UPLC-Q-TOF/MS based untargeted metabolite and lipid analysis on premature ovarian insufficiency plasma samples


Taşcı Y., Fındık R. B. , Pekcan M. K. , KAPLAN O., ÇELEBİER M.

Current Pharmaceutical Analysis, vol.17, no.4, pp.474-483, 2021 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.2174/1573412916666200102112339
  • Journal Name: Current Pharmaceutical Analysis
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Biotechnology Research Abstracts, Chemical Abstracts Core, EMBASE
  • Page Numbers: pp.474-483
  • Keywords: Lipidomics, Liquid chromatography mass spectrometry, Metabolomics, Premature ovarian insufficiency, Ultra performance liquid chromatography
  • Kütahya Health Sciences University Affiliated: Yes

Abstract

© 2021 Bentham Science Publishers.Background: Metabolomics is one of the main areas to understand cellular process at molecular level by analyzing metabolites. In recent years metabolomics has emerged as a key tool to understand molecular basis of diseases, to find diagnostic and prognostic biomarkers and develop new treatment opportunities and drug molecules. Objective: In this study, untargeted metabolite and lipid analysis were performed to identify potential biomarkers on premature ovarian insufficiency plasma samples. 43 POI subject plasma samples were compared with 32 healthy subject plasma samples. Methods: Plasma samples were pooled and extracted using chloroform:methanol:water (3:3:1 v/v/v) mixture. Agilent 6530 LC/MS Q-TOF instrument equipped with ESI source was used for analysis. A C18 column (Agilent Zorbax 1.8 µM, 50 x 2.1 mm) was used for separation of the metabolites and li-pids. XCMS, an “R software” based freeware program, was used for peak picking, grouping and compar-ing the findings. Isotopologue Parameter Optimization (IPO) software was used to optimize XCMS pa-rameters. The analytical methodology and data mining process were validated according to the literature. Results: 83 metabolite peaks and 213 lipid peaks were found to be in semi-quantitatively and statisti-cally different (fold change >1.5, p <0.05) between the POI plasma samples and control subjects. Conclusion: According to the results, two groups were successfully separated through principal component analysis. Among the peaks, phenyl alanine, decanoyl-L-carnitine, 1-palmitoyl lysophosphati-dylcholine and PC(O-16:0/2:0) were identified through auto MS/MS and matched with human metabo-lome database and proposed as plasma biomarker for POI and monitoring the patients in treatment pe-riod.