Proteomics analysis revealed the mechanism of differential egg productive in geese

[A análise proteômica revelou o mecanismo da produção diferencial de ovos em gansos]

ABSTRACT

The Western Anhui white goose is a highly esteemed breed in our country, renowned for its superior meat quality and exceptional egg-laying performance. As the primary reproductive organ in poultry, the ovary significantly influences the sexual maturation and reproductive capacity of female animals. Understanding the molecular basis underlying nesting behavior in geese relies on understanding the variations in ovarian proteins during different nesting periods. To investigate the effect of proteins on nesting behavior and fecundity, we selected ovarian tissues from three high-and three low-fertility Wanxi white geese. By employing a tandem mass-tag-based quantitative proteomics method, we successfully identified 4611 proteins, of which 111 exhibited significant differences. KEGG enrichment analysis revealed that these differentially expressed proteins were primarily associated with glycolysis and folate biosynthesis pathways. Furthermore, our protein-protein interaction network analysis demonstrated that the interacting proteins predominantly participated in ribosome function as well as amino acid and carbohydrate metabolism. The findings of this study establish a foundation for elucidating the molecular mechanism underlying variations in ovarian protein levels among Wanxi white geese with different fecundity.

Keywords:
broodiness; proteomics; goose; egg productive

RESUMO

O ganso branco do oeste de Anhui é uma raça muito apreciada em nosso país, famosa pela qualidade superior de sua carne e pelo excepcional desempenho na postura de ovos. Como principal órgão reprodutor das aves, o ovário influencia significativamente a maturação sexual e a capacidade reprodutiva das fêmeas. Compreendwier a base molecular subjacente ao comportamento de nidificação dos gansos depende da compreensão das variações nas proteínas ovarianas durante os diferentes períodos de nidificação. Para investigar o efeito das proteínas no comportamento de nidificação e na fecundidade, selecionamos tecidos ovarianos de três gansos brancos Wanxi de alta fertilidade e três de baixa fertilidade. Empregando um método de proteômica quantitativa baseado em marcação em massa em tandem, identificamos com sucesso 4611 proteínas, das quais 111 apresentaram diferenças significativas. A análise de enriquecimento KEGG revelou que essas proteínas diferencialmente expressas estavam principalmente associadas às vias de glicólise e biossíntese de folato. Além disso, nossa análise da rede de interação proteína-proteína demonstrou que as proteínas interagentes participavam predominantemente da função ribossômica, bem como do metabolismo de aminoácidos e carboidratos. Os resultados deste estudo estabelecem uma base para elucidar o mecanismo molecular subjacente às variações nos níveis de proteínas ovarianas entre gansos brancos Wanxi com diferentes níveis de fecundidade.

Palavras-chave:
incubação; proteômica; ganso; produção de ovos

INTRODUCTION

Although China is the largest producer of geese, its egg production performance is lower than that of the main poultry breeds, including chickens and ducks, which restricts the development of the goose industry. As a local breed in Anhui province, Wanxi White geese have strong disease resistance, large body size, and excellent fluffy quality (Bai et al., 2019). To improve yield, it is necessary to breed geese with high egg production.

Nesting behavior is a prevalent phenomenon in poultry production (Romanov et al., 2002) Various types of poultry such as geese exhibit nesting behavior following egg laying, which significantly affects poultry production (Romanov et al., 2002; Radi et al., 2013) The incidence of this phenomenon is higher in local goose breeds, with some breeds exhibiting a prevalence rate as high as 100%, which further affects the production and fecundity of goose eggs (Yao et al., 2019). The behavior at different stages of reproduction is tightly regulated by the hypothalamic-pituitary-gonadal axis (Zhang et al., 2019. As a crucial component and essential organ for oviposition, ovarian hormone levels directly affect the hatching behavior and production performance of poultry Ye et al., 2019a.

Owing to the significance of nesting behavior in the goose industry, breeding scientists have been endeavoring to enhance this behavior in recent years, to reduce or even eliminate the duration of geese nesting. Previous studies on the nesting behavior of poultry were mainly based on the level of gene polymorphisms and transcriptomics, and there are few reports on the protein level (Bai et al., 2019; Liu et al., 2018; Ye et al., 2019b). Pengfei et al. conducted a transcriptome analysis to identify gene expression levels in the hypothalamus during different nesting stages of Muscovy ducks and subsequently identified differentially expressed genes across these stages. Analysis of these genes revealed a potential association between oxidative stress and nesting behavior in Muscovy ducks (Liu et al., 2018). The agreement between the transcriptome and the proteome is not as high as expected (Schwanhäusser et al., 2011), and the changes and potential effects of proteins as executors of molecular functions in nesting behavior are rarely reported. Mass spectrometry-based proteomics has been widely used in animal breeding research owing to the rapid development of mass spectrometry technology in recent years. María et al. used proteomics to analyze proteins that differed among beef samples (López-Pedrouso et al., 2021). Hou et al. used proteomics to analyze the association between pork quality and protein content before and after slaughter (Hou et al., 2020). Currently, most proteomic studies in the animal field have focused on meat quality, and there are no reports on the nest behavior of poultry.

In this study, a tandem mass tag (TMT)-based proteomic strategy of ovary tissue was employed to differentiate between high- and low-egg-producing geese. Our goal was to determine the molecular mechanism underlying the nest behavior of Wanxi white geese at the protein level to provide a new method for improving production efficiency.

MATERIALS AND METHODS

A total of 115 white Wanxi geese, including 92 female geese (300 days old) and 23 male geese (600 days old), were bred at the Anhui Wanxi White Goose Conservation Farm. All geese were housed in individual pens (1♂ and 4♀ per pen) with playground fields. All geese were the same diet and had free access to food and water. The eggs were collected daily at 5:00 pm every day. Based on continuous and complete egg production records (laying period from December 2020 to May 2021), we selected 4 high-egg production (HEP) and 4 low-egg production (LEP) geese. HEP Wanxi white geese laid 31, 33, 32, and 32 eggs, respectively. LEP Wanxi white geese laid 12, 11, 11, and 10 eggs, respectively.

The selected geese were humanely euthanized via sustained isoflurane inhalation (5% concentration for 10 minutes induction followed by 8% maintenance dose) in accordance with the American Veterinary Medical Association (AVMA) guidelines for avian euthanasia.

The entire ovaries were surgically excised, and the ovarian medulla was specifically dissected from the ovary mid-portion under sterile conditions at 4℃, with careful removal of the outer cortical layer and attached follicles. All dissected tissues were immediately frozen in liquid nitrogen and stored at -80℃ until subsequent protein extraction.

Three biological replicates were used for each treatment group (Fig. 1). Immediately after snap-freezing in liquid nitrogen, the samples were added to a lysis buffer (8 M urea and 1% protease inhibitors) for sonication. Cell debris was removed by centrifugation at 12,000 g for 10 min at 4℃, and the supernatant was transferred to a new centrifuge tube and the protein concentration was determined using the BCA kit.

The protein was removed for equal amounts of lysis, an appropriate amount of standard protein was added, and the lysis buffer was adjusted to an equal volume. The protein lysate was adjusted to a final concentration of 20% trichloroacetic acid (TCA), followed by centrifugation at 4,500 × g for 5 min at 4 ℃ to pellet the proteins, after which the supernatant was discarded. The resulting pellets were washed with ice-cold acetone to remove residual TCA, air-dried, and subsequently resuspended in triethylammonium bicarbonate (TEAB) buffer adjusted to a final concentration of 200 mM. The resuspended protein pellets were briefly sonicated to ensure complete dissolution, and enzymatic digestion was initiated by adding trypsin at a 1:50 (enzyme-to-protein mass ratio) followed by incubation at 37 ℃ overnight. Dithiothreitol (DTT) was added to a final concentration of 5 mM, and incubated at 56 ℃ for 30 min, then iodoacetamide (IAA) was added to a final concentration of 11 mM and incubated at room temperature for 15 min in the dark. According to the manufacturer’s protocol (126 N, 127C, and 128 N for Group N and 129C, 130 N, and 131C for Group S; Thermo Fisher Scientific, USA), they were then incubated at 37 ℃ for 3 h and dried in a speed-vac (Cox and Mathias, 2008).

The TMT-labeled mixture was separated by high-performance reversed-phase high-performance liquid chromatography using an Agilent 300Extend C18 column (C18, 5 μm, 4.6 × 250mm). Mobile Phase A consisted of 0.1% formic acid and 2% acetonitrile in water, and mobile Phase B consisted of 0.1% formic acid and 90% acetonitrile in water. Smooth gradient: 0-26 min, 6-25% B; 26-34 min, 25-35% B; 34-37 min, 35-80% B; 37-40 min, 80% B, flow rate maintained at 500nL/min. The 60 components were separated within 60 min using an 8-32% acetonitrile gradient. Peptides were separated by UHPLC, ionized by injecting an NSI ion source and analyzed by Q Exactive™ HF-X mass spectrometry. The scan range was 400-1600 m/z with a mass resolution of 120,000. The secondary mass spectrometer scan range was set to a threshold of 100 m/z and the secondary scan resolution was set to 15,000.

The MaxQuant search engine was used for database searches; full trypsin specificity was required, and tolerance was set to four missing cleavages. The tandem mass spectra were searched against the UniProt database. The mass tolerance for precursor ions was set at 20 ppm in the first round of search, and the mass tolerance was set at 0.02 Da for the fragment. For protein identification, data were filtered with a false discovery rate (FDR) of < 1% and at least one matched unique peptide (Tyanova et al., 2016).

The (GO) annotation proteome was obtained from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). First, the identified protein ID was converted to a UniProt ID and then mapped to GO IDs using the protein ID. If identified proteins were not annotated using the UniProt-GOA database, InterProScan software was used to annotate the GO function of the protein based on the protein sequence alignment method. Proteins were classified by Gene Ontology annotation based on three categories: biological processes, cellular components, and molecular functions. The identified protein domain functional descriptions were annotated using InterProScan (a sequence analysis application) based on the protein sequence alignment method and the InterPro domain database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate protein pathways. First, the KEGG online service tool KAAS was used to annotate the protein KEGG database description. The annotation results were mapped to the KEGG pathway database using the KEGG online service tool, KEGG Mapper. We used Wolfpsort, a subcellular localization prediction software, to predict the subcellular localization.

To construct the basic network of interactions from different expression proteins, the Search Tool for the Retrieval of Interacting Genes (STRING, https://cn.string-db.org/) was applied with default confidence at 400 and minimum required interaction score> 0.15. Cytoscape software (version 3.7.1) was used to construct the network (Shannon et al., 2003).

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD050879.

RESULTS

In our study, 250795, 55441, 32948, 30771, 5352, and 4611 total spectra, matched spectra, peptides, unique peptides, identified proteins, and quantifiable proteins were identified (Fig 1A). The distributions of the peptide length and number of charges are shown in Fig 1B. The number of peptides per protein and the corresponding number of proteins are shown in Fig 1C. Most of the proteins contained more than two peptides. The coverage of the identified proteins was also calculated. Most of these were less than 30%, and only 657 proteins had over 30% coverage (Fig 1D).

Figure 1
Distribution of mass spectrometric quality control results

Principal Component Analysis was used to distinguish geese with different egg production, Principal Component Analysis (PCA) was applied. As shown in Fig 2A, geese with different egg production levels were separated in the PCA score plot, showing significant differences. Similarly, the abundance of proteins was highly consistent among different samples in the same group (Fig 2B).

Differential abundance protein (DAP) was defined as fold-change (FC) > 1.3 or < 1/1.3 with a p-value < 0.05. A total of 111 DAPs were identified by the HEP/LEP comparison, of which 62 were upregulated and 49 were downregulated (Fig 2C). All DAPs are listed in Table S1.

Figure 2
Results of overall proteome analysis

GO annotations were used to describe the distinct properties of differentially abundant proteins grouped into three broad categories: biological process (BP), cellular component (CC), and molecular function (MF). The upregulated and downregulated DAPs are shown separately in Fig 3A and 3B. According to the annotations, cellular processes, biological regulation, and multicellular organism processes had the most members among the biological processes. Analysis of cellular components revealed that these proteins were mainly distributed in the cell and intracellular space, with functions such as binding, catalytic activity, and structural molecule activity.

Figure 3
GO enrichment of up- and down-regulated proteins

The KEGG pathway was also divided into upregulated and downregulated pathways for analysis, and there were obvious differences between the upregulated and downregulated pathways in which proteins were enriched. Upregulated proteins mainly exist in ribosomes and ribosome biogenesis in eukaryotes, whereas downregulated proteins mainly exist in glycolysis, folate biosynthesis, RNA degradation, and insulin resistance (Fig. S2A,B). Subcellular localization analysis revealed that 17.74% of the upregulated proteins were in the extracellular space, which was more than twice that of the downregulated proteins (Fig. S2C,D).

According to the different multiples of these differential proteins, we divided them into four parts (Fig. 5A): heavily downregulated (Q1, FC<0.667), slightly downregulated (Q2, 0.667<FC<0.769), slightly upregulated (Q3, 1.3<FC<1.5), and severely upregulated (Q4, FC>1.5). Functional annotation of the proteins in these four parts showed that the four categories had significant differences at various functional levels, although the functions of proteins with different degrees of upregulation were somewhat different.

Figure 4
Cluster analysis of protein expression patterns. A. Bar chart of different cluster proteins. B. Protein domain enrichment of different clusters. C. KEGG pathway enrichment of different clusters. D. E. and F. present biological process, cellular components, and molecular functions in Gene Ontology enrichment. Colors of the columns represent different clusters and the color of the squares represents saliency.

To explore the interactions between these proteins, an interaction network diagram was constructed using STRING and Cytoscape to visually depict the interplay between these distinct proteins. The results demonstrated that These proteins were categorized into distinct interaction networks. The interacting proteins primarily exhibited functions related to ribosomes, amino acid metabolism, and carbohydrate metabolism, with notable upregulation in the expression of ribosome-related proteins. (Fig. 6).

Figure 5
Protein-protein interaction network

Interaction network diagram of the differential proteins between the two groups; nodes represent the differential proteins, and the connecting lines represent the direct interactions between the two linker proteins. Red represents upregulated protein expression, blue represents downregulated protein expression, the size of the node represents the degree of the node, and the larger the node, the greater the number of interactions.

DISCUSSION

The West Anhui white goose, a high-quality local breed in the Anhui Province, exhibits distinct nesting behavior (Zhao et al., 2025). A total of 5352 proteins were identified, of which 4611 provided quantitative information. Consistent with the production traits, 111 differentially expressed proteins were observed between the two groups and the upregulated and downregulated proteins were enriched in diverse functional categories.

Glycolysis in the granulosa cells is the primary source of energy for follicular development (Cao et al., 2022). Lactate, a byproduct of glycolysis, acts as an environmental stimulant for follicular development. There was a positive correlation between lactate levels in granulosa cells and their functionality. Enrst et al. discovered that the expression of glycolysis-related genes, such as GLUT4, HK1, and PKM2, increased during primordial follicle activation in humans, indicating an enhancement of glycolytic activity during this process (Ernst et al., 2018). Through pyruvate deprivation-induced enhanced glycolysis, Zhang et al. (2022) activated the mTOR signaling and downstream pathways to stimulate mouse and human follicles Zhang et al., 2022). Differentially expressed proteins were significantly enriched in the glycolysis/ gluconeogenesis pathway, suggesting that variations in nesting behavior may be attributed to differences in glycolytic levels influenced by lactate concentrations.

Differentially expressed proteins were significantly enriched in the glycolysis/ gluconeogenesis pathway, suggesting that variations in nesting behavior may be attributed to differences in glycolytic levels influenced by lactate concentrations. In our study, LDHA (Lactate Dehydrogenase A) and PKM2 (Pyruvate Kinase M2) were downregulated in low-egg-producing geese. LDHA catalyzes the conversion of pyruvate to lactate, a critical step for maintaining glycolytic flux and NAD+ regeneration in granulosa cells. Reduced LDHA activity may impair lactate production, limiting its role as an energy substrate for oocyte maturation (Jia et al., 2023). Similarly, PKM2, a key regulator of glycolysis, modulates metabolic reprogramming by interacting with HIF-1α under hypoxic conditions (Liu et al., 2021). In poultry, PKM2 downregulation correlates with diminished follicular steroidogenesis and oocyte competence, as observed in low-egg-producing geese. These findings align with studies in chickens, where enhanced glycolysis supports follicle selection by activating mTOR signaling Li et al., 2022). The observed downregulation of glycolytic enzymes in LEP geese suggests compromised energy metabolism, potentially delaying follicular development and prolonging broodiness.

Egg hatching induces oxidative stress, which can affect apoptosis and autophagy in granulosa cells (Agarwal et al., 2012; Sun et al., 2024). Hou et al. (2023) discovered that the pre-fertile period is accompanied by elevated levels of oxidative stress, which triggers excessive autophagy and apoptosis, ultimately resulting in granule cell death (Hou et al., 2023). Our findings indicate variations in phagosomes among geese with different egg production levels, suggesting that a potential correlation between individual responses to oxidative stress and enhanced oxidative stress levels may be beneficial for improving egg production.

Methylglyoxal (MG), a highly reactive dicarbonyl group produced during metabolism, is the most potent precursor of advanced glycation end products (Mano et al., 2012; Khajali et al., 2011). MG induces glycation stress in mouse oocytes. GLO1 and GLO2 play crucial roles in the detoxification system by upregulating their expression under stress conditions (Rabbani and Paul, 2015). MG exerts a detrimental effect on oocyte maturation in vitro that is attributed to oxidative stress (Di Emidio et al., 2019b). Previous studies have reported an increase in oxidative stress, superoxide dismutase (SOD) activity, and lactate dehydrogenase (LDH) activity during poultry nesting, resulting in autophagy and apoptosis (Hou et al., 2023; Lou et al., 2017). Our findings demonstrate that the LEP group exhibited a prolonged nesting time, indicative of higher levels of oxidative damage, corresponding to increased expression of GLO1. Furthermore, our investigation into the relationship between sirt1 and this detoxification system revealed that inhibition of Sirt1 decreased GLO1 expression, whereas activation with resveratrol increased its expression (Mano et al., 2012). Sirt1 is a widely recognized histone acetylation inhibitor. Based on these results, we hypothesized that histone acetylation negatively regulates GLO1 expression. However, further verification is required.

Folic acid is a dietary micronutrient essential for one-carbon metabolism and has a significant effect on female pregnancies (Shulpekova et al., 2021; Chen et al., 2024). In 1931, Wills successfully treated megaloblastic anemia during pregnancy with yeast extract (Wills, 1931), and subsequent studies demonstrated that folic acid supplementation can reduce the prevalence of folic acid deficiency during pregnancy and effectively improve pregnancy-related defects (Bulloch et al., 2020; Bukowski et al., 2009). Within a certain range, folate levels are positively correlated with pregnancy safety (Antony, 2007; Fekete et al., 2010).

Interestingly, our results revealed a lower expression of proteins associated with the folate synthesis pathway in the HEP group than in the LEP group. However, the ability of animals to incorporate FA into the one-carbon metabolic pathway is limited, resulting in increased circulating levels of unmetabolized folate. Excess folate decreases methylenetetrahydrofolate reductase activity, leading to an imbalance between thymidylate synthase and methionine synthase activities, and an increase in homocysteine. Dysregulation of homocysteine is associated with oxidative stress that may contribute to gestational diabetes mellitus (Williamson et al., 2022).

Dysregulation of homocysteine is associated with oxidative stress that may contribute to gestational diabetes mellitus. Our proteomic data revealed downregulation of MTHFR (Methylenetetrahydrofolate Reductase) and GLO1 (Glyoxalase 1) in high-egg-producing geese. MTHFR is a pivotal enzyme in folate metabolism, balancing homocysteine remethylation and thymidylate synthesis. Reduced MTHFR activity may lower homocysteine levels, mitigating oxidative damage to granulosa cells (Raghubeer and Matsha, 2021). In contrast, GLO1 detoxifies methylglyoxal (MG), a byproduct of glycolysis linked to oocyte glycative stress (Di Emidio et al., 2019a). While GLO1 expression was elevated in LEP geese, prolonged broodiness likely exacerbates MG accumulation, overwhelming the detoxification system and impairing ovarian function. This aligns with findings in mice, where Sirt1-mediated GLO1 upregulation protects oocytes from glycative stress (Di Emidio et al., 2019a). The interplay between folate metabolism and oxidative stress highlights a trade-off: HEP geese may prioritize folate utilization for nucleotide synthesis (supporting rapid folliculogenesis) while tolerating mild oxidative stress, whereas LEP geese exhibit compensatory antioxidant responses at the cost of reduced fecundity.

Ribosomal proteins are the most highly expressed genes in virtually all cells, and their products play a pivotal role in ribosome biogenesis, thereby influencing protein folding (Petibon et al., 2021). The PPI results revealed that the differentially expressed proteins were predominantly associated with ribosomes, exhibiting higher expression levels in the HEP group, suggesting their potential involvement in fecundity. Further investigations into the role of ribosome-associated proteins and their impact on fecundity are warranted.

Fibrosis resulting from collagen accumulation is a prevalent characteristic of aging in various tissues, including the lung and heart (Biernacka and Nikolaos, 2011; Giménez et al., 2017). Similar to other organs, ovaries undergo progressive fibrosis during the aging process (Amargant et al., 2020). Ovarian fibrosis is reversible, and the removal of collagen by drugs can improve ovarian fibrosis, restore ovarian function, and prolong fertility (Umehara et al. 2022). However, there are no previous reports on the association between egg nests and collagen. In our study, two types of collagen VIII proteins exhibited significantly high expression levels in the HEP group, with fold changes of 11.049 and 5.089, respectively, ranking them first and third among the upregulated proteins. We hypothesized that this phenomenon may be attributed to the distinct reproductive patterns observed in poultry compared to mammals, in which collagen accumulation accompanied by fibrosis potentially contributes to enhanced production capabilities. In future studies, we will further explore this phenomenon and elucidate its underlying molecular mechanisms.

A recent study utilizing whole-genome sequencing (WGS) in couples experiencing recurrent miscarriages revealed that SV, associated with embryo loss and fetal death, affects the functionality of well-known reproductive genes, including FBN2 (Workalemahu et al., 2023). Genetic variations in FBN2 can also contribute to pregnancy loss (Kline et al., 2021). Our findings demonstrated a heightened level of FBN2 expression in the HEP group, further highlighting the positive correlation between FBN2 and fecundity.

The expression of Fibulin5 (FLBN5), which plays a role in regulating cell growth and binding to signaling receptors, was found to be 3.2-fold higher in HEP than in LEP. In a 2015 study by Zhang et al., a GWAS analysis of Jinghai yellow chickens identified seven SNPs associated with reproductive traits, including GLBN5, as genes harboring these variants. Our findings corroborate the conclusions of this study (Zhang et al., 2015).

CONCLUSION

In practice, geese have a prolonged nesting period, which significantly hampers their productivity. Proteins, as crucial functional molecules in organisms, may directly regulate the nesting behavior of geese. To address the issue of low egg production caused by the extended nesting time of geese, we endeavored to elucidate the molecular mechanisms influencing nesting behavior and fecundity through a mass spectrometry-based proteomic study of Wanxi white geese with varying levels of fecundity. In this study, 111 differentially expressed proteins were identified, comprising 62 upregulated and 49 downregulated proteins. Functional enrichment analysis revealed that the downregulated proteins were primarily involved in glycolysis and folate biosynthesis pathways. These protein disparities could potentially serve as influential factors that contribute to diverse levels of fecundity. This study revealed variations in Wanxi white goose fecundity at the protein level and offered insights into the identification of specifically regulated functional molecules. Subsequent studies will entail comprehensive functional assessments to select key proteins and validate their regulatory mechanisms.

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Edited by

• Editor-chefe:
  Marcelo Resende de Souza

• Editor- científico:
  Antônio de Pinho Marques Jr

Publication Dates

• Publication in this collection
  12 Jan 2026
• Date of issue
  Jul-Aug 2025

History

• Received
  26 Dec 2024
• Accepted
  21 Mar 2025