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      Nontargeted metabolomics analysis of follicular fluid in patients with endometriosis provides a new direction for the study of oocyte quality

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          Abstract

          Endometriosis is a common, estrogen‐dependent chronic gynecological disease that endangers the reproductive system and systemic metabolism of patients. We aimed to investigate the differences in metabolic profiles in the follicular fluid between infertile patients with endometriosis and controls. A total of 25 infertile patients with endometriosis and 25 infertile controls who were similar in age, BMI, fertilization method and ovulation induction treatment were recruited in this study. Metabolomics analysis of follicular fluid was performed by two methods of high‐performance liquid chromatography tandem mass spectrometry. There were 36 upregulated and 17 downregulated metabolites in the follicular fluid of patients in the endometriosis group. KEGG pathway analysis revealed that these metabolites were enriched in phenylalanine, tyrosine and tryptophan biosynthesis, aminoacyl‐tRNA biosynthesis, phenylalanine metabolism and pyrimidine metabolism pathways. A biomarker panel consisting of 20 metabolites was constructed by random forest, with an accuracy of 0.946 and an AUC of 0.988. This study characterizes differences in follicular fluid metabolites and associated pathway profiles in infertile patients with endometriosis. These findings can provide a better comprehensive understanding of the disease and a new direction for the study of oocyte quality, as well as potential metabolic markers for the prognosis of endometriosis.

          Abstract

          This study found differences in follicular fluid metabolites and related pathways in infertile patients with endometriosis. It can provide a better comprehensive understanding and new direction for the study of oocyte quality and potential metabolic markers for the prognosis of endometriosis.

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          Most cited references54

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          MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

          Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca . Graphical Abstract From raw data to statistical and functional insights using MetaboAnalyst 5.0.
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            HMDB 4.0: the human metabolome database for 2018

            Abstract The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
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              Metabolomics: beyond biomarkers and towards mechanisms.

              Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases.
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                Author and article information

                Contributors
                yujie.zou@whu.edu.cn
                sm.chen@whu.edu.cn
                peneyyan@mail.ustc.edu.cn
                Journal
                MedComm (2020)
                MedComm (2020)
                10.1002/(ISSN)2688-2663
                MCO2
                MedComm
                John Wiley and Sons Inc. (Hoboken )
                2688-2663
                30 May 2023
                June 2023
                : 4
                : 3 ( doiID: 10.1002/mco2.v4.3 )
                : e302
                Affiliations
                [ 1 ] Reproductive Medicine Center Renmin Hospital of Wuhan University Wuhan Hubei China
                [ 2 ] The Institute for Advanced Studies Wuhan University Wuhan Hubei China
                [ 3 ] Department of Obstetrics and Gynecology Key Laboratory of Birth Defects and Related of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University Chengdu Sichuan China
                [ 4 ] Department of Clinical Laboratory Renmin Hospital of Wuhan University Wuhan Hubei China
                Author notes
                [*] [* ] Correspondence

                Yan Zhang, Department of Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China.

                Email: peneyyan@ 123456mail.ustc.edu.cn

                Suming Chen, The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China.

                Email: sm.chen@ 123456whu.edu.cn

                Yujie Zou, Reproductive Medicine Center, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, China.

                Email: yujie.zou@ 123456whu.edu.cn

                [#]

                Yiqiu Wei, Zhourui Zhang and Yaoyao Zhang have contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-1556-7432
                Article
                MCO2302
                10.1002/mco2.302
                10229744
                37265938
                82d2a780-f7d1-434d-b857-a63df23870b0
                © 2023 The Authors. MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 May 2023
                : 28 December 2022
                : 11 May 2023
                Page count
                Figures: 6, Tables: 3, Pages: 16, Words: 7885
                Funding
                Funded by: National Key Research and Development Program of China , doi 10.13039/501100012166;
                Award ID: 2021YFC2700700
                Funded by: the National Natural Science Foundation of China
                Award ID: 22074111
                Award ID: 22004093
                Award ID: 82271672
                Funded by: Fundamental Research Funds for the Central Universities , doi 10.13039/501100012226;
                Award ID: 2042022kf1210
                Funded by: Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University
                Award ID: JCRCWL‐2022‐001
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                June 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.8 mode:remove_FC converted:30.05.2023

                endometriosis,follicular fluid,infertile,lc‒ms,metabolomics,reproductive

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