Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

Proceedings of the National Academy of Sciences of the United States of America

Volumn 114, Issue 45, 2017, Pages E9740-E9749

  Ryu, Jae Yong ^a   Kim, Hyun Uk ^a,b   Lee, Sang Yup ^a,b  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Chalmers University of Technology   (Denmark)

Author keywords

Alternative splicing; Gene transcript protein reaction associations; Human genome scale metabolic model; Protein isoform; Recon

Indexed keywords

ADENOSINE TRIPHOSPHATE; GLYCERALDEHYDE 3 PHOSPHATE DEHYDROGENASE; LACTATE DEHYDROGENASE; PHOSPHOGLYCERATE KINASE; PROTON TRANSPORTING ADENOSINE TRIPHOSPHATE SYNTHASE; PYRUVATE KINASE; ANTINEOPLASTIC AGENT; ISOPROTEIN; TRANSCRIPTOME;

ACHE GENE; AEROBIC GLYCOLYSIS; AMPD1 GENE; AMPD2 GENE; AMPD3 GENE; ARTICLE; CCBL1 GENE; CONTROLLED STUDY; FGA GENE; GCNT2 GENE; GENE; GENE EXPRESSION; GENE TRANSCRIPT PROTEIN REACTION ASSOCIATION; GENETIC ASSOCIATION; GENETIC TRANSCRIPTION; GENETIC VARIABILITY; GLS2 GENE; GLUCOSE TRANSPORT; HUMAN; HUMAN GENOME; METABOLISM; MOGAT2 GENE; OXIDATIVE PHOSPHORYLATION; OXYGEN CONSUMPTION; PRIORITY JOURNAL; RNA SEQUENCE; SLC14A1 GENE; SLC15A2 GENE; SLC4A7 GENE; SLC7A10 GENE; SLC7A7 GENE; SLC7A8 GENE; ST3GAL3 GENE; TXNRD2 GENE; ALTERNATIVE RNA SPLICING; GENE EXPRESSION REGULATION; GENETICS; NEOPLASM; PHYSIOLOGY; PROTEIN SYNTHESIS;

ALTERNATIVE SPLICING; ANTINEOPLASTIC AGENTS; GENE EXPRESSION REGULATION; GENOME, HUMAN; HUMANS; METABOLISM; NEOPLASMS; PROTEIN BIOSYNTHESIS; PROTEIN ISOFORMS; TRANSCRIPTOME;

EID: 85033457644     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1713050114     Document Type: Article

References (55)

1. Maniatis T (1991) Mechanisms of alternative pre-mRNA splicing. Science 251:33–34.
2. Barash Y, et al. (2010) Deciphering the splicing code. Nature 465:53–59.
3. Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415.
4. Li HD, Menon R, Omenn GS, Guan Y (2014) The emerging era of genomic data integration for analyzing splice isoform function. Trends Genet 30:340–347.
5. Rodriguez JM, Carro A, Valencia A, Tress ML (2015) APPRIS webserver and webservices. Nucleic Acids Res 43:W455-9.
6. Medina MW, Krauss RM (2013) Alternative splicing in the regulation of cholesterol homeostasis. Curr Opin Lipidol 24:147–152.
7. Calabretta S, et al. (2016) Modulation of PKM alternative splicing by PTBP1 promotes gemcitabine resistance in pancreatic cancer cells. Oncogene 35:2031–2039.
8. Sebestyén E, Zawisza M, Eyras E (2015) Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer. Nucleic Acids Res 43: 1345–1356.
9. Rodriguez JM, et al. (2013) APPRIS: Annotation of principal and alternative splice isoforms. Nucleic Acids Res 41:D110–D117.
10. Uhlén M, et al. (2015) Proteomics. Tissue-based map of the human proteome. Science 347:1260419.
11. Ryu JY, Kim HU, Lee SY (2015) Reconstruction of genome-scale human metabolic models using omics data. Integr Biol 7:859–868.
12. Mardinoglu A, Gatto F, Nielsen J (2013) Genome-scale modeling of human metabolism–A systems biology approach. Biotechnol J 8:985–996.
13. Quek LE, et al. (2014) Reducing Recon 2 for steady-state flux analysis of HEK cell culture. J Biotechnol 184:172–178.
14. Mardinoglu A, Nielsen J (2015) New paradigms for metabolic modeling of human cells. Curr Opin Biotechnol 34:91–97.
15. Hyötyläinen T, et al. (2016) Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat Commun 7:8994.
16. Ma H, et al. (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 3:135.
17. Kim HU, Sohn SB, Lee SY (2012) Metabolic network modeling and simulation for drug targeting and discovery. Biotechnol J 7:330–342.
18. Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107–120.
19. Duarte NC, et al. (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 104:1777–1782.
20. Thiele I, et al. (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425.
21. Swainston N, et al. (2016) Recon 2.2: From reconstruction to model of human metabolism. Metabolomics 12:109.
22. Mardinoglu A, et al. (2013) Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol Syst Biol 9:649.
23. Mardinoglu A, et al. (2014) Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun 5:3083.
24. Agren R, et al. (2014) Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 10:721.
25. Yizhak K, et al. (2014) A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Mol Syst Biol 10:744.
26. Folger O, et al. (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501.
27. Nam H, et al. (2014) A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. PLoS Comput Biol 10:e1003837.
28. Väremo L, et al. (2015) Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes. Cell Rep 11:921–933.
29. Mardinoglu A, et al. (2015) The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol Syst Biol 11:834.
30. Blais EM, et al. (2017) Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat Commun 8:14250.
31. Ryu JY, Kim HU, Lee SY (2015) Human genes with a greater number of transcript variants tend to show biological features of housekeeping and essential genes. Mol Biosyst 11:2798–2807.
32. Leoncikas V, Wu H, Ward LT, Kierzek AM, Plant NJ (2016) Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production. Sci Rep 6:19771.
33. Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA (2015) Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. Proc Natl Acad Sci USA 112:12217–12222.
34. Pfau T, Pacheco MP, Sauter T (2016) Towards improved genome-scale metabolic network reconstructions: Unification, transcript specificity and beyond. Brief Bioinform 17: 1060–1069.
35. Wang T, et al. (2015) Identification and characterization of essential genes in the human genome. Science 350:1096–1101.
36. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: The metabolic requirements of cell proliferation. Science 324:1029–1033.
37. Cunningham F, et al. (2015) Ensembl 2015. Nucleic Acids Res 43:D662–D669.
38. Pruitt KD, et al. (2014) RefSeq: An update on mammalian reference sequences. Nucleic Acids Res 42:D756–D763.
39. Karolchik D, et al.; University of California Santa Cruz (2003) The UCSC genome browser database. Nucleic Acids Res 31:51–54.
40. Agren R, et al. (2012) Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 8:e1002518.
41. Kumar N, Skolnick J (2012) EFICAz2.5: Application of a high-precision enzyme function predictor to 396 proteomes. Bioinformatics 28:2687–2688.
42. Horton P, et al. (2007) WoLF PSORT: Protein localization predictor. Nucleic Acids Res 35:W585–W587.
43. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30.
44. Scheer M, et al. (2011) BRENDA, the enzyme information system in 2011. Nucleic Acids Res 39:D670–D676.
45. UniProt Consortium (2015) UniProt: A hub for protein information. Nucleic Acids Res 43:D204–D212.
46. Thul PJ, et al. (2017) A subcellular map of the human proteome. Science 356:eaal3321.
47. Lee D, et al. (2012) Improving metabolic flux predictions using absolute gene expression data. BMC Syst Biol 6:73.
48. Kim HU, Kim TY, Lee SY (2011) Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network. BMC Syst Biol 5(Suppl 2):S14.
49. Ozsvári B, Lamb R, Lisanti MP (2016) Repurposing of FDA-approved drugs against cancer–Focus on metastasis. Aging (Albany NY) 8:567–568.
50. Vanhaelen Q, et al. (2017) Design of efficient computational workflows for in silico drug repurposing. Drug Discov Today 22:210–222.
51. Kim HU, Ryu JY, Lee JO, Lee SY (2015) A systems approach to traditional oriental medicine. Nat Biotechnol 33:264–268.
52. Kinsella RJ, et al. (2011) Ensembl BioMarts: A hub for data retrieval across taxonomic space. Database (Oxford) 2011:bar030.
53. Bernard T, et al. (2014) Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform 15:123–135.
54. Moretti S, et al. (2016) MetaNetX/MNXref–Reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks. Nucleic Acids Res 44:D523–D526.
55. Law V, et al. (2014) DrugBank 4.0: Shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097.