Integrated transcriptomic and metabolomic analysis reveals potential candidate genes and regulatory pathways associated with egg weight in geese

Abstract

Egg weight (EW) is a key trait in both breeding and production for poultry. Although Zi geese demonstrate good egg-laying performance, they have relatively low EW. Identifying candidate genes and pathways associated with EW in geese is essential for enhancing genetic progress. In this study, 260 female Zi geese at 30 weeks of age were monitored individually for egg number (EN) and EW. Thirty geese, showing no significant differences in body weight and EN but significant variation in EW (P < 0.05), were selected and divided into a high egg weight (HEW) group (n = 15) and a low egg weight (LEW) group (n = 15). Egg quality traits were evaluated for both groups. From each group, six geese were randomly selected for serum biochemical profiling and untargeted metabolomics; four of these were further analyzed for transcriptomic data from the liver, ovary, and oviduct magnum. The results showed significant differences in albumen weight, yolk weight, and shell weight between HEW and LEW (P < 0.01). Serum biochemical analysis revealed higher levels of total cholesterol, triglycerides, estradiol, very low-density lipoprotein, and vitellogenin in the HEW group (P < 0.05). Transcriptomic analysis identified 278 differentially expressed genes (DEGs) in the liver, primarily enriched in antigen processing and presentation pathways, with ACACB, FDFT1, and HSPA2 highlighted as candidate genes. In the ovary, 18 DEGs were identified, with RBP1 as the key gene. In the oviduct magnum, 1,605 DEGs were enriched in protein processing in the endoplasmic reticulum, with HSPA8, HSP90AA1, and ST3GAL4 identified as candidate genes. Serum metabolomics revealed metabolites associated with EW, including cholesterol sulfate, l-tryptophan, indoxyl sulfate, and LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0). Integrated multi-omics analysis identified ACSL5-LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) and ACSL5-L-tryptophan as key gene-metabolite pairs potentially regulating EW. These findings provide a theoretical foundation for understanding the genetic mechanisms underlying EW regulation in geese.

Introduction

The Zi goose is a prominent indigenous breed in northern China, known for its excellent egg-laying performance, with an average annual egg production of approximately 90 eggs (Hu et al., 2025). As an important maternal breed, it plays a key role in goose breeding programs. However, the Zi goose has relatively low egg weight (EW), ranging from 122 to 144 g (National Standard of the P.R. China, 2018). Lower EW is linked to reduced hatchling weight and slower post-hatch growth, which may ultimately hinder production efficiency and breeding improvement (Kucharska-Gaca et al., 2016).

Egg weight (EW) is a critical reproductive trait in geese, regulated by coordinated mechanisms across multiple tissues (Salamon, 2020). The liver plays a central role in egg formation by synthesizing lipid-based yolk precursors, such as very low-density lipoprotein (VLDL) and vitellogenin (VTG) (Tang et al., 2023). Under estrogenic regulation, the liver synthesizes and secretes these yolk precursors through the liver-blood-ovary axis. The precursors circulate in the bloodstream and are selectively taken up by ovarian follicles, influencing the rate and extent of yolk deposition (Cui et al., 2020). In addition to yolk formation, albumen deposition is essential for egg composition. The avian oviduct magnum, the longest and most tightly coiled section of the oviduct, is the primary site for egg-white protein synthesis and secretion (Yin et al., 2020).

From a genetic perspective, EW is a complex quantitative trait influenced by the combined effects of multiple genes (Yang et al., 2025). Transcriptomics identifies differentially expressed genes and associated regulatory pathways, while metabolomics reveals changes in small-molecule metabolites, reflecting alterations in metabolic states (Patti et al., 2012; Stark et al., 2019). Integrating these omics layers provides a more comprehensive view of the molecular architecture underlying EW variation.

In recent years, multi-omics approaches have been widely used to investigate the molecular mechanisms underlying EW in poultry. Yan et al. (2023) integrated transcriptomic data from the liver, magnum, and follicular tissues with serum metabolomics in laying ducks, revealing coordinated molecular changes associated with EW differences. Ding et al. (2025) combined population-level phenotyping with multi-tissue transcriptomics and serum metabolomics in chickens, identifying endocrine- and lipid-related regulatory networks involved in EW modulation. Wang et al. (2024) analyzed liver transcriptomes and serum metabolomes between the early and peak laying stages in hens, showing that increases in EW were linked to lipid metabolic remodeling. However, compared with chickens and ducks, multi-omics evidence for EW regulation in geese remains scarce.

This study investigated the molecular mechanisms underlying EW in Zi geese. Using population-level phenotyping of egg production traits, we selected two groups with divergent EW for integrated transcriptomic profiling of the liver, ovary, and oviduct magnum, as well as untargeted serum metabolomics. This approach identified potential candidate genes and regulatory pathways, providing a theoretical basis for improving EW in geese breeding.

Materials and methods

Ethics approval and consent to participate

The study protocol was approved by the Ethics Committee of Science and Technology of Heilongjiang Bayi Agricultural University (Daqing, China) (Number: DWKJXY2024062).

Experimental animals

A total of 260 female Zi geese (Anser cygnoides, a small breed), were used in this experiment. The experimental period lasted 18 weeks (from 30 to 48 weeks of age). At 30 weeks, the geese were transferred to individual cages and provided ad libitum access to feed. They were housed at a stocking density of one goose per square meter, under 12 to 15 hours of natural light and a temperature range of 10 to 28°C. The dietary nutrient composition is presented in Table 1. The experiment was conducted at the Institute of Animal Husbandry, Heilongjiang Academy of Agricultural Sciences, Qiqihar, Heilongjiang Province, China (47°19′ N, 123°45′ E; altitude 155 m).

Table 1.

Composition and nutrient levels of basal diets (air-dry basis, %).

Ingredients	Content	Nutrient levels	Content
Corn	61.50	ME/(MJ/kg)2	10.92
Soybean meal	25.00	Crude protein	16.02
Rice bran	7.00	Crude fibre	5.60
Wheat bran	3.00	Calcium	0.72
NaCl	0.30	Phosphorus	0.54
Premix1	1.00	Lysine	0.80
DL-Met	0.10	Methionine	0.33
CaHPO4	1.10
Mountain flour	1.00

¹. The premix provided the following per kilogram of diet: Zn 60 mg, Cu 10 mg, Mn 80 mg, Fe 50 mg, I 0.5 mg, Se 0.2 mg, vitamin A 10000 IU, vitamin E 30 mg, vitamin D3 2000 IU.

². ME is a calculated value, whereas other values are quantified by measurement.

From the onset of laying to the peak laying period (weeks 31 to 40), egg number (EN) and EW were recorded twice daily at 09:00 and 16:00. At week 41, 30 geese with no significant differences in body weight (BW, 3.51 ± 0.23 kg) and EN (37.83 ± 3.36) were selected. Their EW, which met the national standard (National Standard of the P.R. China, 2018), ranged from 122 to 144 g. These geese were assigned to a high egg weight group (HEW; n = 15) and a low egg weight group (LEW; n = 15), with mean EW differing significantly between the two groups (P < 0.05).

Testing of egg production and egg quality traits

Starting at week 41, three consecutive eggs from each of the 15 geese per group (90 eggs in total) were collected to assess egg weight (EW₁), egg shape index (ESI₁), shell strength (SS), yolk weight (YW), albumen weight (AW), shell weight (SW), yolk height (YH), albumen height (AH), yolk percentage (YP), shell thickness (ST), and Haugh unit (HU). EW₁, HU, AH, ST, and SS were measured according to the method described by Gulabrai et al. (2025). YW, AW, and SW were measured following Yan et al. (2023), and ESI₁ and YH were assessed based on Kumar et al. (2022). YP was calculated using the formula: YP (%) = (YW/EW₁) × 100. At weeks 47-48, egg weight (EW₂) and egg shape index (ESI₂) were re-measured for 30 geese from both the HEW and LEW groups, using the same methods as at week 41.

At week 48, six geese were randomly selected from each group. The geese were fasted for 12 h, and whole blood was collected from the wing vein. Blood samples were allowed to clot for 30 min and then centrifuged at 3,000 × g for 10 min at 4 °C. The resulting serum was transferred into sterile cryovials and stored at −80 °C for metabolomic and biochemical analysis.

Subsequently, four geese were randomly selected from the six geese in each group and anesthetized by inhalation of 1.5% to 3.0% isoflurane. Upon confirmation of adequate anesthesia, the geese were euthanized via carotid artery severance and exsanguination. Liver, ovary, and oviduct magnum were collected. Portions of each tissue were immediately frozen in liquid nitrogen and stored at −80 °C for transcriptomic analysis.

RNA extraction and transcriptome analysis

Liver, ovary, and oviduct magnum tissues were collected from the HEW and LEW groups, with four biological replicates per tissue per group (n = 4). Total RNA was extracted using TRIzol reagent (Thermo Fisher, MA, USA). RNA concentration and integrity were assessed using a Qubit 3.0 Fluorometer (Thermo Fisher, MA, USA), and an Agilent 5300 Fragment Analyzer (Agilent Technologies, CA, USA). Only samples with RIN > 7.0 were used for library construction.

Poly(A)+ mRNA was purified from 2 μg of total RNA using mRNA Capture Beads 2.0 (Yeasen, Shanghai, China) and fragmented at 94 °C in the presence of magnesium ions (Yeasen, Shanghai, China). First- and second-strand cDNA were synthesized with dUTP incorporation to ensure strand specificity. After end repair, A-tailing, dual-index adapter ligation, and PCR amplification (14 cycles), libraries with an average insert size of 400 ± 50 bp were purified and sequenced on the Illumina NovaSeq X Plus platform (2 × 150 bp, paired-end) (Illumina, CA, USA).

Raw reads were trimmed using Cutadapt (v 1.9) and evaluated using FastQC (v 0.11.9). Clean reads were aligned to the Taihu_goose_T2T (GCF_040182565.1) reference genome using HISAT2 (v2.2.1). Transcripts were assembled with StringTie (v2.1.6) and merged using gffcompare (v0.9.8). Gene expression levels were quantified as Fragments Per Kilobase of transcript per Million mapped reads (FPKM) using StringTie and Ballgown. Differentially expressed genes (DEGs) were identified using DESeq2 with a false discovery rate (FDR) < 0.05 and |fold change| ≥ 2, and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses.

Nontargeted metabolomics analysis

Untargeted metabolomics was performed on a Thermo Vanquish Flex UPLC system (Thermo Fisher, MA, USA) coupled to a Q-Exactive Plus mass spectrometer (Thermo Fisher, MA, USA). Serum (100 μL) was precipitated with cold methanol (400 μL), incubated at −20°C for 30 min, centrifuged at 20,000 × g for 10 min (4°C), and the supernatant was subjected to LC–MS analysis. A pooled QC sample (equal aliquots of all samples) was injected intermittently to monitor analytical stability.

Chromatographic separation was achieved on an ACQUITY UPLC HSS T3 column (Waters, MA, USA) (100 mm × 2.1 mm, 1.8 μm) using mobile phase A (5 mmol/L ammonium acetate + 5 mmol/L acetic acid in water) and mobile phase B (acetonitrile) at 0.35 mL/min (4 μL injection, 40°C). The gradient was: 0-0.8 min, 2-70% B; 0.8-2.8 min, 70-90% B; 2.8-5.3 min, 90-99% B; 5.3-5.9 min, 99% B; 5.9-7.5 min, 99-2% B; 7.5-10.0 min, 2% B.

MS data were acquired in both positive and negative ESI modes (m/z 70–1050). Full-scan MS was performed at 70,000 resolution (AGC 3 × 10^6, maximum injection time 100 ms), followed by DDA-MS/MS of the top 5 precursor ions (intensity > 100,000) at 17,500 resolution (maximum injection time 50 ms) with dynamic exclusion of 6 s.

Raw data were processed using XCMS and metaX in R. Metabolites were annotated by matching MS1 accurate mass to KEGG and HMDB (10 ppm) with isotope/adduct verification, further supported by MS/MS spectral matching against in-house and public databases (MS1/MS2 tolerances 20 ppm; score cutoff 70%).

Determination of serum biochemical indicators

Serum concentrations of estradiol (E2), follicle-stimulating hormone (FSH), luteinizing hormone (LH), VTG, phosphatidylcholine (PC), and VLDL were determined using commercial enzyme-linked immunosorbent assay (ELISA) kits (Yuanju Biology, Shanghai, China), following the manufacturer’s instructions. Levels of triglycerides (TG), total cholesterol (TC), total protein (TP), and albumin (ALB) were measured using commercial biochemical assay kits (Grace Biotechnology, Jiangsu, China), in accordance with the manufacturer's protocols. All measurements were performed using a SpectraMax ABS microplate reader (Molecular Devices, CA, USA) equipped with SoftMax Pro software (version 7.1.2).

Statistical analysis

Statistical analyses were conducted using SPSS Statistics (version 27.0). Data are presented as mean ± SD. Normality and homogeneity of variance were tested prior to analysis. Partial correlation analysis was used to examine relationships among phenotypic traits. Differences between the HEW and LEW groups were assessed using an independent-samples t-test. Pearson correlation analysis was performed to assess the association between transcriptomic and metabolomic features. To control for the false discovery rate, P values were adjusted using the Benjamini-Hochberg procedure (BH-FDR) for multiple testing. All statistical tests were two-tailed.

Figures were generated using GraphPad Prism (version 10.4.1) and R software (version 4.5.0). Network analysis was performed using STRING and visualized in Cytoscape (version 3.10.3). Differences were considered statistically significant at P < 0.05 and highly significant at P < 0.01.

Results

Comparison of egg production and egg quality traits between HEW and LEW groups

The egg production and egg quality traits between the HEW and LEW groups are shown in Table 2. The HEW group had significantly higher EW₁, EW₂, ESI₁, ESI₂, YW, AW, and SW than the LEW group (P < 0.05). In contrast, no significant differences were observed for EN, BW, SS, AH, HU, YH, YP, and ST (P > 0.05) (Table 2). Correlation analysis further showed that EW₁ was strongly and positively correlated with AW (r = 0.96, P < 0.001) and was also strongly correlated with EW₂ (r = 0.90, P < 0.001). AH exhibited a strong positive correlation with HU (r = 0.94, P < 0.001). In addition, EW₂ was significantly positively correlated with AW (r = 0.85, P < 0.001), and ESI₁ was significantly correlated with ESI₂ (r = 0.64, P < 0.001) (Table 3). Notably, the significant difference between groups in EW₂, together with the strong correlation between EW₁ and EW₂, indicates that EW remained largely consistent between the egg-quality assessment and the later recording period.

Table 2.

Differences in production traits and egg quality traits between HEW and LEW groups.

Items	HEW (n = 15)	LEW (n = 15)	P-Value
EN(n)	37.20±3.05	38.47±3.64	0.227
BW (kg)	3.62±0.24	3.41±1.79	0.732
EW1 (g)	143.38±5.53a	123.17±2.88b	<0.001
ESI1	1.51±0.04a	1.46±0.04b	0.006
SS (N)	96.99±20.08	101.06±21.39	0.595
AH (mm)	10.05±0.91	9.68±0.94	0.280
HU	83.67±5.31	84.73±5.77	0.606
YH (mm)	19.32±1.25	19.53±0.48	0.551
YP (%)	32.11±2.47	33.51±2.30	0.118
YW (g)	45.96±3.06a	41.27±2.66b	<0.001
AW (g)	73.88±4.23a	60.93±4.06b	<0.001
ST (mm)	0.42±0.05	0.39±0.09	0.325
SW (g)	15.11±1.47a	13.22±1.76b	0.004
EW2 (g)	140.64±7.23a	122.42±2.64b	<0.001
ESI2	1.49±0.04a	1.43±0.10b	0.031

Different superscript letters (a, b) within the same row indicate a significant difference (P < 0.05). Data are presented as mean ± SD.

EN:egg number; BW: body wight; EW₁: egg weight of three consecutive eggs collected starting at 41 weeks; ESI₁: egg shape index of three consecutive eggs collected starting at 41 weeks; SS: shell strength; AH: albumen height; HU: Haugh unit; YH: yolk height; YP: yolk percentage; YW: yolk weight; AW: albumen weight; ST: shell thickness; SW: shell weight; EW₂: egg weight at 47-48 weeks; ESI₂: egg shape index at 47-48 weeks.

Table 3.

Correlation coefficients of production traits and egg quality traits.

Item	BW (kg)	EN (n)	EW1 (g)	EW2 (g)	ESI1	ESI2	SS (N)	AH (mm)	HU	YH (mm)	YP (%)	YW (g)	AW (g)	ST (mm)	SW (g)
BW (kg)	1.00	0.01	0.46*	0.52⁎⁎	−0.08	0.05	−0.35	−0.11	−0.28	−0.22	−0.19	0.27	0.47⁎⁎	0.23	0.35
EN (n)	0.01	1.00	−0.22	−0.10	−0.20	0.16	−0.02	0.00	0.05	−0.11	−0.12	−0.33	−0.13	0.12	−0.08
EW1 (g)	0.46*	−0.22	1	0.90⁎⁎	0.38*	0.26	−0.13	0.34	0.02	−0.09	−0.41*	0.61⁎⁎	0.96⁎⁎	0.20	0.54⁎⁎
EW2 (g)	0.52⁎⁎	−0.10	0.90⁎⁎	1	0.35	0.29	−0.21	0.27	−0.04	−0.12	−0.41*	0.50⁎⁎	0.85⁎⁎	0.23	0.53⁎⁎
ESI1	−0.08	−0.20	0.38*	0.35	1	0.64⁎⁎	0.05	−0.03	−0.17	−0.15	−0.00	0.38*	0.34	−0.20	0.28
ESI2	0.05	0.16	0.26	0.29	0.64⁎⁎	1.00	−0.04	−0.03	−0.12	−0.06	−0.13	0.14	0.26	−0.17	0.24
SS (N)	−0.35	−0.02	−0.13	−0.21	0.05	−0.04	1.00	−0.01	0.07	0.03	−0.01	−0.11	−0.19	0.26	0.06
AH (mm)	−0.11	0.00	0.34	0.27	−0.03	−0.03	−0.01	1.00	0.94⁎⁎	0.34	−0.50⁎⁎	−0.11	0.38*	0.32	−0.05
HU	−0.28	0.05	0.02	−0.04	−0.17	−0.12	0.07	0.94⁎⁎	1.00	0.42*	−0.38*	−0.31	0.07	0.27	−0.24
YH (mm)	−0.22	−0.11	−0.09	−0.12	−0.15	−0.06	0.03	0.34	0.42*	1.00	0.18	0.07	−0.16	−0.03	−0.11
YP (%)	−0.19	−0.12	−0.41*	−0.41*	−0.00	−0.13	−0.01	−0.50⁎⁎	−0.38*	0.18	1.00	0.47⁎⁎	−0.55⁎⁎	−0.28	−0.21
YW (g)	0.27	−0.33	0.61⁎⁎	0.50⁎⁎	0.38*	0.14	−0.11	−0.11	−0.31	0.07	0.47⁎⁎	1.00	0.44*	−0.04	0.35
AW (g)	0.47⁎⁎	−0.13	0.96⁎⁎	0.85⁎⁎	0.34	0.26	−0.19	0.38*	0.07	−0.16	−0.55⁎⁎	0.44*	1.00	0.21	0.53⁎⁎
ST (mm)	0.23	0.12	0.20	0.23	−0.20	−0.17	0.26	0.32	0.27	−0.03	−0.28	−0.04	0.21	1.00	0.41*
SW (g)	0.35	−0.08	0.54⁎⁎	0.53⁎⁎	0.28	0.24	0.06	−0.05	−0.24	−0.11	−0.21	0.35	0.53⁎⁎	0.41*	1.00

BW: body wight; EN:egg number; EW₁: egg weight of three consecutive eggs collected starting at 41 weeks; EW₂: egg weight at 47-48 weeks; ESI₁: egg shape index of three consecutive eggs collected starting at 41 weeks; ESI₂: egg shape index at 47-48 weeks; SS: shell strength; AH: albumen height; HU: Haugh unit; YH: yolk height; YP: yolk percentage; YW: yolk weight; AW: albumen weight; ST: shell thickness; SW: shell weight.

^⁎represents a significant difference;

^⁎⁎represents a very significant difference.

Comparison of serum biochemical characteristics between the HEW and LEW groups

The serum biochemical characteristics between the HEW and LEW groups are shown in Table 4. The concentrations of TC, TG, E2, VLDL, and VTG were significantly higher in the HEW group than in the LEW group (P < 0.05). No significant differences were observed in ALB, TP, LH, FSH, and PC between the two groups (P > 0.05).

Table 4.

Comparison of serum biochemical parameters between HEW and LEW groups.

Items	HEW (n = 6)	LEW (n = 6)	P-Value
TC (mmol/L)	4.33±1.12a	3.19±0.51b	0.046
ALB (g/L)	23.18±4.83	26.15±3.75	0.261
TP (mg/mL)	34.49±8.20	42.00±19.86	0.412
TG (mmol/L)	3.35±0.83a	2.26±0.53b	0.020
E2 (pg/mL)	328.66±24a	282.37±23.37b	0.007
VLDL (mmol/L)	3.37±0.25a	2.82±0.28b	0.005
LH (ng/mL)	14.37±0.62	14.10±1.14	0.200
FSH (ng/mL)	59.14±2.74	62.97±5.02	0.132
VTG (ng/mL)	479.45±28a	427.15±22.32b	0.005
PC (pg/mL)	128.18±13.10	117.36±13.58	0.190

Different superscript letters (a, b) within the same row indicate a significant difference (P < 0.05). Data are presented as mean ± SD.

E2: estradiol; FSH: follicle-stimulating hormone; LH: luteinizing hormone; VTG: vitellogenin; PC: phosphatidylcholine; VLDL: very low-density lipoprotein; TG: triglycerides; TC: total cholesterol; TP: total protein; ALB: albumin.

Transcriptomic analysis of the liver, ovary, and oviduct magnum between the HEW and LEW groups

In the liver, the EW difference was primarily associated with lipid metabolism and proteostasis-related transcriptional changes. A total of 278 DEGs were identified, of which 37 were upregulated and 241 were downregulated (Fig. 1A). A circular heatmap of the top 30 DEGs is shown in Fig. 1B. GO enrichment analysis identified 1,608 GO terms associated with these DEGs, of which 356 were significantly enriched (P < 0.05), including protein refolding, malonyl-CoA biosynthetic process, and negative regulation of fatty acid beta-oxidation (Fig. 1C). KEGG pathway analysis mapped the DEGs to 203 pathways, with 14 showing significant enrichment (P < 0.05) (Fig. 1D), primarily involving antigen processing and presentation, fatty acid biosynthesis, steroid biosynthesis, and the adipocytokine signaling pathway. By assessing the relative expression levels of DEGs and their relevance to the significantly enriched liver pathways, we identified ACACB, FDFT1, and HSPA2 as candidate genes associated with EW divergence. ACACB and FDFT1 were downregulated, whereas HSPA2 was upregulated in the HEW group. These genes were mainly associated with pathways and processes related to fatty acid metabolism, steroid biosynthesis, and protein refolding and proteostasis.

Fig. 1.

DEGs in the liver between HEW and LEW and functional enrichment analyses. (A) Volcano plot of DEGs. (B) circular heatmap of DEGs (Top 30). (C) GO term enrichment (Top 20). (D) KEGG pathway enrichment (Top 20).

In the ovary, transcriptomic differences between groups were limited, only 18 DEGs were identified, including 3 upregulated and 15 downregulated genes (Fig. 2A). A circular heatmap of these genes is shown in Fig. 2B. These DEGs were enriched in 118 GO terms, 74 of which were significantly enriched. The top 20 GO terms are shown in Fig. 2C. KEGG analysis showed significant enrichment in 4 pathways, including the B cell receptor signaling pathway (Fig. 2D). Based on DEG expression patterns and their relevance to the enriched pathways, RBP1 was highlighted as a candidate gene associated with EW divergence.

Fig. 2.

DEGs in the ovary between HEW and LEW and functional enrichment analyses. (A) Volcano plot of DEGs. (B) circular heatmap of DEGs. (C) GO term enrichment (Top 20). (D) KEGG pathway enrichment.

The oviduct magnum exhibited the most pronounced transcriptomic differences, primarily associated with secretion and protein folding. 1,605 DEGs were identified between the HEW and LEW groups, with 1,393 upregulated and 212 downregulated in the HEW group (Fig. 3A). A circular heatmap of the top 30 DEGs is shown in Fig. 3B. GO enrichment analysis revealed 5,916 terms, of which 615 were significantly enriched; the top 20 are shown in Fig. 3C. These DEGs were primarily associated with biological processes. KEGG analysis indicated enrichment in 326 pathways, including 50 significantly enriched pathways. The top 20 pathways are shown in Fig. 3D. These pathways were mainly related to the MAPK signaling pathway, protein processing in the endoplasmic reticulum, cell adhesion molecules, glycosphingolipid biosynthesis (lacto and neolacto series), and cholesterol metabolism. Based on expression patterns and pathway associations, HSPA8, HSP90AA1, and ST3GAL4 were identified as candidate genes potentially involved in EW regulation. Functionally, HSPA8 and HSP90AA1 are molecular chaperones involved in endoplasmic reticulum protein processing and folding, whereas ST3GAL4 is implicated in glycosylation and glycosphingolipid biosynthesis.

Fig. 3.

DEGs in the oviduct magnum between HEW and LEW and functional enrichment analyses. (A) Volcano plot of DEGs. (B) circular heatmap of DEGs (Top 30). (C) GO term enrichment (Top 20). (D) KEGG pathway enrichment (Top 20).

Serum metabolomics analysis between the HEW and LEW groups

Serum metabolomic profiles were compared between the HEW and LEW groups. A total of 20,583 ion features were detected across both positive and negative ion modes, of which 795 were annotated with putative metabolite names and classifications by searching multiple databases. Using strict criteria (P < 0.05, |log₂FC| > 0.263, and VIP ≥ 1), 123 differential metabolites (DMs) were identified between the two groups, with 56 upregulated and 67 downregulated in the HEW group (Fig. 4A).

Fig. 4.

Functional enrichment analysis of serum DMs between the HEW and LEW groups. (A) Volcano plot of DMs. (B) Network of DMs and enriched KEGG pathways. Red circles represent metabolites, and blue circles represent pathways. (C) KEGG pathway enrichment (Top 20).

All DMs were mapped to KEGG pathways using KEGG annotation as a reference. A total of 32 DMs were assigned to 87 pathways (Fig. 4B), with 20 pathways showing significant enrichment. These pathways included tryptophan metabolism, mineral absorption, and phenylalanine, tyrosine, and tryptophan biosynthesis (Fig. 4C). Based on these DMs, cholesterol sulfate, L-Tryptophan, indoxyl sulfate, and LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) were identified as potential metabolites associated with EW.

Integrated analysis of transcriptome and metabolome data

Integration of the ovarian transcriptome with the liver and oviduct magnum transcriptomes and the serum metabolome revealed five shared enriched pathways (Fig. 5A). Given the limited pathway overlap involving the ovary and the small number of ovarian DEGs, we next focused the integrative analysis on the liver and oviduct magnum transcriptomes along with the serum metabolome.

Fig. 5.

Integration analysis of KEGG pathways between the transcriptome and metabolome. (A) Venn diagram of KEGG enriched pathways in the liver, ovary, oviduct magnum transcriptomes and the serum metabolome. (B) Venn diagram of KEGG enriched pathways in the liver and oviduct magnum transcriptomes and the serum metabolome. (C) Pathway analysis of shared enrichment of DEGs and DMs.

Integrated analysis of the liver and oviduct magnum transcriptomes with the serum metabolome revealed concurrent enrichment of DEGs and differential metabolites in 31 pathways (Fig. 5B; Fig. 6). These shared pathways were primarily related to amino sugar and nucleotide sugar metabolism, biosynthesis of unsaturated fatty acids, and steroid hormone biosynthesis (Fig. 5C). Within these 31 pathways, 28 liver genes, 180 oviduct magnum genes, and 19 serum metabolites were involved.

Fig. 6.

Integrated KEGG pathway analysis of the liver and oviduct magnum transcriptomes and the serum metabolome.

Network of enriched KEGG pathways identified from multi-omics analyses. Each node represents an enriched pathway. A red triangle (Both) indicates pathways jointly enriched in the liver transcriptome (L-Tran), oviduct magnum transcriptome (M-Tran), and serum metabolomics (Meta). Blue circles (M-Tran-only) indicate pathways enriched only in the oviduct magnum transcriptome, green circles (Meta-only) indicate pathways enriched only in serum metabolomics, and pink circles (L-Tran_only) indicate pathways enriched only in the liver transcriptome. Purple arrow-shaped nodes (L-Tran-Meta) represent pathways jointly enriched in the liver transcriptome and serum metabolomics, orange arrow-shaped nodes (M-Tran-Meta) represent pathways jointly enriched in the oviduct magnum transcriptome and serum metabolomics, and yellow arrow-shaped nodes (L-Tran-M-Tran) represent pathways jointly enriched in the liver transcriptome and oviduct magnum transcriptome.

To explore gene-metabolite associations, correlation analyses were performed only in individuals with both transcriptomic and metabolomic data (n = 8). The top 20 significant genes were selected separately from the liver and oviduct magnum transcriptomes, and their expression levels were tested for correlation with five serum metabolites (Fig. 7). Given the small sample size and multiple correlation tests, these analyses were considered exploratory, and P values were adjusted using the BH-FDR (Table S1).

Fig. 7.

Correlation network of DEGs and DMs in the liver and oviduct magnum transcriptomes. The heatmap shows pairwise Pearson’s correlation coefficients (r) among the selected genes (color scale: red, positive; blue, negative). Differential metabolites are displayed on the left, and curved lines connect metabolites with genes based on correlation analysis. Line type indicates statistical significance (solid, P < 0.05; dashed, P ≥ 0.05). Line width represents the absolute correlation strength (|r|: 0-0.3, and ≥ 0.3). Genes in blue boxes represent DEGs from the liver transcriptome, and genes in red boxes represent DEGs from the oviduct magnum transcriptome.

Interestingly, ACSL5 was identified in both the liver and oviduct candidate gene sets and mapped to the shared PPAR signaling pathway. ACSL5 expression was positively associated with LysoPC(20:5) in the liver and negatively associated with L-Tryptophan in the oviduct magnum. However, these associations did not remain significant after multiple-testing correction (BH-FDR) and were therefore considered exploratory.

Discussion

Our multi-omics data support a working model for EW divergence in Zi geese, involving the liver-blood-ovary axis and the secretory activity of the oviduct magnum. In this framework, EW differences may arise from two linked processes. First, the body needs to synthesize and transport yolk precursors under hormonal and metabolic conditions that are favorable for follicular deposition. Second, the oviduct magnum requires efficient protein processing and glycosylation to support albumen formation.

Liver-blood-ovary axis: yolk precursor supply and endocrine–metabolic support

A defining feature of the HEW group was higher circulating yolk precursor availability, reflected by significantly elevated serum VLDL and VTG. Steroid hormone signaling stimulates hepatic synthesis and secretion of VLDL and VTG (Li et al., 2014). Therefore, these serum changes link liver regulation to substrate supply for follicles. These serum results match liver transcript changes in lipid and sterol handling and proteostasis (e.g., ACACB, FDFT1, and HSPA2). Together, these findings suggest that hepatic metabolic activity may contribute to variation in yolk precursor output.

ACACB encodes acetyl-CoA carboxylase 2, which produces malonyl-CoA (Wang et al., 2022). Malonyl-CoA is a precursor for fatty acid synthesis. It also inhibits CPT1, which limits the mitochondrial entry of long-chain fatty acids and reduces β-oxidation (Liang, 2023; Rodriguez-Rodriguez et al., 2025). This mechanism suggests that ACACB may balance lipid synthesis and oxidation in the liver. This balance may help maintain lipid precursor output during egg formation.

In parallel, HSPA2 was upregulated in the liver of the HEW group. HSPA2 is an HSP70-family chaperone that supports protein folding and quality control (Rosenzweig et al., 2019; Sojka et al., 2020). Its higher expression may reflect greater demand for protein quality control during reproduction. This idea matches findings in ducks with different EW. In ducks, HSPA2 was proposed as a candidate gene and linked to ER protein-processing pathways (Yan et al., 2023).

In contrast, FDFT1 (squalene synthase), a key enzyme in sterol and cholesterol biosynthesis (Coman et al., 2018), was downregulated in the liver of the HEW group. This pattern suggests tighter control of sterol biosynthesis in the liver, rather than broad activation of this pathway during heavier-egg formation. This result differs from findings in ducks. In ducks, higher yolk percentage was associated with higher expression of cholesterol biosynthesis genes, including FDFT1 (Du et al., 2019). This difference may be because YP did not differ between HEW and LEW geese in our study.

Serum metabolites also suggested hormonal signaling and whole-body metabolic status that may affect egg formation. Cholesterol sulfate was higher in the HEW group. Because it can be reversibly desulfated, this change may alter sterol availability and steroid hormone activity (Cook and Leyh, 2023). LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) (LPC-EPA) was also higher in HEW geese. This agrees with reports of lipid remodeling during yolk formation, including stage-specific changes in lysophospholipids such as LPC (Wang et al., 2023). Tryptophan-related metabolites also suggested systemic differences linked to reproductive performance. L-Tryptophan is a precursor of serotonin (Richard et al., 2009). Dietary supplementation has been reported to reduce corticosterone, increase serotonin, and improve laying performance and EW (Son et al., 2025). In contrast, indoxyl sulfate is derived from microbial tryptophan metabolism and liver processing (Graboski et al., 2023). It has been linked to oxidative and inflammatory signaling and disrupted mineral balance under chronic stress (Nanto-Hara and Ohtsu, 2024), which may hinder egg formation. Taken together, these circulating metabolic differences suggest that HEW geese may have a more favorable endocrine–metabolic milieu for yolk precursor transport and deposition.

Among the ovarian DEGs, RBP1 was downregulated in the HEW group. RBP1 (also known as CRBP1) encodes cellular retinol-binding protein 1, which transports retinol to metabolic enzymes and contributes to the regulation of retinoic acid levels (Zalesak‐Kravec et al., 2022). Retinoic acid can promote granulosa cell proliferation and enhance steroid hormone synthesis (Cai et al., 2024), suggesting that lower RBP1 expression may influence these processes. Overall, ovarian transcriptional changes were limited, suggesting that the ovary may mainly respond to circulating signals rather than show broad transcriptional changes.

Oviduct magnum: protein processing and glycosylation in albumen deposition

Our data show that the oviduct magnum is closely associated with EW divergence. It had the largest number of differentially expressed genes among the tissues analyzed. The magnum is the main site of albumen synthesis and secretion, which places high demands on protein folding and secretion. HSPA8 and HSP90AA1 were enriched in the protein processing in the endoplasmic reticulum pathway. HSP90, in particular, has been reported to participate in steroid hormone receptor signaling and to support protein homeostasis in reproductive tissues (Yu et al., 2016). As molecular chaperones, HSPs help refold unfolded or misfolded proteins, maintaining proteostasis in reproductive tissues. Similarly, Yan et al. (2023) found that DEGs in the magnum of ducks with different egg weights were enriched in the ER protein processing pathway. HSP90AA1 and HSPA8 were identified as core nodes in protein-protein interaction networks. This supports the relevance of these mechanisms across bird species.

We also detected ST3GAL4, which is involved in the glycosphingolipid biosynthesis-lacto and neolacto series pathway. It encodes an α2,3-sialyltransferase that catalyzes terminal α2,3-sialylation on specific glycoprotein or glycolipid receptors (Qi et al., 2020). Given its functional annotation, altered expression of ST3GAL4 in the oviduct magnum may contribute to coordinated remodeling of glycoprotein and glycosphingolipid glycosylation during albumen formation. This finding is consistent with the results of Wan et al. (2017), who reported that ST3GAL4 was upregulated in hens with a higher thick albumen ratio and showed a trend toward enrichment in the glycosphingolipid biosynthesis pathway. Notably, our data show that in all eggs measured for egg quality, the albumen is heavier than the yolk, which, along with the higher number of differentially expressed genes in the oviduct magnum, may indicate that its protein-processing capacity plays a role as a limiting factor in EW divergence.

Integrated transcriptome–metabolome associations

Integrated transcriptome-metabolome analysis highlighted two gene-metabolite associations of possible relevance to EW: ACSL5-LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) and ACSL5-l-tryptophan. ACSL5 was co-expressed in both the liver and oviduct magnum of Zi geese. It is involved in long-chain fatty acid activation and lipid metabolic partitioning (Luo et al., 2023). LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) is related to phosphatidylcholine turnover (Engel et al., 2021), so its association with ACSL5 may be consistent with coordinated variation in lipid metabolism. In addition, ACSL5 showed a negative correlation with l-tryptophan, suggesting a possible link between lipid metabolism and tryptophan-related metabolic status. However, these associations are correlative and require further validation.

Conclusion

ACACB, FDFT1, and HSPA2 in the liver; RBP1 in the ovary; and HSPA8, HSP90AA1, and ST3GAL4 in the oviduct magnum were identified as the key genes regulating EW in Zi geese. Cholesterol sulfate, L-Tryptophan, indoxyl sulfate, and LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) were identified as the potential molecular markers associated with EW. Integrated transcriptomic and metabolomic analysis indicate that ACSL5-LysoPC(20:5(5Z,8Z,11Z,14Z,17Z)/0:0) and ACSL5-L-Tryptophan could potentially be gene-metabolite interaction pairs related to EW variation. Collectively, these findings offer a theoretical foundation for comprehending the molecular mechanisms underlying EW.

Funding

This work was supported by the Bio-breeding Industry Innovation and Development Project of Heilongjiang Province (No. 2024-515). Ministry of Agriculture and Rural Affairs of the People's Republic of China (No. 19240940). Heilongjiang Bayi Agricultural University Graduate Innovative Research Project (YJSCX2025-XCZX34).

CRediT authorship contribution statement

Kexin Cong: Writing – original draft, Visualization, Software, Methodology. Yunuo Liu: Software, Formal analysis, Data curation. Hechuan Wang: Project administration, Investigation. Ke Jiang: Investigation. Jiaxin Yin: Investigation, Data curation. Xinyi Miao: Investigation. Weiran Yang: Investigation. Xiang Xiao: Investigation. Zhifeng Chen: Supervision, Resources, Conceptualization. Shengjun Liu: Writing – review & editing, Funding acquisition, Conceptualization.

Disclosures

The authors declare that they have no competing interests.