Abstract
Abdominal aortic aneurysm (AAA) and atherosclerosis (AS) have considerable similarities in clinical risk factors and molecular pathogenesis. The aim of our study was to investigate the differences between AAA and AS from the perspective of metabolomics, and to explore the potential mechanisms of differential metabolites via integration analysis with transcriptomics. Plasma samples from 32 AAA and 32 AS patients were applied to characterize the metabolite profiles using untargeted liquid chromatography-mass spectrometry (LC-MS). A total of 18 remarkably different metabolites were identified, and a combination of seven metabolites could potentially serve as a biomarker to distinguish AAA and AS, with an area under the curve (AUC) of 0.93. Subsequently, we analyzed both the metabolomics and transcriptomics data and found that seven metabolites, especially 2′-deoxy-d-ribose (2dDR), were significantly correlated with differentially expressed genes. In conclusion, our study presents a comprehensive landscape of plasma metabolites in AAA and AS patients, and provides a research direction for pathogenetic mechanisms in atherosclerotic AAA.
概要
目的
腹主动脉瘤(AAA)和动脉粥样硬化(AS)在临床危险因素和分子发病机制上有相当大的相似之处. 我们的研究旨在从代谢组学的角度研究AAA和AS之间的差异, 并通过与转录组学的整合分析探索差异代谢物的潜在机制.
创新点
从代谢组学的角度探究了AAA和AS之间的差异; 采用关联分析, 探究差异代谢物和差异基因的交互作用整合了代谢组学和转录组学.
方法
应用32例AAA和32例AS患者的血浆样本表征代谢物谱, 采用非靶向液相色谱质谱法(LC-MS), 探究AAA与AS之间代谢物水平的差异; 应用GEO数据集GSE57691, 探究AAA与AS之间基因表达的差异; 最后应用Spearman相关分析探究差异代谢物与差异基因之间的相关性.
结论
本研究共鉴定出18种显著差异代谢物, 其中7种代谢物的组合可能作为区分AAA和AS的生物标志物, 曲线下面积(AUC)为0.93. 通过整合代谢组学和转录组学数据, 发现这7种代谢物, 尤其是2′-脱氧-d-核糖(2dDR)与差异表达基因显著相关. 本研究提供了AAA和AS患者血浆代谢物的全面概况, 并为动脉粥样硬化性AAA的发病机制提供了研究方向.
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References
Alcorn HG, Wolfson SK, Sutton-Tyrrell K, et al., 1996. Risk factors for abdominal aortic aneurysms in older adults enrolled in the Cardiovascular Health Study. Arterioscler Thromb Vasc Biol, 16(8):963–970. https://doi.org/10.1161/01.atv.16.8.963
Ardestani A, Yazdanparast R, Nejad AS, 2008. 2-Deoxy-d-ribose-induced oxidative stress causes apoptosis in human monocytic cells: prevention by pyridoxal-5′-phosphate. Toxicol in Vitro, 22(4):968–979. https://doi.org/10.1016/j.tiv.2008.02.010
Biros E, Gäbel G, Moran CS, et al., 2015. Differential gene expression in human abdominal aortic aneurysm and aortic occlusive disease. Oncotarget, 6(15):12984–12996. https://doi.org/10.18632/oncotarget.3848
Bradley DT, Hughes AE, Badger SA, et al., 2013. A variant in LDLR is associated with abdominal aortic aneurysm. Circ Cardiovasc Genet, 6(5):498–504. https://doi.org/10.1161/CIRCGENETICS.113.000165
Cornuz J, Sidoti Pinto C, Tevaearai H, et al., 2004. Risk factors for asymptomatic abdominal aortic aneurysm: systematic review and meta-analysis of population-based screening studies. Eur J Public Health, 14(4):343–349. https://doi.org/10.1093/eurpub/14.4.343
Dikici S, Bullock AJ, Yar M, et al., 2020. 2-Deoxy-d-ribose (2dDR) upregulates vascular endothelial growth factor (VEGF) and stimulates angiogenesis. Microvasc Res, 131:104035. https://doi.org/10.1016/j.mvr.2020.104035
Dunn WB, Broadhurst D, Begley P, et al., 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc, 6(7):1060–1083. https://doi.org/10.1038/nprot.2011.335
Ferrara N, Winer J, Burton T, 1991. Aortic smooth muscle cells express and secrete vascular endothelial growth factor. Growth Factors, 5(2): 141–148. https://doi.org/10.3109/08977199109000278
Fico A, Manganelli G, Cigliano L, et al., 2008. 2-Deoxy-d-ribose induces apoptosis by inhibiting the synthesis and increasing the efflux of glutathione. Free Radic Biol Med, 45(2):211–217. https://doi.org/10.1016/j.freeradbiomed.2008.04.017
Gan SJ, Ye B, Qian SX, et al., 2015. Immune- and ribosome-related genes were associated with systemic vasculitis. Scand J Immunol, 81(2):96–101. https://doi.org/10.1111/sji.12252
Golledge J, Norman PE, 2010. Atherosclerosis and abdominal aortic aneurysm: cause, response, or common risk factors? Arterioscler Thromb Vasc Biol, 30(6):1075–1077. https://doi.org/10.1161/ATVBAHA.110.206573
Haraguchi M, Miyadera K, Uemura K, et al., 1994. Angiogenic activity of enzymes. Nature, 368(6468):198. https://doi.org/10.1038/368198a0
Haring B, Selvin E, He XT, et al., 2018. Adherence to the dietary approaches to stop hypertension dietary pattern and risk of abdominal aortic aneurysm: results from the ARIC study. J Am Heart Assoc, 7(21):e009340. https://doi.org/10.1161/JAHA.118.009340
Harrison SC, Smith AJP, Jones GT, et al., 2013. Interleukin-6 receptor pathways in abdominal aortic aneurysm. Eur Heart J, 34(48):3707–3716. https://doi.org/10.1093/eurheartj/ehs354
Ito S, Akutsu K, Tamori Y, et al., 2008. Differences in atherosclerotic profiles between patients with thoracic and abdominal aortic aneurysms. Am J Cardiol, 101(5):696–699. https://doi.org/10.1016/j.amjcard.2007.10.039
Ji L, Chen SL, Gu GC, et al., 2021. Exploration of crucial mediators for carotid atherosclerosis pathogenesis through integration of microbiome, metabolome, and transcriptome. Front Physiol, 12:645212. https://doi.org/10.3389/fphys.2021.645212
Johnson CH, Ivanisevic J, Siuzdak G, 2016. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol, 17(7):451–459. https://doi.org/10.1038/nrm.2016.25
Jones GT, Bown MJ, Gretarsdottir S, et al., 2013. A sequence variant associated with sortilin-1 (SORT1) on 1p13.3 is independently associated with abdominal aortic aneurysm. Hum Mol Genet, 22(14):2941–2947. https://doi.org/10.1093/hmg/ddt141
Kaluza J, Stackelberg O, Harris HR, et al., 2019. Anti-inflammatory diet and risk of abdominal aortic aneurysm in two Swedish cohorts. Heart, 105(24):1876–1883. https://doi.org/10.1136/heartjnl-2019-315031
Kent KC, 2014. Abdominal aortic aneurysms. N Engl J Med, 371(22):2101–2108. https://doi.org/10.1056/NEJMcp1401430
Kim K, Zakharkin SO, Allison DB, 2010. Expectations, validity, and reality in gene expression profiling. J Clin Epidemiol, 63(9):950–959. https://doi.org/10.1016/j.jclinepi.2010.02.018
Knox AJ, Corbett L, Stocks J, et al., 2001. Human airway smooth muscle cells secrete vascular endothelial growth factor: up-regulation by bradykinin via a protein kinase C and prostanoid-dependent mechanism. FASEB J, 15(13): 2480–2488. https://doi.org/10.1096/fj.01-0256com
Leeper NJ, Raiesdana A, Kojima Y, et al., 2013. Loss of CDKN2B promotes p53-dependent smooth muscle cell apoptosis and aneurysm formation. Arterioscler Thromb Vasc Biol, 33(1):e1–e10. https://doi.org/10.1161/atvbaha.112.300399
Li XS, Wang ZN, Cajka T, et al., 2018. Untargeted metabolomics identifies trimethyllysine, a TMAO-producing nutrient precursor, as a predictor of incident cardiovascular disease risk. JCI Insight, 3(6):e99096. https://doi.org/10.1172/jci.insight.99096
Lindquist Liljeqvist M, Hultgren R, Bergman O, et al., 2020. Tunica-specific transcriptome of abdominal aortic aneurysm and the effect of intraluminal thrombus, smoking, and diameter growth rate. Arterioscler Thromb Vasc Biol, 40(11): 2700–2713. https://doi.org/10.1161/atvbaha.120.314264
Ma XH, Yao HR, Yang YH, et al., 2018. miR-195 suppresses abdominal aortic aneurysm through the TNF-α/NF-κB and VEGF/PI3K/Akt pathway. Int J Mol Med, 41(4):2350–2358. https://doi.org/10.3892/ijmm.2018.3426
Macel M, van Dam NM, Keurentjes JJB, 2010. Metabolomics: the chemistry between ecology and genetics. Mol Ecol Resour, 10(4):583–593. https://doi.org/10.1111/j.1755-0998.2010.02854.x
Moxon JV, Liu DW, Wong G, et al., 2014. Comparison of the serum lipidome in patients with abdominal aortic aneurysm and peripheral artery disease. Circ Cardiovasc Genet, 7(1):71–79. https://doi.org/10.1161/CIRCGENETICS.113.000343
Murakami M, Iwai S, Hiratsuka S, et al., 2006. Signaling of vascular endothelial growth factor receptor-1 tyrosine kinase promotes rheumatoid arthritis through activation of monocytes/macrophages. Blood, 108(6): 1849–1856. https://doi.org/10.1182/blood-2006-04-016030
NASCET (North American Symptomatic Carotid Endarterectomy Trial) collaborators, 1991. Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis. N Engl J Med, 325(7):445–453. https://doi.org/10.1056/NEJM199108153250701
Ning XJ, Ding N, Ballew SH, et al., 2020. Diabetes, its duration, and the long-term risk of abdominal aortic aneurysm: the Atherosclerosis Risk in Communities (ARIC) Study. Atherosclerosis, 313:137–143. https://doi.org/10.1016/j.atherosclerosis.2020.09.031
Ofir-Rosenfeld Y, Boggs K, Michael D, et al., 2008. Mdm2 regulates p53 mRNA translation through inhibitory interactions with ribosomal protein L26. Mol Cell, 32(2):180–189. https://doi.org/10.1016/j.molcel.2008.08.031
Palazzuoli A, Gallotta M, Guerrieri G, et al., 2008. Prevalence of risk factors, coronary and systemic atherosclerosis in abdominal aortic aneurysm: comparison with high cardiovascular risk population. Vasc Health Risk Manag, 4(4): 877–883. https://doi.org/10.2147/vhrm.s1866
Pearce WH, Shively VP, 2006. Abdominal aortic aneurysm as a complex multifactorial disease: interactions of polymorphisms of inflammatory genes, features of autoimmunity, and current status of MMPs. Ann N Y Acad Sci, 1085(1): 117–132. https://doi.org/10.1196/annals.1383.025
Qureshi MI, Greco M, Vorkas PA, et al., 2017. Application of metabolic profiling to abdominal aortic aneurysm research. J Proteome Res, 16(7):2325–2332. https://doi.org/10.1021/acs.jproteome.6b00894
Schrimpe-Rutledge AC, Codreanu SG, Sherrod SD, et al., 2016. Untargeted metabolomics strategies-challenges and emerging directions. J Am Soc Mass Spectrom, 27(12):1897–1905. https://doi.org/10.1007/s13361-016-1469-y
Shannon P, Markiel A, Ozier O, et al., 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13(11):2498–2504. https://doi.org/10.1101/gr.1239303
Sharma R, Ramanathan A, 2020. The aging metabolome-biomarkers to hub metabolites. Proteomics, 20(5–6): 1800407. https://doi.org/10.1002/pmic.201800407
Spitler KM, Davies BSJ, 2020. Aging and plasma triglyceride metabolism. J Lipid Res, 61(8): 1161–1167. https://doi.org/10.1194/jlr.R120000922
Subcommittee on Reporting Standards for Arterial Aneurysms, Ad Hoc Committee on Reporting Standards, Society for Vascular Surgery and North American Chapter, et al., 1991. Suggested standards for reporting on arterial aneurysms. J Vasc Surg, 13(3):452–458. https://doi.org/10.1067/mva.1991.26737
Takagi M, Absalon MJ, McLure KG, et al., 2005. Regulation of p53 translation and induction after DNA damage by ribosomal protein L26 and nucleolin. Cell, 123(1):49–63. https://doi.org/10.1016/j.cell.2005.07.034
Ucuzian AA, Gassman AA, East AT, et al., 2010. Molecular mediators of angiogenesis. J Burn Care Res, 31(1): 158–175. https://doi.org/10.1097/BCR.0b013e3181c7ed82
Ufnal M, Zadlo A, Ostaszewski R, 2015. TMAO: a small molecule of great expectations. Nutrition, 31(11–12): 1317–1323. https://doi.org/10.1016/j.nut.2015.05.006
US Preventive Services Task Force, 2019. Screening for abdominal aortic aneurysm: US Preventive Services Task Force Recommendation Statement. JAMA, 322(22):2211–2218. https://doi.org/10.1001/jama.2019.18928
Ussher JR, Elmariah S, Gerszten RE, et al., 2016. The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. J Am Coll Cardiol, 68(25):2850–2870. https://doi.org/10.1016/j.jacc.2016.09.972
van Hove AH, Benoit DSW, 2015. Depot-based delivery systems for pro-angiogenic peptides: a review. Front Bioeng Biotechnol, 3:102. https://doi.org/10.3389/fbioe.2015.00102
Wassef M, Baxter BT, Chisholm RL, et al., 2001. Pathogenesis of abdominal aortic aneurysms: a multidisciplinary research program supported by the National Heart, Lung, and Blood Institute. J Vasc Surg, 34(4):730–738. https://doi.org/10.1067/mva.2001.116966
Xu BH, Iida Y, Glover KJ, et al., 2019. Inhibition of VEGF (vascular endothelial growth factor)-A or its receptor activity suppresses experimental aneurysm progression in the aortic elastase infusion model. Arterioscler Thromb Vasc Biol, 39(8):1652–1666. https://doi.org/10.1161/ATVBAHA.119.312497
Zampieri M, Sekar K, Zamboni N, et al., 2017. Frontiers of high-throughput metabolomics. Curr Opin Chem Biol, 36:15–23. https://doi.org/10.1016/j.cbpa.2016.12.006
Zannas AS, Jia MW, Hafner K, et al., 2019. Epigenetic upregulation of FKBP5 by aging and stress contributes to NF-κB-driven inflammation and cardiovascular risk. Proc Natl Acad Sci USA, 116(23):11370–11379. https://doi.org/10.1073/pnas.1816847116
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 51890894, 81770481, and 82070492) and the Chinese Academy of Medical Sciences, Innovation Fund for Medical Sciences (CIFMS 2017-I2M-1-008). We thank Biotree (Shanghai, China) for assistance with LC-MS.
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Lei JI and Siliang CHEN performed the experimental research and data analysis, wrote and edited the manuscript. Guangchao GU, Wei WANG, Jinrui Ren, Fang XU, Fangda LI, and Jianqiang WU collected the samples. Yuehong ZHENG, Dan YANG, Lei JI, and Siliang CHEN contributed to the study design, data analysis, writing and editing of the manuscript. All authors have read and approved the final manuscript and, therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data.
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Lei JI, Siliang CHEN, Guangchao GU, Wei WANG, Jinrui REN, Fang XU, Fangda LI, Jianqiang WU, Dan YANG, and Yuehong ZHENG declare that they have no conflict of interest.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975; as revised in 2008 (5). This study was approved by the Ethics Committee of the Peking Union Medical College Hospital, China (JS-2629). Informed consent was obtained from all patients for being included in the study. Additional informed consent was obtained from all patients for whom identifying information is included in this article.
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Ji, L., Chen, S., Gu, G. et al. Discovery of potential biomarkers for human atherosclerotic abdominal aortic aneurysm through untargeted metabolomics and transcriptomics. J. Zhejiang Univ. Sci. B 22, 733–745 (2021). https://doi.org/10.1631/jzus.B2000713
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DOI: https://doi.org/10.1631/jzus.B2000713