Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen’s biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genome-scale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets. Piscirickettsiosis or Salmonid Rickettsial Septicaemia (SRS) is a severe systemic fish disease that targets multiple organs and tissues . While iPiscirickettsia salmonis, a fastidious facultative intracellular bacterium of the gamma-proteobacteria class. Over two decades ago, P. salmonis LF-89, the first known strain of this pathogen was isolated. Since then, studies on the fish pathogen have unveiled relevant clues, showing that it infects primarily macrophage cells and replicates inside intracellular vacuoles  and use of constraint based analysis . GSMs arialities . The useP. salmonis a first metabolic model for strain LF-89 was recently presented, accounting for 215 of the pathogen’s genes  was obtained from the ATCC culture collection on 2013 and used to perform batch cultures. A P. salmonis LF-89 preculture was prepared by inoculating 50-ml Falcon tubes containing 5 ml of Austral SRS medium supplemented with 1 g/L Cysteine, 14 mL/L Fetal Bovine Serum, 30 g/L Casein-Peptone Soymeal-Peptone (CASO) Broth and 15 g/L sodium chloride. After approximately 7 days of cultivation, cells were extracted from the preculture and inoculated into 50 mL Falcon tubes containing 5 mL of a chemically defined medium to achieve an initial optical density at 600 nm (OD600) of approximately 0.1. The Falcon tubes were incubated at 18°C at a constant stirring of 160 rpm. OD600 was periodically measured. Experiments were performed in triplicate for five different defined media with different carbon sources. Briefly, Medium 1: Basal Medium (BM) containing glucose, amino acids and TCA compounds as carbon sources; Medium 2: BM without glucose; Medium 3: BM without TCA compounds; Medium 4: BM without glucose and TCA compounds; and Medium 5: Equal to Medium 4 but with higher concentration of glutamate (Full Media composition detailed in Supplementary Tables).P. salmonis strains (CRG01 and EM-90-like), one from clade G1 and one for clade G2. Clade membership was confirmed through a phylogenetic tree analysis  and 1492R (5′-CGGTTACCTTGTTACGACTT-3′).Additionally, growth cultures were performed for two additional Strains CRG01 and EM-90-like were cultivated in Media 3 and 4 using the same procedures used for strain LF-89 cultures.DNA was purified from samples of batch cultures at the beginning and end of the experiment, and then 16S rDNA was amplified using primers 27F/1292R. Amplified 16S rDNAs were digested using the restriction enzyme PmaCI and a 2% (w/v) agarose gel electrophoresis was performed to compare band patterns which were visualized in a UV transilluminator  and P. salmonis (RefSeq accession NZ_CP011849.2) were determined using OrthoMCL v 1.4 and INPARANOID v 4.0. The second draft was generated using the PathoLogic component of Pathway-Tools software  which was built in the same way as that of strain LF-89, including blast searches against databases KEGG, NR, UNIPROT, and TCDB.http://psalmonis.cmm.uchile.cl/models/.The genome-scale models for strains LF-89 and A1-15972 in sbml format are included in Supplementary Materials and they can also be downloaded from E. coli iJO1366 (Supplementary Tables). We kept the same main macromolecular components weight fractions. DNA composition was estimated from the GC content of P. salmonis LF-89 genome; RNA composition was estimated based on P. salmonis LF-89 genome coding sequences; Protein composition was based on P. salmonis LF-89 predicted amino acid sequences; and P. salmonis LPS composition was taken from published experimental data  from a given universe of compounds required to produce a set of target compounds. The components of the biomass function were used as the target set and the universe of possible precursors was built including a variety of compounds such as sugars, fatty acids and the full set of amino acids in addition to cofactors and ions listed in Supplementary Tables.Flux Balance Analysis (FBA) was carried out using the python package COBRApy  and the l SASITA  was usedAll non-exchange reactions in the model were mass and charge balanced. Reaction reversibility information was taken from BiGG and MetaCyc databases. As common practice, reactions fluxes lower and upper bounds were set to –1000 and 1000 mmol/g DW/h for reversible reactions and to 0 and 1000 mmol/g DW/h for irreversible reactions.In silico uptake rates for essential metabolites were left unconstrained (–1000 mmol/g DW/h flux lower bounds) for all FBA simulations. Main carbon uptake was limited to 6 mmol/gDW/h. Uptake rates of metabolites used as main carbon sources were constrained accordingly. Supplementary Tables contains the complete list of different in silico growth media used in FBA to simulate growth.Piscirickettsia salmonis iPS584 metabolic model was compared with models iYL1228 . Ortholog groups between all pathogens were determined by OrthoMCL v1.4 using as input their corresponding protein sequences . iYL1228 , iPC815  iYL1228 , STM_v1. iYL1228 , iMO1056 iYL1228 , and iRS iYL1228  for the We constructed metabolite graphs for model iPS584 and for each of the reference pathogen models mentioned above. These graphs are directed graphs where nodes represent metabolites in the model network. A directed edge between metabolites S and P was included in the graph if there is a reaction R in the network where S is a substrate and P is a product. In the case that R is reversible an edge from P to S was also incorporated in the graph. Transport reaction involving metabolites from different compartments were not included in the graphs.i that are substrates in reactions where M is a product. Conversely, the out-degree of M is the sum of metabolites Pi that are products in reactions where M is a substrate  between iPS584 and the average values for the reference networks. In this calculation, we only considered metabolites present in iPS584. The average degree value in the reference networks was calculated considering only the pathogen networks that also presented the metabolite.P. salmonis strains available in the NCBI database were downloaded (Accession numbers in Supplementary Tables) and orthology among them was calculated with OrthoMCL v1.4. A binary matrix of ortholog gene groups versus strain was built and used to calculate a hamming distance between strains. Using these distances, a hierarchical clustering of P. salmonis strains was conducted to group strains.All 19 complete genomes for P. salmonis iPS584 presented here, was generated by a combination of two complementary automatic approaches followed by thorough manual curation . A first draft reconstruction was generated using as a template E. coli iJO1366 metabolic model. We used this model because it is to date the most detailed metabolic reconstruction available for a gamma-proteobacteria. To enrich our first draft, a second de novo reconstruction was made based solely on the annotation of P. salmonis LF-89 genome, assuring in this way that all genes were considered. Both reconstructions were merged, and the result was manually evaluated and curated. In particular, reactions involved in lipopolysaccharide (LPS) biosynthesis inherited from E. coli were modified to be consistent with the reported lipid A carbohydrate backbone and fatty acid composition of P. salmonis  as all three genes for its biosynthesis are in P. salmonis genome while no indication of glycogen biosynthesis was found.The reconstructed metabolic network of salmonis . AdditioP. salmonis LF-89 genome; 801 unique metabolites and a total of 1,323 reactions. Among these reactions, 80% have a gene association. Reactions without a gene linked to them, are mostly transport or synthetic exchange reactions . An overview of iPS584 is presented in Figure 1, while the full set of reactions, metabolites and genes in the reconstruction are listed in Supplementary Tables.The iPS584 model contains 584 genes, accounting for 19% of genes in P. salmonis LF-89 . Moreover, among these shared reactions, there are 30 reactions that were not linked to any gene in iPF215, but are now associated to genes in our model. We did not include in iPS584 the remaining reactions from iPF215 for either modeling differences  or because we did not find enough evidence to support their incorporation. For example, reactions involving menaquinones and their biosynthesis form part of iPF215 even though no gene for their biosynthesis is present in the genome of strain LF-89. Additionally, we incorporated in iPS584 specific aspect of the pathogen that are absent in iPF215 apparently due to simplifications made in the biomass function that describes cell composition in this model. For example, we included biosynthesis pathways for PHB and for particular sugar nucleotides precursors of P. salmonis LPS. These pathways are not a part of iPF215 where it was assumed that P. salmonis stores glycogen and LPS composition was taken to be the same as in Francisella tularensis, which has a different sugar backbone to the one described for P. salmonis .We analyzed the components and structure of P. salmonis reconstructed metabolic network is considerably smaller than that of most of the compared pathogens (Supplementary Tables). This can be largely explained by looking at the genome size of these pathogens, which, with the exception of F. tularensis, are between 40 and 82% larger than that of P. salmonis. This translates into a reduced number of genes and hence a potentially reduced metabolic capacity for the fish pathogen.The size of P. salmonis LF-89 shares a total of 1,140 ortholog gene groups distributed among the compared pathogens as shown in Figure 2A. A core of 549 orthologs (core+ set) is shared between P. salmonis and all other five pathogens and 56% of them are accounted for in their metabolic models and are related to a wide range of metabolic functions . They include 45% of the genes in P. salmonis iPS584 model. The remaining 8,879 gene groups from the five compared pathogens that do not have orthologs in P. salmonis LF-89 (pan- set) are distributed as shown in Figure 2B. Most of them correspond to unique proteins in each pathogen and only 1,490 are part of their metabolic reconstructions. They are associated to reactions where the main categories are transport, amino acid and alternate carbon metabolism . This suggest that these categories are either reduced in P. salmonis metabolic network or they are filled by reactions associated to alternative genes.After an orthology analysis between all pathogens we found that P. salmonis genes that are not shared with the compared bacterial pathogens, 59 are associated to reactions in the iPS584 reconstruction . A large part of them corresponds to transporter genes, mostly putative peptide or amino acid transport systems. This correlates well with the inability of P. salmonis to produce several amino acids and the presence of several amino acid degradation pathways within its metabolic network . One of these pathways is the homogentisate pathway for the degradation of tyrosine and phenylalanine. This pathway is also present in P. aeruginosa, however, sequence similarity between the associated genes in P. salmonis and P. aeruginosa was insufficient to place them in the same ortholog groups.Among the 1,116 P. salmonis metabolism included in iPS584 and associated to non-shared genes are the biosynthesis of CMP-pseudoaminate, the siderophore transport system ExbB-ExbD-TonB and a set of fatty acid desaturases.Other noteworthy elements of P. salmonis LPS. The ExbB-ExbD-TonB siderophore transport system is a key element for the acquisition of iron from the host environment  and an additional one is related to P. aeruginosa DesA (47% identity). Both are acyl-lipid desaturases that incorporate double bonds in membrane phospholipids  that is 50% larger than the average FC pairs in the other networks. The presence of a high number of FC pairs could be an indication of missing reactions in the reconstruction or it could imply a lower flexibility of P. salmonis metabolic network associated to a smaller number of alternative pathways.Another important difference between P. salmonis iPS584 in relation to the reference networks. We evaluated the level of connectivity in each network by calculating their metabolites in- and out-degrees . For each metabolite, we compared its degree values in iPS584 to its average values in the set of pathogen networks that also contain it  and a smaller set +psal, containing metabolites more connected in iPS584 . Metabolites from both sets are listed in Supplementary Tables.To explore this result, we further assessed the topological characteristics of -psal, we looked for reactions in the other pathogens networks that involve these metabolites and that are absent in iPS584. From these reactions, we identified the main metabolic pathways associated to them, present in most of the reference networks and either incomplete or missing in P. salmonis. We grouped these pathways into five main larger subsystems or categories listed in Table 1.For the less connected metabolites set P. salmonis.In the Amino Acids Metabolism subsystem, unsurprisingly, we find metabolites that are associated to several amino acid biosynthetic pathways in the compared networks but absent or incomplete in P. salmonis growth. The biosynthetic pathways of essential cofactors thiamine diphosphate and pyridoxal-5-phosphate are part of this category. In iPS584 glyceraldehyde 3-phosphate (g3p) out-degree is lower than in the rest of the pathogen networks because the enzyme 1-deoxyxylulose-5-phosphate synthase (DXS) is missing. DXS uses g3p and pyruvate to produce deoxyxylulose-5-phosphate and feed it to the aforementioned cofactors biosynthetic pathways. The absence of DXS indicates that P. salmonis requires the presence of these cofactors in its media for growth.In the Cofactor and Prosthetic Group subsystem we find additional clues about the requirements for P. salmonis lacks the enzymes required to synthesize cardiolipins and phosphatidylglycerols, and as a consequence several metabolites from these classes have lower in-degree in iPS584.The Lipids and Cell Envelope Metabolism subsystem includes several less connected metabolites. This is mainly because P. salmonis lacks key enzymes needed for the degradation and utilization of several sugars and sugar derivatives. These include sugar phosphatases part of the degradation pathways of sugar phosphates, dehydrogenases and aldolases required to process sugar alcohols like mannitol or sorbitol, and sugars such as fucose or L-rhamnose and also sugar acids like galactonate or glucuronate. Additionally, several known genes for PTS transport systems used for the uptake of sugars are notably missing from P. salmonis genome. These observations indicate that these sugar compounds are unsuitable nutrients for P. salmonis and that this fish pathogen is adapted to a significantly different kind of diet than the compared pathogens.In the Alternate Carbon Metabolism subsystem, there are several metabolites that are less connected in iPS584 because P. salmonis network than in those of the compared pathogens shows that P. salmonis network is indeed less flexible because it possesses less alternative pathways to carry out central metabolic tasks.We also found several metabolites from the Central Metabolism subsystem that are less connected in iPS584 because pathways from this category are missing or incomplete. Among them, we find the glyoxylate cycle, several oxidative phosphorylation redox reactions and also sugar catabolism related pathways including the oxidative branch of the pentose-phosphate pathway and the Entner–Dourdoroff pathway. The fact that central metabolism metabolites are less connected in +psal set of metabolites with higher in- or out-degree in P. salmonis is made of only 9 metabolites (Supplementary Tables). The largest differences are found in Fe+2 and acetoacetyl-CoA. Fe+2 is more connected in iPS584 due to a difference in the modeling of the metabolic networks under comparison. We included the biosynthesis of Fe-S clusters in the metabolic reconstruction of P. salmonis, while for the other pathogens this pathway was omitted, making Fe+2 involved in a lower number of reactions. Acetoacetyl-CoA, on the other hand, has higher in- and out-degrees in iPS584 because it is involved in pathways absent in the other pathogens metabolic networks, namely the mevalonate pathway for isopentenyl diphosphate (IPP) biosynthesis and the biosynthesis and degradation pathways of poly-3-hydroxybutanoate (PHB). The metabolic tasks associated to these pathways are not unfulfilled in the compared pathogens networks but are accomplished using alternatives pathways. They synthesize IPP using the methylerythritol phosphate pathway and they can use glycogen as storage compound instead of PHB.The P. salmonis metabolic network is considerable more rigid. Even by expanding the +psal set with additional metabolites more connected in P. salmonis network but to a lesser extent, it is still almost two thirds smaller than the set of least connected metabolites. Moreover, these additional metabolites are also associated to alternative or peripheral pathways like the mevalonate or the CMP-pseudaminate biosynthesis pathway.The smaller number of metabolites with higher degrees than with lower degrees with respect to the compared pathogens further indicates that P. salmonis LF-89 to fulfill its nutrients requirements. To do this, we analyzed different growth scenarios in silico using iPS584. We first defined what were the different minimal growth media that enable the in silico production of biomass.After observing this lower flexibility in its metabolic network, we explored what compounds could be exploited by psets)  . These apsets made of a total of 14 compounds including 12 that are essential metabolites found in both psets (Table 2). In line with our previous analysis of the network we found that these essential metabolites are amino acids and cofactors that compensate for incomplete or absent biosynthetic pathways. We also observed that differences among psets arise from the alternative use of glutathione and cysteine. Glutathione (γ-glutamyl-cysteinyl-glycine) can be used as cysteine source since it can be degraded through γ-glutamyl transpeptidase.We obtained two L-histidine and L-arginine that make up the calculated psets also serve as carbon and nitrogen source because their degradation pathways can feed central metabolism. Since these metabolites are in themselves a requirement and psets are minimal, no other metabolites that could also feed central metabolism are included in these sets. To search for them, we did a second pset analysis, this time blocking in our model the degradation pathways of the mentioned amino acids . As a result, we obtained 26 new sets (Supplementary Tables) where alternative metabolites listed in Table 2 can act as carbon and/or nitrogen sources.In addition to compensate for their auxotrophy, the amino acids pset analysis enter central metabolism at different points. Sugars glucose and galactose enter at the beginning of glycolysis, while TCA intermediaries and amino acids enter at the level of the TCA cycle. Glycerol, on the other hand, enters central metabolism after being degraded to glycerone phosphate (dhap) which feeds both glycolysis and gluconeogenesis . Since central metabolism is fed at different levels by these nutrients, different pathways must be active for them to act as sole carbon sources. For sugars, glycolysis must be active, for TCA intermediaries and amino acids gluconeogenesis is a requirement for essential sugar compounds biosynthesis, while for glycerol both pathways must function.Carbon sources obtained through pset analysis we simulated different growth scenarios. For each in silico media we evaluated growth with combinations of blocked or unblocked gluconeogenesis and glycolysis in our network. The main carbon input was arbitrarily limited to 6 mmol/gCDW-h and this flux was distributed between glucose uptake and an additional main carbon source identified from our psets . Additionally, in each simulation cysteine and all essential metabolites found in all psets were allowed to freely enter the system. We also allowed the free uptake of ammonium so that nitrogen would not be a limiting substrate. Figure 3 shows that all carbon sources can sustain the growth. In simulations where glycolysis was not blocked (glycolysis+), the largest growth rates are obtained when the highest fraction of carbon input comes from glucose, since its degradation through glycolysis generates additional ATP. The exception is glycerol, that also enters glycolysis but at a lower point and reaches slightly higher growth rates. Among the rest of the tested carbon sources, the highest growth rates were obtained for L-alanine, since it is transformed directly to pyruvate. Conversely, when glycolysis is blocked (glycolysis-), higher growth rates are obtained when most of the carbon that enters the network comes from TCA intermediaries or amino acids. Simulations with unblocked or blocked gluconeogenesis (gluconeogenesis+/–) did not result in major differences in growth rates since in both cases glucose was used for the synthesis of sugar compounds. The only significant difference is that in the case of an inactive gluconeogenesis (gluconeogenesis–) no growth is obtained without the addition of glucose.To assess the use of the main carbon sources obtained through the previous We performed the same simulations without allowing ammonium to enter the network, therefore making amino acids the sole source of nitrogen . Each media contained the essential metabolites previously mentioned and different sources of carbon. They were as follows: (1) a BM rich in carbon and energy sources made of the full set of standard amino acids, glucose and TCA intermediaries compounds; (2) BM medium without glucose; (3) BM medium without TCA compounds; (4) BM with no glucose nor TCA compounds; and (5) the same as the last medium but with a higher concentration of glutamate. Table 3 shows the measured growth level (OD600) in each medium after 10 days of culture. We found growth was only achieved in media containing glucose pointing to an inactive gluconeogenesis. In particular, the use of a higher concentration of glutamate, could not compensate the absence of sugar in the medium. However, in the work of P. salmonis LF-89 metabolic network.To study the growth of Figure 4). This indicates that in addition to glucose, P. salmonis also profits from TCA compounds for growth as suggested by our in silico analysis.We obtained similar biomass levels in media (1) and (3) at the end of the culture but we observed different growth profiles with an earlier exponential phase start in media containing TCA intermediaries . Among them is the gene for 5-aminolevulinate synthase, which allows these strains to produce heme precursor uroporphyrinogen-III from glycine. This gives their metabolic networks additional flexibility with respect to clade G2 strains which can only synthesize it using glutamate. Additionally, two of these clade G1 specific genes code for glycosyltransferases that we tentatively associated to the LPS formation reaction in iPS584. They are part of a large cluster of more than 10 genes related to cell wall polysaccharides biosynthesis present in all 19 P. salmonis strains. However, inside this cluster, two alternative groups of three genes differentiate clade G1 and G2 . These genes include one for bifunctional alcohol/acetaldehyde dehydrogenase AdhE and one for an aldehyde dehydrogenase. These enzymes confer clade G2 strains the ability to produce ethanol from acetyl-CoA, a feature that is absent in G1 strains. Also among iPSA590 clade G2 particular features are cytidine and deoxycytidine deaminases, enzymes part of salvages pathways that allow the use of available cytidine and deoxycytidine as resources.Given these differences, we decided to build an additional metabolic model for clade G2 strain A1-15972, and therefore cover both clades. We used our iPS584 model as a template in addition to the annotation of A1-15972 genes. The resulting model iPSA590 is made of 1,291 reactions, 1,118 metabolites, and 590 genes (See Supplementary Tables). This model shares most of its reactions with iPS584 but it also incorporates additional reactions associated to clade G2 specific genes  and a member of clade G2 (EM90-like strain). This clade assignation was confirmed through a phylogenetic analysis based on their 16S rDNA sequences . Interestingly, this result was shown experimentally in a study in the closely related Francisella tularensis, which also requires cysteine for growth  and one from a different group (clade G2). We observed the same behavior that we observed for strain LF-89, that is, growth was obtained in the medium containing glucose and after 10 days, no growth was detected in the medium without the sugar. Notably, the same behavior was observed for the two additional strains. Even though we observed differences in final biomass concentration, all strains were able to grow in the medium containing glucose. We highlight that we verified the presence of P. salmonis and absence of contaminant agents in our cultures using 16S PCR-RFLP. Therefore, we confirmed the phenotypes observed for P. salmonis.We only observed growth in media containing glucose, which was also associated to higher growth rates in E. coli it has been shown that the presence of particular salts in the growth media can greatly limit glycerol fermentation due to enzyme inhibition as well as toxicity (P. salmonis LF-89 in AUSTRAL-SRS, a complex medium containing a high concentration of glucose (We believe that differences between results reported by toxicity . Another glucose , Fuentea glucose  which coP. salmonis, for example to analyze what pathways could be active in different stages. In order to do this, additional information should be incorporated into the models to accurately describe these different scenarios. For instance, different biomass functions describing the cellular composition of each stage should be included.Additionally, LF-89 strain growth profile differences between media with and without TCA intermediary compounds indicated that these substrates were also exploited by the fish pathogen. In other bacterial intracellular pathogens, such as legionella or chlamydia, an adaptation to a co-substrate diet has been observed . MoreoveP. salmonis metabolism through the analysis of its pangenome. This showed that most genes from model iPS584 for strain LF-89 are conserved and found in all 19 analyzed strains, which is consistent with pangenome analysis of other bacterial species where the majority of metabolic genes have been found to be core elements that constitute a species metabolic backbone (We broaden our study of backbone .Additionally, we found that most non-core genes in iPS584 were not specific for strain LF-89 but characteristic of the genotypic clade G1 of which it is member. Furthermore, we constructed iPSA590, an additional genome-scale model for strain A1-15972, member of clade G2. Analysis of this model lead us to the same result, specific non-core genes in the model were conserved among G2 strains. This shows that small metabolic traits can differentiate these clades.E. coli O157:H7 by directing the flux from the acetyl-CoA pool and avoiding intracellular acetate accumulation (P. salmonis, the difference on AdhE presence between clades could still suggest that alternative virulence regulation mechanisms related to metabolism are in play.A particularly interesting differentiating feature is the bifunctional alcohol/acetaldehyde dehydrogenase AdhE whose coding gene is present in G2 strains only. It has been shown that AdhE controls virulence genes in mulation . Even thin vitro growth profiles and host preferences (Further analysis could show if the metabolic particularities of G1 and G2 clades included in models iPS584 and iPSA590 can be linked to clade phenotypic differences such as those previously observed related to antibiotic susceptibility, particular ferences .MC carried out metabolic reconstructions, model definitions, constraint based analysis, and model comparisons. SM and AG designed and carried out all growth experiments. DT performed pangenome orthology analysis and genome annotations. AM and VC conceived the project and coordinated and oversaw its progress with AS. MC wrote the manuscript with critical revisions from AS, AM, and VC. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor declared a shared affiliation, though no other collaboration, with one of the authors, AS.


The purpose of this investigation was to compute the fractional binding of H+, K+, Na+ and Mg2+ to 21 metabolites of skeletal muscle non-mitochondrial energy metabolism, resulting in 104 different metabolite-cation complexes. Fractional binding of H+ to these metabolite-cation complexes were applied to 17 reactions of skeletal muscle non-mitochondrial energy metabolism, and 8 conditions of the glycolytic pathway based on the source of substrate (glycogen vs. glucose), completeness of glycolytic flux, and the end-point of pyruvate vs. lactate. For pH conditions of 6.0 and 7.0, respectively, H+ coefficients  for the creatine kinase, adenylate kinase, AMP deaminase and ATPase reactions were 0.8 and 0.97, -0.13 and -0.02, 1.2 and 1.09, and -0.01 and -0.66, respectively. The glycolytic pathway is net H+ releasing, regardless of lactate production, which consumes 1 H+. For glycolysis fueled by glycogen and ending in either pyruvate or lactate, H+ coefficients for pH 6.0 and 7.0 were -3.97 and -2.01 (pyruvate), and -1.96 and -0.01 (lactate), respectively. When starting with glucose, the same conditions result in H+ coefficients of -3.98 and -2.67, and -1.97 and –0.67, respectively. The most H+ releasing reaction of glycolysis is the glyceraldehyde-3-phosphate dehydrogenase reaction, with H+ coefficients for pH 6.0 and 7.0 of -1.58 and -0.76, respectively. Incomplete flux of substrate through glycolysis would increase net H+ release due to the absence of the pyruvate kinase and lactate dehydrogenase reactions, which collectively result in H+ coefficients for pH 6.0 and 7.0 of 1.35 and 1.88, respectively. The data presented provide an extensive reference source for academics and researchers to accurately profile the balance of protons for all metabolites and reactions of non-mitochondrial energy metabolism, and reveal the greater role of glycolysis in net H+ release than previously assumed. The data can also be used to improve the understanding of the cause of metabolic acidosis, and reveal mechanistic connections between H+ release within and from muscle and the electrochemical neutrality concepts that further refine acid-base balance in biological solutions.Limited research and data has been published for the H For example, despite the prior work by Kushmerick et al. ) and M ([Mn+]), 3) the concentration(s) of any other cation(s) that can bind to L, and 4) the pKM+ values of the L and all Mn+ binding. Consequently, for conditions where [Mn+] is relatively high, a low pKM+ may still coincide with high Mn+ binding. In addition, when more than one Mn+ competes for L binding, the actual stoichiometry of binding for a specific Mn+ will be less than that computed singularly. This fact renders computations of the balance of H+ for specific reactions solely from the pKa of the metabolite to be erroneous. The magnitude of the error in computing singular H+ binding increases with increasing competition between multiple Mn+, especially when the sum of K and [Mn+] are large, as has been explained by Kushmerick et al. ) When each side of Ln−]) , and theLn−]) .Ltot was calculated using The Ln−] was known, Now that [+ complex fraction without correction for multiple cation binding. The H+ fractional content of the metabolite-cation complexes was then computed by summing all H+ fractions, adjusted by H+ content of each metabolite-cation complex, to derive the H+ fraction of each metabolite  for the fraction of each metabolite-cation complex for the previously stated pH range.The use of this “α equation” provided the mathematical derivation presumed to be operational in the MathLab application of Kushmerick  and Vinntabolite . The dat+ balance (coefficient) for each reaction, based on the fraction of cation-metabolite species containing H+ computed from Eqs + coefficients are not as simple as depicted by Kushmerick et al. [+ coefficient can be from differences in ionization between substrates and products, consumption from H+ in solution, or production from covalent modification of substrates (e.g. hydrolysis reactions). Each condition requires a unique formula. The formula used for each reaction is presented in The reactions of k et al.  or Vinnak et al. , as the + coefficients for chemical reactions provided negative coefficients for H+ release and positive coefficients for H+ consumption (metabolic buffering), consistent with thermodynamic computations. Data of the pH profile of the H+ coefficient of each reaction of nd, 3rd or 4th order polynomial) for the proton coefficient of each reaction for the physiological pH (x-variable) range from 6.0 to 7.0.Computations of the H+ coefficient of glycolysis when starting with glycogen vs. glucose by summing the net proton balance of each reaction of the pathway. For the reactions of the 3-carbon glycolytic intermediates, the reaction proton coefficient was doubled to balance carbon flux between phases 1 and 2 of glycolysis. Added calculations were also made for glycolysis ending in lactate production, as well as for phase 1 (glycogen or glucose to DHP + G3P) and 2 of glycolysis (DHP or G3P to pyruvate or lactate). Each data set was fit with nonlinear curve fitting to derive equations  for the net H+ coefficient for the series of reactions the physiological pH (x-variable) range from 6.0 to 7.0.Computations were made for the H+ binding to metabolites and the pH dependent H+ coefficients of all reactions of non-mitochondrial energy metabolism. Consequently, as much data as feasible is reported for each metabolite and reaction. In addition, supporting information is presented in three different data files; + exchange across the pH range of 6.0 to 7.0; + exchange of metabolites and chemical reactions of the phosphagen and glycolytic energy systems; and + exchange of all metabolites.Data presented from this research can be used as reference data for the pH dependent fractional H+ based dissociation, reference data was acquired and computed as presented in Tables + exchange coefficients.In order to compute fractional cation binding and show differences to singular H+ and Mg2+, K+ and Na+. For comparison, data for the main metabolite-H+ complex are also provided based on non-competitive binding computation. Differences between competitive and non-competitive binding computations reveal the error of using metabolite specific pKM+ data in computations. Exponential equations are provided for calculations of the fraction of bound metabolite-cation complexes across the pH range of 6.0 to 7.0. For visual clarity, the main metabolite-cation complexes of interest, based on large or highly pH dependent H+ binding, are presented in + coefficients for the reactions of the phosphagen system, and phase 1 and 2 of glycolysis are presented in + coefficients of all reactions are provided in + buffering capacity of the CK and AMPD reactions, and the H+ releasing potential of the ATPase reaction. + coefficients for the 4 reactions of the phosphagen system for pH conditions of 6.0 and 7.0. This presentation clearly reveals the potential of the phosphagen system for metabolically buffering H+ release.The H+ releasing, while reactions of phase II remain net H+ releasing. Of particular importance is the H+ coefficient change for the G3PD reaction, which becomes increasingly negative towards a value of -1.5 as pH falls from 7.0 to 6.0. This is caused by the increased fraction of H2PO-1 as pH decreases, which is a substrate for this reaction, and thereby can potentially contribute  2 H+ to the net reaction H+ release of the G3PD reaction. Finally, note that H+ exchange occurs for reactions that do not have H+ traditionally expressed as a substrate or product in the reaction. This occurs due to differences in the pKa and therefore extent of H+ binding or dissociation between substrates and products.For glycolysis , the rea+ coefficients for all reactions of glycogenolysis and glycolysis, including lactate production. As explained, the negative H+ coefficient of the glyceraldehyde-3-phosphate dehydrogenase reaction significantly contributes to the net acidifying potential of the glycolytic pathway, even when accounting for the H+ consumed in lactate production. This illustration also reveals that the incomplete flux of substrate through glycolysis, ending at the enolase reaction and preventing both the PK and LDH reactions, increases the H+ release of glycogen fueled glycolysis. Actual changes in the H+ coefficients would be from -2.01 to -3.78 at pH = 7.0, and from -3.97 to -4.67 at pH = 6.0.+ coefficients for phases 1 and 2 of glycolysis, and the total glycolytic pathway are presented in The H2+, K+, Na+ and H+) to 21 metabolites, resulting in 104 different metabolite-cation complexes. Data of the fractional binding of H+ to these metabolite-cation complexes were applied to 17 reactions of skeletal muscle non-mitochondrial energy metabolism, and 8 conditions of the glycolytic pathway.Fractional binding data have been presented for the competitive binding between four cations  of IMP. Kushmerick et al. [2+ and K+) binding for CrP, Pi, ADP and ATP, with data summed for cation free and H+ bound fractions of each metabolite. As Kushmerick et al. [+ bound fractions of each metabolite, it is difficult to interpet and compare the data of Kushmerick to these results. This is especially true for ADP, where the cation free complex varies from 17 to 27% of the total from pH 6.0 to 7.0. Nevertheless, for comparison of the present H+ coefficient data for the creatine kinase reaction from + coefficient results to the data of Vinnakota et al. [The phosphagen system comprises the metabolites ATP, ADP, AMP, IMP, NHdatabase  presente+, Mg2+, K+ and Na+ to AMP, IMP and NH3. Interestingly, the AMPD reaction adds to the H+ uptake capacity of the CK reaction because of the high pKa (9.26) of NH3-H. Consequently, the production of NH3 results in an immediate and near complete (fraction = 0.99945) consumption of a H+ to form the ammonium ion (NH4+). However, as AMPD is activated by acidosis, and that the capacity of this reaction is relatively small in skeletal muscle at < 20% of the capacity of the CK reaction, this reaction would do little to retard a developing acidosis in skeletal muscle.No prior published data exists for competitive cation binding of H0.5) of CrP during such conditions is still in the order of 1 min (6). More recent data applying advanced 31P NMR equipment and methodologies during intense exercise [Some researchers have assumed that the ATP hydrolysis of muscle contraction and the CK reaction are coupled, with the coupled reaction referred to as the Lohman reaction ,4,6–8. Texercise  and in rexercise , further+ coefficient data for two of the reactions of glycolysis  and the LDH reaction can be compared to Vinnakota et al. [+ coefficients for these reactions are almost identical for this investigation and that of Vinnakota et al. [+ release of this reaction, with added H+ release during increasing acidosis, is an important feature of glycolysis. The data of this investigation and that of Vinnakota et al. [+ release assumption (2 H+ release per glucose flux) of the glycolytic pathway. This investigation shows that the net H+ release of glycolysis at pH 7.0 is 2 from glycogen, but 2.67 from glucose , cellular pH and the source of glucose substrate.Traditionally, the Hoduction –12. Howeelow 7.0 . When co+ binding for all metabolites and reactions of non-mitochondrial energy metabolism.The academic and research inquiry topics of the metabolic biochemistry of contracting skeletal muscle, as well as the buffer capacity of skeletal muscle, can benefit from this research. For the first time, comprehensive data is presented allowing computations of the fraction of H+ balance of key reactions and the glycolytic pathway, which for the first time, has been revealed to be always acidifying to skeletal muscle and with up to twice the H+ release as previously assumed. Academics and reach scientists alike are now equipped with data to more validly profile the balance of H+ during intermediary metabolism, and thereby allow more valid biochemical understanding of the cause of metabolic acidosis, and the components of metabolic H+ buffering.Such data has computed the actual H+ binding can be lowered when accounting for competitive cation binding in computations. This may be meaningful for the glycolytic intermediates that currently have no data of the influence of the competing cations Mg2+, K+ and Na+ to fractional H+ binding . It is possible that future progress with research that quantifies the KM+ data for cations and glycolytic intermediates will result in slight reductions in the H+ release for glycolysis. However, until this happens, the present data are the most valid that exist.This data is by no means final. Of the 17 metabolites studied in this investigation, incomplete data for competitive cation binding occurred for 6 (29%), and it remains unclear how many of the remaining metabolites that had data were also incomplete in cation binding representation. As shown in + stoichiometry presented for specific pathways.It is also important to understand that the computations for metabolic pathways such as glycolysis ending in pyruvate vs. lactate, assume complete flux of metabolic intermediates through the pathway . Any accumulation of intermediates within the pathway, which does happen in-vivo in contracting skeletal muscle, would require adjustment to the HS1 File(XLS)Click here for additional data file.S2 File(PZF)Click here for additional data file.S3 File(PZM)Click here for additional data file.


Mitochondrial respiratory chain dysfunction causes a variety of life-threatening diseases affecting about 1 in 4300 adults. These diseases are genetically heterogeneous, but have the same outcome; reduced activity of mitochondrial respiratory chain complexes causing decreased ATP production and potentially toxic accumulation of metabolites. Severity and tissue specificity of these effects varies between patients by unknown mechanisms and treatment options are limited. So far most research has focused on the complexes themselves, and the impact on overall cellular metabolism is largely unclear. To illustrate how computer modelling can be used to better understand the potential impact of these disorders and inspire new research directions and treatments, we simulated them using a computer model of human cardiomyocyte mitochondrial metabolism containing over 300 characterised reactions and transport steps with experimental parameters taken from the literature. Overall, simulations were consistent with patient symptoms, supporting their biological and medical significance. These simulations predicted: complex I deficiencies could be compensated using multiple pathways; complex II deficiencies had less metabolic flexibility due to impacting both the TCA cycle and the respiratory chain; and complex III and IV deficiencies caused greatest decreases in ATP production with metabolic consequences that parallel hypoxia. Our study demonstrates how results from computer models can be compared to a clinical phenotype and used as a tool for hypothesis generation for subsequent experimental testing. These simulations can enhance understanding of dysfunctional mitochondrial metabolism and suggest new avenues for research into treatment of mitochondrial disease and other areas of mitochondrial dysfunction. •Mitochondrial diseases are life-threatening, but lack effective treatments.•The impact of respiratory chain diseases on central metabolism is largely unclear.•We built a computer model of mitochondrial and associated cytosolic metabolism.•We simulated dysfunction of each complex of the mitochondrial respiratory chain.•Simulations show different impacts to metabolism and suggest interventions. IsolateEffective diagnosis of mitochondrial disorders is hampered by their wide genetic and metabolic heterogeneity, and most therapies focus on palliative care . These tA challenging aspect of mitochondrial disorders is studying their effect on overall cellular metabolism. Addressing this is non-trivial as metabolism is a complex network of interlinked reactions with interactions that can be unintuitive. Studies have identified perturbed metabolic pathways in dysfunctional mitochondrial by using transcriptomics and gene enrichment analyses , but theWe simulated the metabolic networks using flux balance analysis  and demo22.1+ ratio was fixed in the simulations, i.e. the rate of NADH production was equal to its oxidation so that NADH and NAD+ are coupled. Thus in situations where the NADH/NAD+ ratio may be expected to change; the model will find pathways to circumvent this. For example, in scenarios where experimentally the NADH/NAD+ ratio may be expected to increase (e.g. complex I dysfunction), flux balance analysis will find alternative pathways that attempt to compensate for complex I dysfunction and reoxidise NADH. In FBA the fluxes around the network are constrained by reaction stoichiometry and directionality, as well as capacity constraints that specify upper and lower bounds on reaction fluxes and metabolite uptake. These constraints are defined from experimental information such as enzymatic activity, estimates of Gibbs free energies, or metabolite uptake rates such as glucose uptakes or oxygen consumption. Defining ranges allows the uncertainty of a flux value to be taken into account. But where the reaction flux is unknown, the default is unlimited flux in either direction. To find the flux distribution most likely to resemble that in vivo, an objective function is chosen that best matches the primary purpose of the system, and FBA is used to calculate the optimal fluxes that maximise this function. For the mitochondrial respiratory chain, we chose maximum ATP production to represent its primary purpose in energy generation. Unfortunately, the residual function of respiratory chain components is often reported only as enzyme activity. But, the relationship between enzyme activity and flux is usually complex and non-linear , which calculates fluxes (flows) of metabolites through a network of biochemical reactions . FBA reqn-linear . To accohttp://www.gnu.org/software/glpk). The simulations were visualized using Cytoscape  with the COBRA Toolbox , and theytoscape .2.2iAS253 . This increases the likelihood of finding metabolic bypasses, and lacks specialised tissue specific reactions (such as found in kidney and liver) that may make any mechanisms found less generalizable. We applied Flux Balance Analysis  to simul3.1To establish baseline flux values as controls to compare against fluxes from simulations of respiratory chain disorders, we simulated a cardiomyocyte model under basal conditions. In this simulation (as reported previously )  deficiencies are the most common respiratory chain disorders, accounting for up to 40% of cases . Mammali+ and bypassing dysfunctional complex I.To investigate how metabolism may compensate for complex I deficiency, we simulated inhibition of complex I to varying degrees and examined how ATP production was maintained. When a compensatory mechanism emerged, it was subsequently disabled to reveal further, albeit less efficient, mechanisms. In this way, we identified several pathways predicted by the model to maintain ATP production that share a common mechanism of transferring electrons from mitochondrial NADH to other redox couples—which then enter the respiratory chain via the quinone pool—thus regenerating NAD3.2.1+ ratio is high.Completely inhibiting reaction flux through complex I reduced ATP production from 138.1 to 95.6 μmol/min/gDW (Table S2); indicating substantial compensation for complex I inhibition, and corroborates accounts of short-term survival of patients with undetectable complex I activity . As flux3.2.2Limiting the reaction flux through the glycerol phosphate shuttle had a negligible effect on maximum ATP production (Table S3), suggesting the model found further compensatory mechanisms. However, this inhibition disfavoured the production of cytosolic NADH from lactate and glucose metabolism, and resulted in the efflux of lactate (at up to 1.5 μmol/min/gDW when the shuttle was inhibited completely). This suggests variable lactic acidosis seen in patients could depend on the capacity of a mechanism (such as the glycerol phosphate shuttle) to compensate for the extent of the deficiency. In the simulations, β-oxidation of fatty acids increased, which produced mitochondrial NADH and reduced the quinone pool via the electron transfer flavoprotein (ETF) and also compensated for the loss of acetyl-CoA needed for the TCA cycle (otherwise derived from the pyruvate created by glycolysis and lactate degradation).3.2.3Next flux through the putative proline cycle was limited (to 7.0 μmol/min/gDW), in addition to inhibiting the glycerol phosphate shuttle (Table S4). This predicted a further compensatory mechanism in the model with metabolite cycling by enzymes from the β-oxidation of fatty acids pathway . In this3.2.4To uncover any remaining compensatory mechanisms in the model, the glycerol phosphate shuttle, NNT, and the proline cycle were all disabled (Table S5). This caused a major decrease of flux through the respiratory chain and consequent decrease of ATP production (30 μmol/min/gDW), suggesting only minor mechanisms remained. As complex I flux was reduced, pyruvate carboxylase converted pyruvate into oxaloacetate and then malate by reversing these steps of the TCA cycle. This removed mitochondrial NADH that otherwise could not be oxidised. Further pyruvate was converted into malate (via citrate and oxoglutarate) by using steps in the forward direction of the TCA cycle. Malate from both sources was then exported into the cytosol, which is consistent with organic acids accumulating in some complex I deficient patients . This bi3.2.5Supply of certain metabolites may help compensate complex I deficiency. But, the cellular availability of metabolites is unknown in complex I deficiencies, and may differ to the model parameters that were taken from experimental measurements of healthy resting hearts. Therefore, we performed a series of simulations where the availability of particular metabolites was increased greatly, to explore their predicted affect on metabolism in complex I deficiency. We found maximum ATP production was increased in the model by supplying several amino acids—glutamate, arginine, proline, valine, aspartate, lysine and glutamine—due to their degradation and this occurred for all levels of complex I activity . The degA variety of different metabolic phenotypes occur in patients with complex I deficiency. The simulation results indicate this may arise from the different tissue-specific availabilities of amino acids and other metabolites that can boost ATP production. Cardiac catabolism of branched chain amino acids is crucial in ATP production and normal cardiac physiology , hence i3.3SDHA subunit and SDHAF1 assembly factor (SDHB and membrane-anchoring SDHD subunits (Complex II (succinate dehydrogenase) deficiencies are rare and account for about 2% of respiratory chain disorders . Complexy factor , but alssubunits . Phenotysubunits , and comsubunits , but thisubunits .In our simulations of complex II deficiency, the model showed less ability to compensate compared to complex I deficiency, and ATP production fell from 138.1 to 66 μmol/min/gDW when complex II activity was inhibited completely (Table S8). This was due to the deficiency affecting both the respiratory chain and TCA cycle; fundamentally disrupting central metabolism into two ways. When we performed multiple simulations exploring different compensatory pathways it became clear that maximising ATP production in complex II deficiency required maximising NADH production while minimising the production of acetyl-CoA that fed into the TCA cycle.3.3.1Initial simulations of complex II deficiency showed intermediates of glycolysis were diverted through the pentose phosphate pathway . By usin3.3.2To identify additional compensatory mechanisms in the model, the folate shuttle was disabled by preventing the uptake of formate (Table S10). This increased flux through the TCA cycle, but ATP production still fell (from 66.0 to 55.1 μmol/min/gDW at 0% complex II activity). The major change was the activity of another NADPH shuttle that used cytosolic reductive carboxylation  – whereb3.3.3A key symptom of complex II deficiencies is organic acids accumulate in the blood and urine. In many patients succinate also accumulates as complex II is the major enzyme to metabolise it and cellular concentrations cannot be buffered . In some3.3.4To predict the effect of increased metabolite availability on complex II deficiency, we individually simulated increased availability of different metabolites (3.4c reductase) deficiencies are considered rare  deficiencies account for about 28% of respiratory chain disorders . Although this system is commonly seen in anaerobic bacteria and some intracellular parasites . This is because the two complexes function as a pair as they are neighbouring in the metabolic network with a single metabolite connecting them. As a result, inhibiting the flux of one complex in simulations consequently constrained the other. As the flux through these complexes was reduced in simulations, this reduced the flux through all the pathways contributing to the quinone pool, including complexes I and II, fatty acid β-oxidation, valine and proline degradation, and the glycerol phosphate shuttle. As a consequence, the model's ability to regenerate NADarasites  it has barasites . HoweverAs complex IV was the major consumer of oxygen in our model, simulating deficiencies in this part of the respiratory chain produced results similar to modelling ischemia . In this+ or avoiding production of NADH. In simulations with glutamine and 5% or lower flux activity of complex III and IV, proline biosynthesis was predicted to become active and used as an additional pathway to remove NADH with the produced proline effluxed from the model.When the results of these simulations were combined into an overview , it becaAlthough these simulations predict the NFRS is not useful for increasing ATP production directly in complex III or IV deficiency, there may be indirect benefits, such as supporting the proton motive force across the mitochondrial inner membrane to maintain mitochondrial viability. One key difference between these simulations and those of ischemia is the in vivo role of succinate accumulation. In ischemia, succinate is responsible for ischaemic reperfusion injury due to its rapid metabolism by complex II when oxygen is reintroduced . This ca4We have shown computer models of mitochondrial metabolism can simulate known features of respiratory chain disorders, supporting their biological applicability. The model's comprehensive coverage enabled prediction of multiple pathways that have the potential to improve the energetic state of diseased mitochondria and cells. Thus we show computer modelling can generate hypotheses about compensatory pathways and by their testing provide new opportunities for future research on mitochondrial disease. Further, modelling and simulation can also provide a framework to interpret the data from large-scale ‘omics experiments on mitochondrial dysfunction.Simulations of complex I deficiency showed the smallest reduction in ATP production, and predicted three metabolic mechanisms that compensate for the deficiency. A proline cycle and cycling of fatty acid β-oxidation reactions allowed additional electrons to enter the respiratory chain via the quinone pool, in addition to the well-characterised glycerol phosphate shuttle. A folate cycle provided additional NADPH to the mitochondrial matrix to drive the fatty acid cycling. This predicted metabolic flexibility may explain the high proportion of isolated complex I disorders among respiratory chain diseases, because patients have the highest chance of survival via alternate compensating metabolic pathways.Simulations of complex II deficiency showed a much larger drop in ATP production than complex I deficiencies because of the dual impact on the TCA cycle and respiratory chain. In the model this impaired the use of the main cellular fuel sources , as the acetyl-CoA produced could not be fully metabolised. This made the model more reliant on alternative mechanisms to maintain complex I activity, such as a predicted folate and reductive carboxylation shuttles to take cytosolic NADPH produced from the pentose phosphate pathway and convert it to NADH in the mitochondrial matrix. The lack of pathways found in the model to compensate for complex II deficiency may explain the rarity of patients with complex II deficiency, perhaps due to embryo lethality. It may also imply that compensatory shuttles identified in the simulations cannot support the necessary fluxes in vivo, as also suggested by their predicted inefficiency in producing ATP.Simulations of deficiencies of complex III and IV both had the same effect on metabolism and lacked alternative compensatory pathways and metabolites. The changes were strikingly similar to experimentally validated simulations of ischemia . AlthougCertain amino acids commonly were predicted to increase ATP production in simulations of all respiratory chain disorders. It is possible that the plethora of symptoms in mitochondrial diseases arise from differences in the substrates that are available to different tissues and in different patients. The model we used was based on a cardiomyocyte mitochondrion with default parameters to reflect the wide range of substrates cardiomyocytes can access. The reactions included in this model are likely shared with many other tissue types. But the substrate availabilities are likely different among tissues, thus limiting the compensatory options available to some tissues and placing them at greater risk of energy deficiency.In the future, the quality of simulation results and compensation strategies could be improved and personalised by measuring and incorporating data from patients about metabolite changes, expression of metabolic enzymes, metabolic fluxes, and regulatory interactions. We anticipate our work can guide future experimental studies that can confirm or reject the hypothetical mechanisms revealed by computer modelling and their importance in mitochondrial respiratory chain disorders. We hope that our results will promote the use of computational modelling by the mitochondrial diseases community and will spur investigation into new research directions and therapies for mitochondrial respiratory diseases, such as changes in diet or the provision of adjuvants to boost anaplerotic pathways.SBML-formatted file of the flux balance analysis model used for the simulations.Supplementary File 1Supplementary tables. Calculated fluxes of reactions during simulations of the models under control conditions, mitochondrial dysfunction, and with the addition of selected metabolites.Image 1The contribution of a folate shuttle to ATP production in simulations of complex I deficiency. During simulations of complex I deficiency, a folate shuttle emerged (blue) to support ATP production when the compensatory mechanisms of the glycerol phosphate shuttle, NNT and proline cycle were disabled. This folate shuttle used enzymes of tetrahydofolate metabolism to shuttle NADPH produced from the pentose phosphate pathway into the mitochondrial matrix. This NADPH was used to drive the cycling of metabolites between trans-2-enoyl reductase (purple) and acyl-CoA dehydrogenase (red), reducing the quinone pool and contributing to flux through the respiratory chain to produce ATP.Supplementary Fig. 1The contribution of a folate shuttle to ATP production in simulations of complex II deficiency. Initial simulations of complex II deficiency showed a folate shuttle that used enzymes from tetrahydofolate metabolism to shuttle NADPH produced from the pentose phosphate pathway into the mitochondrial matrix (blue), as seen previously in simulations of complex I deficiency. However, in complex II deficiency this was coupled to an additional cycle involving glutamate to convert NADPH into NADH that was then oxidised by complex I. This reduction of the quinone pool contributed to flux through the respiratory chain to produce ATP.Supplementary Fig. 2Other shuttles contributing to ATP production in simulations of complex II deficiency. A. Initial simulations of complex II deficiency showed the citrate malate antiporter used to shuttle cytosolic NADH into the mitochondrion, which was oxidised by complex I. B. A reductive carboxylation shuttle emerged (purple) during simulations of complex II deficiency when the folate shuttle  was disaSupplementary Fig. 3Sources of fumarate for the NADH-fumarate reductase system in simulations of complex III & IV deficiency. During simulations of deficiencies of complex III and complex IV, the NADH-fumarate reductase system (NFRS) (orange) emerged as a mechanism to support ATP production. The NFRS uses complex II running in reverse with fumarate as the terminal electron acceptor. For the necessary fumarate, the model used a series of reactions to convert pyruvate into malate and then fumarate (purple).Supplementary Fig. 4The following are the supplementary data related to this article.AJR conceived the study. AJR and LZ designed the simulation procedures. LZ and AGS conducted the simulations. ACS developed and provided the model. LZ and ACS analysed the results and drafted the manuscript. All authors reviewed, edited and approved the final manuscript.The authors declare that they have no conflict of interest.


The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the  It is believed that the general architecture of living systems is based on the structure and function of modules . HoweverThe metabolism is perhaps the best network of interactions ever characterized in biology because a large number of studies has defined metabolic pathways , 5. For Data extracted from plant genomes make it possible to investigate correlations of the molecular backbone of living beings that are related to their adaptations to the environment –8. ComplHowever, it is not known exactly how a general design on the distribution of matter in metabolism can be developed based on the observation of the topology from metabolic networks. Consequently, in order to reveal functional, hierarchical and even phylogenetic information hidden in the network structure, new models need to be investigated. The content of plant metabolic networks reflects the species phylogeny, thus groups of plants that are evolutionary closer share a larger proportion of common metabolites. In this case, the specialized metabolism is responsible for the greatest differences; gene coding for specialized metabolic functions have proliferated to a much greater degree and by different mechanisms and display lineage-specific patterns of physical clustering within the genome . Thus, uAlthough some studies have shown that the hierarchical organization and modularity inside the metabolic network is related to the chemical classes of metabolites , these skcore decomposition [kcore percolation, a network is pealed as a set of successively kcores (layers (k)) until a maximum core is achieved. Thus, small values of k define the periphery of the network and the innermost network core corresponds to a large k. This algorithm has been proposed to understand the network hierarchical organization by extracting highly interconnected parts and thus providing ways to find relationships within these substructures [kcores can provide useful insights into the global network topology and also establish a hierarchy of connections from each network node [For instance, some attempts have been made to find fully-connected parts of the metabolic networks . Completposition , 12. It position . Throughructures . Therefoork node .kcore decomposition of plant metabolic networks modeled according to the plant metabolic reaction dataset from 17 plants selected from the PlantCyc database [Brachypodium distachyon (BD), Hordeum vulgare (HV), Oryza sativa japonica (OSJ), Panicum virgatum (PV), Setaria italica (SI), Sorghum bicolor (SB), Zea mays (ZM); Arabidopsis thaliana (AT), Brassica rapa pekinensis (BRP), Carica papaya (CP), Glycine max (GM), Manihot esculenta (ME), Populus trichocarpa (PT), Vitis vinifera (VV); Selaginella moellendorffii (SM); Physcomitrella patens (PP) and Chlamydomonas reinhardtii (CR) were considered.In this study, we present a percolation analysis based on the database . The metkcore decomposition of plant metabolic networks reveals information about the metabolic centralization and functional hierarchy of certain metabolite groups distributed across its layers.Our main objective is to demonstrate that a percolation through the kcore layers of the networks in an opposite way relative to the secondary and non-common metabolism between plants. The common metabolism between plants is completely centralized in the layers with greater connectivity and the metabolism secondary and uncommon between the plants is peripheral along the layers. Together these data show that the decomposition by a layering tool is useful for building hierarchical models of the metabolism organization and brings information that can be used in studies about the evolution of the metabolism.This functional analysis shows that a high proportion of the metabolites from central layers of the networks contain the basic components from basal metabolism. In addition, we show that the metabolites found in the more centralized level of the networks integrate all plant metabolisms because they participate in the absolute majority of their metabolic reactions. As confirmed by previous studies that show that metabolic networks are very modularized , we alsokcore percolation algorithm to 17 plant metabolic networks whose set of metabolic reactions were obtained from the PlantCyc database [We applied the database . The setdatabase .kcore decomposition is applied successively, as shown in The kcore percolation from the metabolic network of Arabidopsis thaliana shows that the last full connected level (maximum kcore) contains 21 metabolites, while the outermost layer contains 3,546 metabolites, from which 149 belong exclusively to k1. As expected, there is a decrease in the number of metabolites in each layer until the central core. It can be observed that the maximum number of layers kmax for all the studied plants varies between 16 and 18. Thus, these plants were grouped into three sets: BD, HV, OSJ, SI and SB with 16 layers; PV, ZM, BRP, CP, GM, ME, VV, SM and CR with 17 layers; AT and PP with 18 layers.The kmax is not related to the number of metabolites (N). For instance, networks with relatively small sizes such as moss Physicomitrela patens (N = 2651) reached the highest number of cores (k = 18), while networks with larger sizes such as the dicotyledonous Manihot esculenta (N = 2991) reached fewer cores (k = 16). The detailed network properties, such as the number of layers and network size for the 17 plant metabolic networks studied here, can be found in We found that the number of layers kcore from all plants studied here namely: ADP, ammonia, AMP, ATP, CO2, coenzyme A, diphosphate, H+, H2O, L-glutamate, NAD+, NADH, NADPH, and phosphate. These 14 metabolites extracted from the central core, found in the 17 studied plants, represent a very connected dataset.We also analyzed the chemical composition of the central core in order to obtain the contents of the innermost network for the 17 plants, which is shown in We observed that the metabolites extracted from the central core of the metabolic networks participate in almost all the sets of the metabolic reactions from plants studied here. This is shown in kcore to the metabolic network from Arabidopsis thaliana  with the maximum kcore of the network. It can be seen for example in kcore).The graphs in kcore class contains only 21 metabolites while the lipids contain around 500. This is almost 20 times higher than the content of the metabolites from the maximum kcore, while the number of the links at the maximum kcore is almost the same as the lipids.There are differences in the number of the metabolites in each class shown in klayer. These metabolites were extracted from the intersection dataset between the 17 plants studied in this work. The klayers for plants belonging to five major clades, namely: the algae Chlamydomonas reinhardtii, the bryophyta moss Physicomitrela patens, the lycophyte Selaginella moelendorffi and two angiosperms, the monocotyledon Zea mays and the dicotyledon Arabidopsis thaliana. The analysis of the proportion from common metabolites in the kcore layers reveals that its content is centered in the innermost kcore, where these metabolites reach to almost the total content of the layers. The distribution of the metabolites per each kcore layer for plants that were not shown in kcore layers, as it is a complementary set of the common metabolites, as shown in klayers as that shown to non-common metabolites between the plants , amino acids (L-glutamate) and the most primary photosynthesis product, the oxygen (O2)  see . This faThe metabolite distribution across the plant’s metabolic reactions showed that at least one element of the set of metabolites belonging to the most central layer of the networks is present in more than 90% of the metabolic reactions from all plants studied here see . It is sArabidopsis thaliana are highly connected within themselves, as the number of connections inside each metabolic class  is higher than with other classes. Interestingly all classes are very connected with metabolites present at the maximum kcore layer. This shows that in addition to being very modularized, the network is also very centralized in the metabolites found in the last level of the network percolation. Earlier studies have demonstrated that the metabolic networks have a modular and hierarchical structure [The analysis of the cross connection between metabolic classes showed that the metabolic classes in the tructure .kcores. Moreover, it is known that the basal metabolism arose early in the evolution of species, therefore it is not unreasonable to assume that the percolation by kcores provides a tool for studying molecular evolution metabolism, which is supposed to occur with the direction of the center to the periphery of the network percolation layers.It is also interesting to note that the common metabolism, represented by common metabolites between all plants studied here, is centralized in the kcore layers also corroborates the hypothesis that the kcore percolation algorithm may reveal evolutionary hierarchies in the metabolism and it is appropriate to demonstrate connectivity hierarchies in plant metabolic networks and their correlation with the metabolic functionality.The peripheral location of the secondary and non-common metabolites between plants along the It should be noted that the secondary metabolism provides alternative ways of molecular interactions between the plant and its environment and for this reason it appeared later in the evolution of species. This metabolism has a differential evolution in relation to the basal metabolism and genes related to it proliferate and diverge more rapidly throughout the evolution .kcore percolation. We observed that the distribution of these metabolites is very similar to that of the secondary metabolites  along the topology of the networks, as the vast majority of the reactions contain at least one element of the central layer and therefore, appear to be embedded in the central module. This suggests that transport of matter at the metabolic pathways does not have an occasional structure and it can be centered in terms of routes. Moreover, it can be assumed that transport is performed by a chemical network, which although is not spatially defined, has logical connections that distribute material components (molecules). Therefore, theoretical correlations about the genesis and dynamics of pathways can be drawn from the knowledge of key metabolic nodes, which could be shown by metabolomic and transcriptomic data, for example. Therefore the kcore analysis can reveal the hierarchy and emergence of these nodes in various topological states of the metabolic network.The kcore also participate in almost all chemical reactions of the secondary metabolism. This observation leads to proposing a model in which the kcore central layer is a kind of network processing unit, which is responsible for anchoring the plant metabolic reactions.All elements of the central core are considered hubs in plant metabolic networks. These metabolites are the basic bricks of which all metabolic pathways are built. Meanwhile, we observed that the metabolites at the maximum kcore percolation layers. It suggests that studies on the modularization of metabolism could be performed with kcore percolation to reveals hierarchies of modularity in metabolism.Hierarchical network models show that their assembly hierarchy is possibly associated with the specialization of metabolism . At the Finally, some questions remain: Can we assume that the molecular evolution of metabolism, i.e. adding and integrating new components can be simulated by the percolation proposed in this study? Is this addition anchored to the metabolites of the central layers?The plant metabolic networks studied here were constructed from the metabolic reactions of 17 plants available at PlantCyc database (Version 9.5) , furtherCommon metabolites This dataset is composed by the intersection among all metabolites of plants analyzed here. The dataset is available in Non-common metabolites This dataset is complementary to the one described above, i.e. it is composed by those elements that do not belong to the intersection of metabolites among all the plants studied.Secondary metabolites This dataset was obtained by discriminating the complete dataset of metabolites of each plant. The secondary metabolites were identified using well-established literature regarding secondary metabolism [tabolism . The numtabolism  and the tabolism  were useMetabolic classes This data set was obtained from the attribution of metabolites belonging to the classes: amino acids, nucleotides, lipids, carbohydrates and those metabolites found in the maximum kcore of the metabolic network of the plant Arabidopsis thaliana -core. A kshell Gk is composed by all the nodes whose shell index is k [k in a given Gk is denoted by kmax [The ures see . A kcoreual to k , 12. As ual to k . Fig 1b dex is k . The max by kmax .kcore were discriminated by identifying metabolites from each respective class present within the full content of metabolites from Arabidopsis thaliana.Metabolites belonging to the classes amino acids, nucleotides, lipids, carbohydrates and maximum The quantification of the cross connection of metabolites was performed by counting the number of links between metabolites belonging to a specific module with metabolites from other modules.S1 File(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(ZIP)Click here for additional data file.S7 Filekcore decomposition for Arabidopsis thaliana metabolic network (A video corresponding to the  network . The vid(MOV)Click here for additional data file.