{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:33:59Z","timestamp":1784180039207,"version":"3.55.0"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T00:00:00Z","timestamp":1562544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting<jats:italic>in silico<\/jats:italic>model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver\u2019s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected \u201chub\u201d genes (<jats:italic>Pklr<\/jats:italic>,<jats:italic>Hadha<\/jats:italic>,<jats:italic>Tkt<\/jats:italic>,<jats:italic>Pgm1<\/jats:italic>,<jats:italic>Tpi1<\/jats:italic>, and<jats:italic>Eno3<\/jats:italic>) that showed differing trends between early and late time points. Taken together, our results suggest that the liver\u2019s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.<\/jats:p>","DOI":"10.1038\/s41598-019-46245-1","type":"journal-article","created":{"date-parts":[[2019,7,8]],"date-time":"2019-07-08T10:32:20Z","timestamp":1562581940000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Genome-scale metabolic model of the rat liver predicts effects of diet restriction"],"prefix":"10.1038","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3382-4941","authenticated-orcid":false,"given":"Priyanka","family":"Baloni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vineet","family":"Sangar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-509X","authenticated-orcid":false,"given":"James T.","family":"Yurkovich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0878-114X","authenticated-orcid":false,"given":"Max","family":"Robinson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Taylor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine M.","family":"Karbowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisham K.","family":"Hamadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudong D.","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4157-0267","authenticated-orcid":false,"given":"Nathan D.","family":"Price","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,7,8]]},"reference":[{"key":"46245_CR1","doi-asserted-by":"crossref","unstructured":"Leiskau, C. & Baumann, U. Structure, Function, and Repair of the Liver. In Diseases of the Liver and Biliary System in Children 1\u201317 (2017).","DOI":"10.1002\/9781119046936.ch1"},{"key":"46245_CR2","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/S1262-3636(07)70133-7","volume":"30","author":"C Postic","year":"2004","unstructured":"Postic, C., Dentin, R. & Girard, J. Role of the liver in the control of carbohydrate and lipid homeostasis. Diabetes Metab. 30, 398\u2013408 (2004).","journal-title":"Diabetes Metab."},{"key":"46245_CR3","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1016\/j.cell.2015.05.019","volume":"161","author":"EJ O\u2019Brien","year":"2015","unstructured":"O\u2019Brien, E. J., Monk, J. M. & Palsson, B. O. Using Genome-scale Models to Predict Biological Capabilities. Cell 161, 971\u2013987 (2015).","journal-title":"Cell"},{"key":"46245_CR4","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth, J. D., Thiele, I. & Palsson, B. \u00d8. What is flux balance analysis? Nat. Biotechnol. 28, 245\u2013248 (2010).","journal-title":"Nat. Biotechnol."},{"key":"46245_CR5","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1093\/bib\/bbp011","volume":"10","author":"K Raman","year":"2009","unstructured":"Raman, K. & Chandra, N. Flux balance analysis of biological systems: applications and challenges. Brief. Bioinform. 10, 435\u2013449 (2009).","journal-title":"Brief. Bioinform."},{"key":"46245_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/srep46249","volume":"7","author":"A Bordbar","year":"2017","unstructured":"Bordbar, A. et al. Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci. Rep. 7, 46249 (2017).","journal-title":"Sci. Rep."},{"key":"46245_CR7","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.copbio.2018.01.009","volume":"51","author":"JT Yurkovich","year":"2018","unstructured":"Yurkovich, J. T. & Palsson, B. O. Quantitative -omic data empowers bottom-up systems biology. Curr. Opin. Biotechnol. 51, 130\u2013136 (2018).","journal-title":"Curr. Opin. Biotechnol."},{"key":"46245_CR8","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/nrmicro2737","volume":"10","author":"NE Lewis","year":"2012","unstructured":"Lewis, N. E., Nagarajan, H. & Palsson, B. O. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 10, 291\u2013305 (2012).","journal-title":"Nat. Rev. Microbiol."},{"key":"46245_CR9","doi-asserted-by":"publisher","first-page":"e1005489","DOI":"10.1371\/journal.pcbi.1005489","volume":"13","author":"Z Wang","year":"2017","unstructured":"Wang, Z. et al. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast. PLoS Comput. Biol. 13, e1005489 (2017).","journal-title":"PLoS Comput. Biol."},{"key":"46245_CR10","doi-asserted-by":"publisher","first-page":"17845","DOI":"10.1073\/pnas.1005139107","volume":"107","author":"S Chandrasekaran","year":"2010","unstructured":"Chandrasekaran, S. & Price, N. D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA 107, 17845\u201317850 (2010).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"46245_CR11","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1093\/bioinformatics\/btn352","volume":"24","author":"MW Covert","year":"2008","unstructured":"Covert, M. W., Xiao, N., Chen, T. J. & Karr, J. R. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 24, 2044\u20132050 (2008).","journal-title":"Bioinformatics"},{"key":"46245_CR12","doi-asserted-by":"publisher","first-page":"e1000082","DOI":"10.1371\/journal.pcbi.1000082","volume":"4","author":"SA Becker","year":"2008","unstructured":"Becker, S. A. & Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4, e1000082 (2008).","journal-title":"PLoS Comput. Biol."},{"key":"46245_CR13","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1073\/pnas.0610772104","volume":"104","author":"NC Duarte","year":"2007","unstructured":"Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA 104, 1777\u20131782 (2007).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"46245_CR14","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1038\/nbt.4072","volume":"36","author":"E Brunk","year":"2018","unstructured":"Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272\u2013281 (2018).","journal-title":"Nat. Biotechnol."},{"key":"46245_CR15","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1038\/nbt.2488","volume":"31","author":"I Thiele","year":"2013","unstructured":"Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419\u2013425 (2013).","journal-title":"Nat. Biotechnol."},{"key":"46245_CR16","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-6-153","volume":"6","author":"Y Wang","year":"2012","unstructured":"Wang, Y., Eddy, J. A. & Price, N. D. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst. Biol. 6, 153 (2012).","journal-title":"BMC Syst. Biol."},{"key":"46245_CR17","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-5-180","volume":"5","author":"A Bordbar","year":"2011","unstructured":"Bordbar, A. et al. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst. Biol. 5, 180 (2011).","journal-title":"BMC Syst. Biol."},{"key":"46245_CR18","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.cels.2017.01.010","volume":"4","author":"S Opdam","year":"2017","unstructured":"Opdam, S. et al. A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models. Cell Systems 4, 318\u2013329.e6 (2017).","journal-title":"Cell Systems"},{"key":"46245_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-5-110","volume":"5","author":"A Bordbar","year":"2011","unstructured":"Bordbar, A., Jamshidi, N. & Palsson, B. O. iAB-RBC-283: A proteomically derived knowledge-base of erythrocyte metabolism that can be used to simulate its physiological and patho-physiological states. BMC Syst. Biol. 5, 110 (2011).","journal-title":"BMC Syst. Biol."},{"key":"46245_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/srep03925","volume":"4","author":"A Thomas","year":"2014","unstructured":"Thomas, A., Rahmanian, S., Bordbar, A., Palsson, B. \u00d8. & Jamshidi, N. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance. Sci. Rep. 4, 3925 (2014).","journal-title":"Sci. Rep."},{"key":"46245_CR21","first-page":"237","volume":"4","author":"NE Lewis","year":"2013","unstructured":"Lewis, N. E. & Abdel-Haleem, A. M. The evolution of genome-scale models of cancer metabolism. Front. Physiol. 4, 237 (2013).","journal-title":"Front. Physiol."},{"key":"46245_CR22","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1038\/nrg3643","volume":"15","author":"A Bordbar","year":"2014","unstructured":"Bordbar, A., Monk, J. M., King, Z. A. & Palsson, B. O. Constraint-based models predict metabolic and associated cellular functions. Nat. Rev. Genet. 15, 107\u2013120 (2014).","journal-title":"Nat. Rev. Genet."},{"key":"46245_CR23","doi-asserted-by":"publisher","first-page":"e1000938","DOI":"10.1371\/journal.pcbi.1000938","volume":"6","author":"RL Chang","year":"2010","unstructured":"Chang, R. L., Xie, L., Xie, L., Bourne, P. E. & Palsson, B. \u00d8. Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput. Biol. 6, e1000938 (2010).","journal-title":"PLoS Comput. Biol."},{"key":"46245_CR24","doi-asserted-by":"publisher","first-page":"e1005039","DOI":"10.1371\/journal.pcbi.1005039","volume":"12","author":"N Mih","year":"2016","unstructured":"Mih, N., Brunk, E., Bordbar, A. & Palsson, B. O. A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism. Plos Computational Biology 12, e1005039 (2016).","journal-title":"Plos Computational Biology"},{"key":"46245_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms3632","volume":"4","author":"K Yizhak","year":"2013","unstructured":"Yizhak, K., Gabay, O., Cohen, H. & Ruppin, E. Model-based identification of drug targets that revert disrupted metabolism and its application to ageing. Nat. Commun. 4, 2632 (2013).","journal-title":"Nat. Commun."},{"key":"46245_CR26","doi-asserted-by":"crossref","unstructured":"Gille, C. et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Molecular Systems Biology 6 (2010).","DOI":"10.1038\/msb.2010.62"},{"key":"46245_CR27","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1038\/msb.2010.56","volume":"6","author":"L Jerby","year":"2010","unstructured":"Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol. Syst. Biol. 6, 401 (2010).","journal-title":"Mol. Syst. Biol."},{"key":"46245_CR28","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1089\/cmb.2012.0276","volume":"20","author":"R Pagliarini","year":"2013","unstructured":"Pagliarini, R. & di Bernardo, D. A genome-scale modeling approach to study inborn errors of liver metabolism: toward an in silico patient. J. Comput. Biol. 20, 383\u2013397 (2013).","journal-title":"J. Comput. Biol."},{"key":"46245_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms4083","volume":"5","author":"A Mardinoglu","year":"2014","unstructured":"Mardinoglu, A. et al. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat. Commun. 5, 3083 (2014).","journal-title":"Nat. Commun."},{"key":"46245_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms14250","volume":"8","author":"EM Blais","year":"2017","unstructured":"Blais, E. M. et al. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat. Commun. 8, 14250 (2017).","journal-title":"Nat. Commun."},{"key":"46245_CR31","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1586\/17474124.2015.1056156","volume":"9","author":"FR Ponziani","year":"2015","unstructured":"Ponziani, F. R., Pecere, S., Gasbarrini, A. & Ojetti, V. Physiology and pathophysiology of liver lipid metabolism. Expert Rev. Gastroenterol. Hepatol. 9, 1055\u20131067 (2015).","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"key":"46245_CR32","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/s11357-997-0021-1","volume":"20","author":"RJ Feuers","year":"1997","unstructured":"Feuers, R. J., Weindruch, R., Leakey, J. E., Duffy, P. H. & Hart, R. W. Increased effective activity of rat liver catalase by dietary restriction. Age 20, 215\u2013220 (1997).","journal-title":"Age"},{"key":"46245_CR33","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1042\/bj1110713","volume":"111","author":"F Novello","year":"1969","unstructured":"Novello, F., Gumaa, J. A. & McLean, P. The pentose phosphate pathway of glucose metabolism. Hormonal and dietary control of the oxidative and non-oxidative reactions of the cycle in liver. Biochem. J 111, 713\u2013725 (1969).","journal-title":"Biochem. J"},{"key":"46245_CR34","doi-asserted-by":"publisher","first-page":"e21342","DOI":"10.1371\/journal.pone.0021342","volume":"6","author":"Y Zhang","year":"2011","unstructured":"Zhang, Y. et al. Insulin-Regulated Srebp-1c and Pck1 mRNA Expression in Primary Hepatocytes from Zucker Fatty but Not Lean Rats Is Affected by Feeding Conditions. PLoS One 6, e21342 (2011).","journal-title":"PLoS One"},{"key":"46245_CR35","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/nprot.2009.203","volume":"5","author":"I Thiele","year":"2010","unstructured":"Thiele, I. & Palsson, B. \u00d8. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93\u2013121 (2010).","journal-title":"Nat. Protoc."},{"key":"46245_CR36","doi-asserted-by":"publisher","first-page":"D515","DOI":"10.1093\/nar\/gkv1049","volume":"44","author":"ZA King","year":"2016","unstructured":"King, Z. A. et al. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515\u201322 (2016).","journal-title":"Nucleic Acids Res."},{"key":"46245_CR37","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1038\/emm.2000.6","volume":"32","author":"BH Kim","year":"2000","unstructured":"Kim, B. H. et al. Dedifferentiation of conditionally immortalized hepatocytes with long-term in vitro passage. Exp. Mol. Med. 32, 29\u201337 (2000).","journal-title":"Exp. Mol. Med."},{"key":"46245_CR38","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1517\/17425255.2.6.875","volume":"2","author":"M Martignoni","year":"2006","unstructured":"Martignoni, M., Groothuis, G. M. M. & de Kanter, R. Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin. Drug Metab. Toxicol. 2, 875\u2013894 (2006).","journal-title":"Expert Opin. Drug Metab. Toxicol."},{"key":"46245_CR39","first-page":"842","volume":"27","author":"WT Jewell","year":"1999","unstructured":"Jewell, W. T. & Miller, M. G. Comparison of human and rat metabolism of molinate in liver microsomes and slices. Drug Metab. Dispos. 27, 842\u2013847 (1999).","journal-title":"Drug Metab. Dispos."},{"key":"46245_CR40","doi-asserted-by":"publisher","first-page":"948","DOI":"10.3109\/00498254.2013.787155","volume":"43","author":"H Nishimuta","year":"2013","unstructured":"Nishimuta, H., Nakagawa, T., Nomura, N. & Yabuki, M. Species differences in hepatic and intestinal metabolic activities for 43 human cytochrome P450 substrates between humans and rats or dogs. Xenobiotica 43, 948\u2013955 (2013).","journal-title":"Xenobiotica"},{"key":"46245_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2156-3-18","volume":"3","author":"AJ Edgar","year":"2002","unstructured":"Edgar, A. J. The human L-threonine 3-dehydrogenase gene is an expressed pseudogene. BMC Genet. 3, 18 (2002).","journal-title":"BMC Genet."},{"key":"46245_CR42","doi-asserted-by":"publisher","first-page":"16625","DOI":"10.1073\/pnas.1010390107","volume":"107","author":"WS Stevenson","year":"2010","unstructured":"Stevenson, W. S. et al. Deficiency of 5-hydroxyisourate hydrolase causes hepatomegaly and hepatocellular carcinoma in mice. Proc. Natl. Acad. Sci. USA 107, 16625\u201316630 (2010).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"46245_CR43","doi-asserted-by":"publisher","first-page":"1203S","DOI":"10.1093\/ajcn\/54.6.1203s","volume":"54","author":"M Nishikimi","year":"1991","unstructured":"Nishikimi, M. & Yagi, K. Molecular basis for the deficiency in humans of gulonolactone oxidase, a key enzyme for ascorbic acid biosynthesis. Am. J. Clin. Nutr. 54, 1203S\u20131208S (1991).","journal-title":"Am. J. Clin. Nutr."},{"key":"46245_CR44","doi-asserted-by":"publisher","first-page":"9081","DOI":"10.1073\/pnas.85.23.9081","volume":"85","author":"PG Reddy","year":"1988","unstructured":"Reddy, P. G. et al. Isolation and sequence determination of a cDNA clone for rat peroxisomal urate oxidase: liver-specific expression in the rat. Proceedings of the National Academy of Sciences 85, 9081\u20139085 (1988).","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"46245_CR45","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1093\/rheumatology\/keq204","volume":"49","author":"B Alvarez-Lario","year":"2010","unstructured":"Alvarez-Lario, B. & Macarron-Vicente, J. Uric acid and evolution. Rheumatology 49, 2010\u20132015 (2010).","journal-title":"Rheumatology"},{"key":"46245_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-30149-7","volume":"8","author":"VR Pannala","year":"2018","unstructured":"Pannala, V. R. et al. Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat. Sci. Rep. 8, 1364 (2018).","journal-title":"Sci. Rep."},{"key":"46245_CR47","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.taap.2019.04.001","volume":"372","author":"VR Pannala","year":"2019","unstructured":"Pannala, V. R. et al. Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats. Toxicol. Appl. Pharmacol. 372, 19\u201332 (2019).","journal-title":"Toxicol. Appl. Pharmacol."},{"key":"46245_CR48","doi-asserted-by":"publisher","first-page":"161","DOI":"10.3389\/fphys.2019.00161","volume":"10","author":"KC Vinnakota","year":"2019","unstructured":"Vinnakota, K. C. et al. Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat. Front. Physiol. 10, 161 (2019).","journal-title":"Front. Physiol."},{"key":"46245_CR49","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.arr.2011.12.006","volume":"11","author":"WR Swindell","year":"2012","unstructured":"Swindell, W. R. Dietary restriction in rats and mice: a meta-analysis and review of the evidence for genotype-dependent effects on lifespan. Ageing Res. Rev. 11, 254\u2013270 (2012).","journal-title":"Ageing Res. Rev."},{"key":"46245_CR50","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1007\/s00213-013-3300-9","volume":"231","author":"KL Keller","year":"2014","unstructured":"Keller, K. L., Vollrath-Smith, F. R., Jafari, M. & Ikemoto, S. Synergistic interaction between caloric restriction and amphetamine in food-unrelated approach behavior of rats. Psychopharmacology 231, 825\u2013840 (2014).","journal-title":"Psychopharmacology"},{"key":"46245_CR51","doi-asserted-by":"publisher","first-page":"5266","DOI":"10.1074\/jbc.M110.185991","volume":"286","author":"SC Kalhan","year":"2011","unstructured":"Kalhan, S. C. et al. Metabolic and genomic response to dietary isocaloric protein restriction in the rat. J. Biol. Chem. 286, 5266\u20135277 (2011).","journal-title":"J. Biol. Chem."},{"key":"46245_CR52","doi-asserted-by":"publisher","first-page":"e1000489","DOI":"10.1371\/journal.pcbi.1000489","volume":"5","author":"C Colijn","year":"2009","unstructured":"Colijn, C. et al. Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5, e1000489 (2009).","journal-title":"PLoS Comput. Biol."},{"key":"46245_CR53","unstructured":"Robinson, M. Projective Decomposition and Matrix Equivalence up to Scale. arXiv 12 (2019)."},{"key":"46245_CR54","doi-asserted-by":"publisher","unstructured":"Robinson, M. et al. Scale-invariant geometric data analysis (SIGDA) provides robust, detailed visualizations of human ancestry specific to individuals and populations, https:\/\/doi.org\/10.1101\/431585 (2018).","DOI":"10.1101\/431585"},{"key":"46245_CR55","doi-asserted-by":"crossref","unstructured":"Rui, L. Energy Metabolism in the Liver. In Comprehensive Physiology 177\u2013197 (2014).","DOI":"10.1002\/cphy.c130024"},{"key":"46245_CR56","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1172\/JCI200422422","volume":"114","author":"JD Browning","year":"2004","unstructured":"Browning, J. D. & Horton, J. D. Molecular mediators of hepatic steatosis and liver injury. J. Clin. Invest. 114, 147\u2013152 (2004).","journal-title":"J. Clin. Invest."},{"key":"46245_CR57","doi-asserted-by":"publisher","first-page":"D362","DOI":"10.1093\/nar\/gkw937","volume":"45","author":"D Szklarczyk","year":"2017","unstructured":"Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362\u2013D368 (2017).","journal-title":"Nucleic Acids Res."},{"key":"46245_CR58","doi-asserted-by":"publisher","first-page":"848","DOI":"10.15252\/msb.20156351","volume":"11","author":"J Snider","year":"2015","unstructured":"Snider, J. et al. Fundamentals of protein interaction network mapping. Mol. Syst. Biol. 11, 848 (2015).","journal-title":"Mol. Syst. Biol."},{"key":"46245_CR59","unstructured":"de Brouwer, A. P. M., Duley, J. A. & Christodoulou, J. Phosphoribosylpyrophosphate Synthetase Superactivity. In Gene Reviews (eds Adam, M. P. et al.) (University of Washington, Seattle, 2008)."},{"key":"46245_CR60","doi-asserted-by":"publisher","first-page":"127013","DOI":"10.1155\/2015\/127013","volume":"2015","author":"R Mittal","year":"2015","unstructured":"Mittal, R. et al. Association of PRPS1 Mutations with Disease Phenotypes. Dis. Markers 2015, 127013 (2015).","journal-title":"Dis. Markers"},{"key":"46245_CR61","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.molcel.2014.05.028","volume":"55","author":"A Saha","year":"2014","unstructured":"Saha, A. et al. Akt Phosphorylation and Regulation of Transketolase Is a Nodal Point for Amino Acid Control of Purine Synthesis. Mol. Cell 55, 264\u2013276 (2014).","journal-title":"Mol. Cell"},{"key":"46245_CR62","doi-asserted-by":"publisher","first-page":"3074","DOI":"10.1016\/j.febslet.2014.06.034","volume":"588","author":"E Bae","year":"2014","unstructured":"Bae, E., Kim, H. E., Koh, E. & Kim, K.-S. Phosphoglucomutase1 is necessary for sustained cell growth under repetitive glucose depletion. FEBS Lett. 588, 3074\u20133080 (2014).","journal-title":"FEBS Lett."},{"key":"46245_CR63","doi-asserted-by":"publisher","first-page":"D710","DOI":"10.1093\/nar\/gkv1157","volume":"44","author":"A Yates","year":"2016","unstructured":"Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710\u20136 (2016).","journal-title":"Nucleic Acids Res."},{"key":"46245_CR64","doi-asserted-by":"crossref","unstructured":"Database resources of the National Center for Biotechnology Information. Nucleic Acids Research 42, D7\u2013D17 (2014).","DOI":"10.1093\/nar\/gkt1146"},{"key":"46245_CR65","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","volume":"25","author":"S Altschul","year":"1997","unstructured":"Altschul, S. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 3389\u20133402 (1997).","journal-title":"Nucleic Acids Research"},{"key":"46245_CR66","doi-asserted-by":"publisher","DOI":"10.1186\/1751-0473-9-19","volume":"9","author":"A Porollo","year":"2014","unstructured":"Porollo, A. EC2KEGG: a command line tool for comparison of metabolic pathways. Source Code Biol. Med. 9, 6633 (2014).","journal-title":"Source Code Biol. Med."},{"key":"46245_CR67","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 28, 27\u201330 (2000).","journal-title":"Nucleic Acids Research"},{"key":"46245_CR68","doi-asserted-by":"publisher","unstructured":"Karp, P. D. et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief. Bioinform., https:\/\/doi.org\/10.1093\/bib\/bbx085 (2017).","DOI":"10.1093\/bib\/bbx085"},{"key":"46245_CR69","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-4-140","volume":"4","author":"MI Sigurdsson","year":"2010","unstructured":"Sigurdsson, M. I., Jamshidi, N., Steingrimsson, E., Thiele, I. & Palsson, B. \u00d8. A detailed genome-wide reconstruction of mouse metabolism based on human Recon 1. BMC Syst. Biol. 4, 140 (2010).","journal-title":"BMC Syst. Biol."},{"key":"46245_CR70","doi-asserted-by":"publisher","first-page":"D542","DOI":"10.1093\/nar\/gku1181","volume":"43","author":"V Chelliah","year":"2015","unstructured":"Chelliah, V. et al. BioModels: ten-year anniversary. Nucleic Acids Res. 43, D542\u20138 (2015).","journal-title":"Nucleic Acids Res."},{"key":"46245_CR71","doi-asserted-by":"publisher","unstructured":"Guide for the Care and Use of Laboratory Animals\u2013French Version, https:\/\/doi.org\/10.17226\/9852 (1996).","DOI":"10.17226\/9852"},{"key":"46245_CR72","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/nar\/30.1.207","volume":"30","author":"R Edgar","year":"2002","unstructured":"Edgar, R., Domrachev, M. & Lash, A. E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207\u2013210 (2002).","journal-title":"Nucleic Acids Res."},{"key":"46245_CR73","doi-asserted-by":"publisher","first-page":"299","DOI":"10.3389\/fphys.2012.00299","volume":"3","author":"AS Blazier","year":"2012","unstructured":"Blazier, A. S. & Papin, J. A. Integration of expression data in genome-scale metabolic network reconstructions. Front. Physiol. 3, 299 (2012).","journal-title":"Front. Physiol."},{"key":"46245_CR74","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.ymben.2003.09.002","volume":"5","author":"R Mahadevan","year":"2003","unstructured":"Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264\u2013276 (2003).","journal-title":"Metab. Eng."},{"key":"46245_CR75","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1038\/nprot.2011.308","volume":"6","author":"J Schellenberger","year":"2011","unstructured":"Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6, 1290\u20131307 (2011).","journal-title":"Nat. Protoc."},{"key":"46245_CR76","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1038\/s41596-018-0098-2","volume":"14","author":"L Heirendt","year":"2019","unstructured":"Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14, 639\u2013702 (2019).","journal-title":"Nat. Protoc."},{"key":"46245_CR77","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289\u2013300 (1995).","journal-title":"J. R. Stat. Soc. Series B Stat. Methodol."},{"key":"46245_CR78","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498\u20132504 (2003).","journal-title":"Genome Res."},{"key":"46245_CR79","doi-asserted-by":"publisher","first-page":"15866","DOI":"10.1074\/jbc.273.25.15866","volume":"273","author":"A Irie","year":"1998","unstructured":"Irie, A., Koyama, S., Kozutsumi, Y., Kawasaki, T. & Suzuki, A. The Molecular Basis for the Absence ofN-Glycolylneuraminic Acid in Humans. J. Biol. Chem. 273, 15866\u201315871 (1998).","journal-title":"J. Biol. Chem."},{"key":"46245_CR80","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1093\/glycob\/cwp017","volume":"19","author":"F Casals","year":"2009","unstructured":"Casals, F. et al. Human pseudogenes of the ABO family show a complex evolutionary dynamics and loss of function. Glycobiology 19, 583\u2013591 (2009).","journal-title":"Glycobiology"},{"key":"46245_CR81","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2010-11-3-r26","volume":"11","author":"ZD Zhang","year":"2010","unstructured":"Zhang, Z. D., Frankish, A., Hunt, T., Harrow, J. & Gerstein, M. Identification and analysis of unitary pseudogenes: historic and contemporary gene losses in humans and other primates. Genome Biol. 11, R26 (2010).","journal-title":"Genome Biol."},{"key":"46245_CR82","doi-asserted-by":"publisher","first-page":"19591","DOI":"10.1074\/jbc.M210364200","volume":"278","author":"LA Stevens","year":"2003","unstructured":"Stevens, L. A., Bourgeois, C., Bortell, R. & Moss, J. Regulatory role of arginine 204 in the catalytic activity of rat alloantigens ART2a and ART2b. J. Biol. Chem. 278, 19591\u201319596 (2003).","journal-title":"J. Biol. Chem."},{"key":"46245_CR83","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-6-32","volume":"6","author":"AJ Edgar","year":"2005","unstructured":"Edgar, A. J. Mice have a transcribed L-threonine aldolase\/GLY1 gene, but the human GLY1 gene is a non-processed pseudogene. BMC Genomics 6, 32 (2005).","journal-title":"BMC Genomics"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-46245-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-46245-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-46245-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,20]],"date-time":"2024-07-20T18:39:41Z","timestamp":1721500781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-019-46245-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,8]]},"references-count":83,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["46245"],"URL":"https:\/\/doi.org\/10.1038\/s41598-019-46245-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,8]]},"assertion":[{"value":"8 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"C.M.K., Y.D.H., S.T. are employed by Amgen Inc. and own stock in Amgen Inc. H.K.H. currently works at Genmab and owns stock in the company. The remaining authors declare no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"9807"}}