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. 2026 Mar 20;105(6):106811. doi: 10.1016/j.psj.2026.106811

Integrated analysis of transcriptome and metabolomic uncover molecular mechanisms of ovarian aging in laying hens

Huiting He 1, Qing Ma 1, Xingyu Zhang 1, Chunjie Zhou 1, Fangtan Liu 1, Yinglin Lu 1, Minli Yu 1,⁎
PMCID: PMC13068545  PMID: 41916059

Abstract

Ovarian aging shortens the productive lifespan and diminishes economic value of poultry, yet its mechanisms are unclear. This study investigated ovarian aging using transcriptomics and metabolomics. Result suggested that the number of primitive, primary, and secondary follicles significantly reduced in aged hens, while atretic follicles increased. Ovaries showed fibrosis with increased collagen deposition, and the thickness of follicle granulosa cell layers was remarkably reduced, exhibiting a disorganized and loose structure. The mitochondria of granulosa cells in aged hens exhibited vacuolation and sparse cristae. Additionally, antioxidant (GSH-Px, SOD) and reproductive hormone (AMH, E2) levels were significantly lower in aged hens (p < 0.05). Transcriptomic analysis revealed altered pathways including PPAR, ECM-receptor interaction, and cytokine-cytokine receptor interaction in aging ovaries. Metabolomic profiling further implicated biosynthesis of unsaturated fatty acids and purine metabolism pathways in aging ovaries. Integrated analysis revealed that FABP4 was a critical mediator linking lipid metabolism and inflammation, and it showed a negative correlation with conjugated linoleic acids (CLA) and cis-4,7,10,13,16,19-docosahexaenoic acid (DHA), suggesting therapeutic potential of their dietary supplementation. To understand how lipid metabolism dysregulation induced ovarian aging, the expression of key genes was assessed by qRT-PCR. The expression of senescence markers (p16, p21, p53), apoptosis-related genes (Bax/Bcl-2) (p < 0.001), autophagy-related gene (p62) (p < 0.01) and inflammation-related genes (IL6, TNFα, NOS2) (p < 0.01) in aged hens signifficantly increased, while the expression of autophagy-related genes (LC3B/LC3A) decreased (p < 0.05). This work provides a theoretical basis for strategies to delay ovarian aging and improve reproductive efficiency.

Keywords: Ovary, Aging, Transcriptome, Non-targeted metabolomic, Inflammation

Introduction

Egg production is an important trait of chickens, which affects the production efficiency and profit of the laying hen industry (Zhang et al., 2019). In females, the ovary is a critical reproduction organ, which generates oocytes and acts as the primary source of steroid sex hormones (Wang et al., 2020). Female reproductive capacity is correlated with aging, as follicle number and oocyte quality decrease, resulting in diminished ovarian reserve and reduced fertility (Park et al., 2021). Additionally, the aging of hens is accompanied by a decrease in estrogen levels, which is mostly produced by the ovary. This reduction in estrogen affects the synthesis and transport of yolk precursors in the liver, thereby impacting the egg-laying performance of laying hens (Amevor et al., 2021). Ovarian aging in hens causes a rapid decline in egg production, reducing both yield and commercial value (Liu et al., 2018). However, the underlying mechanisms of ovarian aging remain unclear. Thus, understanding the mechanisms of ovarian aging is essential for improving egg production efficiency.

Multiple factors contribute to the mechanisms of ovarian aging, such as mitochondrial dysfunction, genomic instability, and impaired autophagy (Wu et al., 2025). Additional contributing factors comprise cellular senescence, oxidative stress, chronic inflammation (Wu et al., 2025), and lipid metabolism (Wu et al., 2022). Late in the laying period, following the high-metabolic-demand period, oxidative stress and inflammation arise in the ovaries of laying hens (Wu et al., 2024), the antioxidant and immune capacities are reduced (Wang et al., 2024), elevated production of pro-inflammatory cytokines, resulting in a reduction in ovarian function. Decreased ovarian function manifests through follicular depletion, accelerated atresia, and hormonal deficits, ultimately reducing egg output and hindering gains in egg production efficiency (Qiang et al., 2023; Xu et al., 2023). Similarly, the decline in fertility observed in aging hens is closely associated with the functional deterioration of the liver-blood ovary axis (Wu et al., 2024). In aging laying hens, fatty liver syndrome significantly diminishes egg production, fertility, and hatchability, impairing overall reproductive performance (Dai et al., 2021).

Improving reproductive efficiency is a key goal in poultry breeding, while advancement via traditional methods remains a slow process. Using RNA-seq, gene expression differences related to reproductive performance have been extensively characterized. Transcriptome analysis revealed that the PPAR signaling pathway, and neuroactive ligand-receptor interaction are key pathways associated with egg production (Sun et al., 2021). The expression of PPARγ in granulosa cells is regulated by cyclic hormonal changes and plays crucial roles in regulating follicular development, gametogenesis, steroidogenesis, cell differentiation, and cholesterol metabolism (Meling et al., 2022). Transcriptomic analysis revealed that FABP4 functions through the PPARγ signaling pathway. It was highly expressed in lamb granulosa cells, and its overexpression promoted granulosa cell apoptosis. In addition, single-cell sequencing has revealed that dysregulation of lipid metabolism can lead to granulosa cell apoptosis (Zhang et al., 2024). Transcriptomic and metabolomic analyses transcriptome data and metabolomic profiles to identify key biomarkers, delineate relationships among samples, and reveal the underlying biological significance (Zhang et al., 2025). For example, through transcriptome and metabolomics analysis, it was found that supplementation with spermidine promotes oocyte maturation, enhances fertilization rates, and improves embryonic developmental potential in aged female mice, thereby improving overall reproductive capacity (Zhang et al., 2023).

The economic returns in layer farming are directly determined by the hens' laying performance. Aging leads to systemic senescence and reproductive decline, manifested as a reduction in follicle number and oocyte quality, which ultimately diminishes the egg production rate and compromises egg quality. Therefore, this study elucidates the mechanism by which ovarian aging affects reproductive performance through integrated transcriptomic and metabolomic analyses, focusing on the relationships between genes and metabolites. Furthermore, this study investigated the phenotypic changes associated with ovarian aging in laying hens. Subsequently, we employed integrated transcriptomic and metabolomic sequencing to profile alterations in gene expression and metabolite profiles, thereby investigating the molecular mechanisms of ovarian aging in laying hens. These findings provide a theoretical foundation for developing strategies to delay ovarian aging and to enhance both the egg production rate and reproductive performance.

Materials and methods

Ethics statement

All experimental procedures in our experiment were conducted in accordance with the guidelines for experimental animals established by the ministry of science and technology of the people’s republic of China and was approved by the animal care committee of Nanjing Agricultural University (NO: SYXK-2021-0086).

Animals and sample collection

Commercial laying hens (Jinghong No.1) were raised under identical conditions and provided with the same complete feed. To investigate age-related ovarian changes with strict longitudinal consistency, the same cohort of hens was maintained and tracked from hatch through 80 weeks of age. The hens were fasted for 12 h prior to euthanasia while maintaining free access to water. A total of 30 chickens (30 weeks (Young) and 80 weeks (Aged) chickens, n = 15, respectively) were randomly selected and sacrificed by cervical dislocation after ether respiratory anesthesia. Liver and ovarian tissues, and different-stage follicles (small white follicle, SWF; large white follicle, LWF; small yellow follicle, SYF; large yellow follicle, LYF) were collected, quickly cryopreserved or fixed for further experiments. All sampling procedures adhered to principles of animal welfare.

Hematoxylin-eosin (H&E) staining

Following fixation in 4 % paraformaldehyde for 24 h, LYF, SYF, LWF, SWF, liver and ovarian tissues were dehydrated through a graded ethanol series, cleared in xylene, embedded in paraffin, and sectioned at 5 μm. Sections were deparaffinized in xylene, rehydrated in a descending ethanol gradient, and subjected to HE staining. Histological images were acquired with an Grundium Ocus scanner (Grundium, Ocus®20, FIN). Five distinct regions per samples were randomly selected for imaging and analyzed using slide viewer software.

Transmission electron microscopy

Liver tissues and granulosa cells isolated from small yellow follicles were fixed overnight in 2.5 % glutaraldehyde prepared in 0.1 M phosphate-buffered saline (PBS, pH 7.0). The samples were then post-fixed with 1 % osmium tetroxide (OsO₄) for 1.5 h, followed by three 15 min washes in PBS. Dehydration was carried out through a graded series of ethanol and acetone. Subsequently, the samples were embedded, se ctioned into ultrathin slices, and stained. Ultrastructural examination was performed using a Hitachi H-7650 transmission electron microscope (Hitachi, Ibaraki, Japan).

Assay of reproductive hormones in serum

Prior to euthanasia, blood samples were collected from the wing vein of 6 hens and centrifuged at 4000 r/min for 15 min at 4 °C. The resulting serum was separated and stored at −20 °C. Subsequently, the serum concentrations of anti-Müllerian hormone (AMH) (cat#ANG-E32039C), follicle-stimulating hormone (FSH) (cat#ANG-E32056C), and estradiol (E2) (cat#ANG-E32143C) were measured using commercial enzyme-linked immunosorbent assay (ELISA) kits from Aoqing biotechnology company (Nanjing, Jiangsu, China), following the instructions provided by the manufacturer.

Assay of antioxidant index in serum

Prior to euthanasia, blood samples were collected from the wing vein of 6 hens and centrifuged at 4000 r/min for 15 min at 4 °C. The resulting serum was separated and stored at −20 °C. Subsequently, the serum levels of superoxide dismutase (SOD) (cat#YH1202) and glutathione peroxidase (GSH-Px) activities (cat#YH1267), as well as malondialdehyde (MDA) content (cat#YH1217), were determined using commercial assay kits from Aoqing biotechnology company (Nanjing, Jiangsu, China), following the instructions provided by the manufacturer.

Masson’s trichrome staining of ovaries

Ovarian fibrosis was assessed using a masson’s trichrome stain kit (Solarbio, G1340, China). Paraffin-embedded sections were deparaffinized and rehydrated through a graded series of ethanol to distilled water. The sections were then stained with weigert's iron hematoxylin solution (prepared by mixing solutions A1 and A2 in equal volumes) for 5-10 min. Following this, they were rinsed with distilled water to remove excess stain, differentiated in acid differentiation solution for 5-10 s, and washed with distilled water for 30 s. Next, the sections were blued in bluing solution for 3-5 min and rinsed in distilled water for 30 s. Staining was continued by incubation with ponceau-acid fuchsin solution for 5-10 min. Prior to this step, a weak acid working solution was prepared by mixing distilled water with weak acid solution in a 2:1 ratio. The sections were then rinsed with this weak acid working solution for 30 s. After discarding the excess solution, the sections were differentiated in phosphomolybic acid solution for 1-2 min, followed by another 30 s rinse with the weak acid working solution. Upon discarding the excess solution, the sections were counterstained with aniline blue solution for 1-2 min and again rinsed with the weak acid working solution for 30 s. Finally, the sections were dehydrated through a brief immersion in 95 % ethanol for 2-3 s, followed by two changes of absolute ethanol (5-10 s each). The sections were then cleared in two changes of xylene (1-2 min each) and mounted with a resinous medium. The images was analyzed using Slide Viewer software. Collagen-rich fibrotic areas were identified by blue staining, whereas muscle fibers, cytoplasm, cellulose, keratin, and erythrocytes appeared red.

Ovarian RNA library construction and transcriptome sequencing

Total RNA was extracted from the samples (n = 6), and mRNA was enriched from the total RNA using Oligo dT magnetic beads for library construction. The enriched mRNA was fragmented, after which the first strand of cDNA was synthesized with random hexamer primers, and the second strand of cDNA was generated subsequently. The library construction workflow included end repair, poly (A) tailing, adapter ligation, fragment selection, PCR amplification and purification.

After passing quality inspection, the qualified libraries were pooled according to their effective concentrations and the required output data volume, and sequenced on an Illumina sequencing platform based on the principle of Sequencing by Synthesis. Four types of fluorescently labeled dNTPs, DNA polymerase and adapter primers were added to the sequencing flow cell for amplification; the sequencer captured the fluorescent signals released upon the incorporation of each fluorescently labeled dNTP, and converted these signals into sequencing peaks via computer software to obtain the sequence information of target fragments.

Raw reads generated by sequencing were first processed with fastp software. Adapter-containing reads, poly-N-containing reads and low-quality reads were filtered out to generate clean reads, and the Q20, Q30 and GC content of the clean data were calculated at the same time. All subsequent analyses were performed based on these high-quality clean reads. HISAT2 (v2.2.1) was used to construct the reference genome index, and this software was also applied to align paired-end clean reads to the reference genome. FeatureCounts (v2.0.6) was employed to count the mapped reads for each gene, and the FPKM value of each gene was calculated based on gene length and the number of mapped reads to quantify gene expression levels.

For differential expression analysis between the two groups, the DESeq2 R package (v1.42.0) was used for samples with biological replicates, while the edgeR R package (v4.0.16) was adopted for samples without biological replicates. The Benjamini and Hochberg method was used to control the false discovery rate, and genes with a padj ≤ 0.05 and |log2 (fold change) | ≥ 1 were identified as significantly differentially expressed genes (DEGs). GO and KEGG enrichment analyses of DEGs were conducted with clusterProfiler (v4.8.1), with gene length bias corrected in the analysis. GO terms and KEGG pathways with an adjusted p-value < 0.05 were regarded as significantly enriched.

Nontarget metabolomics analysis

An untargeted metabolomics approach was used to detect metabolite changes. The extraction, detection, and quantitative analysis of metabolites in ovarian samples were performed by Novogene Technology Co., Ltd. (Beijing, China). XCMS processed the data files generated by UHPLC-MS/MS to perform peak alignment, peak picking, and quantitation for each metabolite. Then, based on adduct ions and setting mass deviation to 10 ppm, a comparison was made between these data and the high-quality secondary spectrum database to obtain results for metabolite identification. After eliminating background ions according to blank samples, the original quantitative results were normalized by the following formula to obtain relative peak areas: Relative peak areas = Raw quantitative value of samples/(The sum of quantitative value of samples/The sum of quantitative value of QC1); Compounds with a coefficient of variation (CV) of relative peak areas in QC samples greater than 30 % were removed. Finally, the identification and relative quantification results of metabolites were obtained. Data processing is based on the Linux operating system (CentOS version 6.6), using R and Python.

These metabolites were annotated using the KEGG database (https://www.genome.jp/kegg/pathway.html), HMDB database (https://hmdb.cametabolites) and LIPIDMaps database (http://www.lipidmaps.org/). Principal components analysis (PCA) and Partial least squares discriminant analysis (PLS-DA)were performed at Metaxl6 (a flexible and comprehensive software for processing metabolomics data). We applied univariate analysis (t-test) to calculate the statistical significance (p-value). The metabolites with VIP > 1 and p-value< 0.05 and FC > 1.5 or FC≤ 0.667 were considered to be differential metabolites. Volcano plots were used to filter metabolites of interest which based on log2 (FoldChange) and log10(p-value) of metabolites by ggplot2 in R language. For clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were plotted by the Pheatmap package in R. The correlation between differential metabolites was analyzed by the R language (method = pearson). Statistically significant correlation between differential metabolites was calculated by the R language.p-value < 0.05 was considered statistically significant, and the corrplot package in the R language plotted correlation plots. The functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathways enrichment of differential metabolites was performed, when ratios were satisfied by x/n > y/Nmetabolic pathways were considered as enrichment, when the p-value of the metabolic pathway < 0.05, metabolic pathways were considered as statistically significant enrichment.

Integrative analysis of the transcriptome and metabolome

The correlation analysis between DEGs and differentially expressed metabolites was performed using Pearson's correlation coefficient based on the NovoMagic analysis platform (https://magic-plus.novogene.com/#/). Furthermore, we mapped all concurrently identified differentially expressed genes and significant metabolites to the KEGG pathway database to determine their shared pathway involvement.

Quantitative real-time PCR analysis

RNA was extracted from liver and ovary tissues using the EASYspin Plus RNAprep pure Micro Kit (Aidlab, Beijing, China, RN28). cDNA was synthesized using the RNA reverse transcription kit (TransGen, Beijing, China, AT311-03) according to the instructions. The reaction conditions were 25 °C for 10 min, 42 °C for 15 min, and 85 °C for 30 s. The quality and quantity of cDNA was stored at −20 °C. qRT-PCR analyses were conducted using SYBR® Premix Ex Taq™ II (Takara, Dalian, China, RR820A) on a Roche 480 light cycler real-time PCR instrument (Roche, Germany). Samples from all two periods were processed strictly according to standard protocols, with three independent biological replicates (n = 3) established for each period. After normalization to the β-actin gene, fold change was calculated, and mRNA abundance was estimated using the 2−ΔΔCT method. The primer sequences for the target genes are listed in Table 1.

Table 1.

Specific primers for qRT-PCR.

Gene Accession number Primer sequences (5′ → 3′) Product length (bp)
β-actin NM_205518.2 F:CCGTGCTGTGTTCCCATCTATCG 80
R:CGTAGCTGTCTTTCTGGCCCATAC
Bcl2 XM_046910476.1 F:ATGACCGAGTACCTGAACCG 200
R:CAAGAGTGATGCAAGCTCCC
Bax XM_001235092.6 F:CCGCATGGCTTTTCTACGAA 193
R:ATGCTGGTGTCTGTAGAGGG
LC3A XM_040688401.2 F:TTACACCCATATCAGATTCTTG 143
R:ATTCCAACCTGTCCCTCA
LC3B NM_001031461.2 F:AGTGAAGTGTAGCAGGATGA 193
R:AAGCCTTGTGAACGAGAT
p62 XM_003642061.6 F:GACCCAGCCAAGACTACCAT 240
R:CAGAGGCATGTAGTTTCGGC
p53 NM_205264.1 F:GAGATGCTGAAGGAGATCAATGAG 145
R:GTGGTCAGTCCGAGCCTTTT
p21 XM_040670583.2 F:CCGGAAGTGGTGACGAGAAA 66
R:TGCCATGATCCCAAGTGACC
p16 NM_204433.2 F:GCTGCGGATGAACTAGCCAA 106
R:TCCGACCGAAGGAGTTGACA
IL6 NM_204628.2 F:AAATCCCTCCTCGCCAATCT 106
R:CCCTCACGGTCTTCTCCATAAA
TNFα XM_046927262.1 F:CCCATCTGCACCACCTTCAT 221
R:AACTCATCTGAACTGGGCGG
NOS2 NM_204961.2 F:GCAACCTGGGCAGCCTAAAGTC 123
R:GCCATGCGTACATCTCCACAGAC
FABP4 NM_204290.2 F:ATGTGCGACCAGTTTGT 143
R:TCACCATTGATGCTGATAG
IGF-1 NM_001004384.3 F:TACCTTGGCCTGTGTTTGCT 170
R:CCCTTGTGGTGTAAGCGTCT
ApoB NM_001044633.2 F:GCAGCCTATGGAACAGA 214
R:TAGTGGAACGCAGAGCA
SPP1 NM_204535.5 F:GCCCAACATCAGAGCGTAGA 204
R:ACGGGTGACCTCGTTGTTTT
CD36 XM_046907113.1 F:CACTGCAATTTGCCAAAAGA 198
R:TCTGCACCACACCCAGTAAC
ADIPOQ NM_206991.2 F:GCCAGGTCTACAAGGTGTCA 86
R:CCATGTGTCCTGGAAATCCT
PPARγ XM_040646063.2 F:CATCAGGTTTGGGCGAATGC 76
R:TAACTGGTCGATGTCGCTGG
NF-kB NM_001396396.1 F:GCTCACAAAGGCAGTCTCACCAG 150
R:AGGTCTCTACGCCGCTGTCAC
VTGⅡ NM_001031276.2 F:AACTACTCGATGCCCGCAAA 179
R:ACCAGCAGTTTCACCTGTCC
ApoV1 XM_015295934.3 F:CCTTAGCACCACTGTCCCTG 130
R:AGCTCTAGGGGACACCTTGT
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Statistical analysis

The experiments were conducted three times. Data analysis was performed using GraphPad Prism9 software, employing one-way analysis of variance (ANOVA), followed by either Tukey’s or Dunnett’s post hoc test. Statistical significance was determined when p < 0.05.

Results

Morphological characteristics of aged ovaries

To investigate the changes of aging hens, the morphology and histology of ovaries in young (30-week-old) and aged (80-week-old) hens was measured. Compared to young hens, the aged group exhibited a marked reduction in follicular number, accompanied by widespread follicular deformation, collapse, and atresia, along with sparser vasculature on the follicular surface (Fig. 1A). Concurrently, aged ovaries showed evident fibrosis with increased collagen deposition (Fig. 1B). HE revealed a significant decrease in the populations of primordial follicles (PrFs), primary follicles (PFs), and secondary follicles (SFs), whereas the number of atretic follicles (AtFs) were significantly increased (Figs. 1C, S1). Ultrastructural analysis by transmission electron microscopy further demonstrated that granulosa cells from aged ovaries contained mitochondria with ruptured outer membranes and disintegrated or absent cristae, alongside an increase in lipid droplets (Fig. 1D).

Fig. 1.

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Histological analysis of the ovary in young and aged hens. (A) Morphology of ovaries in young and aged hens. (B) Masson staining of ovaries tissues. The blue region indicates collagen fibers (fibrosis), and the red region indicates myofibers and cytoplasm. Scale bars: 100 μm. (C) Morphology of ovaries in young and aged hens by HE staining. Abbreviations: PrF, primordial follicle; PF, primary follicle; SF, secondary follicle; AtF, atretic follicle. Scale bars: 50 μm. (D) Morphology of mitochondria in ovaries were observed under an electron microscope. Normal and abnormal mitochondria are indicated by blue and red dashed circles respectively. The yellow dotted circle indicates a lipid droplet. Scale bars: 800 nm.

Morphological characteristics of follicles in aged hens

The morphology of different-stage follicles was assessed by HE staining. As shown in Fig. 2, the granulosa cell (GC) layer in follicules (SWF, LWF, SYF, LYF) and the theca cells in young hens exhibited a clear boundary, with the granulosa cells being tightly packed, well-organized, and thick. In contrast, aged hens showed a marked separation between the granulosa cell layer and the theca layer (Fig. 2). Furthermore, the thickness of granulosa cell layer exhibited a thin and loosely arranged morphology (Fig. 2).

Fig. 2.

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Comparison of the morphological and histological characteristics of follicles. The morphology follicles were observed by HE staining. The yellow dotted line indicates the thickness of the granular cell layer. Abbreviations: SWF, small white follicle; LWF, large white follicle; SYF, small yellow follicle; LYF, large yellow follicle; T, theca cells; G, granulosa cells; O, oocytes. Scale bars: 20 μm.

Changes of serum antioxidant capacity and biochemical indicators

To further elucidate the impact of aging, the hormone levels and antioxidant capacity in serum were assessed by enzyme-linked immunosorbent assay (ELISA) kit and antioxidant marker kit, respectively. Result illustrated the various biochemical parameters changed in the ovaries of aged hens. The data indicated that the level of AMH and E2 was notably decreased in aged hens compared with young hens, but the level of FSH was notable increased in aged hens (Fig. 3A–C) (p < 0.05). Inaddition, the serum antioxidant capacity assessment, in contrast to the young hens, there was a significant decrease in GSH-Px and SOD content (Fig. 3D, F) (p < 0.01). The content of MDA in aged hens serum not significantly changed (Fig. 3E) (p > 0.05).

Fig. 3.

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Changes of serum biochemical indicators and antioxidant capacity in aged hens. Abbreviations: FSH, follicle-stimulating hormone (A); E2, estradiol (B); AMH, anti-müllerian hormone (C); MDA, malondialdehyde (D); GSH-Px, glutathione peroxidase (E); SOD, superoxide dismutase (F). Data are described as mean ± SEM (n = 6). *p < 0.05 and **p < 0.01, ns-No significant difference.

Morphological characteristics of livers in aged hens

The liver is a vital organ that significantly influences the laying performance of hens. To investigate aging-related changes in liver tissue, we performed HE staining to assess general histoarchitecture, and mitochondrial ultrastructure in hepatocytes was examined by transmission electron microscopy. Result showed that the hepatocytes were neatly and clearly arranged, with a pink cytoplasm and light blue nuclei in young hens (Fig. 4). However, the hepatocytes in the aged hens were enlarged, the cytoplasm was loose, the coloring was light, and fat droplets appeared in the liver (Fig. 4). Transmission electron microscopy observation of cellular morphology revealed that liver cells in young hens displayed normal morphology with intact mitochondrial double membrane structures and clear cristae (Fig. 4). In aged hens, the cells showed ruptured mitochondrial outer membranes and reduced or absent cristae (Fig. 4).

Fig. 4.

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Histological analysis in livers of young and aged hens. Morphology of liver in young and aged hens was measured by HE staining. Scale bars: 50 μm. Morphology of mitochondria in liver cells was observed under an transmission electron microscopy. Scale bars: 1 μm. Normal and abnormal mitochondria are indicated by blue and red dashed circles resectively.

Transcriptomics analysis of young and aged hens

To investigate the mechanism of ovarian aging in hens, the comparative transcriptomic analysis of the ovaries from young and aged hens was performed by using RNA-seq. The analysis showed that 1611 differentially expressed genes (DEGs) were identified in the ovaries between aged and young hens, of which 1087 were downregulated and 524 were upregulated (Fig. 5A). The heat map data showed that the transcriptome profile of ovaries from aged hens was different from that of ovaries from young hens (Fig. 5B). Further analysis using GO pathway annotations revealed key processes associated with aging, such as “transporter activity”, “extracellular space”, “extracellular region”, “multicellular organism development” and “cell adhesion” (Fig. 5C). Notably, the gene FABP4, identified as a top ten DEGs, was significantly enriched within the PPAR signaling pathway. Concurrently, KEGG enrichment analysis identified other key altered pathways in aged ovaries, including ECM-receptor interaction, fatty acid biosynthesis, and cytokine-cytokine receptor interaction (Fig. 5D), indicated that ovarian aging in hens is the result of the integration and combined effects of multiple complex pathways. Functional enrichment analysis screened FABP4, ADIPOQ, ApoB, CD36, and other genes related to inflammation and follicular fat deposition. Then, six key genes were selected for validation by qRT-PCR, and the results were basically consistent with the expression patterns of RNA-seq (Fig. 5E).

Fig. 5.

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Transcriptome analysis of ovaries in aged and young hens. (A) The volcano plot of differential genes in ovaries. The x-axis represents the log2 fold change, the y-axis represents the statistical significance. The red dots indicate significantly up-regulated differential genes, the green dots indicate significantly down-regulated differential genes. (B) Heat map of differentially expressed gene clustering.The horizontal coordinate in the figure is the sample names, and the vertical coordinate is the value of the differential gene FPKM normalized. GO enrichment (C) and KEGG analysis (D) of DEGs in the ovaries. (E) The candidate DEGs selected by RNA-seq was confirmed by qRT-PCR.

Non-targeted metabolomics analysis of young and aged hens

To identify the differentially expressed metabolites (DEMs) between aged and young hens, the variable importance in the projection (VIP) of the first component of the PLS-DA model was used, and the significantly differential metabolites were found by combining the p-value of the t-test. The heat maps of DEMs was showed in Fig. 6A. A total of 115 metabolites were identified as significantly different, comprising 74 that were upregulated and 41 that were downregulated in the aged group (Fig. 6B). We detected significant alterations in key metabolites, including various fatty acids (CLA, DHA) and critical energy-related molecules such as ADP, and cyclic ADP-ribose (Fig. 6C). KEGG database enrichment analysis was conducted for DEMs, identifying the top 20 significantly enriched KEGG metabolic pathways in the comparison between the aged and young hens. KEGG enrichment analysis showed that differential metabolites in the chickens were mainly involved in purine metabolism and biosynthesis of unsaturated fatty acids, suggesting these pathways may be critical factors contributing to ovarian aging (Fig. 6D).

Fig. 6.

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Non-targeted metabolomics of ovaries in aged and young hens. (A) Heat map of differential metabolite clustering shows the clustering of metabolites in aged hens compared to young hens. The vertical axis represents the clustering of samples, while the horizontal axis represents the clustering of metabolites. The shorter clustering branches indicate the higher similarity. (B) Volcano plot of DEMs in the ovaries, the red dots indicate significantly up-regulated differential metabolites, the blue dots indicate significantly down-regulated differential metabolites. (C) Correlation heatmap of differential metabolites. The color scale represents Pearson correlation coefficients, ranging from +1 (significant positive correlation, red, p < 0.05) to -1 (significant negative correlation, blue, p < 0.05). Metabolites lacking color indicate statistically non-significant correlations (p > 0.05). The display shows the top 20 differential metabolites ranked by ascending p-value. (D) Significantly enriched KEGG pathways.

Integrated analysis of transcriptomics and non-targeted metabolomics

Pearson's correlation analysis was conducted to examine the relationships between DEMs identified through metabolomics and DEGs from transcriptomics. To identify key genes and metabolites, we focused on the key differential metabolites from the most significantly enriched pathways and their related differentially expressed genes, and accordingly built a gene-metabolite co-expression network (Fig. 7A). Notably, the gene FABP4, which is associated with inflammation and lipid metabolism, showed significant negative correlations with lipid metabolites such as CLA and DHA (Fig. 7A). DEGs in the transcriptome and DEMs in the metabolome were enriched into a total of 20 pathways. Integrated pathway analysis identified the dysregulation of lipid metabolism (arachidonic acid metabolism and linoleic acid metabolism) as a key feature of aging (Fig. 7B). The integrated analysis revealed that ovarian aging is associated with dysregulated lipid metabolism, identifying the key gene FABP4 as a critical mediator linking lipid metabolism and inflammation. Therefore, the expression of key genes involved in lipid metabolism and inflammatory responses was examined by qPCR. Result showed that the expression of PPARγ in the ovary of aged hens was significantly lower than that of young (Fig. 7C) (p < 0.01). In contrast, the expression of FABP4 and NF-kB in the ovary of aged hens was significantly upregulated (Fig. 7C) (p < 0.01).

Fig. 7.

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Integrated analysis of transcriptomics and non-targeted metabolomics. (A) Sankey diagram of the interplay between differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs). Left indicate DEMs, and right indicate DEGs. The colour of the lines indicates the correlation, with red representing a positive correlation and blue representing a negative correlation. The larger the absolute value of the correlation coefficient, the darker the colour. (B) The common pathways of the intersection of DEGs and DEMs enrichment signaling pathways. (C) The mRNA expression of key genes was measured by qPCR. The relative expression of genes was normalized to β-actin. Data are presented as means ± SEM. **p < 0.01, ****p < 0.0001.

Expression level of genes related to yolk precursors synthesis

The development of follicles is inseparable from the synthesis, transportation and deposition of yolk precursor substances, so the mRNA expression level of VTGⅡ and ApoV1 which are involved in yolk protein formation,was measured by qRT-PCR. Result showed that the expression of ApoV1 and VTGⅡ in the ovary of aged hens was significantly lower than that of young hens (Fig. 8A, B) (p < 0.05). The expression level of ApoV1 and VTGII in the liver were consistent with those in the ovary (Fig. 8C, D) (p < 0.0001).

Fig. 8.

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Expression of yolk precursors synthesis related genes in ovaries and livers. Lipoprotein genes (A) and vitellogenin genes (B) expressed in ovaries. Lipoprotein genes (C) and vitellogenin genes (D) expressed in livers. The relative expression of genes was calculated by normalizing to β-actin. Data are presented as means ± SEM. *p < 0.05, ****p < 0.0001.

Changes in aging, apoptosis, autophagy, and inflammation-related genes

To understand how lipid metabolism dysregulation induces ovarian aging, the expression of key genes mediating aging, apoptosis, autophagy, and inflammation was assessed by qRT-PCR. Compared with young, the expression of senescence markers (p16, p21 and p53) in the ovaries of aged hens was significantly increased (Fig. 9A) (p < 0.05). In the ovaries of aged hens, we observed that the level of Bax/Bcl2 was significantly upregulated (Fig. 9B) (p < 0.001), but the level of the autophagy-related gene LC3B/LC3A was significantly reduced (Fig. 9B) (p < 0.05). And the expression of p62 was significantly increased (Fig. 9B) (p < 0.01), as well as the expression of the inflammation-related genes IL-6, TNFα, andNOS2 was significantly increased (Fig. 9C) (p < 0.01).

Fig. 9.

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Gene expression of ovaries in young and aged hens. Expression of the senescence markers p16, p21 and p53 (A), apoptosis-related genes Bax/Bcl2, autophagy-related genes LC3B/LC3A, p62 (B) and inflammation-related genes IL6, TNFα, NOS2 (C) in ovaries. The relative expression of genes was calculated by normalizing to β-actin. Data are presented as means ± SEM. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns-No significant difference.

Discussion

Egg-laying performance is a key economic trait for poultry production. The ovary is a vital reproductive organ, and its health and normal development are critical to oocyte formation and reproductive capacity (Xiang et al., 2023). Ovarian aging leads to reduced egg yield and poor egg quality (Zhang et al., 2022). Studying the ovarian aging process in hens enables the identification of key genes and metabolites associated with ovarian aging, as well as the critical signaling pathways involved in egg production. This study integrated transcriptomic and metabolomic data to uncover the molecular mechanisms underlying ovarian aging, and to screen out key candidate genes and biological processes linked to ovarian aging. The findings of this study clarify the multi-omics expression characteristics of ovarian aging in hens.

The ovary is an essential reproductive organ in hens, responsible for producing oocytes and secreting steroid sex hormones (Wang et al., 2020). Ovarian reserve is a key indicator for evaluating ovarian function, defined as the number of primordial follicles within the ovarian cortex (Tu et al., 2022). In the present study, aged hens showed a reduction in primordial follicles, an increase in atretic follicles, and a thinning of the granulosa cell layer, and these findings demonstrate the progressive depletion of ovarian reserve with advancing age (Yan et al., 2024). Furthermore, follicles are embedded in the ovarian stroma, which is composed of fibroblasts, immune cells, blood and lymphatic vessels, nerves, extracellular matrix and other cellular components (Balough et al., 2024). Our study found exacerbated ovarian fibrosis in aged hens, and age-related fibrosis of the ovarian microenvironment is typically accompanied by inflammatory responses (Umehara et al., 2022; Balough et al., 2024). This therefore confirms that inflammation develops in the ovary during the process of ovarian senescence. Additionally, consistent with the results of other research (Wu et al., 2025), mitochondria in the granulosa cells of aged hen ovaries exhibited fragmentation and vacuolization, and mitochondrial vacuolization serves as a potential marker of cellular apoptotic processes (Balough et al., 2024). The distinct phenotypic differences observed between the ovaries of young and aged hens in this study indicate that inflammatory and apoptotic mechanisms are activated during ovarian aging. This activation disrupts the normal development and selection of follicles in hens, impeding the transition of follicles to the mature ovulatory stage and consequently leading to a decline in egg-laying performance (Huang et al., 2025).

Reproductive hormones regulate ovarian development in hens. As key reproductive hormones, E2 and FSH not only mediate follicular atresia but also regulate cellular proliferation, primordial follicle development and yolk formation (Yang et al., 2024). The levels of anti-Müllerian hormone (AMH) reflect the status of ovarian follicular reserve (di Clemente et al., 2021). In the present study, we found that the serum levels of E2 and AMH were significantly decreased in aged hens, while the FSH level was markedly elevated. These hormonal changes indicate a decline in ovarian follicular reserve during the aging process, and also impair yolk formation and cellular proliferation in the ovary. Similarly, we observed a notable reduction in the activity of antioxidant enzymes including superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in aged hens. This decrease leads to impaired ovarian antioxidant defense capacity and exacerbated oxidative stress, thereby accelerating ovarian senescence (Zhong et al., 2025). Collectively, these age-related alterations may ultimately result in a decline in egg production in laying hens (Ru et al., 2024).

By analyzing the transcriptome results, we found that aging mainly affects cell structure and migration (ECM-receptor interaction, focal adhesion, cell adhesion molecules), metabolism-related pathways (PPAR signaling pathway, fatty acid biosynthesis, adipocytokine signaling pathway, propanoate metabolism, pyruvate metabolism, tyrosine metabolism, retinol metabolism, nitrogen metabolism), immune and inflammatory responses (cytokine-cytokine receptor interaction, phagosome, intestinal immune network for IgA production) and signal transduction pathway (MAPK signaling pathway, calcium signaling pathway, neuroactive ligand-receptor interaction, GnRH signaling pathway). The PPAR signaling pathway was previously reported to affect ovarian follicle development and function by indirectly regulating oocyte maturation and ovulation through its role in steroid hormone synthesis in granulosa cells (Sun et al., 2021). The “neuroactive ligand-receptor interaction” and “cytokine–cytokine receptor interaction” pathways are essential for egg production in chickens (Huang et al., 2022). In this study, we identified that a total of 1611 DEGs were found in the young hens and the aged hens. GO and KEGG enrichment analyses indicate that many of these DEGs are associated with egg-laying. ApoB and VTG are key genes associated with yolk deposition in poultry (Dai et al., 2020). These substances are mostly produced in hepatocytes and deposited in developing oocytes, which ultimately encourages follicle formation (Li et al., 2025). Therefore, yolk deposition is critical for follicle development and laying performance (Song et al., 2023). At 58 weeks of age, laying hens exhibit low expression of ApoB and VTGII, which directly impairs egg production (Wu et al., 2024). ADIPOQ, as downstream genes of CD36, were differentially expressed in the PPAR signaling pathway of the ovarian. ADIPOQ expression was correlated with adiponectin levels. Adiponectin is an important adipokine that regulates reproduction (He et al., 2023). In mammals, adiponectin has been shown to influence ovarian steroidogenesis by increasing the expression of IGF-1, thereby triggering the secretion of E2 (Li et al., 2021). Additionally, we identified FABP4, ADIPOQ and CD36 as genes enriched in the PPAR signalling pathway. Elevated FABP4 promotes lipid deposition and metabolic disorders, whereas inhibiting or knocking down FABP4 elevates PPARγ expression, suppressing NF-κB mediated inflammation (Mao et al., 2021). Inhibiting FABP4 expression also suppresses inflammation induced by saturated fatty acids (Zhou et al., 2023). This study revealed that compared to young laying hens, aged hens exhibited upregulation of ovarian FABP4 expression and downregulation of ApoB, VTGII, and ADIPOQ. We hypothesize that with age, reduced yolk deposition in the follicles and lipid deposition in the ovaries. This lipid metabolism disorder subsequently induces inflammation, which accelerates follicular atresia and ovarian aging, ultimately resulting in diminished egg production performance.

The metabolomic assay identified 41 down-regulated-overlapping DEMs and 74 up-regulated-overlapping DEMs in the ovaries of hens. DEMs showed significant enrichment in the biosynthesis of unsaturated fatty acids (DHA) and purine metabolism. Unsaturated fatty acids (UFAs) serve as fundamental structural components of all cellular membranes. Dietary intervention with polyunsaturated fatty acids (PUFAs) has been demonstrated to remodel the ovarian membrane lipid architecture in infertile mice (Stoffel et al., 2020), while PUFAs administration effectively modulates lipid metabolism and enhances oocyte quality in cattle (Verdurico et al., 2025). Therefore, PUFAs play an important role in maintaining lipid metabolic homeostasis. Integrated pathway analysis combining transcriptomics and metabolomics further revealed that ovarian aging involves perturbations in lipid metabolism. We correlated specific differential genes with metabolites, analyzed specific genes potentially regulating egg production with correlations and integrated gene-metabolite pairs based on the gene-metabolite correlations, and identified important gene-metabolite pairs, FABP4-CLA, and FABP4-DHA. DHA is an n-3 PUFA. Dietary n-3 PUFA supplementation led to an increase in the size of the pre-ovulatory follicles, in the number of total follicles (Maillard et al., 2018). CLA are polyunsaturated fatty acids. Dietary CLA supplementation led to reducing the production of pro-inflammatory factors, decreasing inflammation (Putera et al., 2023). FABP4, a member of the fatty acid-binding protein family, is a key mediator in both lipid metabolism and inflammatory responses (Zhou et al., 2023). Inhibition of FABP4 ameliorates saturated fatty acid-induced inflammation by suppressing NF-κB activation (Bosquet et al., 2018). FABP4 is a potential egg production regulatory gene with a strong negative correlation to CLA and DHA, which are important metabolites in reproduction. Finally, dietary supplementation with CLA or DHA may delay ovarian aging and prolongs the laying period in hens during the late production phase.

Conclusions

The present study characterized the molecular and metabolic landscape of ovaries in aging hens. Ovarian aging in laying hens is characterized by follicle loss, ovarian tissue fibrosis, mitochondrial dysfunction, and reduced antioxidant capacity. These changes may originate from declining physiological function with age, leading to lipid deposition in the ovaries. This triggers lipid metabolism dysregulation and ovarian inflammation, which in turn cause abnormal follicular development and accelerate follicular atresia. Consequently, ovarian reserve and function decline, ultimately resulting in decreased egg production (Fig. 10). These findings may lay the foundation for developing targeted interventions to delay ovarian aging.

Fig. 10.

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Schematic diagram of ovarian aging mechanism. In the present study, we demonstrate that FABP4 affects lipid metabolism through the PPARγ - FABP4/CD36 pathway, while also promoting inflammation via the PPARγ - NF-κB - IL-6/TNFα cascade. These processes collectively lead to mitochondrial dysfunction, follicular atresia, fibrosis, and hormonal dysregulation, ultimately driving ovarian aging and decline in egg production.

CRediT authorship contribution statement

Huiting He: Writing – original draft, Visualization, Software, Methodology, Investigation. Qing Ma: Validation. Xingyu Zhang: Formal analysis. Chunjie Zhou: Data curation. Fangtan Liu: Data curation. Yinglin Lu: Resources. Minli Yu: Writing – review & editing, Supervision, Funding acquisition, Conceptualization.

Disclosures

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China (32372825) and Hainan Provincial Natural Science Foundation of China (325MS112).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2026.106811.

Appendix. Supplementary materials

mmc1.docx (549.3KB, docx)

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