Systems metabolomics: from metabolomic snapshots to design principles.

Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.

[1]  F. Hu,et al.  Use of Metabolomics in Improving Assessment of Dietary Intake. , 2018, Clinical chemistry.

[2]  C. V. von Arnim,et al.  NADH Autofluorescence—A Marker on its Way to Boost Bioenergetic Research , 2018, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[3]  Z. Nikoloski,et al.  Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment. , 2015, The Plant journal : for cell and molecular biology.

[4]  Nicolas Fontaine,et al.  Flux prediction using artificial neural network (ANN) for the upper part of glycolysis , 2019, PloS one.

[5]  Avlant Nilsson,et al.  Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism , 2018, Nature Biotechnology.

[6]  Christian M. Metallo,et al.  Quantifying Intermediary Metabolism and Lipogenesis in Cultured Mammalian Cells Using Stable Isotope Tracing and Mass Spectrometry. , 2019, Methods in molecular biology.

[7]  E. Giannoni,et al.  5-Fluorouracil resistant colon cancer cells are addicted to OXPHOS to survive and enhance stem-like traits , 2015, Oncotarget.

[8]  Zoran Nikoloski,et al.  iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models , 2016, Bioinform..

[9]  M. Terris,et al.  Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer , 2019, Clinical Cancer Research.

[10]  Nicola Zamboni,et al.  Defining the metabolome: size, flux, and regulation. , 2015, Molecular cell.

[11]  C. Thompson,et al.  Metabolic regulation of cell growth and proliferation , 2019, Nature Reviews Molecular Cell Biology.

[12]  Mingrui Liu,et al.  IntLIM: integration using linear models of metabolomics and gene expression data , 2018, BMC Bioinformatics.

[13]  A. Stegh,et al.  Cancer-associated mutation and beyond: The emerging biology of isocitrate dehydrogenases in human disease , 2019, Science Advances.

[14]  Ronan M. T. Fleming,et al.  A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines , 2017, PLoS Comput. Biol..

[15]  Davide Castelvecchi,et al.  Can we open the black box of AI? , 2016, Nature.

[16]  J. Loscalzo,et al.  Analysis of redox landscapes and dynamics in living cells and in vivo using genetically encoded fluorescent sensors , 2018, Nature Protocols.

[18]  B. Palsson,et al.  Large-scale in silico modeling of metabolic interactions between cell types in the human brain , 2010, Nature Biotechnology.

[19]  J. Locasale,et al.  Metabolomics reveals intratumor heterogeneity – Implications for precision medicine , 2017, EBioMedicine.

[20]  S. Kales,et al.  Metabolomics and Microbiomes as Potential Tools to Evaluate the Effects of the Mediterranean Diet , 2019, Nutrients.

[21]  Aedín C. Culhane,et al.  MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data , 2016, Molecular & Cellular Proteomics.

[22]  Desmond S. Lun,et al.  Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production , 2009, PLoS Comput. Biol..

[23]  O. Warburg,et al.  THE METABOLISM OF TUMORS IN THE BODY , 1927, The Journal of general physiology.

[24]  K. Christopher.  Nutritional metabolomics in critical illness , 2017, Current opinion in clinical nutrition and metabolic care.

[25]  S. Fendt.  Metabolic vulnerabilities of metastasizing cancer cells , 2019, BMC Biology.

[26]  Joachim Kopka,et al.  Discovery of food identity markers by metabolomics and machine learning technology , 2019, Scientific Reports.

[27]  F. Gatto,et al.  Pan-cancer analysis of the metabolic reaction network. , 2020, Metabolic engineering.

[28]  Oveis Jamialahmadi,et al.  A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism , 2019, PLoS Comput. Biol..

[29]  S. Lee,et al.  Metabolomics for industrial fermentation , 2018, Bioprocess and Biosystems Engineering.

[30]  V. Mootha,et al.  Metabolite Profiling Identifies a Key Role for Glycine in Rapid Cancer Cell Proliferation , 2012, Science.

[31]  K. D. Duncan,et al.  Advances in mass spectrometry based single-cell metabolomics. , 2019, The Analyst.

[32]  J. Loscalzo,et al.  Genetically encoded fluorescent sensors reveal dynamic regulation of NADPH metabolism , 2017, Nature Methods.

[33]  J. Knutson,et al.  MitoRACE: evaluating mitochondrial function in vivo and in single cells with subcellular resolution using multiphoton NADH autofluorescence , 2019, The Journal of physiology.

[34]  V. Hatzimanikatis,et al.  Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models , 2018, bioRxiv.

[35]  Christoph B. Messner,et al.  Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts , 2018, Cell systems.

[36]  J. Nielsen,et al.  Uncovering transcriptional regulation of metabolism by using metabolic network topology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Giancarlo Mauri,et al.  Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power , 2018, J. Biomed. Informatics.

[38]  A. Intlekofer,et al.  Metabolic signatures of cancer cells and stem cells , 2019, Nature Metabolism.

[39]  S. Villas-Bôas,et al.  Pre-fermentative supplementation of fatty acids alters the metabolic activity of wine yeasts. , 2019, Food research international.

[40]  Giancarlo Mauri,et al.  Integration of single-cell RNA-seq data into population models to characterize cancer metabolism , 2019, PLoS Comput. Biol..

[41]  Maria Pires Pacheco,et al.  Identifying and targeting cancer-specific metabolism with network-based drug target prediction , 2019, EBioMedicine.

[42]  Z. Pirtošek,et al.  Correlations of Neuropsychological and Metabolic Brain Changes in Parkinson's Disease and Other α-Synucleinopathies , 2019, Front. Neurol..

[43]  Jeffrey D Varner,et al.  Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors , 2018, PLoS Comput. Biol..

[44]  Giancarlo Mauri,et al.  Single-cell Digital Twins for Cancer Preclinical Investigation. , 2020, Methods in molecular biology.

[45]  A. Catai,et al.  Metabolomics-Based Studies Assessing Exercise-Induced Alterations of the Human Metabolome: A Systematic Review , 2019, Metabolites.

[46]  Anne Richelle,et al.  A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models. , 2017, Cell systems.

[47]  F. Yin,et al.  Cellular Specificity and Inter-cellular Coordination in the Brain Bioenergetic System: Implications for Aging and Neurodegeneration , 2020, Frontiers in Physiology.

[48]  David Wishart,et al.  Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community , 2019, Metabolites.

[49]  Daniel Machado,et al.  Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism , 2014, PLoS Comput. Biol..

[50]  Yuri Pirola,et al.  Protein Kinase A Activation Promotes Cancer Cell Resistance to Glucose Starvation and Anoikis , 2016, PLoS genetics.

[51]  Robert J Gillies,et al.  Metabolic Profiling of healthy and cancerous tissues in 2D and 3D , 2017, Scientific Reports.

[52]  Zoran Nikoloski,et al.  Integration of metabolomics data into metabolic networks , 2015, Front. Plant Sci..

[53]  Eytan Ruppin,et al.  iMAT: an integrative metabolic analysis tool , 2010, Bioinform..

[54]  Xin Li,et al.  Spatially resolved metabolomics to discover tumor-associated metabolic alterations , 2018, Proceedings of the National Academy of Sciences.

[55]  W. D. De Vos,et al.  Cellular Redox Profiling Using High-content Microscopy. , 2017, Journal of visualized experiments : JoVE.

[56]  G. Krumschnabel,et al.  High-Resolution Respirometry for Simultaneous Measurement of Oxygen and Hydrogen Peroxide Fluxes in Permeabilized Cells, Tissue Homogenate and Isolated Mitochondria , 2015, Biomolecules.

[57]  J. Rabinowitz,et al.  Metabolomics and Isotope Tracing , 2018, Cell.

[58]  R. Deberardinis,et al.  Applications of metabolomics to study cancer metabolism. , 2018, Biochimica et biophysica acta. Reviews on cancer.

[59]  R. Sharan,et al.  Metabolic Network Prediction of Drug Side Effects. , 2016, Cell systems.

[60]  Rui Chen,et al.  Promise of personalized omics to precision medicine , 2013, Wiley interdisciplinary reviews. Systems biology and medicine.

[61]  Manuel Serrano,et al.  The Hallmarks of Aging , 2013, Cell.

[62]  E. Ruppin,et al.  The landscape of tiered regulation of breast cancer cell metabolism , 2019, Scientific Reports.

[63]  C. Frezza,et al.  Oncometabolites: Unconventional triggers of oncogenic signalling cascades , 2016, Free radical biology & medicine.

[64]  Claudio Angione,et al.  Machine and deep learning meet genome-scale metabolic modeling , 2019, PLoS Comput. Biol..

[65]  M. Ferracin,et al.  Reprogramming of Amino Acid Transporters to Support Aspartate and Glutamate Dependency Sustains Endocrine Resistance in Breast Cancer , 2019, Cell reports.

[66]  Adrian J. Verster,et al.  High-Content Screening for Quantitative Cell Biology. , 2016, Trends in cell biology.

[67]  Zak Costello,et al.  A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data , 2018, npj Systems Biology and Applications.

[68]  Giancarlo Mauri,et al.  A metabolic core model elucidates how enhanced utilization of glucose and glutamine, with enhanced glutamine-dependent lactate production, promotes cancer cell growth: The WarburQ effect , 2017, PLoS Comput. Biol..

[69]  Jens Nielsen,et al.  Systems Biology of Metabolism: A Driver for Developing Personalized and Precision Medicine. , 2017, Cell metabolism.

[70]  Bethany J Wolf,et al.  Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning. , 2015, Biochimica et biophysica acta.

[71]  J. Bunch,et al.  Mass Spectrometry Imaging in Oncology Drug Discovery , 2017, Advances in cancer research.

[72]  M. Zampieri,et al.  Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism , 2019, Nature Communications.

[73]  Ashwinikumar Kulkarni,et al.  Beyond bulk: a review of single cell transcriptomics methodologies and applications. , 2019, Current opinion in biotechnology.

[74]  J. Locasale,et al.  Metabolomics: A Primer. , 2017, Trends in biochemical sciences.

[75]  R. Shamir,et al.  Multi-omic and multi-view clustering algorithms: review and cancer benchmark , 2018, bioRxiv.

[76]  E. Marinari,et al.  Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle , 2015, Scientific Reports.

[77]  Kirill Veselkov,et al.  Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer , 2017, Chemical science.

[78]  Jason H. Yang,et al.  A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action , 2019, Cell.

[79]  Rudi Balling,et al.  The hallmarks of Parkinson's disease , 2013, The FEBS journal.

[80]  Natapol Pornputtapong,et al.  Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT , 2012, PLoS Comput. Biol..

[81]  M. Tosun,et al.  Imaging Mass Spectrometry: A New Tool to Assess Molecular Underpinnings of Neurodegeneration , 2019, Metabolites.

[82]  S. Lee,et al.  Systems strategies for developing industrial microbial strains , 2015, Nature Biotechnology.

[83]  Daniele Ramazzotti,et al.  Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival , 2018, Nature Communications.

[84]  T. Sauter,et al.  Constraint Based Modeling Going Multicellular , 2016, Front. Mol. Biosci..

[85]  Giulio Caravagna,et al.  The Influence of Nutrients Diffusion on a Metabolism-driven Model of a Multi-cellular System , 2020, Fundam. Informaticae.

[86]  O. Reséndis-Antonio,et al.  Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies , 2016, Front. Physiol..

[87]  Eytan Ruppin,et al.  A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration , 2014, Molecular systems biology.

[88]  Soumeya Bekri,et al.  Paving the Way to Precision Nutrition Through Metabolomics , 2019, Front. Nutr..

[89]  Farhana R Pinu,et al.  Translational Metabolomics: Current Challenges and Future Opportunities , 2019, Metabolites.

[90]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..