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. 2026 Feb 7;34:103651. doi: 10.1016/j.fochx.2026.103651

Multi-omics analysis reveals breed-specific differences in the quality and nutrition of goose eggs

Yan Zheng b,1, Tiantian Gu a,1, Minglu Bai b, Yue Chen b, Qianhui Wang b, Lei Yu b, Zhongzan Cao b, Li Chen a, Tao Zeng a,⁎,1, Xinhong Luan b,⁎,1
PMCID: PMC12914437  PMID: 41717370

Abstract

Goose eggs are valued for their rich nutrition and unique flavor, yet breed-specific differences in egg quality remain unclear. This study found that meat-type Zhedong White goose eggs (ZDG) had higher weight, yolk ratio, and yolk color, with rounder shape and potentially stronger eggshells, while dual-purpose Huoyan goose eggs (HYG) showed relatively lower values in these traits but a higher yolk index. Proteomics revealed higher activity in the vitamin digestion and absorption pathway in ZDG, and metabolomics indicated enhanced metabolism of unsaturated fatty acids and α-linolenic acid. Integrated analysis with lipidomics showed that ZDG exhibited higher activity in vitamin digestion and absorption and neuroactive ligand–receptor interaction pathways, along with elevated TG, DG, SM, and PC levels, improving flavor and texture. These findings highlight breed-specific differences in egg quality, nutrition, and flavor, providing a theoretical basis for the nutritional improvement and targeted breeding of functional goose eggs.

Keywords: Goose, Egg quality, Proteomics, Metabolomics, Lipidomics

Highlights

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    ZDG eggs have higher weight, yolk proportion, yolk color, and better sensory quality.

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    Proteomic analysis shows enrichment in vitamin digestion and absorption pathways.

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    Metabolomic analysis reveals enhanced unsaturated fatty acid and α-linolenic acid metabolism in ZDG.

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    Vitamin digestion and absorption and neuroactive ligand–receptor interaction pathways influencing egg quality and flavor.

1. Introduction

Eggs are rich in nutrients, containing abundant proteins, essential amino acids, vitamins, and various bioactive compounds, making them an important source of nutrition in the human diet . The composition of egg white and yolk differs significantly. Egg white accounts for about 60% of the total egg and mainly consists of water (88%), protein (11%), and about 1% minerals and carbohydrates (Campbell, Raikos, & Euston, 2003). Although the yolk makes up less than 30% of the egg, it contains much higher levels of protein, lipids, and minerals than the egg white, making it the main source of nutrients in the egg (Z. Zhou et al., 2025). Almost all lipids in eggs come from the yolk, including triglycerides (TG), phospholipids, and cholesterol (Xiao et al., 2020). Among them, bioactive phospholipids such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM) play important biological roles, including antioxidant and anti-inflammatory activities, as well as the regulation of lipid metabolism (L. Zhou et al., 2021).

In poultry eggs, chicken eggs dominate global production and consumption due to their availability, moderate price, and well-defined nutritional properties. In China alone, annual egg production exceeds 29 million tons (Yuan et al., 2021). In terms of nutritional composition, goose eggs offer distinct advantages. Their protein content and amino acid profile differ significantly from those of other poultry eggs, with a notably higher proportion of essential amino acids . In addition, goose eggs are richer in minerals and vitamins (Sharif, Saleem, & Javed, 2018). These nutritional characteristics make them more beneficial to human health. Egg quality directly affects consumer purchasing decisions, processing performance, and economic value. Common indicators of egg quality include egg weight, shape, shell strength, albumen index, yolk index, and Haugh unit. These traits are influenced by multiple factors, such as breed and management practices. Therefore, scientific research on goose egg quality is crucial for promoting the development of the goose egg industry and enhancing the economic value of goose farming.

China is the largest producer and consumer of geese in the world, with a wide variety of breeds. Studies have shown that the nutrition and quality of egg products are closely related to the breed (L. Zhou et al., 2021). Huoyan and Zhedong White geese are local Chinese breeds that hold a significant market share in the goose industry. Huoyan geese are dual-purpose for meat and eggs, with very high egg production, reaching 120–180 eggs per bird annually, while Zhedong White geese are primarily raised for meat, producing 30–45 eggs per bird each year (Luan et al., 2017). Due to differences in reproductive traits, energy metabolism, and hormone levels, the egg quality and nutritional composition of these two breeds may differ significantly. Current research on poultry eggs mostly focuses on the effects of different rearing conditions or dietary supplements on egg quality and nutrition (Shi et al., 2022; Takahashi et al., 2023). However, studies comparing egg quality among breeds with different laying capacities and characteristics are limited, despite being crucial for improving egg quality and nutrition.

In recent years, with the growing demand for poultry eggs with high nutritional value and superior flavor, traditional physicochemical analysis methods have become insufficient to fully reveal the molecular mechanisms underlying egg quality. As the main nutrient reservoir in eggs, the composition and metabolic characteristics of the yolk directly determine the egg's nutritional value and sensory quality. With the development of multi-omics approaches, powerful tools have become available for analyzing egg quality. Proteomics can identify changes in functional proteins related to yolk nutrition and flavor formation, while metabolomics reflects the activity of metabolic pathways and changes in small-molecule metabolites. Combined analysis of the two helps to clarify the molecular basis of yolk quality differences at both functional and metabolic levels. In addition, lipidomics can provide supporting evidence by revealing lipid metabolism. Given the limited research on the quality of goose eggs and their high nutritional value, this study compared the differences in egg quality between geese with different laying capacities and types. Using proteomics, metabolomics, and lipidomics, the study revealed comprehensive differences in yolk protein composition, metabolic pathways, and lipid profiles, thereby elucidating the molecular mechanisms underlying goose egg quality. This provides a basis for improving goose egg quality and developing high-quality egg products, and offers insights into goose breeding and the development of functional egg-based foods.

2. Materials and methods

2.1. Egg samples collection

The Huoyan geese eggs (HYG) were purchased from Liaoning Hengjiu Agriculture and Animal Husbandry Co., Ltd. in China, while the Zhedong white geese eggs (ZDG) were purchased from Xiangshan Element Egg Industry Co., Ltd. in Zhejiang Province, China. The eggs were collected from laying geese of similar body size and free from disease, all raised under the same management conditions. After removing soft-shelled and cracked eggs, a total of 31 eggs from each breed were retained for analysis. From 15 eggs, 100 μL of yolk was collected from each. The yolks from every five eggs were pooled into a single sample for multi-omics analysis. The remaining whole eggs were used for egg quality testing.

2.2. Eggs quality measurement

The eggs were weighed to determine egg weight. A vernier caliper was used to measure the transverse and longitudinal diameters, and the egg shape index was calculated as (transverse diameter/ longitudinal diameter) × 100%. After breaking the eggs, the yolk and albumen were collected for later use, and the eggshells were weighed. The shell ratio was calculated as the percentage of shell weight relative to the total egg weight. After removing the inner membrane of the eggshell, a digital micrometer was used to measure shell thickness at three points—the blunt end, middle, and pointed end—and the average value was calculated. Eggshell strength was measured using an eggshell strength tester (EFR-01, ORKA, Israel). The yolk was weighed, and the yolk ratio was calculated as the percentage of yolk weight relative to total egg weight. A vernier caliper was used to measure the yolk diameter and height, and the yolk index was calculated as the ratio of yolk height to yolk diameter. The yolk color, Haugh unit and albumen height were determined using a multifunctional egg quality tester (EA-01, ORKA, Israel).

2.3. Proteins extraction and proteomic analysis

A 50 mg sample was homogenized in 600 μL of RIPA lysis buffer using an ultrasonic disruptor in an ice–water bath for 20 min. Then, the lysate was centrifuged at 12,000 rpm for 10 min at 4 °C, and the supernatant was transferred into a new EP tube. Protein concentration was measured using the BCA assay. For each sample, 30 μg of total protein was subjected to reduction, alkylation and enzymatic digestion. Then, 200 ng of digested peptides were used for proteomic analysis. Peptide separation was performed on a nano­UPLC (Evosep one) coupled to a timsTOF Pro2 mass spectrometer (Bruker Daltonics, MA, USA) equipped with a nano­electrospray ion source. Chromatographic separation was carried out using a reversed­phase column (Bruker Daltonics, MA, USA). Mobile phases were H2O with 0.1% formic acid (phase A) and acetonitrile with 0.1% formic acid (phase B). The mass spectrometer adopts DIA PaSEF mode for DIA data acquisition, and the scanning range is from 400 to 1200 m/z, the Ramp time is 100 ms, the Accu time is 100 ms. During PASEF MS/MS scanning, the impact energy increases linearly with ion mobility, from 20 eV(1/K0 = 0.85 Vs/cm2) to 59 eV (1/K0 = 1.30 Vs/cm2). Raw data were processed using Spectronaut software (version 19.0.240604.62635, Biognosys AG, Schlieren, Switzerland) and searched against the species­level UniProt FASTA databases (uniprotkb_taxonomy­Anser cygnoides (species)_id_8845_2025_05_22. fasta) .

To improve the robustness of downstream analysis, raw data were subjected to quality control and preprocessing. Proteins with at least one unique peptide (unique peptides≥1) were retained. Missing values were simulated based on a minimum value-bisection method and subsequently imputed to approximate low abundance signals. After quality control, data normalization and missing value imputation were performed. The protein intensities were log-transformed and mean-centered using SIMCA software (V18.0.1, Sartorius Stedim Data Analytics AB, Umea, Sweden). Principal component analysis (PCA) was conducted to assess the clustering and variation among different groups. Differentially expressed proteins (DEPs) were identified using Student's t-test with p < 0.05 and a fold change ≤0.83 or ≥ 1.2. Hierarchical clustering analysis was performed on the DEPs, and a heatmap was generated for visualization. Then, the DEPs were mapped to Anser cygnoides (swan goose) gene IDs and queried against the Gene Ontology database (http://www.geneontology.org/). and functional enrichment analysis was performed. The pathway enrichment analysis was using the KEGG database (www.kegg.jp/kegg/pathway.html).

2.4. Metabolites extraction and metabolomic analysis

The sample was weighed into an EP tube, followed by the addition of two homogenization beads and 500 μL of extraction solvent (methanol:acetonitrile:water = 2:2:1, v/v) containing isotope-labeled internal standards. After mixing, the samples were homogenized in a homogenizer (35 Hz, 4 min), followed by ultrasonic treatment in an ice–water bath using a cell disruptor for 5 min. This step was repeated three times. The samples were then incubated at −40 °C for 1 h, and subsequently centrifuged at 12,000 rpm and 4 °C for 15 min. The supernatant was collected for analysis. Polar metabolites were separated using a Waters ACQUITY UPLC BEH Amide column. The mobile phase consisted of A: water with 25 mM ammonium acetate and 25 mM ammonia, and B: acetonitrile. Non-polar metabolites were separated using a Phenomenex Kinetex C18 column, with mobile phase A: water containing 0.01% acetic acid, and B: isopropanol:acetonitrile (1,1, v/v).

Following the removal of outliers, the filtering of missing values, and the imputation of missing values, standardization of the data was conducted. Principal component analysis (PCA) was then performed, as previously described. The data were subsequently subjected to log-transformation and UV-scaling, employing the SIMCA software for the purpose of generating orthogonal projections to latent structures (OPLS-DA). Differentially metabolites (DMs) were identified using both a p < 0.05 from Student's t-test and a variable importance in the projection (VIP) score > 1 for the first principal component in the OPLS-DA. The Euclidean distance matrix was calculated based on the quantitative values of the DMs, and hierarchical clustering was performed with the complete linkage method. Subsequently, KEGG enrichment analysis was performed on the DMs as previously described.

2.5. Lipid extraction and lipidomic analysis

Approximately 25 mg sample was transferred into an EP tube, followed by the addition of homogenization beads, 200 μL of water, and 480 μL of extraction solvent (methyl tert-butyl ether:methanol = 5:1, v/v). After thoroughly mixing, the mixture was homogenized at 35 HZ for 4 min, followed by ultrasonication in an ice–water bath for 5 min. This process was repeated three times to ensure complete extraction. Next the samples were incubated at −40 °C for 1 h, and centrifuged at 3000 rpm for 15 min. A 300 μL of the supernatant was transferred to an EP tube and dried under vacuum at low temperature. The dried extract was reconstituted in 200 μL of extraction solvent (dichloromethane:methanol = 1:1, v/v). An isotope-labeled internal standard was added to the solvent and thoroughly mixed. The mixture was then subjected to ultrasonication in an ice–water bath for 10 min. Subsequently, the samples were centrifuged at 12,000 rpm for 15 min at 4 °C, and 150 μL of the supernatant was finally transferred to glass vials for analysis.

For lipids, LC-MS/MS analysis were performed using a UHPLC system (Vanquish, Thermo Fisher Scientific, MA, USA) equipped with a Phenomenex Kinetex C18 column coupled to Orbitrap Exploris 120 mass spectrometer(Vanquish, Thermo Fisher Scientific, MA, USA). The mobile phase consisted of A: 40% water and 60% acetonitrile containing 10 mM ammonium formate, and B: 10% acetonitrile and 90% isopropanol containing 10 mM ammonium formate. Following the removal of outliers, the filtering of missing values, and the imputation of missing values, standardization of the data was conducted. Then the data were log-transformed and mean-centered to perform PCA and OPLS-DA as described earlier. Differentially lipids (DLs) were identified using both a p < 0.05 from Student's t-test and a variable importance in the projection (VIP) score > 1 for the first principal component in the OPLS-DA. Subsequently, DLs were subjected to hierarchical clustering and a heatmap was generated for visualization. The quantitative values of DLs were used to calculate correlation coefficients using the Spearman method, with results visualized as a heatmap. Subsequently analyze the extent of change in DLs content and classification information. The multi-omics analysis data are available in Supplementary Tables S1-S4.

2.6. Statistical analysis

Data are presented as mean ± standard error of the mean (SEM), unless otherwise specified. Statistical analysis was performed using GraphPad Prism version 9.0 software (GraphPad Software, San Diego, CA, USA), employing Student's t-tests and one-way analysis of variance (ANOVA). The p < 0.05 was considered statistically significant.

3. Results

3.1. The egg quality of Huoyan goose and Zhedong white goose eggs

The egg quality of HYG and ZDG showed distinct differences. Compared with HYG, ZDG showed significantly higher egg weight but a lower shape index (Fig. 1A, B), indicating that ZDG eggs were larger and rounder, with a more attractive external appearance. Meanwhile, ZDG had lower shell ratio and shell thickness than HYG, while no significant difference was observed in shell strength (Fig. 1C-E). This suggests that the thinner shell did not compromise mechanical resistance. In terms of internal quality, ZDG showed higher yolk color (Fig. 1F) and yolk ratio (Fig. 1G), indicating a greater proportion of yolk with enhanced visual characteristics. However, the yolk freshness of ZDG was lower than that of HYG, as HYG exhibited a higher yolk index (Fig. 1H). In addition, ZDG showed greater albumen height, while no significant difference in Haugh unit was observed between the two breeds (Fig. 1I, J).This suggests that albumen viscosity was higher in ZDG, but its overall freshness was comparable to that of HYG. Collectively, these results demonstrate that ZDG eggs possess a larger size and more prominent yolk proportion but exhibit slightly reduced yolk freshness compared with HYG.

Fig. 1.

Fig. 1

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The egg quality traits of HYG and ZDG. The egg weight (A), shape index (B), shell ratio (C), shell strength (D), shell thickness (E), yolk color (F), yolk ratio (G), yolk index (H), albumen height (I) and haugh unit (J) of HYG and ZDG were measured. * p < 0.05, ** p < 0.01. HYG represents the eggs of Huoyan goose and ZDG represents the eggs of Zhedong White Goose.

3.2. Proteomic profiles of Huoyan goose and Zhedong white goose eggs

To further investigate the proteomic features of the HYG and ZDG, proteomic analysis was performed. The total ion chromatogram of all samples and the MS/MS spectrum of a peptide from a representative DEPs was displayed in Fig. 2A, B. PCA results showed a clear separation between the two breeds, indicating distinct proteomic profiles (Fig. 2C). A total of 22 DEPs were identified between HYG and ZDG, including 15 up-regulated and 7 down-regulated proteins (Fig. 2D, Table S1). Hierarchical clustering analysis of the DEPs was performed to identify expression pattern differences between the experimental groups. Distinct clustering patterns of DEPs were observed between HYG and ZDG (Fig. 2E), further supporting breed-specific protein expression characteristics. To clarify the molecular functions and biological processes of the DEPs, GO enrichment analysis was performed. The results showed that the DEPs between HYG and ZDG were mainly involved in small molecule binding, lipid binding, and vitamin binding, and participated in various immune-related biological processes, such as leukocyte mediated immunity, immune effector process, response to external stimulus (Fig. 2F, Table S2). KEGG enrichment analysis showed that the DEPs were mainly enriched in pathways such as cobalamin transport and metabolism, vitamin digestion and absorption, adherens junction and neuroactive ligand-receptor interaction (Fig. 2G, Table S3). The KEGG chord diagram illustrates the expression of DEPs involved in these pathways. As shown in Fig. 2H cobalamin-binding intrinsic factor (CBLIF) is involved in the cobalamin transport and metabolism and vitamin digestion pathways, while complement component 3 (C3) participates in the neuroactive ligand–receptor interaction pathway. Both CBLIF and C3 are up-regulated in these pathways, whereas Poly(A) polymerase 1 A (PAP1A) is down-regulated and participates in the adherens junction pathway. Collectively, these proteomic findings indicate that breed-specific differences in vitamin metabolism, immune response, and cell–cell adhesion may contribute to the distinct physiological characteristics of HYG and ZDG eggs.

Fig. 2.

Fig. 2

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The proteomic profiles of Huoyan goose and Zhedong White goose eggs. (A) The total ion chromatogram of all samples was displayed, with the horizontal axis representing retention time and the vertical axis representing peak intensity. Different colors indicated different samples. (B) MS/MS spectrum of a peptide from the representative DEPs. The horizontal axis represented mass-to-charge ratio (m/z), the vertical axis represented relative intensity, and ion types are marked with different colors. The PCA score plot (C), DEPs (D), hierarchical cluster analysis (E), GO enrichment analysis (F) and KEGG analysis (G) between HYGC and ZDGC. (H) KEGG chord diagram: the right side represents different pathways, and the left side represents DEPs. A redder color indicates upregulation of the corresponding gene, while a bluer color indicates downregulation.

3.3. Metabolomic profiles of Huoyan goose and Zhedong white goose eggs

To further investigate the metabolic patterns of HYG and ZDG, metabolomic analysis was performed. PCA and OPLS-DA results showed a clear separation between HYG and ZDG (Fig. 3A, B). A total of 365 DMs were identified, with 234 upregulated and 131 downregulated (Fig. 3C). Hierarchical clustering analysis was performed on the top ten upregulated and downregulated DMs. The clustering heatmap showed distinct group separation, with metabolites within each group exhibiting consistent expression trends (Fig. 3D). To explore the potential biological implications of these metabolic alterations, KEGG pathway enrichment analysis was conducted. The results indicated that the DMs between HYG and ZDG were mainly enriched in biosynthesis of unsaturated fatty acids, biotin metabolism, taurine and hypotaurine metabolism, α-linolenic acid metabolism, primary bile acid biosynthesis and cysteine and methionine metabolism pathways (Fig. 3E, Table S4). To assess whether the overall activity of metabolic pathways involving DMs was enhanced or reduced, pathway differential abundance (DA) scoring analysis was performed. Compared with HYG, the biosynthesis of unsaturated fatty acids and α-linolenic acid metabolism pathways were overall up-regulated, while the taurine and hypotaurine metabolism and primary bile acid biosynthesis pathways were overall down-regulated (Fig. 3F). These differences in pathway activity may contribute to the observed differences in egg quality between HYG and ZDG.

Fig. 3.

Fig. 3

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The metabolomic profiles of Huoyan goose and Zhedong White goose eggs. The PCA score plot (A), OPLS-DA score plot (B), DMs (C), hierarchical cluster analysis (D), pathways enrichment analysis of DMs E), DA score analysis (F) between HYG and ZDG.

3.4. Lipidomic profiles of Huoyan goose and Zhedong white goose eggs

To characterize the lipid composition differences between HYG and ZDG, lipidomics analysis was performed. The total ion chromatogram in positive and negative ion mode was shown in Fig. 4A, B. The critical DL extraction ion chromatogram and MS/MS spectrum was displayed in Fig. 4C. The PCA results revealed a clear separation between the two breeds (Fig. 4D), and subsequent OPLS-DA model further confirmed significant intergroup distinctions, indicating that the two types of eggs have distinct lipid composition patterns (Fig. 4E). A total of 359 DLs were identified, including 194 up-regulated and 165 down-regulated (Fig. 4F). Hierarchical clustering of the top ten upregulated and downregulated DLs showed distinct separation between the two groups, with consistent expression trends within each group (Fig. 4G). Spearman correlation analysis showed the relationships among these up-regulated or down-regulated DLs (Fig. 4H). The DLs were classified into 95 lipid superclasses. ZDG exhibited higher lipid abundance, with triglycerides (TG) and sphingomyelins (SM) being particularly elevated, meanwhile, phosphatidylcholines (PC) and diglycerides (DG) were also higher in ZDG, although not as prominently as TG and SM (Fig. 4I). These results indicate significant differences in lipid composition patterns between HYG and ZDG, which may be closely related to their differences in egg quality.

Fig. 4.

Fig. 4

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The lipidomic profiles of Huoyan goose and Zhedong White goose eggs. (A) The total ion chromatogram in positive ion mode. (B) The total ion chromatogram in negative ion mode. (C) The critical DLs extraction ion chromatogram (top) and MS/MS spectrum (bottom). The PCA score plot (D), OPLS-DA score plot (E), DLs (F), hierarchical cluster analysis (G), Spearman correlation analysis (H), percentage change in relative lipid content (I) between HYG and ZDG.

3.5. Integrated proteomic and metabolomic analysis of Huoyan goose and Zhedong white goose eggs

To further elucidate the molecular basis of the differences in egg quality between HYG and ZDG, an integrated analysis based on proteomics and metabolomics data was performed. Hierarchical clustering revealed distinct clustering patterns between the two groups, indicating strong correlations among DEPs and DMs that may collectively contribute to egg quality variation (Fig. 5A). The DEPs were involved in a total of 8 pathways, while the DMs participated in 37 pathways. Among the pathways in which both were involved, there were two: the vitamin digestion and absorption pathway and neuroactive ligand–receptor interaction pathway (Fig. 5B, C). An interaction network of DEPs, DMs, and DLs was constructed, revealing that some DEPs and DMs involved in key pathways interact with numerous DLs (Fig. 5D). This suggests a potential coordinated regulatory mechanism among proteins, metabolites, and lipids, jointly influencing egg quality and nutritional traits. To provide a more intuitive understanding of these mechanisms, pathway maps were constructed for the two pathways in which both DEPs and DMs were involved. In the neuroactive ligand–receptor interaction pathway, C3 and cysteinyl-leukotriene were upregulated in expression or abundance, while taurine levels decreased (Fig. 5E). In the vitamin digestion and absorption pathway, CBLIF and biotin regulated multiple nodes, with CBLIF upregulated and biotin decreased (Fig. 5F). These findings suggest that differential regulation of key proteins and metabolites may underlie the breed-specific differences in egg quality. Moreover, the integrated multi-omics network reveals a potential synergistic interaction among DEPs, DMs, and DLs, particularly within vitamin digestion and absorption and neuroactive ligand–receptor interaction pathways, providing mechanistic insights into how these pathways collectively contribute to the observed phenotypic distinctions between HYG and ZDG.

Fig. 5.

Fig. 5

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Integrated proteomic and metabolomic analysis of Huoyan goose and Zhedong White goose eggs. Correlation heatmap of DEPs and DMs (A), Venn diagram of KEGG pathways enriched by DEPs and DMs (B), pathways jointly involved by DEPs and DMs (C), between HYG and ZDG. (D) Interaction network of DEPs, DMs, and DLs, with red lines indicating positive correlations and blue lines indicating negative correlations. (E) Pathway map of the neuroactive ligand–receptor interaction pathway. (F) Pathway map of the vitamin digestion and absorption pathway. Red indicates upregulation, blue indicates downregulation, and green indicates species-specific proteins. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

Eggs are considered one of the natural foods with high nutritional value and a balanced nutrient profile. They are rich in protein, essential amino acids, bioactive lipids, and various vitamins and minerals, making them important for human health. In recent years, increasing attention has been paid to studying and improving egg quality, as their nutritional composition and physicochemical properties not only affect nutritional value but also directly influence consumer demand and the development of the poultry industry. Among various poultry eggs, goose eggs have attracted attention due to their larger size, richer nutrition, and stronger flavor. Sun et al.(Sun, Liu, Yang, & Xu, 2019) compared eggs from chickens, ducks, geese, pigeons, and quails and found that goose eggs have the highest proportion of essential amino acids. Moreover, lipidomic studies have also shown that, goose eggs possess elevated levels of ether-linked phosphatidylethanolamine (ether PE) and lysophosphatidylinositol (LPI) relative to chicken and duck eggs(Z. Zhou et al., 2025). These lipids may carry important bioactive functions: ether PEs have been associated with lifespan extension across species (Cedillo et al., 2023), while LPI has also been reported to help lower blood pressure and maintain glucose homeostasis (Karpińska et al., 2018). These findings indicate that goose eggs are a high-quality functional food. Although many studies have examined the nutritional composition and sensory properties of poultry eggs, most have focused on chicken eggs, and research on goose eggs remains limited. In this study, two representative types of goose eggs were selected: HYG, a typical high-yield dual-purpose goose, and ZDG, a meat-type goose with lower egg production. The differences in reproductive traits and hormone levels between these two types may lead to significant variations in egg quality and nutritional content. This provides an opportunity to more accurately assess breed-specific effects on egg quality and offers a scientific basis for improving the quality and nutritional value of goose eggs. In this study, we found that the egg quality of HYG and ZDG varied significantly, with ZDG showing superior traits, particularly in egg weight, shell ratio, yolk color, yolk proportion, and albumen height. The higher egg weight in ZDG is likely linked to its faster growth rate and larger body size, as larger poultry have been shown to produce heavier eggs (Sun et al., 2019). These phenotypic traits are consistent with the biological characteristics of a meat-type breed. Meat-type lines typically allocate more nutrients to the yolk to support rapid embryonic development, resulting in a higher yolk proportion (Nolte et al., 2021). The increased yolk color in ZDG may be attributed to a longer laying cycle, which allows the gradual accumulation of carotenoids and other pigments within the yolk (Hammershøj, Kidmose, & Steenfeldt, 2010). The lower egg shape index in ZDG indicates a rounder egg shape. Studies have shown that the egg shape index is related to eggshell strength, and a rounder eggshell structure may have greater strength (Blanco, Icken, Ould-Ali, Cavero, & Schmutz, 2014). In summary, the differences in egg quality between ZDG and HYG reflect their respective breeding goals and biological characteristics. As a meat-type breed, ZDG's faster growth and larger body size give it an advantage in terms of egg weight and yolk proportion. In contrast, HYG, a high-yield laying breed, must control the energy and nutrient allocation per egg to maintain high production, resulting in poorer performance in these traits.

Beyond these phenotypic differences, the multi-omics data provide molecular evidence supporting this breed-specific nutrient allocation strategy. The integration of proteomic, metabolomic, and lipidomic analyses enables the identification of key pathways that regulate egg quality and nutritional composition in HYG and ZDG. Notably, the proteomics results showed the pathways for cobalamin transport and metabolism and vitamin digestion and absorption were significantly enriched. These pathways play a key role in the biological quality of eggs, as they are responsible for supplying nutrients, yolk pigmentation and shell structure. Cobalamin is not only an important nutrient in the yolk but also plays a critical role in egg quality and human health. Studies have shown that supplementing cobalamin in laying hens' diets can improve eggshell quality, regulate lipid and amino acid metabolism, and enhance the nutritional value of eggs (R. Wang, Bai, Yang, Wu, & Li, 2021). Additionally, cobalamin is essential for reproductive health, DNA synthesis, and nervous system function, and its deficiency can lead to various health disorders (Mucha, Kus, Cysewski, Smolenski, & Tomczyk, 2024). The enrichment of the vitamin digestion and absorption pathway further reinforces the link between molecular regulation and egg quality traits. This pathway directly influences the availability of lipid-soluble and water-soluble vitamins, thereby shaping yolk pigmentation, nutrient density, shell strength, and albumen quality. Zurak et al. (Zurak et al., 2025) found that vitamin A supplementation can increase carotenoid content, especially lutein, thereby improving yolk color. Gao et al. (S. Gao et al., 2024) observed that vitamin D₃ supplementation significantly enhanced eggshell strength and albumen height. These findings align well with the phenotypic advantages observed in ZDG in this study.

Metabolomics revealed differential metabolic pathways between HYG and ZDG, clarifying the differences in their nutritional composition. We found that the biosynthesis of unsaturated fatty acids and α-linolenic acid metabolism pathways were more active in ZDG. Unsaturated fatty acids are essential for human health; for example, ω-3 fatty acids benefit cardiovascular health and support brain function (Zailani et al., 2024). And α-Linolenic acid can be converted into EPA and DHA, providing multiple benefits such as supporting cardiovascular health, and exhibiting anti-inflammatory and antioxidant effects (Sala-Vila, Fleming, Kris-Etherton, & Ros, 2022; Yin et al., 2023). Additionally, supplementing α-linolenic acid in feed can significantly increase the ω-3 fatty acid content in yolks, improve yolk fatty acid composition, and enhance nutritional value (Kartikasari, Geier, Hughes, Bastian, & Gibson, 2024). In HYG, the taurine and hypotaurine metabolism and primary bile acid biosynthesis pathways were upregulated. The beneficial effects of taurine on human health are well established, and it is widely used as a food additive (Singh et al., 2023). Studies have shown that taurine can improve eggshell quality in chickens and help maintain egg quality through its antioxidant effects (Hajjarmanesh, Zaghari, Hajati, & Ahmad, 2023). However, high levels of primary bile acids, while capable of increasing egg production, can negatively affect egg quality by reducing yolk color, albumen height, and Haugh units (Cao, Li, Chen, Mu, & Wu, 2025). These findings are consistent with the egg quality characteristics observed in HYG, where relatively poorer yolk color and albumen height were detected, suggesting that excessive allocation of metabolic resources toward bile acid biosynthesis may come at the expense of certain quality traits.

Integrated analysis of proteomic and metabolomic pathways revealed that both DEPs and DMs were involved in the vitamin digestion and absorption pathway and the neuroactive ligand–receptor interaction pathway, suggesting that these two pathways may serve as central regulatory hubs linking nutrient metabolism to egg quality traits. As discussed earlier, the vitamin digestion and absorption pathway is closely associated with yolk nutrient deposition, egg flavor, and nutritional value. Within this pathway, CBLIF was upregulated in ZDG, while biotin levels decreased. CBLIF is a key carrier protein responsible for binding vitamin B12 and mediating its absorption, and its upregulation indicates a more active capacity for vitamin B12 absorption and transport (Francis, 1980), therefore, its increased expression suggests a more efficient vitamin B12 utilization in ZDG. Biotin is a cofactor for acetyl-CoA carboxylase and pyruvate carboxylase, and its reduction limits de novo fatty acid synthesis (Tong, 2013). With decreased efficiency in synthesizing new fatty acids, cells must rely on exogenous or endogenous fatty acids for reutilization (Suchy, Rizzo, & Wolf, 1986). Meanwhile, enhanced vitamin B12 absorption promotes the activity of methylmalonyl-CoA mutase, increasing energy metabolism and generating excess acetyl-CoA and glycerol-3-phosphate, which are then used for fatty acid re-esterification to produce TG and DG (Q. Gao, Shang, Huang, & Wang, 2013; Takahashi-Iñiguez, García-Hernandez, Arreguín-Espinosa, & Flores, 2012). This is consistent with the lipidomics data, which revealed significantly elevated TG and DG levels in ZDG. Watkins (Watkins, 1989) also reported that biotin deficiency leads to increased TG in chicken liver, supporting this observation. The neuroactive ligand–receptor interaction pathway is involved in regulating neuro–immune–endocrine signaling and modulates poultry egg production through the HPG axis (Yan et al., 2022). Differences in egg production and hormone levels between HYG and ZDG lead to significant variations in this pathway. Within the pathway, ZDG shows increased levels of C3 and cysteinyl-leukotriene, while taurine is decreased. C3 is a central component of the innate immune system, and its upregulation is associated with inflammation and oxidative stress (Collard et al., 2000). Cysteinyl-leukotriene can induce lipid oxidation and inflammatory responses through G protein–coupled receptors (Sala, Zarini, & Bolla, 1998). The decrease in taurine indicates reduced antioxidant capacity (Jong, Azuma, & Schaffer, 2012). These changes suggest that in ZDG, immune signaling is activated under high-energy conditions, while the antioxidant system is compromised. Decreases in taurine have been reported to promote TG accumulation, while C3 is associated with elevated SM and PC levels (Kennett, Schenkein, Ellis, & Rutherford, 1984; Z. Wang et al., 2020). These are consistent with the lipidomics results. In addition, high levels of TG, DG, SM, and PC not only enhance texture but also generate better flavor during cooking (Ren et al., 2024; Shahidi & Hossain, 2022). These findings indicate that ZDG exhibits a metabolic strategy favoring nutrient deposition and flavor enhancement, whereas HYG appears to allocate resources toward maintaining antioxidant balance and production efficiency. Although ZDG produces eggs with higher nutritional value and better flavor attributes, its antioxidant capacity is comparatively lower, which may render its eggs more susceptible to lipid oxidation during storage or processing.

In conclusion, this study demonstrates that geese with different reproductive traits exhibit significant differences in egg quality and nutritional composition. ZDG shows superior egg quality, whereas HYG, owing to its high-yield characteristics, adopts a more conservative strategy in nutrient and energy allocation. Multi-omics analysis further revealed that ZDG has higher activity in key pathways such as vitamin digestion and absorption, unsaturated fatty acid synthesis, and neuroactive ligand–receptor interaction, contributing to enhanced egg quality and nutritional value. These findings provide a basis for understanding the impact of breed differences on goose egg quality and offer a theoretical foundation for the nutritional improvement and targeted breeding of high-quality functional goose eggs.

CRediT authorship contribution statement

Yan Zheng: Writing – original draft, Methodology, Formal analysis, Data curation. Tiantian Gu: Visualization, Validation, Methodology, Investigation, Formal analysis. Minglu Bai: Validation, Software, Investigation. Yue Chen: Validation, Investigation, Formal analysis. Qianhui Wang: Validation, Software, Investigation. Lei Yu: Validation, Methodology, Investigation. Zhongzan Cao: Validation, Methodology, Investigation. Li Chen: Validation, Methodology, Investigation. Tao Zeng: Supervision, Resources, Project administration, Funding acquisition, Conceptualization. Xinhong Luan: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

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 funded by the National Natural Science Foundation of China (32072810) and the Open Research Projects of the Zhejiang Key Laboratory of Livestock and Poultry Biotech Breeding (CARS-42). We are grateful for the technical support and assistance provided by Shanghai BIOTREE Biological Technology Co., Ltd. (Shanghai, China).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochx.2026.103651.

Contributor Information

Tao Zeng, Email: zengtao4009@126.com.

Xinhong Luan, Email: xhluan@syau.edu.cn.

Appendix A. Supplementary data

Supplementary material 1

mmc1.docx (13.6KB, docx)

Supplementary material 2

mmc2.docx (25.5KB, docx)

Supplementary material 3

mmc3.docx (22.8KB, docx)

Supplementary material 4

mmc4.docx (25.6KB, docx)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1

mmc1.docx (13.6KB, docx)

Supplementary material 2

mmc2.docx (25.5KB, docx)

Supplementary material 3

mmc3.docx (22.8KB, docx)

Supplementary material 4

mmc4.docx (25.6KB, docx)

Data Availability Statement

Data will be made available on request.

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