Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability.
Humans
Glutamine
/ metabolism
Plaque, Atherosclerotic
Glutamic Acid
/ metabolism
Macrophages
/ metabolism
Carotid Artery Diseases
/ metabolism
Rupture, Spontaneous
Metabolic Networks and Pathways
Phenotype
Carotid Arteries
/ pathology
Metabolomics
Databases, Genetic
Inflammation
/ metabolism
Energy Metabolism
Datasets as Topic
Male
Atherosclerosis
Genome-scale metabolic network
Macrophage
Metabolomics
Plaque rupture
Journal
Cardiovascular diabetology
ISSN: 1475-2840
Titre abrégé: Cardiovasc Diabetol
Pays: England
ID NLM: 101147637
Informations de publication
Date de publication:
08 Jul 2024
08 Jul 2024
Historique:
received:
30
10
2023
accepted:
26
06
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
8
7
2024
Statut:
epublish
Résumé
Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking. In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque. Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment. This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.
Sections du résumé
BACKGROUND
BACKGROUND
Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking.
METHODS
METHODS
In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque.
RESULTS
RESULTS
Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment.
CONCLUSIONS
CONCLUSIONS
This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.
Identifiants
pubmed: 38978031
doi: 10.1186/s12933-024-02339-3
pii: 10.1186/s12933-024-02339-3
doi:
Substances chimiques
Glutamine
0RH81L854J
Glutamic Acid
3KX376GY7L
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
240Subventions
Organisme : China Scholarship Council
ID : 201609120004
Organisme : Tianjin Municipal Education Commission
ID : 2023KJ107
Organisme : Marie Skłodowska-Curie Innovative Training Networks
ID : 675111
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : 40-45700-98-2010
Organisme : Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ID : 190429
Organisme : Hartstichting
ID : 2019T33
Organisme : European Research Area Network on Cardiovascular Diseases
ID : 02-001-0217-T100
Informations de copyright
© 2024. The Author(s).
Références
Chen J, Tung C-H, Mahmood U, Ntziachristos V, Gyurko R, Fishman MC, Huang PL, Weissleder R. In vivo imaging of proteolytic activity in atherosclerosis. Circulation. 2002;105(23):2766–71.
pubmed: 12057992
doi: 10.1161/01.CIR.0000017860.20619.23
Bierhansl L, Conradi L-C, Treps L, Dewerchin M, Carmeliet P. Central role of metabolism in endothelial cell function and vascular disease. Physiology. 2017;32(2):126–40.
pubmed: 28202623
pmcid: 5337830
doi: 10.1152/physiol.00031.2016
Theodorou K, Boon RA. Endothelial cell metabolism in atherosclerosis. Front Cell Dev Biol. 2018;6:82.
pubmed: 30131957
pmcid: 6090045
doi: 10.3389/fcell.2018.00082
Shi J, Yang Y, Cheng A, Xu G, He F. Metabolism of vascular smooth muscle cells in vascular diseases. Am J Physiol Heart Circ Physiol. 2020;319(3):H613–31.
pubmed: 32762559
doi: 10.1152/ajpheart.00220.2020
Bories GFP, Leitinger N. Macrophage metabolism in atherosclerosis. FEBS Lett. 2017;591(19):3042–60.
pubmed: 28796886
doi: 10.1002/1873-3468.12786
Tomas L, Edsfeldt A, Mollet IG, Perisic Matic L, Prehn C, Adamski J, Paulsson-Berne G, Hedin U, Nilsson J, Bengtsson E, et al. Altered metabolism distinguishes high-risk from stable carotid atherosclerotic plaques. Eur Heart J. 2018;39(24):2301–10.
pubmed: 29562241
pmcid: 6012762
doi: 10.1093/eurheartj/ehy124
Karagiannidis E, Sofidis G, Papazoglou AS, Deda O, Panteris E, Moysidis DV, Stalikas N, Kartas A, Papadopoulos A, Stefanopoulos L, et al. Correlation of the severity of coronary artery disease with patients’ metabolic profile-rationale, design and baseline patient characteristics of the CorLipid trial. BMC Cardiovasc Disord. 2021;21(1):79.
pubmed: 33557756
pmcid: 7869241
doi: 10.1186/s12872-021-01865-2
Merlin J, Ivanov S, Dumont A, Sergushichev A, Gall J, Stunault M, Ayrault M, Vaillant N, Castiglione A, Swain A, et al. Non-canonical glutamine transamination sustains efferocytosis by coupling redox buffering to oxidative phosphorylation. Nat Metab. 2021;3(10):1313–26.
doi: 10.1038/s42255-021-00471-y
Patil KR, Nielsen J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci. 2005;102(8):2685–9.
pubmed: 15710883
pmcid: 549453
doi: 10.1073/pnas.0406811102
Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5(1):3083.
pubmed: 24419221
doi: 10.1038/ncomms4083
Hyötyläinen T, Jerby L, Petäjä EM, Mattila I, Jäntti S, Auvinen P, Gastaldelli A, Yki-Järvinen H, Ruppin E, Orešič M. Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat Commun. 2016;7(1):8994.
pubmed: 26839171
pmcid: 4742839
doi: 10.1038/ncomms9994
Lee S, Zhang C, Kilicarslan M, Piening Brian D, Bjornson E, Hallström Björn M, Groen Albert K, Ferrannini E, Laakso M, Snyder M, et al. Integrated network analysis reveals an association between plasma mannose levels and insulin resistance. Cell Metabol. 2016;24(1):172–84.
doi: 10.1016/j.cmet.2016.05.026
Bayraktar A, Lam S, Altay O, Li X, Yuan M, Zhang C, Arif M, Turkez H, Uhlén M, Shoaie S, Mardinoglu A. Revealing the molecular mechanisms of Alzheimer’s disease based on network analysis. Int J Mol Sci. 2021;22(21):11556.
pubmed: 34768988
pmcid: 8584243
doi: 10.3390/ijms222111556
Bidkhori G, Benfeitas R, Klevstig M, Zhang C, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proc Natl Acad Sci. 2018;115(50):E11874–83.
pubmed: 30482855
pmcid: 6294939
doi: 10.1073/pnas.1807305115
Turanli B, Zhang C, Kim W, Benfeitas R, Uhlen M, Arga KY, Mardinoglu A. Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning. EBioMedicine. 2019;42:386–96.
pubmed: 30905848
pmcid: 6491384
doi: 10.1016/j.ebiom.2019.03.009
Yizhak K, Chaneton B, Gottlieb E, Ruppin E. Modeling cancer metabolism on a genome scale. Mol Syst Biol. 2015;11(6):817.
pubmed: 26130389
pmcid: 4501850
doi: 10.15252/msb.20145307
Jin H, Goossens P, Juhasz P, Eijgelaar W, Manca M, Karel JMH, Smirnov E, Sikkink CJJM, Mees BME, Waring O, et al. Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage. Clin Translational Med. 2021;11(6):e458.
doi: 10.1002/ctm2.458
Jin H, Mees BME, Biessen EAL, Sluimer JC. Transcriptional sex dimorphism in human atherosclerosis relates to plaque type. Circul Res. 2021;129(12):1175–7.
doi: 10.1161/CIRCRESAHA.121.320099
Perisic L, Aldi S, Sun Y, Folkersen L, Razuvaev A, Roy J, Lengquist M, Åkesson S, Wheelock CE, Maegdefessel L, et al. Gene expression signatures, pathways and networks in carotid atherosclerosis. J Intern Med. 2016;279(3):293–308.
pubmed: 26620734
doi: 10.1111/joim.12448
Matic LP, Jesus Iglesias M, Vesterlund M, Lengquist M, Hong M-G, Saieed S, Sanchez-Rivera L, Berg M, Razuvaev A, Kronqvist M, et al. Novel Multiomics profiling of human carotid atherosclerotic plaques and plasma reveals biliverdin reductase B as a marker of intraplaque hemorrhage. JACC: Basic Translational Sci. 2018;3(4):464–80.
Virmani R, Kolodgie FD, Burke AP, Farb A, Schwartz SM. Lessons from sudden coronary death. Arterioscler Thromb Vasc Biol. 2000;20(5):1262–75.
pubmed: 10807742
doi: 10.1161/01.ATV.20.5.1262
Juhasz P, Lynch M, Sethuraman M, Campbell J, Hines W, Paniagua M, Song L, Kulkarni M, Adourian A, Guo Y, et al. Semi-targeted plasma proteomics discovery workflow utilizing two-stage protein depletion and off-line LC–MALDI MS/MS. J Proteome Res. 2011;10(1):34–45.
pubmed: 20936781
doi: 10.1021/pr100659e
Koek MM, van der Kloet FM, Kleemann R, Kooistra T, Verheij ER, Hankemeier T. Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies. Metabolomics. 2011;7(1):1–14.
pubmed: 21461033
doi: 10.1007/s11306-010-0219-6
Kleemann R, van Erk M, Verschuren L, van den Hoek AM, Koek M, Wielinga PY, Jie A, Pellis L, Bobeldijk-Pastorova I, Kelder T, et al. Time-resolved and tissue-specific systems analysis of the pathogenesis of insulin resistance. PLoS ONE. 2010;5(1):e8817.
pubmed: 20098690
pmcid: 2809107
doi: 10.1371/journal.pone.0008817
Du P, Kibbe WA, Lin SM. Lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547–8.
pubmed: 18467348
doi: 10.1093/bioinformatics/btn224
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47–47.
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol. 2014;10(3):721.
pubmed: 24646661
pmcid: 4017677
doi: 10.1002/msb.145122
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R Package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal Complex Syst. 2006;1695:1–9.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.
pubmed: 14597658
pmcid: 403769
doi: 10.1101/gr.1239303
Alsaigh T, Evans D, Frankel D, Torkamani A. Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution. Commun Biology. 2022;5(1):1084.
doi: 10.1038/s42003-022-04056-7
McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 2019;8(4):329–e337324.
pubmed: 30954475
pmcid: 6853612
doi: 10.1016/j.cels.2019.03.003
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888–e19021821.
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–20.
pubmed: 29608179
pmcid: 6700744
doi: 10.1038/nbt.4096
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh P-r, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019;16(12):1289–96.
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv Preprint arXiv:180203426 2018.
McQuin C, Goodman A, Chernyshev V, Kamentsky L, Cimini BA, Karhohs KW, Doan M, Ding L, Rafelski SM, Thirstrup D, et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 2018;16(7):e2005970.
pubmed: 29969450
pmcid: 6029841
doi: 10.1371/journal.pbio.2005970
van Genderen H, Kenis H, Lux P, Ungeth L, Maassen C, Deckers N, Narula J, Hofstra L, Reutelingsperger C. In vitro measurement of cell death with the annexin A5 affinity assay. Nat Protoc. 2006;1(1):363–7.
pubmed: 17406257
doi: 10.1038/nprot.2006.55
Redgrave TG, Roberts DCK, West CE. Separation of plasma lipoproteins by density-gradient ultracentrifugation. Anal Biochem. 1975;65(1):42–9.
pubmed: 165752
doi: 10.1016/0003-2697(75)90488-1
Michel J-B, Virmani R, Arbustini E, Pasterkamp G. Intraplaque haemorrhages as the trigger of plaque vulnerability. Eur Heart J. 2011;32(16):1977–85.
pubmed: 21398643
pmcid: 3155759
doi: 10.1093/eurheartj/ehr054
Hellings WE, Peeters W, Moll FL, Piers SRD, Setten Jv S, PJVd, Vries J-PPM, Seldenrijk KA, Bruin PCD, Vink A, et al. Composition of carotid atherosclerotic plaque is associated with cardiovascular outcome. Circulation. 2010;121(17):1941–50.
pubmed: 20404256
doi: 10.1161/CIRCULATIONAHA.109.887497
Liu P-S, Wang H, Li X, Chao T, Teav T, Christen S, Di Conza G, Cheng W-C, Chou C-H, Vavakova M, et al. α-Ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat Immunol. 2017;18(9):985–94.
pubmed: 28714978
doi: 10.1038/ni.3796
Palmieri EM, Menga A, Martín-Pérez R, Quinto A, Riera-Domingo C, De Tullio G, Hooper DC, Lamers WH, Ghesquière B, McVicar DW, et al. Pharmacologic or genetic targeting of glutamine synthetase skews macrophages toward an M1-like phenotype and inhibits tumor metastasis. Cell Rep. 2017;20(7):1654–66.
pubmed: 28813676
pmcid: 5575233
doi: 10.1016/j.celrep.2017.07.054
Edsfeldt A, Dunér P, Ståhlman M, Mollet IG, Asciutto G, Grufman H, Nitulescu M, Persson AF, Fisher RM, Melander O, et al. Sphingolipids contribute to human atherosclerotic plaque inflammation. Arterioscler Thromb Vasc Biol. 2016;36(6):1132–40.
pubmed: 27055903
doi: 10.1161/ATVBAHA.116.305675
Dang VT, Zhong LH, Huang A, Deng A, Werstuck GH. Glycosphingolipids promote pro-atherogenic pathways in the pathogenesis of hyperglycemia-induced accelerated atherosclerosis. Metabolomics. 2018;14(7):92.
pubmed: 30830446
doi: 10.1007/s11306-018-1392-2
Robinson JL, Kocabaş P, Wang H, Cholley P-E, Cook D, Nilsson A, Anton M, Ferreira R, Domenzain I, Billa V, et al. An atlas of human metabolism. Sci Signal. 2020;13(624):eaaz1482.
pubmed: 32209698
pmcid: 7331181
doi: 10.1126/scisignal.aaz1482
Sorto P, Mäyränpää MI, Saksi J, Nuotio K, Ijäs P, Tuimala J, Vikatmaa P, Soinne L, Kovanen PT, Lindsberg PJ. Glutamine synthetase in human carotid plaque macrophages associates with features of plaque vulnerability: an immunohistological study. Atherosclerosis. 2022;352:18–26.
pubmed: 35667160
doi: 10.1016/j.atherosclerosis.2022.05.008
Stöger JL, Gijbels MJJ, van der Velden S, Manca M, van der Loos CM, Biessen EAL, Daemen MJAP, Lutgens E, de Winther MPJ. Distribution of macrophage polarization markers in human atherosclerosis. Atherosclerosis. 2012;225(2):461–8.
pubmed: 23078881
doi: 10.1016/j.atherosclerosis.2012.09.013
Lehn-Stefan A, Peter A, Machann J, Schick F, Randrianarisoa E, Heni M, Wagner R, Birkenfeld AL, Fritsche A, Häring H-U, et al. Elevated circulating glutamate is associated with subclinical atherosclerosis independently of established risk markers: a cross-sectional study. J Clin Endocrinol Metab. 2020;106(2):e982-9.
doi: 10.1210/clinem/dgaa898
Pietzner M, Stewart ID, Raffler J, Khaw K-T, Michelotti GA, Kastenmüller G, Wareham NJ, Langenberg C. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med. 2021;27(3):471–9.
pubmed: 33707775
pmcid: 8127079
doi: 10.1038/s41591-021-01266-0
Ryan DG, O’Neill LAJ. Krebs cycle reborn in macrophage immunometabolism. Annu Rev Immunol. 2020;38(1):289–313.
pubmed: 31986069
doi: 10.1146/annurev-immunol-081619-104850
Palmieri M, Menga E, Lebrun A, Hooper AC, Butterfield D, Mazzone DA, Castegna M. Blockade of glutamine synthetase enhances inflammatory response in microglial cells. Antioxid Redox Signal. 2017;26(8):351–63.
pubmed: 27758118
pmcid: 5346956
doi: 10.1089/ars.2016.6715
Tannahill GM, Curtis AM, Adamik J, Palsson-McDermott EM, McGettrick AF, Goel G, Frezza C, Bernard NJ, Kelly B, Foley NH, et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature. 2013;496(7444):238–42.
pubmed: 23535595
pmcid: 4031686
doi: 10.1038/nature11986
Matés JM, Segura JA, Alonso FJ, Márquez J. Pathways from glutamine to apoptosis. FBL. 2006;11(3):3164–80.
Geeraerts X, Bolli E, Fendt S-M, Van Ginderachter JA. Macrophage metabolism as therapeutic target for cancer, atherosclerosis, and obesity. Front Immunol. 2017;8:289.
pubmed: 28360914
pmcid: 5350105
doi: 10.3389/fimmu.2017.00289
Nagenborg J, Goossens P, Biessen EAL, Donners MMPC. Heterogeneity of atherosclerotic plaque macrophage origin, phenotype and functions: implications for treatment. Eur J Pharmacol. 2017;816:14–24.
pubmed: 28989084
doi: 10.1016/j.ejphar.2017.10.005
Lu SC. Glutathione synthesis. Biochim Biophys Acta (BBA) Gen Subj. 2013;1830(5):3143–53.
doi: 10.1016/j.bbagen.2012.09.008
Kolodgie FD, Burke AP, Nakazawa G, Cheng Q, Xu X, Virmani R. Free cholesterol in atherosclerotic plaques: where does it come from? Curr Opin Lipidol. 2007;18(5):500–7.
pubmed: 17885419
doi: 10.1097/MOL.0b013e3282efa35b
Haines DD, Tosaki A. Heme Degradation in Pathophysiology of and countermeasures to inflammation-associated disease. Int J Mol Sci. 2020;21(24):9698.
pubmed: 33353225
pmcid: 7766613
doi: 10.3390/ijms21249698