Smits Myrthe A J, Janssens Georges E, Goddijn Mariëtte, Hamer Geert, Houtkooper Riekelt H, Mastenbroek Sebastiaan
Amsterdam UMC, University of Amsterdam, Center for Reproductive Medicine, Reproductive Biology Laboratory, Amsterdam Reproduction & Development research institute, Amsterdam, The Netherlands.
Laboratory Genetic Metabolic Diseases, Amsterdam Gastroenterology and Metabolism, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Hum Reprod Open. 2021 May 16;2021(2):hoab020. doi: 10.1093/hropen/hoab020. eCollection 2021.
Are genes known to be involved in somatic cell ageing, particularly related to longevity pathways, associated with the accelerated ageing process of the ovary?
Growth, metabolism, and cell-cycle progression-related pathways that are involved in somatic cell ageing are also associated with ovarian ageing.
Ovarian ageing is characterized by a gradual decline in ovarian follicle quantity, a decline in oocyte quality, and lower chances of pregnancy. Genetic pathways modulating the rate of somatic cell ageing have been researched intensively. Ovarian ageing does not follow the same timeline as somatic cell ageing, as signs of ovarian ageing occur at a younger female age, while the somatic cells are still relatively young. It is not known whether the generally recognized somatic cell longevity genes also play a role during ovarian ageing. Looking at somatic cell longevity genes can lead to new hypotheses and possible treatment options for subfertility caused by ovarian ageing.
In this observational study, we analysed a dataset of individual gene expression profiles of 38 germinal vesicle (GV) oocytes from 38 women aged between 25 and 43 years. We correlated female age (calendar age in years) and biological age (factors known to be associated with ovarian ageing such as dosage of FSH needed for ovarian hyperstimulation, and antral follicle count (AFC)) with gene expression signatures of longevity pathways.
PARTICIPANTS/MATERIALS SETTING METHODS: Transcripts of 38 GV oocytes were used for individual gene expression analysis. R version 3.5.1 was used to process and analyse data. The GeneAge database (build 19) was used to obtain mouse ageing-related genes. Human to mouse orthologues were obtained using the R package biomaRt. Correlations and significance between gene expression data and age were tested for using Pearson's product moment correlation coefficient using ranked expression data. Distributions were compared with an ANOVA, and the Tukey Honest Significant Difference method was used to control for the Type I error rate across multiple comparisons.
Of the 136 genes in the GeneAge database, the expression of 15 anti-longevity genes identified in oocytes showed a positive correlation with female calendar age and FSH dosage administered during ICSI treatment, and a negative correlation with AFC. Expression of 32 pro-longevity genes was negatively correlated with calendar age and FSH dosage, and positively correlated with AFC. In general, anti- and pro-longevity genes changed in opposing directions with advancing maternal age in oocytes. Notably, the anti-longevity genes include many 'growth'-related genes involved in the mechanistic target of rapamycin (mTOR) Complex 1 pathway, such as EIF5A2, EIF3H, EIF4E, and mTOR. The pro-longevity genes include many cell-cycle progression-related genes involved in DNA damage repair (e.g. XRCC6, ERCC2, and MSH2) or cell-cycle checkpoint regulation genes (e.g. ATM, BRCA1, TP53, TP63, TP73, and BUB1B).
Using mature oocytes instead of GV-stage oocytes discarded from ICSI treatments may provide different results. No correction for multiple testing was carried out on individual genes because a small set of longevity-related genes was selected a priori for the analysis. The global trend was corrected for multiple testing and remained significant. This work was an observational study and, as no additional experimental work was performed, the associations described do not directly demonstrate the involvement of such genes in oocyte ageing.
Growth, metabolism, and cell-cycle progression-related pathways that are known to be involved in somatic cell ageing were associated with ovarian ageing. If experimental data are obtained to support these associations, we suggest that interventions known to modulate these processes could benefit women suffering from ovarian ageing.
STUDY FUNDING/COMPETING INTERESTS: G.E.J. is supported by a VENI grant from ZonMw (https://www.zonmw.nl). Work in the Houtkooper group is financially supported by an ERC Starting grant (No. 638290), a VIDI grant from ZonMw (No. 91715305), and the Velux Stiftung (No. 1063). M.G. declares several research and educational grants from Guerbet, Merck and Ferring (all location VUmc), outside the scope of the submitted work. The other authors report no competing interest.
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已知参与体细胞衰老的基因，尤其是与长寿途径相关的基因，是否与卵巢的加速衰老过程有关？
参与体细胞衰老的生长、代谢和细胞周期进程相关途径也与卵巢衰老有关。
卵巢衰老的特征是卵巢卵泡数量逐渐减少、卵母细胞质量下降以及怀孕几率降低。调节体细胞衰老速率的遗传途径已得到深入研究。卵巢衰老与体细胞衰老的时间线不同，因为卵巢衰老的迹象在女性年龄较小时就会出现，而此时体细胞仍相对年轻。尚不清楚普遍认可的体细胞长寿基因在卵巢衰老过程中是否也起作用。研究体细胞长寿基因可能会为卵巢衰老导致的生育力低下带来新的假设和可能的治疗选择。
在这项观察性研究中，我们分析了38名年龄在25至43岁之间女性的38个生发泡（GV）卵母细胞的个体基因表达谱数据集。我们将女性年龄（以年为单位的日历年龄）和生物学年龄（已知与卵巢衰老相关的因素，如卵巢过度刺激所需的促卵泡激素剂量和窦卵泡计数（AFC））与长寿途径的基因表达特征进行了关联分析。
参与者/材料设置方法：使用38个GV卵母细胞的转录本进行个体基因表达分析。使用R 3.5.1版本处理和分析数据。使用GeneAge数据库（版本19）获取小鼠衰老相关基因。使用R包biomaRt获得人与小鼠的直系同源基因。使用Pearson积矩相关系数对基因表达数据与年龄之间的相关性和显著性进行检验，使用排序后的表达数据。通过方差分析比较分布情况，并使用Tukey真实显著性差异方法控制多重比较中的I型错误率。
在GeneAge数据库的136个基因中，卵母细胞中鉴定出的15个抗衰老基因的表达与女性日历年龄和ICSI治疗期间使用的促卵泡激素剂量呈正相关，与AFC呈负相关。32个长寿基因的表达与日历年龄和促卵泡激素剂量呈负相关，与AFC呈正相关。总体而言，随着母龄增加，卵母细胞中的抗衰老和长寿基因变化趋势相反。值得注意的是，抗衰老基因包括许多参与雷帕霉素机制靶点（mTOR）复合体1途径的“生长”相关基因，如EIF5A2、EIF3H、EIF4E和mTOR。长寿基因包括许多参与DNA损伤修复（如XRCC6、ERCC2和MSH2）或细胞周期检查点调节基因（如ATM、BRCA1、TP53、TP63、TP73和BUB1B）的细胞周期进程相关基因。
使用成熟卵母细胞而非ICSI治疗中丢弃的GV期卵母细胞可能会得到不同结果。未对单个基因进行多重检验校正，因为分析时预先选择了一小部分与长寿相关的基因。对整体趋势进行了多重检验校正，结果仍然显著。这项工作是一项观察性研究，由于未进行额外的实验工作，所描述的关联并不能直接证明这些基因参与了卵母细胞衰老。
已知参与体细胞衰老的生长、代谢和细胞周期进程相关途径与卵巢衰老有关。如果获得实验数据支持这些关联，我们建议已知调节这些过程的干预措施可能会使卵巢衰老的女性受益。
研究资金/利益冲突：G.E.J. 得到了ZonMw的VENI资助（https://www.zonmw.nl）。Houtkooper小组的工作得到了欧洲研究委员会启动基金（项目编号638290）、ZonMw的VIDI资助（项目编号91715305）和Velux Stiftung（项目编号1063）的资金支持。M.G. 声明在提交工作范围之外从Guerbet、Merck和Ferring（均位于VUmc）获得了多项研究和教育资助。其他作者报告无利益冲突。
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