Metabolic Flux Analysis in Eukaryotic Cells pp 271-298 | Cite as
Software Supporting a Workflow of Quantitative Dynamic Flux Maps Estimation in Central Metabolism from SIRM Experimental Data
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Abstract
Stable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model. A kinetic model based on ordinary differential equations (ODEs) for isotopomers of metabolites of the corresponding biochemical network supports the final part of the analysis, which provides a dynamic flux map.
Key words
Mass spectrometry Stable isotope tracing Isotopolog distribution Central energy metabolism Metabolic fluxes Computational analysis Kinetic models of metabolism Stable isotope-resolved metabolomicsAbbreviations
- ala
Alanine
- cit
Citrate
- e4p
Erythrose-4-phosphate
- f6p
Fructose-6-phosphate
- FBA
Flux balance analysis
- fbp
Fructose bisphosphate
- fum
Fumarate
- g6p
Glucose-6-phosphate
- GC-MS
Gas chromatography–mass spectrometry
- glc
Glucose
- gln
Glutamine
- glu
Glutamate
- gly
Glycine
- kg
Alpha-ketoglutarate
- mal
Malate
- MS
Mass spectrometry
- NMR
Nuclear magnetic resonance
- oaa
Oxaloacetate
- ODEs
Ordinary differential equations
- ppp
Pentose phosphate pathways
- pro
Proline
- pyr
Pyruvate
- r5p
Ribose-5-phosphate
- s7p
Sedoheptulose-7- phosphate
- ser
Serine
- SIM
Selected ion monitoring
- SIRM
Stable isotope resolved metabolomics
- t3p
Triose-3-phosphate
- UHR-FTMS
Ultrahigh resolution Fourier transformed mass spectrometry
Notes
Acknowledgments
This work was supported by the European Commission (PhenoMeNal EC-654241), MINECO-European Commission FEDER funds—“Una manera de hacer Europa” (SAF2017-89673-R and SAF2015-70270-REDT), Instituto de Salud Carlos III and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, CB17/04/00023 and INB-Bioinformatics Platform, of the ISCIII, PT17/0009/0018), Agència de Gestió d’Ajuts Universitaris i de Recerca—Generalitat de Catalunya (2017SGR-1033), and Ministerio de Educación y Formación Profesional (FPU14-05992); and by the Redox Metabolism Shared Resource of the University of Kentucky Markey Cancer Center (P30CA177558). M.C. also acknowledges the prize “ICREA Academia” for the excellence in research, funded by ICREA foundation—Generalitat de Catalunya.
References
- 1.Ipata PL, Pesi R (2018) Metabolic interaction between purine nucleotide cycle and oxypurine cycle during skeletal muscle contraction of different intensities: a biochemical reappraisal. Metabolomics 14:42PubMedCrossRefGoogle Scholar
- 2.Crown SB, Antoniewicz MR (2013) Publishing 13C metabolic flux analysis studies: a review and future perspectives. Metab Eng 20:42–48PubMedPubMedCentralCrossRefGoogle Scholar
- 3.Feng X, Page L, Rubens J, Chircus L, Colletti P, Pakrasi HB, Tang YJ (2010) Bridging the gap between fluxomics and industrial biotechnology. J Biomed Biotechnol 2010:460717PubMedGoogle Scholar
- 4.Zamboni N, Sauer U (2009) Novel biological insights through metabolomics and 13 C-flux analysis. Curr Opin Microbiol 12:553–558PubMedCrossRefGoogle Scholar
- 5.Ahn WS, Antoniewicz MR (2013) Parallel labeling experiments with [1,2-(13)C]glucose and [U-(13)C]glutamine provide new insights into CHO cell metabolism. Metab Eng 15:34–47PubMedCrossRefGoogle Scholar
- 6.Grimble RF (2001) Stress proteins in disease: metabolism on a knife edge. Clin Nutr 20:469–476PubMedCrossRefGoogle Scholar
- 7.Jin L, Zhou Y (2019) Crucial role of the pentose phosphate pathway in malignant tumors. Oncol Lett 17:4213–4221PubMedPubMedCentralGoogle Scholar
- 8.King BC, Blom AM (2017) Non-traditional roles of complement in type 2 diabetes: metabolism, insulin secretion and homeostasis. Mol Immunol 84:34–42PubMedCrossRefGoogle Scholar
- 9.Fontané L, Benaiges D, Goday A, Llauradó G, Pedro-Botet J (2018) Influence of the microbiota and probiotics in obesity. Clin Investig Arterioscler 30:271–279PubMedGoogle Scholar
- 10.Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674PubMedPubMedCentralCrossRefGoogle Scholar
- 11.Badur MG, Metallo CM (2018) Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metab Eng 45:95–108PubMedCrossRefGoogle Scholar
- 12.Fan TW, Lorkiewicz PK, Sellers K, Moseley HN, Higashi RM, Lane AN (2012) Stable isotope-resolved metabolomics and applications for drug development. Pharmacol Ther 133:366–391PubMedCrossRefGoogle Scholar
- 13.Jayaraman A, Kumar P, Marin S, de Atauri P, Mateo F, Thomson TM, Centelles JJ, Graham SF, Cascante M (2018) Untargeted metabolomics reveals distinct metabolic reprogramming in endothelial cells co-cultured with CSC and non-CSC prostate cancer cell subpopulations. PLoS One 13:e0192175PubMedPubMedCentralCrossRefGoogle Scholar
- 14.Tarrado-Castellarnau M, de Atauri P, Tarragó-Celada J, Perarnau J, Yuneva M, Thomson TM, Cascante M (2017) De novo MYC addiction as an adaptive response of cancer cells to CDK4/6 inhibition. Mol Syst Biol 13:940PubMedPubMedCentralCrossRefGoogle Scholar
- 15.Marín de Mas I, Marín S, Pachón G, Rodríguez-Prados JC, Vizán P, Centelles JJ, Tauler R, Azqueta A, Selivanov V, López de Ceraín A, Cascante M (2017) Unveiling the metabolic changes on muscle cell metabolism underlying p-phenylenediamine toxicity. Front Mol Biosci 4:8PubMedPubMedCentralCrossRefGoogle Scholar
- 16.Tarrado-Castellarnau M, de Atauri P, Cascante M (2016) Oncogenic regulation of tumor metabolic reprogramming. Oncotarget 7:62726–62753PubMedPubMedCentralCrossRefGoogle Scholar
- 17.Diaz-Moralli S, Aguilar E, Marin S, Coy JF, Dewerchin M, Antoniewicz MR, Meca-Cortés O, Notebaert L, Ghesquière B, Eelen G, Thomson TM, Carmeliet P, Cascante M (2016) A key role for transketolase-like 1 in tumor metabolic reprogramming. Oncotarget 7:51875–51897PubMedPubMedCentralCrossRefGoogle Scholar
- 18.Aguilar E, Marin de Mas I, Zodda E, Marin S, Morrish F, Selivanov V, Meca-Cortés Ó, Delowar H, Pons M, Izquierdo I, Celià-Terrassa T, de Atauri P, Centelles JJ, Hockenbery D, Thomson TM, Cascante M (2016) Metabolic reprogramming and dependencies associated with epithelial cancer stem cells independent of the epithelial-mesenchymal transition program. Stem Cells 34:1163–1176PubMedPubMedCentralCrossRefGoogle Scholar
- 19.Jang C, Chen L, Rabinowitz JD (2018) Metabolomics and Isotope Tracing. Cell 173:822–837PubMedPubMedCentralCrossRefGoogle Scholar
- 20.Lane AN, Fan TW, Bousamra M II, Higashi RM, Yan J, Miller DM (2011) Stable isotope-resolved metabolomics (SIRM) in cancer research with clinical application to nonsmall cell lung cancer. OMICS 15:173–182PubMedPubMedCentralCrossRefGoogle Scholar
- 21.Costa C, Maraschin M, Rocha M (2016) An R package for the integrated analysis of metabolomics and spectral data. Comput Methods Prog Biomed 129:117–124CrossRefGoogle Scholar
- 22.Deng K, Zhang F, Tan Q, Huang Y, Song W, Rong Z, Zhu ZJ, Li Z, Li K (2019) WaveICA: a novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis. Anal Chim Acta 1061:60–69PubMedCrossRefGoogle Scholar
- 23.Tautenhahn R, Patti GJ, Kalisiak E, Miyamoto T, Schmidt M, Lo FY, McBee J, Baliga NS, Siuzdak G (2011) metaXCMS: second-order analysis of untargeted metabolomics data. Anal Chem 83:696–700PubMedCrossRefGoogle Scholar
- 24.Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84:5035–5039PubMedPubMedCentralCrossRefGoogle Scholar
- 25.Brauman JI (1966) Least squares analysis and simplification of multi-isotope mass spectra. Anal Chem 38:607–610CrossRefGoogle Scholar
- 26.Katz J (1989) Studies of glycogen synthesis and the Krebs cycle by mass isotopomer analysis with [U-13C]glucose in rats. J Biol Chem 264:12994–13004PubMedGoogle Scholar
- 27.Fernandez CA, Des Rosiers C, Previs SF, David F, Brunengraber H (1996) Correction of 13C mass isotopomer distributions for natural stable isotope abundance. J Mass Spectrom 31:255–262PubMedCrossRefGoogle Scholar
- 28.Lee WN, Byerley LO, Bergner EA, Edmond J (1991) Mass isotopomer analysis: theoretical and practical considerations. Biol Mass Spectrom 20:451–458PubMedCrossRefGoogle Scholar
- 29.van Winden WA, Wittmann C, Heinzle E, Heijnen JJ (2002) Correcting mass isotopomer distributions for naturally occurring isotopes. Biotechnol Bioeng 80:477–479PubMedCrossRefGoogle Scholar
- 30.Millard P, Letisse F, Sokol S, Portais JC (2012) IsoCor: correcting MS data in isotope labeling experiments. Bioinformatics 28:1294–1296CrossRefGoogle Scholar
- 31.Selivanov VA, Benito A, Miranda A, Aguilar E, Polat IH, Centelles JJ et al (2017) MIDcor, an R-program for deciphering mass interferences in mass spectra of metabolites enriched in stable isotopes. BMC Bioinformatics 18:88PubMedPubMedCentralCrossRefGoogle Scholar
- 32.Zamboni N, Fischer E, Sauer U (2005) FiatFlux-a software for metabolic flux analysis from 13C-glucose experiments. BMC Bioinformatics 6:209PubMedPubMedCentralCrossRefGoogle Scholar
- 33.Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, Palsson BØ (2010) Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab Eng 12:173–186PubMedCrossRefGoogle Scholar
- 34.Blazeck J, Alper H (2010) Systems metabolic engineering: genome-scale models and beyond. Biotechnol J 5:647–659PubMedPubMedCentralCrossRefGoogle Scholar
- 35.Chiewchankaset P, Siriwat W, Suksangpanomrung M, Boonseng O, Meechai A, al TM (2019) Understanding carbon utilization routes between high and low starch-producing cultivars of cassava through flux balance analysis. Sci Rep 9:2964PubMedPubMedCentralCrossRefGoogle Scholar
- 36.Foguet C, Marin S, Selivanov VA, Fanchon E, Lee WN, Guinovart JJ et al (2016) HepatoDyn: a dynamic model of hepatocyte metabolism that integrates 13C isotopomer data. PLoS Comput Biol 12:e1004899PubMedPubMedCentralCrossRefGoogle Scholar
- 37.Press WH, Flannery BP, Teukolsky SA, Vetterling WT (2002) Numerical recipes in C: the art of scientific computing. Cambridge University Press, New York, NYGoogle Scholar
- 38.de Mas IM, Selivanov VA, Marin S, Roca J, Orešič M, Agius L, Cascante M (2011) Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions. BMC Syst Biol 5:175PubMedCrossRefPubMedCentralGoogle Scholar
- 39.Higashi RM, Fan TW, Lorkiewicz PK, Moseley HN, Lane AN (2014) Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS. Methods Mol Biol 1198:147–167PubMedPubMedCentralCrossRefGoogle Scholar
- 40.Fan TW, Lane AN, Higashi RM, Yan J (2011) Stable isotope resolved metabolomics of lung cancer in a SCID mouse model. Metabolomics 7:257–269PubMedPubMedCentralCrossRefGoogle Scholar
- 41.Nöh K, Grönke K, Luo B, Takors R, Oldiges M, Wiechert W (2007) Metabolic flux analysis at ultra short time scale: isotopically non-stationary 13C labeling experiments. J Biotechnol 129:249–267PubMedCrossRefGoogle Scholar
- 42.Farbehi N, Patrick R, Dorison A, Xaymardan M, Janbandhu V, Wystub-Lis K et al (2019) Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. elife 8:e43882PubMedPubMedCentralCrossRefGoogle Scholar
- 43.Zhao X, Noack S, Wiechert W, Lieres EV (2017 Dec) Dynamic flux balance analysis with nonlinear objective function. J Math Biol 75(6–7):1487–1515PubMedCrossRefGoogle Scholar
- 44.Selivanov VA, Marin S, Lee PW, Cascante M (2006) Software for dynamic analysis of tracer-based metabolomic data: estimation of metabolic fluxes and their statistical analysis. Bioinformatics 22:2806–2812PubMedCrossRefGoogle Scholar
- 45.Selivanov VA, Meshalkina LE, Solovjeva ON, Kuchel PW, Ramos-Montoya A, Kochetov GA et al (2005) Rapid simulation and analysis of isotopomer distributions using constraints based on enzyme mechanisms: an example from HT29 cancer cells. Bioinformatics 21:3558–3564PubMedCrossRefGoogle Scholar
- 46.Selivanov VA, Puigjaner J, Sillero A, Centelles JJ, Ramos-Montoya A, Lee PW, Cascante M (2004) An optimized algorithm for flux estimation from isotopomer distribution in glucose metabolites. Bioinformatics 20:3387–3397PubMedCrossRefGoogle Scholar
- 47.Henze AT, Mazzone M (2016) The impact of hypoxia on tumor-associated macrophages. J Clin Invest 126:3672–3679PubMedPubMedCentralCrossRefGoogle Scholar
- 48.Kuang DM, Zhao Q, Peng C, Xu J, Zhang JP, Wu C, Zheng L (2009) Activated monocytes in peritumoral stroma of hepatocellular carcinoma foster immune privilege and disease progression through PD-L1. J Exp Med 206:1327–1337PubMedPubMedCentralCrossRefGoogle Scholar
- 49.Ganeshan K, Chawla A (2014) Metabolic regulation of immune responses. Annu Rev Immunol 32:609–634PubMedPubMedCentralCrossRefGoogle Scholar
- 50.Qian BZ, Pollard JW (2010) Macrophage diversity enhances tumor progression and metastasis. Cell 141:39–51PubMedPubMedCentralCrossRefGoogle Scholar
- 51.Zhu Y, Herndon JM, Sojka DK, Kim KW, Knolhoff BL, Zuo C et al (2017) Tissue-resident macrophages in pancreatic ductal adenocarcinoma originate from embryonic hematopoiesis and promote tumor progression. Immunity 47:323–338PubMedPubMedCentralCrossRefGoogle Scholar