KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters

Nakaya, Akihiro; Katayama, Toshiaki; Itoh, Masumi; Hiranuka, Kazushi; Kawashima, Seiichiro; Moriya, Yuki; Okuda, Shujiro; Tanaka, Michihiro; Tokimatsu, Toshiaki; Yamanishi, Yoshihiro; Yoshizawa, Akiyasu C.; Kanehisa, Minoru; Goto, Susumu

doi:10.1093/nar/gks1239

Cited by 107 publications

(73 citation statements)

References 22 publications

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“…The analysis of archaeal protein families relied on the assignments in the latest public release of the COG database (Tatusov et al , 2003), currently available at ftp://ftp.ncbi.nlm.nih.gov/pub/COG/COG/whog, the NCBI’s RefSeq database (Pruitt et al , 2012), and the recently updated version of archaeal COGs (Wolf et al , 2012), which is available at ftp://ftp.ncbi.nih.gov/pub/wolf/COGs/arCOG. We also used the latest versions of the eggNOG, KEGG and MetaCyc databases (Caspi et al , 2012; Powell et al , 2012; Nakaya et al , 2013). Pathway details were taken from KEGG and the available literature data.…”

Section: Cog-based Pathway Analysismentioning

confidence: 99%

Phylogenomic reconstruction of archaeal fatty acid metabolism

Dibrova

Galperin

Mulkidjanian

2014

Environmental Microbiology

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While certain archaea appear to synthesize and/or metabolize fatty acids, the respective pathways still remain obscure. By analyzing the genomic distribution of the key lipid-related enzymes, we were able to identify the likely components of the archaeal pathway of fatty acid metabolism, namely, a combination of the enzymes of bacterial-type β-oxidation of fatty acids (acyl-CoA-dehydrogenase, enoyl-CoA hydratase, and 3-hydroxyacyl-CoA dehydrogenase) with paralogs of the archaeal acetyl-CoA C-acetyltransferase, an enzyme of the mevalonate biosynthesis pathway. These three β-oxidation enzymes working in the reverse direction could potentially catalyze biosynthesis of fatty acids, with paralogs of acetyl-CoA C-acetyltransferase performing addition of C2 fragments. The presence in archaea of the genes for energy-transducing membrane enzyme complexes, such as cytochrome bc complex, cytochrome c oxidase, and diverse rhodopsins, was found to correlate with the presence of the proposed system of fatty acid biosynthesis. We speculate that because these membrane complexes functionally depend on fatty acid chains, their genes could have been acquired via lateral gene transfer from bacteria only by those archaea that already possessed a system of fatty acid biosynthesis. The proposed pathway of archaeal fatty acid metabolism operates in extreme conditions and therefore might be of interest in the context of biofuel production and other industrial applications.

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Section: Cog-based Pathway Analysismentioning

confidence: 99%

Phylogenomic reconstruction of archaeal fatty acid metabolism

Dibrova

Galperin

Mulkidjanian

2014

Environmental Microbiology

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“…To analyze the evolutionary conservation, we selected model organisms with rich genome information, i.e., Saccharomyces cerevisiae (fission yeast), Schizosaccharomyces pombe (budding yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Danio rerio (fish), Canis familiaris (dog), Mus musculus (mouse), Pan troglodytes (Chimpanzee), and Homo sapiens (human). We obtained orthologous gene sets from KEGG OC [29] for the nine species. We constructed multiple sequence alignments of phosphoproteins in each orthologous group and identified species where a known phosphosite was conserved in a motif.…”

Section: Resultsmentioning

confidence: 99%

“…The three-letter code for each genome is the species identifier defined by KEGG. Orthologous genes among these genomes were defined by KEGG OC [29]. For each ortholog cluster, multiple sequence alignments were constructed by MAFFT [53], which is a freely available, rapid, and reliable tool compared with other alignment tools.…”

Section: Methodsmentioning

confidence: 99%

Elucidation of the evolutionary expansion of phosphorylation signaling networks using comparative phosphomotif analysis

Yoshizaki

Okuda

2014

BMC Genomics

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BackgroundProtein phosphorylation is catalyzed by kinases and is involved in the regulation of a wide range of processes. The phosphosites in protein sequence motifs determine the types of kinases involved. The development of phosphoproteomics has allowed the identification of huge numbers of phosphosites, some of which are not involved in physiological functions.ResultsWe developed a method for extracting phosphosites with important roles in cellular functions and determined 178 phosphomotifs based on the analysis of 34,366 phosphosites. We compared the conservation of serine/threonine/tyrosine residues observed in humans and seven other species. Consequently, we identified 16 phosphomotifs, where the level of conservation increased among species. The highly conserved phosphomotifs in humans and the worm were kinase regulatory sites. The motifs present in the fly were novel phosphomotifs, including zinc finger motifs involved in the regulation of gene expression. Subsequently, we found that this zinc finger motif contributed to subcellular protein localization. The motifs identified in fish allowed us to detect the expansion of phosphorylation signals related to alternative splicing. We also showed that the motifs present in specific species functioned in an additional network that interacted directly with the core signaling network conserved from yeast to humans.ConclusionsOur method may facilitate the efficient extraction of novel phosphomotifs with physiological functions, thereby contributing greatly to the analysis of complex phosphorylation signaling cascades. Our study suggests that the phosphorylation networks acquired during evolution have added signaling network modules to the core signaling networks.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-546) contains supplementary material, which is available to authorized users.

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“…not AraCyc, are computationally predicted (Zhang et al, 2010;Nakaya et al, 2013;Kanehisa et al, 2014;Seaver et al, 2014). Additionally, in many of the cases, and this problem is particularly acute in plants, the set of computationally predicted genes associated with reactions may be homologous, but do not perform the same catalytic function (i.e., they are out-paralogs).…”

Section: Evidence For Gene-reaction Associationsmentioning

confidence: 99%

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

Seaver¹,

Bradbury²,

Frelin³

et al. 2016

Crop Breeding

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There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

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KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters

Cited by 107 publications

References 22 publications

Phylogenomic reconstruction of archaeal fatty acid metabolism

Phylogenomic reconstruction of archaeal fatty acid metabolism

Elucidation of the evolutionary expansion of phosphorylation signaling networks using comparative phosphomotif analysis

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

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