Analysis of oocyte granulosa cell transcriptome data in aged women

doi:10.12307/2025.070

Abstract

Abstract: BACKGROUND: The granulosa cell state greatly affects the quality of oocytes. As the increased proportion of aged women in assisted reproduction, granulosa cells from patients of different ages are examined at transcript levels in order to better assess oocyte quality.
OBJECTIVE: To investigate the expression changes of mRNA and long non-coding RNA in granulosa cells from the young and aged patients.
METHODS: Waste cumulus granulosa cells obtained during the assisted reproductive cycle. Young group was in 25-35 years old, and the aged group was in 38-45 years old. Granulosa cells collected from the young and aged females were analyzed using RNA sequencing, three replications for each group.
RESULTS AND CONCLUSION: (1) The RNA sequencing analysis results showed that the average sequencing volume of samples was more than 14 G; the data quality Q30 after quality control was more than 93%; the average mapping rate of data was 98.4%, which indicates that the data had good quality. (2) The results of principal component analysis and correlation analysis showed that the samples of the aged group and the young group could be clearly distinguished. (3) The difference analysis results showed that compared with the young group, a total of 410 differentially expressed mRNAs were detected in the aged group (167 up-regulated and 243 down-regulated). GO analysis results showed that the down-regulated genes were mainly enriched in regulatedexocytosis and the emetabolicprocess. GSEA analysis results showed that secretion-related pathways in the aged group were down-regulated. (4) Compared with the young group, 662 differentially expressed long non-coding RNAs were detected in the aged group, and 1 772 protein-coding genes were directly regulated by these long non-coding RNAs, among which 59 genes overlaped with differentially expressed genes. The results showed that the expression of secretion-related genes and pathways in granulosa cells of the aged group was down-regulated, thus affecting oocyte quality. At the same time, these down-regulated genes were regulated by long non-coding RNA. Therefore, the expression of long non-coding RNA might be related to age.

Key words: 年龄, 颗粒细胞, 卵母细胞, 全转录组, 差异基因, 长链非编码基因

CLC Number:

Qin Lifeng, Wang Jiaqiang, Zhu Ling. Analysis of oocyte granulosa cell transcriptome data in aged women[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(19): 4069-4075.

Figures/Tables 5

References

1] CECCONI S, CICCARELLI C, BARBERI M, et al. Granulosa cell-oocyte interactions. Eur J Obstet Gynecol Reprod Biol. 2004;115 Suppl 1: S19-22.
[2] GILULA NB, EPSTEIN ML, BEERS WH. Cell-to-cell communication and ovulation. A study of the cumulus-oocyte complex. J Cell Biol. 1978; 78(1):58-75.
[3] DONG JP, DAI ZH, JIANG ZX, et al. CD24: a marker of granulosa cell subpopulation and a mediator of ovulation. Cell Death Dis. 2019; 10(11):791.
[4] LI Q, MCKENZIE LJ, MATZUK MM. Revisiting oocyte-somatic cell interactions: in search of novel intrafollicular predictors and regulators of oocyte developmental competence. Mol Hum Reprod. 2008;14(12): 673-678.
[5] UYAR A, TORREALDAY S, SELI E. Cumulus and granulosa cell markers of oocyte and embryo quality. Fertil Steril. 2013;99(4):979-997.
[6] SELI E, ROBERT C, SIRARD MA. OMICS in assisted reproduction: possibilities and pitfalls. Mol Hum Reprod. 2010;16(8):513-530.
[7] HERMAN AB, TSITSIPATIS D, GOROSPE M. Integrated lncRNA function upon genomic and epigenomic regulation. Mol Cell. 2022;82(12): 2252-2266.
[8] CHEN S, ZHOU Y, CHEN Y, et al. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-i890.
[9] KIM D, PAGGI JM, PARK C, et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37(8):907-915.
[10] LIAO Y, SMYTH GK, SHI W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923-930.
[11] LUN ATL, SMYTH GK. No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data. Stat Appl Genet Mol Biol. 2017;16(2):83-93.
[12] ZHOU Y, ZHOU B, PACHE L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.
[13] SUBRAMANIAN A, TAMAYO P, MOOTHA VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550.
[14] KULUS J, KRANC W, KULUS M, et al. New Gene Markers of Exosomal Regulation Are Involved in Porcine Granulosa Cell Adhesion, Migration, and Proliferation. Int J Mol Sci. 2023;24(14):11873.
[15] LATHAM KE, WIGGLESWORTH K, MCMENAMIN M, et al. Stage-dependent effects of oocytes and growth differentiation factor 9 on mouse granulosa cell development: advance programming and subsequent control of the transition from preantral secondary follicles to early antral tertiary follicles. Biol Reprod. 2004;70(5):1253-1262.
[16] DAVIS JS, FARESE RV, CLARK MR. Gonadotropin-releasing hormone (GnRH) stimulates phosphatidylinositol metabolism in rat granulosa cells: mechanism of action of GnRH. Proc Natl Acad Sci U S A. 1983; 80(7):2049-2053.
[17] MORELLI MB, BARBERI M, GAMBARDELLA A, et al. Characterization, expression, and functional activity of pituitary adenylate cyclase-activating polypeptide and its receptors in human granulosa-luteal cells. J Clin Endocrinol Metab. 2008;93(12):4924-4932.
[18] PYUN JA, KIM S, CHA DH, et al. Epistasis between IGF2R and ADAMTS19 polymorphisms associates with premature ovarian failure. Hum Reprod. 2013;28(11):3146-3154.
[19] TATONE C, AMICARELLI F, CARBONE MC, et al. Cellular and molecular aspects of ovarian follicle ageing. Hum Reprod Update. 2008;14(2): 131-142.
[20] ALVIGGI C, HUMAIDAN P, HOWLES CM, et al. Biological versus chronological ovarian age: implications for assisted reproductive technology. Reprod Biol Endocrinol. 2009;7:101.
[21] TRIPATHI A, PREMKUMAR KV, PANDEY AN, et al. Melatonin protects against clomiphene citrate-induced generation of hydrogen peroxide and morphological apoptotic changes in rat eggs. Eur J Pharmacol. 2011;667(1-3):419-424.
[22] KAWAMURA K, CHENG Y, KAWAMURA N, et al. Pre-ovulatory LH/hCG surge decreases C-type natriuretic peptide secretion by ovarian granulosa cells to promote meiotic resumption of pre-ovulatory oocytes. Hum Reprod. 2011;26(11):3094-3101.
[23] HSUEH AJ, KAWAMURA K, CHENG Y, et al. Intraovarian control of early folliculogenesis. Endocr Rev. 2015;36(1):1-24.
[24] GENG X, ZHAO J, HUANG J, et al. lnc-MAP3K13-7:1 Inhibits Ovarian GC Proliferation in PCOS via DNMT1 Downregulation-Mediated CDKN1A Promoter Hypomethylation. Mol Ther. 2021;29(3): 1279-1293.
[25] WU Y, XIAO H, PI J, et al. LncRNA lnc_13814 promotes the cells apoptosis in granulosa cells of duck by acting as apla-miR-145-4 sponge. Cell Cycle. 2021;20(9):927-942.
[26] YAO G, KONG Y, YANG G, et al. Lnc-GULP1-2:1 affects granulosa cell proliferation by regulating COL3A1 expression and localization. J Ovarian Res. 2021;14(1):16.
[27] CHERMUŁA B, HUTCHINGS G, KRANC W, et al. Expression Profile of New Gene Markers and Signaling Pathways Involved in Immunological Processes in Human Cumulus-Oophorus Cells. Genes (Basel). 2021; 12(9):1369.
[28] XU D, SONG S, WANG F, et al. Single-cell transcriptomic atlas of goat ovarian aging. J Anim Sci Biotechnol. 2023;14(1):151.
[29] ORIÁ RB, DE ALMEIDA JZ, MOREIRA CN, et al. Apolipoprotein E Effects on Mammalian Ovarian Steroidogenesis and Human Fertility. Trends Endocrinol Metab. 2020;31(11):872-883.
[30] ZHAO ZH, WANG XY, SCHATTEN H, et al. Single cell RNA sequencing techniques and applications in research of ovary development and related diseases. Reprod Toxicol. 2022;107:97-103.
[31] D’AURORA M, SPERDUTI S, DI EMIDIO G, et al. Inside the granulosa transcriptome. Gynecol Endocrinol. 2016;32(12):951-956.
[32] CHRONOWSKA E. High-throughput analysis of ovarian granulosa cell transcriptome. Biomed Res Int. 2014;2014:213570.