Elucidating the metabolic characteristics of pancreatic β-cells from patients with type 2 diabetes (T2D) using a genome-scale metabolic modeling
Tài liệu tham khảo
Chen, 2012, The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives, Nat. Rev. Endocrinol., 8, 228, 10.1038/nrendo.2011.183
Khan, 2020, Epidemiology of type 2 diabetes–global burden of disease and forecasted trends, J Epidemiol Glob Health, 10, 107, 10.2991/jegh.k.191028.001
Halban, 2014, β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment, Diabetes Care, 37, 1751, 10.2337/dc14-0396
Roden, 2019, The integrative biology of type 2 diabetes, Nature, 576, 51, 10.1038/s41586-019-1797-8
Fu, 2013, Regulation of insulin synthesis and secretion and pancreatic β-cell dysfunction in diabetes, Curr. Diabetes Rev., 9, 25, 10.2174/157339913804143225
White, 2016, Type 2 diabetes: the pathologic basis of reversible β-cell dysfunction, Diabetes Care, 39, 2080, 10.2337/dc16-0619
Park, 2019, Pancreatic β cells: gatekeepers of type 2 diabetes, J. Cell Biol., 218, 1094, 10.1083/jcb.201810097
Weir, 2020, Glucolipotoxicity, β-cells, and diabetes: the emperor has no clothes, Diabetes, 69, 273, 10.2337/db19-0138
Rahman, 2019, Bioinformatics methodologies to identify interactions between type 2 diabetes and neurological comorbidities, IEEE Access, 7, 183948, 10.1109/ACCESS.2019.2960037
Lytrivi, 2020, Recent insights into mechanisms of β-cell lipo- and glucolipotoxicity in type 2 diabetes, J. Mol. Biol., 432, 1514, 10.1016/j.jmb.2019.09.016
Khin, 2021, A brief Review of the mechanisms of β-cell dedifferentiation in type 2 diabetes, Nutrients, 13, 1593, 10.3390/nu13051593
Weir, 2020, Inadequate β-cell mass is essential for the pathogenesis of type 2 diabetes, Lancet Diabetes Endocrinol., 8, 249, 10.1016/S2213-8587(20)30022-X
Campbell, 2021, Mechanisms controlling pancreatic islet cell function in insulin secretion, Nat. Rev. Mol. Cell Biol., 22, 142, 10.1038/s41580-020-00317-7
Maechler, 2001, Mitochondrial function in normal and diabetic β-cells, Nature, 414, 807, 10.1038/414807a
Halban, 2014, β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment, J. Clin. Endocrinol. Metab., 99, 1983, 10.1210/jc.2014-1425
Rocha, 2020, Mitochondria and T2D: role of autophagy, ER stress, and inflammasome, Trends Endocrinol. Metabol., 31, 725, 10.1016/j.tem.2020.03.004
Otani, 2004, Reduced β-cell mass and altered glucose sensing impair insulin-secretory function in βIRKO mice, Am. J. Physiol. Endocrinol, 286, E41, 10.1152/ajpendo.00533.2001
Kulkarni, 2005, New insights into the roles of insulin/IGF-I in the development and maintenance of β-cell mass, Rev. Endocr. Metab. Disord., 6, 199, 10.1007/s11154-005-3051-y
Fernandez-Ruiz, 2014, Protein tyrosine phosphatase-1B modulates pancreatic β-cell mass, PLoS One, 9, 10.1371/journal.pone.0090344
Kushner, 2004, Islet-sparing effects of protein tyrosine phosphatase-1b deficiency delays onset of diabetes in IRS2 knockout mice, Diabetes, 53, 61, 10.2337/diabetes.53.1.61
Hilmarsdottir, 2017, Inhibition of PTP1B disrupts cell–cell adhesion and induces anoikis in breast epithelial cells, Cell Death Dis., 8, 10.1038/cddis.2017.177
Anello, 2005, Functional and morphological alterations of mitochondria in pancreatic beta cells from type 2 diabetic patients, Diabetologia, 48, 282, 10.1007/s00125-004-1627-9
Segerstolpe, 2016, Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes, Cell Metabol., 24, 593, 10.1016/j.cmet.2016.08.020
Brereton, 2016, Hyperglycaemia induces metabolic dysfunction and glycogen accumulation in pancreatic β-cells, Nat. Commun., 7, 13496, 10.1038/ncomms13496
Adam, 2017, Fumarate hydratase deletion in pancreatic β cells leads to progressive diabetes, Cell Rep., 20, 3135, 10.1016/j.celrep.2017.08.093
Aichler, 2017, N-acyl taurines and acylcarnitines cause an imbalance in insulin synthesis and secretion provoking β cell dysfunction in type 2 diabetes, Cell Metabol., 25, 1334, 10.1016/j.cmet.2017.04.012
Göhring, 2014, Chronic high glucose and pyruvate levels differentially affect mitochondrial bioenergetics and fuel-stimulated insulin secretion from clonal INS-1 832/13 cells, J. Biol. Chem., 289, 3786, 10.1074/jbc.M113.507335
Fernandez, 2008, Metabolomic and proteomic analysis of a clonal insulin-producing β-cell line (INS-1 832/13), J. Proteome Res., 7, 400, 10.1021/pr070547d
Haythorne, 2019, Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic β-cells, Nat. Commun., 10, 2474, 10.1038/s41467-019-10189-x
Las, 2020, Emerging roles of β-cell mitochondria in type-2-diabetes, Mol. Aspect. Med., 71, 100843, 10.1016/j.mam.2019.100843
Murao, 2022, Increased glycolysis affects β-cell function and identity in aging and diabetes, Mol. Metabol., 55, 101414, 10.1016/j.molmet.2021.101414
Zhang, 2021, Reductive TCA cycle metabolism fuels glutamine- and glucose-stimulated insulin secretion, Cell Metabol., 33, 804, 10.1016/j.cmet.2020.11.020
Lai, 2020, Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: a metabolic profiling study, PLoS Med., 17, 10.1371/journal.pmed.1003112
Newsholme, 2007, Amino acid metabolism, insulin secretion and diabetes, Biochem. Soc. Trans., 35, 1180, 10.1042/BST0351180
Menge, 2010, Selective amino acid deficiency in patients with impaired glucose tolerance and type 2 diabetes, Regul. Pept., 160, 75, 10.1016/j.regpep.2009.08.001
Oh, 2018, Fatty acid-induced lipotoxicity in pancreatic beta-cells during development of type 2 diabetes, Front. Endocrinol., 9, 384, 10.3389/fendo.2018.00384
Mardinoglu, 2013, Genome‐scale modeling of human metabolism–a systems biology approach, Biotechnol. J., 8, 985, 10.1002/biot.201200275
Frayan, 2019
Karstädt, 2012, CardioNet: a human metabolic network suited for the study of cardiomyocyte metabolism, BMC Syst. Biol., 6, 114, 10.1186/1752-0509-6-114
Moolamalla, 2020, Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders, Comput. Biol. Med., 125, 10399, 10.1016/j.compbiomed.2020.103994
Mardinoglu, 2014, Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease, Nat. Commun., 5, 3083, 10.1038/ncomms4083
Paul, 2021, Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models, Sci. Rep., 11, 213, 10.1038/s41598-020-80561-1
Mardinoglu, 2017, Personal model‐assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD, Mol. Syst. Biol., 13, 916, 10.15252/msb.20167422
Turanli, 2019, Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning, EBioMedicine, 42, 386, 10.1016/j.ebiom.2019.03.009
Shlomi, 2009, Predicting metabolic biomarkers of human inborn errors of metabolism, Mol. Syst. Biol., 5, 263, 10.1038/msb.2009.22
Calimlioglu, 2015, Tissue-specific molecular biomarker signatures of type 2 diabetes: an integrative analysis of transcriptomics and protein–protein interaction data, OMICS A J. Integr. Biol., 19, 563, 10.1089/omi.2015.0088
Marselli, 2010, Gene expression profiles of Beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes, PLoS One, 5, 10.1371/journal.pone.0011499
Langfelder, 2008, WGCNA: an R package for weighted correlation network analysis, BMC Bioinf., 9, 1, 10.1186/1471-2105-9-559
Zhang, 2005, A general framework for weighted gene co-expression network analysis, Stat. Appl. Genet. Mol. Biol., 4, 10.2202/1544-6115.1128
Langfelder, 2008, Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R, Bioinformatics, 24, 719, 10.1093/bioinformatics/btm563
Kuleshov, 2016, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res., 44, W90, 10.1093/nar/gkw377
Thiele, 2013, A community-driven global reconstruction of human metabolism, Nat. Biotechnol., 31, 419, 10.1038/nbt.2488
Colijn, 2009, Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production, PLoS Comput. Biol., 5, 10.1371/journal.pcbi.1000489
Schellenberger, 2011, Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2. 0, Nat. Protoc., 6, 1290, 10.1038/nprot.2011.308
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
Thiele, 2020, Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome, Mol. Syst. Biol., 16, 10.15252/msb.20198982
Richelle, 2019, Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions, PLoS Comput. Biol., 15, 10.1371/journal.pcbi.1006867
Bashary, 2020, An insight of alpha-amylase inhibitors as a valuable tool in the management of type 2 diabetes mellitus, Curr. Diabetes Rev., 16, 117
Boslem, 2012, Roles of ceramide and sphingolipids in pancreatic β-cell function and dysfunction, Islets, 4, 177, 10.4161/isl.20102
Véret, 2014, Roles of sphingolipid metabolism in pancreatic β cell dysfunction induced by lipotoxicity, J. Clin. Med., 3, 646, 10.3390/jcm3020646
Fernandez-Alvarez, 1994, Enzymatic, metabolic and secretory patterns in human islets of type 2 (non-insulin-dependent) diabetic patients, Diabetologia, 37, 177, 10.1007/s001250050090
Ueda, 1998, Overexpression of mitochondrial FAD-linked glycerol-3-phosphate dehydrogenase does not correct glucose-stimulated insulin secretion from diabetic GK rat pancreatic islets, Diabetologia, 41, 649, 10.1007/s001250050963
Noronha, 2019, The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease, Nucleic Acids Res., 47, D614, 10.1093/nar/gky992
Shi, 2018, Plasma metabolites associated with type 2 diabetes in a Swedish population: a case–control study nested in a prospective cohort, Diabetologia, 61, 849, 10.1007/s00125-017-4521-y
Meikle, 2013, Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes, PLoS One, 8, 10.1371/journal.pone.0074341
Carraway, 1988, Presence of neurotensin and neuromedin-N within a common precursor from a human pancreatic neuroendocrine tumor, J. Clin. Endocrinol. Metab., 66, 1323, 10.1210/jcem-66-6-1323
Liu, 2010, Discovery and comparison of serum biomarkers for diabetes mellitus and metabolic syndrome based on UPLC-Q-TOF/MS, Clin. Biochem., 82, 40, 10.1016/j.clinbiochem.2020.03.007
Barradas, 1988, Intraplatelet serotonin in patients with diabetes mellitus and peripheral vascular disease, Eur. J. Clin. Invest., 18, 399, 10.1111/j.1365-2362.1988.tb01030.x
Malyszko, 1994, Daily variations of platelet aggregation in relation to blood and plasma serotonin in diabetes, Thromb. Res., 75, 569, 10.1016/0049-3848(94)90231-3
Weiderkehr, 2006, Minireview: implication of mitochondria in insulin secretion and action, Endocrinology, 147, 2643, 10.1210/en.2006-0057
Jitrapakdee, 2010, Regulation of insulin secretion: role of mitochondrial signaling, Diabetologia, 53, 1019, 10.1007/s00125-010-1685-0
Fex, 2018, The pathogenetic role of β-cell mitochondria in type 2 diabetes, J. Endocrinol., 236, R145, 10.1530/JOE-17-0367
Kobayashi, 1997, In situ characterization of islets in diabetes with a mitochondrial DNA mutation at nucleotide position 3243, Diabetes, 46, 1567, 10.2337/diacare.46.10.1567
Lu, 2010, Molecular and metabolic evidence for mitochondrial defects associated with β-cell dysfunction in a mouse model of type 2 diabetes, Diabetes, 59, 448, 10.2337/db09-0129
Ma, 2012, Diabetes reduces β-cell mitochondria and induces distinct morphological abnormalities, which are reproducible by high glucose in vitro with attendant dysfunction, Islets, 4, 233, 10.4161/isl.20516
Cernea, 2013, Diabetes and beta cell function: from mechanisms to evaluation and clinical implications, Biochem. Med., 23, 266, 10.11613/BM.2013.033
Elsner, 2011, Peroxisome-generated hydrogen peroxide as important mediator of lipotoxicity in insulin-producing cells, Diabetes, 60, 200, 10.2337/db09-1401
Sawatani, 2019, Dual effect of reduced type I diacylglycerol kinase activity on insulin secretion from MIN6 β-cells, J. Pharmacol. Sci., 140, 178, 10.1016/j.jphs.2019.06.001
Kaneko, 2015, Diacylglycerol signaling pathway in pancreatic β-cells: an essential role of diacylglycerol kinase in the regulation of insulin secretion, Biol. Pharm. Bull., 38, 669, 10.1248/bpb.b15-00060
Espinosa-Diez, 2015, Antioxidant responses and cellular adjustments to oxidative stress, Redox Biol., 6, 183, 10.1016/j.redox.2015.07.008
Dypbukt, 1994, Different prooxidant levels stimulate growth, trigger apoptosis, or produce necrosis of insulin-secreting RINm5F cells. The role of intracellular polyamines, J. Biol. Chem., 269, 30553, 10.1016/S0021-9258(18)43849-5
Schulze, 2016, Lipid use and misuse by the heart, Circ. Res., 118, 1736, 10.1161/CIRCRESAHA.116.306842
Lever, 2014, Betaine and trimethylamine-N-oxide as predictors of cardiovascular outcomes show different patterns in diabetes mellitus: an observational study, PLoS One, 9, 10.1371/journal.pone.0114969
Selim, 2017, Plasma serotonin in heart failure: possible marker and potential treatment target, Heart Lung Circ., 26, 442, 10.1016/j.hlc.2016.08.003
Frishman, 2000, Serotonin and the heart, Ann. Med., 32, 195, 10.3109/07853890008998827
Ban, 2007, Impact of increased plasma serotonin levels and carotid atherosclerosis on vascular dementia, Atherosclerosis, 195, 153, 10.1016/j.atherosclerosis.2006.09.005
Vikenes, 1999, Serotonin is associated with coronary artery disease and cardiac events, Circulation, 100, 483, 10.1161/01.CIR.100.5.483
Van den Berg, 1989, Transcardiac serotonin concentration is increased in selected patients with limiting angina and complex coronary lesion morphology, Circulation, 79, 116, 10.1161/01.CIR.79.1.116
Mohammed, 2021, Kinetensin increases blood pressure by activation of angiotensin‐II type 1 receptors, in isoflurane anesthetized male mice, Faseb. J., 35, 10.1096/fasebj.2021.35.S1.04168
Dobner, 1987, Cloning and sequence analysis of cDNA for the canine neurotensin/neuromedin N precursor, Proc. Natl. Acad. Sci. U.S.A., 84, 3516, 10.1073/pnas.84.10.3516
Melander, 2012, Plasma proneurotensin and incidence of diabetes, cardiovascular disease, breast cancer, and mortality, JAMA, 308, 1469, 10.1001/jama.2012.12998
Hackett, 2016, Systems-level analysis of mechanisms regulating yeast metabolic flux, Science, 354, 10.1126/science.aaf2786
Shlomi, 2008, Network-based prediction of human tissue-specific metabolism, Nat. Biotechnol., 26, 1003, 10.1038/nbt.1487
Vieira, 2021, A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale, bioRxiv
Richelle, 2019, Assessing key decisions for transcriptomic data integration in biochemical networks, PLoS Comput. Biol., 15, 10.1371/journal.pcbi.1007185
Li, 2017, NOREVA: normalization and evaluation of MS-based metabolomics data, Nucleic Acids Res., 45, W162, 10.1093/nar/gkx449
Fu, 2021, Optimization of metabolomic data processing using NOREVA, Nat. Protoc., 17, 129, 10.1038/s41596-021-00636-9
Yang, 2019, A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies, Briefings Bioinf., 21, 2142, 10.1093/bib/bbz137
Yang, 2021, MMEASE: online meta-analysis of metabolomic data by enhanced metabolite annotation, marker selection and enrichment analysis, J. Proteonomics, 232, 104023, 10.1016/j.jprot.2020.104023
Ruiz, 2021, Identification of disease treatment mechanisms through the multiscale interactome, Nat. Commun., 12, 1, 10.1038/s41467-021-21770-8
Waeber B, Feihl F, Ruilope L. Diabetes and hypertension. Blood Pres.. 200; 10(5–6):311-321. https://doi.org/10.1080/080370501753400610.
Collins, 2015, United States Renal Data System public health surveillance of chronic kidney disease and end-stage renal disease, Kidney Int. Suppl., 5, 2, 10.1038/kisup.2015.2
Rahman, 2020, A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases, Int. J. Environ. Res. Publ. Health, 17, 1035, 10.3390/ijerph17031035