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Article

Comparative N-Glycoproteomic Analysis of Transparent and Opaque Pigeon Egg Albumen

by
Jinxin Liu
1,2,
Lingling Chang
1,
Qingping Tang
1,
Chunyu Mu
1,
Darong Cheng
2,
Rui Zhang
1,* and
Zhu Bu
1,*
1
Jiangsu Institute of Poultry Science, Yangzhou 225100, China
2
College of Veterinary Medicine, Yangzhou University, Wenhuilu Campus, Yangzhou 225009, China
*
Authors to whom correspondence should be addressed.
Foods 2026, 15(5), 909; https://doi.org/10.3390/foods15050909
Submission received: 30 January 2026 / Revised: 24 February 2026 / Accepted: 2 March 2026 / Published: 6 March 2026
(This article belongs to the Special Issue Eggs and Egg Products: Production, Processing, and Safety: 2nd Edition)
Browse Figures
Versions Notes

Abstract

Albumen transparency is an important quality trait of pigeon eggs that directly influences consumer preference and market value; however, its molecular basis remains unclear. This study aimed to characterize the key molecular differences between transparent and opaque pigeon egg albumen from an N-glycoproteomic perspective and to explore their associations with macroscopic textural properties. Transparent and opaque pigeon eggs were selected, and N-glycoproteomic analysis combined with texture profile analysis was conducted to compare glycosylation modifications and textural characteristics between the two groups. The results showed that transparent pigeon egg albumen exhibited significantly lower hardness, fracturability, gumminess, and chewiness than opaque albumen. Comparative glycoproteomic analysis revealed that the abundance of 122 glycopeptides was significantly lower in the transparent group, primarily originating from ovalbumin-related proteins and transferrin. Functional enrichment and protein–protein interaction analyses indicated that these proteins are closely associated with the extracellular space and serine-type endopeptidase inhibitor activity, and form a functional interaction module dominated by ovalbumin family proteins and transferrin. Overall, reduced N-glycosylation of key egg white proteins may influence protein aggregation behavior and gel network formation during heating, thereby contributing to differences in albumen textural properties and transparency. These findings provide glycoproteomic insights into the molecular mechanisms underlying transparency differences in pigeon egg albumen and identify specific glycosylation-related targets that may be exploited to modulate gel properties during thermal processing. This knowledge may support precision quality control of pigeon eggs and facilitate the development of transparent protein-based foods and functional gel products in the food industry.

1. Introduction

Egg albumen is an important functional ingredient and raw material in the food industry. Pigeon egg albumen has gained increasing consumer attention due to its composition and gel characteristics, particularly its high transparency [1]. Compared with eggs from domesticated chicken, duck, goose, turkey, and quail, pigeon eggs exhibit particularly distinctive gel-related physical properties [2]. Heat-induced gel formation is an important functional property of egg albumen [3]. Under thermal gelation conditions, transparent pigeon egg white can be used as a protein source for the preparation of transparent and durable hydrogels, and the thermal gel of pigeon egg white (PEW) is transparent and visually attractive to consumers [4]. However, there are variability in albumen transparency of different pigeon eggs. Transparent pigeon eggs are crystal clear, glossy, and elastic in texture, making them more attractive to consumers and conferring broader market prospects [5]. During commercialization, transparent pigeon eggs are generally preferred and command higher prices than ordinary pigeon eggs [6]. Albumen transparency has become an important appearance quality indicator and has attracted growing interest from both researchers and consumers. Therefore, studying the factors influencing the transparency of egg-white thermal gels is of significance for food science, biomedical fields, and protein-based product development [7]. However, the molecular basis underlying transparency differences in pigeon eggs, particularly the regulatory roles of protein post-translational modifications in egg albumen, remains insufficiently understood.
Proteins are central functional molecules that mediate diverse physiological processes, including signal transduction and metabolic catalysis. The dynamic regulation of these functions largely depends on post-translational modifications, such as phosphorylation and glycosylation, which influence protein conformation, stability, subcellular localization, and interaction networks [8,9,10].Glycosylation is a prevalent post-translational modification involving the enzymatic attachment of monosaccharides or oligosaccharides (glycans) to proteins or lipids, which collectively constitute the glycome [11]. Protein glycosylation mainly occurs as N-glycosylation and O-glycosylation [12]. As a highly regulated and site-specific post-translational modification, N-glycosylation is the most common form of protein glycosylation, with up to 70% of glycoproteins being N-glycosylated [13]. Site-specific N-glycosylation plays important roles in diverse physiological and pathological processes [14]. Egg white proteins account for approximately 80.8–88.3% of total egg protein, and more than 80% of these proteins are glycosylated [15]. Using deglycosylation-assisted liquid chromatography–tandem mass spectrometry, Geng Fang et al. [16] identified 26 glycoproteins and 71 N-glycosylation sites in chicken egg white. In chicken egg white, improved gel properties of glycosylated egg white proteins (GEWP) were evidenced by increased gel hardness, water-holding capacity, rheological parameters, and finer gel microstructures [17]. Multiple studies have shown that glycosylation modifications of egg white proteins play an important role in gel structure formation [18,19], indicating that glycosylation may contribute to the regulation of protein network structure and transparency in pigeon egg albumen. Comprehensive proteomic analyses of whole pigeon egg liquid have identified 189 proteins, including 27 phosphoproteins (90 phosphorylation sites) and 73 N-glycoproteins (203 N-glycosylation sites) [20]. At present, studies investigating differences in pigeon egg albumen transparency have mainly focused on phenotypic characteristics and production-related factors. It has been reported that storage at moderate temperatures (approximately 20 °C) is beneficial for improving albumen transparency [21]. In addition, pigeon eggs with lower egg weight and lower albumen proportion exhibit higher transparency. Previous studies have demonstrated that the fundamental cause of transparency variation lies in differences in albumen protein composition rather than in yolk or eggshell components [22]. However, comparative glycoproteomic studies focusing on pigeon egg albumen with different transparency levels have not yet been reported.
Based on this background, the present study applied N-glycoproteomic techniques to systematically compare N-glycosylation differences between transparent and opaque pigeon egg albumen. Differentially modified glycopeptides, glycoproteins, and glycosylation sites were identified, followed by functional enrichment and protein–protein interaction analyses. In parallel, textural properties of transparent and opaque pigeon eggs were evaluated, aiming to explore potential molecular bases for differences in albumen transparency and gel characteristics from a glycosylation perspective and to provide potential targets for pigeon egg quality regulation.

2. Materials and Methods

2.1. Sample Collection

Fresh pigeon eggs were collected randomly from the Jiangsu Institute of Poultry Science within 24 h after being laid and utilized in this study. The eggs were collected in April 2025 at 2:00 pm every day. The composition of the diet is shown in Table 1.

2.2. Materials and Reagents

Ammonium bicarbonate, triethylammonium bicarbonate (TEAB), urea, dithiothreitol (DTT), iodoacetamide (IAA), sequencing-grade trypsin, acetonitrile, ammonia solution, and formic acid were of analytical or LC–MS grade and purchased from commercial suppliers. Protein concentration was determined using a commercial protein assay kit, with bovine serum albumin used as the standard. Zip Tip C18 pipette tips (Beijing Qinglian Biotech Co., Ltd., Beijing, China) were used for peptide desalting prior to mass spectrometric analysis.
Magnetic bead–based kits for low-abundance protein enrichment, exosome enrichment, and proteomic sample preparation, as well as an automated proteomics workstation, were obtained from a commercial supplier and used according to the manufacturer’s instructions.

2.3. Preparation of Pigeon Egg Albumen

Seventy pigeon eggs were randomly selected, and egg whites were manually separated from yolks. The egg whites were divided into two portions: one portion was used as raw egg white, and the other was placed in test tubes and heated in a boiling water bath to obtain cooked egg white. The L* value (where L* represents lightness, with higher values indicating increased opacity) of cooked egg white was measured using a portable RM200QC colorimeter and used as the criterion for evaluating albumen transparency. According to previous reports [5], pigeon eggs were classified as transparent (L* ≤ 45), translucent (45 < L*< 55), or opaque (L* ≥ 55).
Based on this classification, three eggs with the highest transparency and three eggs with the lowest transparency were selected. The corresponding raw egg whites were assigned to group A (transparent; biological replicates A1–A3) and group B (opaque; biological replicates B1–B3), respectively, for N-glycoproteomic analysis and stored at −80 °C until further processing.
In addition, seventy-five pigeon eggs were randomly selected, and ten transparent and ten opaque eggs were chosen based on L* values. These samples were used for texture profile analysis.
Although the N-glycoproteomic analysis was performed on representative samples with distinct transparency phenotypes, the limited number of biological replicates may influence the generalizability of the conclusions. Future studies including larger cohorts are needed to validate these findings.

2.4. Texture Profile Analysis

Egg white samples were cut into cubes of approximately 1 cm3. Texture profile analysis (TPA) was performed using a texture analyzer equipped with a TA/5S probe. The pre-test speed, test speed, and post-test speed were set to 1.0, 0.5, and 1.0 mm/s, respectively. The compression mode was applied with a target deformation of 60% and a time interval of 5 s. The trigger type was force with a trigger value of 1 gf. Texture parameters including hardness, fracturability, adhesiveness, springiness, gumminess, cohesiveness, and resilience were recorded.

2.5. Sample Pretreatment

2.5.1. Protein Extraction and Quality Control

Samples were lysed by adding 500 μL of 8 M urea, followed by the addition of protease inhibitor at 10% (v/v) of the lysate. After centrifugation at 14,100× g for 20 min, the supernatant was collected. The protein concentration of each sample was determined using the Bradford assay prior to digestion, and equal amounts of protein were used for subsequent enzymatic digestion and LC-MS/MS analysis.

2.5.2. Protein Digestion and Desalting

Protein samples were adjusted to final concentrations of 10 mM tris(2-carboxyethyl) phosphine (TCEP) and 25 mM chloroacetamide (CAA), vortexed, briefly centrifuged, and incubated at 37 °C for 30 min. After cooling to room temperature, samples were diluted with 10 mM TEAB to a trypsin-compatible concentration. Trypsin was added at a protein-to-enzyme ratio of 50:1, and digestion was carried out overnight at 37 °C.
Formic acid was added to adjust the pH to below 3 to terminate digestion. Peptides were desalted using C18 spin columns. The columns were activated with 100 μL of 100% acetonitrile, washed with 0.1% formic acid, loaded with samples, washed sequentially, and eluted with 70% acetonitrile. Eluates were combined, lyophilized, and stored at −80 °C until further analysis.

2.5.3. Enrichment of Glycosylated Peptides

Glycopeptides were enriched using hydrophilic interaction liquid chromatography (HILIC). The enrichment column was activated with 0.5% trifluoroacetic acid (TFA), equilibrated with 80% acetonitrile containing 5% formic acid, and incubated with peptide samples for 2 h. After washing, glycopeptides were eluted sequentially with 0.5% formic acid and 5% acetonitrile containing 0.5% formic acid. The eluates were collected and lyophilized.

2.6. LC–MS/MS Analysis

Mobile phase A (100% water containing 0.1% formic acid) and mobile phase B (80% acetonitrile containing 0.1% formic acid) were prepared. The freeze-dried peptide powder was dissolved in 10 µL of solution A and centrifuged at 14,000× g at 4 °C for 20 min. A total of 400 ng of the supernatant was collected for injection and subjected to liquid chromatography analysis.LC–MS/MS analysis was performed using a timsTOF-HT mass spectrometer(Bruker Daltonics, Bremen, Germany) equipped with a protein analysis column (item number: -HPLC-100 × 15; Beijing Qinglian Biotech Co., Ltd., Beijing, China) and a CaptiveSpray ion source. Mass spectra were acquired in data-dependent acquisition (DDA) mode over an m/z range of 100–3500, with a primary mass resolution of 60,000 at m/z 1222. In the TIMS tunnel, the ion accumulation time was set to 100 ms. The capillary voltage was set to 1.6 kV, and the ion mobility range was 0.7–1.4 cm2/V. The total cycle time was 1.17 s, including 10 PASEF cycles.

2.7. Data Analysis

2.7.1. Protein Identification and Quantification

Fragmentation spectra of glycopeptides were searched against the Columba livia uniProt proteome database using Byonic software (version 2.13.17, Protein Metrics, Cupertino, CA, USA). N-glycans were specified for N-glycopeptide searches. Trypsin was set as the digestion enzyme with up to two missed cleavages allowed. The minimum and maximum peptide lengths were set to 6 and 144 amino acids, respectively. The precursor and fragment mass tolerances were both set to 20 ppm. For N-glycopeptide searches, glycosylation was set as a rare modification, while methionine oxidation (+15.995 Da) and N-terminal acetylation were set as variable modifications. Carbamidomethyl (57.021 Da) were set as static modifications. A target–decoy strategy was used, and the false discovery rate (FDR) was controlled at 1% at both peptide and protein levels. Median normalization was applied to raw data, and features with more than 50% missing values were removed. Missing values were imputed using the Perseus algorithm. Differential glycopeptides were defined as those with an average fold change >1.2 or <0.833 and a p-value < 0.05 based on independent-sample t-tests. Differential glycopeptides were mapped back to corresponding proteins and site-specific glycosylation sites. For motif analysis, seven amino acids upstream and downstream of the modified sites (15 amino acids in total) were extracted and analyzed using the Motif-X algorithm in the MoMo module of the MEME Suite(https://meme-suite.org/meme/doc/momo.html accessed on 21 September 2025).

2.7.2. Bioinformatics and Statistical Analysis

The databases COG (Clusters of Orthologous Groups), KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to analyze the protein family and pathway. GO annotation of the proteome was derived from the GO database (https://www.ebi.ac.uk/QuickGO/ accessed on 7 October 2025). Proteins were classified by GO annotation based on three categories as follows: molecular function, biological process, and cellular component. The pathway analysis was performed using the KEGG pathway (KEGG protein database: http://www.kegg.jp/kegg/pathway.html accessed on 28 October 2025). The KEGG database was used in this study to identify enriched pathways and to test the enrichment of the different proteins against all identified proteins by Hypergeometric distribution. Protein–protein interaction networks were constructed using the STRING database (version 12.0). Figures were generated using Origin 2021 software. Texture profile analysis data were analyzed using IBM SPSS Statistics 27.0.0. Differences between transparent and opaque groups were evaluated using independent-sample t-tests, with p < 0.05 considered statistically significant.

3. Results

3.1. Differences in Texture Parameters Between Transparent and Opaque Pigeon Eggs

Differences in texture parameters between transparent and opaque pigeon eggs are shown in Table 2. Significant differences (p < 0.05) were observed in hardness, fracturability, chewiness, and gumminess between the transparent and opaque groups. Compared with the opaque group, transparent pigeon eggs exhibited lower hardness, fracturability, chewiness, and gumminess.
Table 2. Differences in Texture Parameters between Transparent and Opaque Pigeon Eggs.
Table 2. Differences in Texture Parameters between Transparent and Opaque Pigeon Eggs.
ParameterTransparent GroupOpaque Group
Hardness0.256 ± 0.0960.432 ± 0.170 *
Fracturability0.256 ± 0.0960.432 ± 0.170 *
Adhesiveness−0.188 ± 0.265−0.079 ± 0.144
Springiness0.818 ± 0.0660.856 ± 0.068
Chewiness0.194 ± 0.0910.341 ± 0.144 *
Gumminess0.232 ± 0.0920.389 ± 0.148 *
Cohesiveness0.896 ± 0.0360.910 ± 0.042
Resilience0.205 ± 0.0120.198 ± 0.011
Values in the same row marked with * indicate a significant difference (p < 0.05).

3.2. Identification of N-Glycosylation Modifications in Pigeon Egg Albumen

In this study, a total of 27 proteins, 417 glycopeptides, and 33 N-glycosylation sites were identified. In addition, 15 N-glycosylated peptides were uniquely identified in group A (transparent), whereas 348 N-glycosylated peptides were uniquely identified in group B (opaque). A total of 54 glycopeptides were shared between the two groups (Figure 1a). According to the identification results of individual samples (Table 3), group B samples exhibited significantly higher numbers of identified proteins, total glycopeptides, and glycosylation sites than group A.
All identified glycopeptides were further classified according to their attached glycan structures. The N-glycosylation glycan types identified in pigeon egg albumen included Complex, Sialylated, Fucosylated, Other High Mannose, Man5, Hybrid, Man8, and Man9. The results showed that complex-type glycopeptides were the most abundant, followed by sialylated and fucosylated glycopeptides, indicating that N-glycosylation modifications in pigeon egg albumen were predominantly characterized by complex, processed glycans (Figure 1b).
The conserved motif of N-glycosylation is N-X-S/T, in which the asparagine residue (N) represents the glycosylation site. Motif analysis demonstrated that the identified N-glycosylation sites in pigeon egg albumen conformed to the conserved N-glycosylation motif (Figure 1c). Median normalization was applied to the original quantitative data to correct systematic errors, and the distribution of samples across groups became consistent after normalization.

3.3. Multivariate Statistical Analysis

To evaluate the overall differences in N-glycosylation profiles between groups A and B, principal component analysis (PCA) was first performed. PCA is a multivariate statistical method that transforms the original glycopeptide variables into a set of new, mutually uncorrelated principal components based on their abundance patterns, thereby maximizing the variance explained by a limited number of components and enabling visualization of sample discrimination after dimensionality reduction. The PCA results showed a clear separation between groups A and B along the PC1 axis, with good clustering within each group (Figure 2a), indicating pronounced differences in glycopeptide expression profiles between the two groups. To further validate this grouping pattern, partial least squares discriminant analysis (PLS-DA) was subsequently conducted, and the results were consistent with those of the PCA (Figure 2b). Given the relatively limited sample size, these multivariate analyses should be interpreted as exploratory and descriptive rather than predictive models.

3.4. Differential Analysis

Differential analysis was performed on the 417 identified glycopeptides. Glycopeptides with an average ratio fold change > 1.2 or <1/1.2 (≈0.833) and a p-value < 0.05 were defined as differentially expressed. The overall distribution of glycopeptide differences was visualized using a volcano plot (Figure 3), in which the −log10(p-value) was plotted against the log2 fold change for each glycopeptide. Specifically, a total of 122 glycopeptides were significantly downregulated in group A compared with group B (FC < 0.833, p-value < 0.05), whereas no glycopeptides were significantly upregulated in group A relative to group B. Among these 122 differentially expressed glycopeptides, 104 were derived from the 348 glycopeptides uniquely identified in group B, and the remaining 18 originated from the 54 glycopeptides shared between the two groups.
Subsequent classification of N-glycan structures associated with the 122 glycopeptides that were downregulated in group A revealed that the glycan types included Complex, Sialylated, Fucosylated, and Man5 (Figure 4), with complex-type glycans remaining the most abundant. Based on these differentially expressed glycopeptides, the corresponding differential proteins were further identified. Among the 122 glycopeptides, 21 were assigned to Ovalbumin-related protein X (OVAX), 13 to Ovalbumin-like (OVAL), 66 to Ovalbumin-related protein Y (OVAY), 2 to Dynein, axonemal, heavy chain 8 (DNAH8), 1 to Ovostatin (OVOS), and 19 to Transferrin (TF) (Figure 5).

3.5. Enrichment Analysis of N-Glycosylated Proteins

3.5.1. Global Functional Characteristics of Glycosylated Proteins

To elucidate the overall functional characteristics of N-glycosylated proteins in pigeon samples, Gene Ontology (GO) enrichment analysis was performed on the 27 identified glycoproteins, revealing their detailed functional distribution. As shown in the bar chart (Figure 6a), within the biological process (BP) category, these proteins were significantly enriched in “antibacterial humoral response”, “iron ion transport”, and “microtubule-based movement”.
In the cellular component (CC) category, based on the size and color of the bubbles, “extracellular space” was identified as the most significantly enriched term, indicating that the majority of the identified glycoproteins were localized in this compartment.
Within the molecular function (MF) category, “serine-type endopeptidase inhibitor activity” was significantly enriched. Specifically, in the CC category, five proteins were enriched in “extracellular space”, while in the MF category, four proteins were associated with “serine-type endopeptidase inhibitor activity”. The bubble plot (Figure 6b) indicated that “extracellular space” and “serine-type endopeptidase inhibitor activity” were the most significantly enriched terms.

3.5.2. Functional Enrichment of Differentially Glycosylated Proteins

The 122 differentially expressed glycopeptides were assigned to six proteins. To explore the functional relationships among these proteins, functional enrichment analyses were further performed. The GO enrichment results (Figure 7a) showed that, within the biological process category, these proteins were associated with “antibacterial humoral response”, “iron ion transport”, and “microtubule-based movement”.
In the cellular component category, enrichment was observed in seven terms, among which “extracellular space” contained the largest number of proteins, with five proteins enriched in this term. In the molecular function category, six terms were identified, and “serine-type endopeptidase inhibitor activity” was the most significantly enriched term, involving four proteins. The bubble plot (Figure 7b) further indicated that “extracellular space” and “serine-type endopeptidase inhibitor activity” remained the two most prominently enriched terms.
KEGG pathway enrichment analysis of these six proteins revealed that transferrin was significantly enriched in the ferroptosis pathway, with a rich factor of 0.0213 and a p-value of 0.0062.
To further investigate the functional associations among these proteins, a protein–protein interaction (PPI) network was constructed using the STRING database. As shown in (Figure 7c), four of the six proteins exhibited functional interactions, including Ovalbumin-related protein X, Ovalbumin-like, Ovalbumin-related protein Y, and Transferrin. Transferrin served as a central node connecting members of the ovalbumin family, forming a functional module.

4. Discussion

In this study, N-glycoproteomic analysis revealed pronounced differences in N-glycosylation profiles between transparent and opaque pigeon egg albumen. Overall, a substantially higher number and abundance of N-glycosylated peptides were detected in the opaque group, whereas no glycopeptides were significantly upregulated in the transparent group. Instead, A total of 122 glycopeptides was significantly less abundant in the transparent group compared with the opaque group, suggesting that reduced N-glycosylation may represent one molecular feature associated with albumen transparency. However, it remains unclear whether decreased glycosylation reflects unmodified sites or is accompanied by alternative post-translational modifications, which may also influence protein conformation and aggregation behavior. The majority of these differentially abundant glycopeptides originated from ovalbumin-related proteins, including OVAY, OVAX, and OVAL. Ovalbumin is the most abundant protein in egg white [23]. The pronounced glycosylation differences observed within the ovalbumin family suggest that ovalbumin-related glycoproteins may be involved in, or potentially associated with, transparency differences in pigeon egg white. Previous studies have shown that glycosylation of ovalbumin can significantly influence its physicochemical properties and molecular structure [24]. At the molecular level, N-glycosylation at Asn-292 has been demonstrated to be essential for proper folding and efficient secretion of ovalbumin [25]. Glycosylation-induced structural alterations of ovalbumin have been experimentally demonstrated. Ultrasound-assisted and enzymolysis–glycosylation modifications were shown to redistribute secondary structure elements and enhance ordered aggregation during thermal gelation, accompanied by changes in gel strength and microstructure [26,27]. In addition, glycosylation has been reported to modulate surface hydrophobicity and intermolecular interactions of ovalbumin, thereby influencing protein aggregation behavior during heating [28]. Collectively, these findings suggest that glycosylation may regulate gel network formation by reshaping protein conformation and intermolecular interaction patterns during thermal treatment. The molecular weight of ovalbumin in pigeon egg albumen is approximately 55 kDa, which is significantly higher than that of chicken ovalbumin (45 kDa) [29]. This difference has been suggested to be associated with a higher degree of glycosylation in pigeon egg albumen proteins [1]. Moreover, a higher proportion of high-molecular-weight ovalbumin has been proposed as one of the factors contributing to the formation of a transparent heat-induced gel in pigeon egg albumen [29]. Collectively, these studies support a plausible association between changes in glycosylation status and alterations in conformational stability and gel network formation; however, direct mechanistic validation remains to be established. These findings provide important context for understanding the potential association between ovalbumin glycosylation and differences in pigeon egg white transparency.
In addition to ovalbumin-related proteins, transferrin also exhibited notable changes in glycosylation. This protein family includes serum transferrin, lactoferrin, and ovotransferrin, which share common structural and functional features and are typically monomeric glycoproteins with molecular weights of approximately 80 kDa [30,31]. Ovotransferrin is the second most abundant protein in egg white and exhibits iron-binding capacity as well as antibacterial and antioxidant activities [32]. Ultrasound-assisted glycosylation has been reported to alter its secondary and tertiary structures, increase molecular weight, and improve thermal stability and physicochemical properties [33]. During egg white gel formation, ovotransferrin has also been suggested to affect protein network assembly. Ovotransferrin can interfere with fibril formation in egg white proteins, and its removal has been shown to facilitate the formation of transparent gels [34]. Suppression of heat-induced aggregation of ovotransferrin can promote transparent gel formation in egg whites from major poultry species [35]. Therefore, the observed reduction in transferrin glycosylation in transparent pigeon egg albumen may be associated with altered aggregation behavior during heating, which could potentially relate to the formation of translucent gel structures. Two differentially abundant glycopeptides were assigned to Dynein, axonemal, heavy chain 8 (DNAH8). Given its limited representation and its primary role in cellular motility [36], the potential contribution of DNAH8 glycosylation to egg white gel properties is likely indirect and requires further investigation. It should be noted that the present conclusions are based on comparative and correlative glycoproteomic evidence. Although the data support plausible mechanistic interpretations, direct experimental validation of causative relationships was beyond the scope of this study.
Glycan-type analysis showed that N-glycosylation in pigeon egg white was mainly composed of complex, sialylated, and fucosylated glycans, with complex-type glycans accounting for the highest proportion, which is consistent with glycosylation characteristics reported for chicken egg white [37]. The 122 downregulated glycopeptides were also predominantly associated with these glycan types. N-glycosylation represents a highly complex biosynthetic network capable of generating diverse glycan structures, and changes in glycan composition have been linked to the regulation of cellular functions and disease processes. For example, fucosylation and sialylation have been reported to mediate cell adhesion and immune regulation [38,39]. Sialylation, defined as the covalent addition of sialic acid to the terminal positions of glycoproteins, plays important roles in embryonic development, neurodevelopment, oncogenesis, and immune responses [40]. Core fucosylation has also been shown to influence glycoprotein stability, binding affinity, and cell–cell interactions [41]. These studies indicate that such glycan structures are closely associated with protein stability, intermolecular interactions, and functional regulation. The predominance of these glycan types in pigeon egg white suggests a potential association between glycosylation patterns and the macroscopic properties of egg white proteins.
Texture profile analysis demonstrated that transparent pigeon eggs exhibited lower hardness, brittleness, adhesiveness, and chewiness. Scanning electron microscopy observations reported in previous studies showed that transparent and opaque pigeon egg white gels display distinct microstructures: transparent egg white gels exhibit loose, porous, and irregular network structures, whereas opaque egg white gels are more compact and denser, with smaller mesh sizes and aggregated gel blocks [6]. Such loose network structures may contribute to softer macroscopic textural properties, as reduced aggregate compactness leads to lower hardness and chewiness. Structural features including altered secondary structure distribution and surface reactivity may favor the formation of smaller thermal aggregates, resulting in finer gel networks and enhanced transparency. In addition to glycosylation, disulfide bond formation is a key factor in egg white gel network assembly during heating [7]. Although the present proteomic analyses were conducted under reducing conditions, potential differences in native disulfide bonding patterns cannot be excluded and may also contribute to transparency variations. Future non-reducing structural analyses or thiol quantification would help clarify this possibility. In addition, covalent modification of egg white proteins with polysaccharides to alter spatial protein arrangement has also been shown to significantly affect gel network structure and transparency [42]. These studies collectively demonstrate that the spatial arrangement of proteins and their thermal aggregation patterns are critical factors in regulating the transparency and texture properties of egg white gels. On this basis, protein–protein interaction (PPI) network analysis revealed close interactions among OVAX, OVAL, OVAY, and Transferrin, forming a functional module centered on transferrin. This network organization suggests that key egg white proteins may act cooperatively to influence protein spatial arrangement and jointly regulate gel network formation and stability, which may ultimately manifest as differences in gel properties and transparency at the macroscopic level. While these observations support a plausible mechanistic link between glycosylation status and protein aggregation behavior, direct structural and functional validation remains to be established.
GO enrichment analysis of the six proteins associated with the 122 differentially expressed glycopeptides showed significant enrichment in “extracellular space” and “serine-type endopeptidase inhibitor activity”. The “extracellular space” was enriched with five proteins, namely OVAX, OVAL, OVAY, OVOS, and TF. This characteristic of significant enrichment in the extracellular space is similar to the identification results observed in quail eggs [43]. These differential proteins may serve as components of the egg white secretory proteins, influencing the transparency of the egg white. The serine-type endopeptidase inhibitor activity was demonstrated by four proteins: OVAX, OVAL, OVAY, and OVOS. OVA, OVAY, and OVAX belong to the serine protease inhibitor (serpin) family, which comprises proteins with identical three-dimensional structures consisting of 8 to 9 α-helices and 3 β-sheets [44]. This α-helix and β-sheet-dominated structural characteristic makes the conformational stability of serpin family proteins highly dependent on variations in the secondary structure ratio. Conformational changes in OVAX during chicken embryonic development, such as decreased α-helix content and increased β-sheet content, lead to altered surface charge and functional properties [45]. Similarly, post-translational modifications have been shown to affect secondary structure composition in other protein systems. For example, combined glycosylation and phosphorylation of parvalbumin resulted in decreased α-helix content and increased β-sheet content [46], and glycosylation–acylation modification induced similar changes in rapeseed proteins [47]. In egg white protein systems, OVAX is a heparin-binding protein with highly heterogeneous glycosylation patterns, meaning that glycosylation sites and degrees can vary among protein molecules [48]. Compared with chicken egg white, the major glycoprotein OVAY in pigeon egg white exhibits a higher degree of glycosylation and distinct glycan structures, and pigeon egg white thermal gels show higher transparency than chicken egg white thermal gels [1]. These findings suggest that variations in glycosylation status may be associated with changes in secondary structure proportions, which could potentially relate to egg white gel formation and transparency.
KEGG enrichment analysis further revealed that transferrin was enriched in the ferroptosis pathway. Ferroptosis is an iron-dependent form of regulated cell death first described by Dixon et al. in 2012 [49], characterized by excessive accumulation of lipid peroxides driven by iron-dependent reactions. Cellular sensitivity to ferroptosis is tightly regulated by lipid composition, lipid-metabolizing enzymes (such as ACSL4, GPX4, and FSP1), and endogenous antioxidant systems, with polyunsaturated fatty acid peroxidation acting as a key driver, while monounsaturated fatty acids exert inhibitory effects [50]. Previous studies have suggested that glycosylation can regulate the function of ferroptosis-related proteins [51]. In this context, the enrichment of transferrin in the ferroptosis pathway observed in this study suggests that iron-dependent oxidative processes may influence egg white protein oxidation status and thereby participate in regulating gel transparency and texture.
Beyond mechanistic insights, transparent heat-induced pigeon egg white gels possess practical value in food and biomedical applications where optical clarity and controlled texture are desired, such as desserts, bakery fillings, edible coatings, and encapsulation matrices [52]. The glycosylation-associated differences identified in this study provide a molecular framework for selecting and processing pigeon eggs with tailored gel properties. This knowledge may support raw material grading, targeted breeding, and processing optimization, thereby promoting the development of value-added pigeon egg products.
In conclusion, this study elucidates N-glycosylation differences underlying transparency and gel property variation in pigeon egg white. Reduced glycosylation of ovalbumin-related proteins and transferrin was associated with altered protein aggregation behavior and gel network characteristics during thermal processing. Although direct functional validation of specific glycosylation sites is still required, these findings advance our understanding of molecular factors influencing albumen quality and offer a foundation for future studies integrating targeted modification and multi-omics approaches to improve food processing and quality control strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods15050909/s1. Table S1. Venn Diagram Data of N-Glycopeptides Unique to and Shared Between Groups A and B. Table S2. Downregulated Differential Glycopeptides Identified in Group A (Transparent) Compared to Group B (Opaque). Table S3. Downregulated N-Glycoproteins and Their Associated Gene Ontology (GO) Terms. Table S4. GO Enrichment Bubble Plot Analysis Based on All Identified N-Glycoproteins. Table S5. GO Enrichment Analysis Based on Differentially Expressed N-Glycoproteins.

Author Contributions

Conceptualization, J.L., R.Z. and Z.B.; methodology, R.Z. and L.C.; validation, D.C. and C.M.; formal analysis, R.Z. and Q.T.; investigation, L.C., D.C. and Q.T.; resources, Q.T. and Z.B.; data curation, J.L., L.C. and C.M.; writing—original draft preparation, J.L.; supervision, R.Z. and Z.B.; project administration, Z.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key R&D Program of China, grant number 2022YFD1600105.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in Supplementary Materials.

Acknowledgments

We are grateful to LC-Bio Technologies (Hangzhou) Co., Ltd. (Hangzhou, China) for assisting with metabolite detection. We thank the reviewers for their critical reading and discussion of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Comprehensive characterization of N-glycosylation profiles reveals distinct glycopeptide composition and motif features between groups A and B. (a) Venn diagram showing the distribution of identified N-glycosylated glycopeptides in groups A and B. (b) Classification of N-glycan types among all identified glycopeptides, indicating the predominance of complex-type glycans. (c) Motif analysis of N-glycosylation sites. The x-axis represents the sequence window surrounding the modified site, with the central position indicating the glycosylated asparagine (N). The size of each amino acid letter reflects its relative frequency at the corresponding position.
Figure 1. Comprehensive characterization of N-glycosylation profiles reveals distinct glycopeptide composition and motif features between groups A and B. (a) Venn diagram showing the distribution of identified N-glycosylated glycopeptides in groups A and B. (b) Classification of N-glycan types among all identified glycopeptides, indicating the predominance of complex-type glycans. (c) Motif analysis of N-glycosylation sites. The x-axis represents the sequence window surrounding the modified site, with the central position indicating the glycosylated asparagine (N). The size of each amino acid letter reflects its relative frequency at the corresponding position.
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Figure 2. Multivariate analysis demonstrates clear separation of glycopeptide profiles between groups A and B. (a) Principal component analysis (PCA) of identified glycopeptides showing distinct clustering of groups A and B. (b) Partial least squares–discriminant analysis (PLS-DA) score plot further confirming group separation.
Figure 2. Multivariate analysis demonstrates clear separation of glycopeptide profiles between groups A and B. (a) Principal component analysis (PCA) of identified glycopeptides showing distinct clustering of groups A and B. (b) Partial least squares–discriminant analysis (PLS-DA) score plot further confirming group separation.
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Figure 3. Volcano plot of differentially expressed glycopeptides between groups. Each dot represents one glycopeptide. The x-axis shows the log2 (fold change), indicating the change in expression level, with negative values representing downregulation in group A relative to group B. The y-axis shows −log10 (p-value), indicating the statistical significance of the difference. The two vertical dashed lines on the x-axis indicate the fold-change thresholds, corresponding to FC < 1/1.2 and FC > 1.2, respectively. The horizontal dashed line on the y-axis indicates the p-value threshold, with data points above the line representing p-value < 0.05. Based on the combined fold-change and p-value criteria, green dots represent significantly downregulated glycopeptides (n = 122), whereas blue dots represent glycopeptides with no significant difference. Sig: significantly regulated; NotSig: not significantly regulated; FC: fold change.
Figure 3. Volcano plot of differentially expressed glycopeptides between groups. Each dot represents one glycopeptide. The x-axis shows the log2 (fold change), indicating the change in expression level, with negative values representing downregulation in group A relative to group B. The y-axis shows −log10 (p-value), indicating the statistical significance of the difference. The two vertical dashed lines on the x-axis indicate the fold-change thresholds, corresponding to FC < 1/1.2 and FC > 1.2, respectively. The horizontal dashed line on the y-axis indicates the p-value threshold, with data points above the line representing p-value < 0.05. Based on the combined fold-change and p-value criteria, green dots represent significantly downregulated glycopeptides (n = 122), whereas blue dots represent glycopeptides with no significant difference. Sig: significantly regulated; NotSig: not significantly regulated; FC: fold change.
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Figure 4. Classification of N-glycan types of glycopeptides downregulated in group A compared with group B.
Figure 4. Classification of N-glycan types of glycopeptides downregulated in group A compared with group B.
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Figure 5. Relative proportion of glycopeptides assigned to each N-glycoprotein significantly downregulated in group A compared with group B.
Figure 5. Relative proportion of glycopeptides assigned to each N-glycoprotein significantly downregulated in group A compared with group B.
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Figure 6. Identified N-glycoproteins are predominantly enriched in extracellular region and serine-type endopeptidase inhibitor activity. (a) GO enrichment bar chart based on all identified N-glycoproteins. The x-axis represents the number of proteins identified for each GO term, and the y-axis shows GO terms grouped by different colors corresponding to the three GO categories: biological process (BP), cellular component (CC), and molecular function (MF). (b) GO enrichment bubble plot based on all identified N-glycoproteins. The y-axis represents GO terms, and the x-axis indicates the proportion of proteins enriched in each term relative to the total number of proteins annotated to that term. Circles represent the proteins enriched in each term; circle size corresponds to the number of enriched proteins, with larger circles indicating higher protein counts. Circle color represents the significance of each term (−Log10 p value), with redder colors indicating higher statistical significance.
Figure 6. Identified N-glycoproteins are predominantly enriched in extracellular region and serine-type endopeptidase inhibitor activity. (a) GO enrichment bar chart based on all identified N-glycoproteins. The x-axis represents the number of proteins identified for each GO term, and the y-axis shows GO terms grouped by different colors corresponding to the three GO categories: biological process (BP), cellular component (CC), and molecular function (MF). (b) GO enrichment bubble plot based on all identified N-glycoproteins. The y-axis represents GO terms, and the x-axis indicates the proportion of proteins enriched in each term relative to the total number of proteins annotated to that term. Circles represent the proteins enriched in each term; circle size corresponds to the number of enriched proteins, with larger circles indicating higher protein counts. Circle color represents the significance of each term (−Log10 p value), with redder colors indicating higher statistical significance.
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Figure 7. Differentially expressed N-glycoproteins are functionally enriched in extracellular processes and exhibit interaction networks related to protein regulation. (a) GO enrichment bar chart based on differentially expressed N-glycoproteins. The x-axis represents the number of proteins identified for each GO term, and the y-axis shows GO terms grouped by different colors corresponding to the three GO categories: biological process (BP), cellular component (CC), and molecular function (MF). (b) GO enrichment bubble plot based on differentially expressed N-glycoproteins. The y-axis represents GO terms, and the x-axis indicates the proportion of proteins enriched in each term relative to the total number of proteins annotated to that term. Circles represent the proteins enriched in each term; circle size corresponds to the number of enriched proteins, with larger circles indicating higher protein counts. Circle color represents the significance of each term (−Log10 p value), with redder colors indicating higher statistical significance. (c) Protein–protein interaction (PPI) network analysis of differentially expressed N-glycoproteins, illustrating potential functional connectivity among key proteins.
Figure 7. Differentially expressed N-glycoproteins are functionally enriched in extracellular processes and exhibit interaction networks related to protein regulation. (a) GO enrichment bar chart based on differentially expressed N-glycoproteins. The x-axis represents the number of proteins identified for each GO term, and the y-axis shows GO terms grouped by different colors corresponding to the three GO categories: biological process (BP), cellular component (CC), and molecular function (MF). (b) GO enrichment bubble plot based on differentially expressed N-glycoproteins. The y-axis represents GO terms, and the x-axis indicates the proportion of proteins enriched in each term relative to the total number of proteins annotated to that term. Circles represent the proteins enriched in each term; circle size corresponds to the number of enriched proteins, with larger circles indicating higher protein counts. Circle color represents the significance of each term (−Log10 p value), with redder colors indicating higher statistical significance. (c) Protein–protein interaction (PPI) network analysis of differentially expressed N-glycoproteins, illustrating potential functional connectivity among key proteins.
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Table 1. Ingredients and composition of the diets of different poultry.
Table 1. Ingredients and composition of the diets of different poultry.
SpeciesPigeon
Ingredients
Maize, %65.00
Soybean meal, %19.45
Wheat, %10.00
Soybean oil, %0.00
Trace elements 1, %0.20
Vitamins 2, %0.05
Limestone, %3.00
Sodium chloride, %0.30
Dicalcium phosphate, %2.00
Total, %100.00
Nutrition level 
Metabolizable energy, MJ/kg12.08
Protein, %15.50
Calcium, %1.60
Phosphorus (total), %0.67
1 The premix provided the following per kg of diets: Fe (as ferrous sulfate) 60 mg, Cu (as copper sulfate) 8 mg, Zn (as zinc sulfate) 66 mg, Mn 65 mg, Se 0.3 mg, and I 1 mg. 2 The premix provided the following per kg of diets: VA 12,500 IU, VD 34,125 IU, VE 15 IU, VK 2 mg, thiamine 1 mg, riboflavin 8.5 mg, calcium pantothenate 50 mg, nicotinic acid 32.5 mg, pyridoxine 8 mg, VB 12 5 mg, and biotin 2 mg.
Table 3. Summary of N-glycosylation identification results for each sample.
Table 3. Summary of N-glycosylation identification results for each sample.
SampleProteinGlycopeptideSite
A16238
A27239
A36589
B11328116
B21624920
B31627019
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Liu, J.; Chang, L.; Tang, Q.; Mu, C.; Cheng, D.; Zhang, R.; Bu, Z. Comparative N-Glycoproteomic Analysis of Transparent and Opaque Pigeon Egg Albumen. Foods 2026, 15, 909. https://doi.org/10.3390/foods15050909

AMA Style

Liu J, Chang L, Tang Q, Mu C, Cheng D, Zhang R, Bu Z. Comparative N-Glycoproteomic Analysis of Transparent and Opaque Pigeon Egg Albumen. Foods. 2026; 15(5):909. https://doi.org/10.3390/foods15050909

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Liu, Jinxin, Lingling Chang, Qingping Tang, Chunyu Mu, Darong Cheng, Rui Zhang, and Zhu Bu. 2026. "Comparative N-Glycoproteomic Analysis of Transparent and Opaque Pigeon Egg Albumen" Foods 15, no. 5: 909. https://doi.org/10.3390/foods15050909

APA Style

Liu, J., Chang, L., Tang, Q., Mu, C., Cheng, D., Zhang, R., & Bu, Z. (2026). Comparative N-Glycoproteomic Analysis of Transparent and Opaque Pigeon Egg Albumen. Foods, 15(5), 909. https://doi.org/10.3390/foods15050909

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