NADase CD38 is a key determinant of ovarian aging

Yang, Qingling; Chen, Wenhui; Cong, Luping; Wang, Mengchen; Li, Hui; Wang, Huan; Luo, Xiaoyan; Zhu, Jing; Zeng, Xinxin; Zhu, Zhenye; Xu, Yining; Lei, Min; Zhao, Yanqing; Wei, Chenlu; Sun, Yingpu

doi:10.1038/s43587-023-00532-9

Download PDF

Article
Open access
Published: 21 December 2023

NADase CD38 is a key determinant of ovarian aging

Qingling Yang ORCID: orcid.org/0000-0002-9187-7961^1,2,3^na1,
Wenhui Chen^1,2,3^na1,
Luping Cong^1,2,3^na1,
Mengchen Wang^1,2,3,
Hui Li^1,2,3,
Huan Wang^1,2,3,
Xiaoyan Luo^1,2,3,
Jing Zhu^1,2,3,
Xinxin Zeng^1,2,3,
Zhenye Zhu^1,2,3,
Yining Xu^1,2,3,
Min Lei^1,2,3,
Yanqing Zhao^1,2,3,
Chenlu Wei^1,2,3 &
…
Yingpu Sun ORCID: orcid.org/0000-0003-3783-6509^1,2,3

Nature Aging volume 4, pages 110–128 (2024)Cite this article

8333 Accesses
3 Citations
42 Altmetric
Metrics details

Subjects

Abstract

The ovary ages earlier than most other tissues, yet the underlying mechanisms remain elusive. Here a comprehensive analysis of transcriptomic landscapes in different organs in young and middle-aged mice revealed that the ovaries showed earlier expression of age-associated genes, identifying increased NADase CD38 expression and decreased NAD⁺ levels in the ovary of middle-aged mice. Bulk and single-cell RNA sequencing revealed that CD38 deletion mitigated ovarian aging, preserving fertility and follicle reserve in aged mice by countering age-related gene expression changes and intercellular communication alterations. Mechanistically, the earlier onset of inflammation induced higher expression levels of CD38 and decreased NAD⁺ levels in the ovary, thereby accelerating ovarian aging. Consistently, pharmacological inhibition of CD38 enhanced fertility in middle-aged mice. Our findings revealed the mechanisms underlying the earlier aging of the ovary relative to other organs, providing a potential therapeutic target for ameliorating age-related female infertility.

Spatiotemporal transcriptomic changes of human ovarian aging and the regulatory role of FOXP1

Article Open access 09 April 2024

A single-cell atlas of the aging mouse ovary

Article Open access 10 January 2024

Telomere associated gene expression as well as TERT protein level and telomerase activity are altered in the ovarian follicles of aged mice

Article Open access 30 July 2021

Main

Ovarian functional lifespan is determined by the size of the nonrenewable ovarian follicle reserve at birth, as well as the rate at which this endowment is depleted, ending with menopause¹. Modern trends of delayed childbearing lead to elevated infertility, miscarriages and birth defects in the offspring². Consequently, there is increased demand for the treatment of age-induced female infertility due to decreased oocyte quantity and quality³. Thus, comprehending physiological and molecular mechanisms of ovarian aging is crucial for effective infertility treatment and healthcare management.

The ovary ages early in females, with menopause around 50–52 years, much shorter than modern life expectancy of up to 90 years^4,5. Ovarian aging, marked by fertility decline and hormonal changes, impacts overall health due to the influence of ovarian hormones on various tissues, such as reproductive, bone, brain and skin tissues^6,7. Ovarian aging is associated with an increased risk of diabetes, heart disease, cancer and other conditions^6,8,9,10,11. Transplanting young mouse ovaries into older mice extends lifespan¹², improving cognitive, immune and renal functions¹³. Nevertheless, the molecular basis for the accelerated onset of ovarian aging compared to other organs remains unclear.

The ovarian follicle consists of an oocyte surrounded by granulosa and theca cells¹⁴. Oocyte–granulosa–theca cell interactions regulate follicular growth and atresia¹⁵. Dysfunction of follicles leads to premature ovarian insufficiency^16,17,18,19. Although the detailed mechanisms underlying ovarian aging are unknown, studies have found that ovarian aging leads to the accumulation of reactive oxygen species (ROS) and mitochondrial dysfunction in oocytes, resulting in aneuploidy associated with decreased embryonic development^20,21,22,23. Accumulating evidence has revealed that oxidative stress is one of the key factors inducing oocyte and granulosa cell apoptosis in mice²⁴. Monkey studies have also demonstrated that oxidative damage is a crucial factor in ovarian aging²⁵. In addition, inflammation genes play an important role in maintaining ovary function, and genetic deletion of inflammation-related genes, such as those encoding tumor necrosis factor (TNF), interleukin-1 alpha (IL-1α) and NLR family pyrin domain-containing 3 (NLRP3), delayed ovarian aging by inhibiting follicular atresia and improving oocyte quality^26,27,28,29.

In the present study, we analyzed the transcriptome of the ovaries, together with those of the liver, muscle, brain, heart, kidney and lung from young (2-month-old) and middle-aged (8-month-old) mice. We found an earlier onset of inflammation-induced CD38 activation and decreased NAD⁺ levels in the ovary compared with other organs in middle-aged mice, resulting in follicle depletion and decreased oocyte quality during aging. Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) revealed that genetic deletion of CD38 rescued age-related changes in gene expression. These changes involve genes associated with pathways such as the senescence-associated secretory phenotype (SASP), DNA repair, inflammation, core regulatory transcription factors (TFs) and cell–cell communication networks, ultimately resulting in higher female fertility. Furthermore, pharmacological inhibition of CD38 increased ovarian NAD⁺ levels and reduced inflammation, leading to improved oocyte quality. Overall, this study provides a comprehensive molecular understanding of ovarian senescence earlier than in other organs, offering potential clinical treatment strategies for ovarian aging.

Results

Ovary ages earlier than several other organs in middle-aged mice

Although the ovary ages earlier than most other organs in the body^30,31, a systematic molecular comparison among tissues is lacking. We performed comparative transcriptomic analysis of the ovary, heart, kidney, brain, lung, liver and muscle by using bulk RNA-seq from young and middle-aged female mice at 2 and 8 months of age, respectively. As shown in Fig. 1a, middle-aged ovaries had more differentially expressed genes (DEGs), followed by liver, muscle, heart, kidney, lung and brain (Supplementary Table 1 and Extended Data Fig. 1a–g). By analyzing gene transcription in senescence-related pathways, including ‘SASP’, ‘cell cycle’, ‘inflammation’ and ‘DNA repair’, we found that a larger number of transcripts in these pathways had altered expression in the ovary relative to other organs when comparing 8-month-old with 2-month-old mice (Fig. 1b). Increased expression of two senescence markers, namely, p16 and p21, was confirmed by using real-time PCR with reverse transcription (RT–PCR) and western blotting in middle-aged ovaries, but not in other tissues, when comparing 2-month-old and 8-month-old mice (Fig. 1c–e). These results suggest that ovarian senescence occurred earlier than that of most other organs in mice.

**Fig. 1: Earlier onset of senescence in the ovary than in other organs in middle-aged (8-month-old) compared with young (2-month-old) mice.**

Inflammation activation was earlier in the middle-aged ovary

To unveil mechanisms underlying ovarian aging, we performed Gene Ontology (GO) enrichment analysis on DEGs from the RNA-seq data between young (2-month-old) and middle-aged (8-month-old) ovaries. Upregulated DEGs in middle-aged ovaries were enriched in inflammation pathways encompassing ‘immune response activity’ and ‘immunoglobulin binding’ (Fig. 1f). In contrast, downregulated genes showed enrichment in pathways like ‘female gamete generation’, ‘meiotic cell cycle process’ and ‘oogenesis’ (Fig. 1g), indicating decreased ovarian function in middle-aged mice. Real-time RT–PCR and western blotting showed that ovarian gene transcripts for diverse inflammation factors were increased in middle-aged versus young mice, whereas no differences were observed in the other tissues tested (Fig. 1h–j).

The top five pathway-related genes from the GO ‘molecular function’ category are shown in Fig. 1k. Of note, the NAD⁺ consumption enzyme CD38, which is involved in three different pathways (NAD⁺ nucleosidase activity, NAD(P)⁺ nucleosidase activity and NAD⁺ nucleosidase), was upregulated in the ovaries of middle-aged mice. Considering that the decline in the NAD⁺ level induced by the increase in CD38 expression plays an important role in the liver, skeletal muscle and adipose tissue in aged mice³², we focused on the role of CD38 in the ovary.

Increased CD38 expression in middle-aged ovaries

NAD⁺ metabolism plays an important role during aging in mammals^{33,34,35,36,37}. The transcription levels of key enzymes responsible for NAD⁺ biogenesis and consumption (Fig. 2a) were analyzed in ovaries using transcriptomic sequencing. As shown in Fig. 2b, the expression levels of NAD⁺ biogenesis genes were not changed in the middle-aged ovary. Although no changes in most NAD⁺ consumption genes were found, increased transcription of Cd38 was evident (Fig. 2b). To confirm this finding, we further determined the expression of some key enzymes for NAD⁺ biogenesis and consumption in different tissues of middle-aged mice using real-time RT‒PCR and immunoblotting. The rate-limiting NAD⁺ biogenesis enzyme NAMPT’s transcription and protein expression remained unchanged in both the middle-aged ovary and other tested tissues (Fig. 2c–e). Furthermore, the key genes (Nmnat1, Nmnat2 and Nmnat3) responsible for NAD⁺ biogenesis exhibited consistent expression across all tissues tested, indicating unaltered NAD⁺ biogenesis in middle-aged tissues. Conversely, the NADase CD38 exhibited a notable increase in expression in the middle-aged ovary (Fig. 2c–e), whereas other enzymes (SIRT1 and PARP2) in NAD⁺ consumption pathways remained unchanged across all tissues (Fig. 2c–e). Enzyme activity assessment revealed an augmented CD38 activity specifically in the ovaries of middle-aged mice compared to young mice, while no such change was observed in other tissues (Fig. 2f). Consistent with the increased ovarian expression of CD38, we found a marked decrease in ovarian NAD⁺ levels in middle-aged mice but nonotable differences in other tissues (Fig. 2g). To determine if the increased expression of CD38 observed in middle-aged mice is also present in the human female reproductive system, follicular fluids were collected from young (20–25 years old) and middle-aged (>35 years old) individuals with blocked fallopian tubes. Decreased ovarian reserve was observed in middle-aged individuals, as demonstrated by the lower anti-Mullerian hormone (AMH) levels, fewer antral follicles and the lower number of retrieved oocytes compared with those in young individuals (Supplementary Table 2). CD38-positive cells were isolated using flow cytometry (Supplementary Fig. 1). Interestingly, human CD38 mRNA expression was higher in purified cells from middle-aged individuals compared to young individuals (Fig. 2h). Real-time RT–PCR analysis also revealed elevated transcript levels of age-related inflammation factors, including IL1A, IL1B, IL6, IL8, IL10 and TNF, in CD38-positive cells from middle-aged individuals relative to younger individuals (Fig. 2i). These results suggest that increased expression of CD38 in middle-aged ovaries is a common feature in mammals, indicating that the earlier onset of increased CD38 expression and declining NAD⁺ levels may underlie the earlier senescence in the ovary relative to other organs.

**Fig. 2: Increased expression of CD38 and decreased NAD⁺ levels in ovaries but not in other tissues in middle-aged mice.**

Deletion of CD38 improved the fecundity in female mice

A Cd38-deficient mouse model lacking exons 2–4 of the Cd38 gene was generated using the CRISPR–Cas9 approach to evaluate the impact of CD38 on female fertility (Extended Data Fig. 2a). PCR and western blotting analysis confirmed the Cd38 mutation and the absence of CD38 protein in the ovaries of Cd38^−/− mice (Extended Data Fig. 2b). As shown in Fig. 3a, an increase in the ovarian NAD⁺ content was observed in Cd38-deficient mice compared with controls at 2, 8 and 12 months of age. Ovarian weight was also higher in Cd38^−/− versus wild-type (WT) mice (Fig. 3b) at all ages tested. In addition, serum AMH levels were higher in 8-month-old and 12-month-old Cd38^−/− mice than in their WT littermates (Fig. 3c). Comparison of the reproductive potential between Cd38^−/− and control mice showed a notably larger litter size from Cd38^−/− mice compared with controls at young (2–6 months), middle-aged (7–9 months) and aged (10–12 months) mice (Fig. 3d).

Fig. 3: CD38 deletion increased ovarian NAD⁺ levels and improved ovarian follicle reserve, increased reproductive ability of aging mice and shifted the ovarian transcriptome toward a more youthful profile.

Ovarian reserve was analyzed by counting the number of follicles at different developmental stages. Representative images of Cd38^−/− and WT ovarian sections from mice at different ages are shown in Fig. 3e. The numbers of primordial and total follicles were notably higher in Cd38^−/− ovaries than in WT ovaries from 2-month-old (Fig. 3f), 8-month-old (Fig. 3g) and 12-month-old (Fig. 3h) mice. However, no differences were found in the number of primary, secondary, antral and atretic follicles between Cd38^−/− and WT ovaries (Fig. 3f–h). Additionally, CD38 deletion reduced the expression of inflammation-related genes (Extended Data Fig. 2c). Given a prior study indicating fibrosis in the ovarian stromal compartment affecting oocyte release³⁸, we examined ovarian fibrosis in aged mice using picrosirius red (PSR) staining. Notably, CD38 deletion led to decreased collagen accumulation in stromal compartment in the aged ovary (Extended Data Fig. 2d,e). CD38 deletion also increased DNA damage (Extended Data Fig. 2f,g) and apoptosis (Extended Data Fig. 2h,i) in follicles in aged (12-month-old) mice compared with WT controls. Following gonadotropin treatment to induce ovulation, 12-month-old Cd38^−/− mice displayed larger ovaries compared to age-matched WT mice (Extended Data Fig. 2j,k), indicating enhanced ovulatory potential in aged Cd38^−/− mice. An increased number of ovulated oocytes and a decreased rate of oocytes with fragmentation were observed in 12-month-old Cd38^−/− mice compared with age-matched control mice (Extended Data Fig. 2l–n). These observations suggested that disruption of CD38 improved the fecundity in female mice.

CD38 deletion reversed ovarian aging-related gene expression

We performed transcriptome analysis of ovaries from Cd38^−/− and WT mice in the diestrus stage at different ages using bulk RNA-seq to assess whether a longer reproductive lifespan in Cd38-null mice was accompanied by molecular changes. The heat map data showed that the transcriptome profile of ovaries from middle-aged (8-month-old) WT mice was different from that of ovaries from young (2-month-old) WT mice (Fig. 3i and Supplementary Table 1). Of interest, the transcriptome profile of ovaries from middle-aged Cd38^−/− mice clustered in an intermediate position between the middle-aged and young WT ovaries (Fig. 3i and Supplementary Table 3). The expression of key upregulated and downregulated genes in ovaries from each group was further confirmed using real-time RT‒PCR (Supplementary Fig. 2). GO analysis revealed that downregulated genes in middle-aged Cd38^−/− ovaries were mostly enriched in pathways related to inflammation and NAD⁺ metabolism (Fig. 3j). Upregulated genes in the middle-aged Cd38^−/− ovaries were enriched in the processes of reproductive development (Fig. 3k). Analysis of DEGs in senescence-related pathways, including ‘SASP’, ‘cell cycle’, ‘DNA repair’ and ‘inflammation’, showed that the transcriptional signature of 8-month-old Cd38^−/− ovaries for these gene sets was intermediate between 2-month-old and 8-month-old WT ovaries (Fig. 3l). Gene set enrichment analysis (GSEA) further demonstrated that deletion of CD38 improved ovarian function, as shown by enrichment of pathways such as ‘female gamete generation’ and ‘reproductive system development’ (Extended Data Fig. 3a). Ovarian transcriptome analysis was also performed with 12-month-old Cd38^−/− and age-matched WT mice. Similarly, the results showed that deletion of CD38 partially reversed the age-related changes in pathways related to ovarian function, mitochondria, inflammation and SASP (Extended Data Figs. 3b and 4a–d and Supplementary Table 4). These results suggest that CD38 deletion prevents ovarian senescence.

In situ immunohistochemistry (IHC) staining of CD38 on ovarian sections revealed predominant localization of CD38 in the stroma, corpora lutea and vessel (Fig. 4a). These findings are consistent with those reported in a recent study, which highlighted the role of CD38 in regulating ovarian function and fecundity through NAD⁺ metabolism³⁹. To gain further insights into the impact of aging and CD38 deletion on cell-type transcriptional profiles, we generated single-cell atlases for young (2-month-old) WT mice, old (12-month-old) WT mice and Cd38^−/− (12-month-old) mice. After stringent cell filtration, 64,479 cells were retained for subsequent analyses. Global ovarian cell populations were visualized using uniform manifold approximation and projection (UMAP), which led to the identification of six distinct cell types based on specific marker expression (Supplementary Table 5): granulosa cells, stromal cells, immune cells, endothelial cells, epithelial cells and oocytes (Fig. 4b). Analysis of Cd38 expression levels across the six ovarian cell types revealed prominent expression in endothelial cells and immune cells (Fig. 4c). The proportions of cell types captured by single-cell sequencing were comparable across young, old and CD38-deleted old mice. As shown in Fig. 4d, most of the cell types exhibited changes in proportions during aging, which were reversed after deletion of CD38, including granulosa cells, stromal cells, epithelial cells and oocytes. Consistent with the findings from bulk-seq data of the entire ovary, scRNA-seq data also showed upregulation of SASP-related genes and downregulation of DNA repair and cell cycle-related genes in the aging ovary (Fig. 4e). Specifically, in the aged ovary after CD38 deletion, the expression of SASP-related genes was repressed in endothelial, immune, granulosa, stromal and epithelial cells. However, all these changes were reversed upon CD38 deletion (Fig. 4f). Conversely, DNA repair and cell cycle-related genes exhibited higher expression in endothelial, immune, granulosa and stromal cells in the aged CD38-deleted ovary compared to WT controls (Fig. 4g,h). To elucidate the molecular events associated with aging and CD38 deletion, we identified DEGs between old and young ovaries and between Cd38^−/− and old ovaries, referred to as ‘aging DEGs’ and ‘Cd38^−/− DEGs,’ respectively. CD38 deletion rescued 1,165 genes and the upregulated and downregulated DEGs in each cell type are shown in Fig. 4i. GO term analysis revealed that CD38 deletion reduced the expression of aging-induced genes related to immune response pathways in endothelial, granulosa, immune and stromal cells (Fig. 4j). As expected, CD38 deletion increased the expression of aging-induced downregulated genes related to cell growth and cell proliferation in endothelial cells, female pregnancy in epithelial cells, ovulation cycle and oocyte maturation in granulosa cells, recombinational repair in immune cells and regulation of embryonic development pathways in stromal cells (Fig. 4k). These results suggest the presence of a coordinated intercellular regulation of ovarian function and the deletion of CD38 improved overall ovarian functions.

**Fig. 4: CD38 deletion reversed aging-related cell-type-specific gene expression changes.**

Restoration of transcriptional regulatory networks and aging-related aberrant cell–cell communication patterns in the ovary after CD38 deletion

To understand the effect of CD38 on the transcriptional regulatory networks underlying ovarian aging, we used SCENIC to predict the core TFs responsible for regulating ovarian aging after CD38 deletion. The changes of core TFs with aging and CD38 deletion are shown in Fig. 5a,b. The CD38 deletion-rescued TF DEGs are shown in Fig. 5c. The results revealed that the rescued TFs and their target DEGs were found in all six types of cells. Specifically, Myc, a TF that regulates the expression of many genes involved in critical cellular functions and has been linked to longevity⁴⁰, showed increased expression during aging but was downregulated after CD38 deletion (Fig. 5d). Furthermore, CEBPD, a core transcriptional factor associated with chronic inflammatory diseases⁴¹, also exhibited increased expression during aging and decreased expression after CD38 deletion (Fig. 5d). Additionally, Foxo1, a TF that plays essential roles in cellular homeostasis⁴², was downregulated during aging but upregulated in the Cd38-knockout ovary (Fig. 5e). These findings suggest that CD38 deletion reshapes the aging-associated transcriptional regulatory networks in the ovary, impacting genes and pathways involved in inflammation processes and ovarian functions.

**Fig. 5: CD38 deletion rescued aging-related changes in core regulatory TFs and ligand–receptor interactions between different cell types.**

To investigate the impact of CD38 deletion on cell–cell communications in the ovary during aging, we initially analyzed the ligand–receptor interactions among the ovarian cell types. The results showed a general decline in cell–cell communications in most cell types in the aged ovary (Fig. 5f). However, interestingly, in the aged CD38 deletion ovary, several types of cell–cell communications between specific cell types were actually enhanced (Fig. 5f). To gain further insights, we conducted GO enrichment analyses on the interaction pairs for each cell type. The data showed that CD38 deletion rescued important interactions in various cellular processes (Fig. 5g). For instance, in epithelial cells, endothelial cells, stromal cells and oocytes, CD38 deletion positively enhanced cell–cell communications involved in reproductive system development. In granulosa cells, CD38 deletion was associated with improved interactions related to germ cell development. Additionally, in immune cells, CD38 deletion enhanced leukocyte cell–cell adhesion. Altered intercellular communication is an integrative hallmark of aging⁴³. These results demonstrated that CD38 deletion notably reversed the disrupted cell–cell communication patterns associated with ovarian aging.

CD38 deletion ameliorated age-related oocyte quality decline

Metaphase II (MII) oocyte quality decline is a hallmark of ovarian senescence^44,45. Due to the larger size of MII oocytes, scRNA-seq for the whole-ovary methods was unable to provide transcriptomic data for these oocytes. To investigate the potential impact of CD38 deletion on the quality of aging oocytes, we conducted single-cell transcript analysis on ovulated MII oocytes from young (2-month-old) and old (12-month-old) Cd38^−/− and WT mice using the SMART-seq method (Fig. 6a). Principal component analysis (PCA) was performed, and PC1 revealed distinct expression patterns between young and old oocytes (Fig. 6b and Supplementary Table 6). Notably, the oocyte gene transcription profile of 12-month-old Cd38^−/− mice was found to be intermediate between that of 2-month-old and 12-month-old WT oocytes. Furthermore, the heat map indicated that the DEGs following oocyte aging were partially reversed in 12-month-old Cd38^−/− mice (Fig. 6c), particularly concerning senescence-related pathways (Extended Data Fig. 5a). Among the identified DEGs, 407 genes were found to be downregulated during aging but upregulated following CD38 deletion (Fig. 6d and Supplementary Table 7). GO and GSEA analyses revealed that these DEGs were associated with processes such as ‘DNA repair’, ‘reproductive system development’, ‘cell cycle phase transition’, ‘spindle organization’, ‘female gamete generation’, ‘oocyte meiosis’ and ‘mitochondrial membrane permeability’ (Fig. 6d and Extended Data Fig. 5b,c). Given the close relationship between mitochondrial dysfunction and declining oocyte quality with aging, these findings suggest a potential role of CD38 in modulating oocyte aging and mitochondrial function^46,47. Considering that mitochondrial dysfunction is closely related to declining oocyte quality with age, mitochondria-related gene expression (Supplementary Table 8) was further analyzed by GSEA, which revealed that these genes were upregulated in Cd38^−/− oocytes compared with age-matched WT oocytes (Fig. 6e and Extended Data Fig. 5c).

Fig. 6: Single-cell sequencing showed that the oocyte transcriptome of 12-month-old *Cd38*^−/− mice resembled that of WT mice at 2 months of age, and age-related oocyte quality decline was attenuated after the deletion of CD38.

The ROS contents in oocytes were assessed to investigate the effects of CD38 deletion on oocyte quality during aging. Deletion of CD38 reduced ROS accumulation in aging oocytes (Fig. 6f,g). As shown in Fig. 6h,i, the mitochondrial membrane potential (ΔΨm) in oocytes was notably increased in 12-month-old Cd38^−/− mice compared with WT mice, indicative of improvement in mitochondrial activity. We further investigated whether age-related meiotic defects in oocytes could be improved with the loss of CD38. As shown in Fig. 6j,k, the abnormal spindle/chromosome structural ratios in WT oocytes of aged mice were decreased in Cd38^−/− counterparts, indicating that loss of CD38 reduced meiotic defects. These results revealed that CD38 deficiency ameliorated the age-related deterioration of oocyte quality.

CD38 deletion prevented inflammation-induced follicle loss

The above findings indicated that inflammation-related pathways were activated and that CD38 expression was increased in middle-aged ovaries. To focus on the relationship between inflammation activation and CD38 expression, 8-month-old mice were treated with 1 mg ml⁻¹ lipopolysaccharide (LPS) in vivo (Fig. 7a). After 24 h, LPS-treated mice had increased expression of inflammatory cytokines in the ovary (Fig. 7b–d). In vitro, ovaries from 10-day-old mice were cultured with LPS (10 μg ml⁻¹) for 4 d, at which point they exhibited elevated expression of inflammation-associated genes (Fig. 7e). Additionally, LPS treatment increased ovarian CD38 expression in vivo and in vitro (Fig. 7b–e).

**Fig. 7: Inflammation-induced primordial follicle loss was mediated by activation of CD38 and a decline in NAD⁺ levels.**

We further investigated the effect of CD38 and LPS on ovarian NAD⁺ levels. Ovaries from 10-day-old Cd38^−/− mice cultured and treated with LPS showed higher expression of inflammation-related genes, but ovarian NAD⁺ levels were not affected, in contrast to LPS-treated WT ovaries, which showed a reduction in NAD⁺ levels (Fig. 7f). Histological analysis and follicle counting were performed to investigate the effects of inflammation-induced CD38 activation on the ovarian primordial follicle reserve pool (Fig. 7g). As shown in Fig. 7h, LPS-treated ovaries had a notably reduced proportion of primordial follicles compared with that of controls, whereas the proportion of growing follicles (primary, secondary and antral follicles) was larger in the LPS-treated ovaries. However, these changes were not observed in the ovaries after deletion of CD38 (Fig. 7h).

The TF Foxo3 is required for suppression of primordial follicle activation, and nuclear exclusion of Foxo3 in oocytes results in primordial follicle activation^48,49. The intracellular location of Foxo3 in primordial follicle oocytes of cultured ovary sections was analyzed in the different groups. As shown in Fig. 7i, LPS treatment increased the nuclear export of Foxo3 in primordial follicle oocytes. Quantitative analyses indicated that 61.87% of primordial follicle oocytes exhibited Foxo3 export (Fig. 7j) in WT LPS-treated ovaries. In contrast, only 40.14% of primordial follicle oocytes showed Foxo3 export in Cd38^−/− LPS-treated ovaries. These results suggested that the inflammation-induced decrease in the primordial follicle pool was mediated by CD38.

78c treatment rescued age-related female fertility decline

We conducted further investigations to assess the impact of a CD38 (ADP ribosyl cyclase/hydrolase) inhibitor, 78c, on the ovarian function of middle-aged female mice. Initially, we administered 78c injections to 8-month-old mice for 8 d to verify its effect on ovarian NAD⁺ levels. We observed a significant increase in ovarian NAD⁺ levels in middle-aged mice following treatment with 78c (Fig. 8a). However, this effect was not observed in Cd38-knockout mice (Extended Data Fig. 6a), highlighting the essential role of CD38 in mediating 78c’s actions on ovarian NAD⁺ levels. Moreover, we also found that administration of FK866, an inhibitor of nicotinamide phosphoribosyltransferase (NAMPT) for 3 d, could reduce the 78c-induced increase in ovarian NAD⁺ (Extended Data Fig. 6b). These results demonstrated that the effect of 78c on ovarian NAD⁺ levels was indeed dependent on CD38. To investigate its potential benefits on fertility, we treated 8-month-old mice with 78c for 3 weeks. The 78c-treated mice exhibited elevated serum levels of AMH, a marker for ovarian reserve (Fig. 8b). Furthermore, after mating, the 78c-treated mice showed a notable increase in the mean number of pups compared to the control group (Fig. 8c). Moreover, the 78c-treated mice had approximately twofold more oocytes retrieved after undergoing superovulation induced by gonadotropins, and a reduced proportion of abnormal oocytes showing vacuolization and fragmentation (Fig. 8d–f). Furthermore, the analysis of spindle morphology revealed that 38.03% of MII oocytes from the control group exhibited abnormal spindle/chromosome structures, whereas the 78c-treated group showed only 17.66% with abnormalities (Fig. 8g,h). These findings clearly indicate that short-term treatment with 78c notably improves the fertility of middle-aged female mice, similar to the effect observed with CD38 deletion.

**Fig. 8: Pharmacological inhibition of CD38 improved female fertility in middle-aged mice.**

Discussion

Delayed childbearing is prevalent worldwide, and ovarian senescence occurs earlier than most of the other organs in females. Ovarian function decreased dramatically in middle age, as shown by a decrease in oocyte quality and ovarian reserve^50,51. We hypothesized that middle-aged mice may be useful for investigating the molecular mechanisms underlying ovarian senescence. Our study showed the transcriptome changes that occur in the ovaries of middle-aged mice when many other organs showed no aging-related gene changes. In particular, gene transcripts in aging-related pathways, including SASP, cell cycle, inflammation and DNA repair, were misregulated in the ovary but not in multiple other organs when comparing middle-aged with young mice. Indeed, increased expression of aging markers, namely, p16 and p21, and inflammation-related factors was observed in the ovary but not in other organs from middle-aged mice. Our findings are consistent with a report classifying the aging-associated alterations in gene expression patterns of different tissues into four stages with ovarian aging occurring in 6-month-old to 12-month-old mice⁵², which is earlier than for most of the other organs.

Importantly, the current study showed that the expression of inflammation-related genes rapidly increased in the middle-aged ovary, accompanied by activation of the NAD⁺ metabolizing enzyme CD38, whereas other key enzymes for NAD⁺ generation and metabolism were not changed in the ovaries from middle-aged mice. The activation of CD38 and inflammation-related transcripts was not observed in other organs. A previous study showed that CD38 levels increased in the liver, adipose tissue, spleen and skeletal muscle in aged (approximately 18-month-old) mice³², indicating that the increases in CD38 expression during middle age are likely a key event during ovarian senescence. We and several groups have reported that ovarian NAD⁺ levels decline during aging, whereas boosting NAD⁺ by supplementation with NAD⁺ precursors, such as nicotinamide riboside or nicotinamide mononucleotide, increased ovarian NAD⁺ levels and delayed ovarian aging by improving mitochondrial function^20,21,22,23. The present work found that deletion of CD38 prevented ovarian NAD⁺ decline, extended ovarian lifespan and resulted in increased litter sizes in aged mice. Importantly, increased ovarian follicle reserve was found in aged Cd38^−/− mice compared with WT mice. Consistent with these findings, higher levels of serum AMH and decreased cell DNA damage and apoptosis were observed in the ovarian follicles of Cd38^−/− mice than in those of age-matched WT mice. Additionally, aged (12-month-old) Cd38^−/− mice exhibited a markedly increased response to gonadotropins, which yielded a higher number of healthy preovulatory follicles than those of age-matched WT mice.

To further investigate the impact of CD38 deletion on ovarian function, we conducted bulk RNA-seq and scRNA-seq analyses, revealing notable changes in the transcriptome of the aged ovary, particularly affecting genes related to the senescence pathway, which were largely suppressed in Cd38-deficient mice. These results strongly suggest that the earlier onset of ovarian senescence is likely attributed to increased expression of CD38, resulting in a decline in NAD⁺ levels. Additionally, the scRNA-seq data highlighted that CD38 expression was primarily observed in endothelial cells and immune cells. The ovarian stroma encompasses essential components, including immune cells, blood vessels, nerves and the extracellular matrix⁵³. In situ immunostaining identified that CD38 protein mainly localized in ovarian stroma. The ovarian stroma not only offers structural support for follicle development but also engages in intricate bidirectional paracrine signaling with the follicles⁵⁴. We observed notable alterations in the transcriptome of various cell types within the ovary upon CD38 deletion, with many genes related to ovarian function pathways being upregulated. Moreover, CD38 deletion facilitated the restoration of core TF expression, consequently rejuvenating the aging-related disruptions in transcriptional regulatory networks. Importantly, we found that CD38 deletion restored the expression of core TFs, leading to the restoration of transcriptional regulatory networks during aging. The ovary is a complex organ composed of multiple cell types that require intricate systemic regulation for follicle development²⁵, and alteration of cell–cell communication is considered a hallmark of tissue aging⁴³. Our research provides insights into the beneficial effects of CD38 deletion, highlighting its role in restoring core TF expression and cell–cell communication patterns to counteract ovarian aging.

The present study also revealed that CD38 deletion improved oocyte quality, with single-oocyte transcriptomic analysis indicating enhancement of pathways, including ‘DNA repair’ and ‘female gamete generation’, as well as ‘mitochondrial membrane permeability’, in oocytes from Cd38^−/− mice. Analysis of ROS levels, spindle/chromosome organization and mitochondrial membrane potential demonstrated that CD38 deletion reduced oocyte abnormalities in aged animals. Human female reproductive ability decreases during the fourth decade of life because of age-related changes in oocyte quality and quantity⁵⁵. The accumulation of ROS and mitochondrial dysfunction, together with spindle/chromosome organization failure, are hallmarks of the age-related decline in oocyte quality, which is a major cause of pregnancy loss and developmental disorders in offspring⁵⁶. While our studies primarily focused on the NADase activity of CD38, it is important to acknowledge that CD38 may play roles in various other cellular functions, including cell adhesion, signal transduction and calcium signaling⁵⁷. Notably, despite the absence of observed CD38 expression in oocytes, our research and findings from other research groups consistently demonstrate reduced NAD⁺ levels in aging mouse oocytes^20,22. Furthermore, senescent cells could induce the upregulation of CD38 mRNA and protein expression, leading to increased CD38-NADase activity in non-senescent cells⁵⁸. These cumulative results further strengthen the proposition that CD38 exerts regulatory control over ovarian aging, operating through both direct mechanisms and indirect influence on cell–cell communication within the ovarian microenvironment.

Most CD38 inhibitors are antibodies, such as Darzalex (daratumumab), which has been approved by the US Food and Drug Administration to treat multiple myeloma patients⁵⁹. However, these antibodies are unable to inhibit intracellular CD38. Recently, the small molecule 78c was found to ameliorate age-related metabolic dysfunction by reversing tissue NAD⁺ decline⁶⁰, thereby increasing the lifespan in mice⁶¹. The current study further investigated the effects of 78c on ovarian function in mice and showed that 78c treatment increased ovarian NAD⁺ levels. Administration of 78c for 3 weeks also increased serum AMH levels and ovulated oocyte numbers after gonadotropin treatment. In addition, oocyte quality was improved in 78c-treated mice, as evidenced by the reduction in abnormal oocyte morphology and spindle assembly, along with increased litter size. These findings suggest that short-term inhibition of CD38 might be a promising approach for treating infertility in middle-aged cisgender women and other people with ovaries.

As we observed a notable increase in the expression of CD38 and inflammation-related genes in 8-month-old mice, we aimed to elucidate the regulatory relationship between these two factors and explore potential mechanisms contributing to ovarian aging. To investigate this, we induced inflammation using LPS treatment both in vivo and in vitro, which resulted in an increase in ovarian CD38 expression and a reduction in ovarian NAD⁺ levels. These findings were consistent with other reports that indicate inflammation can upregulate CD38 and decrease NAD⁺ levels in other tissues^62,63. We further demonstrated that inflammation induced a decrease in the primordial follicle pool, consistent with a previous study in rats⁶⁴. In contrast, deletion of CD38 prevented the inflammation-induced loss of the primordial follicle pool, highlighting the crucial role of CD38 in the inflammation-induced decline in ovarian reserve with age.

We also identified substantial shifts in the proportions of most cell types during ovarian aging, and these changes were notably reversed upon CD38 deletion. However, our scRNA-seq data did not reveal a substantial increase in immune cell populations, contrary to what has been reported in other studies^65,66,67, while one study also did not show a decrease in the number of oocytes in aging ovaries based on their scRNA-seq data⁶⁷. Hence, an adequate number of biological replicates are required to accurately represent all cell-type populations, considering that single-cell capture can be influenced by various factors, such as cell size and the preparation of single-cell samples. In addition, although we observed CD38 to be predominantly expressed in endothelial cells and immune cells, we did not elucidate whether CD38 regulates ovarian aging through their combined action or if one of these cell types predominantly influences it. Future work with cell-specific Cd38-knockout models will provide further insights into the mechanisms driving ovarian aging.

Taken together, our study describes the transcriptomic changes in early senescence of the ovary relative to other organs in middle-aged mice. At the molecular level, the early onset of inflammation-induced CD38 accumulation in middle-aged ovaries likely leads to a decrease in NAD⁺ level, accelerating cell senescence, disrupting transcriptional regulatory networks and altering cell–cell communication patterns in the ovary. These molecular changes ultimately result in reduced ovarian follicle reserve and compromised oocyte quality, contributing to age-related subfertility. Genetic deletion of CD38 attenuated ovarian aging and prolonged fertility in female mice. Importantly, our study demonstrates that CD38 plays a role in ovarian aging and may offer a valuable therapeutic approach to prolong ovarian lifespan, thereby mitigating the age-related decline in female fertility.

Methods

This study received approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University. Research with human biospecimens as well as animal studies were carried out in compliance with all relevant ethical regulations. Informed consent was obtained from the participants for the collection of follicular fluids.

Reagents, antibodies and chemicals

Key resources in the form of reagents, antibodies and chemicals used in this study are shown in Supplementary Table 9.

Animal feeding and treatments

Two-month-old and eight-month-old WT female C57BL/6 mice were obtained from Beijing Vital River Experimental Animals Center (Beijing, China). Cd38-knockout mice were generated by using CRISPR–Cas9 technology (Cyagen).

For genotyping of Cd38^−/− mice, PCR was performed on DNA extracted from mouse tails, and the Cd38-knockout mutant allele (509 bp) was assayed by the primers 5′-ACGGTTGGGACTTAGAACAGAG-3′ (forward) and 5′-TAGAAAGGGAAGCCCAGGTAAG -3′ (reverse). The Cd38 WT allele (457 bp) was assayed by the primers 5′-TAGTGTCCAGTGCAGAGTATCTTC-3′ (forward) and 5′-GTAGAAAGGGAAGCCCAGGTAAG-3′ (reverse). All animals were housed in a pathogen-free environment in filter-top cages. For breeding experiments, WT, Cd38^−/− and CD38 inhibitor-treated females were individually caged with male C57BL/6 mice with proven fertility before monitoring the number of offspring. All mice were maintained under a 12-h light/dark cycle, at a room temperature of 20–25 °C, humidity of 55% ± 10%, and provided with food and water ad libitum.

LPS treatment in vivo

For ovarian LPS treatments in vivo, 8-month-old female mice were injected intraperitoneally with LPS (Sigma) at a concentration of 1 mg ml⁻¹ based on a previous study⁶⁸, and then the ovaries were collected after 24 h for subsequent experiments.

LPS treatment in vitro

For LPS treatment in vitro, ovaries from 10-day-old mice were placed on culture plate inserts (Millipore Corporation) and cultured in 400 µl of DMEM/F12 culture medium (Solarbio) supplemented with 10% FBS (Biological Industries) and 1% penicillin–streptomycin 100× solution (HyClone) in an incubator at 37 °C with 5% CO₂ as previously reported⁶⁹. A thin layer of medium covered the ovaries on the inserts. Ovaries were treated with 10 µg ml⁻¹ LPS with media changes every 2 d for 4 d. At the end of the culture, ovaries were fixed in Bouin’s solution, paraffin-embedded and cut into continuous sections before staining with H&E. The growing and primordial follicle percentages were analyzed in these sections as previously reported⁷⁰.

CD38 inhibitor (78c) treatment in vivo

78c was administered intraperitoneally to 8-month-old female C57BL/6 mice at previously characterized doses of 15 mg per kg body weight twice daily for 8 d for NAD⁺ level measurement and 21 d for biological effects⁶⁰. Control female mice received vehicle solutions with 10% dimethylsulfoxide, 40% PEG400, 5% Tween-80 and 45% PBS injections. Moreover, the FK866 (50 mg per kg body weight), an inhibitor for NAMPT, was used to treat the mice with or without 78c for 3 d, to verify the role of CD38 on ovarian NAD⁺ level regulation.

Flow cytometry of human follicular fluid

Human follicular fluid was collected from young (20–25 years old) and middle-aged (>35 years old) participants who underwent an assisted reproductive technology procedure due to fallopian tube issues. Follicular fluid cells were isolated by density centrifugation at 850g for 15 min with Human Lymphocyte Separation Medium (Solarbio). After centrifugation, the intermediate layer was collected and centrifuged at 400g for 10 min. Then, cells were incubated in PBS labeled with the following antibody panel: CD38-APC (BioLegend) for 30 min at room temperature. After twice washing, samples were resuspended in PBS and subjected to a FACSMelody (BD Biosciences) for flow sorting. CD38-positive cells were extracted and frozen in TRIzol reagent for subsequent real-time RT–PCR experiments.

IHC staining

Paraffin-embedded sections were initially deparaffinized with twice 100% xylene washes, rehydrated via graded alcohols and briefly washed in distilled water. The sections were treated using heat-mediated antigen retrieval with sodium citrate buffer (pH of 6.0) for 20 min, permeabilized and blocked in 1% BSA with 0.5% Triton X-100 for 1 h. The primary CD38 antibody was used at 4 °C overnight. After a wash with PBS, horseradish peroxidase-conjugated secondary antibodies were used before the 3,3′-diaminobenzidine application. The ovary sections were then counterstained with eosin and dehydrated in a series of graded alcohols and 100% xylene before they were coverslipped with resinous mounting medium.

RNA isolation and quantitative real-time PCR

Total RNA was isolated from oocytes and other organs with TRIzol reagent (Invitrogen). cDNA was generated with HiScript III RT SuperMix for qPCR (+gDNA wiper; Vazyme). Quantitative real-time PCR was performed using ChamQ Universal SYBR qPCR Master Mix (Vazyme) on a QuantStudio 12K Flex (Applied Biosystems). The primer sequences are shown in Supplementary Table 10. Experiments were performed at least three times. Relative expression levels were determined by the delta CT method with normalization to Gapdh.

Western blotting

Western blotting was performed as previously described²⁰. Briefly, samples were sonicated in lysis buffer. After SDS‒PAGE, separated proteins were transferred to a PVDF transfer membrane (Millipore), and the membranes were incubated with specific primary and secondary antibodies sequentially. Protein bands were detected using an enhanced chemiluminescence detection system (Bio-Rad). ImageJ software was used to analyze protein expression levels after measuring the protein band intensity. All the antibodies used were validated by western blotting or IHC (Supplementary Fig. 3).

NAD⁺ measurement

Ovarian NAD⁺ levels were measured using an NAD/NADH Assay Kit (Fluorometric; Abcam). Briefly, ovaries were collected and lysed in NAD/NADH lysis buffer before collecting the supernatant. Then, 25 ml of NAD⁺ extraction buffer was added to the lysate sample, followed by incubation at 37 °C for 15 min and the addition of 25 ml of NADH extraction buffer and 75 ml of NAD/NADH reaction mixture before incubation at room temperature for 1 h in the dark. Total NAD⁺ levels were quantified using a colorimetric assay at an excitation and emission of 540 and 590 nm, respectively, using a Varioskan Flash Multimode Reader (Thermo Fisher Scientific).

Oocyte collection

Mice were superovulated by an intraperitoneal injection of 5 IU of pregnant mare serum gonadotropin, followed by 5 IU of human chorionic gonadotropin 48 h later. The mice were euthanized at 14 h after human chorionic gonadotropin injection. Cumulus–oocyte complexes were collected by dissecting oviductal ampullae in Gmops buffer (Vitrolife). Then, cumulus cells were removed by pipetting the cumulus–oocyte complexes in 0.1% hyaluronidase (Solarbio) in Gmops.

ROS level monitoring

For measurement of the ROS content, MII oocytes were incubated in IVF medium with 5 mM MitoSOX Red reagent prepared according to the instructions of the MitoSOX Red mitochondrial superoxide indicator (Thermo Fisher Scientific) at 37 °C for 20 min in a 5% CO₂ incubator. After washing three times with IVF medium, oocyte images were captured under a laser scanning confocal microscope (LSM 700; Zeiss). The fluorescence value was calculated using ImageJ software (National Institutes of Health).

Mitochondrial membrane potential (Δψm) measurement

Oocytes were incubated with 10 µM JC-1 (Beyotime) at 37 °C in 5% CO₂ for 20 min in the dark. After three washes in PBS, oocyte images were captured in both the green and red channels using an LSM 700 laser scanning confocal microscope. Fluorescence intensities were detected using ImageJ. The ΔΨm was calculated as the ratio of red-to-green fluorescence pixels.

Spindle assembly analysis

MII oocytes were obtained and fixed in PBS (Solarbio) containing 4% paraformaldehyde for 30 min, followed by permeabilization in PBS with 0.5% Triton X-100 for 20 min. After blocking with 1% BSA in PBS for 30 min, oocytes were incubated with an anti-α-tubulin antibody (Cell Signaling Technology) overnight at 4 °C. After three washes, oocytes were incubated with a FITC-conjugated secondary Alexa Fluor 488 antibody (Thermo Fisher Scientific) for 1 h at room temperature. For spindle assembly analysis, oocytes were stained with propidium iodide and then viewed under an LSM 700 laser scanning confocal microscope.

Sectioning ovaries for follicle counting and serum AMH measurement

Ovaries were fixed with 4% formaldehyde for 24 h, washed with PBS two times and stored in 70% ethanol. After embedding in paraffin wax and serial sectioning at a thickness of 5 μm, ovarian sections were deparaffinized and rehydrated through a graded ethanol series. The samples were then stained with H&E. Quantification of ovarian follicles in different developmental stages, including primordial, primary, secondary, antral and atretic follicles, was performed as previously described⁶⁹. Blood samples were drawn via the retro-orbital sinus, and serum samples were extracted and kept at −80 °C until use. Serum AMH measurements were performed using an enzyme-linked immunosorbent assay (ELISA) according to the manufacturer’s instructions (CUSABIO).

TUNEL assay and immunofluorescence on ovarian sections

Paraffin-embedded ovarian sections were heated for 45 min at 65 °C for deparaffinization, and the slides were then treated with 100% xylene three times followed by washing three times with 100% ethanol. The slides were rehydrated with citrate buffer (pH 6.0) for 20 min, permeabilized and blocked in 1% BSA with 0.5% Triton X-100 for 1 h. The TUNEL assay was conducted according to the manufacturer’s protocol (Roche, Swiss). For immunofluorescence, the slides were incubated with primary antibodies against γH2AX (1:100 dilution) overnight at 4 °C, followed by incubation with secondary antibodies conjugated with Alexa Fluor 555 (Thermo Fisher Scientific; 1:200 dilution) and then with Antifade Mounting Medium with DAPI. Images were captured using a fluorescence microscope (Ni2 Nikon).

PSR staining

The PSR Stain Kit (Polysciences) was used following the manufacturer’s protocol. Briefly, ovary sections underwent deparaffinization with xylene and ethanol gradient, followed by water washes. Subsequently, sections were treated with solution A for 2 min and solution B for 1 h at room temperature, and slides were cleared using solution C. For each separate experiment, all PSR-stained slides were processed simultaneously to reduce staining intensity variation. Fibrotic area in imaged ovaries stained with PSR was measured using ImageJ.

Measurement of CD38 enzymatic activities

CD38 activities were measured according to the manufacturer’s protocol (CUSABIO). Briefly, tissues were washed with PBS, homogenized in 1 ml of PBS and stored overnight at −20 °C. After two freeze–thaw cycles to break cell membranes, the homogenates were centrifuged for 5 min. The supernatant was removed and assayed immediately, and the CD38 activity was calculated.

Tissue RNA-seq data processing

For gene expression quantification, uniquely mapped reads were submitted as reads per kilobase of exon per million reads mapped for the gene algorithm to calculate counts of fragments per kilobase of exon and per million mapped reads (FPKMs). Samples from different groups were initially analyzed by PCA based on FPKM values of the expression to characterize the trend of intergroup separation in the experimental model. The PCA plots and differential gene expression were analyzed with the R package DESeq2. Genes with |log₂FC| > 1 and adjusted P value (by the Benjamini–Hochberg method) < 0.05 were considered DEGs. Heat maps generated by the R package pheatmap were used to visualize the expression patterns of DEGs in different groups. The DEGs were further divided into upregulated and downregulated genes according to log₂FC for enrichment analysis. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed by the R package clusterProfiler. GO terms and KEGG pathways with a P value < 0.05 were considered statistically significant and are shown by dot plots. P values were calculated by Fisher’s exact test and adjusted by the Benjamini–Hochberg method. To compare the differences in biological process signaling pathways between different groups, GSEA was also used. GSEA analysis was performed by the R package clusterProfiler, and P values were calculated based on Fisher’s exact test. Only gene sets with P value < 0.05 were shown by using the R package enrichplot.

Single-cell RNA-seq library construction and sequencing

Ovaries were washed in ice-cold RPMI 1640 and dissociated using collagenase IV, Dispase and DNase I. After cell counting, fresh cells were washed twice in the RPMI 1640 and then resuspended at 1 × 10⁶ cells per ml in PBS with 0.04% BSA. Then, the single-cell RNA-seq libraries were prepared using SeekOne Digital Droplet Single Cell 3′ library preparation kit (SeekGene) and libraries were sequenced on an Illumina NovaSeq 6000 with PE150 read length.

Analysis of scRNA-seq data of ovaries

The raw sequencing data were processed by Fastp firstly to trim primer sequence and eliminate low-quality bases. Subsequently, SeekOneTools was used to preprocess sequencing data and align to GRCm38 in order to obtain gene expression matrix. For further analysis of scRNA-seq data, R package Seurat (version 4.3.0.1)⁷¹ and Harmony (version 0.1.1)⁷² were used for filtering, data normalization, dimensionality reduction, clustering and further analysis of the single-cell RNA-seq data. Briefly, data from three groups of ovaries (WT-2M, WT-12M and Cd38^−/−-12M) were combined by Harmony package and were clustered to 22 cell types. Then the clusters were annotated using cell-type-specific signatures and marker genes^73,74 into six cell types: granulosa cells, stromal cells, immune cells, endothelial cells, epithelial cells and oocytes. Gene-set scores of ‘SASP’, ‘DNA repair’ and ‘cell cycle’ for each input cell were calculated using the Seurat function ‘AddModuleScore’. Changes in the scores in the ‘aging group’ (WT-12M versus WT-2M) or ‘Cd38^−/− group’ (Cd38^−/−-12M versus WT-12M) were analyzed by the Wilcoxon test. Then differential gene expression analysis was performed with the ‘FindMarkers’ function of Seurat in the ‘aging group’ and ‘Cd38^−/− group’. DEGs were defined as those genes with P values < 0.05 and |log₂FC| > 0.25. GO enrichment was performed on DEGs in each cell type.

Then TF regulatory network analysis was conducted using pySCENIC (version 0.10.4) with default settings⁷⁵. The transcriptional regulatory network was visualized by Cytoscape (version 3.9.1). CellPhoneDB Python package (version 2.1.7)⁷⁶ was used to detect ligand–receptor interactions and predict communications among different cell types. Only those specific interactions classified by ligand–receptor expression in more than 10% of cells within a cell type were selected. Average expression of each ligand–receptor pair was compared across cell types, and only those with a significance level of P < 0.05 were used for subsequent cell–cell communication prediction.

Single-oocyte RNA-seq library construction and sequencing

Mouse MII oocytes were obtained as described above. A single MII oocyte was transferred to a PCR tube with lysis buffer containing dNTP and oligo (dT) oligonucleotides. Then, a sequencing library from a single oocyte was constructed using a SMART-seq HT Kit (Takara). Purified libraries were quantified and validated by a Qubit 3.0 Fluorometer and Agilent 2100 Bioanalyzer to confirm the insert size and calculate the concentration. Then, sequencing was performed on an Illumina NovaSeq 6000 (Illumina). RNA-seq reads were aligned to the GRCm38.100 reference genome using Hisat2 software. For subsequent downstream analysis, the original expression reads were converted to FPKMs according to the method described above.

Single-oocyte RNA-seq data processing and pathway analysis

The single-cell sequencing data analysis was similar to that described for the tissue RNA-seq data above. The PCA plot for the three groups was drawn, and then DEGs of two groups were calculated by using data from 12-month-old versus 2-month-old WT mice and 12-month-old Cd38^−/− versus 12-month-old WT mice. The expression patterns of the DEGs are shown in a heat map. Next, the intersecting genes among the downregulated DEGs due to aging and upregulated DEGs due to the knockout assay were selected. GO and KEGG analyses were performed to explore the functional enrichment of these intersecting genes. All the pathway-related genes mentioned in this paper were obtained by the combination of related terms from the GO database. The gene list is shown in Supplementary Table 11.

Statistics and reproducibility

We did not use a statistical method to determine the sample size in advance, but we consistently used similar sample sizes for each experiment. Percentages or values are shown as the mean ± s.e.m. Statistical significance was measured using Student’s t-test, chi-squared test with Prism software (GraphPad). Values of P < 0.05 were considered statistically significant. The specific sample sizes (the n numbers) are provided in the figure legends. In addition, for all experiments except RNA-seq data, each experiment was conducted independently at least three times. The statistical test methods and exact P values are detailed in the corresponding figure legends. No specific methods were used for random allocation of samples into groups. All data were included in the analysis without any exclusions. Data collection and analysis were conducted without blinding to the experimental conditions. Although we assumed a normal data distribution with equal variance, this assumption was not formally tested.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The raw data of scRNA-seq and bulk RNA-seq presented in this study have been deposited into the Sequence Read Archive database under the accession number PRJNA1002222. Source data are provided with this paper. Any other data underlying this study will be provided by the corresponding authors upon reasonable request.

Code availability

The software used in this study is publicly available and accessed without restriction. R package DESeq2 v.1.34.0 is available at https://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. R package clusterProfiler v.4.2.0 is available at https://www.bioconductor.org/packages/release/bioc/html/clusterProfiler.html. R package pheatmap v.1.0.12 is available at https://rdocumentation.org/packages/pheatmap/versions/1.0.2. R package enrichplot v.1.14.1 is available at https://www.bioconductor.org/packages/release/bioc/html/enrichplot.html. R package GSEABase v.1.56.0 is available at https://www.bioconductor.org/packages/release/bioc/html/GSEABase.html. R package ggplot2 v.3.3.6 is available at https://cran.r-project.org/web/packages/ggplot2/index.html. R package stringr v.1.4.0 is available at https://cran.r-project.org/web/packages/stringr/index.html. R package tidyverse v.1.3.1 is available at https://cran.r-project.org/web/packages/tidyverse/index.html. R package VennDiagram v.1.7.1 is available at https://cran.r-project.org/web/packages/VennDiagram/index.html. R package Seurat v.4.3.0.1 is available at https://github.com/satijalab/seurat/. Harmony v.0.1.1 is available at https://cran.r-project.org/web/packages/harmony/index.html. R package AUCell v.1.22.0 is available at https://www.bioconductor.org/packages/release/bioc/html/AUCell.html. Python package pySCENIC v.0.12.0 is available at https://github.com/aertslab/pySCENIC/. For analysis of cell–cell interaction, CellPhoneDB v.3.1.0 is available at https://github.com/ventolab/CellphoneDB/. R Studio v.4.1.2 and Python v.3.7.12 were used to format data for analyses.

References

Morita, Y. et al. Oocyte apoptosis is suppressed by disruption of the acid sphingomyelinase gene or by sphingosine-1-phosphate therapy. Nat. Med. 6, 1109–1114 (2000).
Article CAS PubMed Google Scholar
Yang, Q., Mumusoglu, S., Qin, Y., Sun, Y. & Hsueh, A. J. A kaleidoscopic view of ovarian genes associated with premature ovarian insufficiency and senescence. FASEB J. 35, e21753 (2021).
Article CAS PubMed Google Scholar
Takahashi, T., Igarashi, H., Amita, M., Hara, S. & Kurachi, H. Cellular and molecular mechanisms of various types of oocyte aging. Reprod. Med. Biol. 10, 239–249 (2011).
Article CAS PubMed PubMed Central Google Scholar
Inter, L. S. T. Variations in reproductive events across life: a pooled analysis of data from 505,147 women across 10 countries. Hum. Reprod. 34, 881–893 (2019).
Article Google Scholar
Sirard, M. A. Why is the human ovary aging faster than other organs? Women Health 62, 577–579 (2022).
Article PubMed Google Scholar
Broekmans, F. J., Soules, M. R. & Fauser, B. C. Ovarian aging: mechanisms and clinical consequences. Endocr. Rev. 30, 465–493 (2009).
Article CAS PubMed Google Scholar
Buckler, H. The menopause transition: endocrine changes and clinical symptoms. J. Br. Menopause Soc. 11, 61–65 (2005).
Article PubMed Google Scholar
Wellons, M. F., Matthews, J. J. & Kim, C. Ovarian aging in women with diabetes: an overview. Maturitas 96, 109–113 (2017).
Article CAS PubMed Google Scholar
Yang, L. et al. The role of oxidative stress and natural antioxidants in ovarian aging. Front. Pharmacol. 11, 617843 (2020).
Article CAS PubMed Google Scholar
Hagg, S. & Jylhava, J. Sex differences in biological aging with a focus on human studies. eLife 10, e63425 (2021).
Price, M. A. et al. Early and surgical menopause associated with higher Framingham risk scores for cardiovascular disease in the Canadian Longitudinal Study on Aging. Menopause 28, 484–490 (2021).
Mason, J. B., Parkinson, K. C. & Habermehl, T. L. Orthotopic ovarian transplantation procedures to investigate the life- and health-span influence of ovarian senescence in female mice. J. Vis. Exp. 132, 56638 (2018).
Mason, J. B. et al. Transplantation of young ovaries restored cardioprotective influence in postreproductive-aged mice. Aging Cell 10, 448–456 (2011).
Article CAS PubMed Google Scholar
Li, L., Shi, X., Shi, Y. & Wang, Z. The signaling pathways involved in ovarian follicle development. Front. Physiol. 12, 730196 (2021).
Article PubMed PubMed Central Google Scholar
Hsueh, A. J., Kawamura, K., Cheng, Y. & Fauser, B. C. Intraovarian control of early folliculogenesis. Endocr. Rev. 36, 1–24 (2015).
Article CAS PubMed Google Scholar
Jiao, X. et al. T_reg deficiency-mediated TH₁ response causes human premature ovarian insufficiency through apoptosis and steroidogenesis dysfunction of granulosa cells. Clin. Transl. Med. 11, e448 (2021).
Article CAS PubMed PubMed Central Google Scholar
Shao, T. et al. Autophagy regulates differentiation of ovarian granulosa cells through degradation of WT1. Autophagy 18, 1864–1878 (2022).
Article CAS PubMed PubMed Central Google Scholar
Lin, X. et al. Excessive oxidative stress in cumulus granulosa cells induced cell senescence contributes to endometriosis-associated infertility. Redox Biol. 30, 101431 (2020).
Article CAS PubMed PubMed Central Google Scholar
Sreerangaraja Urs, D. B. et al. Mitochondrial function in modulating human granulosa cell steroidogenesis and female fertility. Int. J. Mol. Sci. 21, 3592 (2020).
Article PubMed PubMed Central Google Scholar
Yang, Q. et al. Increasing ovarian NAD⁺ levels improve mitochondrial functions and reverse ovarian aging. Free Radic. Biol. Med. 156, 1–10 (2020).
Article CAS PubMed Google Scholar
Miao, Y., Cui, Z., Gao, Q., Rui, R. & Xiong, B. Nicotinamide mononucleotide supplementation reverses the declining quality of maternally aged oocytes. Cell Rep. 32, 107987 (2020).
Article CAS PubMed Google Scholar
Bertoldo, M. J. et al. NAD⁺ repletion rescues female fertility during reproductive aging. Cell Rep. 30, 1670–1681 (2020).
Article CAS PubMed PubMed Central Google Scholar
Wu, X. et al. NMNAT2-mediated NAD⁺ generation is essential for quality control of aged oocytes. Aging Cell 18, e12955 (2019).
Article PubMed PubMed Central Google Scholar
Prasad, S., Tiwari, M., Pandey, A. N., Shrivastav, T. G. & Chaube, S. K. Impact of stress on oocyte quality and reproductive outcome. J. Biomed. Sci. 23, 36 (2016).
Article PubMed PubMed Central Google Scholar
Wang, S. et al. Single-cell transcriptomic atlas of primate ovarian aging. Cell 180, 585–600 (2020).
Article CAS PubMed Google Scholar
Navarro-Pando, J. M. et al. Inhibition of the NLRP3 inflammasome prevents ovarian aging. Sci. Adv. 7, eabc7409 (2021).
Article CAS PubMed PubMed Central Google Scholar
Uri-Belapolsky, S. et al. Interleukin-1 deficiency prolongs ovarian lifespan in mice. Proc. Natl Acad. Sci. USA 111, 12492–12497 (2014).
Article CAS PubMed PubMed Central Google Scholar
Cui, L. L., Yang, G., Pan, J. & Zhang, C. Tumor necrosis factor alpha knockout increases fertility of mice. Theriogenology 75, 867–876 (2011).
Article CAS PubMed Google Scholar
Lliberos, C., Liew, S. H., Mansell, A. & Hutt, K. J. The inflammasome contributes to depletion of the ovarian reserve during aging in mice. Front. Cell Dev. Biol. 8, 628473 (2020).
Article PubMed Google Scholar
Wu, J. et al. Aging conundrum: a perspective for ovarian aging. Front. Endocrinol. 13, 952471 (2022).
Article Google Scholar
Zhang, J. et al. Can ovarian aging be delayed by pharmacological strategies? Aging 11, 817–832 (2019).
Article PubMed PubMed Central Google Scholar
Camacho-Pereira, J. et al. CD38 dictates age-related NAD decline and mitochondrial dysfunction through an SIRT3-dependent mechanism. Cell Metab. 23, 1127–1139 (2016).
Article CAS PubMed PubMed Central Google Scholar
Covarrubias, A. J., Perrone, R., Grozio, A. & Verdin, E. NAD⁺ metabolism and its roles in cellular processes during ageing. Nat. Rev. Mol. Cell Biol. 22, 119–141 (2021).
Article CAS PubMed Google Scholar
Xie, N. et al. NAD⁺ metabolism: pathophysiologic mechanisms and therapeutic potential. Signal. Transduct. Target Ther. 5, 227 (2020).
Article CAS PubMed PubMed Central Google Scholar
Bonkowski, M. S. & Sinclair, D. A. Slowing ageing by design: the rise of NAD⁺ and sirtuin-activating compounds. Nat. Rev. Mol. Cell Biol. 17, 679–690 (2016).
Article CAS PubMed PubMed Central Google Scholar
Yoshino, J., Baur, J. A. & Imai, S. I. NAD⁺ intermediates: the biology and therapeutic potential of NMN and NR. Cell Metab. 27, 513–528 (2018).
Article CAS PubMed Google Scholar
Rajman, L., Chwalek, K. & Sinclair, D. A. Therapeutic potential of NAD-boosting molecules: the in vivo evidence. Cell Metab. 27, 529–547 (2018).
Article CAS PubMed PubMed Central Google Scholar
Umehara, T. et al. Female reproductive life span is extended by targeted removal of fibrotic collagen from the mouse ovary. Sci. Adv. 8, eabn4564 (2022).
Article CAS PubMed PubMed Central Google Scholar
Perrone, R. et al. CD38 regulates ovarian function and fecundity via NAD⁺ metabolism.iScience 26, 107949 (2023).
Grieb, B. C., Boyd, K., Mitra, R. & Eischen, C. M. Haploinsufficiency of the Myc regulator Mtbp extends survival and delays tumor development in aging mice. Aging 8, 2590–2602 (2016).
Article CAS PubMed PubMed Central Google Scholar
Ko, C. Y., Chang, W. C. & Wang, J. M. Biological roles of CCAAT/enhancer-binding protein delta during inflammation. J. Biomed. Sci. 22, 6 (2015).
Article CAS PubMed PubMed Central Google Scholar
Lee, K. I. et al. FOXO1 and FOXO3 transcription factors have unique functions in meniscus development and homeostasis during aging and osteoarthritis. Proc. Natl Acad. Sci. USA 117, 3135–3143 (2020).
Article CAS PubMed PubMed Central Google Scholar
Ma, S. et al. Caloric restriction reprograms the single-cell transcriptional landscape of Rattus norvegicus aging. Cell 180, 984–1001 (2020).
Bebbere, D., Coticchio, G., Borini, A. & Ledda, S. Oocyte aging: looking beyond chromosome segregation errors. J. Assist. Reprod. Genet. 39, 793–800 (2022).
Article PubMed PubMed Central Google Scholar
Secomandi, L., Borghesan, M., Velarde, M. & Demaria, M. The role of cellular senescence in female reproductive aging and the potential for senotherapeutic interventions. Hum. Reprod. Update 28, 172–189 (2022).
Article CAS PubMed Google Scholar
Kasapoglu, I. & Seli, E. Mitochondrial dysfunction and ovarian aging. Endocrinology 161, bqaa001 (2020).
May-Panloup, P. et al. Ovarian ageing: the role of mitochondria in oocytes and follicles. Hum. Reprod. Update 22, 725–743 (2016).
Article CAS PubMed Google Scholar
Liu, L. et al. Infertility caused by retardation of follicular development in mice with oocyte-specific expression of Foxo3a. Development 134, 199–209 (2007).
Article CAS PubMed Google Scholar
Pelosi, E. et al. Constitutively active Foxo3 in oocytes preserves ovarian reserve in mice. Nat. Commun. 4, 1843 (2013).
Article PubMed Google Scholar
American College of Obstetricians and Gynecologists Committee on Gynecologic Practice and Practice Committee. Female age-related fertility decline. Committee Opinion No. 589. Fertil. Steril. 101, 633–634 (2014).
Garcia, D., Brazal, S., Rodriguez, A., Prat, A. & Vassena, R. Knowledge of age-related fertility decline in women: a systematic review. Eur. J. Obstet. Gynecol. Reprod. Biol. 230, 109–118 (2018).
Article PubMed Google Scholar
Zhou, Z. et al. Temporal transcriptomic landscape of postnatal mouse ovaries reveals dynamic gene signatures associated with ovarian aging. Hum. Mol. Genet. 30, 1941–1954 (2021).
Article CAS PubMed PubMed Central Google Scholar
Navarini, H. & Reeves, G. Specific stroma in the cortex and medulla of the ovary. 3. Changes induced by ionizing radiation. Obstet. Gynecol. 39, 556–561 (1972).
CAS PubMed Google Scholar
Baker, S. J. & Spears, N. The role of intra-ovarian interactions in the regulation of follicle dominance. Hum. Reprod. Update 5, 153–165 (1999).
Article CAS PubMed Google Scholar
Charalambous, C., Webster, A. & Schuh, M. Aneuploidy in mammalian oocytes and the impact of maternal ageing. Nat. Rev. Mol. Cell Biol. 24, 27–44 (2022).
Mikwar, M., MacFarlane, A. J. & Marchetti, F. Mechanisms of oocyte aneuploidy associated with advanced maternal age. Mutat. Res. Rev. Mutat. Res. 785, 108320 (2020).
Article CAS PubMed Google Scholar
Quarona, V. et al. CD38 and CD157: a long journey from activation markers to multifunctional molecules. Cytometry B 84, 207–217 (2013).
Chini, C. et al. The NADase CD38 is induced by factors secreted from senescent cells providing a potential link between senescence and age-related cellular NAD⁺ decline. Biochem. Biophys. Res. Commun. 513, 486–493 (2019).
Article CAS PubMed PubMed Central Google Scholar
Raedler, L. A. Darzalex (daratumumab): first anti-CD38 monoclonal antibody approved for patients with relapsed multiple myeloma. Am. Health Drug Benefits 9, 70–73 (2016).
PubMed PubMed Central Google Scholar
Tarrago, M. G. et al. A potent and specific CD38 inhibitor ameliorates age-related metabolic dysfunction by reversing tissue NAD⁺ decline. Cell Metab. 27, 1081–1095 (2018).
Article CAS PubMed PubMed Central Google Scholar
Peclat, T. R. et al. CD38 inhibitor 78c increases mice lifespan and healthspan in a model of chronological aging. Aging Cell 21, e13589 (2022).
Article CAS PubMed PubMed Central Google Scholar
Covarrubias, A. J. et al. Senescent cells promote tissue NAD⁺ decline during ageing via the activation of CD38⁺ macrophages. Nat. Metab. 2, 1265–1283 (2020).
Chini, C. C. S. et al. CD38 ecto-enzyme in immune cells is induced during aging and regulates NAD⁺ and NMN levels. Nat. Metab. 2, 1284–1304 (2020).
Fuller, E. A. et al. Neonatal immune activation depletes the ovarian follicle reserve and alters ovarian acute inflammatory mediators in neonatal rats. Biol. Reprod. 97, 719–730 (2017).
Article PubMed Google Scholar
Ben Yaakov, T., Wasserman, T., Aknin, E. & Savir, Y. Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function. eLife 12, e74915 (2023).
Lliberos, C. et al. Evaluation of inflammation and follicle depletion during ovarian ageing in mice. Sci. Rep. 11, 278 (2021).
Article CAS PubMed PubMed Central Google Scholar
Isola, J. et al. A single-cell atlas of the aging murine ovary. Preprint at bioRxiv https://doi.org/10.1101/2023.04.29.538828 (2023).
Bromfield, J. J. & Sheldon, I. M. Lipopolysaccharide reduces the primordial follicle pool in the bovine ovarian cortex ex vivo and in the murine ovary in vivo. Biol. Reprod. 88, 98 (2013).
Article PubMed Google Scholar
Sato, Y., Cheng, Y., Kawamura, K., Takae, S. & Hsueh, A. J. C-type natriuretic peptide stimulates ovarian follicle development. Mol. Endocrinol. 26, 1158–1166 (2012).
Article CAS PubMed PubMed Central Google Scholar
Yang, C. et al. Melatonin delays ovarian aging in mice by slowing down the exhaustion of ovarian reserve. Commun. Biol. 4, 534 (2021).
Article CAS PubMed PubMed Central Google Scholar
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Article CAS PubMed PubMed Central Google Scholar
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Article CAS PubMed PubMed Central Google Scholar
Morris, M. E. et al. A single-cell atlas of the cycling murine ovary. eLife 11, e77239 (2022).
Wang, J. J. et al. Single-cell transcriptome landscape of ovarian cells during primordial follicle assembly in mice. PLoS Biol. 18, e3001025 (2020).
Article CAS PubMed PubMed Central Google Scholar
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Article CAS PubMed PubMed Central Google Scholar
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
Article CAS PubMed Google Scholar

Download references

Acknowledgements

This study was funded by the National Key R&D Program of China (2019YFA0110900), key international (regional) cooperative research projects of China (81820108016), the National Natural Science Foundation of China (31970800 and 32370917) and Funding for Scientific Research and Innovation Team of The First Affiliated Hospital of Zhengzhou University (QNCXTD2023017).

Author information

These authors contributed equally: Qingling Yang, Wenhui Chen, Luping Cong.

Authors and Affiliations

Center for Reproductive Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Qingling Yang, Wenhui Chen, Luping Cong, Mengchen Wang, Hui Li, Huan Wang, Xiaoyan Luo, Jing Zhu, Xinxin Zeng, Zhenye Zhu, Yining Xu, Min Lei, Yanqing Zhao, Chenlu Wei & Yingpu Sun
Henan Key Laboratory of Reproduction and Genetics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Qingling Yang, Wenhui Chen, Luping Cong, Mengchen Wang, Hui Li, Huan Wang, Xiaoyan Luo, Jing Zhu, Xinxin Zeng, Zhenye Zhu, Yining Xu, Min Lei, Yanqing Zhao, Chenlu Wei & Yingpu Sun
Henan Provincial Obstetrical and Gynecological Diseases (Reproductive Medicine) Clinical Research Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Qingling Yang, Wenhui Chen, Luping Cong, Mengchen Wang, Hui Li, Huan Wang, Xiaoyan Luo, Jing Zhu, Xinxin Zeng, Zhenye Zhu, Yining Xu, Min Lei, Yanqing Zhao, Chenlu Wei & Yingpu Sun

Authors

Qingling Yang
View author publications
You can also search for this author in PubMed Google Scholar
Wenhui Chen
View author publications
You can also search for this author in PubMed Google Scholar
Luping Cong
View author publications
You can also search for this author in PubMed Google Scholar
Mengchen Wang
View author publications
You can also search for this author in PubMed Google Scholar
Hui Li
View author publications
You can also search for this author in PubMed Google Scholar
Huan Wang
View author publications
You can also search for this author in PubMed Google Scholar
Xiaoyan Luo
View author publications
You can also search for this author in PubMed Google Scholar
Jing Zhu
View author publications
You can also search for this author in PubMed Google Scholar
Xinxin Zeng
View author publications
You can also search for this author in PubMed Google Scholar
Zhenye Zhu
View author publications
You can also search for this author in PubMed Google Scholar
Yining Xu
View author publications
You can also search for this author in PubMed Google Scholar
Min Lei
View author publications
You can also search for this author in PubMed Google Scholar
Yanqing Zhao
View author publications
You can also search for this author in PubMed Google Scholar
Chenlu Wei
View author publications
You can also search for this author in PubMed Google Scholar
Yingpu Sun
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

Q.Y. conceptualized the work. Q.Y., W.C., L.C., M.W., H.L., H.W., X.L., J.Z., X.Z., Z.Z., Y.X., M.L., Y.Z. and C.W. developed the methodology and performed experiments. L.C. and W.C. carried out the formal analysis. Q.Y. and Y.S. supervised the work. Q.Y. wrote the paper. All authors reviewed the paper and approved its submission.

Corresponding authors

Correspondence to Qingling Yang or Yingpu Sun.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Eduardo Chini, Michael Stout and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Transcriptome data from different organs in 2-month and 8-month-old mice.

a–g. Principal component analysis (PCA) of RNA transcriptomes in the ovary (a), muscle (b), liver (c), heart (d), kidney (e), brain (f), and lung (g).

Source data

Extended Data Fig. 2 CD38 deletion reduced ovarian aging and improved ovulation potential in aged mice.

a. Schematic diagram of the generation of Cd38 knockout mice using CRISPR/Cas9. b. PCR genotyping and western blotting confirmed the absence of the CD38 protein in Cd38^−/− ovaries compared with the presence in WT ovaries. c. Expression of inflammation-associated factors in ovaries from 8-month-old WT and Cd38^−/− mice determined by RT-PCR (n = 6 for each group). d. Representative images of ovarian fibrosis in different from 2-month-old, 12-month-old WT and Cd38^−/− mice by PSR staining of collagen. Bar = 100μm. e. PSR positive area was calculated relative to 2-month-old mice. f. γH2AX staining of ovarian sections from 12 months old WT and Cd38^−/− mice. Arrows indicated the cells with DNA damage. Bar = 100μm. g. Quantitative analysis of γH2AX-positive cells in ovarian sections (n= 4-7 for each group). h. TUNEL staining of ovarian sections from 12-month-old WT and Cd38^−/− mice. Arrows indicated the apoptotic cells. Bar = 100μm. i. Quantitative analysis of TUNEL-positive cells in ovarian sections (n = 4-5 for each group). j. Representative morphology of ovarian sections from 12-month-old WT and Cd8^−/− mice pretreated with gonadotropins (n = 3-5 mice for each group). Bar = 100μm. k. Ovarian weights of WT and Cd38^−/− mice after gonadotropin treatment (n = 9–13 mice per group). l. Representative images of oocytes from 12-month-old WT and Cd38^−/− mice. Arrows indicate abnormal oocytes showing fragmentation. Bar = 100μm. m. Number of ovulated oocytes from 12-month-old WT and Cd38^−/− mice after gonadotropin-induced ovulation (n = 5–6 mice per group). Bar = 100μm. n. Percentage of abnormal oocytes from 12-month-old WT and Cd38^−/− mice (n = 5–7 mice per group). Data are represented as mean ± s.e.m. P value was determined by the unpaired t-test, two-tailed.

Source data

Extended Data Fig. 3 GSEA enrichment analysis for the pathways related to ovarian function and mitochondrial functions.

a. GSEA analysis showing that the pathways of ovulation cycles, female gamete generation, reproductive system development, embryonic morphogenesis, embryonic organ development, and mitotic cell cycle were enriched in 8-month-old Cd38^−/− compared to age-matched WT mice. b. GSEA analysis showed the pathways of female gamete generation, mitochondrial matrix, mitochondrial transport, ovulation cycle process, oogenesis, and ovarian follicle development in 12-month-old Cd38^−/− compared to age-matched WT mice. P values were calculated based on Fisher′s Exact Test.

Extended Data Fig. 4 CD38 deficiency shifted the ovarian transcriptome towards a more youthful state in 12-month-old mice.

a. Heatmap representation of ovarian DEGs between 12-M-old WT and Cd38^−/− mice. b. and c. GO analysis identified downregulated (b) and upregulated (c) genes in enriched pathways in 12-month-old Cd38^−/− mice. P values were calculated by Fisher′s Exact Test and adjusted by the Benjamini-Hochberg method. d. Heatmap showing the DEGs in cell cycle, DNA repair, inflammation, and SASP pathways in ovaries from 12-month-old WT and Cd38^−/− mice.

Extended Data Fig. 5 Single-oocyte sequencing revealed that CD38 deletion down-regulates senescence-related pathways and enhanced mitochondrial functions.

a. Heatmap showing the DEGs enriched in aging-related pathways of senescence-associated secretory phenotype (SASP), cell cycle, inflammation, and DNA repair in young, aged WT and aged Cd38^−/− oocytes. b. Venn diagram showing the overlap between age-related downregulated genes (Aged vs. Young) and upregulated after deletion of CD38. 407 DEGs were found, and KEGG analyses showed related pathways of the 407 DEGs. P values were calculated by Fisher′s Exact Test and adjusted by the Benjamini-Hochberg method. c. GSEA enrichment plots of GO pathways related to mitochondrial functions in 12-month-old WT and Cd38^−/− oocytes. P values were calculated based on Fisher′s Exact Test.

Extended Data Fig. 6 NAD⁺ levels in the ovaries from different groups.

a. Ovarian NAD⁺ levels in the control (WT) and Cd38^−/− mice treated with 78c for 8 days. (n = 16 for control, n = 7 for Cd38^−/− and Cd38^−/−+78c group). b. Ovarian NAD⁺ levels in the WT, 78c-treated and 78c+FK866-treated mice for 3 days. (n = 16 for control, n = 10 for 78c and 78c+FK866 group). Data are represented as mean ± s.e.m. P value was determined by the unpaired t-test, two-tailed.

Source data

Supplementary information

Supplementary Information

Legends for Supplementary Tables 1–11, Supplementary Figs. 1–3 and image source data for Supplementary Fig. 3.

Reporting Summary

Supplementary Table

Supplementary Table 1. RNA-seq data of different tissues. Supplementary Table 2. Basic clinical characteristics of young and middle-aged participants. RNA-seq expression data between Cd38-knockout and WT mice in 8-month-old ovaries. Supplementary Table 3. RNA-seq expression data between Cd38-knockout and WT in 8-month-old ovaries. Supplementary Table 4. RNA-seq expression data of Cd38-knockout and WT in 12-month-old ovaries. Supplementary Table 5. Specific markers used to annotate cell clusters. Supplementary Table 6. Single-oocyte sequencing data showing the Cd38-knockout and WT at 2 months and 12 months old. Supplementary Table 7. The overlap genes between age-related downregulated (aged versus young) and upregulated after deletion of CD38. Supplementary Table 8. Mitochondrial-related gene list. Supplementary Table 9. Reagents, antibodies and chemicals. Supplementary Table 10. Real time RT–PCR primer sequences. Supplementary Table 11. Pathway genelists include SASP, inflammation, DNA repair and cell cycle. Statistical Source Data for Supplementary Fig. 2

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 2

Unprocessed western blots.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 7

Unprocessed western blots.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 6

Statistical source data.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Yang, Q., Chen, W., Cong, L. et al. NADase CD38 is a key determinant of ovarian aging. Nat Aging 4, 110–128 (2024). https://doi.org/10.1038/s43587-023-00532-9

Download citation

Received: 06 January 2023
Accepted: 27 October 2023
Published: 21 December 2023
Issue Date: January 2024
DOI: https://doi.org/10.1038/s43587-023-00532-9

Subjects

Abstract

Similar content being viewed by others

Main

Results

Ovary ages earlier than several other organs in middle-aged mice

Inflammation activation was earlier in the middle-aged ovary

Increased CD38 expression in middle-aged ovaries

Deletion of CD38 improved the fecundity in female mice

CD38 deletion reversed ovarian aging-related gene expression

Restoration of transcriptional regulatory networks and aging-related aberrant cell–cell communication patterns in the ovary after CD38 deletion

CD38 deletion ameliorated age-related oocyte quality decline

CD38 deletion prevented inflammation-induced follicle loss

78c treatment rescued age-related female fertility decline

Discussion

Methods

Reagents, antibodies and chemicals

Animal feeding and treatments

LPS treatment in vivo

LPS treatment in vitro

CD38 inhibitor (78c) treatment in vivo

Flow cytometry of human follicular fluid

IHC staining

RNA isolation and quantitative real-time PCR

Western blotting

NAD+ measurement

Oocyte collection

ROS level monitoring

Mitochondrial membrane potential (Δψm) measurement

Spindle assembly analysis

Sectioning ovaries for follicle counting and serum AMH measurement

TUNEL assay and immunofluorescence on ovarian sections

PSR staining

Measurement of CD38 enzymatic activities

Tissue RNA-seq data processing

Single-cell RNA-seq library construction and sequencing

Analysis of scRNA-seq data of ovaries

Single-oocyte RNA-seq library construction and sequencing

Single-oocyte RNA-seq data processing and pathway analysis

Statistics and reproducibility

Reporting summary

Data availability

Code availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Extended data

Supplementary information

Source data

Rights and permissions

About this article

Cite this article

Share this article

Search

Quick links

NAD⁺ measurement