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Enumerating all possible biosynthetic pathways in metabolic networks.

ABSTRACT: Exhaustive identification of all possible alternate pathways that exist in metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities. We developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular size between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways of a specified size producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from identifying amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying large graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein. MetQuest is available as a Python package, and the source codes can be found at https://github.com/RamanLab/metquest .

SUBMITTER: Ravikrishnan A

PROVIDER: S-EPMC6028704 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Enumerating all possible biosynthetic pathways in metabolic networks.

Ravikrishnan Aarthi A Nasre Meghana M Raman Karthik K

Scientific reports 20180702 1

Exhaustive identification of all possible alternate pathways that exist in metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities. We developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular size betwee ...[more]

PMID: 29967471

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Project description:BackgroundA wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds.ResultsThe developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates.ConclusionWith this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts.

Project description:BackgroundA central problem in computational metabolic modelling is how to find biochemically plausible pathways between metabolites in a metabolic network. Two general, complementary frameworks have been utilized to find metabolic pathways: constraint-based modelling and graph-theoretical path finding approaches. In constraint-based modelling, one aims to find pathways where metabolites are balanced in a pseudo steady-state. Constraint-based methods, such as elementary flux mode analysis, have typically a high computational cost stemming from a large number of steady-state pathways in a typical metabolic network. On the other hand, graph-theoretical approaches avoid the computational complexity of constraint-based methods by solving a simpler problem of finding shortest paths. However, while scaling well with network size, graph-theoretic methods generally tend to return more false positive pathways than constraint-based methods.ResultsIn this paper, we introduce a computational method, ReTrace, for finding biochemically relevant, branching metabolic pathways in an atom-level representation of metabolic networks. The method finds compact pathways which transfer a high fraction of atoms from source to target metabolites by considering combinations of linear shortest paths. In contrast to current steady-state pathway analysis methods, our method scales up well and is able to operate on genome-scale models. Further, we show that the pathways produced are biochemically meaningful by an example involving the biosynthesis of inosine 5'-monophosphate (IMP). In particular, the method is able to avoid typical problems associated with graph-theoretic approaches such as the need to define side metabolites or pathways not carrying any net carbon flux appearing in results. Finally, we discuss an application involving reconstruction of amino acid pathways of a recently sequenced organism demonstrating how measurement data can be easily incorporated into ReTrace analysis. ReTrace is licensed under GPL and is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/retrace/.ConclusionReTrace is a useful method in metabolic path finding tasks, combining some of the best aspects in constraint-based and graph-theoretic methods. It finds use in a multitude of tasks ranging from metabolic engineering to metabolic reconstruction of recently sequenced organisms.

Project description:A comprehensive analysis to survey heme-binding proteins produced by the white-rot fungus Phanerochaete chrysosporium was achieved using a biotinylated heme-streptavidin beads system. Mitochondrial citrate synthase (PcCS), glyceraldehyde 3-phosphate dehydrogenase (PcGAPDH), and 2-Cys thioredoxin peroxidase (mammalian HBP23 homolog) were identified as putative heme-binding proteins. Among these, PcCS and PcGAPDH were further characterized using heterologously expressed recombinant proteins. Difference spectra of PcCS titrated with hemin exhibited an increase in the Soret absorbance at 414 nm, suggesting that the axial ligand of the heme is a His residue. The activity of PcCS was strongly inhibited by hemin with Ki oxaloacetate of 8.7 μM and Ki acetyl-CoA of 5.8 μM. Since the final step of heme biosynthesis occurred at the mitochondrial inner membrane, the inhibition of PcCS by heme is thought to be a physiological event. The inhibitory mode of the heme was similar to that of CoA analogues, suggesting that heme binds to PcCS at His347 at the AcCoA-CoA binding site, which was supported by the homology model of PcCS. PcGAPDH was also inhibited by heme, with a lower concentration than that for PcCS. This might be caused by the different location of these enzymes. From the integration of these phenomena, it was concluded that metabolic regulations by heme in the central metabolic and heme synthetic pathways occurred in the mitochondria and cytosol. This novel pathway crosstalk between the central metabolic and heme biosynthetic pathways, via a heme molecule, is important in regulating the metabolic balance (heme synthesis, ATP synthesis, flux balance of the tricarboxylic acid (TCA) cycle and cellular redox balance (NADPH production) during fungal aromatic degradation. KEY POINTS: • A comprehensive survey of heme-binding proteins in P. chrysosporium was achieved. • Several heme-binding proteins including CS and GAPDH were identified. • A novel metabolic regulation by heme in the central metabolic pathways was found.

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Enumerating all possible biosynthetic pathways in metabolic networks.

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Enumerating all possible biosynthetic pathways in metabolic networks.

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