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 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.
Metabolic knowledge is crucial to understand physiology and biotic interactions. Supported by an unprecedented rise of sequencing technologies, the last decade saw the increasing understanding of metabolic capacities using genomic knowledge. In particular, in 2010, Thiele and Palsson [1] described a general protocol enabling the reconstruction of high-quality metabolic networks, and several approaches have since been proposed to automate this process [2–4]. These methods rely primarily on two distinct steps. First, they provide automatic reconstructions of networks, called draft metabolic networks [5,6], and in a second step fill the gaps of the draft networks. To this end, reference databases of metabolic reactions are used to check whether adding reactions to networks allows compounds of interest to be produced from given growth media. Identifying these missing reactions constitutes the so-called gap-filling problem (n. b. considering the diversity of compound producibility and network consistency definitions, we here refer to a family of gap-filling problems rather than a single one) [2]. Several approaches exist to solve gap-filling problems and to select, within databases, those reactions that must be added to the draft network to restore its consistency and a metabolic behavior. Reactions may be chosen to optimize a graph-based criterion [7], or to optimize a linear score modelling the quantitative metabolic production of the system, as in the GapFill tool [8], and its derivative fastGapFill [9]. Some approaches also integrate complementary knowledge such as taxonomic information [10] or compartment modularity [11]. More generally, the selection of reactions may be performed by optimizing a linear score modelling the consistency of a network with phenotypic knowledge, i. e. experimental flux data [12] or growth/no growth results [13]. Lastly, some tools combine several of the previously mentioned approaches. An example of these is MIRAGE, which selects reactions in a database in order to maintain biomass producibility with respect to a score based on co-expression and taxonomic distance between the target species and the species for which enzymes were evidenced [14]. The approach presented in [15] is similar although based on a different definition of producibility. In Table in S1 Table, we report the main characteristics of such methods in terms of required input data, the technological platform to be run, and examples of applications. Together these examples illustrate that methods to reconstruct metabolic networks have been very fruitful. However, as noted in [16], most published genome-scale metabolic networks (GEMs) concern either prokaryotic or eukaryotic organisms for which genomic and physiological knowledge results from years of intensive studies. Indeed, GEM reconstruction is very sensitive to both genome annotation and the availability of complementary knowledge. It cannot, for instance, take into account genes of unknown function, which are common in incomplete or roughly annotated genomes. Nowadays, next generation sequencing (NGS) technologies are commonly employed to study strains and species distantly related to common model organisms. Draft metabolic networks based on these technologies are frequently quite degraded compared to those for standard model organisms. For instance, when comparing the number of reactions in the BioCyc repository [17] version 19. 5, we noticed that the 7,296 automatically reconstructed bacterial networks (Tier 3) contained on average 8% fewer reactions than the 27 curated bacterial metabolic networks contained in the manually curated repositories (Tier 1 & Tier 2). For the sake of illustration, let us introduce two examples of organisms with a complex evolutionary history and recently studied using NGS technologies. Euglena mutabilis is a photosynthetic protist and important primary producer in acidic aquatic environments. Despite the crucial role of E. mutabilis in these ecosystems and the fact that it has often been considered as an indicator species for acid mine drainages (AMDs), this organism has only been poorly described so far, in contrast to another species of the same genus, Euglena gracilis. The available data for E. mutabilis consists in assembled transcript sequences obtained from de novo transcriptomics and metabolomics experiments previously published in [18] and in [19], respectively. This sparse dataset prevented us from using most of the tools described above to construct a metabolic network: the absence of a sequenced genome for the Euglena genus and the fact that this genus is not closely related to any common model organism rendered taxonomy-based methods of network reconstruction unusable [10,11]. E. mutabilis is difficult to cultivate in controlled conditions and to obtain as clonal cultures, preventing the use of phenotype-based tools [12,13]. The family of tools [8,9, 14] that could be used here are of functional nature. Another application for gap-filling methods has emerged as a tool for studying the coexistence of organisms living in communities. As an example, Candidatus Phaeomarinobacter ectocarpi is a symbiotic bacterium associated with Ectocarpus siliculosus. Its genome and a draft of its metabolism could be produced [20]. Its host E. siliculosus has been studied for a longer time, and a functional metabolic network was reconstructed to explain the production of characteristic compounds of its metabolic profile [21]. Additional transcriptomic datasets were used in this study to identify 1,125 internal or external compounds produced by at least one reaction of E. siliculosus for which the corresponding enzyme was transcribed. Only 317 compounds could be produced according to the E. siliculosus draft network. In the framework of systems ecology [22], a natural question is whether the symbiotic bacterial network can resolve some of this non-producibility. This issue can be rephrased as follows: how can the Candidatus Phaeomarinobacter ectocarpi metabolic network be used to fill gaps in the E. siliculosus draft GEM? As above, this issue is of functional nature, and can be addressed only with functional gap-filling methods. Let us point out, however, that applying functional GEM gap-filling techniques [8,9, 14] to organisms distantly related to common model organisms raises several problems. A first problem is related to the determination of the biomass reaction of the system. This reaction is often copied from well-established model organisms and therefore cannot capture all of the characteristics of the studied organism, especially when dealing with extremophiles. Shortcomings in the determination of an adequate biomass reaction lead to a second problem, that is, the determination of the boundary compounds, dead-end metabolites, and cofactors in the system. These may be hard to characterize from experiments or literature, despite their strong potential impact on the capacity of the system to produce biomass according to stoichiometry-based formalisms [8]. In particular, the score-based methods mentioned above depend on the stoichiometric balance of metabolic reactions, a criterion which may be prone to errors, especially with respect to cofactors and when using large-scale databases of metabolic reactions [2]. As a natural consequence, we advocate the need of GEM gap-filling techniques suitable for newly developed model organisms, in particular those with a complex evolutionary history and/or living in extreme environments for which phenotypic data are lacking. This study reformulates the gap-filling problem as a qualitative combinatorial (optimization) one. We introduce the tool Meneco (Metabolic Network Completion) that solves this problem, using Answer Set Programming (ASP), a declarative programming paradigm including SAT-based solving technologies. Meneco considers reactions as achievable only if all their reactants are available, either as nutrients or provided by other metabolic reactions. Starting from given nutrients (e. g. growth medium), referred to as seeds, this tool computes their scope defined as all the metabolites that can be synthesized from them using a graph-based approach. For metabolic network gap-filling, a database of metabolic reactions is queried to look for minimal sets of reactions that can restore the observed bio-synthetic behaviour (i. e producibility of target metabolites). The Meneco tool was included in a pipeline implemented to construct EctoGEM, a metabolic network for the brown algal model E. siliculosus. The analysis of EctoGEM highlighted several interesting biochemical reactions, shedding light on the organization and evolution of some primary metabolic pathways of photosynthetic organisms [21]. In the present work, the case for Meneco as an important tool for hypothesis generation is further supported by new observations related to a benchmark of networks on a model organism and two case studies. First, this study simulates different degrees of manual curation using the model Escherichia coli [1]. For this purpose, 3,600 metabolic networks were generated from randomly degraded E. coli metabolic networks. In this benchmark, when the reference database used for completion was the real-case study Metacyc, Meneco outperformed the GapFill, the fastGapFill and the MIRAGE algorithms in terms of performance or accuracy. On a larger benchmark of 10,800 metabolic networks, our analysis suggests that Meneco is functionally relevant by identifying all essential reactions for more than 95% of the degraded networks. We advocate that the identification of such essential reactions is a key step towards the understanding of the metabolic capabilities of the species of interest, because they are related to key enzymes which, when removed, most likely prevent the viability of the species. Our results show that, when focusing on networks with a 10% degradation rate, the Meneco tool is able to restore the functionality of the network in 82% of cases. This suggests that Meneco is an important tool to study metabolic networks produced for organisms distantly related to common model organisms. In our first case study we use Meneco to assess the capability of the EctoGEM metabolic network to exchange metabolites with Candidatus Phaeomarinobacter ectocarpi, the aforementioned symbiotic bacterium associated with E. siliculosus. Combining the metabolic capacities of the draft GEM of E. siliculosus with those of the bacterial network enabled the in-silico production of 83 previously non producible algal targets. All of them were studied in detail allowing us to put forward hypotheses on possible exchanges between both organisms. Our second case study presents the first metabolic network for E. mutabilis, based on transcript sequences assembled from previously published transcriptomic and metabolomic data [18] [19]. In order to complete this draft with Meneco, we selected a set of targets from the list of metabolites that E. mutabilis can accumulate or secrete in minimum mineral medium [19]. Except for cobalamine, a cofactor that is not produced by this organism but is required for methionine synthesis, E. mutabilis can grow on a strictly mineral medium and is able to synthesize all the basic components of its biomass from mineral compounds only. Gaps in the draft network were filled iteratively with Meneco, using for each iteration a different subset of the 72 targets to solve the problem of cycles and circular dependencies. We thus obtained a network which was functional in Flux Balance analysis (FBA) for the photosynthetic production of biomass and excreted metabolites.
Our results suggest that the topological parsimony criterion used in 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 (for an a posteriori manual curation), with a reasonable loss of impact on the restoration of biomass production compared to parsimonious approaches. To further evaluate the quality of the output of Meneco, we completed the 3,600 degraded GEMs of our benchmark derived from the iJ904 E. coli metabolic network by 7,200 (2*3,600) additional degraded GEMs of the iAF1260 [32] and iJO1366 [33] 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. As a second case study, we focused on the reconstruction of a metabolic network for 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. Due to their complexity and size, no genome sequences are available so far within the genus Euglena. Therefore, we used assembled transcript sequences as described in [18] and annotated them by similarity search against enzyme reference sequences from MetaCyc. We used a stringent similarity threshold for function assignation to reduce as much as possible the rate of false positive reactions included in the initial network that was subsequently completed by 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 [19] who showed that they were secreted or accumulated in E. mutabilis cultured in a minimal mineral medium. The 17 other targets were myristic acid, shown to be present in the E. mutabilis membrane [36], and the remaining proteinogenic amino acids, nucleotides, and chlorophyll A and B. Running Meneco 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.
The main characteristic of Meneco is that it relies on a topological description of the notion of producibility. The qualitative approximation of the topological notion of producibility is more robust than stoichiometry-based notions, which are often forced to find more complete solutions that additionally fulfill the stoichiometric constraints. The logical formalization of the topological producibility allows users of Meneco to benefit from the performance of recent solvers for combinatorial problems based on ASP technologies. Meneco relies on these efficient solvers for the completion of degraded draft metabolic networks built, for instance, from NGS data. The other characteristic of the Meneco tool is that it is very flexible with respect to the definition of seeds (composition of the medium and potentially some cofactors) 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. 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 [8,10,27], or a top-down approach starting from all available information and removing reactions without added-value to the solution of the problem [15,23,24]. Bottom-up methods often report few solutions and may miss alternative ones while top-down approaches capture more solutions but are computationally demanding, and sometimes require sampling of the solution space. Therefore, families of gap-filling methods can be classified with respect to four characteristics: (i) the set of compounds whose producibility should be restored; (ii) the definition of producibility they rely on; (iii) the criteria they optimize; (iv) the number of solution sets they return. Meneco checks the same topological criteria for producibility as the subset-minimality top-down method [15], but it is able to enumerate all solution sets by using the parsimony criterion of the GapFill family. This allows an exhaustive computation of the family of solutions. 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. Alternative qualitative semantics could have been used to assess the producibility of a metabolic compound. In [26,37], Cottret 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 (S1 Files). This can be explained by self producing cycles which can occur with that definition. Our experiments using the E. coli benchmark evidence that, in half of the cases, Meneco (and GapFill) 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 [10,15], the space or compatible sets of reactions may become intractable, especially when many targets are considered together. In this case, most methods rely on a sampling of the solution space, which may introduce biases and errors. 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 [10] to improve GapFill 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. As stated by Satish Kumar et al. in [8], “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. Meneco 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 [5,38], (ii) parsimonious gap-filling based on metabolite profiles or RNA-seq datasets with Meneco, (iii) refinement of the model with stoichiometric-based approaches relying on additional data ([14] [13]), and (iv) manual curation process of metabolic networks described in [1]. From a biological point of view, the examples show that Meneco 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.
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 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.
metabolic networks
enzymology
database searching
reactants
genomic databases
plant science
metabolites
network analysis
genome analysis
enzyme metabolism
photosynthesis
plants
enzyme chemistry
research and analysis methods
computer and information sciences
biological databases
chemistry
algae
biochemistry
plant biochemistry
database and informatics methods
genetics
biology and life sciences
chemical reactions
physical sciences
genomics
metabolism
computational biology
organisms
Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks