Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Krumholz, Elias W.; Libourel, Igor G. L.

doi:10.1074/jbc.m114.634121

Cited by 14 publications

(13 citation statements)

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“…Even after those additions an average of one-third of the reactions in each model were still blocked, meaning that there were still enough reactions missing in the network to preclude metabolic flux through those reactions [25, 26]. In addition, the number of reactions that can partake in a gap filling solution is vast (3270 in the case of E. coli ), and the sets of reactions generated by different gap filling algorithms may have little or no overlap with each other [27]. Clearly, a more complete identification and annotation of metabolic reactions would be preferable to the addition of dozens of poorly supported reactions just to patch the holes in the network.…”

Section: Introductionmentioning

confidence: 99%

Combining multiple functional annotation tools increases coverage of metabolic annotation

et al. 2018

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BackgroundGenome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30–50% of genes without functional annotation, severely limiting our knowledge of the “parts lists” that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes.ResultsHere, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3–8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli.ConclusionsMetabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-5221-9) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Combining multiple functional annotation tools increases coverage of metabolic annotation

et al. 2018

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“…All methods that use various kinds of omics data to fill the gaps of a metabolic model are in the third group, e.g. , Sequence-based (45) and Likelihood-based (46) methods, Mirage (47), and GAUGE (26).…”

Section: Resultsmentioning

confidence: 99%

Systematically gap-filling the genome-scale metabolic model of CHO cells

Fouladiha

Marashi

et al. 2020

Preprint

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ObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In comparison to other mammalian cell types, in vitro handling of CHO cells is relatively easy. For example, CHO cells can grow in suspension and reach high cellular densities in bioreactors. Therefore, studying the metabolism of CHO cells to improve the bio-production of these cells is an important subject of research. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism.ResultsHere, we expanded the existing model of Chinese hamster metabolism (iCHO1766) with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. This updated model substantially increased the number of reactions capable of carrying flux.ConclusionsThe addition of these new data provides an important step towards more complete metabolic models of CHO cells.

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“…We should also note that, one way to improve the GAUGE predictions is to use BLAST-weighted dataset of reactions like strategies used recently 18 19 . This way, the presence of unrelated or orphan reactions may be reduced in possible solutions of GAUGE.…”

Section: Discussionmentioning

confidence: 99%

“…This is also true about 13 C labeling data or metabolomics data which are required for OMNI 14 and minimal extension 15 methods, respectively. The four last methods in Table 1 usually take a draft metabolic network that cannot produce biomass, and try to add minimum number of reactions to the model, such that biomass producing reaction can carry flux 16 17 18 19 . In the present study we aim to use gene expression data for finding the gaps.…”

Section: Gap Analysis: Gap Finding and Gap Fillingmentioning

confidence: 99%

Discovering missing reactions of metabolic networks by using gene co-expression data

Hosseini

Marashi

2017

Sci Rep

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Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

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Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Cited by 14 publications

References 51 publications

Combining multiple functional annotation tools increases coverage of metabolic annotation

Combining multiple functional annotation tools increases coverage of metabolic annotation

Systematically gap-filling the genome-scale metabolic model of CHO cells

Discovering missing reactions of metabolic networks by using gene co-expression data

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