Digital organisms with an evolvable genome structure encoding an evolvable metabolic network evolved for tens of thousands of generations in environments mimicking the dynamics of real controlled environments, including chemostat or batch culture providing a single limiting resource. We show here that the evolution of stable cross-feeding interactions requires seasonal batch conditions. In this case, adaptive diversification events result in two stably co-existing ecotypes, with one feeding on the primary resource and the other on by-products. We show that the regularity of serial transfers is essential for the maintenance of the polymorphism, as it allows for at least two stable seasons and thus two temporal niches. A first season is externally generated by the transfer into fresh medium, while a second one is internally generated by niche construction as the provided nutrient is replaced by secreted by-products derived from bacterial growth. In chemostat conditions, even if cross-feeding interactions emerge, they are not stable on the long-term because fitter mutants eventually invade the whole population. We also show that the long-term evolution of the two stable ecotypes leads to character displacement, at the level of the metabolic network but also of the genome structure. This difference of genome structure between both ecotypes impacts the stability of the cross-feeding interaction, when the population is propagated in chemostat conditions. This study shows the crucial role played by seasonality in temporal niche partitioning and in promoting cross-feeding subgroups into stable ecotypes, a premise to sympatric speciation.Metabolic cross-feeding interactions between microbial strains are common in nature, and emerge during evolution experiments in the laboratory, even in homogeneous environments providing a single carbon source. In sympatry, when the environment is well-mixed, the reasons why emerging cross-feeding interactions may sometimes become stable and lead to monophyletic genotypic clusters occupying specific niches, named ecotypes, remain unclear. As an alternative to evolution experiments in the laboratory, we developed Evo Escherichia coli as a model organism. Yet it is not clear how cross-feeding interactions can resist the invasion of a fitter mutant when the environment contains a single resource since there seems to be a single ecological niche. Here, we used another kind of “model organism”, a digital one, allowing for detailed and fast investigations, and providing a way to disentangle generic evolutionary mechanisms from specificities associated with E. coli. Digital organisms with evolvable genomes and metabolic networks compete for resources in conditions mimicking laboratory evolution experiments. In chemostat simulations, although cross-feeding interactions regularly emerged, selective sweeps regularly purged the population of its diversity. By contrast, batch culture allowed for much more stable cross-feeding interactions, because it includes seasons and thus distinct temporal niches, thereby favoring the adaptive diversification of proto-species.Stable bacterial cross-feeding interactions, where one strain feeds on the waste of the other, are important to understand, as they can be a first step toward bacterial speciation. Their emergence is commonly observed in laboratory experiments using  Stable metabolic cross-feeding interactions between microbial strains are commonly observed in nature –4. For eecotype, which itself relies on the ecological and evolutionary dynamics of the subpopulations. Two bacterial subpopulations may be considered as different ecotypes if they form monophyletic clusters, occupy different ecological niches and if periodic selection purges diversity in one subpopulation independently from the other  representing the production rate of the protein(s) depending on the promoter. All the parameters and their units are listed in s and kcat ∈ ±, and the ratio kcat/KM ∈  describing the enzymatic kinetics (see the description of the metabolic network below). In the special case where s = p, the enzyme is considered as a pump, actively pumping in (or out) the metabolite s if kcat is positive . Initial genomes of 50 genomic units are generated. These genomes contain ten P and ten E, all with random positions and attribute values.Non-coding units (NC) have no particular function. They constitute the non-coding part of the genome. Promoter units (P) contain a floating-point number s, p, kcat and kcat/KM for an E. Each unit attribute mutates at a rate of 10−3 per attribute per replication. For the substrate/product tags, a mutation consists in randomly incrementing/decrementing s or p respectively. For kcat or kcat/KM, a random number drawn from β mutates by adding a random number drawn from −3 per genomic unit per replication. All types of genomic units are actually implemented as a tuple containing all possible attributes, like . The unit type tells us which parameters are functionally relevant and the others are free to mutate neutrally.Upon cell division, the parental genome is replicated with mutations in the two daughter cells. Each genomic unit can undergo point mutations, meaning here changes in the numbers it contains, like the values of −3 per genomic unit per replication, hence the number of rearrangements is related to the genome size thereby limiting genome expansion , [s] and [p] over time are then driven by β is the basal production rate specified in the promoter unit, ϕ is the enzyme degradation rate , KM and kcat are the kinetic attributes of the enzyme (KM being deduced from kcat and kcat/KM attributes).Let us consider an enzyme in the cytoplasm, that catalyzes one specific reaction s] and [p] describe the internal and external concentrations of the same metabolite. If kcat is positive (resp. negative), [s] is the external  concentration of the metabolite and [p] the internal  concentration. The dynamics of metabolic concentrations [s] and [p] are thus also driven by Pumps are treated here as special enzymes for which [(i) the concentrations of all metabolites inside the organism, i.e., internal concentrations, (ii) the concentrations of all metabolites at the organism’s location on the grid, i.e., external concentrations, and (iii) the concentrations of all proteins (pumps and enzymes) in the cytoplasm. For a very simple organism whose genome merely encodes one pump importing metabolite #10 into the cell, and one enzyme converting #10 to #7, the ODE system would read:Each organism has an ODE  system that keeps track of: The number of equations in the ODE system generally differs across individuals within a population because it depends on the number of functional genes, and chromosomal rearrangements like duplications and deletions can alter gene number. In practice, the size of the ODE system goes from tens to thousands of equations depending on the individual. Similarly, the parameter values of the ODE system also vary across individuals, as they are encoded in the organism’s genes and thus result from the mutation process.β/ϕ, and each metabolite starts with an internal concentration of 0.0 ACU (Arbitrary Concentration Unit). At time-step 0, for all grid spots, external concentrations are initialized to 0.0 ACU for all nutrients except for metabolite #10 (the exogenous carbon source). Between time-steps 0 and 1, the ODE system computes the dynamics of the metabolite and protein concentrations, using the adaptive Runge-Kutta-Cash-Karp method (RKCK), during 100 centi-time-steps. In organisms that possess a pump for metabolite #10, this metabolite will enter the cell. If the genome of this organism also encodes an enzyme to transform #10 into #7 for example, then the internal concentrations will show an accumulation of #7. At time-step 1, each organism will either die, divide or just survive (see paragraph “Population and environment” below for details). If the organism merely survives without dividing, its current internal concentrations are used as initial conditions for the computation of the next 100 centi-time-steps . If the organism divides, each of the two daughter cells inherits half of each metabolite and each protein amounts. These will constitute the initial conditions for each cell’s ODE system for the next 100 centi-time-steps. If the organism dies, its internal content is released into the environment, thereby increasing the local external concentrations. As the metabolites can diffuse across the grid, the metabolites produced by the dead cell, like metabolite #7, will become available to the neighboring cells, which will thus be able to feed on both #10 and #7, if they own the corresponding pumps. This process is repeated for each transition from time-step t to time-step t + 1.Initially, in the individuals used to seed a run at time-step 0, each protein starts at its equilibrium concentration e.g., a 32 × 32 grid is full of organisms , a time-step involves the computation of about a thousand different ODE systems, each of them containing from tens to thousands of equations depending on gene number.Thus, when Some metabolites are essential for an organism’s replication. Here, we arbitrarily define as essential the metabolites whose tag is a prime number. The score of an organism is then simply defined as the sum of its internal concentrations of essential metabolites. However, to prevent organisms from producing a single specific prime number in huge quantities, we also define lethal toxicity thresholds for both essential and non essential metabolites. Here these toxicity thresholds are set to 1.0 ACU for all metabolites.D = 0.1 gridstep2.time-step-1, meaning that a fraction D of each metabolite present at one location will diffuse to each of the eight neighboring grid spots at each time-step. The discrete diffusion equation we are using is inspired from [Dg, meaning that a fraction Dg of each metabolite at each location will disappear at each time-step. We make the simplifying assumption that there are no enzymatic reaction in the environment, and thus that metabolite transformation only occurs inside the organisms. Organisms compete for the external metabolites to produce offspring in empty spots. They interact with their local environment by pumping metabolites in and out and releasing their metabolic content at death. At each time-step, organisms are evaluated and either killed, updated or replicated depending on their current state:Organisms evolve on a two-dimensional toroidal grid, each spot containing at most one organism. The physical environment is described at the grid level: each grid spot contains external metabolites, each with its concentration. These external metabolites diffuse with a diffusion parameter red from . Externae.g., because there is no free space in its neighborhood), its metabolic network is updated, and its score is computed. If lethal toxicity thresholds are reached, the organism dies (see point 2);If the organism does not die and cannot divide .For each empty grid spot, all living organisms in the Moore neighborhood whose score is higher than a minimum score of 10mexo = #10. To initialize an evolutionary run, the entire grid was populated with individuals having random genomes . This initial population was allowed to evolve for 500 time-steps, at which point its viability is assessed. We repeated this procedure until a viable population was found, i.e., with at least 500 viable individuals after the 500 time-steps. In this case, some organisms possess at least one pump to internalize mexo, and  one enzyme to transform mexo into a prime number, thereby producing an “essential metabolite”. Up to a few hundred trials were usually needed to find a viable population, which was then used to seed the evolutionary run. Each evolutionary run was seeded with a different viable population. These organisms grow on the primary resource and start to release by-products (mostly at death), hence modifying their environment. Populations evolved in two different environments:In all our simulations, the environment provided one primary resource with tag periodic environment, in which the resource dynamics of the LTEE [mexo at concentration fin = 10.0 ACU per grid spot. Internal metabolites were not affected by the refresh event. The refresh period was Δt = 333 time-steps. We call a “cycle” this time interval between two environmental resets. The value of Δt was calibrated to let the organisms live for approximately 7 generations per cycle, as in the LTEE. Within each cycle, the metabolites in the environment were conserved (Dg = 0 per time-step). Note that we mimicked the resource dynamics of the LTEE but not the 1% population subsampling occurring during serial transfers, because it would have implied transferring populations of 10 individuals or fewer. Such a low population size would have implied dramatic genetic drift and impeded adaptive evolution . To simulate subsampling, a significantly larger grid would have been needed, making the whole campaign impossible to compute in a reasonable time.The the LTEE  was mimicontinuous environment, in which the resource dynamics of a chemostat environment was mimicked. The medium was constantly provided with a small influx of the primary resource. All the external metabolites were slowly degraded. Specifically, at each time-step, a concentration Δfin = 0.03 ACU of mexo was added in every grid spot, and external metabolites were degraded at rate Dg = 0.003 per time-step.The t = 333 time-steps instead of the exact regular period of 333 time-steps. The simulation parameters common to all the simulations are described in For each environment, 12 independent populations were propagated for 500,000 time-steps . On the long-term, the quantity of resources available in the system was equivalent in both environments. The grid size is 32 × 32. Complementary experiments were also run in a randomized batch environment similar to the periodic environment except that the environment reset intervals followed a Poisson law of parameter Δt. For example, if an organism uptakes metabolite #4, produces #3 from #4 and releases #3, knowing that the largest metabolite tag in the whole grid is #5, then its profile is |00010|00100|00100|. We classified organisms in two trophic groups depending on their trophic profiles:In order to detect the potential cross-feeding interactions in the population, the metabolic activity of each individual was evaluated at each time-step. For each organism, a “trophic profile” was computed from its metabolic network activity. The trophic profile is a binary sequence summarizing the uptake, production and release activity of an organism. The length of the binary string was defined by the largest metabolite tag present in the system at time mexo, and possibly other metabolites,“Group A” pumps in mexo.“Group B” pumps in group A by-products, and possibly other metabolites, but not the primary resource A trophic group is considered an ecotype if the organisms of the group form a monophyletic cluster (see below).PS score) to identify the degree of monophyly of both groups. This phylogenetic structure score was defined as PS = |f1 − f2|, where f1 and f2 are the relative frequencies of group B in both subtrees rooted to the last common ancestor of the whole final population. A high PS value indicates a strong clustering of groups A and B in the phylogenetic tree, i.e., that groups A and B are two different ecotypes. A low PS value indicates a random distribution or the absence of polymorphism.Phylogenetic relationships were exhaustively recorded during each simulation. Since organisms can only divide once per time-step, phylogenetic trees are binary trees. It was possible to recover the line of descent of any organism, and to compare the phylogenetic tree structure with the distribution of the trophic groups in the population. In particular, we can determine if groups A and B are monophyletic, and thus can be considered as ecotypes. To this aim, we computed a phylogenetic structure score , and the grid size. Details and results are described in We tested variations of our parameters set see , by chanFirst, the global evolutionary dynamics of the system can be analyzed by looking at main simulation statistics. The evolution of the mean score, the environmental richness (the number of different metabolites available in the environment), the number of trophic profiles, and the proportion of organisms of group A or B are represented in (i) the mean genome size, (ii) the mean amount of non-coding DNA, (iii) the mean number of enzyme coding units encoding the same metabolic reaction (the “metabolic redundancy”), (iv) the mean number of different essential metabolites pumped in (the “uptake diversity”), and (v) the mean number of different essential metabolites produced (the “production diversity”). For each measure, we performed a two-sample Wilcoxon test with a Bonferroni correction (n = 5).We compared the structure of both the genome and the metabolic network of final A organisms  from the continuous and periodic environments of the main campaign (see above). We evaluated five variables: i.e., maximizing the concentration of essential metabolites in their cytoplasm) and avoiding lethal toxicity thresholds. In the periodic environment, the external resource mexo was introduced by bursts of 10.0 ACU at each serial transfer. Thus, to maximize the score without reaching toxicity thresholds, organisms must avoid specializing on a single essential metabolite and instead spread the toxicity by distributing metabolic fluxes in the production of several essential metabolites. In the continuous environment, the external resource was continuously provided at a lower concentration (0.03 ACU at each time-step). In this case, the selective pressure on toxicity was relaxed and the number of essential metabolites produced was significantly lower. Interestingly, metabolic fluxes were also adjusted by amplifying or deleting genes. Indeed, in the continuous environment, there were more copies of E (enzyme coding units) than in the periodic environment, while the production diversity was lower, meaning that those units were amplified in the continuous environment to maximize metabolic fluxes, thus increasing the genome size.As shown in mexo (this metabolite being a hub in the network), even if the organism also fed on some by-products. Organism B’s metabolic network was less complex, and indicates that the organism mostly grew on A-secreted products. Most parts of both genomes were coding enzymes, revealing large operons all along the genomes.For each simulation, we analyzed the final phylogenetic tree and compared it to the distribution of groups A and B. All the phylogenetic trees are represented in To get more insight into the evolutionary dynamics, we computed the distribution of the Most Recent Common Ancestor (MRCA) age at each time-step for all the simulations. The MRCA age reflects the stability of the polymorphism in a population. As shown in PS . Points corresponding to the end of each simulation are colored in black. On each plot, three areas are identified: the purple area indicates long-diverged clades , the orange area indicates when clades correspond to ecotypes (PS score > 0.9), and the intersection of the previous two areas (inside dashed borders) indicates long-diverged monophyletic ecotypes. In the periodic environment , to polymorphic trees. However, the MRCA age was very short compared to the periodic environment, revealing the instability of the phylogenetic structure. Note that the random environment is the only one where we observed a population extinction (1 out of 12). In the continuous environment  in all the simulations of the periodic and continuous environments , are shown. In all 5 populations, the same pattern emerged: the population was primarily of group A, but niche construction on by-products resulted in adaptive diversification, with one ecotype strongly specializing on by-products, such that it lost the ability to uptake the primary resource.In the 5 populations that evolved a stable cross-feeding, we recovered the mutational history of the lineages of ecotypes A and B. Final phylogenetic trees of the 5 populations are represented in before the loss of the pump provoking the change of trophic group. In the LTEE, the S ecotype specialized on acetate, but was still able to grow on glucose. However, recent work has shown that while the S ecotype improved its ability to grow on acetate since the diversification event, it was not the case on glucose, presaging a possible complete loss of its ability to grow on glucose in the longer term [Interestingly, in all simulations, the loss of this ability was not the source of the adaptive diversification. The diversification event occurred a few hundreds of generations ger term . Conversi.e., 333 time-steps). Then, we computed the log-fitness [To test whether ecotypes A and B coexistence is maintained by negative frequency-dependent interactions, we performed short term competition experiments with the 5 populations that evolved a stable cross-feeding interaction at the end of the simulations in the periodic environment . Initial populations were seeded at 9 different initial frequencies of B  and were propagated in the same periodic environment during 1 cycle  just after adaptive diversification  and at the end of the simulations . As a control, these populations were also propagated in the periodic environment. For each population, 10 repetitions were run in each environment. Then, we evaluated the stability of the A/B cross-feeding interaction by counting the number of simulations where the interaction persisted, the number of simulations where the interaction failed, and the time before interaction failure.To test whether a population with two stable A and B ecotypes (evolved in the periodic environment) could persist when placed in the continuous one, we let populations from the periodic environment evolve in chemostat-like environments for 500,000 time-steps. The 5 populations that evolved a stable cross-feeding in the periodic environment were propagated in a continuous environment at two different stages of their evolution: The proportion of simulations where the interaction persisted are displayed in In order to understand why the A/B interaction failed in half of the continuous environment experiments (50% of the assays in the late populations), and why the A/B interaction failures implied the extinction of ecotype B in most cases (80% of the failures in the late populations), we studied in details the evolution of digital organisms in this environment.(i) significantly increased their genome size , their mean metabolic redundancy  and mean uptake diversity , and (ii) significantly decreased their mean production diversity , when they evolved in the continuous environment for 500,000 time-steps. Indeed, the relaxation of selective pressures to maintain concentrations under the lethal toxicity thresholds led to a restructuring of A organisms towards a genome and metabolic network well-adapted to continuous conditions . The results are presented in i.e., ecotype B significantly increased its uptake diversity and reduced its production diversity). The traits of ecotypes A and B diverged: (i) ecotype A strongly specialized on mexo (with a mean uptake diversity of 1.17 metabolites), and optimized metabolic fluxes according to the trade-off between avoiding lethal toxicity thresholds and maximizing the score . (ii) Ecotype B specialized on by-products by increasing the uptake diversity and reducing the production diversity. This demonstrates that ecotypes A and B specialized to their own niches, and that ecotypes A and B traits diverged by character displacement, in complete agreement with the LTEE [In order to understand why the interaction between A and B ecotypes was more robust in late than early populations, we compared the genomic and metabolic structures of early and late populations, independently for A and B ecotypes. We performed the same statistical tests as in Tables the LTEE , 34.mexo and increase their production diversity. On the other hand, B organisms specialized on a large number of by-products. However, character displacement and niche specialization was stronger in late than early populations. For this reason, late A organisms needed more mutations (and then more evolution time) to adapt to continuous conditions  while the second one is generated by the replacement of the exogeneously-provided nutrient by the secreted by-products through a mechanism of niche construction.Using In the continuous environment, where the primary resource is constantly provided (like in a chemostat), cross-feeding interactions emerged, but were not stable because of competitive exclusion. In this case, organisms enriched their environment via their metabolic activity, such that mutants were temporarily able to feed on by-products. But the absence of seasonality precludes any possibility for the stabilization of cross-feeding interactions.e.g., gene amplification) and of the metabolic network. It also allowed us to dissect the precise mechanism behind the evolved robustness of the cross-feeding interaction. We demonstrated that those results are robust to model parameter variation. Indeed, stable cross-feeding interactions emerged in the periodic environment for a wide range of parameter values, including well-mixed populations and infinite diffusion rate, while they never appeared in the continuous environment, thus reinforcing our conclusions Click here for additional data file.S1 Fig(A) Phylogenetic trees of the 12 repetitions in the periodic environment. (B) Phylogenetic trees of the 12 repetitions in the continuous environment. Tree leaves are colored depending on their trophic group: group A in blue, group B in green. Phylogenetic trees are numbered by repetition. For each tree, the scale is represented in simulation time-steps.(TIF)Click here for additional data file.S2 Fig(A) Periodic environment. (B) Random environment. (C) Continuous environment.For each environment, all the repetitions are represented in different colors. (TIF)Click here for additional data file.S3 FigThe 10 repetitions of population 3 are displayed. The 7 essential metabolites  that were produced by ecotype A organisms at the beginning of the assays are represented vertically for each repetition. Background colors indicate a production loss. Essential metabolites that are consumed by ecotype B organisms are colored in red, the other in green. At the top, the evolution of groups A and B proportions is represented, and is colored in green when the A/B interaction persisted, or in red when the interaction failed. In all simulations where A have ceased to produce a metabolite pumped-in by B, B has gone to extinction.(TIF)Click here for additional data file.S1 AppendixSix key parameters have been explored to evaluate the probability of emergence of a stable cross-feeding in the periodic and continuous environments. Each parameter has been modified independently around default parameter values. The emergence of a stable cross-feeding is evaluated in the same way as in (PDF)Click here for additional data file.S1 VideoThis video shows the evolution of the ecotype B relative fitness during the first cycle of the short-term competition experiment. Each of the 333 frames corresponds to one time-step, the whole video presenting the entire cycle.(MPG)Click here for additional data file.


Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities.N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols.We present iNS934, the first GSMM for N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of The online version of this article (doi:10.1186/s12918-017-0441-1) contains supplementary material, which is available to authorized users. In the last few years, interest in microalgæ has risen because of their ability to produce a wide range of compounds, such as carotenoids –3, lipidomics data, such as transcriptional profiling, allows development of a meaningful systemic representation of metabolism [Escherichia coli [Saccharomyces cerevisiae [Arabidopsis thaliana [Metabolic networks provide an efficient framework to describe cellular metabolism and have become an important tool in metabolic engineering, facilitating strain optimization and reducing the need for expensive in vivo experiments . In additabolism . Genome-hia coli , 16, Sacrevisiae  and Arabthaliana .Chlamydomonas reinhardtii [Botryococcus braunii [Ostreococcus [Phaeodactylum tricornutum [Ectocarpus siliculosus [Emiliania huxleyi [Nannochloropsis salina. Nannochloropsis species has emerged as a leading microorganism for biodiesel production, due to their high photoautotrophic biomass accumulation rates [Nannochloropsis species on a large scale using natural sunlight has been achieved by several companies [Nannochloropsis has gained great interest because of its potential for bio-production of eicosapentaenoic acid (EPA) which is a relevant additive for human health [Nannochloropsis. Indeed, it could represent over 30% of total fatty acid content under heterotrophic conditions [Nannochloropsis.Several efforts have been made to model algæ metabolism . Green anhardtii –27, one  braunii , five fo braunii –33 and teococcus . Additiocornutum –41, one iculosus  and one  huxleyi . One impon rates  and highon rates , either ompanies . Moreoven health –49 and nn health –52. EPA nditions  and overnditions  in NannoN. salina, built with a strategy that integrates metabolic knowledge from several related species, genomic and transcriptomic data. In particular, we generated transcriptomic data for N. salina which allowed us to confirm coding sequences (CDS) in its genome and also discover new ones. iNS934 provides a detailed description of biosynthesis of lipids for the Nannochloropsis genus. Specifically, it describes reactions for the biosynthesis of polyunsaturated fatty acids such as EPA, arachidonic acid (ARA) and eicosatetraenoic acid (ETA). iNS934 was validated both qualitative and quantitatively, with an average error of 15% in the latter. Moreover, the model was used to propose knockouts that could improve the production of triacylglycerols (TAGs).We present here iNS934, the first genome-scale functional metabolic model for Setting up culture conditions for RNA extractionN. salina cells were obtained from Commonwealth Scientific and Industrial Research Organization (CSIRO) and identified as CS-190 Nannochloropsis salina CCAP 849/2. They were cultured in Artificial Sea Water (ASW), supplemented with f/2 medium [μE photons m-2s-1) on a 24 h light/day cycle, primary in batch cultures, as described by Chen et al. [N. salina cells were collected at the exponential growth phase (~ 5×106cell/mL) at the following conditions: (1) Dark, Low CO2 (DLC): 4 h dark with 1 L/min air influx (CO2 0.03%), (2) High light, Low CO2 (HLLC): 2 h high light (1000μE photons m-2s-1) and 1 L/min air influx (0.03% CO2) and (3) High light, High CO2 (HLHC): 2 h high light (1000μE photons m-2s-1) and 1 L/min air influx (1.5% CO2). Total RNA was extracted from frozen cells, which were ground using a mortar and pestle, using TRIzol RNA Isolation Reagents (Invitrogen) according to the manufacturer. Total RNA and mRNA integrity were analyzed by running them on agarose gel and in an Agilent Bioanalyzer to evaluate its quality before sending it to library construction.2 medium  at 20 °Cn et al. . For mRNEST collection/library construction and sequencingN. salina transcriptome, two different sequencing techniques were adopted: GS FLX+ System (Roche), sequencing a normalized cDNA library, and Illumina sequencing a cDNA library. Regular and normalized library construction and sequencing was performed by Eurofin MWG Operon, USA.To obtain a good coverage of the For the Roche GS FLX sequencing, we made a RNA pool, including all 3 conditions previously described . To build the normalized cDNA library construction, from a total RNA sample, poly(A)+ RNA was isolated and used for cDNA synthesis. The poly(A)+ was fragmented by ultrasound (1 pulse of 30 s at 4 °C). First-strand cDNA synthesis was primed with a N6 randomized primer. Then 454 adapters A and B were ligated to the 5’ and 3’ ends of the cDNA. The cDNA was finally amplified with 13 PCR cycles using a proof reading enzyme. Normalization was carried out by one cycle of denaturation and reassociation of the cDNA, resulting in N1-cDNA. Reassociated ds-cDNA was separated from the remaining ss-cDNA  by passing the mixture over a hydroxylapatite column. After hydroxylapatite chromatography, the ss-cDNA was amplified with 14 PCR cycles. For Titanium sequencing the cDNA in the size range of 500–700 bp was eluted from a preparative agarose gel. Half a plate of GS FLX+ System (Roche) was sequenced.Library preparation of total RNA from conditions HLLC and HLHC was carried out using the Illumina TruSeq kit. Cluster formation and sequencing on HiSeq2000 were done according to the manufacturer’s instructions. Two samples, one from each condition, were prepared in 250 bp paired-end sequenced in 2 different lanes, delivering around 40 million reads per lane.De novotranscriptome assembly1 software. The third dataset corresponds to the transcripts assembled by wgs-assembler, using 454 reads that did not have hits against Illumina transcripts. Transcripts from these three sets of data with less than 30 mapping reads or length under 300 bp were discarded. The remaining transcripts constitute our de novo transcriptome.We divided this process in 4 steps: (1) Illumina raw data was error corrected, and then sequences were assembled using the Trinity package. (2) Roche 454 GS FLX raw reads were cleaned and trimmed with Figaro . (3) UsiMapping transcriptome to reference genomede novo transcriptome assembly to the N. salina CCMP537 genome assembly [2.We mapped this assembly   against Swissprot, KEGG, PRIAM and NR protein databases. Moreover, InterProScan (default parameters) was used to identify protein domains and GO numbers. In order to build a consensus annotation for each gene, the Gene name , EC number , KO number (KEGG), GO number (Interpro), InterPro number (Interpro) and protein product  attributes were obtained from BLAST and Interpro results; using an in-house PERL script; In parenthesis we show the databases from which attributes were obtained. Afterwards, a single value for each attribute was defined by picking the most frequent from the set of 10 best hits times the number of databases the attribute was parsed . One exception to the previous rule was the protein product attribute; we chose it prioritizing the databases result in the following decreasing priority order: Swissprot, KEGG and NR.Functional annotation of coding regions from the reference genome and the supplementary transcripts from the The complete set of annotated CDS is included as Additional file N. salina metabolic network was generated following the five stages described in the protocol for generating high-quality GSMMs [Chlamydomonas reinhardtii and its iRC1080 metabolic model [N. salina genome annotation and literature. First, we searched for orthologs between our N. salina CDS and the C. reinhardtii protein sequences, using Inparanoid [N. salina using Pantograph [C. reinhardtii. Then, we looked at the list of genes not present in the draft model, but which annotation included an Enzyme Commission (EC) number and we mapped those ECs to BiGG reaction identifiers. We imported those BiGG reactions into our model, using the BiGG web API3. Afterwards, we manually determined a list of reactions using different resources of information such as BIGG, MetaCyc and KEGG. These reactions were added to our reconstruction with their corresponding identifiers and N. salina gene associations. For reactions and metabolites without BiGG identifiers, BiGG-like identifiers were assigned. After adding reactions, we manually curated compartments, changed reversibility for some reactions, moved species among compartments, renamed and pruned unused elements, among other changes. In order to generate a functional model, we used meneco [The reconstruction of ty GSMMs . We usedic model  in additparanoid  and Orthparanoid . Then wentograph , taking d meneco , 64 to lFormulas and charges for metabolites in our model were revised and all reactions in our model were subjected to mass and charge balances. Except light and exchange reactions, all reactions in our model are mass balanced and only 6% could not be charge balanced. The complete list of metabolites in the model with their formula, charge, BiGG ID and ID from the external database MetaNetX  is incluWe produced a version of our model in Systems Biology Markup Language (SBML) format in order to analyze it with compatible existing tools, and share it with the community. The model can be obtained as Additional file Growth experiments for model validation In order to validate our metabolic network, we prepared a battery of simulation tests based on 32 previously published growth experiments . We used the complete f/2 medium as described by Guillard [−4 for ammonium and nitrate and a molar concentration of 4.4×10−4 for urea. Before inoculation, cells were washed with ASW. We inoculated 1 L flasks with 2×106 cells/mL, which were maintained at 25 °C, aireation of 0.5 VVM and to 60 μmol m −2s−1 of light intensity. For batch cultures growing with different carbon dioxide levels, it was supplied through the gas inlet in concentrations of 2 and 5%, respectively.For growth experiments conducted in our laboratory we used Guillard , which cIn order to diminish the internal reservoir of phosphate in cells for phosphate evaluation, inocula cells came from a culture with diminished phosphate concentration (0.4 mg/L phosphate). All cultures were followed by cell counting (Newbauer chamber and OD750 nm), biomass estimation (dry weight) and in some cultures nitrate consumption, evaluated using a microplate technique as described . All expTo analyze our iNS934 model, we used Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) and dynamic Flux Balance Analysis (dFBA) from the Cobra Toolbox in MATLAB . Flux CoN. salina: autotrophic growth with nitrate, ammonium and urea. Second, we used data from three batch cultures grown with nitrate at 0.03%, 2% and 5% of CO2 in the inflow gas. An in silico growth rate was obtained for each FBA simulation using fixed experimental uptake rates of nutrients and setting all other uptake reactions to zero. The error between experimental and predicted growth rates was then calculated.For model validation, we performed FBA with growth rate as the objective function to predict growth in different growth conditions mentioned in the previous section. For each test, we modified the flux boundaries of exchange reactions to simulate the composition of each growth media. We performed both a qualitative and a quantitative validation. Qualitative validation was used in cases where growth and/or uptake rates could not be obtained from the experimental data gathered from the literature and we only had growth/non-growth data. In these cases, we used an in silico scenario that simulates a rich medium by allowing free uptake of nutrients. The growth rate obtained in this condition was considered the reference maximal growth rate. For each experiment, we established that growth was achieved if the obtained growth rate was greater than 1/3 of the reference value , 70. TheCO2 by using the formula described previously [We estimated uptake of eviously : 1\docuCO2. The specific uptake rate of CO2 used to perform simulations was calculated as the average specific uptake rate in exponential phase.Values of biofixation were further transformed to specific uptake rates by dividing them by cell concentrations and the molecular weight of N. salina which may be useful for lipid overproduction, we developed a method for in silico strain optimization based on reaction knockouts. Our method guarantees the production of a target metabolism, while conserving the functional property, that is, the production of biomass. We iterated 200 times and sorted the resulting knockout sets. The metric to sort the results was defined as μ is the specific growth rate, rt is the specific production rate of the target t and k is the knockout set.In order to find mutants of This method consists of four steps: (1) We randomly traverse the reactions in the iNS934 model, removing reactions that are not needed for the production of the target metabolite. (2) Starting again from iNS934, we randomly traverse the model removing all reactions that are not needed for biomass production, while keeping those conserved in step 1. (3) Using FBA we check if the resulting model is able to produce the target metabolite when optimizing growth rate. If so, we continue to step 4. Otherwise we restart from step 1. (4) We try to recover the reactions removed during step 1 one at a time, checking that their inclusion maintains the integrity of the model to produce biomass and the target metabolite simultaneously. When a reaction breaks this restriction, it is included in the list of reactions to knockout.N. salina and then combined them with the reported draft CCMP527 genome [N. salina. From this set, 10913 putative genes identified in the transcripts of our de novo assembly were not contained in the reference genome  such as ETA(20:4), ARA(20:4) and EPA(20:5), which were absent in the C. reinhardtii model iRC1080 [Nannochloropsis. Therefore their inclusion represents a major advance towards in silico simulation of lipid production in this algæ. Additionally, we also added unsaturated fatty acids, such as tetradecenoic acid (14:1) and hexadecadienoic acid (16:2), among others.The lipid metabolism in microalgæ is biotechnologically relevant, given that it is key for the production of biodiesel and food additives. Therefore the inclusion of an accurate and species-specific description of lipid pathways into a GSMM is needed if we want to use it as a platform to guide the production of these biotechnological targets. In order to describe the lipid metabolism of  iRC1080 , 75. ThiNannochloropsis-specific reactions that define the bioynthesis of TAG, diacylglycerol (DAG), phosphatidylcholine (PC), monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerol (DGDG), sulfoquinovosyl diacylglycerol (SQDG), phosphatidylglycerol (PG), phosphatidylethanolamine (PE), phosphatidylinositol (PI) diacylglyceryl-O-4’- homoserine (DGTS) and free fatty acids. In particular, these 503 reactions account for specific proportions of glycerolipids into the biomass of Nannochloropsis and specific types of glycerolipids of Nannochloropsis with respect to other algæ. These glycerolipids could represent an important percentage of the Nannochloropsis biomass [Nannochloropsis. The new reactions enable the use of iNS934 as a predictor of lipid metabolism in Nannochloropsis.Once we added the pathways for all the required fatty acids, we replaced the glycerolipid pathways from the initial draft with 503 new  biomass . Given iFurthermore, based on current knowledge of species of chromista , we assoUrea cycleN. salina has a complete urea cycle, including ornithine carbamoyltransferase, argininosuccinate synthase, argininosuccinate lyase, and arginase  and different CO2 concentrations  in the gas inlet. Measured uptake rates of nitrogen sources as well as estimated uptake rates of CO2 used as constraints in the model can be found in Additional file CO2 uptake rate could improve growth rate predictions.We assessed iNS934 quantitative predictive power by simulating We also analyzed the inter-compartment fluxes of experimental conditions with different nitrogen sources in order to understand the main metabolic mechanisms that are involved in each case. In the first condition, the cell consumes nitrate as the nitrogen source. This is transformed to nitrite by the nitrate redutase in the cytosol. The nitrite is then transported to the chloroplast where it is further transformed to ammonium by the nitrite reductase. This ammonium is used to build some building blocks such as L-serine by the threonine ammonia-lyase (EC: 4.3.1.19). For carbon fixation, carbon dioxide entered the cytosol and was transported to the chloroplast where it participated in the Calvin Cycle. The malonyl-CoA used to build fatty acids is also created from carbon dioxide. Fatty acids such as decanoic acid and PUFAs such as eicosanopentanoic acid were synthesized in the chloroplast. PUFAs leave the chloroplast and travel to the endoplasmic reticulum to synthesize glycerolipids.N. salina consumes ammonium which is transformed in the cytosol to urea (EC: 3.5.1.5) and amino acids such as L-glutamine, L-threonine and glycine. Urea enters the urea cycle and is further transformed to L-arginine. To simulate carbon fluxes, acetyl-CoA, which is synthesized in the cytosol, is transported to the mitochondria in order to generate energy and reducing power through the tricarboxylic acid cycle.In the second condition, N. salina consumes urea. The urea is transformed to ammonium by two consecutive reactions (EC: 6.3.4.6 and 3.5.1.54) in the cytosol. Additionally, ammonium is transported to the chloroplast where it is also used to synthesize glutamine and L-serine. The same mechanisms of carbon fixation and biosynthesis of fatty acids observed in experimental condition one was observed for cases where N. salina consumed ammonium or urea.In the third condition Nannochloropsis changes when cells growing on a nitrogen replete media are transferred to a nitrogen-depleted condition. In particular, the content of TAG increases at least 100 fold [−1 according to Simionato et al. [−1 [It has been shown that lipid content in 100 fold . We wanto et al. . At the  al. [−1 . At the N. salina consumed nitrate and generated biomass according to the biomass equation for a nitrogen-replete condition. The glycerolipids were consequently increased as biomass increased. At the second stage, the lipid production increased significantly with respect to stage one. In the third stage, the growth rate and the lipid production were the same as stage one. This preliminary simulation showed that iNS934 is able to accurately describe the behavior in both nitrogen replete and nitrogen depleted conditions. Therefore this a feature that could be further exploited in biotechnological applications related to lipid optimization.As shown in Fig. in silico rate of TAG production in N. salina. This is a powerful and low-cost tool to predict the behavior of N. salina in different genetic and media contexts, working as a guide for metabolic engineering efforts. In order to find those in silico mutants, we initially used OptKnock [in house algorithm to determine sets of reactions that guarantee a minimum desired production, while keeping the capacity to produce biomass (see Methods). We applied this algorithm to optimize our target metabolites.As an example for iNS934 use, we focused on TAG production optimization as a case study. We used our model to search for sets of reactions whose blockage resulted in a higher OptKnock  to obtaiOptKnock , but we TAGs, like other glycerolipids, are represented as biomass constituents in GSMMs. This represents a challenge when optimizing these types of compounds because most strain design algorithms are conceived to optimize metabolic products which are secreted from the cell, such as succinate or lactate, not biomass constituents. To simulate an additional production of TAG, we created artificial reactions leading to the production of additional TAG. A demand reaction for that additional TAG was considered as the target objective function. Using our in-house algorithm for strain optimization, we found 82 sets of reactions whose independent elimination guaranteed a minimum TAG production CDS. New set of CDS of Additional file 2N. salina model iNS934. (XLSX 122 kb)Metabolites in iNS934. List of all metabolites in Additional file 3N. salina iNS934 model. SBML representation of the reconstructed model of N. salina. This file is compatible with SBML Level 3, Version 1, fbc ver. 2. It has been tested with COBRA Toolbox (2.0). (XML 4137 kb)Model. Additional file 4Extra tables and figures. Tables and figures with details on model reconstruction and analysis. (XLSX 484 kb)Additional file 5N. salina to CCMP537 genome assembly [ID mapping. Mapping of transcriptome of assembly . Constraints used to predict growth rates. Measured uptake rates of nitrogen sources (Nitrate/ammonium/urea) and estimated Additional file 7N. salina. (XLSX 107 kb)Strain optimization. Proposed reaction knockouts for improving production of lipids in


The complexity of metabolic networks can make the origin and impact of changes in central metabolism occurring during diseases difficult to understand. Computer simulations can help unravel this complexity, and progress has advanced in genome-scale metabolic models. However, many models produce unrealistic results when challenged to simulate abnormal metabolism as they include incorrect specification and localisation of reactions and transport steps, incorrect reaction parameters, and confounding of prosthetic groups and free metabolites in reactions. Other common drawbacks are due to their scale, making them difficult to parameterise and simulation results hard to interpret. Therefore, it remains important to develop smaller, manually curated models.We present MitoCore, a manually curated constraint-based computer model of human metabolism that incorporates the complexity of central metabolism and simulates this metabolism successfully under normal and abnormal physiological conditions, including hypoxia and mitochondrial diseases. MitoCore describes 324 metabolic reactions, 83 transport steps between mitochondrion and cytosol, and 74 metabolite inputs and outputs through the plasma membrane, to produce a model of manageable scale for easy interpretation of results. Its key innovations include a more accurate partitioning of metabolism between cytosol and mitochondrial matrix; better modelling of connecting transport steps; differentiation of prosthetic groups and free co-factors in reactions; and a new representation of the respiratory chain and the proton motive force. MitoCore’s default parameters simulate normal cardiomyocyte metabolism, and to improve usability and allow comparison with other models and types of analysis, its reactions and metabolites have extensive annotation, and cross-reference identifiers from Virtual Metabolic Human database and KEGG. These innovations—including over 100 reactions absent or modified from Recon 2—are necessary to model central metabolism more accurately.We anticipate MitoCore as a research tool for scientists, from experimentalists looking to interpret their data and test hypotheses, to experienced modellers predicting the consequences of disease or using computationally intensive methods that are infeasible with larger models, as well as a teaching tool for those new to modelling and needing a small, manageable model on which to learn and experiment.The online version of this article (10.1186/s12918-017-0500-7) contains supplementary material, which is available to authorized users. Human central metabolism is a large and complex system under sensitive homeostatic control, and its disturbance is causative or associated with many diseases and responses to toxins. However, it is often difficult to relate more than a handful of these changes to their underlying origin or their down-stream impact, due to the highly connected nature of the reactions of central metabolism. Computer models are widely accepted in many fields as a tool to incorporate complexity and simulate changes, allowing predictions to be made and providing a unifying framework to interpret empirical data, especially from large, noisy and incomplete data sets. Yet modelling is treated with scepticism by many biomedical researchers despite their potential broad utility . Simple iAS253 model of the mitochondrion, which we used to simulate metabolic diseases of the tricarboxylic acid cycle [Many problems of genome-scale models can be avoided by using smaller, curated models validated against data from normal and disease metabolism. A more focussed and carefully defined model allows the user to examine and be confident in each reaction, and more clearly elucidate the behaviour of the system, including any short-comings, as well as interpret the results more easily. These ‘core’ models have been shown to be useful in a range of areas , 17 and id cycle . This moid cycle , fumaratid cycle  and commid cycle . These sHere we present MitoCore, a new constraint-based model of central metabolism that addresses these issues and comprehensively expands upon and refines our previous mitochondrial models. This model has been designed to be easy-to-use, includes extensive annotation, has default parameters to simulate human cardiomyocyte metabolism, and is encoded in the widely used SBML format . We antiWe designed MitoCore as a constraint-based model of central metabolism with two compartments; one representing the cytosol, outer mitochondrial membrane, inter-membrane space and cytosolic side of the inner mitochondrial membrane, and the other the mitochondrial side of the inner membrane and the mitochondrial matrix. The accuracy and utility of a metabolic model is dependent upon the reactions included and the correct partitioning of these reactions between compartments. Thus to create MitoCore, we built a list of candidate reactions to include by considering human reactions in the KEGG , HumanCy3− and exported ATP4− nucleotides to reflect the charge difference that affects only ΔΨ  and the role of protons crossing the inner mitochondrial membrane as part of oxidative phosphorylation, which produces the majority of cellular ATP. The PMF is achieved by complexes I, III and IV pumping matrix protons across the mitochondrial inner membrane and into the intermembrane space. This proton-pumping creates a proton motive force (PMF) across the membrane that has two components: a proton gradient (ΔpH) coupled with an electrical membrane potential (ΔΨ). The energy for proton-pumping comes from the transfer of electrons down the respiratory chain from NADH and ubiquinone to oxygen, to form water. Additional electrons are passed into the respiratory chain from the TCA cycle by complex II, and from the degradation of fatty acids and amino acids by the electron-transfer flavoprotein (ETF). Mitochondrial ATP synthase uses the PMF to power ATP synthesis from ADP and phosphate by channelling protons back across the inner mitochondrial membrane. It is thus clear that it is necessary to distinguish as Peter Mitchell did , the proy ΔΨ Eq. , whereasy ΔΨ Eq. :1\documeWe next modelled the remaining transport steps that connect the compartments, and the influx and efflux of metabolites to the cytoplasm. We defined four different transport categories. First were transport steps based on the characterised mitochondrial transport proteins. Many carriers can transport a range of related substrates  and we modelled each metabolite combination as a separate step including counter exchange and proton-coupling. In some cases proton-coupled transporters were represented by two reactions to model separately the forward and reverse directions, and a proton (plus co-transported PMF metabolite) only used for movement down the proton gradient. This prevented transporters being used in reverse to pump protons artificially (and so transferring PMF) and thereby unrealistically contributing to ATP production by the mitochondrial ATP synthase. The second category was for metabolites whose transporters are unidentified. We modelled these steps as uniporters that are not proton coupled. If the metabolite was charged and moving into the mitochondrial matrix we assumed this would impact ΔΨ and co-transported 0.82 PMF metabolite accordingly. The third category was for metabolites that can diffuse across the inner mitochondrial membrane—including oxygen, carbon dioxide and water—and these were modelled as reversible uniport transport steps. Finally we modelled the insertion of lipids into the mitochondrial inner membrane via the flippases, which was coupled to ATP hydrolysis.To account for the generation of reactive oxygen species (ROS) from the respiratory chain in MitoCore, we defined their production as 0.001% of the flux through complex I—the primary site of ROS generation from the respiratory chain—and hence also reduce the efficiency of proton-pumping , 33. SubVarious reactions defined in KEGG do not distinguish between free and prosthetic redox cofactors, e.g. FAD/FADH, which can lead to unrealistic electron transport being mediated between prosthetic redox co-factors. To prevent this in MitoCore, we removed prosthetic co-factors from reactions (such as for complex II and the ETF), and instead directly coupled their reactions, e.g. to the reduction of ubiquinone to ubiquinol.For convenience in setting up simulations, we created four ‘pseudo’ reactions that summarise aspects of MitoCore’s biological activity and can be used by flux balance analysis as objective functions: ATP hydrolysis (representing cellular ATP demand), and the biosynthesis of heme, lipids and amino acids.https://vmh.uni.lu) for MitoCore where possible. However, it was necessary to create 105 new reactions for MitoCore . Fumarate deficiency is a mitochondrial disease resulting from a defect in the fumarate hydratase gene (ENSG00000091483) that encodes both cytosolic and mitochondrial isoforms, and that converts fumarate to malate—a key step in the TCA cycle. This can leave patients with almost no residual enzyme activity and causes developmental delay, severe mental retardation, seizures and dysmorphic facial features –37. A keTo simulate the disease in each model we used the default cardiomyocyte uptake parameters of MitoCore (replicating them in Recon 2.2) and inactivated both the cytosolic and mitochondrial reactions that represent the enzyme. The MitoCore model showed a 72% reduction in ATP production upon deletion of fumarate hydratase compared to the default model Fig. . Flux thTo show the MitoCore model can simulate experimentally induced conditions that are directly affected by the proton motive force, we performed a series of simulations representing an increasing proton leak across the mitochondrial inner membrane. We introduced a flux through the transport step that represents the leak of matrix protons to the cytosol through the uncoupling protein 2 (UCP2) in cardiomyocytes . The optHere we present MitoCore, a curated, constraint-based model of human central metabolism, designed as a predictive model of metabolism in disease and toxicology, and for use by a wide range of researchers. It covers all major pathways involved in central metabolism using 407 reactions and mitochondrial transport steps, and 74 transport steps over the plasma membrane. To increase the metabolic flexibility of MitoCore, we included a large number of reactions that were not assigned to classical metabolic pathways, but could have potentially important roles in supporting central metabolism. MitoCore was parameterised and annotated for cardiomyocyte metabolism, which is useful for many types of analyses as the cardiomyocyte can metabolise a wide range of substrates and has reactions common to many other cell types, as well as representing the metabolism of an organ of utmost importance in human health, disease and toxicology. This allows the simulation results to be generalisable, without having features that are particularly cell specific, such as those found only in hepatocytes. However, we have included reactions that are inactivated in the default cardiac model, but can be activated to represent the metabolic capabilities of other cell types, e.g. gluconeogenesis, ketogenesis, β-alanine synthesis and folate degradation. Thus, the model allows biologically relevant flux distributions to be generated ‘out of the box’ without altering the model, while allowing for easy modification to represent metabolism in other cell types.MitoCore has several unique features. The first is the more accurate partitioning of metabolism between the mitochondrion and the cytosol by using extensive localisation data and annotation. This partitioning has an important impact on model behaviour as the limited transport steps into the mitochondrial matrix result in dramatic differences in metabolite availability in the matrix compared to the cytosol. Therefore, it is important that reactions are assigned to the correct compartment. We achieved this by manually evaluating the subcellular localisation of each reaction’s catalysing protein by using the mitochondrial localisation evidence in the MitoMiner database .To model oxidative phosphorylation in MitoCore, we devised a new representation of the PMF and mitochondrial respiratory chain—the second unique feature of MitoCore. MitoCore’s representation of the respiratory chain differs in many key aspects to other metabolic models, due to how it models vectorial protons and accounts for both components of the PMF. MitoCore represents the PMF as a metabolite that is co-transported in steps that transport charged metabolites or protons across the inner mitochondrial membrane, such as the reactions of the respiratory complexes, and in proton-coupled and electrogenic transport steps. This separate modelling of vectorial protons using an additional new PMF metabolite enabled the impact of electrogenic transporters on the PMF to be accounted for in flux balance analysis simulations for the first time, and prevented simulation artefacts where  protons generated or removed from other parts of metabolism allow unrealistically high ATP production.The third unique feature of MitoCore is the physiochemical modelling of the transport steps that connect the cytosolic and mitochondrial compartments including their impact on the PMF, and the influx and efflux of metabolites over the plasma membrane. 83 transport steps connect the two compartments, of which 30 are modelled on the known transport mechanisms of characterised transport proteins of the inner mitochondrial membrane, whereas the other 53 represent known transport capabilities of the membrane, such as diffusion of small metabolites. 74 transport steps at the cytosolic boundary represent the import and export of metabolites across the plasma membrane, such as oxygen, carbon dioxide, glucose, fatty acids and amino acids.MitoCore improves over other models by distinguishing between free and prosthetic redox co-factors. This keeps separate the electrons entering the respiratory chain from different sources, which can otherwise become connected via e.g. a shared flavin adenine nucleotide metabolite, and is particularly relevant under perturbed conditions where the erroneous connection of free and prosthetic flavin adenine nucleotides can cause unrealistic electron transport bypasses to occur. This problem is endemic in large-scale models that auto-generate reaction networks directly from metabolic databases without manual curation of reactions.Other improvements to MitoCore included modelling protein complexes as one reaction rather than a series of linked reactions, as can often be found in metabolic pathway databases and other models. This is particularly important for simulations of gene knockouts, as a gene deficiency will disrupt the activity of the whole protein complex rather than just one of its subunit’s reactions. Further, to facilitate gene-based analyses (such as knock out studies) we provide reaction-to-gene mappings with both gene symbols and Ensembl identifiers. Finally, to summarise different aspects of central metabolism during simulations with MitoCore, we defined four ‘pseudo’ reactions for biosynthesis of amino acids, lipids and heme that are required by cells for maintenance and growth, and ATP hydrolysis to reflect the energy demand of the cell. These pseudo reactions are designed to be used as objective functions during flux balance analysis and can determine if particular functions of central metabolism have become impaired during simulations of disease.A weakness when it comes to interpreting many metabolic models is the lack of provenance about their components. For example recording why reactions have been included, why directionality constraints have been set, or the origin of reaction parameters. Thus for MitoCore we created a supplementary annotation spreadsheet  and compared the results to those generated by the genome-scale model Recon 2.2  and fromFurther differences could be seen when comparing the results of preliminary simulations of fumarase deficiency Fig.  that shoAn important aspect of modelling is exploring different hypotheses by running multiple simulations with different parameters. This is especially true when simulating mechanisms of disease, which may reveal compensatory mechanisms and the effect of supplying different metabolites to compensate for metabolite deficiencies. Therefore, it is beneficial if simulation runtimes are comparatively short. Even when using computationally expensive types of flux balance analysis, such as geometric FBA, MitoCore takes ~10 s per run, compared to ~3 h per run for Recon 2.2. The short runtime for MitoCore simulations makes the exploration of varying parameters much easier and routine, computationally expensive methods feasible, and allows rapid testing of new ideas and hypotheses.iAS253 model using the same parameters [0 cells to maintain a proton motive force in mitochondria [When comparing maximum ATP production using default parameters to the previous rameters , ATP prochondria , 43.Taken together these simulation results show the MitoCore model is capable of producing realistic results using a wide range of metabolites while avoiding some of the common problems of genome-scale models, including: unlikely shuttling between compartments, reactions that are not compartmentalized correctly, incorrect directionality constraints and the inclusion of reactions that are unlikely to be present in human. These problems, while understandable due to the scale of these models, nevertheless make producing biological relevant flux distributions difficult, especially under perturbed conditions, and show the continued need for carefully curated ‘core’ models for some types of simulation. Further, these comparisons illustrate an additional role for ‘core’ models, which is the ability to identify and diagnose problem areas for improvement in genome-scale models.MitoCore is a constraint-based model of human central metabolism, provided with default parameters that provide physiologically realistic reaction fluxes for cardiomyocytes in FBA simulations. The model has several innovations including a new representation of the respiratory chain and proton motive force, and partitioning of reactions to subcellular compartments based on the latest localisation evidence. To achieve an accurate depiction of central metabolism, each of MitoCore’s 407 reactions and transport steps was manually evaluated for directionality, expression of its gene in heart, and subcellular localisation of its protein. To allow MitoCore to be easily used and to make the results comparable with other models and compatible with other types of analyses, we used identifiers from the Virtual Metabolic Human database for both reactions and metabolites where possible, and recorded KEGG identifiers in the annotation. To help ease of use, we also provide an annotation spreadsheet that provides gene mappings, localisation and heart expression evidence, and notes on parameter choice. MitoCore is provided in SBML format to be compatible with a wide range of software. We hope MitoCore will be of use as a research tool to a wide range of biomedical scientists and students—from experienced modellers interested in central metabolism or using computationally intensive methods that are infeasible on a genome-scale model, to those new to modelling who would like to begin by using a small manageable model, with application as a predictive model of disease.iAS253 mitochondrial model [iAS253 model and could impact central metabolism. Each reaction was reassessed for subcellular localisation and directionality (see below), and to ensure the reactions were occurring in most tissues including cardiac, tissue expression of their genes and proteins was verified by using the Human Protein Atlas [The starting point for the MitoCore model was an updated version of the al model . The modal model , HumanCyal model  and BRENin Atlas .https://vmh.uni.lu) and used its identifiers for reactions and metabolites where possible.To partition the reactions into either the cytosol or mitochondrion, each enzyme was manually evaluated by using the mitochondrial localisation evidence in the MitoMiner database . MitoMinReaction directionality was manually evaluated for each reaction in MitoCore. The KEGG , HumanCy+ were removed from all reactions and replaced with ubiquinone and ubiquinol. Finally, to represent better the current understanding of central metabolism pathways, some reactions were rewritten, such as the generation of ROS from complex I, and the stoichiometry of the proton-pumping of the respiratory complexes. In some cases a reaction’s subcellular localisation was changed by defining mitochondrial-specific metabolite species, or new reactions were written using existing metabolite species.Modelling of the proton motive force (PMF) was improved across the mitochondrial inner membrane, a pseudo-metabolite was introduced to model the effect on the PMF of proton and electrogenic transport steps across the inner mitochondrial membrane. In total 43 reactions representing respiratory complexes as well as proton-coupled transport steps were rewritten to use this new species. Next, to prevent unrealistic bypasses between free and complex-bound prosthetic flavin adenine dinucleotides, FADH and FADTo highlight where there are differences between a MitoCore reaction and the corresponding reaction in the Virtual Metabolic Human database, the MitoCore identifier used the Virtual Metabolic Human database identifier with the suffix ‘MitoCore’. The model was extensively tested: each reaction in the model was set as the objective function in a series of simulations to ensure all reactions were capable of having flux, with directionality constraints reevaluated for both the reaction and other members of the same pathway if this was not the case; erroneous energy-generating cycles were manually identified and removed by running a succession of simulations where ATP production was maximised and a wide range of different ‘fuel’ metabolites provided; that the model used physiologically characterised pathways under normal conditions and used plausible mechanisms under perturbed conditions.The MitoCore model was encoded in SBML v2.1 and its validity checked with the SBML online validator .Metabolism was simulated by using flux balance analysis (FBA), which can be summarised as calculating the reaction turnover, or fluxes (flows) of metabolites through a network of biochemical reactions assuming a pseudo steady state . The fluFor simulations of ATP yield, all cytosol boundary uptake fluxes for metabolites that can be degraded to produce ATP were set to zero, oxygen was increased to 50 μmol/min/g of dry weight (so that limited oxygen availability did not affect results), while other cytosol boundary conditions were unaltered. The uptake flux of each metabolite of interest was then increased to 1 μmol/min/g of dry weight. Geometric FBA simulations were then performed with maximum ATP production as the objective function. For simulations of fumarase deficiency, the reactions corresponding to mitochondrial and cytosolic fumarate hydratase were inactivated in each model, whilst the uptakes values were left at default values. Simulations were performed with geometric FBA and an objective function of maximum ATP production. To enable geometric FBA of the Recon 2.2 model to converge to a solution, the efflux was prevented of acetone (Recon 2.2 reaction ID: EX_acetone), tetradecanoate (Recon 2.2 reaction ID: EX_ttdca), 3-(4-hydroxyphenyl)pyruvate (Recon 2.2 reaction ID: EX_34hpp_), 4-methyl-2-oxopentanoate (Recon 2.2 reaction ID: EX_34hpp_) and bicarbonate (Recon 2.2 reaction ID: EX_hco3).For simulations of the effect of proton leak on maximum ATP production, the lower bound of the reaction representing the gene UCP2 (MitoCore reaction ID: HtmB_MitoCore) was increased over a series of simulations, thus forcing a minimum flux–representing proton leak—through the reaction.http://www.gnu.org/software/glpk).FBA simulations were performed by using MATLAB  and the COBRA Toolbox , with thAdditional file 1:Companion annotation spreadsheet recording evidence and provenance for reactions and parameters used in MitoCore, and reaction fluxes when simulating maximum ATP production using default parameters for cardiomyocytes. (XLSX 289 kb)Additional file 2:Experimental measurements of the relative contributions of ΔpH and ΔΨ to the proton motive force. (XLSX 11 kb)Additional file 3:105 new or altered reactions in the MitoCore model that differ from those in the Virtual Metabolic Human database (and used by the Recon 2.2 model). (XLSX 82 kb)Additional file 4:MitoCore model encoded in the SBML format. (XML 776 kb)Additional file 5:Flux distributions from the MitoCore and Recon 2.2 models for maximum ATP production with different metabolic fuels. (XLSX 445 kb)Additional file 6:Flux distributions from MitoCore and Recon 2.2 models when simulating fumarase deficiency and maximum ATP production. (XLSX 444 kb)Additional file 7:Flux distributions from the MitoCore model when simulating maximum ATP production with varying proton leak through the reaction representing UCP2. (XLSX 245 kb)


The microbial world displays an immense taxonomic diversity. This diversity is manifested also in a multitude of metabolic pathways that can utilise different substrates and produce different products. Here, we propose that these observations directly link to thermodynamic constraints that inherently arise from the metabolic basis of microbial growth. We show that thermodynamic constraints can enable coexistence of microbes that utilise the same substrate but produce different end products. We find that this thermodynamics-driven emergence of diversity is most relevant for metabolic conversions with low free energy as seen for example under anaerobic conditions, where population dynamics is governed by thermodynamic effects rather than kinetic factors such as substrate uptake rates. These findings provide a general understanding of the microbial diversity based on the first principles of thermodynamics. As such they provide a thermodynamics-based framework for explaining the observed microbial diversity in different natural and synthetic environments. There is an immense diversity of microbes in the natural environment .In ecology, a historically dominant idea in the study of diversity is the ‘competitive exclusion principle', which states that at equilibrium no two species can coexist occupying the same niche . This prAlthough spatial and temporal variation in a single substrate can certainly contribute to microbial diversity, it cannot explain species harbouring different metabolic pathways that enable conversion of the same substrate into different end products . This caArguably the most fundamental inhibitory constraint on microbial growth is that arising from thermodynamics of metabolism. As microbial growth depends on energy harvested from substrates converted into end products, it is governed by the thermodynamics of such metabolic conversions. Here, we consider this relation between the thermodynamic constraints placed on growth sustaining metabolic conversions and the resulting population-level dynamics. Using thermodynamic models of microbial growth, we show that the inevitable slowing down of microbial growth ensuing from product built-up can lead to coexistence of different species implementing different metabolic conversions and consuming the same substrate. As each metabolic conversion operates with different thermodynamics, species utilising these are governed by different growth and product-inhibition dynamics that result in their coexistence. We find this ‘thermodynamics inhibition' effect to be strongest for reactions leading to a low change in free energy, where it dominates over kinetic factors such as substrate uptake rates. In line with this fundamental observation, we find that several biologically relevant microbial conversions as well as theoretically possible metabolic conversions of glucose fit in the regime of strong thermodynamic effects and readily lead to coexistence of different species on a single substrate. These findings provide a thermodynamic basis to evaluate observed microbial diversity.The change of free energy generated by a metabolic conversion under non-standard conditions can be calculated according to R is the gas constant, T is the temperature, a and m are the chemical activity and stoichiometric coefficient of each compound involved in the reaction, i and j are indices over products and substrates, respectively, and m concentration of all solutes, 1 atm, 25 °C, and pH 7).where We use here a previously proposed thermodynamic model  that capK and kr are the half saturation constant for substrate turnover and the ratio of maximum forward over maximum reverse reaction rate, respectively ≈0).Note that this equation results also from the thermodynamic model  when theX1 and X2 in a chemostat, where they are consuming the same substrate, S. Species X1 and X2 are assumed to process the substrate to produce different end products, P1 and P2, respectively. The feed-in rates of the species to the chemostat are considered to be zero, while the substrate concentration in the feed is given by S0. The dilution rate (per hour) of the chemostat is considered to be λ. Using these notations, and a given microbial growth model, we can construct ordinary differential equations (ODEs) to model the dynamics in a chemostat as follows:We consider two species Y is a biomass yield parameter and vi represents the reaction rates of species i as given by Equations 2 or 3where ν1 × Y1=ν2 × Y2, which can be achieved only by a unique combination of the yield, maximal growth and uptake parameters or when all these parameters are the same   and parameters K1 and K2 are much larger compared with the steady-state substrate concentration, we can re-arrange this equation to derive a condition for the steady state asAssuming that A=1. In this case, we can substitute where A is a composite parameter given by Since the product concentrations at steady state relate to species concentrations at steady state see , the rel2 , formate , pyruvate , acetaldehyde , ethanol , H2 , H+ , water , butyrate  and methane .To analyse the prevalence of reactions with low Gibbs free energy change, we consider stoichiometrically balanced, fermentation reactions starting from glucose and involving 12 additional common compounds. We generate all possible reactions among these compounds, where each reaction must feature glucose as a substrate. To automate this reaction generation, we define stoichiometric coefficient ranges for each compound, where negative and positive coefficients indicate compounds taking part in a reaction as product or substrate, respectively. We then computationally iterate through these stoichiometric coefficient ranges to generate all possible reactions within these limits. The 13 compounds used in this analysis and their stoichiometric ranges are : glucose , lactate , acetate , COm, 1 atm, 298.13 °K, pH=7) and using tabulated formation energies  and are then evaluated for chemical and stoichiometric balance. To do so, we multiply each reaction vector with a mass- and charge-balance matrix that holds the number of carbon, hydrogen and oxygen atoms as well as the charge of each molecule see . For a rm concentration of all solutes, 1 atm, 25 °C, and pH 7), the apparent thermodynamic energy ΔGrxn available for microbial growth can be calculated according to To analyse the relation between cellular metabolism and microbial ecology, we consider the thermodynamic basis of microbial growth. Microbes achieve cellular growth through harvesting of free energy change available from metabolic conversions. As with any other chemical reaction, these conversions are governed by thermodynamics whereby the free energy change for the reaction is given by the difference between the free energy of formation of the products To account for the inherent thermodynamic constraints on microbial metabolism given by Using this thermodynamic growth model, we first consider the simplest and most idealised ecological case of two species living on a single substrate in a homogenous environment. This scenario can be realised in an ideal chemostat, where influx of substrate, dilution of metabolites and cells can be taken into account and modelled through appropriate ODEs . Chemostat models that consider microbial growth solely as a function of substrate uptake kinetics have been used to derive the exclusion principle; under the assumption of species having the same maximal growth rate and growth yield, there can only be one species existing in a chemostat with a single substrate and this species would be the one with the most favourable substrate uptake kinetics . When ki−1 =1 atm, none of the reactions is energy yielding. However, when applying more realistic conditions of milli molar concentrations of substrates and products, there is a H2 pressure at which any of the three reactions will be exothermic. Interesting here is that when we consider the full range of biologically feasible H2 pressures, we find that the reaction that can generate the highest amount of energy per propionate gets inhibited at a lower ambient H2 pressure than the other two reactions . In other words, the thermodynamic advantage at low H2 pressures for the highest energy yielding reaction is removed by the rapid accumulation of H2 from its own reaction turnover. This example further highlights the fact that microbial growth will be dominated by the thermodynamics of the supporting reactions, which can then lead to new regimes of coexistence and diversity among competing reactions due to product accumulation.To further demonstrate the thermodynamic inhibition effect with an example, we consider three different anaerobic oxidation reactions for propionate . Two of eactions . This isIn the above analyses, we considered species coexistence solely due to differences in their metabolic conversions and resulting thermodynamic effects. In reality, species can diversify in many different traits governing their metabolism and growth. Among such traits, those relating to kinetic properties such as substrate uptake rates could be particularly relevant for the maintenance of species diversity as highlighted by the mathematical studies on the exclusion principle . We findth model ; as the −1 of the reaction with highest change in free energy would coexist at significant frequency. An analysis of known and chemically possible metabolic conversion reactions shows that this is a biologically relevant regime, where many biochemical reactions known to sustain microbial growth are found. This indicates that significant amounts of microbial diversity on a single substrate could have initially emerged from, or are being sustained by thermodynamic constraints.We have considered a thermodynamic model for microbial growth and analysed the ensuing population dynamics in the context of competition and coexistence. The model is derived from the fundamental principles of conservation of energy and thermodynamics in metabolic conversions fuelling microbial growth. The key result from this first-principles model is that utilisation of different metabolic conversions by different species can allow for their coexistence on a single substrate under a homogenous environment. We find that the abundance of different species under this circumstance is governed by the change in standard free energy of the reaction that they utilise, and that species utilising reactions that are within ~50 kJ (mol Substrate)−1 can still coexist even with a 10-fold difference in their substrate half saturation constants  would easily disrupt thermodynamic-driven diversity emerging under metabolic conversions with large change in free energy. The same is not true for metabolic conversions with small change in Gibbs free energy, where we find that species utilising metabolic pathways with onstants . We emphonstants . In realThe presented model considers the thermodynamics of microbial growth, by considering a overall growth-supporting metabolic reaction . In reality, cellular metabolism takes place over many reactions that finally reach a metabolic end product. Thus, the reaction Gibbs free energy change from the overall reaction is split among all the individual reactions and some of it needs to be invested to achieve an appropriate flux for these reactions (per se, but emerge from the drive towards chemical equilibrium in the given metabolic reaction sustaining growth. Therefore, each given species is specifically affected by the build-up of its own products. The inherent thermodynamic mechanism is thus to punish specifically the fastest growing organisms the earliest, and thereby favouring the coexistence of a high number of different metabolic conversions in the environment. The products of these diverse metabolic conversions can then be utilised by the same species or different ones as energy source, resulting in an example of ‘niche creation', and potentially leading to the emergence of further microbial diversity and interactions through adaptation. Indeed, anaerobic microbial communities are frequently characterized by abundance of such interlinked metabolic conversions (Although the thermodynamic constraints highlighted here act similar to inhibition driven by metabolic byproducts , it is iversions , 2002.The findings of this study suggest that microbial coexistence can readily arise under metabolic growth-supporting reactions with low free energy change. A direct experimental test for this proposition for this would be to grow two different species, or synthetically tagged variants of the same species, on a single substrate. The choice of the species and substrate should be such that each species can only utilise metabolic growth-supporting reactions with low free energy change. Such an experiment can be run under chemostat conditions, as well as under batch conditions that test mutual invasion from low frequency. Examples would include anaerobic growth on propionate or glycerol, where different metabolic pathways are known to exist. A broader suggestion from this study is that environments that mainly allow for growth-supporting metabolic reactions with low free energy change should harbour more metabolic diversity compared with environments that allow for metabolic reactions with high free energy change. This proposition could potentially be tested through the use of increasingly available metagenomics data from different environments. Examples for former type of environments would include anaerobic digesters, animal guts, wet soils and ocean sediments of highly productive regions. In these environments, high concentrations of substrates along with the lack of strong oxidising agents like oxygen or nitrate lead to accumulation of high concentrations of waste compounds. Thus, we expect microbial growth in these environments to be mainly limited by the lack of free energy available from the specific metabolic conversions a given species utilises. Microbes can overcome this limitation by evolving an ability to produce different waste products from the same substrate molecule. This way they can maintain growth by producing those products that are only present at very low concentrations in the environment, and overcoming any thermodynamic inhibition from accumulated products. This situation is analogous to a river flowing from the mountains to the sea, at its steeper parts the river is maintained in a single basin, while running over the flat surface close to the coast it starts to meander and form many little rivers in a delta.