Differential metabolomics networks analysis of menopausal status
Autoři:
Xiujuan Cui aff001; Xiaoyan Yu aff001; Guang Sun aff003; Ting Hu aff004; Sergei Likhodii aff005; Jingmin Zhang aff001; Edward Randell aff006; Xiang Gao aff007; Zhaozhi Fan aff008; Weidong Zhang aff001
Působiště autorů:
School of Pharmaceutical Sciences, Jilin University, Changchun, P.R. China
aff001; Department of Pharmacy, Daqing Oil-Field General Hospital, Daqing, China
aff002; Discipline of Medicine, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
aff003; Department of Computer Science, Memorial University, St John’s, NL, Canada
aff004; BC Provincial Toxicology Centre, Provincial Health Services Authority, Vancouver, British Columbia, Canada
aff005; Department of Laboratory Medicine, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
aff006; College of Life Sciences, Qingdao University, Qingdao, China
aff007; Department of Mathematics and Statistics, Memorial University, St. John’s, NL, Canada
aff008; Discipline of Genetics, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
aff009
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222353
Souhrn
Menopause is an endocrine-related transition that induces a number of physiological and potentially pathological changes in middle-aged and elderly women. The intention of this research was to investigate the influence of menopause on the intricate relationships between major biochemical metabolites. The study involved metabolic profiling of 186 metabolic markers measured in blood plasma collected from 120 healthy female participants. We developed a method of network analysis using differential correlation that enabled us to detect and characterize differences in metabolites and changes in inter-relationships in pre- and post-menopausal women. A topological analysis was performed on the differential network that uncovered metabolite differences in pre-and post-menopausal women. In this analysis, our method identified two key metabolites, sphingomyelins and phosphatidylcholines, which may be useful in directing further studies into menopause-specific differences in the metabolome, and how these differences may underlie the body's response to stress and disease following the transition from pre- to post-menopausal status for women.
Klíčová slova:
Biology and life sciences – Biochemistry – Research and analysis methods – Molecular biology – Molecular biology techniques – Computer and information sciences – Network analysis – Medicine and health sciences – Physiology – Molecular biology assays and analysis techniques – Endocrinology – Pharmacology – Pharmacokinetics – Drug metabolism – Lipids – Metabolism – Endocrine physiology – Menopause – Metabolites – Amino acid analysis – Metabolomics – Sphingolipids – Metabolic networks
Zdroje
1. Twiss JJ, Wegner J, Hunter M, Kelsay M, Rathe-Hart M, Salado W. Perimenopausal symptoms, quality of life, and health behaviors in users and nonusers of hormone therapy. J Am Acad Nurse Pract. 2007;19: 602–613. JAAN260 [pii]. doi: 10.1111/j.1745-7599.2007.00260.x 17970860
2. Sammaritano LR. Menopause in patients with autoimmune diseases. Autoimmun Rev. 2012;11: A430–6. doi: 10.1016/j.autrev.2011.11.006 22120060
3. Srikanth VK, Fryer JL, Zhai G, Winzenberg TM, Hosmer D, Jones G. A meta-analysis of sex differences prevalence, incidence and severity of osteoarthritis. Osteoarthritis Cartilage. 2005;13: 769–781. S1063-4584(05)00112-3 [pii]. doi: 10.1016/j.joca.2005.04.014 15978850
4. Lawrence RC, Helmick CG, Arnett FC, Deyo RA, Felson DT, Giannini EH, et al. Estimates of the prevalence of arthritis and selected musculoskeletal disorders in the United States. Arthritis Rheum. 1998;41: 778–799. doi: 10.1002/1529-0131(199805)41:5<778::AID-ART4>3.0.CO;2-V 9588729
5. Zhang W, Sun G, Likhodii S, Aref-Eshghi E, Harper PE, Randell E, et al. Metabolomic analysis of human synovial fluid and plasma reveals that phosphatidylcholine metabolism is associated with both osteoarthritis and diabetes mellitus. metabolomics. 2016;12: 24.
6. Zhang W, Sun G, Likhodii S, Liu M, Aref-Eshghi E, Harper PE, et al. Metabolomic analysis of human plasma reveals that arginine is depleted in knee osteoarthritis patients. Osteoarthritis Cartilage. 2016;24: 827–834. doi: 10.1016/j.joca.2015.12.004 26708258
7. Zhang L, Wei TT, Li Y, Li J, Fan Y, Huang FQ, et al. Functional Metabolomics Characterizes a Key Role for N-Acetylneuraminic Acid in Coronary Artery Diseases. Circulation. 2018;137: 1374–1390. doi: 10.1161/CIRCULATIONAHA.117.031139 29212895
8. Pinto RC. Chemometrics Methods and Strategies in Metabolomics. Adv Exp Med Biol. 2017;965: 163–190. doi: 10.1007/978-3-319-47656-8_7 28132180
9. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461: 218–223. doi: 10.1038/nature08454 19741703
10. Steuer R. Review: on the analysis and interpretation of correlations in metabolomic data. Brief Bioinform. 2006;7: 151–158. bbl009 [pii]. doi: 10.1093/bib/bbl009 16772265
11. Gao X, Zhang W, Wang Y, Pedram P, Cahill F, Zhai G, et al. Serum metabolic biomarkers distinguish metabolically healthy peripherally obese from unhealthy centrally obese individuals. Nutr Metab (Lond). 2016;13: 33-016-0095-9. eCollection 2016. doi: 10.1186/s12986-016-0095-9 27175209
12. Zhang W, Likhodii S, Zhang Y, Aref-Eshghi E, Harper PE, Randell E, et al. Classification of osteoarthritis phenotypes by metabolomics analysis. BMJ Open. 2014;4: e006286-2014-006286. doi: 10.1136/bmjopen-2014-006286 25410606
13. Hu T, Oksanen K, Zhang W, Randell E, Furey A, Sun G, et al. An evolutionary learning and network approach to identifying key metabolites for osteoarthritis. PLoS Comput Biol. 2018;14: e1005986. doi: 10.1371/journal.pcbi.1005986 29494586
14. Hu T, Zhang W, Fan Z, Sun G, Likhodi S, Randell E, et al. Metabolomics Differential Correlation Network Analysis of Osteoarthritis. Pac Symp Biocomput. 2016;21: 120–131. 9789814749411_0012 [pii]. 26776179
15. Auro K, Joensuu A, Fischer K, Kettunen J, Salo P, Mattsson H, et al. A metabolic view on menopause and ageing. Nat Commun. 2014;5: 4708. doi: 10.1038/ncomms5708 25144627
16. Yamatani H, Takahashi K, Yoshida T, Soga T, Kurachi H. Differences in the fatty acid metabolism of visceral adipose tissue in postmenopausal women. Menopause. 2014;21: 170–176. doi: 10.1097/GME.0b013e318296431a 23760430
17. Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C, et al. Human serum metabolic profiles are age dependent. Aging Cell. 2012;11: 960–967. doi: 10.1111/j.1474-9726.2012.00865.x 22834969
18. Lizardo DY, Parisi LR, Li N, Atilla-Gokcumen GE. Noncanonical Roles of Lipids in Different Cellular Fates. Biochemistry. 2018;57: 22–29. doi: 10.1021/acs.biochem.7b00862 29019646
19. Fischer LM, daCosta KA, Kwock L, Stewart PW, Lu TS, Stabler SP, et al. Sex and menopausal status influence human dietary requirements for the nutrient choline. Am J Clin Nutr. 2007;85: 1275–1285. 85/5/1275 [pii]. doi: 10.1093/ajcn/85.5.1275 17490963
20. Zeisel SH, Mar MH, Zhou Z, da Costa KA. Pregnancy and lactation are associated with diminished concentrations of choline and its metabolites in rat liver. J Nutr. 1995;125: 3049–3054. doi: 10.1093/jn/125.12.3049 7500183
21. Zeisel SH, Niculescu MD. Perinatal choline influences brain structure and function. Nutr Rev. 2006;64: 197–203. doi: 10.1111/j.1753-4887.2006.tb00202.x 16673755
22. Mattsson C, Olsson T. Estrogens and glucocorticoid hormones in adipose tissue metabolism. Curr Med Chem. 2007;14: 2918–2924. doi: 10.2174/092986707782359972 18045137
23. Santosa S, Jensen MD. Adipocyte fatty acid storage factors enhance subcutaneous fat storage in postmenopausal women. Diabetes. 2013;62: 775–782. doi: 10.2337/db12-0912 23209188
24. Maynar M, Mahedero G, Maynar I, Maynar JI, Tuya IR, Caballero MJ. Menopause-induced changes in lipid fractions and total fatty acids in plasma. Endocr Res. 2001;27: 357–365. 11678583
25. Milewicz A, Tworowska U, Demissie M. Menopausal obesity—myth or fact? Climacteric. 2001;4: 273–283. 11770183
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Úspěšná resuscitativní thorakotomie v přednemocniční neodkladné péči
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania