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Metabolic signatures in follicular fluid from human ovarian follicles during growth progression reveals that LPC as a predictor of follicular development and ovarian sensitivity
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  • Ji hong Yang,
  • Yangbai Li,
  • Suying Li,
  • Yan Zhang,
  • Ruizhi Feng,
  • Rui Huang,
  • Min jian Chen,
  • Yun Qian
Ji hong Yang
Nanjing Medical University Second Affiliated Hospital
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Yangbai Li
Nanjing Medical University Second Affiliated Hospital
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Suying Li
Nanjing Medical University Second Affiliated Hospital
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Yan Zhang
Nanjing Medical University Second Affiliated Hospital
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Ruizhi Feng
Nanjing Medical University
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Rui Huang
Nanjing Medical University
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Min jian Chen
Nanjing Medical University
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Yun Qian
Nanjing Medical University Second Affiliated Hospital

Corresponding Author:[email protected]

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Abstract

Objective To investigate the metabolic characteristics of follicular fluid (FF) from human ovarian follicles of different developmental stages, and whether there are metabolic predictors of follicular development (FD) with clinical implications. Design A population-based study. Setting A two-stage study from a reproductive medicine center in Nanjing, China between 2019 and 2020. Population A total of 226 infertile women, with tubal complications or unexplained sterility, experiencing their first in vitro fertilization cycle. Methods and main outcome measures The FF from both large follicles (LFs) and matched-small follicles (SFs) was analyzed with the wide range of targeted metabolome technique. The metabolic signatures were described by multi-omics integration technology including our metabolomic data and published transcriptomic data. The potential biomarkers of FD were next verified using enzyme-linked immunoassay with FF and blood serum from an independent 200 participants. Results Overall, 116 differential metabolites were found in 26 LFs and 26 SFs. The combination of pathway analysis and network analysis revealed the importance of lysophosphatidylcholine (LPC) metabolism in FF during FD. Moreover, differential metabolites and mRNAs were connected into a network. Upon linking metabolites with clinical information, followed by an independent population verification, we found that LPC was able to predict FD and ovarian sensitivity parameters both in FF and blood serum. Conclusions We described the FF metabolic signatures from ovarian follicles of different developmental stages, and LPC can be used as a biomarker of FD and ovarian sensitivity, offering potential detection and therapeutic targets for enhancing follicle and oocyte health in humans.