Systems metabolomics: from metabolomic snapshots to design principles

Zamboni, 2015, Defining the metabolome: size, flux, and regulation, Mol Cell, 58, 699, 10.1016/j.molcel.2015.04.021 Cordes, 2019, Quantifying intermediary metabolism and lipogenesis in cultured mammalian cells using stable isotope tracing and mass spectrometry, Methods Mol Biol, 1978, 219, 10.1007/978-1-4939-9236-2_14 Nielsen, 2017, Systems biology of metabolism: a driver for developing personalized and precision medicine, Cell Metab, 25, 572, 10.1016/j.cmet.2017.02.002 Liu, 2017, Metabolomics: a primer, Trends Biochem Sci, 42, 274, 10.1016/j.tibs.2017.01.004 Jang, 2018, Metabolomics and isotope tracing, Cell, 173, 822, 10.1016/j.cell.2018.03.055 Liu, 2017, Metabolomics reveals intratumor heterogeneity - implications for precision medicine, EBioMedicine, 19, 4, 10.1016/j.ebiom.2017.04.030 Pinu, 2019, Systems biology and multi-omics integration: viewpoints from the metabolomics research community, Metabolites, 9, 10.3390/metabo9040076 Pinu, 2019, Translational metabolomics: current challenges and future opportunities, Metabolites, 9, 10.3390/metabo9060108 Pinu, 2019, Pre-fermentative supplementation of fatty acids alters the metabolic activity of wine yeasts, Food Res Int, 121, 835, 10.1016/j.foodres.2019.01.005 Lee, 2015, Systems strategies for developing industrial microbial strains, Nat Biotechnol, 33, 1061, 10.1038/nbt.3365 Choi, 2018, Metabolomics for industrial fermentation, Bioprocess Biosyst Eng, 41, 1073, 10.1007/s00449-018-1967-3 Guasch-Ferré, 2018, Use of metabolomics in improving assessment of dietary intake, Clin Chem, 64, 82, 10.1373/clinchem.2017.272344 Sakaguchi, 2019, Metabolomics-based studies assessing exercise-induced alterations of the human metabolome: a systematic review, Metabolites, 9, 10.3390/metabo9080164 Jin, 2019, Metabolomics and microbiomes as potential tools to evaluate the effects of the Mediterranean diet, Nutrients, 11, 10.3390/nu11010207 Tebani, 2019, Paving the way to precision nutrition through metabolomics, Front Nutr, 6, 41, 10.3389/fnut.2019.00041 Christopher, 2018, Nutritional metabolomics in critical illness, Curr Opin Clin Nutr Metab Care, 21, 121, 10.1097/MCO.0000000000000451 Erban, 2019, Discovery of food identity markers by metabolomics and machine learning technology, Sci Rep, 9, 10.1038/s41598-019-46113-y López-Otín, 2013, The hallmarks of aging, Cell, 153, 1194, 10.1016/j.cell.2013.05.039 Qi, 2019, Cellular specificity and inter-cellular coordination in the brain bioenergetic system: implications for aging and neurodegeneration, Front Physiol, 10, 1531, 10.3389/fphys.2019.01531 Trošt, 2019, Correlations of neuropsychological and metabolic brain changes in Parkinson’s disease and other α-synucleinopathies, Front Neurol, 10, 1204, 10.3389/fneur.2019.01204 Antony, 2013, The hallmarks of Parkinson’s disease, FEBS J, 280, 5981, 10.1111/febs.12335 Kaushik, 2018, Applications of metabolomics to study cancer metabolism, Biochim Biophys Acta Rev Cancer, 1870, 2, 10.1016/j.bbcan.2018.04.009 Zhu, 2019, Metabolic regulation of cell growth and proliferation, Nat Rev Mol Cell Biol, 20, 436, 10.1038/s41580-019-0123-5 Warburg, 1927, The metabolism of tumors in the body, J Gen Physiol, 8, 519, 10.1085/jgp.8.6.519 Sciacovelli, 2016, Oncometabolites: unconventional triggers of oncogenic signalling cascades, Free Radic Biol Med, 100, 175, 10.1016/j.freeradbiomed.2016.04.025 Tommasini-Ghelfi, 2019, Cancer-associated mutation and beyond: the emerging biology of isocitrate dehydrogenases in human disease, Sci Adv, 5, 10.1126/sciadv.aaw4543 Makrecka-Kuka, 2015, High-resolution respirometry for simultaneous measurement of oxygen and hydrogen peroxide fluxes in permeabilized cells, tissue homogenate and isolated mitochondria, Biomolecules, 5, 1319, 10.3390/biom5031319 Bacci, 2019, Reprogramming of amino acid transporters to support aspartate and glutamate dependency sustains endocrine resistance in breast cancer, Cell Rep, 28, 104, 10.1016/j.celrep.2019.06.010 Denise, 2015, 5-Fluorouracil resistant colon cancer cells are addicted to OXPHOS to survive and enhance stem-like traits, Oncotarget, 6, 41706, 10.18632/oncotarget.5991 Russell, 2017, Metabolic profiling of healthy and cancerous tissues in 2D and 3D, Sci Rep, 7, 10.1038/s41598-017-15325-5 Palorini, 2016, Protein kinase A activation promotes cancer cell resistance to glucose starvation and anoikis, PLoS Genet, 12, 10.1371/journal.pgen.1005931 Ortmayr, 2019, Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism, Nat Commun, 10, 10.1038/s41467-019-09695-9 Intlekofer, 2019, Metabolic signatures of cancer cells and stem cells, Nat Metab, 1, 177, 10.1038/s42255-019-0032-0 Chen, 2013, Promise of personalized omics to precision medicine, Wiley Interdiscip Rev Syst Biol Med, 5, 73, 10.1002/wsbm.1198 Editorial, 2019, Why the metabolism field risks missing out on the AI revolution, Nat Metab, 2 Zelezniak, 2018, Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts, Cell Syst, 7, 269, 10.1016/j.cels.2018.08.001 Costello, 2018, A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data, NPJ Syst Biol Appl, 4, 19, 10.1038/s41540-018-0054-3 Ajjolli Nagaraja, 2019, Flux prediction using artificial neural network (ANN) for the upper part of glycolysis, PLoS One, 14, 10.1371/journal.pone.0216178 Castelvecchi, 2016, Can we open the black box of AI?, Nature, 538, 20, 10.1038/538020a Brunk, 2018, Recon3D enables a three-dimensional view of gene variation in human metabolism, Nat Biotechnol, 36, 272, 10.1038/nbt.4072 Damiani, 2017, 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, PLoS Comput Biol, 13, 10.1371/journal.pcbi.1005758 Jain, 2012, Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation, Science, 336, 1040, 10.1126/science.1218595 Siddiqui, 2018, IntLIM: integration using linear models of metabolomics and gene expression data, BMC Bioinformatics, 19, 81, 10.1186/s12859-018-2085-6 Diener, 2016, Personalized prediction of proliferation rates and metabolic liabilities in cancer biopsies, Front Physiol, 7, 644, 10.3389/fphys.2016.00644 Inglese, 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chem Sci, 8, 3500, 10.1039/C6SC03738K Aurich, 2017, A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines, PLoS Comput Biol, 13, 10.1371/journal.pcbi.1005698 Patil, 2005, Uncovering transcriptional regulation of metabolism by using metabolic network topology, Proc Natl Acad Sci U S A, 102, 2685, 10.1073/pnas.0406811102 Graudenzi, 2018, Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power, J Biomed Inform, 87, 37, 10.1016/j.jbi.2018.09.010 Damiani, 2019, Integration of single-cell RNA-seq data into population models to characterize cancer metabolism, PLoS Comput Biol, 15, 10.1371/journal.pcbi.1006733 Colijn, 2009, Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production, PLoS Comput Biol, 5, 10.1371/journal.pcbi.1000489 Becker, 2008, Context-specific metabolic networks are consistent with experiments, PLoS Comput Biol, 4, 10.1371/journal.pcbi.1000082 Agren, 2012, Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT, PLoS Comput Biol, 8, 10.1371/journal.pcbi.1002518 Zur, 2010, iMAT: an integrative metabolic analysis tool, Bioinformatics, 26, 3140, 10.1093/bioinformatics/btq602 Jamialahmadi, 2019, A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism, PLoS Comput Biol, 15, 10.1371/journal.pcbi.1006936 Opdam, 2017, A systematic evaluation of methods for tailoring genome-scale metabolic models, Cell Syst, 4, 318, 10.1016/j.cels.2017.01.010 Machado, 2014, Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism, PLoS Comput Biol, 10, 10.1371/journal.pcbi.1003580 Kleessen, 2015, Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment, Plant J, 81, 822, 10.1111/tpj.12763 Töpfer, 2015, Integration of metabolomics data into metabolic networks, Front Plant Sci, 6, 49 Sajitz-Hermstein, 2016, iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models, Bioinformatics, 32, i755, 10.1093/bioinformatics/btw465 Pandey, 2019, Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models, PLoS Comput Biol, 15, 10.1371/journal.pcbi.1007036 Pacheco, 2019, Towards the network-based prediction of repurposed drugs using patient-specific metabolic models, EBioMedicine, 43, 26, 10.1016/j.ebiom.2019.04.017 Pacheco, 2019, Identifying and targeting cancer-specific metabolism with network-based drug target prediction, EBioMedicine, 43, 98, 10.1016/j.ebiom.2019.04.046 Zampieri, 2019, Machine and deep learning meet genome-scale metabolic modeling, PLoS Comput Biol, 15, 10.1371/journal.pcbi.1007084 Gatto, 2019, Pan-cancer analysis of the metabolic reaction network, Metab Eng, 57, 51, 10.1016/j.ymben.2019.09.006 Shaked, 2016, Metabolic network prediction of drug side effects, Cell Syst, 2, 209, 10.1016/j.cels.2016.03.001 Yang, 2019, A white-box machine learning approach for revealing antibiotic mechanisms of action, Cell, 177, 1649, 10.1016/j.cell.2019.04.016 Vantaku, 2019, Multi-omics integration analysis robustly predicts high-grade patient survival and identifies CPT1B effect on fatty acid metabolism in bladder cancer, Clin Cancer Res, 25, 3689, 10.1158/1078-0432.CCR-18-1515 Ramazzotti, 2018, Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival, Nat Commun, 9, 10.1038/s41467-018-06921-8 Rappoport, 2018, Multi-omic and multi-view clustering algorithms: review and cancer benchmark, Nucleic Acids Res, 46, 10546, 10.1093/nar/gky889 Meng, 2019, MOGSA: integrative single sample gene-set analysis of multiple omics data, Mol Cell Proteomics, 18, S153, 10.1074/mcp.TIR118.001251 Katzir, 2019, The landscape of tiered regulation of breast cancer cell metabolism, Sci Rep, 9, 10.1038/s41598-019-54221-y Capuani, 2015, Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle, Sci Rep, 5, 10.1038/srep11880 Lewis, 2010, Large-scale in silico modeling of metabolic interactions between cell types in the human brain, Nat Biotechnol, 28, 1279, 10.1038/nbt.1711 Martins Conde PoR, 2016, Constraint based modeling going multicellular, Front Mol Biosci, 3, 3 Duncan, 2019, Advances in mass spectrometry based single-cell metabolomics, Analyst, 144, 782, 10.1039/C8AN01581C Sun, 2019, Spatially resolved metabolomics to discover tumor-associated metabolic alterations, Proc Natl Acad Sci U S A, 116, 52, 10.1073/pnas.1808950116 Goodwin, 2017, Mass spectrometry imaging in oncology drug discovery, Adv Cancer Res, 134, 133, 10.1016/bs.acr.2016.11.005 Chen, 2019, Imaging mass spectrometry: a new tool to assess molecular underpinnings of neurodegeneration, Metabolites, 9, 10.3390/metabo9070135 Mattiazzi Usaj, 2016, High-content screening for quantitative cell biology, Trends Cell Biol, 26, 598, 10.1016/j.tcb.2016.03.008 Leonard, 2015, Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning, Biochim Biophys Acta, 1853, 348, 10.1016/j.bbamcr.2014.11.002 Sieprath, 2017, Cellular redox profiling using high-content microscopy, J Vis Exp, 123, e55449 Tao, 2017, Genetically encoded fluorescent sensors reveal dynamic regulation of NADPH metabolism, Nat Methods, 14, 720, 10.1038/nmeth.4306 Zou, 2018, Analysis of redox landscapes and dynamics in living cells and in vivo using genetically encoded fluorescent sensors, Nat Protoc, 13, 2362, 10.1038/s41596-018-0042-5 Schaefer, 2019, NADH autofluorescence-a marker on its way to boost bioenergetic research, Cytometry A, 95, 34, 10.1002/cyto.a.23597 Willingham, 2019, MitoRACE: evaluating mitochondrial function in vivo and in single cells with subcellular resolution using multiphoton NADH autofluorescence, J Physiol, 597, 5411, 10.1113/JP278611 Kulkarni, 2019, Beyond bulk: a review of single cell transcriptomics methodologies and applications, Curr Opin Biotechnol, 58, 129, 10.1016/j.copbio.2019.03.001 Maspero, 2020, The influence of nutrients diffusion on a metabolism-driven model of a multi-cellular system, Fundam Inform, 171, 17 Shan, 2018, Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors, PLoS Comput Biol, 14, 10.1371/journal.pcbi.1006584 Di Filippo, 2020, Single-cell digital twins for cancer preclinical investigation, Methods Mol Biol, 2088, 331, 10.1007/978-1-0716-0159-4_15 Yizhak, 2014, A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration, Mol Syst Biol, 10, 744, 10.15252/msb.20134993 Fendt, 2019, Metabolic vulnerabilities of metastasizing cancer cells, BMC Biol, 17, 54, 10.1186/s12915-019-0672-2