxic response through literature mining and were integrated into a comprehensive gene regulatory network. The role of transcription factors (TF) network which orchestrates the gene regulatory events during Mtb hypoxic response was analysed using a multi-level Boolean modelling  which were observed in an earlier reported study [A set of 286 genes . Interestingly, despite being a high degree TF with a known role in hypoxic response, the dosR mutant did not exhibit any appreciable changes in the pathway enrichment patterns, when compared to the wild type Mtb. This indicates that, although DosR plays a crucial role in initiating hypoxic response, its role in maintenance of bacteriostasis and enduring hypoxic response is probably not very significant. An earlier study had also indicated a nominal effect of dosR deletion on bacteriostasis under hypoxic environment [In addition to studying the static properties, the dynamic properties of the gene regulatory network (GRN) were also investigated through simulation of ironment . On the dosR regulon as well as enduring hypoxic response (EHR), provided further interesting insights into pathway level changes that are likely to occur in Mtb under hypoxic stress. Despite a large set of genes pertaining to both dosR regulon (33 out of 57) and enduring hypoxic response (EHR) (160 out of 230) remains unannotated till date, a majority of the ‘annotated’ set was observed to be involved either in metabolic processes or stress response pathways. A pathway enrichment analysis was performed in order to identify the up and down regulated pathways under hypoxic stress  and glpQ1 (Rv3842c). OtsB1 has been suggested to act as a trehalose monomycolate phosphatase, which participates in the synthesis of a cell-wall component arabinogalactan-mycolate [The results of the analysis performed on the genes for initial hypoxic response further revealed enriched pathways related to stress response and carbohydrate metabolism. Considering the imminent slower growth phase the pathogen is likely to encounter in the subsequent EHR stage, enhanced carbohydrate metabolism is contrary to expectations. Thus exact functional roles of the genes contributing to the enriched carbohydrate metabolic process were investigated. Only two initial hypoxic response genes were observed to be associated to this process, namely, mycolate . Howevermycolate . These odosR and EHR regulons during hypoxic adaptation. While the pathogen, when exposed to host intra-cellular environment, initially employs certain stress response mechanisms to counter the host immune system, later stages of hypoxia is additionally characterized by gene regulatory events leading to a slower (or arrested) growth and a preference for lipids as energy source.Pathways which were observed to be differentially ‘enriched’ during early and enduring hypoxic response hinted at the distinct responsibilities shouldered by in silico flux balance analysis (FBA) study  gene-knock out studies was performed.In order to adapt to the host intra-cellular environment , the invading Mtb is expected to undergo substantial changes in its metabolism. We made an attempt to identify the key enzymes which could be pivotal in bringing about these changes. A systematic gene knock-out simulation study under low oxygen conditions was performed using the iNJ661 FBA model after incorporating the missing reactions for cholesterol metabolism. Figure Δfba, Δpgi, ΔglpX, Δgap, Δpgk, and Δtpi mutants). In particular, the bacterial cells failed to grow in the absence of fba, pgi, and glpX. It may be noted that these three enzymes, apart from contributing to glycolysis/ gluconeogenesis pathways, are also key components of the pentose phosphate shunt pathway (PPP) which has been reported to play an important role in Mtb survival under low pH conditions and oxidative stress [ΔglpK and ΔfolD mutants. glpK encodes for glycerol kinase and is a key enzyme in the regulation of glycerol uptake and metabolism [ΔglpK Mtb cells [ΔfolD mutants are severely compromised for growth under intra-cellular hypoxic conditions. In addition, mutations pertaining to enzymes from the TCA cycle, oxidative phosphorylation, and pyruvate metabolism were also seen to affect the growth of the Mtb cell to a certain degree. To summarize, certain enzymes corresponding to the PPP were found to be crucial for the survival of Mtb during hypoxia. Re-routing of metabolic flux through the PPP generates reducing equivalents (in the form of NADPH) which may help the Mtb cells to counter the oxidative stress asserted by the host during hypoxia.The growth of the simulated Mtb cell was seen to be most affected in the mutants lacking genes from the glycolysis or gluconeogenesis pathways , can influence other biological pathways/ processes, gene ontology (GO) enrichment analyses were performed  was observed to be up-regulated only during the early time points of infection. It is likely that intermediates of carbohydrate catabolic process are utilized for generation of ATP during early stages of infection. However, since prolonged hypoxia-induced glycolysis may have toxic effects on mycobacteria , a subsePentose-phosphate shunt pathway (GO:0006098) was also observed to be enriched during the early stages of infection. This pathway is known to provide reducing equivalents for reductive biosynthetic reactions in Mtb , and it’The iron-sulfur cluster assembly process of Mtb (GO:0016226) was found to be up-regulated during all stages of infection. Given that vital cellular processes like electron transport, energy metabolism and DNA synthesis require iron-containing cofactors, this observation is in line with expectations. In addition, it is also likely that this up-regulation confers resistance to iron limitation and oxidative stress, as suggested in an earlier report .While evaluating the influence of hypoxic gene regulation on cellular processes, the set of proteins which are downstream to the transcription factors controlling hypoxia, and were ‘active’ (i.e. ‘turned-on’) during the hypoxic steady state (obtained through Boolean model simulations – see Supplementary Results), were considered for GO enrichment. The enriched GO terms thus obtained consisted of pathways like sulfate assimilation, metabolic compound salvage, cell cycle, etc.  at late stage of infection. The role of pyrimidine salvage pathway in intra-cellular survival of Mtb at latent state has been implicated earlier . Thus thIn addition to the above mentioned pathways, cell cycle (GO:0007049) was also observed to be enriched, as well as up-regulated. Since this observation is contrary to the anticipated non-replicating physiology of Mtb inside the host cell, the exact roles of the genes/ proteins contributing to the observed enrichment were investigated. Out of six such proteins, three  were found to be homologs of a cell division protein FtsK. These proteins have been reported to belong to the ESAT-6 Secretion System (ESX), involved in virulence and viability of the pathogen , 40. WagOverall, the above observations suggest an up-regulation/ enrichment of cellular functions in Mtb which are helpful to counter the hostile environment of the host macrophage. This includes pathways for cell wall synthesis, sulfur assimilation, and oxidative stress mitigation. In addition, in an attempt to minimize its energy expenses, the Mtb cells probably utilize salvage pathways, especially for acquisition of nitrogen, iron and sulfur when present inside the macrophage.The previous sections pertained to understanding three different aspects of Mtb adaptation to the host intra-cellular environment. While the Mtb proteins involved in HPIs are expected to play a major role in sensing the host environment, its GRN is likely to govern the necessary adaptive changes. In other words, the shift in the metabolic paradigm during intra-cellular/ hypoxic adaptation can be expected to be driven by an altered flow of information through the host-pathogen interactome network and the gene regulatory cascade.In order to gauge the possible paths of information flow through and between (A) HPI-network, (B) the Mtb hypoxic-GRN, and (C) the hypoxic-metabolism network of Mtb during its sustenance inside the host cell, a ‘shortest-path analysis’ connecting individual components of the functional modules A, B and C was performed  connected the components of (A) HPI with (C) the enzymes identified to be the key modulators of metabolic flux during hypoxia. In contrast, the flow of information between (B) the TFs from hypoxic-GRN and (C) the metabolic enzymes, was mediated through 4035 shortest-paths. 2554 shortest-paths were observed between (A) the HPIs and (B) the hypoxic-GRN. One would expect that (B) the gene regulatory network controlling hypoxic response would have a significant effect on (C) the hypoxic metabolism. However, the above observations suggest that the effect of (A) the HPIs on (C) hypoxic metabolism may be far more pronounced. The findings are probably indicative of the fact that many TFs that could be responsible for adaptation of Mtb to hypoxia are not yet known or characterized. Consequently, the shortest paths between (A) HPI and (C) hypoxic-metabolism were screened to ascertain whether they consisted of any TFs which have not been reported to be associated with hypoxic gene regulation in any earlier study. Six Mtb TFs could be identified which are not part of (B) the hypoxic-GRN presented in this attempt. Two of these TFs, viz. Rv3676 and Rv3862c have been implicated in hypoxic response in recent studies , 44. HowTo further assess whether the connections (shortest paths) between (A) the HPI network and (C) metabolism were actually relevant in driving metabolic changes during hypoxia, the lengths of the paths connecting each of the metabolic enzymes (to the HPI network) with the fold-changes observed in the corresponding reaction fluxes during hypoxia were computed. The observations Fig.  indicateM. tuberculosis H37Rv (Mtb) during the course of its adaptation to the host intra-cellular environment, we have analyzed various pertinent aspects using different computational approaches. These include – (i) the components (genes/proteins) that participate in interactions between the pathogen (Mtb) and the human (host), (ii) the Mtb gene regulatory network that may drive the adaptation of Mtb to hypoxia, and (iii) alterations in the metabolic pathways of Mtb in response to hypoxia.In order to obtain a holistic view of the molecular changes that occur in Mtb as well as human components involved in the predicted set of HPIs were observed to be substantially connected, probably helping it to trigger a cascade of molecular events that are required for intra-cellular adaptation of Mtb once it senses the changes in environment. While some of these predicted HPIs provided important insights into probable pathogenic strategies , others indicated at the host immune response  , which was observed to be a hub regulator of EHR. Given the known role of copper in mitigating oxidative stress (through SodC-mediation) in Mtb [A key aspect of Mtb’s survival in the intra-cellular environment is its adaptation to hypoxia. Earlier studies dealing with this aspect of Mtb survival had identified several genes involved in the process through computational as well as experimental studies –9. Given) in Mtb , furtherThe hypoxic metabolism of Mtb was studied using FBA simulations. In contrast to earlier attempts , a minimIn addition to looking at the genes/ proteins/ enzymes directly involved in the investigated HPI, GRN or the metabolic network, the functional aspects of their neighbours were assessed in order to understand the influence of the former on other cellular processes. Despite no overlap between the constructed HPI network and the 24 TFs controlling hypoxic gene regulation, key cellular processes enriched in the ‘downstream’ PPI cascade of both these networks pertained to iron and sulfur assimilation and carbohydrate metabolism pathways involved in synthesis of cell wall components. Other processes associated with the HPI network which probably play an active role during intercellular survival of Mtb included PPP and synthesis of precursor metabolites. The hypoxic-GRN on the other hand had sulfate uptake, metabolic salvage pathways, and cell cycle amongst its list of downstream pathways/ processes that were seen to be modulated during hypoxia. While sulfate uptake and metabolic salvage pathways have obvious implication in a phagocytosed Mtb cell sourcing precursor metabolites from the host, redirection of flux towards cell wall synthesis and gluconeogenesis have earlier been reported in other studies , 47 as win silico metabolic simulations in this work suggest that mutant strains of Mtb lacking genes for glycolysis or gluconeogenesis and PPP could be susceptible to impeded growth/ metabolism during hypoxia. Experimental validation of the above observation, by studying Δfba, Δpgi, and ΔglpX mutants in particular, can provide further understanding of Mtb metabolism during latency and stress.Experimental validation of certain interesting regulatory and metabolic aspects identified in this study can potentially enrich our current understanding of tuberculosis infection. Amongst the identified HPIs, proteins pertaining to two of the predicted interactions (‘Human Visfatin-Mtb GGT’ and ‘Human NINJ1-Mtb NarH’) with suspected role in subversion of host defence mechanisms, have been partially validated based on available literature and are potential candidates for further experimental validation. Experiments directed towards understanding the interaction dynamics of another predicted HPI, ‘Human p53-Mtb YchF’, would be intriguing given the critical role of p53 in cell cycle and virulence associated role of YchF. Further, validation of predicted interactions involving Mtb SodA and human immune system proteins could offer a new perspective on SodA-mediated mechanisms of immune modulation. With respect to the hypoxic gene regulatory network, the predicted crucial roles of the Mtb regulators CsoR and Rv0324 would be interesting aspects for experimental validation, which may aid in designing control strategies against latent tuberculosis. The Mycobacterium tuberculosis have been investigated in the current study. The insights obtained from the three connected aspects of intra-cellular survival of Mtb are expected to augment our current understanding of tuberculosis infection and guide in experimental design towards deeper investigations into host-pathogen interplay. In addition, the host-pathogen interaction network, the Boolean model of Mtb hypoxic gene-regulation, as well as the genome scale metabolic model of Mtb, presented in this study are expected to be useful resources for future studies on tuberculosis infection.The role of HPIs and Mtb transcription factors in driving metabolic changes during hypoxic adaptation of A detailed methodology adopted for the various analysis performed in this study is provided in Additional files dosR regulon genes, (ii) enduring hypoxic response (EHR) genes, and (iii) unclassified (genes lacking any time series expression data) as mentioned in Additional file A total of 286 genes/ proteins associated to hypoxia were mined from literature and subsequently categorized based on the time of gene expression into one of (i) The available whole genome metabolic network of Mtb, iNJ661  was used in the present study  the HPI-network, (B) the Mtb hypoxic-GRN, and (C) the hypoxic-metabolism network of Mtb were computed from the Mtb PPI network  proteins. (DOCX 96 kb)Details of the method used for prediction of host-pathogen interactions (HPIs) between human and Additional file 2:M. tuberculosis H37Rv (Mtb) cells. (DOCX 50 kb)Details of the Host-Pathogen Interactions (HPI) that were predicted between human and Additional file 3:M. tuberculosis H37Rv (Mtb) and (B) human gene expression at different time-points post infection. The mean of expression values of a gene from different experimental datasets (collected from literature) has been considered. Expression values exhibiting at least 2-fold differential expression are indicated. (XLSX 2703 kb)Perturbations in (A) Additional file 4:Human pathways  associated with the host-pathogen interaction network. (DOCX 635 kb)Additional file 5:M. tuberculosis H37Rv. (XLSX 14 kb)List of genes involved in hypoxic response regulation of Additional file 6:M. tuberculosis H37Rv. (DOCX 412 kb)Detailed method for analysis of gene regulatory network (GRN) controlling hypoxic response in Additional file 7:M. tuberculosis H37Rv collated from literature. (XLSX 102 kb)List of interacting “transcription factor - gene” pairs of Additional file 8:Comparison of results of the multi-level Boolean model simulation with experimentally obtained gene expression data. (DOCX 71 kb)Additional file 9:M. tuberculosis H37Rv. (DOCX 14 kb)Results of the analysis on gene regulatory network (GRN) controlling hypoxic response in Additional file 10:M. tuberculosis H37Rv metabolism during hypoxia. (DOCX 27 kb)Details of the adopted method and corresponding results of Flux Balance Analysis (FBA) of Additional file 11:M. tuberculosis H37Rv (Mtb) which was used for metabolic simulations in the current study . (XML 1043 kb)The metabolic network model of Additional file 12:M. tuberculosis H37Rv which were significantly perturbed (over 2-fold) during hypoxia as compared to aerobic condition (results obtained through FBA simulations) (.xlsx format). (XLSX 14 kb)List of metabolic reactions in Additional file 13:M. tuberculosis H37Rv proteins involved in HPIs are listed. Up-regulation or down-regulation of the biological processes during early and late infection time points, as inferred from the expression profiles of the genes constituting the respective processes, are also indicated. (XLSX 13 kb)Significantly enriched GO biological process terms in the set of Additional file 14:M. tuberculosis H37Rv proteins, which were observed to be ‘active’/ switched ON in the ‘hypoxic’ stable state. This stable state was obtained through simulation of Boolean model corresponding to the gene regulatory network collated in the present study. (XLSX 10 kb)Significantly enriched GO biological process terms in the set of Additional file 15:M. tuberculosis H37Rv hypoxic- gene regulatory network, and (C) the hypoxic-metabolism network of M. tuberculosis H37Rv. (DOCX 19 kb)Details of the methodology adopted for identifying the shortest-paths among (A) the HPI-network, (B) the Additional file 16:M. tuberculosis H37Rv (Mtb) interaction network demonstrating the cross-talks between (A) HPI-network, (B) the Mtb hypoxic-GRN, and (C) the hypoxic-metabolism network during hypoxic adaptation. (XLSX 179 kb)Additional File 17:M. tuberculosis H37Rv (Mtb) interaction network demonstrating the cross-talks between (A) HPI-network, (B) the Mtb hypoxic-GRN, and (C) the hypoxic-metabolism network during hypoxic adaptation . (CYS 313 kb)


Background: The integration of computational and mathematical approaches is used to provide a key insight into the biological systems. Through systems biology approaches we seek to find detailed and more robust information on Leishmanial metabolic network. Forman/Forman-Ricci curvature measures were applied to identify important nodes in the network(s). This was followed by flux balance analysis (FBA) to decipher important drug targets.Results: Our results revealed several key high curvature nodes (metabolites) belonging to common yet crucial metabolic networks, thus, maintaining the integrity of the network which signifies its robustness. Further analysis revealed the presence of some of these metabolites, MGO, in redox metabolism of the parasite. Being a component in the glyoxalase pathway and highly cytotoxic, we further attempted to study the outcome of the deletion of the key enzyme (GLOI) mainly involved in the neutralization of MGO by utilizing FBA. The model and the objective function kept as simple as possible demonstrated an interesting emergent behavior. The non-functional GLOI in the model contributed to “zero” flux which signifies the key role of GLOI as a rate limiting enzyme. This has led to several fold increase production of MGO, thereby, causing an increased level of MGO•− generation.Conclusions: The integrated computational approaches have deciphered GLOI as a potential target both from curvature measures as well as FBA which could further be explored for kinetic modeling by implying various redox-dependent constraints on the model. Furthermore, a constraint-based FBA on a larger model could further be explored to get broader picture to understand the exact underlying mechanisms. Designing various in vitro experimental perspectives could churn the therapeutic importance of GLOI. FBA calculations rely on the assumption of steady-state growth and mass balance and can be used to predict the flux of a metabolite flowing through a metabolic network. To predict the growth rate or to find a specific cellular function, FBA tries to find possible solutions to optimize the stated objective function(s) and gives quantitative insights into the genotype-phenotype relationship of the model. Although, FBA is incapable of predicting the concentrations of metabolites and does not account for any regulatory effects like activation/inactivation of enzymes or gene-expression regulations, there are extensive embodiment of literature showing its applications in many fields including drug target identification . A value of “1” was assigned for connection, if a metabolite (substrate) is connected (producing or converting into) to other metabolite (product), and “0” for no connection.The reconstructed bipartite directed graphs were visualized in Cytoscape (3.6.0) , In Forman curvatureFI(v) and Out Forman curvatureFO(v) were computed as follows:To distinguish between incoming, And, the total flow on a given node was obtained as:FR(v)   and FRO(v) are In and out Forman-Ricci curvature, respectively, deg(v) is in and out-degree and F(ev) is the Forman curvature of edge ev, and ev ~ v represents the set of edges incoming or outgoing on that node v. Subsequently, total flow on node v was obtained as follows:Where, For the reconstruction of stoichiometric model COBRA Toolbox , S is the stoichiometric matrix of The reactions for the model were input in COPASI v4.19.140 , electron carriers , Waters (H2O), and electron transporters  with high betweenness centrality were part of M7 model  and unnormalized (Forman-Ricci) Forman curvatures of directed edges and nodes were calculated in all reconstructed models . Forman M” model   except M3 (−0.576), the correlation between Forman curvature of nodes and clustering coefficient was very weak negative in comparison to correlation between Forman curvature and degree. Interestingly, the highest negative correlation between Forman curvature and degree was mainly noted for model M5, M6 and M7  . FurtherOn the other hand, a good negative correlation of betweenness centrality with Fi and FRi was observed only in case of M5, M7 and M4, M7, respectively. Surprisingly, in contrast to Fi and FRi, correlation for Fo and FRo with out-degree and betweenness centrality was noted very weak to moderate negative. Among the small scale models, only M7 indicated high negative correlation for Fo and FRo with betweenness centrality. It was found similar to the correlation for betweenness centrality and In- and Out-degree for M7 model (high positive correlation) .backbone of the network those act as bridges between major network communities  via Forman curvature and Forman-Ricci curvature measures was found important for maintaining the integrity of the master model  consisting of the pseudo consumption of precursors from different pathways present in the model. To observe the effect of metabolism of MGO, substrate for glyoxalase I (GLO I), on the system; hence, we focused on the formulation of objective functions (OF) by taking into account the metabolites only used in the model:The end product of glyoxalase pathway is D-lactate that ultimately leads to the formation of pyruvate. For simplicity, the stoichiometric coefficients of the precursors and metabolites used in formulation were kept as default. We considered, for our model, the end products of both the pathways, glycolysis (pyruvate) and glyoxalase pathway (D-lactate), contributing to the total biomass production. The system was investigated under two scenarios, first, functional GLO I, and second, nonfunctional (absent from the system) GLO I  formed at very minimal level. Although, both the pathways carried different flux levels, the maximization of OF was seemed to be majorly contributed through MGO synthesis and GLO I/II activities . Other m•− free radicals  was constructed by incorporating the reactions from glycolysis, Glyoxalase system and T[SH]2 non-enzymatically, we assumed that the introduced inhibition reaction, along with level of HTA, should also affect the level of MGO. However, when the comparison was made between non-inhibited and inhibited models, it was observed that even after the incorporation of inhibition reaction for GLOI, only HTA was accumulated in the system and not MGO  have very important role in the regulation of redox homeostasis of the parasite. MGO is highly cytotoxic carbonyl and can irreversibly modifies DNA, proteins/enzymes , to S-D-lactoyltrypanothione, followed by GLOII mediated D-lactate formation  have been studied in a piecewise manner considering functional and non-functional GLO(I). We are well aware that there might be a change in metabolite vs. enzyme levels and any feedback introduced at all enzyme levels in the pathway of interest may give rise to different phenotype and correspondingly stable or sustained oscillations. Having said that, with our simplified model using a mathematical formalism wherein output depends on only two conditions in the pathway: functional and non-functional GLO I, these studies have been taken into consideration. Moreover, a measure of robustness in metabolic network accounts for the relative changes in the concentration of metabolites to all other metabolites. Our reduced model with local perturbations has a larger impact in a restricted sub-network model system which is able to absorb the impact which a whole system model might not be able to bear.in silico analog of the curvature measure and FBA. Therefore, the strategy outlined here may become an indispensable tool in future for analyzing smaller sub-network models created from a larger model. Using a model like this amounts to endowing the system with enhanced robustness and investigate how the dynamics might get affected. GLO I as a target deciphered in present work can further be validated through kinetic modeling and experimental validation to pinpoint its influence on direct or indirect metabolites.Having stated above, for most part of the reconstructed network, taking into account the different point of observations, interaction dynamics does not drastically changed. The integrated analysis laid in this paper from computational perspectives helped pinpoint an interesting phenomenon that the underlined network model structure is largely preserved across the large common substructure models. Nonetheless, there might be instances at which major changes can occur in the network often in response to the metabolic or environmental stress. With the theoretical framework laid in this manuscript we are able to represent an In the quest to improve the current understanding of biological networks we have devised a newer strategy of formulation of curvature measure along with FBA in order to explain and predict biological targets. The methodology adopted in this paper has enabled us to determine the implicit relationship between metabolites. The model prototype developed in current paper largely depends on its structure and topological components. On the broader note, we have developed our mathematical model in an iterative fashion to optimize the flux of the metabolic system under limitation of certain enzymatic reactions thus leveraging the unifying framework for curvature measures along with FBA. GLOI deciphered as an important target both from curvature as well as FBA enabled our deeper understanding into the parasite redox metabolism.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.SS laid the design idea and implementation of the project. NC performed the computational modeling. NC and SS wrote the original manuscript and approved 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.


Here we find that Intrasporangium calvum C5, a novel dual-pathway denitrifier/respiratory ammonifier, disproportionately utilizes ammonification rather than denitrification when grown under low C concentrations, even at low C:NO3− ratios. This finding is in conflict with the paradigm that high C:NO3− ratios promote ammonification and low C:NO3− ratios promote denitrification. We find that the protein atomic composition for denitrification modules (NirK) are significantly cost minimized for C and N compared to ammonification modules (NrfA), indicating that limitation for C and N is a major evolutionary selective pressure imprinted in the architecture of these proteins. The evolutionary precedent for these findings suggests ecological importance for microbial activity as evidenced by higher growth rates when I. calvum grows predominantly using its ammonification pathway and by assimilating its end-product (ammonium) for growth under ammonium-free conditions. Genomic analysis of I. calvum further reveals a versatile ecophysiology to cope with nutrient stress and redox conditions. Metabolite and transcriptional profiles during growth indicate that enzyme modules, NrfAH and NirK, are not constitutively expressed but rather induced by nitrite production via NarG. Mechanistically, our results suggest that pathway selection is driven by intracellular redox potential (redox poise), which may be lowered when resource concentrations are low, thereby decreasing catalytic activity of upstream electron transport steps  needed for denitrification enzymes. Our work advances our understanding of the biogeochemical flexibility of N-cycling organisms, pathway evolution, and ecological food-webs.Respiratory ammonification and denitrification are two evolutionarily unrelated dissimilatory nitrogen (N) processes central to the global N cycle, the activity of which is thought to be controlled by carbon (C) to nitrate (NO The env3−) , have ga3−)  due to i3−) . Isolati3−) , respira3−) , and pro3−) . Understcd1 nitrite reductases used in denitrification receive electrons from a soluble electron carrier  via the bc1 complex  heme-based cytochrome c nitrite reductase used in respiratory ammonification receive electrons directly from the quinone (Q) pool  while  drive respiratory ammonification while low C:NO3− ratios (stoichiometric limitation of C relative to nitrate) drive denitrification. Denitrification theoretically yields more free energy per electron, but respiratory ammonification yields more free energy per nitrate due to reaction stoichiometry: one nitrate is needed to make ammonium while two are needed to make N2O/N2 .It has been postulated that C:NOfication . This hye N2O/N2 . Thus, w3− control on pathway selection. While dual-pathway nitrite reducers are capable of differential energy conservation through pathway bifurcation of nitrite, no mechanistic or metabolic models exist to explain how these dual-pathway organisms partition electron flow and proton translocation between pathways to maximize resource-use efficiency. It is not known if these organisms differentially distribute electron flow through each pathway in response to shifting resource availability or control each pathway independently in response to resource thresholds. In order to resolve the mechanisms of C:NO3− control on pathway selection and better understand branched respiratory chains in LP-based N-reducing organisms, we undertook the characterization of Intrasporangium calvum C5: A Gram-positive Actinobacterium and dual-pathway nitrite reducer that uses MK as sole pool quinone. We show that over a range of C:NO3− ratios (0.1–4), duplicated at two substrate concentrations (mM–μM ranges), batch cultures of I. calvum disproportionately utilize its ammonification pathway during low C concentrations (≤ 0.4 mM lactate), when C:NO3− ratios are < 1 (an observation contrary to the current paradigm). Using a genome-guided approach coupled to time-series transcriptomics and metabolite profiles, we identified differentially expressed genes in the bacterium’s ETC and central metabolic pathways. Using this information to inform a metabolic reconstruction of the ETC and extensive literature on the biochemistry of the bc1 complex, we propose a new mechanism by which these two pathways are regulated at the biochemical level.Dual-pathway respiratory nitrite reducers, where respiratory ammonification and denitrification modules are encoded in the same genome, provide a unique system to investigate the molecular mechanisms of C:NOIntrasporangium calvum was isolated from a groundwater well (GW247) collected on 2/18/2013 at Oak Ridge National Laboratory  with a groundwater temperature of 15.57°C, conductivity of 521.9 μS/cm, and pH of 7.71. I. calvum C5 was isolated and obtained from Dr. R. Chakraborty .4Cl (1.4 mM) (for ammonium replete conditions but not used in NH4+-free conditions), MgCl2 (0.2 mM), CaCl2 (0.04 mM), KCl (0.1 mM), K2HPO4 (1.1 mM), NaHCO3− (30 mM), cysteine (1 mM) as reducing agent, resazurin as redox indicator, and trace elements and trace vitamin solutions as reported .All cultures were grown at 30°C and shaken at 250 rpm. Nitrate reducing minimal media was prepared with the following final concentrations: NaCl (0.06 mM), NHreported . 1 M stebased on  where thbased on . See SI based on , measure600 nm . All experiments began with cells grown in the same media for each respective C:NO3− ratio/concentration treatment, which were harvested from late exponential early/stationary phase. This method ensured that the inoculum was in the same nutrient-limited metabolic state when inoculated into fresh media for each respective treatment. Thus, the inoculum was in a nutrient-limited state, as defined by the continuous culture method (3− treatment)  ion chromatograph (IC) was used to measure organic  and inorganic (nitrite and nitrate) anions on an AS11-HC column and cations (ammonium) on a CS-16 column from the bacterial growth media. A complete description is provided in SI Materials and Methods.I. calvum cells were grown in 300 mL batch cultures under 8 mM and 0.8 mM lactate  (n = 3). Growth curves were monitored in real-time to collect cells during early-exponential, late-exponential, and stationary phase. Upon sampling, culturing vessels were immediately incubated on ice, aseptically transferred to 50 mL falcon tubes and immediately centrifuged at 15000 G for 5 min at 4°C. Supernatant was immediately poured off and the cell pellets were harvested and stored in TRIzol Reagent (Ambion RNA by Life Technologies) at –80°C until further processing. RNA was extracted with TRIzol Reagent (Ambion RNA by Life Technologies), following the manufacturer’s protocol. The extraction was scaled up to 20 ml of TRIzol and performed in 50 ml conical tubes. RNA was resuspended in water, treated with Baseline-ZERO DNase (Epicenter) and cleaned up with RNeasy MinElute Cleanup Kit (QIAGEN). Total extracted RNA from each growth phase was quantified (RiboGreen RNA quantification), analyzed for RNA integrity number (RIN) (RIN ≥ 0.8 were used for sequencing), treated with RiboZero rRNA removal kit . Library preparation was performed with the TruSeq Stranded mRNA Library Prep kit  and sequenced using Illumina NextSeq 500 2 × 150 Mid Output v2 platform, resulting in 10–15 million reads per sample.DNA for genome sequencing was extracted with phenol-chloroform. The DNA quality and integrity was verified on Agilent 2200 TapeStation and DNA was sequenced on the PacBio platform . For gene expression analysis, I. calvum 7KIP genome (Acc: NC_014830.1) to compare sequence similarity in Mauve  using the Isolate Genome Gene Calling method  under the submission ID 172966. The complete genome sequence and annotation is available in the NCBI database under the BioProject number PRJNA475609. A complete description of the phylogenetic, pathway analysis, and cost-minimization calculations is provided in SI Material and Methods. For transcriptomic analysis, the resulting raw reads were inspected using FastQC  with an estimated genome size of five million base pairs . The resin Mauve , 2010. Gg FastQC  to deterg FastQC . A complIntrasporangium calvum C5 was isolated from a nitrate contaminated groundwater well (well GW247: > 200 mM nitrate) at the Oak Ridge National Laboratory Field Research Station in Oak Ridge, TN. This species was first isolated from the air in a school dining hall . Genome size of C5 was 4,025,044 base pairs (bp), only 662 bp longer than 7KIP. Genomic analysis of the ETC revealed the typical suite of complexes common to facultative aerobes, including primary dehydrogenases , alternative NDH-2 NADH dehydrogenase, cytochrome bc1 complex, high-oxygen adapted cytochrome c oxidase (A-family), and low-oxygen adapted cytochrome bd oxidase. The bc1 complex subunits are also located immediately upstream of cytochrome c oxidase, suggesting that these enzymes are encoded in a single operon creating a supercomplex. Despite I. calvum’s propensity for aerobic growth on a number of growth media . In addition, to NarG, its dissimilatory N module composition consists of a truncated denitrification pathway  using a copper nitrite reductase NirK and quinol-dependent nitric oxide reductase qNor. I. calvum also possesses both catalytic and membrane anchor subunits , for a pentaheme cytochrome c module involved in respiratory nitrite ammonification. I. calvum’s nitrogen cycling capabilities appear to be unique to the genus as other Intrasporangium species, such as I. chromatireducens and I. oryzae do not possess any known dissimilatory nitrogen cycling genes.ing hall  and has ing hall  and in oing hall . The strth media , employsth media . I. calvI. calvum is a member of the CxxCH first heme motif group . Due to the exclusive NirK representation in dual-pathway membership, we asked whether there might be differences in protein atomic composition between NirK and NrfA, given the disparate evolutionary origins of these modules , indicating that resource constraints are imprinted on the evolution of these proteins.To gain insight into possible function of the NrfAH complex, we aligned the NrfA protein sequences from C5 and 7KIP to a collection of 33 recognized cytochrome c nitrite reductases from published annotated genomes  . We then performed a state-transition where biomass from late-exponential growth phase was collected and anaerobically inoculated into ammonia-free media  over a 48 h incubation, indicating consumption of ammonium produced by NrfA. Net ammonium production was 13 ± 2.7 μmoles with the remainder of dissimilated N being used by the denitrification pathway (24 ± 4.2 μmoles N2O-N), resulting in a recovery of 97.4% dissimilated N. These results confirmed that I. calvum C5 has a functional Nrf complex and also consumes the product (ammonium) of respiratory nitrite ammonification.The potential routes for N-assimilation were screened using aerobic minimal media with defined C-source/e-donor and e-acceptor, and with nitrate or ammonium as assimilatory N-sources. Based on genomic information, the bacterium possesses no known assimilatory nitrate reductase, but encodes for an ammonium transporter (Intca_RS11655) and GS/GOGAT pathways , suggesting that ammonium is its sole assimilatory N source. We observed no aerobic growth with 8mM lactate, Oa Figure . Despite3− control on respiratory ammonification versus denitrification on cultures of I. calvum C5 over a high resource C:NO3− range  and low resource C:NO3− range . Under all the treatments tested, results showed products of both respiratory pathways, differing only in the relative fraction of N2O versus ammonium production across treatments   of RNA extracted from three growth phases: early exponential (EE), late exponential (LE), and stationary , respectively. Some acetate was observed in the media (100–200 μM range), indicating minor incomplete lactate oxidation, however, I. calvum predominately oxidizes lactate completely to CO2. I. calvum encodes for two parallel metabolic pathways for pyruvate conversion to acetyl-CoA: pyruvate dehydrogenase (PDH) (Intca_01255) and pyruvate ferredoxin oxidoreductase (PFOR) (Intca_15510). For PDH, there was a significant and moderate increase in transcript abundance between EE-LE and -ST , respectively. For PFOR, there was a minor decrease in transcript abundance between EE-LE (lfc = −0.43 ± 0.5), and then a moderate increase in transcript abundance between EE-ST (1.1 ± 0.5). Citrate synthase (Intca_04135), the enzyme catalyzing the conversion of acetyl-CoA to citrate and the first step of the tricarboxylic acid (TCA) cycle, showed a highly significant increase in transcript abundance between EE-LE and -ST .In order to gain insight into mechanisms of gene regulation and transcriptional organization of e Figure . This apnuo complex; Intca_03465–03539) between EE-LE and -ST , respectively. Nitrate reductase subunits showed no change in transcript abundance between EE-LE (lfc = 0.01 ± 0.07) and moderately decreased in abundance by ST (lfc = −1.2 ± 0.1), which was corroborated by the depletion of nitrate during stationary phase. There was a significant increase in transcript abundance of nirK (Intca_17170)  and quinol dehydrogenase/membrane anchor subunit nrfH (Intca_09465)  by EE-LT and EE-ST, respectively, which coincided with nitrite production  (Intca_09460) also increased moderately in transcript abundance by EE-LT and EE-ST , respectively. Contrary to the transcript abundance patterns of nirK and nrfAH, nitric oxide reductase  transcripts moderately increased between EE-LT (lfc = 1.6 ± 0.6) but decreased in the successive time periods .Within the ETC, there was moderate and significant decrease in transcript abundance for all subunits from the primary dehydrogenase  between EE-ST, as well as LE-ST . We verified the production of formate in our ion chromatography measurements in the range of 100–200 μM following late exponential growth. We also observed a moderate increase in transcript abundance of formate dehydrogenase (FDH) subunits (Intca_11150–11160). These results implicate the activity of formate oxidation, which would contribute to a proton motive force in the periplasm via a Q-loop mechanism and the reduction of MK for electron transfer to nitrite via cytochrome c nitrite reductase. Considering that formate was not provided in our media recipe, an alternative pathway for formate production must exist in I. calvum. We also observed acetate production in similar concentrations as formate (100–200 μM). In E. coli, formate is produced anaerobically from the action of pyruvate formate lyase (PFL). We identified a putative PFL based on genome annotation (Intca_12230), where transcript abundance also significantly increased by ST. PFL is also highly sensitive to oxygen (bd oxidase (Intca_01110 and Intca_01115), which is thought to protect anaerobic enzymes against oxidative stress  to high and low resource loadings under the same range of C:NO3− ratios to better resolve mechanisms of pathway selection. We demonstrated that resource concentration, not C:NO3− ratio, influences pathway selection in I. calvum. We found stronger support for the preference of respiratory ammonification under low C concentrations (at low C:NO3− ratios) in I. calvum, which grew at significantly higher growth rates when using its ammonification pathway . This framework is further bolstered by the observation that growth yields of pure denitrifiers are significantly lower than expected compared to ammonifiers , can have higher growth yields  without MKH2 regeneration , which may explain why these cultures had lower growth rates , in which we observed both denitrification and respiratory ammonification occurring simultaneously, for RNA-Seq in order to gain insight into the transcriptional organization of actively growing I. calvum cells . When Geobacter lovleyi  and I. calvum use different types of nitrate reducing modules. S. loihica PV-4 utilizes NapA whereas I. calvum utilizes NarG  suggests that Nrf may have originated prior to the bc1 complex. Furthermore, the quinol oxidation site (Qo) of cytochrome b contains a PDWY motif, indicative of an ancestral LP respiratory chain found in many Gram-positive organisms  coupled to molecular evolutionary analysis has revealed respiratory ammonification was likely a major component of the Archean N-cycle . Abiotic product ] .DV wrote the manuscript. DV, RR, M-KW, RC, DS, and JG contributed to the conception and designed the study. DV, BS, IN, RB, EL, and DM performed the lab work. DV, RR, JH, BS, JA, RB, M-KW, DS, and JG analyzed the data and interpreted the results. All authors contributed to manuscript revision, read, and approved the submitted version.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.