Abstract
Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the pathogen Mycobacterium tuberculosis (Mtb), this is particularly important in informing the development of effective drugs targeting Mtb’s metabolism. Here we performed 13C15N dual isotopic labelling of mycobacterial steady state cultures and quantified intracellular carbon-nitrogen (CN) and nitrogen (N) fluxes in addition to carbon (C) fluxes and inferred their reaction bidirectionalities. The combination of 13C15N-MFA with a Bayesian multi-model approach allowed us to resolve C and N fluxes simultaneously which was not possible with classical 13C-MFA. We quantified CN fluxes for amino acid and, for the first time, nucleotide biosynthesis. Our analysis identified glutamate as the central CN and N node in mycobacteria, and improved resolution of the anaplerotic node. Our study describes a powerful platform to measure carbon and nitrogen metabolism in any biological system with statistical rigor.
Introduction
In recent decades, a great deal of progress has been made in unravelling the complexity of intracellular metabolism in microbial, animal and plant cells by measuring metabolic fluxes through the reactions that constitute central metabolism. The state-of-the-art technique is 13C-Metabolic Flux Analysis (MFA) in which cells, at metabolic steady-state, are fed a mixture of 12C and 13C-labelled substrates that are incorporated into the central carbon (C) metabolism to yield stable end products, such as the proteinogenic amino acids. The method infers in vivo metabolic reaction rates (fluxes) by using a system-wide biochemical reaction model that tracks C atom rearrangements throughout the metabolic pathways, and by fitting these fluxes to the emerging labelling patterns (typically isotopically 12C and 13C labelled fractional enrichments measured by mass spectrometry (MS) or nuclear magnetic resonance (NMR)) (1–4). 13C-MFA resolves the activity of biochemical reactions through computational modelling which can differentiate between parallel pathways and determine bidirectional fluxes (mass exchange of reactions that proceed forwards and backwards at the same time) (5, 6).
Besides central C metabolism, nitrogen (N) metabolism plays a key role, not only in amino acid and nucleotide metabolism, but also in the synthesis of many cofactors (7). In many microbes including the pathogenic Mycobacterium tuberculosis, nitrate acts as a terminal oxygen acceptor in addition to molecular oxygen during hypoxic respiration (8). Although N fixation and assimilation play a key role in medical research, agriculture and biotechnology, quantitative insights into N metabolism are currently limited. Consequently, only a few drugs have been developed that target N metabolism (9). The progress in quantifying N metabolism has been challenging, mostly because there is limited information derived from the isotopic labelling profiles of N versus C atoms. The equivalent of 13C-MFA, namely 15N-MFA therefore needs to involve time-resolved labeling data and the measurement of intracellular intermediate metabolite concentrations (pool sizes) to deploy the isotopically non-stationary (INST) MFA framework (10–14). This technique was utilized to study ammonium assimilation in Corynebacterium glutamicum by quantifying central N fluxes in vivo (12).
C and N metabolism are interdependent in all organisms (13, 14). For instance, the tricarboxylic acid cycle (TCA), glycolysis and pentose phosphate pathway (PPP), which are C based, synthesize amino acids and nucleotides. Biosynthesis of amino acids primarily involves addition of N to the C backbone, requiring reductants and energy, generated primarily from C metabolism (Fig. 1). To understand C and N co-assimilation, a systems-based analysis is required which includes both central C and N metabolism. The information about 13C labelling enrichments of the intermediates in amino acid biosynthesis alone is limited, because such measurements cannot resolve alternative pathways in which no C scrambling occurs, as in the case of arginine biosynthesis pathway (Fig.1, inset). The key for quantification of C and N fluxes simultaneously is to administer 13C and 15N isotopic tracers. Using 13C615N4 labelled arginine as the tracer, this co-labelling strategy was implemented to study arginine metabolism in Kluyveromyces lactis (15); this study showed that 13C incorporation into the amino acids proceeded at a slower timescale than the 15N-label. The time-deconvolution of C and N label incorporation allowed approximation of C and N fluxes using a “staggered” INST 13C-MFA and 15N-MFA approach. However, the efficacy of this approach critically depends on the validity of the underlying timescale separation, which is induced by the efficacious choice of C- and N-sources. Here we have established an approach that simultaneously quantifies C and N metabolic fluxes independently of the co-labelling strategy applied. Our 13C15N-MFA platform specifically tracks C and N atom interconversions throughout the entire metabolic network, without the need to acquire the pool sizes of metabolic intermediates or the deconvolution of 13C and 15N isotopologues using time-deconvolution or specialized analytical measurement platforms. We demonstrate the versatility of this platform by quantifying intracellular C and N fluxes of the vaccine strain of mycobacteria Mycobacterium bovis BCG, as a model for one of the world’s most important pathogens,Mycobacterium tuberculosis (Mtb) that causes tuberculosis (TB).
TB is one of the leading causes of human mortality from a single infectious agent that kills over a million people every year (16). Drug resistance is a major problem affecting TB therapy (17, 18), so new drugs are urgently needed. Measurement of N metabolic fluxes has the potential to identify novel anti-TB drug targets, but the current progress is hampered by the limitation of tools and technology to measure N along with C fluxes in vivo. We previously developed 13C-flux spectral analysis (FSA) and 15N-flux spectral ratio analysis (FSRA) for identifying the probable spectrum of C and N substrates in Mtb ex vivo (19, 20). Using FSRA, we found aspartate, glutamate, and glutamine to be the primary nitrogen sources for intracellular Mtb (20). Although 13C-FSA and 15N-FSRA provided qualitative conclusions about C and N sources, the available measurements did not allow for the flux quantifications. Multiple studies have successfully measured C fluxes in Mtb growing as batch cultures (21–23). We also applied 13C-MFA to quantify intracellular C fluxes of Mtb and BCG during slow and fast growth in a chemostat (22). Here using C and N isotopic co-labeling, metabolic modeling, and Bayesian statistics we resolved the central C and N co-metabolism with an increased resolution.
The measurement of N metabolic fluxes, simultaneously with the C fluxes in a principled, system-wide manner has not been attempted in Mtb or in any organism. The simultaneous measurement of C and N fluxes requires constructing an enlarged metabolic reaction model that describes both central C and N metabolism along with multi-atom transitions. As a result, the number of unknown flux parameters to be inferred from the experimental measurements, increases significantly. The increase in dimensionality stems primarily from the reaction steps for CN co-assimilation (mainly reactions catalyzed by transaminases) as these reactions must be modelled bidirectional to describe the co-labeling enrichments. In this situation, employing the standard best-fit approach commonly used in 13C-MFA is prone to overfitting (24, 25). This problem is exacerbated when measurements cannot distinguish between labelling contributions stemming from 13C and 15N tracers, such as measurements obtained from single quadrupole MS instruments with insufficient mass resolution. To overcome the impediments of current single-model 13C-MFA approaches, we used a statistically rigorous multi-model inference approach (26), which we here generalize to 13C15N-MFA. This multi-model-based 13C15N-MFA platform for analyzing co-labeling datasets enabled us to measure intracellular metabolic fluxes for the central C and N metabolism in M. bovis BCG under steady-state conditions.
Results
Roadmap for Bayesian multi-model 13C15N-metabolic flux analysis
The 13C15N-MFA co-labelling general workflow is summarized in Fig. 2. Cultivation experiments are performed under metabolic (pseudo) steady state conditions, in a C or N limited chemostat. Steady state cultures are switched to media containing 13C- and 15N-labelled substrates, and samples are drawn after an isotopic steady state labelling is achieved for both C and N. The samples are then analyzed by MS, providing mass isotopomer distributions (MIDs) (27). Although we focus on MS as mainstream analytics, the workflow is equally valid for NMR delivering heteronuclear NMR moieties (28), or a combination of MS and NMR measurements. In terms of mass shifts, low-resolution gas-chromatography (GC-MS) and liquid-chromatography mass spectrometry (LC-MS) are often not sufficiently sensitive to distinguish between 13C and 15N isotopomers (29), resulting in convoluted univariate (13C15N) MIDs (30). For example, in lysine with six C (#C = 6) and two N (#N = 2) atoms, #C + #N + 1 = 9 univariate MIDs exist. More advanced analytical platforms such as high-resolution LC-MS (orbitrap) (27), FT-ICR-MS (31), multi-reflection time-of-flight (ToF) MS or tailored derivatization approaches combined with two-stage LC-MS (29) can distinguish between 13C and 15N isotopomers, providing multivariate MIDs. In the lysine example, there are (#C + 1) × (#N + 1) = 21 multivariate mass isotopomers. Ultra-high-resolution orbitrap and FT-ICR analyzers allow resolving the full spectrum. In practice, however, those analytical platforms operate at a trade-off between resolving power (~65,000 fwhm needed to separate 13C and 15N mass isotopomers) and acquisition accuracy. Also, low intracellular metabolite concentrations often limits the precise measurements of multivariate MIDs. Therefore, we opted to measure univariate MIDs from the co-labelling experiment using a single-quadrupole GC-MS system, which is a robust analytical device for MID analysis. Our workflow is transferable and equally applicable to other analytical platforms.
MIDs corrected for natural abundance, along with the extracellular rates and biomass proportions, are incorporated into a metabolic network model that precisely specifies the transition of C and N atoms throughout the intracellular reactions. Tracking N in addition to C requires not only an extension of C mappings by N mappings and the addition of the reactions of nitrogen metabolism, but also requires a refined formulation of biosynthesis reactions that are usually lumped in 13C-MFA (an example is shown in Fig. 1 inset). These extensions enable us to infer CN fluxes from the co-labelling data. In addition to the transition network, information on the mass exchange between intermediates of these biosynthetic reactions i.e., whether the mass flow through these reversible reactions is unidirectional or bidirectional is required (6). Transaminases are suspected to operate at near thermodynamic equilibrium, but quantitative evidence regarding their activity are largely missing (32). As such all transaminase catalyzed biochemical reactions carrying CN fluxes should be considered potentially bidirectional (33), implying that they are characterized by two flux parameters, a net and an exchange flux, instead of a unidirectional reaction, which is described by a net flux only. Consequently, this introduces additional challenges to identify flux parameters into the CN model, which renders the model susceptible to overfitting. A general solution for dealing with model under-determinacy in a statistically rigorous manner, while making as few assumptions as possible, has been proposed within the framework of Bayesian Model Averaging (BMA) (34). When applied to 13C15N-MFA, BMA determines the probability distributions of net fluxes (v) given the data (D), in the Bayesian paradigm expressed as p(v|D), by averaging the flux posterior probabilities over all possible models (Mi, i = 1,… N), weighted by the model probability in view of the data p(Mi|D):
Here, models Mi, are structural variants that differ in their bidirectionality setting and, hence, number of flux parameters (vMi). Equation (EQ 1) is solved computationally by using a recently developed tailored Markov chain Monte Carlo (MCMC) approach (26) (see Materials and Methods for details). This results in so-called marginal posterior probability distributions for the net fluxes, as well as the probabilities of reversible reactions being uni- or bidirectional. The marginal posterior probability distributions provide credible intervals (CrI) and expected values (EV) for the net fluxes. Finally, this outcome is visually summarized in a Bayesian flux map (Fig. 2). In addition, two-dimensional marginal posterior probability distributions give insights into possibly non-linear net flux correlations. It should be noted that all traditional flux maps, including those in our previous work (22), instead report maximum likelihood-based point estimates and confidence intervals while flux correlations are exclusive to the Bayesian framework.
BMA-based 13C15N-MFA validates and refines carbon fluxes determined using 13C-MFA in mycobacteria
We applied the 13C15N-MFA workflow to the mycobacterial model system M. bovis BCG. The experimental conditions were comparable to those described in Beste et al. 2011 (22). M. bovis BCG was cultivated in continuous culture with glycerol and ammonia as sole C and N sources respectively at a dilution rate of 0.03 h-1 (Table 1, Fig. S1A-C). Cultures were grown with 10% [13C3]-glycerol (GLYC) and 20% [15N1]-ammonium chloride (NH4Cl) until an isotopic steady state was reached, as confirmed by GC-MS of amino acids (Fig. S1C). Harvested samples were analyzed using GC-MS, providing univariate MID measurements (MIDs which do not distinguish between 13C and 15N isotopomers) of 15 proteinogenic amino acids.
The resulting Bayesian flux map is shown in Fig. 3 with fluxes relative to the glycerol (GLYC) uptake rate, while absolute fluxes are given in Fig. 4 (see also supplementary Fig. S2 for the absolute net flux posterior probability distributions). As expected, the primary C metabolic route is directed from GLYC over lower glycolysis towards lipid and fatty acid synthesis (via the acetyl-CoA drain flux). While glycolytic fluxes were the highest, the TCA cycle, PPP and anaplerotic fluxes are significantly lower. The fluxes through the decarboxylating arm of the TCA cycle, oxoglutarate ferredoxin oxidoreductase (kor) and succinyl-CoA synthetase (scs) reactions are reduced; decarboxylation is bypassed using glyoxylate shunt, instantiating the GAS pathway (22). The net C flux distributions through the upper glycolysis, the TCA and anaplerosis for BCG growing at a growth rate (0.03h-1) measured using Bayesian 13C15N-MFA are very similar to those derived by traditional 13C-MFA in our previous study (22) (supplementary Fig. S3).
There are, however, also differences in the flux maps of central C metabolism. Our previous flux map showed cyclic fluxes around fructose 6-phosphate (F6P) node, involving the fluxes pgi, gnd, tkt2, tal and tkt1. This cycle is also confirmed by the present 13C15N-MFA study but with fluxes lower than previously reported. Notice, however, that the previously reported fluxes of the PPP represent best-fit values that were fixed in the statistical analysis due to their non-identifiability (22), and thus should serve for a qualitative comparison only. More accentuated differences are found in the fluxes of lower glycolysis. This is explained by the differences in the experimental setup between the two studies: In this study, tyloxapol was used as dispersant in the medium as replacement for tween-80 or oleic acid, which was a medium component in our previous study, and is known to be a C source for mycobacteria (35). Our results reveal that the lack of oleic acid as C source is compensated by an increase in lower glycolytic flux. It is interesting that whether using tween or tyloxapol in the medium there is no “global” effect on C fluxes under the investigated conditions.
As in our former 13C-MFA analysis (22), net fluxes of the anaplerotic reactions, pyruvate carboxylase (pca), PEP carboxykinase (pck), and malic enzyme (mez), could not be resolved as seen from their large CrIs in Fig. 4. This is a consequence of the cyclic network topology of the anaplerotic node (36). In our previous analysis pca and mez were aggregated by lumping oxaloacetate (OAA) and malate (MAL), but in this study the three anaplerotic reactions were modelled in detail without imposing any assumption on their (bi)directionalities. Despite limited information in the co-labelling data for identification of the directionalities of the anaplerotic reactions, we were able to improve the resolution of the flux map and limit the absolute flux values to a narrow range of ± 0.06 mmol gbiomass-1 h-1. Furthermore, the two-dimensional (2D) marginal posterior probability distributions provide new insights into anaplerotic flux correlations (Fig. 5), which effectively narrows down the joint space of possible values to concise ring-like areas of the flux space. From our analyses many flux constellations can be ruled out. For instance, it is unlikely that two of the reactions, pca and pck or pck and mez, both carry zero flux. The flux pairs pck/mez and pck/pca are largely positively correlated, meaning that a larger value of one flux implies a larger value of the other. In both cases, two different possible modes emerge, explaining the data equally well, one with larger and one with smaller values of mez and pca, respectively. In contrast, pca and mez are largely negatively correlated. The results show that at least one of the three anaplerotic reactions is operating in gluconeogenetic direction. In a larger context, Fig. S4 shows that mdh and pyk are highly correlated with mez and pck, respectively. In conclusion, the C flux profile for BCG growing at a faster growth rate (0.03h-1) inferred here by BMA-based 13C15N-MFA represents an independent replication of the previous 13C-MFA-derived C fluxes. The comparable C flux maps derived from the two labelling approaches and the two MFA platforms validate the application of our Bayesian approach, which imposes fewer modeling assumptions, and delivers an increased flux resolution for the previously non-inferable anaplerotic cyclic nodes.
13C15N-MFA quantifies CN-fluxes, and thereby N-fluxes
In addition to the C fluxes discussed in the previous section, 13C15N-MFA together with the extended scope of the network allowed us, for the first time, to quantify CN net fluxes in M. bovis BCG, i.e. reaction fluxes in the amino acid and nucleotide biosynthetic pathways. These fluxes are shown in Fig. 3 (hexagons with thick red borders) and Fig. 4 in relative and absolute numbers. The largest CN flux is bsGLU (glutamate dehydrogenase), with an EV of 0.072 mmol g biomass-1 h-1 (95% CrI: 0.071 – 0.074 mmol g biomass-1 h-1), followed by bsGLN (glutamine synthetase), bsASP (aspartate transaminase) and bsSER (serine deaminase). The CN-fluxes of the remaining amino acid and nucleotide synthesis including adenosine monophosphate (AMP), guanosine monophosphate (GMP), cytidine monophosphate (CMP), inosine monophosphate (IMP) and uridine monophosphate (UMP), are one order of magnitude lower, with values rendering the proportion to which they contribute to biomass formation (Fig. 4).
When comparing CN fluxes with the C fluxes in Fig. 4, or with any previously reported flux values, it is important to realize that fluxes must be interpreted in relation to the actual material flows they represent. For example, it has been previously reported (22), that alanine aminotransferase (bsALA) is the largest biosynthetic net carbon flux, being one order of magnitude larger than glutamate dehydrogenase (bsGLU). In this study, we found that bsALA (95% CrI: 0.006 – 0.007 mmol g biomass-1 h-1) is one order of magnitude lower than bsGLU. This apparent discrepancy is explained by the nitrogen demand that is not within the scope of 13C-MFA. More precisely, conventional 13C-MFA can only give a lower bound for the nitrogen requirement of an amino acid by summing up the (C) fluxes of reactions that incorporate nitrogen from the donor. For example, adding the fluxes to alanine (ALA), asparagine (ASN), glutamate, glutamine (GLN), leucine (LEU), lysine (LYS), phenylalanine (PHE), serine (SER), tyrosine (TYR), tryptophan (TRP), valine (VAL), ornithine (ORN), arginine (ARG), and the contributions to nucleotide synthesis, gives a value of 0.061 mmol g biomass-1 h-1, which underestimates the EV of the bsGLU net CN flux 0.071 mmol g biomass-1 h-1 derived by 13C15N-MFA in this study.
The strength of 13C15N-MFA is that it provides quantitative flux measurements for N metabolism, via the CN fluxes. The N flux map shown in Fig. 6 highlights the central role of glutamate (GLU) as a N donor. To a lesser extent, glutamine (GLN) and aspartate (ASP) are also N donors, which explains their significantly higher CN flux as compared to the C fluxes previously reported (22). Glutamate donates its N to other amino acids through various transamination reactions. The centrality of this node for N assimilation was experimentally confirmed by examining substrate utilization of a glutamate auxotroph of M. bovis BCG with a transposon mutation in gltBD, a gene encoding glutamine oxoglutarate aminotransferase (GOGAT) that catalyzes the synthesis of GLU from OXG and GLN (37). Whereas the wild type M. bovis BCG strain could grow with GLYC as sole C and NH3, ASP, GLU and GLN as sole N sources (slope m >0), the gltBD mutant was able to grow only on glutamate as the N source (Fig. 7). The CN-fluxes for GLU and ASP-derived amino acids including ASN, threonine (THR), isoleucine (ILE), LYS, methionine (MET), ARG and PRO and phosphoglyceric acid (PGA)/phosphoenlypuruvate (PEP) derived ALA, SER, cysteine (CYS), VAL and leucine (LEU) are higher than the PPP-derived amino acids (Fig. 4). Interestingly, ALA had the largest pool size amongst the protein-derived amino acids, but the alanine aminotransferase CN flux was by far not the highest. Also, for the other amino acids, no direct relationship between pool sizes and fluxes is visible, highlighting that pool size and CN flux are complementary but independent measures of metabolism (Fig. 8; Fig. S5) (38).
Bayesian Multi-Model 13C15N-MFA uncovers reversibility of glycine biosynthesis and unidirectionality of leucine and isoleucine biosynthesis
GLU and GLN serve as the main amino donors for the synthesis of other amino acids in BCG (Fig. 4 and Fig. 6). To meet the demands for protein, RNA and DNA synthesis, the N net flux is principally directed towards amination. However, the reversibility of the enzymes responsible for catalyzing transaminases provide cells with the ability to adapt rapidly to environmental conditions such as changes in N availability. Indeed, fine-tuning the activities of transaminases to modulate C flux has been demonstrated in the CHO eukaryotic cell line (39). This means that while the biosynthetic net reaction flux is directed towards amination, a simultaneous forward and backward (bidirectional) flux is likely occurring in vivo.
Due to the lack of evidence about the reversibility of mycobacterial transaminases, all transamination reactions were modelled as bidirectional reaction steps, resulting in a flux estimation problem with 86 degrees of freedom (71 fluxes and 15 measurement group scales) to be recovered from 144 measurements (109 univariate MIDs and 34 rate and biomass efflux measurements). Here, BMA was used to minimise overfitting (see Materials and Methods). This technique can explore the space of all possible combinations of uni- and bidirectional reactions (each combination codes for a model) and weights each model variant by its ability to explain the labelling data (see also Fig. 2). In contrast to the conventionally used single-model approaches, the BMA-based 13C15N-MFA enables the rigorous statistical inference of reaction bidirectionality (26). The univariate MIDs did not allow all bidirectional reactions of biosynthesis to be classified as either bi- or unidirectional (Fig. S6) with distinct exceptions: glycine hydroxymethyltransferase (bsGLY) that catalyzes glycine (GLY) biosynthesis was determined to be bidirectional with 100 ± 0% probability. In contrast serine deaminase, isoleucine, and leucine transaminase (bsSER, bsILE and bsLEU) were found to be unidirectional with high probability (98.6 ± 0.3% probability, 99.8 ± 0.05%, and 100 ± 0% probability, respectively).
Discussion
The interplay between C and N assimilation and dissimilation is required to sustain cell metabolism and function. To date, the knowledge about C and N co-assimilation and these fluxes within a cell remains limited. For measuring metabolic fluxes, isotope tracing studies in combination with computational modelling is a gold standard. Recently, multiple stable isotopic tracers have been used to measure C and N enrichments and to derive insights into N assimilation in eukaryotic systems including yeast, plant, and human cancer cells (27, 31). Two pioneering studies determined metabolic fluxes of the GS-GOGAT pathway in C. glutamicum by INST 15N-MFA (12) and the CN fluxes for arginine metabolism in K. lactis (15). Both these studies had limitations; the former was limited to measuring CN fluxes in a small sub-network, and the latter 13C15N-flux analysis relied on a tailored labelling strategy that exploited the fact that the 13C tracer was incorporated faster into metabolites than the 15N tracer.
Here we generalized the well-established methodology of 13C-MFA towards 13C15N-MFA for deriving a system-wide CN, and thereby N fluxes from a combination of isotopic co-labelling experiments and comprehensive C and N atom transition modelling. The generalization is paired with the use of Bayesian model averaging, which provided rigorous quantification of CN metabolic fluxes with low- and medium-resolution MS data by measuring intracellular metabolic fluxes through the central C and N network of M. bovis BCG. Key advantages of BMA-based 13C15N-MFA are the extension of scope and refinement in terms of the metabolic network, while, simultaneously, tackling the consequent introduced uncertainties in the network formulation. This enabled the rigorous statistical assessment of all reactions, even those with unknown bidirectionality and cyclic network structures that were previously unresolvable.
We and others have demonstrated that the TB pathogen co-metabolizes multiple host nutrients during infection (19–21, 40, 41). Co-catabolism of C sources by mycobacteria and the associated metabolic regulations have been previously demonstrated by multiple studies using transcriptional, metabolomics and genetic approaches (21, 42–47). Although multiple studies have explored the C and N metabolism of Mtb in vitro and within ex vivo and in vivo animal models, the information about CN and N fluxes and the key metabolic steps that could be targeted for drug development remain limited. Here we used [13C3]-glycerol and [15N1]-ammonium chloride dual isotopic labelling of steady state BCG cultures to measure intracellular CN and N fluxes. We provide the first comprehensive intracellular CN and N flux distributions in a biological system. A comparison with our previously published 13C-MFA in Mtb and M. bovis BCG (22) showed a broad agreement for the net fluxes of central C metabolism. C fluxes through glycolysis and PPP including phosphoglucose isomerase (pgi), fructose bisphosphatase (fbp), aldolase (fba), glucose-6-phosphate dehydrogenase/glucolactonase (gnd), transketolase (tkt1, tkt2) and transaldolase (tal) were not quantified accurately with our previous 13C-MFA and recent 13C15N-MFA study because PPP reactions lack of sufficient labelling information from proteinogenic amino acids. Incorporating labelling information from glycogen, ribose moiety of DNA, glucosamine moiety from peptidoglycan and lipopolysaccharides, such as suggested by (48) is a promising approach to improve flux precision in this area of metabolism. The fluxes of the three anaplerotic enzymes pyruvate carboxylase (pca), PEP carboxy kinase (pck), and malic enzyme (mez), which play an important role in metabolism connecting catabolism and anabolism with energy generation (49), are notoriously difficult to determine. We demonstrate that BMA-based 13C15N-MFA provides an effective tool to constrain the anaplerotic net fluxes. The Bayesian flux map is consistent with our previous results showing non-identifiability of individual anaplerotic fluxes (22), however, the co-labeling data in this study informs about distinct likely flux couplings of pca, pck and mez which could not be measured in our previous work. From our analysis we conclude that at least one of the three reactions is operating in the gluconeogenetic direction, and it is unlikely that two of the associated fluxes are zero at the same time.
Beyond C fluxes, we measured CN and N fluxes to amino acid and nucleotide (purine and pyrimidine) biosynthesis, providing novel information which cannot be deduced from our former 13C-MFA. We previously identified ASP, GLU and GLN as primary C and N sources for Mtb in human host macrophages (19, 20). Here we quantified the CN and N flux for the biosynthesis of these amino acids. We identified glutamate biosynthesis bsGLU as the primary node for CN flux. This is consistent with the finding that ASP, GLN and NH4+ as sole N sources in Roisin’s minimal medium failed to rescue the growth of a M. bovis BCG mutant lacking functional gltBD gene and thereby lacking de novo glutamate synthesis (37, 50). GLU is a well-established N source for in vitro and intracellular growth of mycobacteria. GLU metabolism is also crucial in mycobacteria to resist acidic and nitric oxide stress inside macrophages (37, 50) and is therefore a prime metabolic and regulatory node. Furthermore, we were able to quantify bidirectionality probabilities for glycine hydroxymethyltransferase (bsGLY), serine deaminase (bsSER) and isoleucine and leucine transaminase (bsILE and bsLEU). C and N metabolic profiling has been attempted using isotopic labelling by other studies. Blank et al. investigated simultaneous C and N incorporation in Saccharomyces cerevisiae administering two different isotopic substrates 13C-glucose and 15N-alanine and measured dual label incorporation in amino acids using FT-ICR-MS (31). Our study demonstrates that conventional GC-MS, which is the traditional workhorse for isotopomer analysis, can also be used to derive dual labelled data sets and univariate MIDs of amino acids for robust and reproducible flux quantification by 13C15N-MFA. Our CN metabolic network is currently limited to amino acid and nucleotide biosynthesis, but there are further scopes for extension of this model through addition of biocomponents such as co-factors NADH and NADPH, and lipids that requires CN assimilation and dissimilation.
In summary, we have developed Bayesian 13C15N-MFA, a powerful tool for simultaneous quantification of intracellular C, CN and N metabolic fluxes in a living system. We applied it to M. bovis BCG. 13C15N-MFA identified glutamate as the central CN node, revealed the most likely operational modes of the anaplerotic fluxes, and resolved the uni/bidirectionalities of glycine, serine, isoleucine, and leucine biosynthesis. Our 13C15N-MFA workflow described here is applicable to any C and N isotopic co-labelling experiment, and the computational platform developed in this work allows analyses of low- and medium-resolution MS data to provide rigorous quantification of CN metabolic fluxes in any biological system.
Author contributions
Conceptualization: JM, KN. Experimental analyses, and data collection: KB, YX, JB, CC, JN. Data analyses and visualization: MB, KB. Computational analyses: MB, KB. Implementation of computational framework: MB, AT. Writing original draft: KB, JM, KN, MB. Writing review and editing: KB, MB, CC, YX, JN, JB, DB, MJB, KN, JM. Funding acquisition: MJB, JM.
Competing interests
The authors declare no competing interests exist.
Funding
This study was supported by Biotechnology and Biological Sciences Research Council (BBSRC) grants BB/L022869/1 and BB/V010611/1 and the Electronics and Physical Sciences Research Council grants EP/R031118/1 and EP/P001440/1, United Kingdom.
Data availability
Mass spectrometry data and 13C-15N atom transition model is provided as supplementary material.
Materials and Methods
Bacterial strains
Mycobacterium bovis BCG Pasteur, which was originally purchased from the American Type Culture Collection (ATCC 35748), was used for this study. M. bovis BCG gltBD transposon mutant (BCG_3922c TnInsertion-8654) was procured from VIB - Vlaams Instituut voor Biotechnologie.
Media composition
Middlebrook 7H11 agar and Middlebrook 7H9 broth containing 5% (vol/vol) oleic acid-albumin-dextrose-catalase enrichment medium supplement (Becton Dickenson) and 0.5% (vol/vol) glycerol were used to grow cultures from frozen stocks and for counting the numbers of culturable bacteria in chemostat samples. Brain heart infusion agar was used to assess culture purity (Sigma Aldrich). For cultivation of M. bovis BCG in the bioreactor, roisins minimal medium with composition-KH2PO4, 1 g litre-1; Na2HPO4, 2.5 g litre-1; NH4Cl, 5.9 g litre-1; K2SO4, 2 g litre-1; ZnCl2, 0.08 mg litre-1; FeCl3, 0.4 mg litre-1; CuCl2, 0.02 mg litre-1; MnCl2, 0.02 mg litre-1; Na2B4O7, 0.02 mg litre-1; NH4MoO4, 0.02 mg litre-1; MgCl2, 0.0476 g litre-1; CaCl2, 0.055 g litre-1; Tyloxapol, 01% (v/v); Glycerol, 0.5% (v/v).
Growth of M. bovis in the bioreactor and chemostat
M. bovis BCG strain was cultured in a 2 litre bioreactor (Electrolab Fermac 310/60) under growth conditions (Table S1). Cultures were grown as batch for 7 days. Continuous cultures were grown under chemostat conditions at a growth rate of 0.03 h-1 maintained by the media flow rate (22). Media was pumped into the chemostat using a peristaltic pump (Rainin Rabbit Plus). Cultures were grown for 3-4 volume changes in the unlabelled media to assure a metabolic steady-state before introducing isotopically labelled media. [13C3] glycerol (12.5%) (Purchased from CK Isotopes, 99% purity) and [15N1] NH4Cl (20%) (Purchased from Merck, 98% atom purity) were the carbon and nitrogen isotopically labelled substrates in the media. Isotopic stationary state was assessed by measuring % label in the proteinogenic amino acids of cultures drawn at different times during label feed (Fig. S1C).
Chemostat measurements and culture analyses
Cultures were monitored every day to check for contamination by plating on BHI agar media and ziehl neelsen stain. Cultures from chemostat were regularly sampled for measuring OD (spectrophotomter from Thermo scientific) and colony forming units (22). Carbon-di-oxide production from the cultures was monitored using Gas analyser (Electrolab Fermac 368). The supernatant was collected, filtered using 0.22 μ unit filters and used for substrate consumption and product excretion analyses. To measure the glycerol uptake, the amounts of glycerol in the supernatant and fresh medium was measured using glycerol assay kit (Sigma Aldrich) by a coupled enzyme assay involving glycerol kinase and glycerol phosphate oxidase, resulting in a colorimetric (570 nm) product, proportional to the glycerol present. To measure NH4Cl uptake, the amounts of NH4Cl was measured using ammonia assay kit (Sigma Aldrich) by reaction of ammonia present in the samples involving L-glutamate dehydrogenase activity. Dry weight of the cells was measured by centrifuging cultures, drying the cell pellet using freeze dryer and weighing the cells. The dried pellet was used for protein analysis using Bicinchoninic Acid Kit for Protein Determination (Sigma Aldrich).
Metabolite extraction
Labelled chemostat cultures were quenched using methanol:chloroform:water (2:1:2) extraction. Briefly, cultures were filtered using membrane Filter, 0.22 μm pore size, filter apparatus (Merck) and the filter was immersed into methanol:chloroform, mixed, incubated on ice for 30 minutes and water was added, followed by centrifuging at room temperature for triphasic metabolite separation. The upper phase was collected separately and dried and used for mass spectrometry analysis. The lower and intermediate phase were mixed into one phase by addition of one more volume of methanol and chloroform and centrifuged for 30 minutes at room temperature. Supernatant was discarded, the pellet was hydrolysed in 6 M hydrochloric acid for 24 hours at 100°C and the hydrolysate was dried for mass spectrometry analysis.
Mass spectrometry analysis of amino acids
Dried upper phase was derivatised using N-Methyl-N-(trimethylsilyl)trifluoroacetamide, MSTFA (sigma Aldrich) and dried hydrolysate were derivatised using tert-Butyldimethylsilyl chloride (TBDMSCl) were analysed using GC-MS (7890-5795 system) (20) Mass spectra were baseline corrected using MetAlign and mas isotopomer distribution (MID) data were extracted using the chemstation software. Identification of metabolites was done using NIST databases, literatures, and qualifier masses. Average 13C15N fractional abundances were calculated from two independent chemostat cultivations (three- or four technical replicates each) and quantitation of metabolite pool sizes was done using calibration curves (20). Further confirmation of 13C and 15N labelling in the amino acids were done using LC-MS orbitrap (Fig. S7). Briefly, hydrophilic interaction liquid chromatography (HILIC) was carried out on a Dionex UltiMate 3000 RSLC system (Thermo Fisher Scientific, Hemel Hempstead, UK) using a C18 and ZIC-pHILIC column (150 mm × 4.6 mm, 5 μm column, Merck Sequant). The column was maintained at 30°C and samples were eluted with a linear gradient (20 mM ammonium carbonate in water, A and acetonitrile, B) over 26 min at a flow rate of 0.3 ml/min. The injection volume was 10 μl and samples were maintained at 4°C prior to injection. For the MS analysis, a Thermo Orbitrap Q Exactive Plus (Thermo Fisher Scientific) was operated in polarity switching mode and the MS settings were used with resolution 70,000, AGC 106, m/z range 70–1400, sheath gas 40, Auxiliary gas 5, sweep gas 1, probe temperature 150°C and capillary temperature 275°C. For positive mode ionisation: source voltage +4.5 kV, capillary voltage +50 V, tube voltage +70 kV, skimmer voltage +20 V. For negative mode ionisation: source voltage-3.5 kV, capillary voltage-50 V, tube voltage-70 V, skimmer voltage-20 V. The data shown in supplementary Fig. S7 is a mass spectrum showing the multivariate 13C and 15N species identification for alanine.
13C15N-Metabolic flux analysis
Metabolic network model: The metabolic model M. bovis BCG used for the analysis was constructed using the network editor Omix v.2.0.7 (Omix Visualization GmbH & Co. KG, Lennestadt/Germany) (51), according to the protocol described in (52), based on the GSMN-TB genome-scale model of M. tuberculosis (53). The constructed model includes reactions of glycolysis, the PPP, the TCA cycle, anaplerosis, nucleotide and amino acid biosynthesis. Uptake reactions were considered for GLYC and ammonium chloride (NH4Cl). All biosynthesis pathway fluxes relevant for growth of M. bovis are modelled as effluxes, whose values represent their share in the biomass composition (54). Reactions were considered bidirectional including all transaminases, unless evidence was found that the reactions operate far from thermodynamic equilibrium under in vivo conditions (supplementary Table S2). Only in the latter case were those reactions modelled unidirectional. Each metabolic reaction was supplemented with carbon and nitrogen atom transitions, following the InChI atom enumeration scheme (55). Carbon symmetries of succinate (SUCC), fumarate (FUM), and diaminopimelic acid (DAP) were accounted for by the formulation appropriate label scrambling reactions. In total, the M. bovis BCG model consists of 248 metabolites (121 balanced intracellular and 127 unbalanced extracellular pools) and 184 metabolic reactions (149 unidirectional, 35 bidirectional). The most comprehensive model in the model set has 71 independent flux parameters (36 net and 35 exchange fluxes). The corresponding CN atom transition network model is formulated in the standardized document format for isotope-based MFA, FluxML v3 (56), and is found in supplementary Data S1.
Measurement models: In total 30 biomass effluxes were considered as measurements that were either obtained from biomass hydrolysates or calculated from intermediates (22) and supplied with Gaussian error of 5%. Uptake rates for glycerol and NH4Cl were fixed for the analysis. Labelling measurements of 15 amino acids, were corrected for the effect of natural abundant isotopes (57), adding up to 109 univariate MIDs (supplementary Table S3). The associated measurement errors were derived from up to 8 replicates (two independent chemostats, each with two-four replicate measurements). The complete measurement specification is given in the FluxML model file in supplementary Data S1.
Labelling system & simulation: The 13C-MFA high-performance simulator 13CFLUX2 v2.2 (58) was extended to simulate 13C15N isotopologues. Briefly, the essential cumomer framework (59) was generalized from single-atom to multi-atom species labelling systems. The resulting balance equations consist of a set of sparse linear equation systems that were solved sequentially with on-the-fly algebraic simplification guaranteeing numerical stability, accuracy, and efficiency. The resulting reduced co-labelling system has a state-space dimension of 471 (a reduction of a factor of 2,563 compared to the full co-labelling system).
Flux inference with Bayesian Model Averaging: Instead of conventional optimization-based single-model flux inference, in this work metabolic fluxes were estimated using a Bayesian multi-model approach. More precisely, net fluxes and reaction bidirectionalities were inferred simultaneously by employing BMA, implemented using a tailored MCMC approach (26). Herein, 13CFLUX2 was used for likelihood computation. For speed, sampling algorithms implemented in the C++ library HOPS v2.0.0 (60) were employed after suitable preprocessing using Polyround v0.1.8 (61). Parallel tempering with dynamic temperature selection was applied to sample from potentially multi-modal distributions. In total, 10 parallel chains were run from independent starting points, where per chain 15×106 forward simulations were performed. For each chain, the first 5×106 samples were discarded (burn-in). Proper convergence of the MCMC runs was diagnosed by measuring the Potential Scale Reduction Factor (PSRF) (62) on a subset of samples, where all but every 2,000th sample was disregarded (thinning). Computations were run on a workstation with dual Intel(R) Xeon(R) Gold CPU (61300 @ 2.8 GHz). PSRF, mixing plots in parameter as well as model spaces are provided in supplementary Table S4, Fig. S8 and Fig S9.
Statistical evaluation: From the MCMC results posterior probability distributions p(v|D) for the net fluxes v were derived, according to (EQ 1). Formally, the posterior probability of a model Mi out of the model set {Mi|i = 1,…, N} is given by where p(Mi), the prior knowledge about model Mi, is considered equal for all models. p(D|Mi) represents the high dimensional marginalization integral over all possible fluxes the model Mi can take. The 95% CrIs were determined for each net flux (i.e. the range that contains the flux with a probability of 95% in view of the data), discarding the upper and lower 2.5% of the values. In addition, as point estimator, the expected value (EV) for the net flux is reported. The marginal distributions for the net fluxes are provided in supplementary Fig. S2. For each reversible reaction, the posterior probability of the reaction being bidirectional was determined, according to (EQ 2), as ratio of models sampled with the reaction set bidirectional divided by the total number of sampled models (supplementary Fig. S6).
Statistical Analysis
Students-t-test, analysis of variance (ANOVA) and linear regression analyses was performed in Graphpad Prism 8.0.
Acknowledgements
The authors are thankful to Wolfgang Wiechert for excellent working conditions at the IBG-1. We are grateful to VIB - Vlaams Instituut voor Biotechnologie for providing us M. bovis BCG gltBD transposon mutant (BCG_3922c TnInsertion-8654).