Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce  Meneco, a tool dedicated to the parsimonious gap-filling of metabolic networks. Unlike other tools, Meneco allows using sparse data (missing stoichiometries) and draft metabolic networks to suggest reactions to fill gaps in the networks. Subsequently, we apply it to two biological case studies and show that the flexibility of Meneco enables it to be adapted to a variety of research questions and types of available data. We show that Meneco performs better than reference tools with respect to large-scale heterogeneous reference database and with respect to the recovery of important reactions in highly degraded networks. Specifically, it allowed the analysis of two interacting metabolic networks and the reconstruction of the first metabolic network of Euglena mutabilis.In the era of fast and massive genome sequencing, one challenge is to transform sequence information into biological knowledge. Reconstructing metabolic networks that include all biochemical reactions of a cell is a way to infer reactions from genomic data. Unfortunately, those data are usually incomplete, poorly annotated, and missing reactions create gaps in the metabolic networks. Here we introduce  In. InE. muStarting point for any metabolic gap-filling problem  is a draa priori criteria to select one rather than the other. Then, a cautious strategy is to compute the set of all reactions contained in at least one solution reported by the algorithm and to manually curate them.Obviously, the solution to a gap-filling problem may not be unique. The sizes of both of the search space and the solution space for the gap-filling problem grow exponentially with the size of the reference database from which the reactions are taken. Any gap-filling method thus needs to find a suitable trade-off between the number of solutions reported by the method, that should not be too large to enable a manual curation, and their biological significance, in order to capture the main properties of the GEM being reconstructed. Gap-filling methods may report one, a few, or many sets of reactions to complete the draft GEM. When several solutions are produced by a gap-filling algorithm, there are no T1, T2, T3.Three main approaches can be distinguished according to the number of solutions they report and the characteristics of these solutions  number of solutions to the problem, although there is no a priori estimation of the number of iterations needed to solve the problem.rategies , 10, whifastGapFill [GapFill approach combined with the Fastcore algorithm, which eliminates the focus on the biomass production by identifying a single set of reactions which unblocks all reactions within the draft metabolic network. The price to pay is to allow import and export fluxes, especially to resolve issues related to the activation of isolated reactions in the GEM. When applied to our toy example, fastGapFill reported seven reactions from the reference database and one import/export reaction of the reactant of the reaction producing T3 that was not present in the database to be added to the model. The reactions R1 and R2 are introduced to produce T2 in the absence of R3. Since no accumulation of the reactant of R3 is possible, fastGapFill adds an import/export reaction to enable the production of T3 without using the previous reactions of this pathway. The reactions R4, R6, and R9 are equivalent to produce T1 and were all chosen in order to unblock all fluxes going threw all reactions present in the draft model . Finally, R7 is added to enable the production of T1.tGapFill  is an exMIRAGE [MIRAGE samples the space of solutions by randomly iterating the search algorithm. In our toy example, the algorithm was iterated 100 times without applying a priori scores to reactions of the database. Four different sets of reactions were obtained {R3, R7}, {R3, R6, R7}, {R3, R4, R7} and {R3, R7, R9}. The reaction R3 is mandatory to produce T3 and it also produces T2. Therefore, no other reaction producing T2 is necessary, so that R1, R2, R5 and R8 were never used. Given the cycle, the reaction R7 would be the minimal completion to produce T1 from a stoichiometric point of view, but the subset-minimality criterion also enables the MIRAGE algorithm to find the reactions R4, R6 and R7.In contrast to parsimonious approaches, top-down approaches start from all available information and remove reactions without added-value to the solution of the problem , 23, 24.MIRAGE  aims at R5 is identified by none of the stoichiometry-based methods.Together, we noticed that the criteria used to complete the network highly impact on the reconstruction procedures which all report different solutions. In particular, the reaction Stoichiometry-based tools fail to address the fact that species-specificity of cofactors in the reactions is often not precisely described in databases neither is stoichiometry of reactions. This may lead to predictions errors for degraded metabolic networks while producing biomass. Topology-based tools on the other hand do not take into account constraints yielded by the stoichiometry that may lead to non-producibility of biomass in some cases.Meneco tool, which uses qualitative constraints to express the producibility of metabolites purely based on the topology of the metabolic network. Following a qualitative approach to elaborate the bio-synthetic capacities of metabolic networks according to [Meneco defines the synthetic capability of the system, with respect to a set of input compounds, as the set of metabolites that can be produced following a route in the network. As described in the third part of Meneco requires the independent production of all inputs in the cycle to “initiate” the reactions [We designed the rding to –27, Meneeactions .S using a topological approach. In contrast, using a FBA-based approach, the stoichiometric coefficient n will have a great influence on the producibility. The first criterion to produce T1 is to have an equal quantity of d and c available. One a will be used to produce one d and a second a is necessary to produce one c. Consequently, the only way to produce T1 using the stoichiometric approach is to have the coefficient n equal to twice the value of the stoichiometric coefficient of b, here 2. It is also interesting to note that, if the objective function was only formed by the reaction producing metabolite d, the only way to enable it from a stoichiometric point of view would be to have n = 1. Using a topologic based approach, d would be always producible from S.In the first part of Meneco uses the ASP paradigm [Meneco attempts to complete the network by importing minimal sets of reactions from a metabolic reference database such that the resulting network provides the required functionality with respect to topological metabolite producibility. The ASP technology is not only able to enumerate all minimal network completions, but also to compute the union and intersection of all minimal network completions without enumerating them, leading to strong gains in efficiency while still being exhaustive. Applied to the example depicted in T1, T2 and T3 are: {R3, R4, R7}, {R3, R6, R7}, {R4, R5, R7}, {R6, R7, R8}, {R4, R7, R8}. Meneco missed the reactions R1 and R2, so did GapFill, because of the parsimonious criteria they used. On the contrary, Meneco reported the reactions R5 and R8 because it checked that T2 could be simply produced by adding R8 as soon as T1 was produced. Finally due to the cycle, Meneco missed the reaction R9. This could have been circumvented by adding one of the metabolites of this cycle to the seeds, like explained in the section dedicated to the application of Meneco to E. mutabilis. In particular, as Meneco is a graph-based approach, it may fail to capture the intrinsic non-linearity of metabolic behaviours [Based on this topological producibility criterion, paradigm  to effichaviours  as well haviours . The othMeneco is the reconstruction of GEMs for non-classical organisms. The automatically reconstructed bacterial GEMs in the BioCyc repository [iJR904 E. coli GEM [iJR904 E. coli GEM. Degradation occurred through all types of pathways, including the central ones, such as the Tri-Carboxylic Acid cycle (TCA)  GEMs. None was capable of producing the corresponding biomass.As described in the introduction, the main application area of pository  containecoli GEM  were ranE. siliculosus), requiring to explore all possible metabolic reactions to fill the network. The identifiers of iJR904 were manually mapped to identifiers of the MetaCyc database and both files were merged to create a complete reference database which contains all the reactions removed from the original GEM.In order to complete these degraded metabolic networks, we selected the MetaCyc reference database (version 18.5) , motivatMeneco, coupled with the fact that stoichoimetry information is not taken into account, can be seen in the benchmark in E. coli networks with the MetaCyc database. The completion was performed using either the fastGapFill, MIRAGE or Meneco tools. Even if this comparison is not entirely representative due to the different backgrounds and aims of the methods, it is worth noting that Meneco in general selected smaller sets of reactions to complete the networks compared to the other methods. Indeed, on average, Meneco returned answers 8 times smaller than fastGapFill and 125 times smaller than MIRAGE. This could enable an easier and faster manual curation of the results. Nevertheless this greatly reduced number of reactions proposed comes with a cost: Meneco restores less functionality, especially when it comes to highly degraded networks. The results of this benchmark are summarized in GapFill could not be used to perform an exhaustive gap-filling of the GEMs in practice. Moreover the analysis suggests that MIRAGE is not suited to be used with minimal data  for gap-filling and that the algorithm needs all the recommended data (phylogenetic and/or transcriptomic scores) to perform correctly and gain robustness.The effect of the parsimonious criterion of Meneco and fastGapFill with regard to the proposition of reactions to produce targets, all reactions added to a functional GEM for gap-filling were classified into essential, blocked and alternative reactions with respect to the production of biomass. The analyses are depicted in fastGapFill were blocked in the completed GEM towards the production of the biomass, i.e. the production of the target compounds. This high rate of blocked reactions was independent of the initial rate of degradation of the considered GEM. This is consistent with the main purpose of fastGapFill which aims at unblocking all reactions in the GEM core set without any focus on a set of targeted reactions. Conversely, only 12% of reactions added by Meneco were blocked with respect to the biomass reaction in the completed GEMs, and even less for 10% and 20% degraded GEMs. This confirms that fastGapFill does not solve the exact same problem as Meneco does, that is proposing sets of reactions to produce unproducible compounds. Since the problem fastGapFill solves is a larger one, it is more difficult to filter and manually curate the proposed solution as it is often required after gap-filling. Meneco is a relevant tool for the preliminary completion of a new draft metabolic network when limited phenotypic information are available. Its parsimonious feature allows suggesting reactions directly related to the production of the targets and manual curation is facilitated through a study of the whole space of solutions.In order to estimate the accuracy of Meneco provides a good trade-off in terms of scalability with respect to the size of the reference database used for completion: the output of Meneco remains reasonable in terms of size , with a reasonable loss of impact on the restoration of biomass production compared to parsimonious approaches.Our results suggest that the topological parsimony criterion used in Meneco, we completed the 3,600 degraded GEMs of our benchmark derived from the iJ904 E. coli metabolic network by 7,200  additional degraded GEMs of the iAF1260 [iJO1366 [E. coli networks. The Meneco tool was applied to complete each of the 10,800 degraded GEMs by using the networks prior to degradation as a reference database. The main motivation for changing the reference database was to be able to analyze how the classification of reactions into essential, alternative and blocked with respect to biomass production evolved with the completion process.To further evaluate the quality of the output of  iAF1260  and iJO1[iJO1366  E. coli Meneco output, even if the size of the solution set increased linearly with the degradation rate of the network. Nevertheless, the quality of the reference E. coli network has a strong impact on the quality of the results as shown in E. Coli network multiplied by 6 the percentage of functional networks after completion  were recovered in the networks filled by the Meneco procedure. Our analysis shows that Meneco was able to recover 97.5% of the essential reactions on average among the 10,800 experiments, and few blocked reactions were included in the networks filled by this tool.The reactions of the 40 degraded iMeneco recovered all essential reactions of the reference network. When failing to restore network functionality, it still recovered all essential reactions in 6,041/7,312 completions (82.3%). In the cases when Meneco did not recover all essential reaction, it generally missed a single essential reaction, and at most 3 .To gain better insights into the importance of essential and alternative reactions in the gap-filling procedure, we classified each gap-filling experiment according to four categories: (i) the network is functional after gap-filling (ii) the network has recovered all essential reactions after gap-filling but it is not functional (iii) the gap-filling procedure missed one essential reaction and (iv) the gap-filling procedure missed more than one essential reaction. Results are depicted in Meneco and capable of producing biomass: among the reactions that were essential in the reconstructed network, on average 40% were classified as alternative in the reference network  can only be applied if biomass can be quantitatively produced, we studied the 3,488 networks  capable of biomass production after their gap-filling. To do so, we compared the classification of reactions before degradation to the classification obtained after degradation and gap-filling. In all cases, reactions that were initially blocked with respect to biomass production remained blocked after the completion. In the same way, 98.6% of the reactions initially essential remained essential after the completion, the others becoming alternative  over the parsimonious gap-filling procedure. The GapFill benchmark was run as single target completion since it was impossible to be run on a global biomass function while being exhaustive. This is due to the combinatorial characteristics of the problem: the number of solutions when filling the networks based on a complete biomass was too high (mandatory bounding of the number of solutions with GapFill) and so was the computation time. Hence, we used as output for GapFill the union of the completion sets  for each individual target. We noticed that only 68 networks reached the limit of 30 completion sets for individual targets among the 10,800 degraded networks.Previous experiments show the added-value of parsimony towards the selection of adequate gap-filling solutions. As a final study, all the analyses were run with the Meneco and GapFill solutions are comparable in sizes, they only share 45.3% of the reactions (see Section 4 in GapFill returned 1.6% more reactions (i.e. 4) than Meneco. Ability to restore biomass production (FBA) was lower for GapFill than for Meneco: 21.6% and 32.3% respectively on 10,800 networks, and particularly for 10% degraded networks: 49.4% and 81.9% respectively. Recovery of essential reactions was less efficient for GapFill: it missed more than one (and up to 21) essential reactions in 13.9% of the cases, a major contrast with Meneco (0.8% and no more than 3 missed reactions). Analyses and results are available in Section 6 in Interestingly, although Altogether these features confirm that the topological over-approximation, when compared to stoichiometric criteria, is relevant for the parsimonious gap-filling of a new draft metabolic network not only in terms of performances but also in terms of functional accuracy.Meneco tool was applied to the analysis of candidate algal-bacterial interactions in the brown algal model E. siliculosus. Interactions between algae and their associated bacteria are of increasing interest because of the potential key roles bacteria may have on the biology of the algae, in particular through metabolic connections between both types of organisms [Ectocarpus genome paper [Meneco we then identified which of these targets were producible in two situations. Firstly, we used as a draft network the algal network itself, more precisely a version of EctoGEM 1.0 [Candidatus Phaeomarinobacter ectocarpi, a bacterium suggested to live in association with E. siliculosus [The rganisms . For thirganisms . In parame paper . For eacoGEM 1.0  for whiciculosus .Ca. P. ectocarpi network to the algal network. These targets and the metabolic pathways enabling their production were then manually examined. In total 83 previously non-producible algal targets became producible according to the Meneco semantics when combining the metabolic capacities of the bacterium with those of the alga. For each of these 83 targets, the essential reactions allowing its producibility by Ca. P. ectocarpi were studied, as well as the target and its related EST in E. siliculosus. The purpose was to determine whether the production of the target by the alga could depend on an interaction with the bacterium. These individual examinations revealed some errors or missing annotations in E. siliculosus as well as some cases of weak sequence homology leading us to no longer consider 21 (25%) of the 83 compounds as targets. Improvements in genome annotations also allowed recovering alternative reactions or pathways in E. siliculosus related to the production of 37 (45%) of the compounds. Nineteen compounds (23%) however are likely to be part of an exchange between the alga and the bacterium (i.e. confirmation of the target), ii) the target was not producible by the algal itself through the presence of other ESTs or genes and iii) the bacterial reaction(s) necessary for the target production was (were) correctly annotated. Details are available in A comparison of both cases allowed the identification of targets that became producible when adding the metabolic capacities of the acterium . The proMeneco were the result of potentially interesting interactions.Several of the reactions identified by E. siliculosus on its own is probably incapable of producing histidine or histidinol because a histidinol-phosphatase (EC 3.1.3.15) is missing from its genome. Meneco has previously identified this mandatory reaction to complete the histidine biosynthetic pathway [E. siliculosus , as well as the recently published genome of Saccharina japonica [Ca. P. ectocarpi, which encodes all enzymes of the histidine biosynthetic pathway.For example,  pathway . Recent japonica , confirmE. siliculosus is capable of producing vitamin B5 from β-alanine via the activity of a pantoate-β-alanine ligase (Esi0070_0043), but it lacks a biosynthetic pathway to produce β-alanine , which is an important vitamin for the formation of coenzyme-A. -alanine . Our anaEctocarpus still remains unknown, the E. siliculosus metabolic network possesses two metabolic reactions  for the degradation of agmatine and polyamine synthesis. These reactions in Ectocarpus are well-supported, despite the fact that it lacks the metabolic capacity to produce agmatine, as no arginine decarboxylase (E.C. 4.1.1.19) is present in its genome. As in the case of histidine synthesis described above, this latter observation also holds true for other sequenced Ectocarpus strains and the S. japonica genome. A plausible explanation is that E. siliculosus  may use external sources of agmatine to produce polyamines. Ca. P. ectocarpi is capable of producing agmatine, which constitutes a potential interaction point between both organisms. Our current working hypothesis is that when external or bacterial agmatine is available, agmatine-derived polyamines may complement or replace ornithine-based polyamine synthesis in E. siliculosus.A third example is agmatine, an aminoguanidine, which is well-studied in human where it modulates for instance receptors of neurotransmitters or ion channels. While the role of agmatine in These three examples provided interesting working hypotheses that will now need to be tested experimentally. Corresponding experiments are ongoing but time-consuming as they first require separate cultivation of algae and bacteria.i.e. candidate algal-bacterial interactions either due to missing gene annotations or false/overly precise annotations. One example for each of these cases is detailed in The aforementioned three examples highlight promising candidate pathways for algal-bacterial interactions. For other identified metabolites and reactions, however, sequence information was insufficient to determine the exact specificity of the enzymes and reactions, and thus to draw conclusions without additional experimentation. Furthermore, in E. mutabilis, a photosynthetic protist and primary producer in acidic aquatic environments. Despite important roles of E. mutabilis in those ecosystems and despite the fact that it has often been considered as an indicator species for acid mine drainages, this organism has been poorly described so far.As a second case study, we focused on the reconstruction of a metabolic network for Euglena. Therefore, we used assembled transcript sequences as described in [Meneco. This ensured that the reconstruction of the E. mutabilis metabolic network would be highly conservative, avoiding the inclusion of reactions based on false annotations. The major drawback of this approach was the resulting high rate of missing reactions in the initial draft network, which did not allow Meneco to restore the production of all targets at once. The 72 targets are listed in Supplementary files S6. Of those targets, 54 were defined according to [E. mutabilis cultured in a minimal mineral medium. The 17 other targets were myristic acid, shown to be present in the E. mutabilis membrane [Due to their complexity and size, no genome sequences are available so far within the genus ribed in  and annording to  who showmembrane , and theMeneco with the 8 mineral seeds and |Light| (Supplementary files S6) could propose a completion solution only for UREA and OXYGEN-MOLECULE among the 72 targets. These two metabolites could be produced only because they were the products of a single reaction involving only mineral reactants, a situation which was not biologically satisfying.Running E. mutabilis metabolic network must be produced from the mineral seeds only, including cofactors that are essential to the vast majority of reactions. These cofactors themselves depend for their biosynthesis on metabolites that they contribute to produce. For instance, NAD is biosynthesized from aspartic acid or tryptophan, two amino-acids whose production requires NAD. In other words, most of the E. mutabilis initial draft network consisted of a complex cycle with long range circular dependencies that no external mineral nutrient could initiate. Thus, this network could not produce the targets with Meneco without adding the appropriate extra seeds to solve the circular dependencies. The addition of NAD to the seeds permitted to initiate the draft network, enabling Meneco to restore the production of 69 targets out of 72. The addition of |ACP| to the seeds was required to restore the production of two remaining targets—PALMITATE, CPD-7836 (myristate) –, and adding |Red-Thioredoxin| to the seeds was necessary to restore the production of DCTP (deoxycytidine-triphosphate).The principal reason for the near impossibility to produce the targets lies in the fact that all targets and internal metabolites of the Meneco because of local cycles and circular dependencies. For instance, in oxygenic photosynthesis, oxygen is produced by photosystem II: light provides the energy for transferring 4 electrons to 2 molecules of plastoquinone from 2 molecules of water which are split into 4 protons and one molecule of oxygen. Since the |PLASTOQUINONE| compound was not available in the network, Meneco could not return the correct reaction PSII-RXN as a solution for the production of OXYGEN-MOLECULE, but proposed instead to reverse the reaction of water-forming NAD oxidation RXN-14692.Although the production of all the targets was enabled by the addition of the 3 seed metabolites NAD, |ACP| and |Red-Thioredoxin|, some pathways could still be non-functional with E. mutabilis is a primary producer, the photosynthesis reactions are at the origin of biomass production. The photosynthesis pathway can be divided into two subpathways: the light reactions collecting the energy from light and the Calvin-Benson-Bassham cycle, also known as the dark reactions, building the precursor components of biomass from carbon dioxide.Since de novo biosynthesis pathway described in MetaCyc, and therefore, the production of the corresponding donor and acceptor are mutually dependent. In order to unblock the whole chain, we added the electron acceptors  to the seeds, with the exception of NADP which could be produced from NAD as shown in Before addressing the capacity of the Calvin cycle to produce biomass precursors, we manually completed the light pathway by adding the missing reactions  and checked whether they could provide a viable route in the draft network . This paMeneco using the same seed metabolites as for the whole network. A single solution was returned with three reactions, NADPYROPHOSPHAT-RXN, APYRASE-RXN, NAD-KIN-RXN, producing respectively AMP from NAD, ATP from AMP, and eventually NADP from NAD and ATP. This confirmed that the four reactions of the photosynthesis light pathway were able to produce NADPH provided that NADP was available.To verify that the chain of photosynthetic light reactions was functional, we isolated the individual reactions  in a subnetwork and checked for the production of NADPH with Meneco on the whole draft network to check its capacity to produce NADPH returned two solutions of one reaction each, RXN-10862 and APYRASE-RXN, both allowing the production of ATP from AMP and unblocking the reaction NAD-KIN-RXN. At this stage, ATP was provisionally added to the seeds until more information could be gathered about which reactions should be included in the network for its production, and to enable the photosynthesis pathway to provide the Calvin-Benson-Bassham cycle with the necessary NADPH.The same dependence on ATP was observed with the whole draft network which could already produce AMP but not NADP although the reaction NAD-KIN-RXN of NAD phosphorylation into NADP was present. Running The Calvin-Benson-Bassham cycle is the pathway of the photosynthesis process that reduces carbon dioxide to produce glucides. The net equation of this cycle can be written as:Meneco with one reaction each: AMP-DEAMINASE-RXN, AMP-NUCLEOSID-RXN and NMNNUCLEOSID-RXN. The latter two reactions directly produce |RIBOSE-5P| whereas the first one yields IMP which can be used to produce |RIBOSE-5P| as well, suggesting that the Calvin-Benson-Bassham cycle requires this metabolite to be functional. We thus added |RIBOSE-5P| to the seeds and all 5 targets previously tested with the whole network were now producible by a subnetwork composed of only the 13 reactions of the Calvin cycle, confirming that this pathway was now functional.The net production of 3-phospho-glyceric acid (G3P) is the main source of organic carbons for further biosynthetic processes, but other metabolites may exit the cycle towards glycogenesis, amino-acid-, nucleotide-, or lipid biosynthesis. In order to assess whether the Calvin-Benson-Bassham cycle was functional we therefore tested the draft network for the production of G3P, FRUCTOSE-6P, RIBULOSE-5P, GAP and DIHYDROXY-ACETONE-PHOSPHATE. Three solutions were proposed by E. mutabilis to initiate the Calvin-Benson-Bassham cycle.It is worth noting that, at this stage, the metabolite seed |RIBOSE-5P| could be replaced by D-RIBULOSE-15-P2. This solution was provisionally preferred as this compound is a reactant of the carbon fixation reaction in photosynthesis. Thus, adding it to the seeds, along with ATP, may simulate the initial batch of metabolites that allows 3), in order to trigger the metabolism of amino-acids and nucleotides. Since the Tri-Carboxylic Acid cycle (TCA) is at the intersection between the metabolisms of glucides, lipids and amino-acids, this pathway was completed manually to ensure that it could use the G3P coming from photosynthesis. The following 5 reactions were thus added to the draft network: MALATE-DEHYDROGENASE-ACCEPTOR-RXN, ACONITATEDEHYDR-RXN, 2OXOGLUTARATEDEH-RXN, ISOCIT-CLEAV-RXN, PYRUVDEH-RXN. Furthermore, CO-A was provisionally added to the seeds as the TCA depends on ACETYL-COA. Assessing with Meneco whether the TCA was able to produce MAL , CIT (citrate) or 2-KETOGLUTARATE suggested that either GLYOX (glyoxylate) or OXALACETIC_ACID should be added to the seeds in order to unblock the TCA cycle. Both compounds are involved in many reactions in MetaCyc, however, as the addition of OXALACETIC_ACID to the seeds would have artificially enabled the production of ATP, GLYOX was used as a seed instead.The photosynthesis pathway being functional, a first route was now open between the mineral nutrients (seed metabolites) and the internal and excreted metabolites. Carbon-based biomass precursors such as G3P or FRUCTOSE-6P were producible and could enter the central metabolism. The next step was therefore to address the incorporation of nitrogen, in the form of ammonium . Since glutamic acid (GLT) is the principal component resulting from the incorporation of ammonium and a key metabolite in nitrogen metabolism, we had thus achieved the completion of the metabolic backbone leading from mineral nutrients through photosynthesis to carbon and nitrogen metabolism.Checking the current draft with The same process was repeated successively for the targets listed in Supplementary files S6 until all were producible. After the addition of new reactions, the provisional seeds were removed to test if they were still needed or if a biologically satisfying completion could replace them.Meneco criteria, we proceeded to verify whether it was functional in FBA. We created 10 import reactions , a biomass reaction, and 54 output reactions for the 54 metabolites that were experimentally shown to be secreted by E. mutabilis. Forty dead-end metabolites, i.e. non-consumed products that may prevent the network from satisfying the stoichiometric constraints for the production of target metabolites (secreted metabolites and biomass) were identified manually and defined as boundaries (boundaryCondition=“true”). Additionally, three reactions  were added to the network in order to regenerate reduced flavodoxins, oxidized ferredoxins, and thioredoxin since the corresponding transcripts could be identified in the transcriptome. We then checked which output reactions (biomass and secretion reactions) had a zero flux and determined the corresponding dead-end metabolites using FVA. Only the secretion of SPERMIDINE was thus blocked for stoichiometric reasons. We therefore added a dummy reaction of 5-METHYLTHIOADENOSINE degradation to satisfy the stoichiometric constraints on this metabolite for SPERMIDINE biosynthesis. The final draft thus contained a total of 1,068 reactions, including 10 entries, 54 secretion reactions, 1 biomass reaction and 1 additional degradation reaction. FBA showed that a positive optimum could be attained when defining the biomass reaction as an objective, and allowing only mineral nutrients and light  and targets (compounds whose producibility has to be restored). This flexibility appears to be crucial to investigate draft GEMs produced from NGS technologies, as illustrated in two applications.The main characteristic of Meneco checks the same topological criteria for producibility as the subset-minimality top-down method [GapFill family. This allows an exhaustive computation of the family of solutions.Most gap-filling methods solve optimization problems over a search space whose size grows exponentially with the size of the reference database from which the reactions are taken. Two different strategies can then be used to explore the search space and compute solution sets to a gap-filling problem: either a parsimonious bottom-up strategy which enriches the draft metabolic network until the targeted properties are satisfied , 10, 27,n method , but it From a biological point of view, having a method that enables the completion of a metabolic network without the need to know the real concentration of metabolites is a real advantage. It enables a completion of the metabolic graph not only for quantified or estimated compounds, but also for those identified by qualitative measurements. Moreover, having access to an exhaustive enumeration of the possible solutions allows researchers to choose the best one among them, instead of having only a subset of solutions without knowing if this subset is representative of the entire solution space or not.et al. introduced refined semantics for producibility taking into account the impact of cycles on the production of metabolites. This alternative definition can be viewed as an over-approximation of FBA-consistent networks, and appears to be very useful for the efficient computation of precursor sets for metabolic targets. However, experiments consisting in encoding this alternative definition in the Meneco framework and performing gap-filling of the same 10,800 degraded networks evidenced that this alternative definition of producibility is not constrained enough to capture essential reactions. Indeed, running our benchmark on the iJR904 network with that definition of producibility returns a set of solutions containing on average only 59.5% of the essential reactions  fail to recover enough alternative reactions to restore the biomass producibility of the network. This bottleneck can be explained by the parsimony criterion used by both tools since they identify sets with a minimal number of reactions allowing either to simultaneously restore the topological producibility of all targets (Meneco) or to enable the production of individual targets (GapFill). However, the identification of alternative routes to produce a targeted set of compounds is crucial to have a global understanding of species metabolic capability, as soon as essential reactions have been properly identified and validated. In order to improve the Meneco tool, we plan to study the impact of additional topological criteria that may take into account larger pathways. The difficulty will be to select relevant metrics in order to extend the search space while sufficiently constraining the search to be able to track the complete solution space. Indeed, with most of the methods based on extended scores and criteria for stoichiometry-based formalisms [Our experiments using the rmalisms , 15, theGapFill approaches with genomic information about alternative functions for genes. In the future, it will be interesting to adapt such scores to the Meneco tool and measure their impact on the functional classification of reactions in the reconstructed network.Alternatively, other metrics can be developed to improve the choice of reactions by the gap-filling methods. Scores based on likelihood value computations have been introduced in  to improet al. in [“clearly, the role of a gap-filling method is to simply pinpoint a number of hypotheses which need to subsequently be tested”. In this line of research, the Meneco tool constitutes a flexible framework to pinpoint hypotheses in the context of large-scale datasets applied to newly investigated organisms.As stated by Satish Kumar t al. in , “clearlMeneco is a versatile tool to complete draft GEMs and to suggest relevant reactions with respect to the response of the system to environmental perturbations. Importantly, it does not aim at providing a complete functional network, but rather at pointing out essential reactions and some of the alternative ones, which are crucial to explain the system response. It can be then combined with refined stoichiometry-based analyses and gap-filling methods to produce functional networks. In this sense, we promote Meneco as a tool to be used as an intermediary step within a workflow consisting of (i) producing a draft GEM [Meneco, (iii) refinement of the model with stoichiometric-based approaches relying on additional data , a), aMenecMeneco cannot replace manual curation and network analysis, but it may provide a flexible tool to aid this process. Analyses are fast, easy to implement, and invaluable because they enable biologists to focus their attention on a few highly interesting compounds and reactions without making a priori assumptions.From a biological point of view, the examples show that R metabolic reactions and M metabolites is represented by a bipartite directed graph G =  where edges in E connect either a metabolic reaction in R to metabolites in M or a metabolite in M to a metabolic reaction in R. Predecessors of a reaction node r are reactants: reac(r) = {m ∈ M| ∈ E}. Successors of a reaction node are products: prod(r) = {m ∈ M| ∈ E}. The biological concept of the producibility of metabolites can be formalized in terms of reachability. Given a metabolic network  and a set S ⊆ M of seed metabolites, a reaction r ∈ R is reachable from the seeds S if all reactants in reac(r) are reachable from S. Pushing forward this definition, a metabolite m ∈ M is reachable from the set of seeds S if m ∈ S or if m ∈ prod(r) is the product of a reaction r ∈ R that is itself reachable from S. Following [S according to a reaction set R contains all metabolites which can be iteratively produced from the seed metabolites. It is the set of metabolites reachable from S through the reactions in R and can be defined by iterating the set relation Mi+1 = Mi ∪ prod({r ∈ R|m ∈ reac(Mi), ∀m,  ∈ E}), with MO = S until it reaches a fixed point. The scope can be computed in polynomial time.A topological metabolic network comprised of ollowing –27, the Rdraft; (ii) a set Mtarget ⊂ M of target metabolites that are produced by the organism of interest; (iii) a set Mseed ⊂ M of seed nutrients found in the growth medium; and (iv) a database of metabolic reactions available to fill the network Rdatabase. The topological gap-filling problem is stated as follows.We assume that we are given (i) a draft network i.e. search space exploration strategies, preserving correctness and completeness of the search procedure. Clasp allows the computation of the brave and cautious consequences  by means of a linear number of calls to a solver  rather than enumerating the entirety of all solutions. This is accomplished by consecutive refinements of an internal constraint by relying on the incremental solving techniques introduced in [This combinatorial problem, expressed as a logic encoding, is solved by the ASP solver clasp 3.0.5  with domduced in . Rather Meneco [http://bioasp.github.io/meneco. Experiments related to the E. coli benchmark were run on a node of 24 cores part of a computational cluster. Case studies were run on personal laptops: the computation time required to get the union of solutions was less than 30 seconds.The ASP encoding, and the solver are encapsulated in a python package named Meneco , 41 whicS. Under steady state conditions, metabolite balances yield ∑j∈RSijvj = bj for all i ∈ M where bi is a parameter standing for uptake or secretion. In this setting, the stoichiometric gap-filling problem consists in minimizing the number of added reactions from the database to restore fluxes through a metabolite i* which is not producible from the draft metabolic network.A stoichiometric metabolic network can be viewed as an enrichment of a topological metabolic network with a stoichiometric matrix GapFill tool, encoded in a GAMS  program [This problem is solved by the so-called  program .To solve this optimization problem, solutions to the stoichiometric gap-filling problems for each individual target were computed by running the GAMS encoding from  on the NMIRAGE algorithm was run to perform a subset-minimal gap-filling using the implementation provided in [a priori information on the importance of the reactions in the draft networks, the biomass reaction was the only one added in the core reactions. The weights for all reactions of the databases were assigned to 1.The vided in . WithoutTo be able to run the software on a computer cluster, the code was compiled with the mcc function of MATLAB (R2014a). The MATLAB runtime software (version 8.3) was used to run the compiled code on a computational cluster consisted of 138 computing nodes .fastGapFill algorithm. Cplex (version 12.6.2) was used as MILP solver to solve the optimisation problem. A weight of 100 was set for the addition of transport and import/export reactions to penalize any modifications of the models. The draft models were used as core sets.The Portable System for the Analysis of Metabolic Models (PSAMM)  was usedE. coli: iJR904 [iAF1260 [iJO1366 [USCD repository. To test the impact of the MetaCyc database on the gapfilling problem, cross-references between the SBML identifiers of metabolites (SBML species) in the networks and MetaCyc identifiers were created. To that end, we followed an iterative strategy based first on metabolite names and formulas, then on similar reactions.We analyzed the following GEMs of : iJR904 , iAF1260[iAF1260  and iJO1[iJO1366 . The corE. coli models iJR904, iAF1260 and iJO1366, we generated 90 random biomass functions obtained by setting a defined percentage of non-zero coefficients of the true biomass to zero. The choice of replaced non-zero coefficients relied on Bernoulli-distributed random binary numbers. For each model consisting of an E. coli metabolic network and a random biomass, we classified the metabolic reactions according to their functionality: blocked , alternative  and essential . We implemented the classification of metabolic reactions within TOMLAB environment [For the three ironment , using GE. coli models iJR904, iAF1260 and iJO1366, we randomly removed 10%, 20%, 30% or 40% of metabolic reactions, with 10 different replicates by percentage. The same percentage of the three types of reactions was removed in each degradation process. The degraded networks were chosen such that none of the 90 random biomass functions could be produced by any of the 40 degraded networks.Then, for the three All together, we obtained a set of 10,800 metabolic networks which were not functional with respect to the production of their associated biomass.http://bioinformatics.psb.ugent.be/orcae/overview/Ectsi) [Ca. P. ectocarpi genome used for network reconstruction is available from the European Nucleotide Archive (accession HG966617). Both networks were mapped against MetaCyc 19.0 as a reference database. The construction of the set of targets for Meneco relied on the dataset of 90,637 ESTs published in the Ectocarpus genome paper ([EctoGEM 1.0 is based on genome data downloaded from the ORCAE website (w/Ectsi)  on June e paper  were discarded. The Meneco completion of the resulting draft metabolic network was performed with the modified version of MetaCyc 17.5 as the repair database and the 71 target metabolites listed in Generic reactions in MetaCyc 17.5 involving generic carbohydrates, amino-acids, nucleotides or NAD-P-OR-NOP (standing for NAD or NADP) were replaced with the appropriately balanced instances involving the corresponding metabolites present in the Meneco. At each step, seed metabolites included the basic mineral compounds and the metabolites required to unblock the cycles. Some metabolites such as ATP were added provisionally in order to solve long range dependencies until more information could be gathered about which reactions should be added to the network to enable their production. The production of the target subsets was addressed in the following order: photosynthesis, TCA cycle and amino-acids, glycolysis and glycogenesis, polyamines, nucleotides, and then all the remaining targets. Respiration was added as a single reaction.In order to avoid mutual dependencies and to solve cycles, the 72 targets were finally arranged into subsets corresponding to subnetworks (pathways and super-pathways) of the whole draft network. Completion was then performed with each subset as the list of targets for E. mutabilis transcriptome by tblastn. Transcripts with an expect value lower than 10−3 were extracted and used to search the protein nr database with blastx on the NCBI web server. The presence of the reaction was validated if the search yielded significant hits  in the Conserved Domain Database (CDD) and with sequences of enzymes that were consistent with the tested reaction.When a reference enzyme sequence corresponding to an added reaction was available in the MetaCyc database, this sequence was used as a query to identify potential homologs in the After each step, if the added reactions produced new metabolites previously absent from the network, the spontaneous reactions described in MetaCyc consuming those metabolites were also added to the network. The appropriate instances were manually added for the generic reactions corresponding classes of the newly added metabolites.The Flux Balance Analyses studies were performed using the python-based toolbox COBRApy (version 0.3.2)  and the Flux Variability Analysis was performed using both COBRApy in association with GLPK, and homemade scripts within the TOMLAB environment using the Gurobi solver.E. gracilis as determined by [E. mutabilis membrane adapted to arsenic contamination was obtained from [The biomass reaction was defined according to  and basemined by –55. The ned from . The proMeneco, the network was tested with COBRA findBlockedReactions command in Matlab R2015b. The dead-end metabolites blocking the production of biomass or of the experimentally determined targets [After completion with  targets  were manThe resulting network was eventually tested in Flux Balance Analysis with COBRA in Matlab R2015b, using the BIOMASS-RXN reaction as the objective function, and the mineral seed metabolites listed in S1 Table(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S1 FilesAll files needed to run gap-filling experiments on the benchmark are provided in an archive as well as a Readme.txt file detailing the commands used to produce the results shown in this study. This archive also contains the global results for FVA and a detailed analysis of the network 256 and the biomass 89 as an example.(ZIP)Click here for additional data file.S2 FilesE. siliculosus and Ca. P. ectocarpi are provided together with the list of seeds and targets used to run the Meneco tool. The exhaustive analysis of essential reactions which allow the production of 83 target metabolites thanks to the combination of E. siliculosus and Ca. P. ectocarpi networks is provided in a separate file. Finally, detailled examples of false positive predicted interactions and their explanation are provided in a separate pdf file.The two draft networks used to study potential cross-feeding relations between (ZIP)Click here for additional data file.S3 FilesE. mutabilis are provided here. The folder also provides the SBML file describing the repair database  required to run the Meneco tool. Files corresponding to targets and seeds are also provided. The file seed_minimum_medium_Emutabilis.sbml describes light and the 8 mineral nutrients of the minimal growth medium for E. mutabilis. As described in the main text, the network is not functional with only these seeds as reactions in cycles are blocked. The file “seeds_min_Emutabilis_final.sbml” provides the minimal set of seeds necessary to unblock the cycles and produce all the targets in “targets_Emutabilis_72.sbml” while the list of seeds and targets is also provided as tables in the file “seedsAndTargets_euglena.pdf”.Both draft and functional reconstructed metabolic networks for (ZIP)Click here for additional data file.


Malassezia species are lipophilic and lipid-dependent yeasts belonging to the human and animal microbiota. Typically, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis, pityriasis versicolor, atopic dermatitis, and folliculitis. The genomes of Malassezia globosa, Malassezia sympodialis, and Malassezia pachydermatis lack the genes related to fatty acid synthesis. Here, the lipid-synthesis pathways of these species, as well as of Malassezia furfur, and of an atypical M. furfur variant were reconstructed using genome data and Constraints Based Reconstruction and Analysis. To this end, the genomes of M. furfur CBS 1878 and the atypical M. furfur 4DS were sequenced and annotated. The resulting Enzyme Commission numbers and predicted reactions were similar to the other Malassezia strains despite the differences in their genome size. Proteomic profiling was utilized to validate flux distributions. Flux differences were observed in the production of steroids in M. furfur and in the metabolism of butanoate in M. pachydermatis. The predictions obtained via these metabolic reconstructions also suggested defects in the assimilation of palmitic acid in M. globosa, M. sympodialis, M. pachydermatis, and the atypical variant of M. furfur, but not in M. furfur. These predictions were validated via physiological characterization, showing the predictive power of metabolic network reconstructions to provide new clues about the metabolic versatility of Malassezia. Malassezia species are lipophilic and lipid-dependent yeasts that are frequently encountered in the human and animal microbiota. Usually, they are isolated from regions rich in sebaceous glands. They have been associated with dermatological diseases such as seborrheic dermatitis (SD), pityriasis versicolor, atopic dermatitis, and folliculitis -based models  can be uMalassezia species and an atypical M. furfur strain. To this end, we used metabolic modeling, using genomics and proteomics data. The results show flux differences in the production of steroid in M. furfur and the metabolism of butanoate in M. pachydermatis. In addition, defects in the assimilation of palmitic acid in Malassezia globosa, Malassezia sympodialis, M. pachydermatis, and the atypical variant of M. furfur (4DS), were suggested, but not for M. furfur CBS 1878. These predictions were validated via culturing on defined media.The aim of this work was to investigate the genomic and metabolic differences between reference strains of four Malassezia strains M. globosa CBS 7986, M. sympodialis CBS 7222, M. pachydermatis CBS 1879, and M. furfur CBS 1878 purchased from Fungal Biodiversity Center , as well as a previously reported isolate of M. furfur with atypical assimilation of Tween 80 (4DS)  , 20 g L-1 Ox-bile , 36 g L-1 malt extract , 2 mL L-1 glycerol , 2 mL L-1 oleic acid , and 10 mL L-1 Tween 40  for 4–5 days at 33°C   were use at 33°C . The gen globosa , M. pachdermatis , and M. podialis  have bees genome  were useM. furfur strains that were sequenced in this study were recovered in modified mDixon for 4–5 days at 33°C (The  at 33°C . Genomic at 33°C .M. furfur strains was sequenced at the Beijing Genomics Institute (Shenzhen) using the Illumina HiSeq 2000 platform. Two runs of 100-bp paired-end reads and 200-bp insert-size libraries were undertaken following standard Illumina protocols. The quality of the reads was analyzed using FastQC software  genes were compared with the predicted genes and proteins of the genomes using blastp, blastn, tblastn from the blast suite version 2.2.29  was obtained from the metabolic network using in-house Perl scripts to obtain a system of linear equations, S × v = 0, where v is the flux vector. System constraints included the lower and upper bounds of reaction fluxes. The system allowed 0.000–1,000 mmol gDW-1 h-1 for irreversible reactions and -1,000 to 1,000 mmol gDW-1 h-1 for reversible reactions. The system was solved to identify the theoretical limits for different fluxes in the metabolic system using GAMS  software. The linear programming presented here was developed with solver CPLEX 12.6.0.0 with an optimization tolerance of 10-6. A modified biomass production reaction (Supplementary Table S. cerevisiae as an objective function (S. cerevisiae (-1 h-1) in anaerobic conditions (oxygen uptake rate of 2 mmol gDW-1h-1) was used for the simulation. This optimization problem aimed to solve the maximization of the flux through the reaction of biomass used as objective functions. The resulting flux distributions were filtered with a cutoff of ±0.05 mmol gDW-1 h-1 to allow a better visualization of the reactions that can carry fluxes and plotted as a heatmap in R , 36 g L-1 malt extract , 6 g L-1 peptone , 2 mL L-1 glycerol , 2 mL L-1 oleic acid , 10 mL L-1 Tween 40 , and 500 mg L-1 chloramphenicol . Aliquots of 5 mL were taken in the early exponential and early stationary phase and centrifuged at 26,000 g for 10 min. The resulting pellet was washed three times with PBS, after which it was resuspended in extraction buffer . Cells were disrupted by vortexing for 10 min with 4-mm silica beads and centrifuged at 26,000 g and 4°C for 10 min. Proteins in the supernatant were precipitated with 20% TCA  (Protein extraction was carried out as described . Strains States) . The amo3. Protein profiling for each sample was carried out using the mass spectrometer Michrom HPLC Paradigm type, the Q-mass spectrometer ionization Proxeon Exactive nano-spray, and the Easy-LC II HPLC.The extracted proteins were sent to the proteomic center of the University of California at DavisMalassezia spp. predicted proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE  and proteins were compared against the Uniprot Ustilaginomycotina proteins and the he PRIDE  partner he PRIDE . To vali-1 K2HPO4; Sigma–Aldrich, United States), 145 g L-1 KH2PO4 , 20 mL M-N , 15 g L-1 NaCl , 1 mL 1% CaCl2.2H2O  (w/v), 10 mL 20% glucose  (w/v), 10 mL 0.01% FeSO4  (w/v), 5 mL spore elements , 100 mg L-1 CuSO4.5H2O , 100 mg L-1 H3BO3 , 100 mg L-1 MnSO4.H2O , l00 mg L-1 Na2MoO4.2H2O , and 2.5 mL 20% NH4NO3  (w/v)] containing 4 mM Tween , or 4 mM oleic acid (Carlo Erba), or palmitic acid (Merck) supplemented with 1% Brij-58 , an emulsifier that is not metabolized. It did not support the growth of the FAS mutant of the yeast S. cerevisiae  contained 98% palmitic acid and 2% elaidic acid, an unsaturated acid. The oleic acid (Carlo Erba) contained 78% oleic acid and, in addition, we detected polyunsaturated fatty acids (10% of linoleic acid), unsaturated fatty acids , and saturated fatty acids .To determine the growth on different Tween varieties and with fatty acids, the strains were first grown on mDixon at 33°C for 7 days. The fungal cells were suspended in 3 ml of water with 0.1% Tween 80 used to inoculate 27 mL of minimal medium (MM) [containing per liter: 10 mL K-buffer pH 7.0 , with 30.8 and 7% of the genomes of M. furfur and of the atypical M. furfur strains, respectively, representing multiple matches or repetitions. In contrast, less than 1% of the genomes of M. globosa, M. pachydermatis, and M. sympodialis corresponded to multiple matches.A pairwise Nucmer comparison at the nucleotide level showed that M. globosa, which only showed 0.8% identity using exact matches of at least 20 bp when compared to M. furfur or the atypical M. furfur genomes. This was <1.2% when the M. globosa genome was compared to M. pachydermatis and M. sympodialis. As expected, the most similar strains were M. furfur and the atypical M. furfur strains with a similarity >80% (Table 2). A higher degree of similarity was observed when evaluating the six translation frames using Promer . The biggest differences were found when M. furfur and M. sympodialis were compared, with 70.7% zero matches. The second highest difference was between M. globosa and M. furfur, with approximately 48% of zero matches. As expected, the highest similarity was observed in the case of the M. furfur strains with only 3.4% of zero matches.The Nucmer alignment showed significant sequence divergence between the four species. The biggest difference was observed for M. furfur sequences in this study with the already published Malassezia genomes, we built a phylogenetic tree using highly conserved genes . This phylogeny showed that atypical M. furfur clustered with M. furfur CBS 7982 and our sequence of M. furfur CBS 1878 clustered with the previously reported sequence of this strain.To establish the taxonomic position of the two reported M. furfur and the atypical M. furfur strain using multiple lines of evidence . Functional annotation showed that both M. furfur strains contained twice the amount of proteins when compared to the other Malassezia species. Yet, the number of EC numbers and reactions were similar, as can be seen in Figure 2. The average genome length, exon length, and the number of exons per gene for the five genomes analyzed are presented in Figure 2.A total of 10,203 and 12,131 protein-encoding genes were predicted for M. furfur and the atypical M. furfur contained 7,570 and 10,434 protein clusters, respectively, as predicted with CD-Hit  were found . The PKSs from the five species shared most of the domains, with the exception of M. globosa, which lacked the S-adenosyl-L-methionine-dependent methyltransferase domain, and M. furfur, which lacked the ketoreductase domain, acyl carrier protein-like domain, NAD-binding domain, and the thioester reductase-like domain in its three predicted PKSs. Out of 954 fungal homologs, the PKS of the basidiomycete Paxillus involutus was the most similar to that of the atypical M. furfur, with an identity of 31%. The most similar PKSs to those of Malassezia were found in basidiomycetes. The closest homolog of the PKS of M. furfur was from Hydnomerulius pinastri, with an identity of 29.8%, while an Auricularia delicata homolog was most similar to the PKS of M. pachydermatis and M. sympodialis, showing 29.9 and 31% similarity, respectively. In the case of M. globosa, the most similar PKS was that of Coniophora puteana, with an identity of 26.8%.To search for genes related to lipid biosynthesis, a total of 2,382 FASs  were compared with the predicted proteins of the  tblastn  and phmmMalassezia strains was similar when analyzing 13 categories of metabolic genes . The metabolic core  .The distribution of the genes in the genomes of the five Malassezia strains were irreversible. Compartmentalization analyses showed a similar distribution of reactions among the species. However, differences were observed in the number of reactions when the M. furfur strains were compared to the other species. This corresponded to cytoplasmic and mitochondrial reactions . After the gap-filling approach to reduce the network pathologies, around 250 reactions were added to M. globosa and M. sympodialis, 300 to M. pachydermatis, 600 to M. furfur, and 660 to the atypical M. furfur strain. The final networks ended up with 2,162 metabolites in M. globosa, 2,303 in M. sympodialis, 1,838 in M. pachydermatis, 3,103 in M. furfur, and 3,642 in the atypical M. furfur strain. The final metabolic networks . The clustering coefficients were around 0.14 and 0.18 and the diameter of the networks ranged between 10 and 15 nodes.The database search and the group contribution method used to calculate the Gibbs free energy predicted that 15–26% of the reactions of the five Table 4. The highest biomass production was found in the atypical M. furfur strain at 2.84 mmol gDW-1 h-1 and the lowest was in M. sympodialis at 0.089 mmol gDW-1 h-1. The number of reactions that that carried flux varied among the species, ranging from 36% in the atypical M. furfur strain to 42% in the M. sympodialis strain.FBA was carried out and the resulting predicted biomass fluxes that were used as objective functions are shown in Figure 7 for visualization as a heatmap, the resulting clusters were found based in the flux carried by each reaction, with M. globosa and M. sympodialis having the most similar flux distribution. The main differences between M. furfur and the other strains were reactions involved in valine, leucine, and isoleucine degradation, and pyrimidine and purine metabolism. Differences in the lipid-metabolism reactions were also found. M. furfur displayed high fluxes in reactions related to fatty acid degradation and elongation, which involved the conversion of hydroxyacyl-CoA to trans-2-enoyl-CoA. These were higher in comparison to those observed in the atypical M. furfur strain. Several reactions associated with fungal steroid biosynthesis were found in the atypical M. furfur and M. furfur isolates. Despite the atypical M. furfur isolate having a larger number of these reactions, they had higher fluxes in the M. furfur strain. In the atypical M. furfur strain, higher fluxes were found in reactions involved in the degradation of maltose, fructose, and starch. M. globosa and M. sympodialis showed similar metabolic behavior. However, some differences in M. sympodialis corresponding to changes in the pyruvate and gluconeogenesis pathways, as well as the pathways involved in the degradation of long-chain fatty acids into acyl-CoA, and in the conversion of hydroxyacyl-CoA to trans-2-enoyl-CoA were found. Finally, in M. pachydermatis, there were differences in the core metabolism of pyruvate and butanoate, and in the biosynthesis of phenylalanine, tyrosine, and tryptophan.The flux distribution of each network is represented in Malassezia species, and lipid metabolism may be related to the pathogenesis of these yeasts, differences in lipid assimilation among the species was further evaluated in the model. In reactions involve in fatty acid biosynthesis such as MNXR2003 , involved in the conversion of hexadecanoic acid to palmitoyl-CoA, higher fluxes were found in M. furfur, even though all the strains’ genomes contain the genes that codify the enzyme that catalyzes these reactions.As lipid assimilation differs among M. furfur peptides were found during the exponential phase (T1), corresponding to an average of 1,865 ± 154 proteins. The lowest number of peptides (828 ± 456) was obtained with M. globosa in the stationary phase (T2), corresponding to an average of 116 ± 60 proteins. Consensus proteins were defined as proteins present in at least two replicates with a quantitative identity of ≥95%. The number of these proteins differed among the strains. M. furfur in the stationary phase had the highest number , while M. globosa presented the lowest number in the exponential phase (201 consensus proteins). The number of reactions was higher in atypical M. furfur in the exponential phase, with 841 reactions, as compared to the other species (Table 5). The principal component analysis (PCA) showed that the proteome profiles behaved differently among the species, with the samples from atypical M. furfur and M. globosa having the most similar profiles. In addition, the clustering and PCA analyses also showed that the replicates behaved similarly within each species in both the stationary and exponential phases .An average number of 8,864 ± 2,400 Figure 9). Based on the validation and taking into account the percentage of the total reactions that can be experimentally validated . The most successful model was that of M. pachydermatis, where ∼40 and 30% of the predicted proteins were expressed in the exponential and stationary phase, respectively. The less accurate models were those of M. globosa and of the atypical M. furfur, with ∼10% of the predicted proteins being found. Similarly, when evaluating the number of predicted enzymes that were expressed, M. pachydermatis showed the best prediction model with ∼90% of its expressed enzymes predicted by the flux distribution in both the exponential and stationary phases. M. globosa, on the contrary, only showed ∼50% of its expressed enzymes, as predicted by the model under both growth phases.Protein validation of the models was conducted and expressed as the percentage of proteins predicted by the model that were detected by proteomic profiling (expressed proteins) as well as the percentage of expressed enzymes that were found to be catalyzing an reaction carrying flux in the model  declined rapidly in the second growth step  in comparison to that of the same strain reported by Table 1); a similar size to the previously reported size of 13.5 Mb. Phylogenetic analysis conducted in this study established that our M. furfur strains are part of the Malassezia cluster A that has been previously described. This cluster consists of several strains of this species that were suggested to be a species complex (M. furfur (10.3 Mb) clustered with M. furfur 7982 (7.7 Mb), thus showing them to be distantly related to other strains, and suggesting that they are implicated in divergence events  7,570 and 10,434 protein clusters, respectively, were detected. Thus, although there are indeed protein duplications, most of the predicted proteins represent unique proteins in the genomes of M. furfur and the atypical M. furfur strains. This is further supported in the case of enzymes by the fact that the unique EC numbers and reactions in both M. furfur strains were higher than those in the other species studied. This diversification in the number of predicted proteins may provide evidence for the metabolic versatility found in M. furfur. The increase in unique proteins and the genetic diversification present in the M. furfur strains may be due to mating of the yeast, since bipolar mating systems have been previously proposed in Malassezia spp. .As expected, the FAS was not found in the genomes of either e domain , and thenjugates , and in c lipids . FurtherAspergillus oryzae  the production of steroid-like fungal hormones in M. furfur for sexual reproduction, as happens in ascomycetes  fun joedes ; and mos ellisii . Finallys strain . In addi. furfur .M. sympodialis and of the M. furfur strain (M. furfur CBS 1878) networks imply a more complex network. Yet, the clustering and diameter of these two networks were lower than for other yeast metabolic networks such as those of S. cerevisiae   in the cytosol and all Malassezia spp. lack a FAS complex and are not able to produce palmitate  mutants of S. cerevisiae  , but becnoyl-CoA  than thealmitate . This phty acids . These drevisiae . FurtherM. globosa and M. restricta are not capable of synthesizing unsaturated fatty acids due to the lack of a Δ 9-desaturase (EC 1.14.19.2) gene  gene , which idruff/SD . In thisM. sympodialis but not in M. globosa or M. pachydermatis. These differences may be associated with the phenotypic differences of each species, reflected by the differential lipid assimilation or the differential use of Tweens. We should, however, explore more deeply the physiology and regulation of β-oxidation in this yeast  as well as to the fatty acid metabolism (M. pachydermatis may be reflected in the production of lipids that may not be produced by the other Malassezia species and that may be corroborated by lipid profiling of the yeasts.The higher number of reactions related to the butanoate metabolism found in tabolism . Thus, tThe proteomic profiling allowed us to validate, on average, 30% of the predicted proteins of the model, implying that the other enzymes that are supposed to be expressed according to the FBA are not present or that their concentrations are substantially low. An average of 70% of the proteins expressed in all the samples were in fact predicted. Given that this technique is still not widely used to validate these kinds of models, the cutoff points and expected percentages are not well known. Furthermore, issues such as the identification of large-scale protein datasets, small protein concentration detection, and the extraction of low soluble proteins such as membrane proteins may affect the profiling .Malassezia. Flux differences were suggested in the production of steroids in M. furfur and in butanoate metabolism in M. pachydermatis. The assimilation defects of palmitic acid were suggested in M. globosa, M. sympodialis, M. pachydermatis, and in the atypical variant of M. furfur. The capability of M. furfur to assimilate palmitic acid was confirmed via culturing on defined media.Together, the metabolic reconstruction and modeling showed versatility within the genus of ST, AC, AG, HdC, and SR contributed to the design of the work. ST, AC, and RO performed the experiments. All authors were involved in the analysis and interpretation of data. ST and AC wrote the manuscript. HdC, HW, SR, and AG made revisions. All authors approved the version to be published and agreed to be accountable for all aspects of the work.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 reviewer LK and handling Editor declared their shared affiliation.


Microorganisms oxidize organic nitrogen to nitrate in a series of steps. Nitrite, an intermediate product, accumulates at the base of the sunlit layer in the subtropical ocean, forming a primary nitrite maximum, but can accumulate throughout the sunlit layer at higher latitudes. We model nitrifying chemoautotrophs in a marine ecosystem and demonstrate that microbial community interactions can explain the nitrite distributions. Our theoretical framework proposes that nitrite can accumulate to a higher concentration than ammonium because of differences in underlying redox chemistry and cell size between ammonia- and nitrite-oxidizing chemoautotrophs. Using ocean circulation models, we demonstrate that nitrifying microorganisms are excluded in the sunlit layer when phytoplankton are nitrogen-limited, but thrive at depth when phytoplankton become light-limited, resulting in nitrite accumulation there. However, nitrifying microorganisms may coexist in the sunlit layer when phytoplankton are iron- or light-limited (often in higher latitudes). These results improve understanding of the controls on nitrification, and provide a framework for representing chemoautotrophs and their biogeochemical effects in ocean models. Nitrite tends to peak at the base of the sunlit zone in the ocean, but the ecological drivers of the local and global distributions of nitrite are not known. Here, Zakem et al. use a marine ecosystem model to show how the interactions of nitrifying microbes mediate nitrite accumulation. Most fixed nitrogen in the ocean is in the oxidized form of nitrate (3) and nitrite  due to anaerobic activity2; however, we focus here only on the dynamics of aerobic environments.The primary nitrite maximum (PNM), an accumulation of mentclass2pt{minimmentclass2pt{minim23: (i) excretion of 25, and (ii) chemoautotrophic 26. Here, we focus on outstanding questions of the latter hypothesis, since isotopic evidence suggests that 21.Despite the widespread occurrence of the PNM, the mechanisms that determine the locations and magnitude of accumulated 30 and incorporated into ecosystem models32, but observations of nitrification in the euphotic zone33 and close to the sea surface36 suggest that this is not universally the case. An alternate (but not exclusive) hypothesis is that phytoplankton outcompete slow-growing, chemoautotrophic nitrifiers for 38, but not deeper where light limits photoautotrophy.First, why does peak nitrification occur at the base of the euphotic zone? Photoinhibition of nitrifying microorganisms has been documented40 and differential temperature sensitivity of their rates41, though some observations conflict with the latter42. Here, we will suggest that this reflects differences in the metabolisms of the distinct clades of nitrifiying microorganisms.Second, while both Third, why does nitrification sometimes occur in the euphotic zone, and why is surface [Here we synthesize and address these questions and hypotheses using a hierarchy of mathematical models and simulations that are grounded in theoretical and laboratory evaluations of the kinetics and efficiencies of marine nitrifying microorganisms. The models are used to interpret both existing and new water column observations in the subtropical North Pacific as well as the global distribution of We first present a general population dynamics model for Bi (mol L−1) is the biomass of type i with population growth rate μi (d−1), loss rate Li (d−1), and physical transport. Loss rate is a function of the population density of grazers Z and other factors (Methods). We describe population growth rate μi using Monod kinetics with limiting resource Rj (determined by Liebig’s Law of the minimum). Growth rate depends on the yield of biomass with respect to the limiting resource yij (mol biomass mol R−1), the maximum specific resource uptake rate R per mol biomass per day), and a half-saturation concentration Kij (mol L−1) as:We consider the clades of microorganisms carrying out the two steps of nitrification as two metabolic functional types: ammonia-oxidizing and nitrite-oxidizing organisms . We describe the population dynamics of each type as:43. For the two nitrifying metabolisms, we assume that DIN and oxygen are the potentially limiting resources (Rj). Over most of the subtropical thermocline, oxygen exists at sufficiently high concentrations (greater than nanomolar) to serve as the terminal electron acceptor for AOO and NOO45.The uptake parameters together give an expression for the specific uptake affinity (Equations  and 2)  2) proviRj that limits the growth of nitrifier type i as:Why does t the PNM,12,13? Ci47. In theory, at a steady state, the population with the lowest R* excludes all others limited by the same resource , and the environmental concentration is set to this 48, we derive stoichiometrically balanced equations to describe catabolic and anabolic processes as a function of the redox chemistry that underlies each step of nitrification. Constraining thermodynamic efficiency with published laboratory observations of marine AOO and NOO growth, we approximate the population-level growth stoichiometries of ammonia-oxidizing biomass BAOO and nitrite-oxidizing biomass BNOO as:4 (Methods). The predicted stoichiometry of 2 demand for the AOO is 1:1.45, close to that observed in culture37 (1:1.52) indicating that the model captures key aspects of real metabolisms. In Eqs. . If we assume a 10-fold larger cell volume for NOO, this estimates a 4.6-fold lower specific affinity.Second, we speculate that NOO specific uptake affinity may be lower than that of AOO if (as is observed) yij) and affinity (Li) are low relative to maximum growth rates (and thus negligible in the denominator), then we predict that [12. If yields are similar, but affinity is 4.6-fold different, we predict a similar order of enhanced Using these differences in yield . For simplicity, we neglect other limitations such as iron availability  also correlate with [12.For comparison with the model, we show relevant observations collected at several stations in the North Pacific subtropical gyre in 2014  to diagnose ecological control , to cal52. However, for picophytoplankton, the effective half-saturation concentrations for nitrogen uptake with respect to growth rate are significantly lower57, and maximum growth rate is higher. This reflects that nitrifiers use DIN for energy production, which is relatively inefficient, while the phytoplankton use it as a nitrogen source for synthesis with a much higher biomass yield.We find that the modeled phytoplankton exclude the chemoautotrophic nitrifiers in the euphotic zone, where both are limited by nitrogen. This is because the photoautotrophs have lower subsistence concentrations for R*s , peak [13. This suggests that either the yield or the affinity difference, or a smaller combination of both, may best represent natural assemblages of nitrifiers.We further demonstrate the control of [AOO Fig. . With bo6. Observed pelagic [13 than the deep concentration in the model here (tens of nanomolar). Assuming no other sinks for [R*s of the NOO, but of a diverse community. Affinities and efficiencies may vary with clades, such as those with maximum growth rates too low for survival at the PNM, or may be plastic in nature. A slower-growing, efficient, deep NOO clade could deplete Even deeper in the water column, [Thaumarchaea) and Nitrospina in the California Current19, a 1–5-fold lower abundance of Nitrospina-like bacteria compared to amoA gene and MG1 abundances in Monterey Bay58, and a 1–4-fold difference at Station ALOHA58. Thus, observed abundances are quantitatively consistent with observed [Modeled nitrifier abundances also reflect the distinctions between the AOO and NOO Fig. . With bo2, though differences in coastal waters have been documented42. Thus, differences in the modeled AOO and NOO emerge in nutrient distributions and nitrifier abundances, but not in subsurface nitrification rates. This reinforces our understanding that exported organic matter determines the rate of all steps in the sequence of remineralizing metabolisms below the euphotic zone22.Rates of nts Fig. . This maR* from the peak of the PNM to about 175 m depth. This indicates that vertical mixing is non-negligible there. In other model simulations, particularly in less stratified water columns, this effect is stronger, and DIN accumulates to concentrations much higher than R*  with the phytoplankton at some locations , nitrifiers may locally sustain growth if sufficient DIN is supplied. This characterizes the base of the euphotic zone, and subpolar surface environments during winter, where phytoplankton are light-limited. Surface nitrification rates may also be significant in high-nutrient, low-chlorophyll regions where iron limits phytoplankton growth, if nitrifiers are less iron-limited. Observations suggest that this may be the case for AOO, which require copper rather than iron for redox machinery47 suggests that the degree of coexisting nitrification and primary production in the euphotic zone reflects the degree of limitation of phytoplankton growth. Less iron or less light availability should allow for a proportionally higher rate of coexisting nitrification.Future observations could test these hypotheses. While timescales of marine nitrifier growth are too long for nitrification to become competitive at the surface at night  can be associated with much higher DIN concentrations where losses and mixing are significant. Our models suggest that this is often the case. This highlights the utility of a dynamic ecosystem model in synthesizing complex interactions. Resolving the fluxes of all of the components results, at times, in unintuitive relationships between the standing stocks of nutrients and biomass.14. Laboratory observations25 and observed associations of Prochlorococcus with the PNM68 support this mechanism. Such excessive 21. Rather, we provide explanations for In the current model, nitrification is sufficient to simulate global distributions of To conclude, we have hypothesized the main controls on the distributions of 48, three half-reactions combine to form the catabolic and anabolic full reactions for each nitrifier metabolism: (1) the oxidation of f represents the fraction of electrons from the electron donor that are channeled into biomass synthesis vs. respiration. Combined, these reactions inform a “whole organism” stoichiometry. The resulting correlation of yield y and growth rate yV (Eq. (y and growth rate may be important in modifying further characteristics).Following established methodologyentclass1pt{minima69, and that both AOO and NOO use reduced nitrogen , the three half-reactions, for generic biomass CcHhOoNn, and their electron-partitioning coefficients, are:d normalizes the biomass synthesis reaction to one electron (see definition below). The sum gives the full metabolism for BAOO (ignoring water and lumping bicarbonate into the CO2 pool for simplification), as a function of f:For the BNOO as:For the y, defined as moles biomass N synthesized per mole DIN used, are:The resulting nitrifier yields d for both functional types. Following previous methodology, d represents the number of electron equivalents that correspond to the oxidation states of the inorganic constituents of that synthesis48. Assuming generic microbial biomass composition of C5H7O2N and d = 4(5) + 1(7) − 2(2) − 3(1) gives d = 20.Since we assume that the f). Smaller f equates to a lower growth efficiency and a lower yield, y. While thermodynamics can provide some theoretical constraints on f, the efficiency of energy production in marine microbes is not well known. Thermodynamics and wastewater treatment studies suggest that f is very similar for the two nitrifying types48—the difference in the free energy of the oxidation of − eq−1, respectively, at standard state with pH = 7.048) does not significantly impact theoretical estimates—so we employ published laboratory observations of marine AOO and NOO growth to constrain a common f for both metabolisms73 −1 mol biomass N per mol −1 mol biomass N synthesized per mol f values corresponding to these yields are 0.030 and 0.032, respectively. A value of 0.03 is about one-fifth of the value of f inferred for wastewater bioreactors48, perhaps explained by the need for marine organisms in oligotrophic environments to have higher-affinity, energetically-expensive nutrient transport systems.Our analysis supports the assumption that f = 0.03, d = 20 ± 4, with uncertainty in d from the 1 mol/mol s.d. of the C:N69, where changes in H and O stoichiometry were neglected to give the largest impact on the resulting stoichiometric uncertainty. We examine the sensitivity of the yields to variation in f and d in Supplementary Note The stoichiometries presented assume Nitrosopumilis provide values for the parameters for the uptake of 37 (Supplementary Table Vmax (in mol 37 of 2 per mg protein per hour: (3.35 × 24.2)36.29−1 = 2.23). Assuming a 16% N content of protein gives a specific maximum Vmax we use in the model. . We then use established empirical and theoretical allometric relationships52 to predict the kinetic parameters for the NOO relative to those of the AOO. Allometric theory predicts that though the cellular uptake rate should increase with cell size, the specific uptake rate should decrease due to a decrease in the surface to volume ratio: the cellular rate scales with surface area as cell radius r2, volume increases as r3, and so the specific rate scales as r−1. Theory also predicts that the half-saturation concentration increases with cell size. The diffusion-limited cellular uptake rate (which explains the steep slope of the Michaelis–Menton form) increases as r. The cellular affinity equates to the quotient of the cellular uptake rate and KN. This suggests that KN scales as r2r−1 = r. Together, allometry thus suggests that the specific affinity decreases with cell size as r−2, which is supported by more detailed analysis of nutrient uptake models43.Known 1/3 = 2.2, and so for the NOO, we estimate a specific uptake rate of about half, and a KN of about double that of the AOO. This gives a 4.6-fold lower specific affinity −1 = 88.2 L per μmol biomass N per d). This literature52 also suggests that picophytoplankton affinity for Prochlorococcus-sized cell for inorganic nitrogen is in the range of 100–1000 L per μmol biomass N per d, bracketing that of the nitrifiers. Thus, couched only in terms of affinity for Prochlorococcus and AOA may be close competitors.Empirical results linking cell size to affinity76, using a Varian Cary 100 Bio ultraviolet-visible spectrophotometer. Ammonia oxidation was measured using 1515NH4Cl, 99%, Cambridge Isotope, 100 nM) incubated for 24 h in 500 mL polycarbonate bottles in the dark and at close to in situ temperatures. A 30 mL sample at the beginning and at the end of the incubation (T0 and T24) was extracted from each bottle, filtered through 0.2 μm pore-sized nylon filters, and frozen. Thawed aliquots were treated with sodium azide to convert all of the 2O gas77. The 14N:15N ratio of the N2O gas was analyzed on an isotope ratio mass spectrometer by the University of California Davis Stable Isotope Facility. Unlabeled carrier −1) was calculated as a function of the atom % 15N (a) of initial and final samples as:V is the sample volume (L), and Δt is the incubation time (d).Measurements were made in the subtropical North Pacific on NEMO (“Nutrient Effects on Marine Microorganisms”) Cruise NH1417 in August and September 2014. Using satellite data, stations thought to be at productive locations were chosen for nitrification rate measurements to increase chances of detectable concentrations and rates. Measurements were taken at stations 44, 58, 70, and 99  measurements, seawater was filtered onto 25 mm Whatman GF/F filters . Filters were extracted in the dark in 5 mL of 90% acetone for 24 h at +3 °C prior to measurement78 on a Turner Designs TD-700 fluorometer calibrated with pure Chl a (Sigma-Aldrich). Abundances of thaumarchaeal ammonia monooxygenase aubunit A (amoA) genes were measured with quantitative PCR.For the Chlorophyll BAOO, nitrite-oxidizing organisms BNOO, phytoplankton P, heterotrophic bacteria Bhet, and microzooplankton grazer Z), three inorganic nutrients , with limitation by nutrients (γN), modification by temperature (γT), and limitation by light. Light limitation was parameterized using an exponential form as a function of the instantaneous photosynthetic rate Γ (d−1) and the Chl a to Carbon ratio θ (g/g)79 as:Phytoplankton grow as a function of a maximum growth rate Γ was computed as a function of photosynthetically active radiation I, the maximum quantum yield of carbon fixation ϕ (mol C mol−1 photons), and the absorption of light by phytoplankton 2 (mgChl)−1) representing a mean value over all wavelengths, as:Photosynthetic rate θ varies with photoacclimation, and is computed using a steady-state solution79 as:θmax is a maximum ratio. In the water column model, θ is allowed to reach an arbitrary minimum value of 0.1θmax, which does not affect solutions of any state variables, but does set the minimum modeled Chl a concentration in Fig. The Chl:C Nutrient limitation is a function of the total concentration of all species of DIN:V (d−1) of each DIN species by the phytoplankton type are resolved as:The inhibition of 52. Cell volume v was converted from diameter assuming a spherical cell, and traits calculated with generic form:Values for the maximum growth rate and the half-saturation constants were computed as functions of cell size following data-based allometric relationshipsP in the water column model), a and b are chosen for small cells80, with v computed assuming the average Prochlorococcus cell diameter of 0.6 μm55. For the larger additional phytoplankton types in the global model, a and b are representative of larger phytoplankton cells using published values54.For the picophytoplankton functional type  as:yD partitions consumption of D into biomass synthesis (as yD) and its remineralization into yD − 1). The growth efficiency yD is assumed as the average bacterial growth efficiency of 0.14 (s.d. 0.14) (mol biomass synthesized mol D−1) for the open ocean81. The maximum uptake rate VDmax and half-saturation constant KD are best estimates that constrain the model heterotrophic bacterial growth rate to about 0.1 d−1, matching the average bulk bacterial growth rate82.The bacterial heterotrophic functional type grows as a function of organic matter , heterotrophic bacteria, and the chemoautotrophic nitrifiers. This parameterization was chosen based on the assumption that grazing preferences are predominantly governed by size83, and that Prochlorococcus and the microbial functional types are all roughly the same size.  The total amount of grazing is calculated as a saturating function of total prey biomass, giving the rate of grazing g (L mol−1 d−1), which is then multiplied by the biomass of each prey in Eqs.  using a formulation that follows the Arrhenius equation54 as:T is the ambient temperature, T0 is a reference temperature, AE regulates the temperature modification, and τ normalizes the maximum value.  and the resulting steady-state gZ (d−1), asg (L mol−1 d−1) is the above grazing rate (equation Z (mol L−1) is the population density of predators. In the model here, the loss rate Li is the same for all of the microbial prey populations, since mB is assumed constant and g depends on the sum of all prey biomass.  boundary conditions result in some accumulation of D at the bottom of the 2000 m domain, conceptually representing a sediment layer. To smooth over numerical error, vertical mixing was allowed to increase there with a 100 m length scale, simulating a bottom boundary mixed layer. KZ (m2 s−1) is thus calculated at cell faces as:H is the height of the domain (2000 m).In the water column model, the mixed layer was imposed by varying the vertical diffusion coefficient I decreases with depth according to the attenuation coefficients for water kw and for chlorophyll kChl, following a previous approach84 as:TChl is the sum of the concentrations of chlorophyll, and kChl is an upper estimate of the absorption by chlorophyll to account for additional absorption by colored dissolved organic matter54. Iin is the incoming irradiance, which is 0.5Imax, or for resolution of the daily cycle, Iin(t) = 0.5Imax(cos(ωt) + 1) where ω = 2π d−1.Light energy T in °C.A temperature curve was fit to the mean observations from the four stations sampled on Cruise NH1417 . Advection for the sinking organic matter pool was carried out using the QUICK advection scheme, consisting of a linear interpolation between points weighted by an upstream second order curvature, resulting in third order accuracy. Fluxes were calculated at the faces of each grid cell, and concentrations at the centers.37. The relative larger size of the NOO as compared to AOO was varied from a factor of 1 to a factor of 20, thus, impacting the relative values of Vmax and KN by a factor of 11/3 to 201/3. Because of their large uncertainty, the following parameters were sampled randomly over the linear range of ±50% of the default value: the maximum growth rate μmax and nutrient half-saturation constants VDmax, KD, and yD; the grazing parameters gmax and Kg, mortality rates mB and mZ, and zooplankton efficiency ζ.For the ~1000 model solutions in Fig. 85, and has a horizontal resolution of 1° × 1° and 23 levels of vertical resolution, from 10 m at the surface to 500 m at depth. The model was numerically integrated until rates of microbial activity equilibrated throughout the thermocline (200 years in the illustrated model).The present configuration of the 3D MITgcm biogeochemical model resolves a total of six phytoplankton populations with parameters that represent the traits of the following six functional types: diatoms, picoplankton, diazotrophs, coccolithophores, and other large and other small phytoplankton. Four zooplankton types graze on the phytoplankton: one each specifically on the picoplankton , the other small phytoplankton, and coccolithophore types, and the fourth grazes on the diatom, the other large phytoplankton, and diazotroph type. The three-dimensional ocean circulation state estimate (the ECCO-GODAE state estimate) is from the configuration of the MITgcm as constrained by observationsIn addition to the six phytoplankton types, six microbial metabolic functional types are included in the global ecosystem model, and are responsible for all organic matter remineralization, nitrification, and denitrification. Particulate and dissolved organic matter (POM and DOM) are consumed and subsequently remineralized by the aerobic heterotrophic bacterial functional type (as in the water column model), an anaerobic nitrate-reducing  is available at http://mitgcm.org and the ecosystem component including the bacteria is available from git://gud.mit.edu/gud1.Fortran code for the ecosystem model and the one-dimensional water column configuration is available at Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1