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Current IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Transcriptomics Reveals the Key Genes and Signalling Pathways Regulating the Nesting Behaviour and Egg Production in Wanxi White Goose

M
Mengmeng Hou1,2,3,#
https://orcid.org/0009-0003-8821-3157
S
Shuai Chen1,2,3,#
https://orcid.org/0009-0007-5520-3525
X
Xinwei Tong1,2,3
https://orcid.org/0009-0008-7066-516X
T
Tonghai Jing1,2,3
https://orcid.org/0009-0005-9465-1772
M
Man Ren1,2,3
https://orcid.org/0000-0002-8033-9420
X
Xiaojin Li1,2,3
https://orcid.org/0000-0003-3101-4440
S
Shenghe Li1,2,3,*
https://orcid.org/0000-0001-8767-6991
1College of Animal Science, Anhui Science and Technology University, Chuzhou 233100, China.
2Anhui Province Key Laboratory of Animal Nutritional Regulation and Health, Chuzhou 233100, China.
3Local Geese Gene Bank in Anhui Province, Chuzhou 233100, China.

ABSTRACT

Background: To explore the important candidate genes and signalling pathways that regulate the laying performance and broodiness behaviour of the Wanxi white goose (WWG).

Methods: The bird’s ovarian tissues in laying (CL) and brooding (JL) periods were analysed using transcriptomics.

Result: A total of 1,015 differentially expressed genes (DEGs) were detected, with 389 DEGs up-regulated and 626 down-regulated. Gene ontology and KEGG functional results indicated the DEGs were mainly and exhibited marked over-representation across biological processes and signalling pathways related to reproductive hormone secretion, such as reproductive system development, steroid biosynthesis and Aplin signalling pathway. Eight hub genes, including FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1, INSR and IGF1 were found to play an important role in the protein-protein interaction network. The GSEA enrichment analysis significantly correlated ribosome, myocardial contraction and other signalling pathways with the laying performance and broodiness behaviour of WWG. Therefore, these eight hub genes may play a key regulatory function within the synthesis of hormonal secretion as well as follicle maturation within geese. This study provides a theoretical basis for further understanding the regulation mechanism of laying performance in geese.

INTRODUCTION

The egg-laying rate is an important index that reflects the individual production performance of poultry and the measurement of its economic benefit (Ji et al., 2025). As the reproductive organ of female animals, the ovary directly determines the laying performance of poultry (Liu et al., 2021). As a premium local breed in China, the Wanxi White goose (WWG) is characterized by excellent meat quality and high down value; however, its short egg-laying period and strong broodiness severely restrict the development of the WWG industry. Nesting is a kind of congenital behaviour unique to birds during which poultry activities such as nutritional intake and egg production are reduced or stopped. Furthermore, severe nesting behavior can cause a premature cessation of egg production, leading the birds to directly enter a non-laying period (Kumar Sarkar, 2022). Therefore, it is essential to study how the ovary affects egg production and broodiness in WWG, along with the underlying mechanisms. This research will help reduce the negative impact of broodiness on reproductive performance. Ultimately, it will accelerate the genetic improvement of superior local breeds.
       
Transcriptome sequencing can provide an in-depth understanding of which genes are activated in specific biological states, including those involved in the physiological and developmental processes of organisms. At present, the transcriptome has been widely used in poultry breeding research. For instance, a short illumination promoted the development of ovaries and follicles in Zhedong white geese and highly upregulated BMPR2 and IGF1 genes (Wang et al., 2024). Chang et al., (2024). analyzed the ovarian transcriptomes of high-and low-yielding Shanma ducks. They identified several genes (NPY, CDK1 and E2F1) involved in regulating ovarian development. In addition, the activity of the neuroactive ligand-receptor interaction signalling pathway of the high-laying Shanma ducks was higher than that of low-laying ducks (Chang et al., 2024). Although numerous studies have investigated poultry ovaries across different stages, our understanding of the ovary’s impact on egg production and broodiness remains limited. Furthermore, the relevant genes and signaling pathways have not been fully explored and the specific underlying mechanisms remain unclear.
       
In this study, transcriptome sequencing was performed on the ovaries of WWG during the egg-laying and broody periods. DEGs and key signaling pathways were identified and a PPI network of the DEGs was constructed. The aim was to gain a deeper understanding of the transcriptional regulatory mechanisms affecting egg-laying performance and brooding behavior, thereby providing a theoretical basis for WWG breeding.

MATERIALS AND METHODS

Experimental animals and sample preparation
 
The present study was carried out at Anhui Science and Technology University Anhui Province Key Laboratory of Animal Nutritional Regulation and Health.between September and December 2025. The WWG used in the experiment were from Dingyuan Junming Ecological Farm (Dingyuan, China) and were fed using normalized feeds and maintained in the same environment. After the geese began to lay eggs, a total of 100 geese were selected for individual cage rearing. Among them, 10 geese that had laid more than 5 eggs were chosen as the laying geese and 10 geese that had been brooding continuously for more than 3 days were chosen as the brooding geese. Then, the egg-laying conditions of the laying and brooding geese were observed and recorded. Based on the behaviour of WWG, the birds were divided into laying and broodiness groups, each with 10 geese and separately fed. Eggs laid by the geese throughout peak production and broodiness windows were observed and counted daily for 10 days. Thereafter three egg-laying geese from the the laying groups (n=3; 3-year-old; 4.2 kg±0.2 kg;female) and three non-egg-laying geese with obvious nests from the brooding group (n=3; 3-year-old; 4.0 kg±0.2kg; female) were selected for subsequent experiments. All the selected geese were sacrificed by cervical dislocation followed by the removal and flushing of their ovaries with phosphate-buffered saline (PBS). The ovarian tissue was placed into cryovials for preservation and immediately immersed in liquid nitrogen for freezing. Subsequently, it was transferred to a -80°C laboratory freezer for storage, awaiting total RNA extraction and subsequent sequencing.The ovary during the laying period is marked as CL1-3 and the ovary during the nesting period is marked as JL1-3.
 
Total RNA was isolated and subjected to RNA-seq
 
Total RNA was isolated with a Trizol kit following the supplier’s protocol (Invitrogen, USA), while its integrity proved evaluated using an RNA integrity was checked on an Agilent 2100 bioanalyzer (Agilent, USA), cleaned and size-selected with QiaQuick PCR spin columns (QIAGEN, China) and converted into TruSeq libraries (Illumina, USA) for 2 ×150-bp sequencing on a HiSeq 4000 platform (Illumina, USA). at the Gideo Biotechnology Co., Ltd. (Guangzhou, China).
 
RNA-seq profiling interrogation
 
To secure data robustness and precision, the original sequence was first processed to remove low-quality reads (Xu et al., 2023) and filtered using Seqtk (https://github.com/lh3/seqtk) to obtain clean reads (Lin et al., 2022). The transcript was assembled using StringTie (v1.3.5) (Kovaka et al., 2019), while Hisat2 v2.0.4 (Kim et al., 2015) software was used to compare the filtered clean reads with the reference genome (Ans Cyg_PRJNA183603_v1.0). DEGs were screened applying the cutoff of |log2FC| > 0.58 and p<0.05 (Hu et al., 2020).
 
Gene ontology (GO) and KEGG enrichment analysis

ClusterProfiler v3.10.1  (Zhang, 2025)  and KOBAS v2.0 (Xie et al., 2011) were used to carry out GO and KEGG pathway enrichment analysis on the DEGs, with p<0.05 indicating significant enrichment of GO terms and KEGG pathways analyses (Pandian et al., 2022) and p<0.01 indicating extremely significant enrichment.
 
Protein interaction network analysis and hub gene screening
 
STRING online analysis (http://string-db.org/) of the DEGs (Sun et al., 2019) was used to screen related protein relationship pairs using Cytoscape software to map the interaction network (Shannon et al., 2003), while four algorithms, including MCC, EPC, MNC and Degree were used to screen the hub genes in the network using the CytoHubba plug-in (Zhou et al., 2024).
 
GSEA gene enrichment analysis
 
GSEA v3.0 (http://www.broadinstitute.org/gsea/index.jsp) software was used to analyze all the genes in the samples. The enrichment pathways were screened and analyzed with a normalized enrichment score (NES) > 1, NOM p-val < 0.05 and false discovery rate (FDR) of q-val < 0.25 (Subramanian et al., 2005).
 
Real-time fluorescence quantitative PCR verification (RT-qPCR)
 
RT-qPCR primers listed in (Table 1) were computationally designed using Oligo-7 software (Rychlik, 2007) and synthesized by Sangon Biotechnology Co., Ltd. (Shanghai, China). Six DEGs were randomly selected and eight genes obtained from the PPI network using four algorithms were subjected to quantitative PCR validation and used to verify the sequencing results with the GAPDH as the internal reference gene (Barber et al., 2005). The RT-qPCR quantification was carried out using SYBR qPCR Master Mix (EZBioscience, USA) with the following reaction conditions, including after an initial 5 min denaturation at 95°C, amplification proceeded for 40 cycles of 95°C for 10s and 60°C for 30s.

Table 1: Primer sequence table.

RESULTS AND DISCUSSION

Sequencing data information statistics
 
We obtained 565,596,988 reads from six ovarian tissue samples, which were filtered to 563,543,690 sequences. The Q30 of each sample was above 93%, while the GC base content was relatively balanced and the base composition was stable. Over 83 % of valid reads aligned to the reference genome (Table 2). The high-quality sequencing output was deemed suitable for downstream analyses.

Table 2: Sequencing data information statistics.


 
Screening and enrichment analysis of differentially expressed genes
 
Compared with the CL group, 1,015 DEGs were screened in the JL group, of which 389 genes displayed increased expression, whereas 626 genes were repressed (Fig 1A-B). GO enrichment analysis revealed that the DEGs were significantly enriched in terms primarily including animal organ development, urogenital system development and the regulation of hormone levels (p<0.01) (Fig 1C). The KEGG enrichment results are shown in (Fig 1D). The DEGs were significantly enriched in five signaling pathways, which mainly included oxidative phosphorylation, steroid biosynthesis and alpine signaling pathway (p<0.05).

Fig 1: A. Differential gene expression bar chart analysis. B. Differential gene expression volcano plot analysis. C. Differentially expressed genes significantly enriched in GO terms. D. assessed for enriched pathways using KEGG enrichment analysis.


                                                                                
Construction of PPI network of DEGs and screening of core genes
 
Using String for data retrieval and Cytoscape for layout, we generated a protein-interaction graph with 147 vertices connected by 348 links (Fig 2). The top 15 hub genes were obtained by MNC, MCC, Degree and EPC algorithms included in the CytoHubba plug-in (Table 3), while the common genes were analyzed by Venn diagram. Eight hub genes, including FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1, INSR and IGF1 were common to the four algorithms (Fig 3), indicating that they play an important role in the interaction network.     

Fig 2: Differentially expressed gene protein interaction network.



Fig 3: MNC, MCC, degree and EPC algorithm common gene wayne graph.



Table 3: Top 15 genes in four algorithms of CytoHubba plug-in.


                              
GSEA analysis
 
The GSEA analysis was performed on all genes in the ovary samples of WWG sampled across peak-lay and broody phases, indicating that myocardial contraction and steroid biosynthesis signalling pathways were significantly enriched (|NES| > 1, FDR < 0.25, P < 0.05), while ribosomes and oxidative phosphorylation were extremely enriched (FDR < 0.01), which is consistent with the results of KEGG enrichment analysis (Fig 4).     

Fig 4: GSEA trend analysis plot.


               
Validation of DEGs by real-time qPCR with fluorescence detection (RT-qPCR)
 
To assess the fidelity of the generated reads, fourteen DEGs were selected by chance for RT-qPCR verification and log2FC values were calculated and compared with transcriptome sequencing data. The RT-qPCR expression trend of DEGs in ovarian tissue samples was consistent with the results of transcriptome sequencing, proving the accuracy of sequencing data (Fig 5).             

Fig 5: Verification of differentially expressed genes by fluorescence quantitative PCR.

                                                 
       
The reproductive performance of poultry is affected by multiple factors, with the nesting behaviour having the most serious impact on their laying performance. Under the influence of light and temperature, gonadotropin-releasing hormone (GnRH) and vasoactive intestinal peptide (VIP) secreted by the hypothalamus reach the critical point and inhibiting the secretion of FSH and LH. This triggers the broodiness behaviour in birds (Stamatiades and Kaiser, 2018) and degrades the ovaries and fallopian tubes (Ran et al., 2023), seriously affecting the laying performance of the WWG.
       
In the early stage, our team measured the serum hormone levels of WWG during the laying period and brooding period. Compared with the laying period, the follicular atresia within WWG ovarian tissue throughout the broody phase was obvious. The levels of TG (triglycerides), TC (total cholesterol) and ALB (albumin) in the serum decreased. This indicates that the brooding behavior of WWG is influenced by reproductive hormones such as E2, FSH  and LH (Raja et al., 2023). Tong et al., (2025) recently found a large number of atretic ovarian follicles within ovarian tissue of WWG during the broodiness period, when follicular development stops. The study also showed a large count of initial, secondary and mature ovarian follicles of the during the laying period, which easily developed in large numbers. Therefore, ovarian architectural shifts across breeding stages modulate WWG laying efficiency.

Ovarian samples from WWG hens in both broody and peak-lay phases were subjected to RNA-seq, yielding 1,015 DEGs. Functional enrichment revealed a significant accumulation of DEGs within oxidative phosphorylation, steroid biosynthesis and apline signalling pathways. The oxidative phosphorylation (OXPHOS) signalling pathway is a key pathway in cell energy metabolism (Kowaltowski and Abdulkader, 2025). Studies in both mice and chickens have demonstrated that a decline in OXPHOS leads to increased GC apoptosis and subsequent follicular atresia (Zhao et al., 2025; Hu et al., 2025). In our dataset, the expression of oxidative phosphorylation-related genes was higher in the ovaries of WWG during laying than during broodiness, which may have led to follicular atresia during brooding in the geese. Steroid hormone biosynthesis is crucial for ovarian development. It directly affects follicular maturation, cell proliferation and apoptosis. Ultimately, this process ensures the normal functioning of the ovary throughout different reproductive stages (Wang et al., 2024). In mammals, apelins are present in ovarian cells and can affect steroid production and ovulation by activating PPARS, a nuclear transcription factor involved in ovarian development and follicular atresia (Lavecchia et al., 2024; Kučka et al., 2021). GSEA results confirmed the results of KEGG pathway analysis by showing the significant enrichment of ribosome, oxidative phosphorylation and steroid biosynthesis signalling pathways, suggesting that these pathways serve a crucial function in the reproductive course of WWG.
               
The PPI network was constructed by the screened DEGs and the best protein interaction results were used to draw the network diagram. Eight hub genes, including FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1, INSR and IGF1 played an important role in the PPI network. FGF8 and BMP15 synergistically promote glycolysis in cumulus cells, there by influencing follicular development and steroidogenesis (Zhai et al., 2023; Yang et al., 2026; Price, 2016). The Fibroblast Growth Factor 18 (FGF18) inhibits the secretion of estradiol and progesterone in granulosa cells and phosphorylates MAPK14, which promotes ovarian cell apoptosis (Estienne and Price, 2018).The brain-derived neurotrophic factor (BDNF) is an ovarian endocrine factor that regulates follicular development by signal transduction through the receptor TrkB (Liu et al., 2024). High expression of BDNF reduces the expression level of TrkB (Dittrich et al., 1996), while its low expression decreases the number of follicles and ovarian abnormalities in mice (Qin et al., 2022). The expression of OTX2 plays a crucial role in the transcriptional regulation of GnRH and normal reproductive functions (Diaczok et al., 2011; Larder and Mellon, 2009). FMS-like tyrosine kinase 3 (FLT3) is expressed in oocytes and primarily affects follicular growth and development (Tingting et al., 2020). Furthermore, as a glutamate receptor, GRIA1 mainly influences the release of reproductive hormones and plays an important role in reproductive regulation (Sugimoto et al., 2010; Xu et al., 2022). On the other hand, Elevated INSR expression levels may decrease plasma FSH levels, leading to reproductive dysfunction (Khan et al., 2023). This investigation monitored the expression pattern of FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1 and INSR was elevated within WWG ovaries throughout the nesting phase, indicating that these genes regulate the broodiness behaviour of the geese by inhibiting the production of gonadotropins and promoting the apoptosis of ovarian cells. 

CONCLUSION

In this study, 1,015 DEGs were identified by the transcriptome WWG ovary transcriptome contrast between egg-producing and nesting windows. Enrichment profiling revealed that the DEGs were markedly over-represented in biological-process categories and signalling pathways related to oxidative phosphorylation, steroid biosynthesis and alpine signalling pathway. Therefore, these signalling pathways may play an important role in regulating the laying performance and broodiness behaviour of WWG by regulating gene-expression levels related to hormone secretion and synthesis and follicular granulosa cell development such as FGF8, FGF18, INSR and IGF1.

ACKNOWLEDGEMENT

This work was supported by grants from the talent introduction project of Anhui Science and Technology University [DKYJ202105, DKYJ202104], Anhui Province Science and Technology Major Project [17030701004], Local goose gene bank in Anhui Province, Science and Technology Project of Chuzhou City, Anhui Province [2022ZN002], Veterinary Science Peak Discipline Project of Anhui Science and Technology University [XK-XJGF002].
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
 
Informed consent
 
All animal procedures for experiments were approved by the Institutional Animal Care and Use Committee of Anhui Science and Technology University, Chuzhou, China.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article.

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    Transcriptomics Reveals the Key Genes and Signalling Pathways Regulating the Nesting Behaviour and Egg Production in Wanxi White Goose

    M
    Mengmeng Hou1,2,3,#
    https://orcid.org/0009-0003-8821-3157
    S
    Shuai Chen1,2,3,#
    https://orcid.org/0009-0007-5520-3525
    X
    Xinwei Tong1,2,3
    https://orcid.org/0009-0008-7066-516X
    T
    Tonghai Jing1,2,3
    https://orcid.org/0009-0005-9465-1772
    M
    Man Ren1,2,3
    https://orcid.org/0000-0002-8033-9420
    X
    Xiaojin Li1,2,3
    https://orcid.org/0000-0003-3101-4440
    S
    Shenghe Li1,2,3,*
    https://orcid.org/0000-0001-8767-6991
    1College of Animal Science, Anhui Science and Technology University, Chuzhou 233100, China.
    2Anhui Province Key Laboratory of Animal Nutritional Regulation and Health, Chuzhou 233100, China.
    3Local Geese Gene Bank in Anhui Province, Chuzhou 233100, China.

    ABSTRACT

    Background: To explore the important candidate genes and signalling pathways that regulate the laying performance and broodiness behaviour of the Wanxi white goose (WWG).

    Methods: The bird’s ovarian tissues in laying (CL) and brooding (JL) periods were analysed using transcriptomics.

    Result: A total of 1,015 differentially expressed genes (DEGs) were detected, with 389 DEGs up-regulated and 626 down-regulated. Gene ontology and KEGG functional results indicated the DEGs were mainly and exhibited marked over-representation across biological processes and signalling pathways related to reproductive hormone secretion, such as reproductive system development, steroid biosynthesis and Aplin signalling pathway. Eight hub genes, including FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1, INSR and IGF1 were found to play an important role in the protein-protein interaction network. The GSEA enrichment analysis significantly correlated ribosome, myocardial contraction and other signalling pathways with the laying performance and broodiness behaviour of WWG. Therefore, these eight hub genes may play a key regulatory function within the synthesis of hormonal secretion as well as follicle maturation within geese. This study provides a theoretical basis for further understanding the regulation mechanism of laying performance in geese.

    INTRODUCTION

    The egg-laying rate is an important index that reflects the individual production performance of poultry and the measurement of its economic benefit (Ji et al., 2025). As the reproductive organ of female animals, the ovary directly determines the laying performance of poultry (Liu et al., 2021). As a premium local breed in China, the Wanxi White goose (WWG) is characterized by excellent meat quality and high down value; however, its short egg-laying period and strong broodiness severely restrict the development of the WWG industry. Nesting is a kind of congenital behaviour unique to birds during which poultry activities such as nutritional intake and egg production are reduced or stopped. Furthermore, severe nesting behavior can cause a premature cessation of egg production, leading the birds to directly enter a non-laying period (Kumar Sarkar, 2022). Therefore, it is essential to study how the ovary affects egg production and broodiness in WWG, along with the underlying mechanisms. This research will help reduce the negative impact of broodiness on reproductive performance. Ultimately, it will accelerate the genetic improvement of superior local breeds.
           
    Transcriptome sequencing can provide an in-depth understanding of which genes are activated in specific biological states, including those involved in the physiological and developmental processes of organisms. At present, the transcriptome has been widely used in poultry breeding research. For instance, a short illumination promoted the development of ovaries and follicles in Zhedong white geese and highly upregulated BMPR2 and IGF1 genes (Wang et al., 2024). Chang et al., (2024). analyzed the ovarian transcriptomes of high-and low-yielding Shanma ducks. They identified several genes (NPY, CDK1 and E2F1) involved in regulating ovarian development. In addition, the activity of the neuroactive ligand-receptor interaction signalling pathway of the high-laying Shanma ducks was higher than that of low-laying ducks (Chang et al., 2024). Although numerous studies have investigated poultry ovaries across different stages, our understanding of the ovary’s impact on egg production and broodiness remains limited. Furthermore, the relevant genes and signaling pathways have not been fully explored and the specific underlying mechanisms remain unclear.
           
    In this study, transcriptome sequencing was performed on the ovaries of WWG during the egg-laying and broody periods. DEGs and key signaling pathways were identified and a PPI network of the DEGs was constructed. The aim was to gain a deeper understanding of the transcriptional regulatory mechanisms affecting egg-laying performance and brooding behavior, thereby providing a theoretical basis for WWG breeding.

    MATERIALS AND METHODS

    Experimental animals and sample preparation
     
    The present study was carried out at Anhui Science and Technology University Anhui Province Key Laboratory of Animal Nutritional Regulation and Health.between September and December 2025. The WWG used in the experiment were from Dingyuan Junming Ecological Farm (Dingyuan, China) and were fed using normalized feeds and maintained in the same environment. After the geese began to lay eggs, a total of 100 geese were selected for individual cage rearing. Among them, 10 geese that had laid more than 5 eggs were chosen as the laying geese and 10 geese that had been brooding continuously for more than 3 days were chosen as the brooding geese. Then, the egg-laying conditions of the laying and brooding geese were observed and recorded. Based on the behaviour of WWG, the birds were divided into laying and broodiness groups, each with 10 geese and separately fed. Eggs laid by the geese throughout peak production and broodiness windows were observed and counted daily for 10 days. Thereafter three egg-laying geese from the the laying groups (n=3; 3-year-old; 4.2 kg±0.2 kg;female) and three non-egg-laying geese with obvious nests from the brooding group (n=3; 3-year-old; 4.0 kg±0.2kg; female) were selected for subsequent experiments. All the selected geese were sacrificed by cervical dislocation followed by the removal and flushing of their ovaries with phosphate-buffered saline (PBS). The ovarian tissue was placed into cryovials for preservation and immediately immersed in liquid nitrogen for freezing. Subsequently, it was transferred to a -80°C laboratory freezer for storage, awaiting total RNA extraction and subsequent sequencing.The ovary during the laying period is marked as CL1-3 and the ovary during the nesting period is marked as JL1-3.
     
    Total RNA was isolated and subjected to RNA-seq
     
    Total RNA was isolated with a Trizol kit following the supplier’s protocol (Invitrogen, USA), while its integrity proved evaluated using an RNA integrity was checked on an Agilent 2100 bioanalyzer (Agilent, USA), cleaned and size-selected with QiaQuick PCR spin columns (QIAGEN, China) and converted into TruSeq libraries (Illumina, USA) for 2 ×150-bp sequencing on a HiSeq 4000 platform (Illumina, USA). at the Gideo Biotechnology Co., Ltd. (Guangzhou, China).
     
    RNA-seq profiling interrogation
     
    To secure data robustness and precision, the original sequence was first processed to remove low-quality reads (Xu et al., 2023) and filtered using Seqtk (https://github.com/lh3/seqtk) to obtain clean reads (Lin et al., 2022). The transcript was assembled using StringTie (v1.3.5) (Kovaka et al., 2019), while Hisat2 v2.0.4 (Kim et al., 2015) software was used to compare the filtered clean reads with the reference genome (Ans Cyg_PRJNA183603_v1.0). DEGs were screened applying the cutoff of |log2FC| > 0.58 and p<0.05 (Hu et al., 2020).
     
    Gene ontology (GO) and KEGG enrichment analysis

    ClusterProfiler v3.10.1  (Zhang, 2025)  and KOBAS v2.0 (Xie et al., 2011) were used to carry out GO and KEGG pathway enrichment analysis on the DEGs, with p<0.05 indicating significant enrichment of GO terms and KEGG pathways analyses (Pandian et al., 2022) and p<0.01 indicating extremely significant enrichment.
     
    Protein interaction network analysis and hub gene screening
     
    STRING online analysis (http://string-db.org/) of the DEGs (Sun et al., 2019) was used to screen related protein relationship pairs using Cytoscape software to map the interaction network (Shannon et al., 2003), while four algorithms, including MCC, EPC, MNC and Degree were used to screen the hub genes in the network using the CytoHubba plug-in (Zhou et al., 2024).
     
    GSEA gene enrichment analysis
     
    GSEA v3.0 (http://www.broadinstitute.org/gsea/index.jsp) software was used to analyze all the genes in the samples. The enrichment pathways were screened and analyzed with a normalized enrichment score (NES) > 1, NOM p-val < 0.05 and false discovery rate (FDR) of q-val < 0.25 (Subramanian et al., 2005).
     
    Real-time fluorescence quantitative PCR verification (RT-qPCR)
     
    RT-qPCR primers listed in (Table 1) were computationally designed using Oligo-7 software (Rychlik, 2007) and synthesized by Sangon Biotechnology Co., Ltd. (Shanghai, China). Six DEGs were randomly selected and eight genes obtained from the PPI network using four algorithms were subjected to quantitative PCR validation and used to verify the sequencing results with the GAPDH as the internal reference gene (Barber et al., 2005). The RT-qPCR quantification was carried out using SYBR qPCR Master Mix (EZBioscience, USA) with the following reaction conditions, including after an initial 5 min denaturation at 95°C, amplification proceeded for 40 cycles of 95°C for 10s and 60°C for 30s.

    Table 1: Primer sequence table.

    RESULTS AND DISCUSSION

    Sequencing data information statistics
     
    We obtained 565,596,988 reads from six ovarian tissue samples, which were filtered to 563,543,690 sequences. The Q30 of each sample was above 93%, while the GC base content was relatively balanced and the base composition was stable. Over 83 % of valid reads aligned to the reference genome (Table 2). The high-quality sequencing output was deemed suitable for downstream analyses.

    Table 2: Sequencing data information statistics.


     
    Screening and enrichment analysis of differentially expressed genes
     
    Compared with the CL group, 1,015 DEGs were screened in the JL group, of which 389 genes displayed increased expression, whereas 626 genes were repressed (Fig 1A-B). GO enrichment analysis revealed that the DEGs were significantly enriched in terms primarily including animal organ development, urogenital system development and the regulation of hormone levels (p<0.01) (Fig 1C). The KEGG enrichment results are shown in (Fig 1D). The DEGs were significantly enriched in five signaling pathways, which mainly included oxidative phosphorylation, steroid biosynthesis and alpine signaling pathway (p<0.05).

    Fig 1: A. Differential gene expression bar chart analysis. B. Differential gene expression volcano plot analysis. C. Differentially expressed genes significantly enriched in GO terms. D. assessed for enriched pathways using KEGG enrichment analysis.


                                                                                    
    Construction of PPI network of DEGs and screening of core genes
     
    Using String for data retrieval and Cytoscape for layout, we generated a protein-interaction graph with 147 vertices connected by 348 links (Fig 2). The top 15 hub genes were obtained by MNC, MCC, Degree and EPC algorithms included in the CytoHubba plug-in (Table 3), while the common genes were analyzed by Venn diagram. Eight hub genes, including FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1, INSR and IGF1 were common to the four algorithms (Fig 3), indicating that they play an important role in the interaction network.     

    Fig 2: Differentially expressed gene protein interaction network.



    Fig 3: MNC, MCC, degree and EPC algorithm common gene wayne graph.



    Table 3: Top 15 genes in four algorithms of CytoHubba plug-in.


                                  
    GSEA analysis
     
    The GSEA analysis was performed on all genes in the ovary samples of WWG sampled across peak-lay and broody phases, indicating that myocardial contraction and steroid biosynthesis signalling pathways were significantly enriched (|NES| > 1, FDR < 0.25, P < 0.05), while ribosomes and oxidative phosphorylation were extremely enriched (FDR < 0.01), which is consistent with the results of KEGG enrichment analysis (Fig 4).     

    Fig 4: GSEA trend analysis plot.


                   
    Validation of DEGs by real-time qPCR with fluorescence detection (RT-qPCR)
     
    To assess the fidelity of the generated reads, fourteen DEGs were selected by chance for RT-qPCR verification and log2FC values were calculated and compared with transcriptome sequencing data. The RT-qPCR expression trend of DEGs in ovarian tissue samples was consistent with the results of transcriptome sequencing, proving the accuracy of sequencing data (Fig 5).             

    Fig 5: Verification of differentially expressed genes by fluorescence quantitative PCR.

                                                     
           
    The reproductive performance of poultry is affected by multiple factors, with the nesting behaviour having the most serious impact on their laying performance. Under the influence of light and temperature, gonadotropin-releasing hormone (GnRH) and vasoactive intestinal peptide (VIP) secreted by the hypothalamus reach the critical point and inhibiting the secretion of FSH and LH. This triggers the broodiness behaviour in birds (Stamatiades and Kaiser, 2018) and degrades the ovaries and fallopian tubes (Ran et al., 2023), seriously affecting the laying performance of the WWG.
           
    In the early stage, our team measured the serum hormone levels of WWG during the laying period and brooding period. Compared with the laying period, the follicular atresia within WWG ovarian tissue throughout the broody phase was obvious. The levels of TG (triglycerides), TC (total cholesterol) and ALB (albumin) in the serum decreased. This indicates that the brooding behavior of WWG is influenced by reproductive hormones such as E2, FSH  and LH (Raja et al., 2023). Tong et al., (2025) recently found a large number of atretic ovarian follicles within ovarian tissue of WWG during the broodiness period, when follicular development stops. The study also showed a large count of initial, secondary and mature ovarian follicles of the during the laying period, which easily developed in large numbers. Therefore, ovarian architectural shifts across breeding stages modulate WWG laying efficiency.

    Ovarian samples from WWG hens in both broody and peak-lay phases were subjected to RNA-seq, yielding 1,015 DEGs. Functional enrichment revealed a significant accumulation of DEGs within oxidative phosphorylation, steroid biosynthesis and apline signalling pathways. The oxidative phosphorylation (OXPHOS) signalling pathway is a key pathway in cell energy metabolism (Kowaltowski and Abdulkader, 2025). Studies in both mice and chickens have demonstrated that a decline in OXPHOS leads to increased GC apoptosis and subsequent follicular atresia (Zhao et al., 2025; Hu et al., 2025). In our dataset, the expression of oxidative phosphorylation-related genes was higher in the ovaries of WWG during laying than during broodiness, which may have led to follicular atresia during brooding in the geese. Steroid hormone biosynthesis is crucial for ovarian development. It directly affects follicular maturation, cell proliferation and apoptosis. Ultimately, this process ensures the normal functioning of the ovary throughout different reproductive stages (Wang et al., 2024). In mammals, apelins are present in ovarian cells and can affect steroid production and ovulation by activating PPARS, a nuclear transcription factor involved in ovarian development and follicular atresia (Lavecchia et al., 2024; Kučka et al., 2021). GSEA results confirmed the results of KEGG pathway analysis by showing the significant enrichment of ribosome, oxidative phosphorylation and steroid biosynthesis signalling pathways, suggesting that these pathways serve a crucial function in the reproductive course of WWG.
                   
    The PPI network was constructed by the screened DEGs and the best protein interaction results were used to draw the network diagram. Eight hub genes, including FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1, INSR and IGF1 played an important role in the PPI network. FGF8 and BMP15 synergistically promote glycolysis in cumulus cells, there by influencing follicular development and steroidogenesis (Zhai et al., 2023; Yang et al., 2026; Price, 2016). The Fibroblast Growth Factor 18 (FGF18) inhibits the secretion of estradiol and progesterone in granulosa cells and phosphorylates MAPK14, which promotes ovarian cell apoptosis (Estienne and Price, 2018).The brain-derived neurotrophic factor (BDNF) is an ovarian endocrine factor that regulates follicular development by signal transduction through the receptor TrkB (Liu et al., 2024). High expression of BDNF reduces the expression level of TrkB (Dittrich et al., 1996), while its low expression decreases the number of follicles and ovarian abnormalities in mice (Qin et al., 2022). The expression of OTX2 plays a crucial role in the transcriptional regulation of GnRH and normal reproductive functions (Diaczok et al., 2011; Larder and Mellon, 2009). FMS-like tyrosine kinase 3 (FLT3) is expressed in oocytes and primarily affects follicular growth and development (Tingting et al., 2020). Furthermore, as a glutamate receptor, GRIA1 mainly influences the release of reproductive hormones and plays an important role in reproductive regulation (Sugimoto et al., 2010; Xu et al., 2022). On the other hand, Elevated INSR expression levels may decrease plasma FSH levels, leading to reproductive dysfunction (Khan et al., 2023). This investigation monitored the expression pattern of FGF8, FGF18, BDNF, OTX2, FLT3, GRIA1 and INSR was elevated within WWG ovaries throughout the nesting phase, indicating that these genes regulate the broodiness behaviour of the geese by inhibiting the production of gonadotropins and promoting the apoptosis of ovarian cells. 

    CONCLUSION

    In this study, 1,015 DEGs were identified by the transcriptome WWG ovary transcriptome contrast between egg-producing and nesting windows. Enrichment profiling revealed that the DEGs were markedly over-represented in biological-process categories and signalling pathways related to oxidative phosphorylation, steroid biosynthesis and alpine signalling pathway. Therefore, these signalling pathways may play an important role in regulating the laying performance and broodiness behaviour of WWG by regulating gene-expression levels related to hormone secretion and synthesis and follicular granulosa cell development such as FGF8, FGF18, INSR and IGF1.

    ACKNOWLEDGEMENT

    This work was supported by grants from the talent introduction project of Anhui Science and Technology University [DKYJ202105, DKYJ202104], Anhui Province Science and Technology Major Project [17030701004], Local goose gene bank in Anhui Province, Science and Technology Project of Chuzhou City, Anhui Province [2022ZN002], Veterinary Science Peak Discipline Project of Anhui Science and Technology University [XK-XJGF002].
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.
     
    Informed consent
     
    All animal procedures for experiments were approved by the Institutional Animal Care and Use Committee of Anhui Science and Technology University, Chuzhou, China.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article.

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